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cc-backend/89/3a1de325b5/0/full.jsonl
Jan Eitzinger 0851f3f75d Checkpoint: 893a1de325b5 
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Entire-Agent: Claude Code
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2026-03-19 21:17:00 +01:00

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{"parentUuid":"7557b65b-9ecb-4d31-a987-15813445639f","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Plan: Add RRDTool-style Average Consolidation Function to Resampler\n\n## Context\n\nThe current downsampler in `cc-lib/v2/resampler` offers two algorithms:\n- **LTTB** (LargestTriangleThreeBucket): Perceptually-aware — picks points that preserve visual shape (peaks/valleys). Used at all call sites.\n- **SimpleResampler**: Decimation — picks every nth point. Fast but lossy.\n\nNeither produces scientifically accurate averages over time intervals. RRDTool's **AVERAGE Consolidation Function (CF)** divides data into fixed-size buckets and computes the arithmetic mean of all points in each bucket. This is the standard approach for monitoring data where the true mean per interval matters more than visual fidelity (e.g., for statistics, SLA reporting, capacity planning).\n\nThe goal: add an `AverageResampler` to cc-lib, then expose it as a per-query option in cc-backend's GraphQL API so the frontend/caller can choose the algorithm.\n\n---\n\n## Part 1: cc-lib changes (`/Users/jan/prg/CC/cc-lib/`)\n\n### 1a. Add `AverageResampler` to `resampler/resampler.go`\n\nNew exported function with the same signature as existing resamplers:\n\n```go\nfunc AverageResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)\n```\n\n**Algorithm (RRDTool AVERAGE CF):**\n1. Call `validateFrequency()` for early-exit checks (reuses existing validation).\n2. Compute `step = newFrequency / oldFrequency` (points per bucket).\n3. For each bucket of `step` consecutive points, compute arithmetic mean skipping NaN values. If all values in a bucket are NaN, output NaN for that bucket.\n4. Return averaged data, `newFrequency`, nil.\n\n**NaN handling**: Skip NaN values and average only valid points (consistent with RRDTool behavior and `AddStats()` in `pkg/metricstore/api.go`). This differs from LTTB which propagates NaN if *any* bucket point is NaN.\n\n### 1b. Add `ResamplerFunc` type and lookup\n\nAdd a function type and string-based lookup to make algorithm selection easy for consumers:\n\n```go\n// ResamplerFunc is the signature shared by all resampler algorithms.\ntype ResamplerFunc func(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)\n\n// GetResampler returns the resampler function for the given name.\n// Valid names: \"lttb\" (default), \"average\", \"simple\". Empty string returns LTTB.\nfunc GetResampler(name string) (ResamplerFunc, error)\n```\n\n### 1c. Add tests to `resampler/resampler_test.go`\n\nFollowing the existing test patterns (table-driven, same helper functions):\n- Basic averaging: `[1,2,3,4,5,6]` step=2 → `[1.5, 3.5, 5.5]`\n- NaN skipping within buckets\n- All-NaN bucket → NaN output\n- Early-exit conditions (same set as SimpleResampler/LTTB tests)\n- Frequency validation errors\n- Benchmark: `BenchmarkAverageResampler`\n\n---\n\n## Part 2: cc-backend changes (`/Users/jan/tmp/cc-backend/`)\n\n### 2a. Add `replace` directive to `go.mod`\n\n```\nreplace github.com/ClusterCockpit/cc-lib/v2 => ../../prg/CC/cc-lib\n```\n\n### 2b. Add GraphQL enum and query parameter\n\nIn `api/schema.graphqls`, add enum and parameter to both queries that accept `resolution`:\n\n```graphql\nenum ResampleAlgo {\n LTTB\n AVERAGE\n SIMPLE\n}\n```\n\nAdd `resampleAlgo: ResampleAlgo` parameter to:\n- `jobMetrics(id: ID!, metrics: [String!], scopes: [MetricScope!], resolution: Int, resampleAlgo: ResampleAlgo): [JobMetricWithName!]!`\n- `nodeMetricsList(..., resolution: Int, resampleAlgo: ResampleAlgo): NodesResultList!`\n\nThen run `make graphql` to regenerate.\n\n### 2c. Update resolvers to pass algorithm through\n\n**`internal/graph/schema.resolvers.go`**:\n\n`JobMetrics` resolver (line ~501): accept new `resampleAlgo *model.ResampleAlgo` parameter (auto-generated by gqlgen), convert to string, pass to `LoadData`.\n\n`NodeMetricsList` resolver (line ~875): same treatment, pass to `LoadNodeListData`.\n\n### 2d. Thread algorithm through data loading\n\n**`internal/metricdispatch/dataLoader.go`**:\n\n- `LoadData()` (line 85): add `resampleAlgo string` parameter. Use `resampler.GetResampler(resampleAlgo)` to get the function, call it instead of hardcoded `resampler.LargestTriangleThreeBucket` at line 145.\n- `LoadNodeListData()` (line 409): add `resampleAlgo string` parameter, pass through to `InternalMetricStore.LoadNodeListData`.\n\n**`pkg/metricstore/query.go`**:\n\n- `InternalMetricStore.LoadNodeListData()` (line 616): add `resampleAlgo string` to signature, include in `APIQueryRequest` or pass to `MemoryStore.Read`.\n\n**`pkg/metricstore/metricstore.go`**:\n\n- `MemoryStore.Read()` (line ~682): add `resampleAlgo string` parameter. At line 740, use `resampler.GetResampler(resampleAlgo)` instead of hardcoded LTTB.\n\n### 2e. Update cache key\n\n**`internal/metricdispatch/dataLoader.go`**: The `cacheKey()` function must include the algorithm name so different algorithms don't serve stale cached results.\n\n---\n\n## Files to modify\n\n| File | Repo | Change |\n|------|------|--------|\n| `resampler/resampler.go` | cc-lib | Add `AverageResampler`, `ResamplerFunc`, `GetResampler` |\n| `resampler/resampler_test.go` | cc-lib | Add tests + benchmark |\n| `go.mod` | cc-backend | Add `replace` directive |\n| `api/schema.graphqls` | cc-backend | Add `ResampleAlgo` enum, update query params |\n| `internal/graph/generated/` | cc-backend | Regenerated (`make graphql`) |\n| `internal/graph/schema.resolvers.go` | cc-backend | Pass algorithm to data loaders |\n| `internal/metricdispatch/dataLoader.go` | cc-backend | Thread algorithm to resampler calls, update cache key |\n| `pkg/metricstore/query.go` | cc-backend | Thread algorithm through `LoadNodeListData` |\n| `pkg/metricstore/metricstore.go` | cc-backend | Thread algorithm through `Read()`, use `GetResampler` |\n\n## Verification\n\n1. `cd /Users/jan/prg/CC/cc-lib && go test ./resampler/...` — new + existing tests pass\n2. `cd /Users/jan/tmp/cc-backend && make graphql` — GraphQL code regenerates cleanly\n3. `make` — full build succeeds\n4. `make test` — all existing tests pass\n5. Manual: GraphQL playground query with `resampleAlgo: AVERAGE` vs default LTTB — average produces smoother curves without artificial peak preservation\n\n\nIf you need specific details from before exiting plan mode (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/jan/.claude/projects/-Users-jan-tmp-cc-backend/190ae749-6a19-47a7-82da-dba5a0e7402d.jsonl"},"uuid":"f6080b4d-410f-4d5f-8683-7ccc8a8d37db","timestamp":"2026-03-19T19:46:16.044Z","planContent":"# Plan: Add RRDTool-style Average Consolidation Function to Resampler\n\n## Context\n\nThe current downsampler in `cc-lib/v2/resampler` offers two algorithms:\n- **LTTB** (LargestTriangleThreeBucket): Perceptually-aware — picks points that preserve visual shape (peaks/valleys). Used at all call sites.\n- **SimpleResampler**: Decimation — picks every nth point. Fast but lossy.\n\nNeither produces scientifically accurate averages over time intervals. RRDTool's **AVERAGE Consolidation Function (CF)** divides data into fixed-size buckets and computes the arithmetic mean of all points in each bucket. This is the standard approach for monitoring data where the true mean per interval matters more than visual fidelity (e.g., for statistics, SLA reporting, capacity planning).\n\nThe goal: add an `AverageResampler` to cc-lib, then expose it as a per-query option in cc-backend's GraphQL API so the frontend/caller can choose the algorithm.\n\n---\n\n## Part 1: cc-lib changes (`/Users/jan/prg/CC/cc-lib/`)\n\n### 1a. Add `AverageResampler` to `resampler/resampler.go`\n\nNew exported function with the same signature as existing resamplers:\n\n```go\nfunc AverageResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)\n```\n\n**Algorithm (RRDTool AVERAGE CF):**\n1. Call `validateFrequency()` for early-exit checks (reuses existing validation).\n2. Compute `step = newFrequency / oldFrequency` (points per bucket).\n3. For each bucket of `step` consecutive points, compute arithmetic mean skipping NaN values. If all values in a bucket are NaN, output NaN for that bucket.\n4. Return averaged data, `newFrequency`, nil.\n\n**NaN handling**: Skip NaN values and average only valid points (consistent with RRDTool behavior and `AddStats()` in `pkg/metricstore/api.go`). This differs from LTTB which propagates NaN if *any* bucket point is NaN.\n\n### 1b. Add `ResamplerFunc` type and lookup\n\nAdd a function type and string-based lookup to make algorithm selection easy for consumers:\n\n```go\n// ResamplerFunc is the signature shared by all resampler algorithms.\ntype ResamplerFunc func(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)\n\n// GetResampler returns the resampler function for the given name.\n// Valid names: \"lttb\" (default), \"average\", \"simple\". Empty string returns LTTB.\nfunc GetResampler(name string) (ResamplerFunc, error)\n```\n\n### 1c. Add tests to `resampler/resampler_test.go`\n\nFollowing the existing test patterns (table-driven, same helper functions):\n- Basic averaging: `[1,2,3,4,5,6]` step=2 → `[1.5, 3.5, 5.5]`\n- NaN skipping within buckets\n- All-NaN bucket → NaN output\n- Early-exit conditions (same set as SimpleResampler/LTTB tests)\n- Frequency validation errors\n- Benchmark: `BenchmarkAverageResampler`\n\n---\n\n## Part 2: cc-backend changes (`/Users/jan/tmp/cc-backend/`)\n\n### 2a. Add `replace` directive to `go.mod`\n\n```\nreplace github.com/ClusterCockpit/cc-lib/v2 => ../../prg/CC/cc-lib\n```\n\n### 2b. Add GraphQL enum and query parameter\n\nIn `api/schema.graphqls`, add enum and parameter to both queries that accept `resolution`:\n\n```graphql\nenum ResampleAlgo {\n LTTB\n AVERAGE\n SIMPLE\n}\n```\n\nAdd `resampleAlgo: ResampleAlgo` parameter to:\n- `jobMetrics(id: ID!, metrics: [String!], scopes: [MetricScope!], resolution: Int, resampleAlgo: ResampleAlgo): [JobMetricWithName!]!`\n- `nodeMetricsList(..., resolution: Int, resampleAlgo: ResampleAlgo): NodesResultList!`\n\nThen run `make graphql` to regenerate.\n\n### 2c. Update resolvers to pass algorithm through\n\n**`internal/graph/schema.resolvers.go`**:\n\n`JobMetrics` resolver (line ~501): accept new `resampleAlgo *model.ResampleAlgo` parameter (auto-generated by gqlgen), convert to string, pass to `LoadData`.\n\n`NodeMetricsList` resolver (line ~875): same treatment, pass to `LoadNodeListData`.\n\n### 2d. Thread algorithm through data loading\n\n**`internal/metricdispatch/dataLoader.go`**:\n\n- `LoadData()` (line 85): add `resampleAlgo string` parameter. Use `resampler.GetResampler(resampleAlgo)` to get the function, call it instead of hardcoded `resampler.LargestTriangleThreeBucket` at line 145.\n- `LoadNodeListData()` (line 409): add `resampleAlgo string` parameter, pass through to `InternalMetricStore.LoadNodeListData`.\n\n**`pkg/metricstore/query.go`**:\n\n- `InternalMetricStore.LoadNodeListData()` (line 616): add `resampleAlgo string` to signature, include in `APIQueryRequest` or pass to `MemoryStore.Read`.\n\n**`pkg/metricstore/metricstore.go`**:\n\n- `MemoryStore.Read()` (line ~682): add `resampleAlgo string` parameter. At line 740, use `resampler.GetResampler(resampleAlgo)` instead of hardcoded LTTB.\n\n### 2e. Update cache key\n\n**`internal/metricdispatch/dataLoader.go`**: The `cacheKey()` function must include the algorithm name so different algorithms don't serve stale cached results.\n\n---\n\n## Files to modify\n\n| File | Repo | Change |\n|------|------|--------|\n| `resampler/resampler.go` | cc-lib | Add `AverageResampler`, `ResamplerFunc`, `GetResampler` |\n| `resampler/resampler_test.go` | cc-lib | Add tests + benchmark |\n| `go.mod` | cc-backend | Add `replace` directive |\n| `api/schema.graphqls` | cc-backend | Add `ResampleAlgo` enum, update query params |\n| `internal/graph/generated/` | cc-backend | Regenerated (`make graphql`) |\n| `internal/graph/schema.resolvers.go` | cc-backend | Pass algorithm to data loaders |\n| `internal/metricdispatch/dataLoader.go` | cc-backend | Thread algorithm to resampler calls, update cache key |\n| `pkg/metricstore/query.go` | cc-backend | Thread algorithm through `LoadNodeListData` |\n| `pkg/metricstore/metricstore.go` | cc-backend | Thread algorithm through `Read()`, use `GetResampler` |\n\n## Verification\n\n1. `cd /Users/jan/prg/CC/cc-lib && go test ./resampler/...` — new + existing tests pass\n2. `cd /Users/jan/tmp/cc-backend && make graphql` — GraphQL code regenerates cleanly\n3. `make` — full build succeeds\n4. `make test` — all existing tests pass\n5. Manual: GraphQL playground query with `resampleAlgo: AVERAGE` vs default LTTB — average produces smoother curves without artificial peak preservation\n","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"1a2a8feb-692c-46b2-a514-6f2e667b818e","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PMMDkWEVbmfUVC1vomjeTe","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-lib.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package resampler provides time-series data downsampling algorithms.\n 7→//\n 8→// This package implements two downsampling strategies for reducing the number\n 9→// of data points in time-series data while preserving important characteristics:\n 10→//\n 11→// - SimpleResampler: A fast, straightforward algorithm that selects every nth point\n 12→// - LargestTriangleThreeBucket (LTTB): A perceptually-aware algorithm that preserves\n 13→// visual characteristics by selecting points that maximize the area of triangles\n 14→// formed with neighboring points\n 15→//\n 16→// Both algorithms are designed to work with schema.Float data and handle NaN values\n 17→// appropriately. They require that the new sampling frequency is a multiple of the\n 18→// old frequency.\n 19→//\n 20→// References:\n 21→// - LTTB Algorithm: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n 22→// - Implementation adapted from: https://github.com/haoel/downsampling\n 23→package resampler\n 24→\n 25→import (\n 26→\t\"fmt\"\n 27→\t\"math\"\n 28→\n 29→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 30→)\n 31→\n 32→// MinimumRequiredPoints is the default number of points required to trigger resampling.\n 33→// If the data has fewer points, the original time series will be returned without resampling.\n 34→var MinimumRequiredPoints int = 1000\n 35→\n 36→// calculateTriangleArea computes the area of a triangle defined by three points.\n 37→//\n 38→// The area is calculated using the cross product formula:\n 39→// Area = 0.5 * |((paX - pcX) * (pbY - paY)) - ((paX - pbX) * (pcY - paY))|\n 40→//\n 41→// This is used by the LTTB algorithm to determine which points preserve the\n 42→// most visual information when downsampling.\n 43→func calculateTriangleArea(paX, paY, pbX, pbY, pcX, pcY schema.Float) float64 {\n 44→\tarea := ((paX-pcX)*(pbY-paY) - (paX-pbX)*(pcY-paY)) * 0.5\n 45→\treturn math.Abs(float64(area))\n 46→}\n 47→\n 48→// calculateAverageDataPoint computes the average point from a slice of data points.\n 49→//\n 50→// Parameters:\n 51→// - points: slice of Y values to average\n 52→// - xStart: starting X coordinate for the points\n 53→//\n 54→// Returns:\n 55→// - avgX: average X coordinate\n 56→// - avgY: average Y value, or NaN if any input point is NaN\n 57→//\n 58→// This function is used by LTTB to find the centroid of points in a bucket,\n 59→// which is then used to calculate triangle areas for point selection.\n 60→func calculateAverageDataPoint(points []schema.Float, xStart int64) (avgX schema.Float, avgY schema.Float) {\n 61→\thasNaN := false\n 62→\tfor _, point := range points {\n 63→\t\tavgX += schema.Float(xStart)\n 64→\t\tavgY += point\n 65→\t\txStart++\n 66→\t\tif math.IsNaN(float64(point)) {\n 67→\t\t\thasNaN = true\n 68→\t\t}\n 69→\t}\n 70→\n 71→\tl := schema.Float(len(points))\n 72→\tavgX /= l\n 73→\tavgY /= l\n 74→\n 75→\tif hasNaN {\n 76→\t\treturn avgX, schema.NaN\n 77→\t}\n 78→\treturn avgX, avgY\n 79→}\n 80→\n 81→// SimpleResampler performs simple downsampling by selecting every nth point.\n 82→//\n 83→// This is the fastest downsampling method but may miss important features in the data.\n 84→// It works by calculating a step size (newFrequency / oldFrequency) and selecting\n 85→// every step-th point from the original data.\n 86→//\n 87→// Parameters:\n 88→// - data: input time-series data points\n 89→// - oldFrequency: original sampling frequency (points per time unit)\n 90→// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n 91→//\n 92→// Returns:\n 93→// - Downsampled data slice\n 94→// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n 95→// - Error if newFrequency is not a multiple of oldFrequency\n 96→//\n 97→// The function returns the original data unchanged if:\n 98→// - Either frequency is 0\n 99→// - newFrequency <= oldFrequency (no downsampling needed)\n 100→// - The resulting data would have fewer than 1 point\n 101→// - The original data has fewer than 100 points\n 102→// - The downsampled data would have the same or more points than the original\n 103→//\n 104→// Example:\n 105→//\n 106→//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}\n 107→//\tdownsampled, freq, err := SimpleResampler(data, 1, 2)\n 108→//\t// Returns: [1.0, 3.0, 5.0], 2, nil\n 109→func SimpleResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n 110→\t// checks if the frequencies are valid or not.\n 111→\tnewDataLength, step, err := validateFrequency(len(data), oldFrequency, newFrequency)\n 112→\tif err != nil {\n 113→\t\treturn nil, 0, err\n 114→\t}\n 115→\tif newDataLength == -1 {\n 116→\t\treturn data, oldFrequency, nil\n 117→\t}\n 118→\n 119→\tnewData := make([]schema.Float, newDataLength)\n 120→\tfor i := range newDataLength {\n 121→\t\tnewData[i] = data[i*step]\n 122→\t}\n 123→\n 124→\treturn newData, newFrequency, nil\n 125→}\n 126→\n 127→func validateFrequency(lenData int, oldFrequency, newFrequency int64) (int, int, error) {\n 128→\t// Validate inputs and check if downsampling is needed\n 129→\tif oldFrequency == 0 || newFrequency == 0 || newFrequency <= oldFrequency {\n 130→\t\treturn -1, 0, nil\n 131→\t}\n 132→\n 133→\t// Ensure new frequency is a multiple of old frequency\n 134→\tif newFrequency%oldFrequency != 0 {\n 135→\t\treturn -1, 0, fmt.Errorf(\"new sampling frequency (%d) must be a multiple of old frequency (%d)\", newFrequency, oldFrequency)\n 136→\t}\n 137→\n 138→\tstep := int(newFrequency / oldFrequency)\n 139→\tnewDataLength := lenData / step\n 140→\n 141→\t// Don't downsample if result would be trivial or counterproductive\n 142→\tif (newDataLength == 0) || (lenData < MinimumRequiredPoints) || (newDataLength >= lenData) {\n 143→\t\treturn -1, 0, nil\n 144→\t}\n 145→\n 146→\treturn newDataLength, step, nil\n 147→}\n 148→\n 149→// LargestTriangleThreeBucket (LTTB) performs perceptually-aware downsampling.\n 150→//\n 151→// LTTB is a downsampling algorithm that preserves the visual characteristics of\n 152→// time-series data by selecting points that form the largest triangles with their\n 153→// neighbors. This ensures that important peaks, valleys, and trends are retained\n 154→// even when significantly reducing the number of points.\n 155→//\n 156→// Algorithm Overview:\n 157→// 1. The data is divided into buckets (except first and last points which are always kept)\n 158→// 2. For each bucket, the algorithm selects the point that forms the largest triangle\n 159→// with the previous selected point and the average of the next bucket\n 160→// 3. This maximizes the visual area and preserves important features\n 161→//\n 162→// Time Complexity: O(n) where n is the number of input points\n 163→// Space Complexity: O(m) where m is the number of output points\n 164→//\n 165→// Parameters:\n 166→// - data: input time-series data points\n 167→// - oldFrequency: original sampling frequency (points per time unit)\n 168→// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n 169→//\n 170→// Returns:\n 171→// - Downsampled data slice\n 172→// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n 173→// - Error if newFrequency is not a multiple of oldFrequency\n 174→//\n 175→// The function returns the original data unchanged if:\n 176→// - Either frequency is 0\n 177→// - newFrequency <= oldFrequency (no downsampling needed)\n 178→// - The resulting data would have fewer than 1 point\n 179→// - The original data has fewer than 100 points\n 180→// - The downsampled data would have the same or more points than the original\n 181→//\n 182→// NaN Handling:\n 183→// The algorithm properly handles NaN values and preserves them in the output when\n 184→// they represent the maximum area point in a bucket.\n 185→//\n 186→// Example:\n 187→//\n 188→//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}\n 189→//\tdownsampled, freq, err := LargestTriangleThreeBucket(data, 1, 2)\n 190→//\t// Returns downsampled data preserving visual characteristics\n 191→//\n 192→// References:\n 193→// - Original paper: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n 194→// - Adapted from: https://github.com/haoel/downsampling/blob/master/core/lttb.go\n 195→func LargestTriangleThreeBucket(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n 196→\t// checks if the frequencies are valid or not.\n 197→\tnewDataLength, _, err := validateFrequency(len(data), oldFrequency, newFrequency)\n 198→\tif err != nil {\n 199→\t\treturn nil, 0, err\n 200→\t}\n 201→\tif newDataLength == -1 {\n 202→\t\treturn data, oldFrequency, nil\n 203→\t}\n 204→\n 205→\tnewData := make([]schema.Float, 0, newDataLength)\n 206→\n 207→\t// Bucket size. Leave room for start and end data points\n 208→\tbucketSize := float64(len(data)-2) / float64(newDataLength-2)\n 209→\n 210→\t// Always add the first point\n 211→\tnewData = append(newData, data[0])\n 212→\n 213→\t// Bucket pointers:\n 214→\t// - bucketLow: current bucket's start\n 215→\t// - bucketMiddle: current bucket's end (also next bucket's start)\n 216→\t// - bucketHigh: next bucket's end\n 217→\tbucketLow := 1\n 218→\tbucketMiddle := int(math.Floor(bucketSize)) + 1\n 219→\n 220→\tvar prevMaxAreaPoint int\n 221→\n 222→\t// Process each bucket (excluding first and last points)\n 223→\tfor i := 0; i < newDataLength-2; i++ {\n 224→\t\tbucketHigh := int(math.Floor(float64(i+2)*bucketSize)) + 1\n 225→\t\tif bucketHigh >= len(data)-1 {\n 226→\t\t\tbucketHigh = len(data) - 2\n 227→\t\t}\n 228→\n 229→\t\t// Calculate average point for next bucket (point c in triangle)\n 230→\t\tavgPointX, avgPointY := calculateAverageDataPoint(data[bucketMiddle:bucketHigh+1], int64(bucketMiddle))\n 231→\n 232→\t\t// Get the range for current bucket\n 233→\t\tcurrBucketStart := bucketLow\n 234→\t\tcurrBucketEnd := bucketMiddle\n 235→\n 236→\t\t// Point a (previously selected point)\n 237→\t\tpointX := prevMaxAreaPoint\n 238→\t\tpointY := data[prevMaxAreaPoint]\n 239→\n 240→\t\tmaxArea := -1.0\n 241→\t\tvar maxAreaPoint int\n 242→\t\tflag_ := 0\n 243→\n 244→\t\t// Find the point in current bucket that forms the largest triangle\n 245→\t\tfor ; currBucketStart < currBucketEnd; currBucketStart++ {\n 246→\t\t\tarea := calculateTriangleArea(\n 247→\t\t\t\tschema.Float(pointX), pointY,\n 248→\t\t\t\tavgPointX, avgPointY,\n 249→\t\t\t\tschema.Float(currBucketStart), data[currBucketStart],\n 250→\t\t\t)\n 251→\n 252→\t\t\tif area > maxArea || math.IsNaN(area) {\n 253→\t\t\t\tmaxArea = area\n 254→\t\t\t\tmaxAreaPoint = currBucketStart\n 255→\t\t\t}\n 256→\t\t\t// if math.IsNaN(float64(avgPointY)) {\n 257→\t\t\t// \tflag_ = 1\n 258→\t\t\t// }\n 259→\t\t}\n 260→\n 261→\t\t// Add the point with maximum area from this bucket\n 262→\t\tif flag_ == 1 {\n 263→\t\t\tnewData = append(newData, schema.NaN) // Pick this point from the bucket\n 264→\t\t} else {\n 265→\t\t\tnewData = append(newData, data[maxAreaPoint]) // Pick this point from the bucket\n 266→\t\t}\n 267→\n 268→\t\tprevMaxAreaPoint = maxAreaPoint\n 269→\n 270→\t\t// Move to the next bucket\n 271→\t\tbucketLow = bucketMiddle\n 272→\t\tbucketMiddle = bucketHigh\n 273→\t}\n 274→\n 275→\t// Always add the last point\n 276→\tnewData = append(newData, data[len(data)-1])\n 277→\n 278→\treturn newData, newFrequency, nil\n 279→}\n 280→\n 281→func SetMinimumRequiredPoints(setVal int) {\n 282→\tMinimumRequiredPoints = setVal\n 283→}\n 284→"}]},"uuid":"6fe9d8c9-e43f-42af-a339-69cf52e1e9cd","timestamp":"2026-03-19T19:46:21.202Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-lib/resampler/resampler.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-lib.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package resampler provides time-series data downsampling algorithms.\n//\n// This package implements two downsampling strategies for reducing the number\n// of data points in time-series data while preserving important characteristics:\n//\n// - SimpleResampler: A fast, straightforward algorithm that selects every nth point\n// - LargestTriangleThreeBucket (LTTB): A perceptually-aware algorithm that preserves\n// visual characteristics by selecting points that maximize the area of triangles\n// formed with neighboring points\n//\n// Both algorithms are designed to work with schema.Float data and handle NaN values\n// appropriately. They require that the new sampling frequency is a multiple of the\n// old frequency.\n//\n// References:\n// - LTTB Algorithm: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n// - Implementation adapted from: https://github.com/haoel/downsampling\npackage resampler\n\nimport (\n\t\"fmt\"\n\t\"math\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// MinimumRequiredPoints is the default number of points required to trigger resampling.\n// If the data has fewer points, the original time series will be returned without resampling.\nvar MinimumRequiredPoints int = 1000\n\n// calculateTriangleArea computes the area of a triangle defined by three points.\n//\n// The area is calculated using the cross product formula:\n// Area = 0.5 * |((paX - pcX) * (pbY - paY)) - ((paX - pbX) * (pcY - paY))|\n//\n// This is used by the LTTB algorithm to determine which points preserve the\n// most visual information when downsampling.\nfunc calculateTriangleArea(paX, paY, pbX, pbY, pcX, pcY schema.Float) float64 {\n\tarea := ((paX-pcX)*(pbY-paY) - (paX-pbX)*(pcY-paY)) * 0.5\n\treturn math.Abs(float64(area))\n}\n\n// calculateAverageDataPoint computes the average point from a slice of data points.\n//\n// Parameters:\n// - points: slice of Y values to average\n// - xStart: starting X coordinate for the points\n//\n// Returns:\n// - avgX: average X coordinate\n// - avgY: average Y value, or NaN if any input point is NaN\n//\n// This function is used by LTTB to find the centroid of points in a bucket,\n// which is then used to calculate triangle areas for point selection.\nfunc calculateAverageDataPoint(points []schema.Float, xStart int64) (avgX schema.Float, avgY schema.Float) {\n\thasNaN := false\n\tfor _, point := range points {\n\t\tavgX += schema.Float(xStart)\n\t\tavgY += point\n\t\txStart++\n\t\tif math.IsNaN(float64(point)) {\n\t\t\thasNaN = true\n\t\t}\n\t}\n\n\tl := schema.Float(len(points))\n\tavgX /= l\n\tavgY /= l\n\n\tif hasNaN {\n\t\treturn avgX, schema.NaN\n\t}\n\treturn avgX, avgY\n}\n\n// SimpleResampler performs simple downsampling by selecting every nth point.\n//\n// This is the fastest downsampling method but may miss important features in the data.\n// It works by calculating a step size (newFrequency / oldFrequency) and selecting\n// every step-th point from the original data.\n//\n// Parameters:\n// - data: input time-series data points\n// - oldFrequency: original sampling frequency (points per time unit)\n// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n//\n// Returns:\n// - Downsampled data slice\n// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n// - Error if newFrequency is not a multiple of oldFrequency\n//\n// The function returns the original data unchanged if:\n// - Either frequency is 0\n// - newFrequency <= oldFrequency (no downsampling needed)\n// - The resulting data would have fewer than 1 point\n// - The original data has fewer than 100 points\n// - The downsampled data would have the same or more points than the original\n//\n// Example:\n//\n//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}\n//\tdownsampled, freq, err := SimpleResampler(data, 1, 2)\n//\t// Returns: [1.0, 3.0, 5.0], 2, nil\nfunc SimpleResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n\t// checks if the frequencies are valid or not.\n\tnewDataLength, step, err := validateFrequency(len(data), oldFrequency, newFrequency)\n\tif err != nil {\n\t\treturn nil, 0, err\n\t}\n\tif newDataLength == -1 {\n\t\treturn data, oldFrequency, nil\n\t}\n\n\tnewData := make([]schema.Float, newDataLength)\n\tfor i := range newDataLength {\n\t\tnewData[i] = data[i*step]\n\t}\n\n\treturn newData, newFrequency, nil\n}\n\nfunc validateFrequency(lenData int, oldFrequency, newFrequency int64) (int, int, error) {\n\t// Validate inputs and check if downsampling is needed\n\tif oldFrequency == 0 || newFrequency == 0 || newFrequency <= oldFrequency {\n\t\treturn -1, 0, nil\n\t}\n\n\t// Ensure new frequency is a multiple of old frequency\n\tif newFrequency%oldFrequency != 0 {\n\t\treturn -1, 0, fmt.Errorf(\"new sampling frequency (%d) must be a multiple of old frequency (%d)\", newFrequency, oldFrequency)\n\t}\n\n\tstep := int(newFrequency / oldFrequency)\n\tnewDataLength := lenData / step\n\n\t// Don't downsample if result would be trivial or counterproductive\n\tif (newDataLength == 0) || (lenData < MinimumRequiredPoints) || (newDataLength >= lenData) {\n\t\treturn -1, 0, nil\n\t}\n\n\treturn newDataLength, step, nil\n}\n\n// LargestTriangleThreeBucket (LTTB) performs perceptually-aware downsampling.\n//\n// LTTB is a downsampling algorithm that preserves the visual characteristics of\n// time-series data by selecting points that form the largest triangles with their\n// neighbors. This ensures that important peaks, valleys, and trends are retained\n// even when significantly reducing the number of points.\n//\n// Algorithm Overview:\n// 1. The data is divided into buckets (except first and last points which are always kept)\n// 2. For each bucket, the algorithm selects the point that forms the largest triangle\n// with the previous selected point and the average of the next bucket\n// 3. This maximizes the visual area and preserves important features\n//\n// Time Complexity: O(n) where n is the number of input points\n// Space Complexity: O(m) where m is the number of output points\n//\n// Parameters:\n// - data: input time-series data points\n// - oldFrequency: original sampling frequency (points per time unit)\n// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n//\n// Returns:\n// - Downsampled data slice\n// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n// - Error if newFrequency is not a multiple of oldFrequency\n//\n// The function returns the original data unchanged if:\n// - Either frequency is 0\n// - newFrequency <= oldFrequency (no downsampling needed)\n// - The resulting data would have fewer than 1 point\n// - The original data has fewer than 100 points\n// - The downsampled data would have the same or more points than the original\n//\n// NaN Handling:\n// The algorithm properly handles NaN values and preserves them in the output when\n// they represent the maximum area point in a bucket.\n//\n// Example:\n//\n//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}\n//\tdownsampled, freq, err := LargestTriangleThreeBucket(data, 1, 2)\n//\t// Returns downsampled data preserving visual characteristics\n//\n// References:\n// - Original paper: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n// - Adapted from: https://github.com/haoel/downsampling/blob/master/core/lttb.go\nfunc LargestTriangleThreeBucket(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n\t// checks if the frequencies are valid or not.\n\tnewDataLength, _, err := validateFrequency(len(data), oldFrequency, newFrequency)\n\tif err != nil {\n\t\treturn nil, 0, err\n\t}\n\tif newDataLength == -1 {\n\t\treturn data, oldFrequency, nil\n\t}\n\n\tnewData := make([]schema.Float, 0, newDataLength)\n\n\t// Bucket size. Leave room for start and end data points\n\tbucketSize := float64(len(data)-2) / float64(newDataLength-2)\n\n\t// Always add the first point\n\tnewData = append(newData, data[0])\n\n\t// Bucket pointers:\n\t// - bucketLow: current bucket's start\n\t// - bucketMiddle: current bucket's end (also next bucket's start)\n\t// - bucketHigh: next bucket's end\n\tbucketLow := 1\n\tbucketMiddle := int(math.Floor(bucketSize)) + 1\n\n\tvar prevMaxAreaPoint int\n\n\t// Process each bucket (excluding first and last points)\n\tfor i := 0; i < newDataLength-2; i++ {\n\t\tbucketHigh := int(math.Floor(float64(i+2)*bucketSize)) + 1\n\t\tif bucketHigh >= len(data)-1 {\n\t\t\tbucketHigh = len(data) - 2\n\t\t}\n\n\t\t// Calculate average point for next bucket (point c in triangle)\n\t\tavgPointX, avgPointY := calculateAverageDataPoint(data[bucketMiddle:bucketHigh+1], int64(bucketMiddle))\n\n\t\t// Get the range for current bucket\n\t\tcurrBucketStart := bucketLow\n\t\tcurrBucketEnd := bucketMiddle\n\n\t\t// Point a (previously selected point)\n\t\tpointX := prevMaxAreaPoint\n\t\tpointY := data[prevMaxAreaPoint]\n\n\t\tmaxArea := -1.0\n\t\tvar maxAreaPoint int\n\t\tflag_ := 0\n\n\t\t// Find the point in current bucket that forms the largest triangle\n\t\tfor ; currBucketStart < currBucketEnd; currBucketStart++ {\n\t\t\tarea := calculateTriangleArea(\n\t\t\t\tschema.Float(pointX), pointY,\n\t\t\t\tavgPointX, avgPointY,\n\t\t\t\tschema.Float(currBucketStart), data[currBucketStart],\n\t\t\t)\n\n\t\t\tif area > maxArea || math.IsNaN(area) {\n\t\t\t\tmaxArea = area\n\t\t\t\tmaxAreaPoint = currBucketStart\n\t\t\t}\n\t\t\t// if math.IsNaN(float64(avgPointY)) {\n\t\t\t// \tflag_ = 1\n\t\t\t// }\n\t\t}\n\n\t\t// Add the point with maximum area from this bucket\n\t\tif flag_ == 1 {\n\t\t\tnewData = append(newData, schema.NaN) // Pick this point from the bucket\n\t\t} else {\n\t\t\tnewData = append(newData, data[maxAreaPoint]) // Pick this point from the bucket\n\t\t}\n\n\t\tprevMaxAreaPoint = maxAreaPoint\n\n\t\t// Move to the next bucket\n\t\tbucketLow = bucketMiddle\n\t\tbucketMiddle = bucketHigh\n\t}\n\n\t// Always add the last point\n\tnewData = append(newData, data[len(data)-1])\n\n\treturn newData, newFrequency, nil\n}\n\nfunc SetMinimumRequiredPoints(setVal int) {\n\tMinimumRequiredPoints = setVal\n}\n","numLines":284,"startLine":1,"totalLines":284}},"sourceToolAssistantUUID":"1a2a8feb-692c-46b2-a514-6f2e667b818e","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"49c2195d-f08e-4f79-a96a-c5844f332310","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WXTsrsHuugmaG1ceBHvyLa","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-lib.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→package resampler\n 6→\n 7→import (\n 8→\t\"math\"\n 9→\t\"testing\"\n 10→\n 11→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 12→)\n 13→\n 14→// TestCalculateTriangleArea tests the triangle area calculation helper function\n 15→func TestCalculateTriangleArea(t *testing.T) {\n 16→\ttests := []struct {\n 17→\t\tname string\n 18→\t\tpaX, paY schema.Float\n 19→\t\tpbX, pbY schema.Float\n 20→\t\tpcX, pcY schema.Float\n 21→\t\texpected float64\n 22→\t}{\n 23→\t\t{\n 24→\t\t\tname: \"Right triangle\",\n 25→\t\t\tpaX: 0, paY: 0,\n 26→\t\t\tpbX: 0, pbY: 3,\n 27→\t\t\tpcX: 4, pcY: 0,\n 28→\t\t\texpected: 6.0,\n 29→\t\t},\n 30→\t\t{\n 31→\t\t\tname: \"Collinear points (zero area)\",\n 32→\t\t\tpaX: 0, paY: 0,\n 33→\t\t\tpbX: 1, pbY: 1,\n 34→\t\t\tpcX: 2, pcY: 2,\n 35→\t\t\texpected: 0.0,\n 36→\t\t},\n 37→\t\t{\n 38→\t\t\tname: \"Negative coordinates\",\n 39→\t\t\tpaX: -1, paY: -1,\n 40→\t\t\tpbX: -1, pbY: 2,\n 41→\t\t\tpcX: 3, pcY: -1,\n 42→\t\t\texpected: 6.0,\n 43→\t\t},\n 44→\t}\n 45→\n 46→\tfor _, tt := range tests {\n 47→\t\tt.Run(tt.name, func(t *testing.T) {\n 48→\t\t\tresult := calculateTriangleArea(tt.paX, tt.paY, tt.pbX, tt.pbY, tt.pcX, tt.pcY)\n 49→\t\t\tif math.Abs(result-tt.expected) > 1e-10 {\n 50→\t\t\t\tt.Errorf(\"calculateTriangleArea() = %v, want %v\", result, tt.expected)\n 51→\t\t\t}\n 52→\t\t})\n 53→\t}\n 54→}\n 55→\n 56→// TestCalculateAverageDataPoint tests the average point calculation helper function\n 57→func TestCalculateAverageDataPoint(t *testing.T) {\n 58→\ttests := []struct {\n 59→\t\tname string\n 60→\t\tpoints []schema.Float\n 61→\t\txStart int64\n 62→\t\texpectedX schema.Float\n 63→\t\texpectedY schema.Float\n 64→\t\texpectNaN bool\n 65→\t}{\n 66→\t\t{\n 67→\t\t\tname: \"Simple average\",\n 68→\t\t\tpoints: []schema.Float{1.0, 2.0, 3.0},\n 69→\t\t\txStart: 0,\n 70→\t\t\texpectedX: 1.0,\n 71→\t\t\texpectedY: 2.0,\n 72→\t\t\texpectNaN: false,\n 73→\t\t},\n 74→\t\t{\n 75→\t\t\tname: \"Single point\",\n 76→\t\t\tpoints: []schema.Float{5.0},\n 77→\t\t\txStart: 10,\n 78→\t\t\texpectedX: 10.0,\n 79→\t\t\texpectedY: 5.0,\n 80→\t\t\texpectNaN: false,\n 81→\t\t},\n 82→\t\t{\n 83→\t\t\tname: \"With NaN value\",\n 84→\t\t\tpoints: []schema.Float{1.0, schema.NaN, 3.0},\n 85→\t\t\txStart: 0,\n 86→\t\t\texpectedX: 1.0,\n 87→\t\t\texpectNaN: true,\n 88→\t\t},\n 89→\t\t{\n 90→\t\t\tname: \"All NaN values\",\n 91→\t\t\tpoints: []schema.Float{schema.NaN, schema.NaN},\n 92→\t\t\txStart: 0,\n 93→\t\t\texpectedX: 0.5,\n 94→\t\t\texpectNaN: true,\n 95→\t\t},\n 96→\t}\n 97→\n 98→\tfor _, tt := range tests {\n 99→\t\tt.Run(tt.name, func(t *testing.T) {\n 100→\t\t\tavgX, avgY := calculateAverageDataPoint(tt.points, tt.xStart)\n 101→\t\t\tif math.Abs(float64(avgX-tt.expectedX)) > 1e-10 {\n 102→\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgX = %v, want %v\", avgX, tt.expectedX)\n 103→\t\t\t}\n 104→\t\t\tif tt.expectNaN {\n 105→\t\t\t\tif !math.IsNaN(float64(avgY)) {\n 106→\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY should be NaN, got %v\", avgY)\n 107→\t\t\t\t}\n 108→\t\t\t} else {\n 109→\t\t\t\tif math.Abs(float64(avgY-tt.expectedY)) > 1e-10 {\n 110→\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY = %v, want %v\", avgY, tt.expectedY)\n 111→\t\t\t\t}\n 112→\t\t\t}\n 113→\t\t})\n 114→\t}\n 115→}\n 116→\n 117→// TestSimpleResampler tests the SimpleResampler function\n 118→func TestSimpleResampler(t *testing.T) {\n 119→\t// Set minimum required points to 100 for testing\n 120→\tSetMinimumRequiredPoints(100)\n 121→\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n 122→\n 123→\ttests := []struct {\n 124→\t\tname string\n 125→\t\tdata []schema.Float\n 126→\t\toldFrequency int64\n 127→\t\tnewFrequency int64\n 128→\t\texpectedLen int\n 129→\t\texpectedFreq int64\n 130→\t\texpectError bool\n 131→\t\tcheckValues bool\n 132→\t\texpectedData []schema.Float\n 133→\t}{\n 134→\t\t{\n 135→\t\t\tname: \"Normal downsampling\",\n 136→\t\t\tdata: makeTestData(200, 1.0),\n 137→\t\t\toldFrequency: 1,\n 138→\t\t\tnewFrequency: 2,\n 139→\t\t\texpectedLen: 100,\n 140→\t\t\texpectedFreq: 2,\n 141→\t\t\texpectError: false,\n 142→\t\t},\n 143→\t\t{\n 144→\t\t\tname: \"No downsampling needed (new <= old)\",\n 145→\t\t\tdata: makeTestData(200, 1.0),\n 146→\t\t\toldFrequency: 2,\n 147→\t\t\tnewFrequency: 1,\n 148→\t\t\texpectedLen: 200,\n 149→\t\t\texpectedFreq: 2,\n 150→\t\t\texpectError: false,\n 151→\t\t},\n 152→\t\t{\n 153→\t\t\tname: \"Zero old frequency\",\n 154→\t\t\tdata: makeTestData(200, 1.0),\n 155→\t\t\toldFrequency: 0,\n 156→\t\t\tnewFrequency: 2,\n 157→\t\t\texpectedLen: 200,\n 158→\t\t\texpectedFreq: 0,\n 159→\t\t\texpectError: false,\n 160→\t\t},\n 161→\t\t{\n 162→\t\t\tname: \"Zero new frequency\",\n 163→\t\t\tdata: makeTestData(200, 1.0),\n 164→\t\t\toldFrequency: 1,\n 165→\t\t\tnewFrequency: 0,\n 166→\t\t\texpectedLen: 200,\n 167→\t\t\texpectedFreq: 1,\n 168→\t\t\texpectError: false,\n 169→\t\t},\n 170→\t\t{\n 171→\t\t\tname: \"Non-multiple frequency\",\n 172→\t\t\tdata: makeTestData(200, 1.0),\n 173→\t\t\toldFrequency: 3,\n 174→\t\t\tnewFrequency: 7,\n 175→\t\t\texpectError: true,\n 176→\t\t},\n 177→\t\t{\n 178→\t\t\tname: \"Small dataset (< 100 points)\",\n 179→\t\t\tdata: makeTestData(50, 1.0),\n 180→\t\t\toldFrequency: 1,\n 181→\t\t\tnewFrequency: 2,\n 182→\t\t\texpectedLen: 50,\n 183→\t\t\texpectedFreq: 1,\n 184→\t\t\texpectError: false,\n 185→\t\t},\n 186→\t\t{\n 187→\t\t\tname: \"Exact values check\",\n 188→\t\t\tdata: []schema.Float{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},\n 189→\t\t\toldFrequency: 1,\n 190→\t\t\tnewFrequency: 2,\n 191→\t\t\texpectedLen: 10,\n 192→\t\t\texpectedFreq: 1,\n 193→\t\t\texpectError: false,\n 194→\t\t\tcheckValues: false, // Too small, won't downsample\n 195→\t\t},\n 196→\t\t{\n 197→\t\t\tname: \"Large downsampling factor\",\n 198→\t\t\tdata: makeTestData(1000, 1.0),\n 199→\t\t\toldFrequency: 1,\n 200→\t\t\tnewFrequency: 10,\n 201→\t\t\texpectedLen: 100,\n 202→\t\t\texpectedFreq: 10,\n 203→\t\t\texpectError: false,\n 204→\t\t},\n 205→\t}\n 206→\n 207→\tfor _, tt := range tests {\n 208→\t\tt.Run(tt.name, func(t *testing.T) {\n 209→\t\t\tresult, freq, err := SimpleResampler(tt.data, tt.oldFrequency, tt.newFrequency)\n 210→\n 211→\t\t\tif tt.expectError {\n 212→\t\t\t\tif err == nil {\n 213→\t\t\t\t\tt.Errorf(\"SimpleResampler() expected error, got nil\")\n 214→\t\t\t\t}\n 215→\t\t\t\treturn\n 216→\t\t\t}\n 217→\n 218→\t\t\tif err != nil {\n 219→\t\t\t\tt.Errorf(\"SimpleResampler() unexpected error: %v\", err)\n 220→\t\t\t\treturn\n 221→\t\t\t}\n 222→\n 223→\t\t\tif len(result) != tt.expectedLen {\n 224→\t\t\t\tt.Errorf(\"SimpleResampler() result length = %v, want %v\", len(result), tt.expectedLen)\n 225→\t\t\t}\n 226→\n 227→\t\t\tif freq != tt.expectedFreq {\n 228→\t\t\t\tt.Errorf(\"SimpleResampler() frequency = %v, want %v\", freq, tt.expectedFreq)\n 229→\t\t\t}\n 230→\n 231→\t\t\tif tt.checkValues && tt.expectedData != nil {\n 232→\t\t\t\tfor i := range result {\n 233→\t\t\t\t\tif result[i] != tt.expectedData[i] {\n 234→\t\t\t\t\t\tt.Errorf(\"SimpleResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])\n 235→\t\t\t\t\t}\n 236→\t\t\t\t}\n 237→\t\t\t}\n 238→\t\t})\n 239→\t}\n 240→}\n 241→\n 242→// TestLargestTriangleThreeBucket tests the LTTB algorithm\n 243→func TestLargestTriangleThreeBucket(t *testing.T) {\n 244→\t// Set minimum required points to 100 for testing\n 245→\tSetMinimumRequiredPoints(100)\n 246→\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n 247→\n 248→\ttests := []struct {\n 249→\t\tname string\n 250→\t\tdata []schema.Float\n 251→\t\toldFrequency int64\n 252→\t\tnewFrequency int64\n 253→\t\texpectedLen int\n 254→\t\texpectedFreq int64\n 255→\t\texpectError bool\n 256→\t}{\n 257→\t\t{\n 258→\t\t\tname: \"Normal downsampling\",\n 259→\t\t\tdata: makeTestData(200, 1.0),\n 260→\t\t\toldFrequency: 1,\n 261→\t\t\tnewFrequency: 2,\n 262→\t\t\texpectedLen: 100,\n 263→\t\t\texpectedFreq: 2,\n 264→\t\t\texpectError: false,\n 265→\t\t},\n 266→\t\t{\n 267→\t\t\tname: \"Sine wave pattern\",\n 268→\t\t\tdata: makeSineWave(500, 10),\n 269→\t\t\toldFrequency: 1,\n 270→\t\t\tnewFrequency: 5,\n 271→\t\t\texpectedLen: 100,\n 272→\t\t\texpectedFreq: 5,\n 273→\t\t\texpectError: false,\n 274→\t\t},\n 275→\t\t{\n 276→\t\t\tname: \"No downsampling needed (new <= old)\",\n 277→\t\t\tdata: makeTestData(200, 1.0),\n 278→\t\t\toldFrequency: 2,\n 279→\t\t\tnewFrequency: 1,\n 280→\t\t\texpectedLen: 200,\n 281→\t\t\texpectedFreq: 2,\n 282→\t\t\texpectError: false,\n 283→\t\t},\n 284→\t\t{\n 285→\t\t\tname: \"Zero old frequency\",\n 286→\t\t\tdata: makeTestData(200, 1.0),\n 287→\t\t\toldFrequency: 0,\n 288→\t\t\tnewFrequency: 2,\n 289→\t\t\texpectedLen: 200,\n 290→\t\t\texpectedFreq: 0,\n 291→\t\t\texpectError: false,\n 292→\t\t},\n 293→\t\t{\n 294→\t\t\tname: \"Zero new frequency\",\n 295→\t\t\tdata: makeTestData(200, 1.0),\n 296→\t\t\toldFrequency: 1,\n 297→\t\t\tnewFrequency: 0,\n 298→\t\t\texpectedLen: 200,\n 299→\t\t\texpectedFreq: 1,\n 300→\t\t\texpectError: false,\n 301→\t\t},\n 302→\t\t{\n 303→\t\t\tname: \"Non-multiple frequency\",\n 304→\t\t\tdata: makeTestData(200, 1.0),\n 305→\t\t\toldFrequency: 3,\n 306→\t\t\tnewFrequency: 7,\n 307→\t\t\texpectError: true,\n 308→\t\t},\n 309→\t\t{\n 310→\t\t\tname: \"Small dataset (< 100 points)\",\n 311→\t\t\tdata: makeTestData(50, 1.0),\n 312→\t\t\toldFrequency: 1,\n 313→\t\t\tnewFrequency: 2,\n 314→\t\t\texpectedLen: 50,\n 315→\t\t\texpectedFreq: 1,\n 316→\t\t\texpectError: false,\n 317→\t\t},\n 318→\t\t{\n 319→\t\t\tname: \"With NaN values\",\n 320→\t\t\tdata: makeTestDataWithNaN(200),\n 321→\t\t\toldFrequency: 1,\n 322→\t\t\tnewFrequency: 2,\n 323→\t\t\texpectedLen: 100,\n 324→\t\t\texpectedFreq: 2,\n 325→\t\t\texpectError: false,\n 326→\t\t},\n 327→\t\t{\n 328→\t\t\tname: \"Large downsampling factor\",\n 329→\t\t\tdata: makeTestData(1000, 1.0),\n 330→\t\t\toldFrequency: 1,\n 331→\t\t\tnewFrequency: 10,\n 332→\t\t\texpectedLen: 100,\n 333→\t\t\texpectedFreq: 10,\n 334→\t\t\texpectError: false,\n 335→\t\t},\n 336→\t}\n 337→\n 338→\tfor _, tt := range tests {\n 339→\t\tt.Run(tt.name, func(t *testing.T) {\n 340→\t\t\tresult, freq, err := LargestTriangleThreeBucket(tt.data, tt.oldFrequency, tt.newFrequency)\n 341→\n 342→\t\t\tif tt.expectError {\n 343→\t\t\t\tif err == nil {\n 344→\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() expected error, got nil\")\n 345→\t\t\t\t}\n 346→\t\t\t\treturn\n 347→\t\t\t}\n 348→\n 349→\t\t\tif err != nil {\n 350→\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() unexpected error: %v\", err)\n 351→\t\t\t\treturn\n 352→\t\t\t}\n 353→\n 354→\t\t\tif len(result) != tt.expectedLen {\n 355→\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() result length = %v, want %v\", len(result), tt.expectedLen)\n 356→\t\t\t}\n 357→\n 358→\t\t\tif freq != tt.expectedFreq {\n 359→\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() frequency = %v, want %v\", freq, tt.expectedFreq)\n 360→\t\t\t}\n 361→\n 362→\t\t\t// Verify first and last points are preserved\n 363→\t\t\tif len(result) > 0 && len(tt.data) > 0 {\n 364→\t\t\t\t// Handle NaN comparison specially since NaN != NaN\n 365→\t\t\t\tif math.IsNaN(float64(tt.data[0])) {\n 366→\t\t\t\t\tif !math.IsNaN(float64(result[0])) {\n 367→\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want NaN\", result[0])\n 368→\t\t\t\t\t}\n 369→\t\t\t\t} else if result[0] != tt.data[0] {\n 370→\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want %v\", result[0], tt.data[0])\n 371→\t\t\t\t}\n 372→\n 373→\t\t\t\tif math.IsNaN(float64(tt.data[len(tt.data)-1])) {\n 374→\t\t\t\t\tif !math.IsNaN(float64(result[len(result)-1])) {\n 375→\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want NaN\", result[len(result)-1])\n 376→\t\t\t\t\t}\n 377→\t\t\t\t} else if result[len(result)-1] != tt.data[len(tt.data)-1] {\n 378→\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want %v\", result[len(result)-1], tt.data[len(tt.data)-1])\n 379→\t\t\t\t}\n 380→\t\t\t}\n 381→\t\t})\n 382→\t}\n 383→}\n 384→\n 385→// TestLTTBPreservesFeatures tests that LTTB preserves important features better than simple resampling\n 386→func TestLTTBPreservesFeatures(t *testing.T) {\n 387→\t// Set minimum required points to 100 for testing\n 388→\tSetMinimumRequiredPoints(100)\n 389→\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n 390→\n 391→\t// Create a dataset with a spike\n 392→\tdata := make([]schema.Float, 200)\n 393→\tfor i := range data {\n 394→\t\tif i == 100 {\n 395→\t\t\tdata[i] = 100.0 // Spike\n 396→\t\t} else {\n 397→\t\t\tdata[i] = 1.0\n 398→\t\t}\n 399→\t}\n 400→\n 401→\tlttbResult, _, _ := LargestTriangleThreeBucket(data, 1, 2)\n 402→\n 403→\t// LTTB should be more likely to preserve the spike\n 404→\tlttbHasSpike := false\n 405→\n 406→\tfor _, v := range lttbResult {\n 407→\t\tif v > 50.0 {\n 408→\t\t\tlttbHasSpike = true\n 409→\t\t\tbreak\n 410→\t\t}\n 411→\t}\n 412→\n 413→\t// This test is probabilistic, but LTTB should generally preserve the spike\n 414→\tif !lttbHasSpike {\n 415→\t\tt.Log(\"Warning: LTTB did not preserve spike (may happen occasionally)\")\n 416→\t}\n 417→}\n 418→\n 419→// Helper functions for test data generation\n 420→\n 421→// makeTestData creates a simple linear test dataset\n 422→func makeTestData(size int, increment schema.Float) []schema.Float {\n 423→\tdata := make([]schema.Float, size)\n 424→\tfor i := range data {\n 425→\t\tdata[i] = schema.Float(i) * increment\n 426→\t}\n 427→\treturn data\n 428→}\n 429→\n 430→// makeSineWave creates a sine wave test dataset\n 431→func makeSineWave(size int, periods float64) []schema.Float {\n 432→\tdata := make([]schema.Float, size)\n 433→\tfor i := range data {\n 434→\t\tangle := 2 * math.Pi * periods * float64(i) / float64(size)\n 435→\t\tdata[i] = schema.Float(math.Sin(angle))\n 436→\t}\n 437→\treturn data\n 438→}\n 439→\n 440→// makeTestDataWithNaN creates test data with some NaN values\n 441→func makeTestDataWithNaN(size int) []schema.Float {\n 442→\tdata := make([]schema.Float, size)\n 443→\tfor i := range data {\n 444→\t\tif i%20 == 0 {\n 445→\t\t\tdata[i] = schema.NaN\n 446→\t\t} else {\n 447→\t\t\tdata[i] = schema.Float(i)\n 448→\t\t}\n 449→\t}\n 450→\treturn data\n 451→}\n 452→\n 453→// Benchmark tests\n 454→\n 455→func BenchmarkSimpleResampler(b *testing.B) {\n 456→\tdata := makeTestData(10000, 1.0)\n 457→\tb.ResetTimer()\n 458→\tfor i := 0; i < b.N; i++ {\n 459→\t\t_, _, _ = SimpleResampler(data, 1, 10)\n 460→\t}\n 461→}\n 462→\n 463→func BenchmarkLargestTriangleThreeBucket(b *testing.B) {\n 464→\tdata := makeTestData(10000, 1.0)\n 465→\tb.ResetTimer()\n 466→\tfor i := 0; i < b.N; i++ {\n 467→\t\t_, _, _ = LargestTriangleThreeBucket(data, 1, 10)\n 468→\t}\n 469→}\n 470→\n 471→func BenchmarkLTTBLargeDataset(b *testing.B) {\n 472→\tdata := makeTestData(100000, 1.0)\n 473→\tb.ResetTimer()\n 474→\tfor i := 0; i < b.N; i++ {\n 475→\t\t_, _, _ = LargestTriangleThreeBucket(data, 1, 100)\n 476→\t}\n 477→}\n 478→"}]},"uuid":"098cc8c4-7772-423a-ba9b-120dae0264af","timestamp":"2026-03-19T19:46:21.603Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-lib/resampler/resampler_test.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-lib.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\npackage resampler\n\nimport (\n\t\"math\"\n\t\"testing\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// TestCalculateTriangleArea tests the triangle area calculation helper function\nfunc TestCalculateTriangleArea(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\tpaX, paY schema.Float\n\t\tpbX, pbY schema.Float\n\t\tpcX, pcY schema.Float\n\t\texpected float64\n\t}{\n\t\t{\n\t\t\tname: \"Right triangle\",\n\t\t\tpaX: 0, paY: 0,\n\t\t\tpbX: 0, pbY: 3,\n\t\t\tpcX: 4, pcY: 0,\n\t\t\texpected: 6.0,\n\t\t},\n\t\t{\n\t\t\tname: \"Collinear points (zero area)\",\n\t\t\tpaX: 0, paY: 0,\n\t\t\tpbX: 1, pbY: 1,\n\t\t\tpcX: 2, pcY: 2,\n\t\t\texpected: 0.0,\n\t\t},\n\t\t{\n\t\t\tname: \"Negative coordinates\",\n\t\t\tpaX: -1, paY: -1,\n\t\t\tpbX: -1, pbY: 2,\n\t\t\tpcX: 3, pcY: -1,\n\t\t\texpected: 6.0,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult := calculateTriangleArea(tt.paX, tt.paY, tt.pbX, tt.pbY, tt.pcX, tt.pcY)\n\t\t\tif math.Abs(result-tt.expected) > 1e-10 {\n\t\t\t\tt.Errorf(\"calculateTriangleArea() = %v, want %v\", result, tt.expected)\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestCalculateAverageDataPoint tests the average point calculation helper function\nfunc TestCalculateAverageDataPoint(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\tpoints []schema.Float\n\t\txStart int64\n\t\texpectedX schema.Float\n\t\texpectedY schema.Float\n\t\texpectNaN bool\n\t}{\n\t\t{\n\t\t\tname: \"Simple average\",\n\t\t\tpoints: []schema.Float{1.0, 2.0, 3.0},\n\t\t\txStart: 0,\n\t\t\texpectedX: 1.0,\n\t\t\texpectedY: 2.0,\n\t\t\texpectNaN: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Single point\",\n\t\t\tpoints: []schema.Float{5.0},\n\t\t\txStart: 10,\n\t\t\texpectedX: 10.0,\n\t\t\texpectedY: 5.0,\n\t\t\texpectNaN: false,\n\t\t},\n\t\t{\n\t\t\tname: \"With NaN value\",\n\t\t\tpoints: []schema.Float{1.0, schema.NaN, 3.0},\n\t\t\txStart: 0,\n\t\t\texpectedX: 1.0,\n\t\t\texpectNaN: true,\n\t\t},\n\t\t{\n\t\t\tname: \"All NaN values\",\n\t\t\tpoints: []schema.Float{schema.NaN, schema.NaN},\n\t\t\txStart: 0,\n\t\t\texpectedX: 0.5,\n\t\t\texpectNaN: true,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tavgX, avgY := calculateAverageDataPoint(tt.points, tt.xStart)\n\t\t\tif math.Abs(float64(avgX-tt.expectedX)) > 1e-10 {\n\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgX = %v, want %v\", avgX, tt.expectedX)\n\t\t\t}\n\t\t\tif tt.expectNaN {\n\t\t\t\tif !math.IsNaN(float64(avgY)) {\n\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY should be NaN, got %v\", avgY)\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif math.Abs(float64(avgY-tt.expectedY)) > 1e-10 {\n\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY = %v, want %v\", avgY, tt.expectedY)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestSimpleResampler tests the SimpleResampler function\nfunc TestSimpleResampler(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t\tcheckValues bool\n\t\texpectedData []schema.Float\n\t}{\n\t\t{\n\t\t\tname: \"Normal downsampling\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Exact values check\",\n\t\t\tdata: []schema.Float{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 10,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t\tcheckValues: false, // Too small, won't downsample\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t\texpectError: false,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := SimpleResampler(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"SimpleResampler() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"SimpleResampler() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"SimpleResampler() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"SimpleResampler() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\tif tt.checkValues && tt.expectedData != nil {\n\t\t\t\tfor i := range result {\n\t\t\t\t\tif result[i] != tt.expectedData[i] {\n\t\t\t\t\t\tt.Errorf(\"SimpleResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestLargestTriangleThreeBucket tests the LTTB algorithm\nfunc TestLargestTriangleThreeBucket(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t}{\n\t\t{\n\t\t\tname: \"Normal downsampling\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Sine wave pattern\",\n\t\t\tdata: makeSineWave(500, 10),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 5,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 5,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"With NaN values\",\n\t\t\tdata: makeTestDataWithNaN(200),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t\texpectError: false,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := LargestTriangleThreeBucket(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\t// Verify first and last points are preserved\n\t\t\tif len(result) > 0 && len(tt.data) > 0 {\n\t\t\t\t// Handle NaN comparison specially since NaN != NaN\n\t\t\t\tif math.IsNaN(float64(tt.data[0])) {\n\t\t\t\t\tif !math.IsNaN(float64(result[0])) {\n\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want NaN\", result[0])\n\t\t\t\t\t}\n\t\t\t\t} else if result[0] != tt.data[0] {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want %v\", result[0], tt.data[0])\n\t\t\t\t}\n\n\t\t\t\tif math.IsNaN(float64(tt.data[len(tt.data)-1])) {\n\t\t\t\t\tif !math.IsNaN(float64(result[len(result)-1])) {\n\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want NaN\", result[len(result)-1])\n\t\t\t\t\t}\n\t\t\t\t} else if result[len(result)-1] != tt.data[len(tt.data)-1] {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want %v\", result[len(result)-1], tt.data[len(tt.data)-1])\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestLTTBPreservesFeatures tests that LTTB preserves important features better than simple resampling\nfunc TestLTTBPreservesFeatures(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\t// Create a dataset with a spike\n\tdata := make([]schema.Float, 200)\n\tfor i := range data {\n\t\tif i == 100 {\n\t\t\tdata[i] = 100.0 // Spike\n\t\t} else {\n\t\t\tdata[i] = 1.0\n\t\t}\n\t}\n\n\tlttbResult, _, _ := LargestTriangleThreeBucket(data, 1, 2)\n\n\t// LTTB should be more likely to preserve the spike\n\tlttbHasSpike := false\n\n\tfor _, v := range lttbResult {\n\t\tif v > 50.0 {\n\t\t\tlttbHasSpike = true\n\t\t\tbreak\n\t\t}\n\t}\n\n\t// This test is probabilistic, but LTTB should generally preserve the spike\n\tif !lttbHasSpike {\n\t\tt.Log(\"Warning: LTTB did not preserve spike (may happen occasionally)\")\n\t}\n}\n\n// Helper functions for test data generation\n\n// makeTestData creates a simple linear test dataset\nfunc makeTestData(size int, increment schema.Float) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tdata[i] = schema.Float(i) * increment\n\t}\n\treturn data\n}\n\n// makeSineWave creates a sine wave test dataset\nfunc makeSineWave(size int, periods float64) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tangle := 2 * math.Pi * periods * float64(i) / float64(size)\n\t\tdata[i] = schema.Float(math.Sin(angle))\n\t}\n\treturn data\n}\n\n// makeTestDataWithNaN creates test data with some NaN values\nfunc makeTestDataWithNaN(size int) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tif i%20 == 0 {\n\t\t\tdata[i] = schema.NaN\n\t\t} else {\n\t\t\tdata[i] = schema.Float(i)\n\t\t}\n\t}\n\treturn data\n}\n\n// Benchmark tests\n\nfunc BenchmarkSimpleResampler(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = SimpleResampler(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkLargestTriangleThreeBucket(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = LargestTriangleThreeBucket(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkLTTBLargeDataset(b *testing.B) {\n\tdata := makeTestData(100000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = 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{"parentUuid":"e89bb510-7bd4-49f7-b0c7-e68efecf3330","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018zJcg6GZet1CQCD9nfo3nD","type":"tool_result","content":" 1→scalar Time\n 2→scalar Any\n 3→\n 4→scalar NullableFloat\n 5→scalar MetricScope\n 6→scalar JobState\n 7→scalar SchedulerState\n 8→scalar MonitoringState\n 9→\n 10→type Node {\n 11→ id: ID!\n 12→ hostname: String!\n 13→ cluster: String!\n 14→ subCluster: String!\n 15→ jobsRunning: Int!\n 16→ cpusAllocated: Int\n 17→ memoryAllocated: Int\n 18→ gpusAllocated: Int\n 19→ schedulerState: SchedulerState!\n 20→ healthState: MonitoringState!\n 21→ metaData: Any\n 22→ healthData: Any\n 23→}\n 24→\n 25→type NodeStates {\n 26→ state: String!\n 27→ count: Int!\n 28→}\n 29→\n 30→type NodeStatesTimed {\n 31→ state: String!\n 32→ counts: [Int!]!\n 33→ times: [Int!]!\n 34→}\n 35→\n 36→type Job {\n 37→ id: ID!\n 38→ jobId: Int!\n 39→ user: String!\n 40→ project: String!\n 41→ cluster: String!\n 42→ subCluster: String!\n 43→ startTime: Time!\n 44→ duration: Int!\n 45→ walltime: Int!\n 46→ numNodes: Int!\n 47→ numHWThreads: Int!\n 48→ numAcc: Int!\n 49→ energy: Float!\n 50→ SMT: Int!\n 51→ shared: String!\n 52→ partition: String!\n 53→ arrayJobId: Int!\n 54→ monitoringStatus: Int!\n 55→ state: JobState!\n 56→ tags: [Tag!]!\n 57→ resources: [Resource!]!\n 58→ concurrentJobs: JobLinkResultList\n 59→ footprint: [FootprintValue]\n 60→ energyFootprint: [EnergyFootprintValue]\n 61→ metaData: Any\n 62→ userData: User\n 63→}\n 64→\n 65→type JobLink {\n 66→ id: ID!\n 67→ jobId: Int!\n 68→}\n 69→\n 70→type Cluster {\n 71→ name: String!\n 72→ partitions: [String!]! # Slurm partitions\n 73→ subClusters: [SubCluster!]! # Hardware partitions/subclusters\n 74→}\n 75→\n 76→type SubCluster {\n 77→ name: String!\n 78→ nodes: String!\n 79→ numberOfNodes: Int!\n 80→ processorType: String!\n 81→ socketsPerNode: Int!\n 82→ coresPerSocket: Int!\n 83→ threadsPerCore: Int!\n 84→ flopRateScalar: MetricValue!\n 85→ flopRateSimd: MetricValue!\n 86→ memoryBandwidth: MetricValue!\n 87→ topology: Topology!\n 88→ metricConfig: [MetricConfig!]!\n 89→ footprint: [String!]!\n 90→}\n 91→\n 92→type FootprintValue {\n 93→ name: String!\n 94→ stat: String!\n 95→ value: Float!\n 96→}\n 97→\n 98→type EnergyFootprintValue {\n 99→ hardware: String!\n 100→ metric: String!\n 101→ value: Float!\n 102→}\n 103→\n 104→type MetricValue {\n 105→ name: String\n 106→ unit: Unit!\n 107→ value: Float!\n 108→}\n 109→\n 110→type Topology {\n 111→ node: [Int!]\n 112→ socket: [[Int!]!]\n 113→ memoryDomain: [[Int!]!]\n 114→ die: [[Int!]!]\n 115→ core: [[Int!]!]\n 116→ accelerators: [Accelerator!]\n 117→}\n 118→\n 119→type Accelerator {\n 120→ id: String!\n 121→ type: String!\n 122→ model: String!\n 123→}\n 124→\n 125→type SubClusterConfig {\n 126→ name: String!\n 127→ peak: Float\n 128→ normal: Float\n 129→ caution: Float\n 130→ alert: Float\n 131→ remove: Boolean\n 132→}\n 133→\n 134→type MetricConfig {\n 135→ name: String!\n 136→ unit: Unit!\n 137→ scope: MetricScope!\n 138→ aggregation: String!\n 139→ timestep: Int!\n 140→ peak: Float!\n 141→ normal: Float\n 142→ caution: Float!\n 143→ alert: Float!\n 144→ lowerIsBetter: Boolean\n 145→ subClusters: [SubClusterConfig!]!\n 146→}\n 147→\n 148→type Tag {\n 149→ id: ID!\n 150→ type: String!\n 151→ name: String!\n 152→ scope: String!\n 153→}\n 154→\n 155→type Resource {\n 156→ hostname: String!\n 157→ hwthreads: [Int!]\n 158→ accelerators: [String!]\n 159→ configuration: String\n 160→}\n 161→\n 162→type JobMetricWithName {\n 163→ name: String!\n 164→ scope: MetricScope!\n 165→ metric: JobMetric!\n 166→}\n 167→\n 168→type ClusterMetricWithName {\n 169→ name: String!\n 170→ unit: Unit\n 171→ timestep: Int!\n 172→ data: [NullableFloat!]!\n 173→}\n 174→\n 175→type JobMetric {\n 176→ unit: Unit\n 177→ timestep: Int!\n 178→ series: [Series!]\n 179→ statisticsSeries: StatsSeries\n 180→}\n 181→\n 182→type Series {\n 183→ hostname: String!\n 184→ id: String\n 185→ statistics: MetricStatistics\n 186→ data: [NullableFloat!]!\n 187→}\n 188→\n 189→type StatsSeries {\n 190→ mean: [NullableFloat!]!\n 191→ median: [NullableFloat!]!\n 192→ min: [NullableFloat!]!\n 193→ max: [NullableFloat!]!\n 194→}\n 195→\n 196→type NamedStatsWithScope {\n 197→ name: String!\n 198→ scope: MetricScope!\n 199→ stats: [ScopedStats!]!\n 200→}\n 201→\n 202→type ScopedStats {\n 203→ hostname: String!\n 204→ id: String\n 205→ data: MetricStatistics!\n 206→}\n 207→\n 208→type JobStats {\n 209→ id: Int!\n 210→ jobId: String!\n 211→ startTime: Int!\n 212→ duration: Int!\n 213→ cluster: String!\n 214→ subCluster: String!\n 215→ numNodes: Int!\n 216→ numHWThreads: Int\n 217→ numAccelerators: Int\n 218→ stats: [NamedStats!]!\n 219→}\n 220→\n 221→type NamedStats {\n 222→ name: String!\n 223→ data: MetricStatistics!\n 224→}\n 225→\n 226→type Unit {\n 227→ base: String!\n 228→ prefix: String\n 229→}\n 230→\n 231→type MetricStatistics {\n 232→ avg: Float!\n 233→ min: Float!\n 234→ max: Float!\n 235→}\n 236→\n 237→type MetricFootprints {\n 238→ metric: String!\n 239→ data: [NullableFloat!]!\n 240→}\n 241→\n 242→type Footprints {\n 243→ timeWeights: TimeWeights!\n 244→ metrics: [MetricFootprints!]!\n 245→}\n 246→\n 247→type TimeWeights {\n 248→ nodeHours: [NullableFloat!]!\n 249→ accHours: [NullableFloat!]!\n 250→ coreHours: [NullableFloat!]!\n 251→}\n 252→\n 253→enum Aggregate {\n 254→ USER\n 255→ PROJECT\n 256→ CLUSTER\n 257→ SUBCLUSTER\n 258→}\n 259→enum SortByAggregate {\n 260→ TOTALWALLTIME\n 261→ TOTALJOBS\n 262→ TOTALUSERS\n 263→ TOTALNODES\n 264→ TOTALNODEHOURS\n 265→ TOTALCORES\n 266→ TOTALCOREHOURS\n 267→ TOTALACCS\n 268→ TOTALACCHOURS\n 269→}\n 270→\n 271→type NodeMetrics {\n 272→ host: String!\n 273→ nodeState: String!\n 274→ metricHealth: String!\n 275→ subCluster: String!\n 276→ metrics: [JobMetricWithName!]!\n 277→}\n 278→\n 279→type ClusterMetrics {\n 280→ nodeCount: Int!\n 281→ metrics: [ClusterMetricWithName!]!\n 282→}\n 283→\n 284→type NodesResultList {\n 285→ items: [NodeMetrics!]!\n 286→ offset: Int\n 287→ limit: Int\n 288→ count: Int\n 289→ totalNodes: Int\n 290→ hasNextPage: Boolean\n 291→}\n 292→\n 293→type ClusterSupport {\n 294→ cluster: String!\n 295→ subClusters: [String!]!\n 296→}\n 297→\n 298→type GlobalMetricListItem {\n 299→ name: String!\n 300→ unit: Unit!\n 301→ scope: MetricScope!\n 302→ footprint: String\n 303→ availability: [ClusterSupport!]!\n 304→}\n 305→\n 306→type Count {\n 307→ name: String!\n 308→ count: Int!\n 309→}\n 310→\n 311→type User {\n 312→ username: String!\n 313→ name: String!\n 314→ email: String!\n 315→}\n 316→\n 317→input MetricStatItem {\n 318→ metricName: String!\n 319→ range: FloatRange!\n 320→}\n 321→\n 322→type Query {\n 323→ clusters: [Cluster!]! # List of all clusters\n 324→ tags: [Tag!]! # List of all tags\n 325→ globalMetrics: [GlobalMetricListItem!]!\n 326→\n 327→ user(username: String!): User\n 328→ allocatedNodes(cluster: String!): [Count!]!\n 329→\n 330→ ## Node Queries New\n 331→ node(id: ID!): Node\n 332→ nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n 333→ nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n 334→ nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n 335→ nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n 336→\n 337→ job(id: ID!): Job\n 338→ jobMetrics(\n 339→ id: ID!\n 340→ metrics: [String!]\n 341→ scopes: [MetricScope!]\n 342→ resolution: Int\n 343→ ): [JobMetricWithName!]!\n 344→\n 345→ jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n 346→\n 347→ scopedJobStats(\n 348→ id: ID!\n 349→ metrics: [String!]\n 350→ scopes: [MetricScope!]\n 351→ ): [NamedStatsWithScope!]!\n 352→\n 353→ jobs(\n 354→ filter: [JobFilter!]\n 355→ page: PageRequest\n 356→ order: OrderByInput\n 357→ ): JobResultList!\n 358→\n 359→ jobsStatistics(\n 360→ filter: [JobFilter!]\n 361→ metrics: [String!]\n 362→ page: PageRequest\n 363→ sortBy: SortByAggregate\n 364→ groupBy: Aggregate\n 365→ numDurationBins: String\n 366→ numMetricBins: Int\n 367→ ): [JobsStatistics!]!\n 368→\n 369→ jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n 370→ jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n 371→\n 372→ rooflineHeatmap(\n 373→ filter: [JobFilter!]!\n 374→ rows: Int!\n 375→ cols: Int!\n 376→ minX: Float!\n 377→ minY: Float!\n 378→ maxX: Float!\n 379→ maxY: Float!\n 380→ ): [[Float!]!]!\n 381→\n 382→ nodeMetrics(\n 383→ cluster: String!\n 384→ nodes: [String!]\n 385→ scopes: [MetricScope!]\n 386→ metrics: [String!]\n 387→ from: Time!\n 388→ to: Time!\n 389→ ): [NodeMetrics!]!\n 390→\n 391→ nodeMetricsList(\n 392→ cluster: String!\n 393→ subCluster: String!\n 394→ stateFilter: String!\n 395→ nodeFilter: String!\n 396→ scopes: [MetricScope!]\n 397→ metrics: [String!]\n 398→ from: Time!\n 399→ to: Time!\n 400→ page: PageRequest\n 401→ resolution: Int\n 402→ ): NodesResultList!\n 403→\n 404→ clusterMetrics(\n 405→ cluster: String!\n 406→ metrics: [String!]\n 407→ from: Time!\n 408→ to: Time!\n 409→ ): ClusterMetrics!\n 410→}\n 411→\n 412→type Mutation {\n 413→ createTag(type: String!, name: String!, scope: String!): Tag!\n 414→ deleteTag(id: ID!): ID!\n 415→ addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n 416→ removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n 417→ removeTagFromList(tagIds: [ID!]!): [Int!]!\n 418→\n 419→ updateConfiguration(name: String!, value: String!): String\n 420→}\n 421→\n 422→type IntRangeOutput {\n 423→ from: Int!\n 424→ to: Int!\n 425→}\n 426→type TimeRangeOutput {\n 427→ range: String\n 428→ from: Time!\n 429→ to: Time!\n 430→}\n 431→\n 432→input NodeFilter {\n 433→ hostname: StringInput\n 434→ cluster: StringInput\n 435→ subCluster: StringInput\n 436→ schedulerState: SchedulerState\n 437→ healthState: MonitoringState\n 438→ timeStart: Int\n 439→}\n 440→\n 441→input JobFilter {\n 442→ tags: [ID!]\n 443→ dbId: [ID!]\n 444→ jobId: StringInput\n 445→ arrayJobId: Int\n 446→ user: StringInput\n 447→ project: StringInput\n 448→ jobName: StringInput\n 449→ cluster: StringInput\n 450→ subCluster: StringInput\n 451→ partition: StringInput\n 452→ duration: IntRange\n 453→ energy: FloatRange\n 454→\n 455→ numNodes: IntRange\n 456→ numAccelerators: IntRange\n 457→ numHWThreads: IntRange\n 458→\n 459→ startTime: TimeRange\n 460→ state: [JobState!]\n 461→ metricStats: [MetricStatItem!]\n 462→ shared: String\n 463→ schedule: String\n 464→ node: StringInput\n 465→}\n 466→\n 467→input OrderByInput {\n 468→ field: String!\n 469→ type: String!\n 470→ order: SortDirectionEnum! = ASC\n 471→}\n 472→\n 473→enum SortDirectionEnum {\n 474→ DESC\n 475→ ASC\n 476→}\n 477→\n 478→input StringInput {\n 479→ eq: String\n 480→ neq: String\n 481→ contains: String\n 482→ startsWith: String\n 483→ endsWith: String\n 484→ in: [String!]\n 485→}\n 486→\n 487→input IntRange {\n 488→ from: Int!\n 489→ to: Int!\n 490→}\n 491→input TimeRange {\n 492→ range: String\n 493→ from: Time\n 494→ to: Time\n 495→}\n 496→\n 497→input FloatRange {\n 498→ from: Float!\n 499→ to: Float!\n 500→}\n 501→\n 502→type NodeStateResultList {\n 503→ items: [Node!]!\n 504→ count: Int\n 505→}\n 506→\n 507→type JobResultList {\n 508→ items: [Job!]!\n 509→ offset: Int\n 510→ limit: Int\n 511→ count: Int\n 512→ hasNextPage: Boolean\n 513→}\n 514→\n 515→type JobLinkResultList {\n 516→ listQuery: String\n 517→ items: [JobLink!]!\n 518→ count: Int\n 519→}\n 520→\n 521→type HistoPoint {\n 522→ count: Int!\n 523→ value: Int!\n 524→}\n 525→\n 526→type MetricHistoPoints {\n 527→ metric: String!\n 528→ unit: String!\n 529→ stat: String\n 530→ data: [MetricHistoPoint!]\n 531→}\n 532→\n 533→type MetricHistoPoint {\n 534→ bin: Int\n 535→ count: Int!\n 536→ min: Int\n 537→ max: Int\n 538→}\n 539→\n 540→type JobsStatistics {\n 541→ id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n 542→ name: String! # if User-Statistics: Given Name of Account (ID) Owner\n 543→ totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n 544→ totalJobs: Int! # Number of jobs\n 545→ runningJobs: Int! # Number of running jobs\n 546→ shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n 547→ totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n 548→ totalNodes: Int! # Sum of the nodes of all matched jobs\n 549→ totalNodeHours: Int! # Sum of the node hours of all matched jobs\n 550→ totalCores: Int! # Sum of the cores of all matched jobs\n 551→ totalCoreHours: Int! # Sum of the core hours of all matched jobs\n 552→ totalAccs: Int! # Sum of the accs of all matched jobs\n 553→ totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n 554→ histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n 555→ histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n 556→ histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n 557→ histNumAccs: [HistoPoint!]! # value: number of accs, count: number of jobs with that number of accs\n 558→ histMetrics: [MetricHistoPoints!]! # metric: metricname, data array of histopoints: value: metric average bin, count: number of jobs with that metric average\n 559→}\n 560→\n 561→input PageRequest {\n 562→ itemsPerPage: Int!\n 563→ page: Int!\n 564→}\n 565→"}]},"uuid":"fc97ab79-506f-4e19-b5a4-38ff7147892a","timestamp":"2026-03-19T19:46:22.327Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/api/schema.graphqls","content":"scalar Time\nscalar Any\n\nscalar NullableFloat\nscalar MetricScope\nscalar JobState\nscalar SchedulerState\nscalar MonitoringState\n\ntype Node {\n id: ID!\n hostname: String!\n cluster: String!\n subCluster: String!\n jobsRunning: Int!\n cpusAllocated: Int\n memoryAllocated: Int\n gpusAllocated: Int\n schedulerState: SchedulerState!\n healthState: MonitoringState!\n metaData: Any\n healthData: Any\n}\n\ntype NodeStates {\n state: String!\n count: Int!\n}\n\ntype NodeStatesTimed {\n state: String!\n counts: [Int!]!\n times: [Int!]!\n}\n\ntype Job {\n id: ID!\n jobId: Int!\n user: String!\n project: String!\n cluster: String!\n subCluster: String!\n startTime: Time!\n duration: Int!\n walltime: Int!\n numNodes: Int!\n numHWThreads: Int!\n numAcc: Int!\n energy: Float!\n SMT: Int!\n shared: String!\n partition: String!\n arrayJobId: Int!\n monitoringStatus: Int!\n state: JobState!\n tags: [Tag!]!\n resources: [Resource!]!\n concurrentJobs: JobLinkResultList\n footprint: [FootprintValue]\n energyFootprint: [EnergyFootprintValue]\n metaData: Any\n userData: User\n}\n\ntype JobLink {\n id: ID!\n jobId: Int!\n}\n\ntype Cluster {\n name: String!\n partitions: [String!]! # Slurm partitions\n subClusters: [SubCluster!]! # Hardware partitions/subclusters\n}\n\ntype SubCluster {\n name: String!\n nodes: String!\n numberOfNodes: Int!\n processorType: String!\n socketsPerNode: Int!\n coresPerSocket: Int!\n threadsPerCore: Int!\n flopRateScalar: MetricValue!\n flopRateSimd: MetricValue!\n memoryBandwidth: MetricValue!\n topology: Topology!\n metricConfig: [MetricConfig!]!\n footprint: [String!]!\n}\n\ntype FootprintValue {\n name: String!\n stat: String!\n value: Float!\n}\n\ntype EnergyFootprintValue {\n hardware: String!\n metric: String!\n value: Float!\n}\n\ntype MetricValue {\n name: String\n unit: Unit!\n value: Float!\n}\n\ntype Topology {\n node: [Int!]\n socket: [[Int!]!]\n memoryDomain: [[Int!]!]\n die: [[Int!]!]\n core: [[Int!]!]\n accelerators: [Accelerator!]\n}\n\ntype Accelerator {\n id: String!\n type: String!\n model: String!\n}\n\ntype SubClusterConfig {\n name: String!\n peak: Float\n normal: Float\n caution: Float\n alert: Float\n remove: Boolean\n}\n\ntype MetricConfig {\n name: String!\n unit: Unit!\n scope: MetricScope!\n aggregation: String!\n timestep: Int!\n peak: Float!\n normal: Float\n caution: Float!\n alert: Float!\n lowerIsBetter: Boolean\n subClusters: [SubClusterConfig!]!\n}\n\ntype Tag {\n id: ID!\n type: String!\n name: String!\n scope: String!\n}\n\ntype Resource {\n hostname: String!\n hwthreads: [Int!]\n accelerators: [String!]\n configuration: String\n}\n\ntype JobMetricWithName {\n name: String!\n scope: MetricScope!\n metric: JobMetric!\n}\n\ntype ClusterMetricWithName {\n name: String!\n unit: Unit\n timestep: Int!\n data: [NullableFloat!]!\n}\n\ntype JobMetric {\n unit: Unit\n timestep: Int!\n series: [Series!]\n statisticsSeries: StatsSeries\n}\n\ntype Series {\n hostname: String!\n id: String\n statistics: MetricStatistics\n data: [NullableFloat!]!\n}\n\ntype StatsSeries {\n mean: [NullableFloat!]!\n median: [NullableFloat!]!\n min: [NullableFloat!]!\n max: [NullableFloat!]!\n}\n\ntype NamedStatsWithScope {\n name: String!\n scope: MetricScope!\n stats: [ScopedStats!]!\n}\n\ntype ScopedStats {\n hostname: String!\n id: String\n data: MetricStatistics!\n}\n\ntype JobStats {\n id: Int!\n jobId: String!\n startTime: Int!\n duration: Int!\n cluster: String!\n subCluster: String!\n numNodes: Int!\n numHWThreads: Int\n numAccelerators: Int\n stats: [NamedStats!]!\n}\n\ntype NamedStats {\n name: String!\n data: MetricStatistics!\n}\n\ntype Unit {\n base: String!\n prefix: String\n}\n\ntype MetricStatistics {\n avg: Float!\n min: Float!\n max: Float!\n}\n\ntype MetricFootprints {\n metric: String!\n data: [NullableFloat!]!\n}\n\ntype Footprints {\n timeWeights: TimeWeights!\n metrics: [MetricFootprints!]!\n}\n\ntype TimeWeights {\n nodeHours: [NullableFloat!]!\n accHours: [NullableFloat!]!\n coreHours: [NullableFloat!]!\n}\n\nenum Aggregate {\n USER\n PROJECT\n CLUSTER\n SUBCLUSTER\n}\nenum SortByAggregate {\n TOTALWALLTIME\n TOTALJOBS\n TOTALUSERS\n TOTALNODES\n TOTALNODEHOURS\n TOTALCORES\n TOTALCOREHOURS\n TOTALACCS\n TOTALACCHOURS\n}\n\ntype NodeMetrics {\n host: String!\n nodeState: String!\n metricHealth: String!\n subCluster: String!\n metrics: [JobMetricWithName!]!\n}\n\ntype ClusterMetrics {\n nodeCount: Int!\n metrics: [ClusterMetricWithName!]!\n}\n\ntype NodesResultList {\n items: [NodeMetrics!]!\n offset: Int\n limit: Int\n count: Int\n totalNodes: Int\n hasNextPage: Boolean\n}\n\ntype ClusterSupport {\n cluster: String!\n subClusters: [String!]!\n}\n\ntype GlobalMetricListItem {\n name: String!\n unit: Unit!\n scope: MetricScope!\n footprint: String\n availability: [ClusterSupport!]!\n}\n\ntype Count {\n name: String!\n count: Int!\n}\n\ntype User {\n username: String!\n name: String!\n email: String!\n}\n\ninput MetricStatItem {\n metricName: String!\n range: FloatRange!\n}\n\ntype Query {\n clusters: [Cluster!]! # List of all clusters\n tags: [Tag!]! # List of all tags\n globalMetrics: [GlobalMetricListItem!]!\n\n user(username: String!): User\n allocatedNodes(cluster: String!): [Count!]!\n\n ## Node Queries New\n node(id: ID!): Node\n nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n\n job(id: ID!): Job\n jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n ): [JobMetricWithName!]!\n\n jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n\n scopedJobStats(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n ): [NamedStatsWithScope!]!\n\n jobs(\n filter: [JobFilter!]\n page: PageRequest\n order: OrderByInput\n ): JobResultList!\n\n jobsStatistics(\n filter: [JobFilter!]\n metrics: [String!]\n page: PageRequest\n sortBy: SortByAggregate\n groupBy: Aggregate\n numDurationBins: String\n numMetricBins: Int\n ): [JobsStatistics!]!\n\n jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n\n rooflineHeatmap(\n filter: [JobFilter!]!\n rows: Int!\n cols: Int!\n minX: Float!\n minY: Float!\n maxX: Float!\n maxY: Float!\n ): [[Float!]!]!\n\n nodeMetrics(\n cluster: String!\n nodes: [String!]\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n ): [NodeMetrics!]!\n\n nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n ): NodesResultList!\n\n clusterMetrics(\n cluster: String!\n metrics: [String!]\n from: Time!\n to: Time!\n ): ClusterMetrics!\n}\n\ntype Mutation {\n createTag(type: String!, name: String!, scope: String!): Tag!\n deleteTag(id: ID!): ID!\n addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagFromList(tagIds: [ID!]!): [Int!]!\n\n updateConfiguration(name: String!, value: String!): String\n}\n\ntype IntRangeOutput {\n from: Int!\n to: Int!\n}\ntype TimeRangeOutput {\n range: String\n from: Time!\n to: Time!\n}\n\ninput NodeFilter {\n hostname: StringInput\n cluster: StringInput\n subCluster: StringInput\n schedulerState: SchedulerState\n healthState: MonitoringState\n timeStart: Int\n}\n\ninput JobFilter {\n tags: [ID!]\n dbId: [ID!]\n jobId: StringInput\n arrayJobId: Int\n user: StringInput\n project: StringInput\n jobName: StringInput\n cluster: StringInput\n subCluster: StringInput\n partition: StringInput\n duration: IntRange\n energy: FloatRange\n\n numNodes: IntRange\n numAccelerators: IntRange\n numHWThreads: IntRange\n\n startTime: TimeRange\n state: [JobState!]\n metricStats: [MetricStatItem!]\n shared: String\n schedule: String\n node: StringInput\n}\n\ninput OrderByInput {\n field: String!\n type: String!\n order: SortDirectionEnum! = ASC\n}\n\nenum SortDirectionEnum {\n DESC\n ASC\n}\n\ninput StringInput {\n eq: String\n neq: String\n contains: String\n startsWith: String\n endsWith: String\n in: [String!]\n}\n\ninput IntRange {\n from: Int!\n to: Int!\n}\ninput TimeRange {\n range: String\n from: Time\n to: Time\n}\n\ninput FloatRange {\n from: Float!\n to: Float!\n}\n\ntype NodeStateResultList {\n items: [Node!]!\n count: Int\n}\n\ntype JobResultList {\n items: [Job!]!\n offset: Int\n limit: Int\n count: Int\n hasNextPage: Boolean\n}\n\ntype JobLinkResultList {\n listQuery: String\n items: [JobLink!]!\n count: Int\n}\n\ntype HistoPoint {\n count: Int!\n value: Int!\n}\n\ntype MetricHistoPoints {\n metric: String!\n unit: String!\n stat: String\n data: [MetricHistoPoint!]\n}\n\ntype MetricHistoPoint {\n bin: Int\n count: Int!\n min: Int\n max: Int\n}\n\ntype JobsStatistics {\n id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n name: String! # if User-Statistics: Given Name of Account (ID) Owner\n totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n totalJobs: Int! # Number of jobs\n runningJobs: Int! # Number of running jobs\n shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n totalNodes: Int! # Sum of the nodes of all matched jobs\n totalNodeHours: Int! # Sum of the node hours of all matched jobs\n totalCores: Int! # Sum of the cores of all matched jobs\n totalCoreHours: Int! # Sum of the core hours of all matched jobs\n totalAccs: Int! # Sum of the accs of all matched jobs\n totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n histNumAccs: [HistoPoint!]! # value: number of accs, count: number of jobs with that number of accs\n histMetrics: [MetricHistoPoints!]! # metric: metricname, data array of histopoints: value: metric average bin, count: number of jobs with that metric average\n}\n\ninput PageRequest {\n itemsPerPage: Int!\n page: Int!\n}\n","numLines":565,"startLine":1,"totalLines":565}},"sourceToolAssistantUUID":"e89bb510-7bd4-49f7-b0c7-e68efecf3330","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"a90bf017-7c03-49f2-87af-00728605cf55","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013cgo7BHHnDHsWAvxsaFb73","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package metricdispatch provides a unified interface for loading and caching job metric data.\n 7→//\n 8→// This package serves as a central dispatcher that routes metric data requests to the appropriate\n 9→// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n 10→// For completed jobs, data is retrieved from the file-based job archive.\n 11→//\n 12→// # Key Features\n 13→//\n 14→// - Automatic backend selection based on job state (running vs. archived)\n 15→// - LRU cache for performance optimization (128 MB default cache size)\n 16→// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n 17→// - Automatic statistics series generation for jobs with many nodes\n 18→// - Support for scoped metrics (node, socket, accelerator, core)\n 19→//\n 20→// # Cache Behavior\n 21→//\n 22→// Cached data has different TTL (time-to-live) values depending on job state:\n 23→// - Running jobs: 2 minutes (data changes frequently)\n 24→// - Completed jobs: 5 hours (data is static)\n 25→//\n 26→// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n 27→//\n 28→// # Usage\n 29→//\n 30→// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n 31→//\n 32→//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n 33→//\tif err != nil {\n 34→//\t // Handle error\n 35→//\t}\n 36→//\n 37→// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\n 38→package metricdispatch\n 39→\n 40→import (\n 41→\t\"context\"\n 42→\t\"fmt\"\n 43→\t\"math\"\n 44→\t\"time\"\n 45→\n 46→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n 47→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 48→\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n 49→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n 50→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 51→)\n 52→\n 53→// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n 54→// The cache reduces load on both the metric store and archive backends.\n 55→var cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n 56→\n 57→// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n 58→// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n 59→// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\n 60→func cacheKey(\n 61→\tjob *schema.Job,\n 62→\tmetrics []string,\n 63→\tscopes []schema.MetricScope,\n 64→\tresolution int,\n 65→) string {\n 66→\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d\",\n 67→\t\t*job.ID, job.State, metrics, scopes, resolution)\n 68→}\n 69→\n 70→// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n 71→// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n 72→//\n 73→// For running jobs or when archive is disabled, data is fetched from the metric store.\n 74→// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n 75→//\n 76→// Parameters:\n 77→// - job: The job for which to load metric data\n 78→// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n 79→// - scopes: Metric scopes to include (nil defaults to node scope)\n 80→// - ctx: Context for cancellation and timeouts\n 81→// - resolution: Target number of data points for resampling (only applies to archived data)\n 82→//\n 83→// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n 84→// the function returns the successfully loaded data with a warning logged.\n 85→func LoadData(job *schema.Job,\n 86→\tmetrics []string,\n 87→\tscopes []schema.MetricScope,\n 88→\tctx context.Context,\n 89→\tresolution int,\n 90→) (schema.JobData, error) {\n 91→\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {\n 92→\t\tvar jd schema.JobData\n 93→\t\tvar err error\n 94→\n 95→\t\tif job.State == schema.JobStateRunning ||\n 96→\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n 97→\n 98→\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n 99→\t\t\tif err != nil {\n 100→\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n 101→\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n 102→\t\t\t\treturn err, 0, 0\n 103→\t\t\t}\n 104→\n 105→\t\t\tif scopes == nil {\n 106→\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n 107→\t\t\t}\n 108→\n 109→\t\t\tif metrics == nil {\n 110→\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n 111→\t\t\t\tfor _, mc := range cluster.MetricConfig {\n 112→\t\t\t\t\tmetrics = append(metrics, mc.Name)\n 113→\t\t\t\t}\n 114→\t\t\t}\n 115→\n 116→\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n 117→\t\t\tif err != nil {\n 118→\t\t\t\tif len(jd) != 0 {\n 119→\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 120→\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 121→\t\t\t\t} else {\n 122→\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 123→\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 124→\t\t\t\t\treturn err, 0, 0\n 125→\t\t\t\t}\n 126→\t\t\t}\n 127→\t\t\tsize = jd.Size()\n 128→\t\t} else {\n 129→\t\t\tvar jdTemp schema.JobData\n 130→\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n 131→\t\t\tif err != nil {\n 132→\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 133→\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 134→\t\t\t\treturn err, 0, 0\n 135→\t\t\t}\n 136→\n 137→\t\t\tjd = deepCopy(jdTemp)\n 138→\n 139→\t\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n 140→\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n 141→\t\t\tfor _, v := range jd {\n 142→\t\t\t\tfor _, v_ := range v {\n 143→\t\t\t\t\ttimestep := int64(0)\n 144→\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n 145→\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n 146→\t\t\t\t\t\tif err != nil {\n 147→\t\t\t\t\t\t\treturn err, 0, 0\n 148→\t\t\t\t\t\t}\n 149→\t\t\t\t\t}\n 150→\t\t\t\t\tv_.Timestep = int(timestep)\n 151→\t\t\t\t}\n 152→\t\t\t}\n 153→\n 154→\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n 155→\t\t\tif metrics != nil || scopes != nil {\n 156→\t\t\t\tif metrics == nil {\n 157→\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n 158→\t\t\t\t\tfor k := range jd {\n 159→\t\t\t\t\t\tmetrics = append(metrics, k)\n 160→\t\t\t\t\t}\n 161→\t\t\t\t}\n 162→\n 163→\t\t\t\tres := schema.JobData{}\n 164→\t\t\t\tfor _, metric := range metrics {\n 165→\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n 166→\t\t\t\t\t\tif len(perscope) > 1 {\n 167→\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n 168→\t\t\t\t\t\t\tfor _, scope := range scopes {\n 169→\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n 170→\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n 171→\t\t\t\t\t\t\t\t}\n 172→\t\t\t\t\t\t\t}\n 173→\n 174→\t\t\t\t\t\t\tif len(subset) > 0 {\n 175→\t\t\t\t\t\t\t\tperscope = subset\n 176→\t\t\t\t\t\t\t}\n 177→\t\t\t\t\t\t}\n 178→\n 179→\t\t\t\t\t\tres[metric] = perscope\n 180→\t\t\t\t\t}\n 181→\t\t\t\t}\n 182→\t\t\t\tjd = res\n 183→\t\t\t}\n 184→\t\t\tsize = jd.Size()\n 185→\t\t}\n 186→\n 187→\t\tttl = 5 * time.Hour\n 188→\t\tif job.State == schema.JobStateRunning {\n 189→\t\t\tttl = 2 * time.Minute\n 190→\t\t}\n 191→\n 192→\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n 193→\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n 194→\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n 195→\t\tconst maxSeriesSize int = 8\n 196→\t\tfor _, scopes := range jd {\n 197→\t\t\tfor _, jm := range scopes {\n 198→\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n 199→\t\t\t\t\tcontinue\n 200→\t\t\t\t}\n 201→\n 202→\t\t\t\tjm.AddStatisticsSeries()\n 203→\t\t\t}\n 204→\t\t}\n 205→\n 206→\t\tnodeScopeRequested := false\n 207→\t\tfor _, scope := range scopes {\n 208→\t\t\tif scope == schema.MetricScopeNode {\n 209→\t\t\t\tnodeScopeRequested = true\n 210→\t\t\t}\n 211→\t\t}\n 212→\n 213→\t\tif nodeScopeRequested {\n 214→\t\t\tjd.AddNodeScope(\"flops_any\")\n 215→\t\t\tjd.AddNodeScope(\"mem_bw\")\n 216→\t\t}\n 217→\n 218→\t\t// Round Resulting Stat Values\n 219→\t\tjd.RoundMetricStats()\n 220→\n 221→\t\treturn jd, ttl, size\n 222→\t})\n 223→\n 224→\tif err, ok := data.(error); ok {\n 225→\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n 226→\t\treturn nil, err\n 227→\t}\n 228→\n 229→\treturn data.(schema.JobData), nil\n 230→}\n 231→\n 232→// LoadAverages computes average values for the specified metrics across all nodes of a job.\n 233→// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n 234→// the pre-calculated averages from the job archive. The results are appended to the data slice.\n 235→func LoadAverages(\n 236→\tjob *schema.Job,\n 237→\tmetrics []string,\n 238→\tdata [][]schema.Float,\n 239→\tctx context.Context,\n 240→) error {\n 241→\tif job.State != schema.JobStateRunning {\n 242→\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n 243→\t}\n 244→\n 245→\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n 246→\tif err != nil {\n 247→\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n 248→\t\t\tjob.Cluster, job.SubCluster, err.Error())\n 249→\t\treturn err\n 250→\t}\n 251→\n 252→\tstats, err := ms.LoadStats(job, metrics, ctx)\n 253→\tif err != nil {\n 254→\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 255→\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 256→\t\treturn err\n 257→\t}\n 258→\n 259→\tfor i, m := range metrics {\n 260→\t\tnodes, ok := stats[m]\n 261→\t\tif !ok {\n 262→\t\t\tdata[i] = append(data[i], schema.NaN)\n 263→\t\t\tcontinue\n 264→\t\t}\n 265→\n 266→\t\tsum := 0.0\n 267→\t\tfor _, node := range nodes {\n 268→\t\t\tsum += node.Avg\n 269→\t\t}\n 270→\t\tdata[i] = append(data[i], schema.Float(sum))\n 271→\t}\n 272→\n 273→\treturn nil\n 274→}\n 275→\n 276→// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n 277→// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n 278→// statistics are loaded from the job archive.\n 279→func LoadScopedJobStats(\n 280→\tjob *schema.Job,\n 281→\tmetrics []string,\n 282→\tscopes []schema.MetricScope,\n 283→\tctx context.Context,\n 284→) (schema.ScopedJobStats, error) {\n 285→\tif job.State != schema.JobStateRunning {\n 286→\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n 287→\t}\n 288→\n 289→\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n 290→\tif err != nil {\n 291→\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n 292→\t\t\tjob.Cluster, job.SubCluster, err.Error())\n 293→\t\treturn nil, err\n 294→\t}\n 295→\n 296→\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n 297→\tif err != nil {\n 298→\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 299→\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 300→\t\treturn nil, err\n 301→\t}\n 302→\n 303→\t// Round Resulting Stat Values\n 304→\tscopedStats.RoundScopedMetricStats()\n 305→\n 306→\treturn scopedStats, nil\n 307→}\n 308→\n 309→// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n 310→// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n 311→// statistics are loaded from the job archive.\n 312→func LoadJobStats(\n 313→\tjob *schema.Job,\n 314→\tmetrics []string,\n 315→\tctx context.Context,\n 316→) (map[string]schema.MetricStatistics, error) {\n 317→\tif job.State != schema.JobStateRunning {\n 318→\t\treturn archive.LoadStatsFromArchive(job, metrics)\n 319→\t}\n 320→\n 321→\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n 322→\tif err != nil {\n 323→\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n 324→\t\t\tjob.Cluster, job.SubCluster, err.Error())\n 325→\t\treturn nil, err\n 326→\t}\n 327→\n 328→\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n 329→\n 330→\tstats, err := ms.LoadStats(job, metrics, ctx)\n 331→\tif err != nil {\n 332→\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n 333→\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n 334→\t\treturn data, err\n 335→\t}\n 336→\n 337→\tfor _, m := range metrics {\n 338→\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n 339→\t\tnodes, ok := stats[m]\n 340→\t\tif !ok {\n 341→\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n 342→\t\t\tcontinue\n 343→\t\t}\n 344→\n 345→\t\tfor _, node := range nodes {\n 346→\t\t\tsum += node.Avg\n 347→\t\t\tmin = math.Min(min, node.Min)\n 348→\t\t\tmax = math.Max(max, node.Max)\n 349→\t\t}\n 350→\n 351→\t\tdata[m] = schema.MetricStatistics{\n 352→\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n 353→\t\t\tMin: (math.Round(min*100) / 100),\n 354→\t\t\tMax: (math.Round(max*100) / 100),\n 355→\t\t}\n 356→\t}\n 357→\n 358→\treturn data, nil\n 359→}\n 360→\n 361→// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n 362→// This is used for node monitoring views and system status pages. Data is always fetched from\n 363→// the metric store (not the archive) since it's for current/recent node status monitoring.\n 364→//\n 365→// Returns a nested map structure: node -> metric -> scoped data.\n 366→// FIXME: Add support for subcluster specific cc-metric-stores\n 367→func LoadNodeData(\n 368→\tcluster string,\n 369→\tmetrics, nodes []string,\n 370→\tscopes []schema.MetricScope,\n 371→\tfrom, to time.Time,\n 372→\tctx context.Context,\n 373→) (map[string]map[string][]*schema.JobMetric, error) {\n 374→\tif metrics == nil {\n 375→\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n 376→\t\t\tmetrics = append(metrics, m.Name)\n 377→\t\t}\n 378→\t}\n 379→\n 380→\tms, err := GetMetricDataRepo(cluster, \"\")\n 381→\tif err != nil {\n 382→\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n 383→\t\t\tcluster, err.Error())\n 384→\t\treturn nil, err\n 385→\t}\n 386→\n 387→\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n 388→\tif err != nil {\n 389→\t\tif len(data) != 0 {\n 390→\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n 391→\t\t} else {\n 392→\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n 393→\t\t\treturn nil, err\n 394→\t\t}\n 395→\t}\n 396→\n 397→\tif data == nil {\n 398→\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n 399→\t}\n 400→\n 401→\treturn data, nil\n 402→}\n 403→\n 404→// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n 405→// with optional resampling and automatic statistics generation for large datasets.\n 406→// This is used for comparing multiple nodes or displaying node status over time.\n 407→//\n 408→// Returns a map of node names to their job-like metric data structures.\n 409→func LoadNodeListData(\n 410→\tcluster, subCluster string,\n 411→\tnodes []string,\n 412→\tmetrics []string,\n 413→\tscopes []schema.MetricScope,\n 414→\tresolution int,\n 415→\tfrom, to time.Time,\n 416→\tctx context.Context,\n 417→) (map[string]schema.JobData, error) {\n 418→\tif metrics == nil {\n 419→\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n 420→\t\t\tmetrics = append(metrics, m.Name)\n 421→\t\t}\n 422→\t}\n 423→\n 424→\tms, err := GetMetricDataRepo(cluster, subCluster)\n 425→\tif err != nil {\n 426→\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n 427→\t\t\tcluster, subCluster, err.Error())\n 428→\t\treturn nil, err\n 429→\t}\n 430→\n 431→\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n 432→\tif err != nil {\n 433→\t\tif len(data) != 0 {\n 434→\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n 435→\t\t\t\tcluster, subCluster, err.Error())\n 436→\t\t} else {\n 437→\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n 438→\t\t\t\tcluster, subCluster, err.Error())\n 439→\t\t\treturn nil, err\n 440→\t\t}\n 441→\t}\n 442→\n 443→\t// Generate statistics series for datasets with many series to improve visualization performance.\n 444→\t// Statistics are calculated as min/median/max.\n 445→\tconst maxSeriesSize int = 8\n 446→\tfor _, jd := range data {\n 447→\t\tfor _, scopes := range jd {\n 448→\t\t\tfor _, jm := range scopes {\n 449→\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n 450→\t\t\t\t\tcontinue\n 451→\t\t\t\t}\n 452→\t\t\t\tjm.AddStatisticsSeries()\n 453→\t\t\t}\n 454→\t\t}\n 455→\t}\n 456→\n 457→\tif data == nil {\n 458→\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n 459→\t}\n 460→\n 461→\treturn data, nil\n 462→}\n 463→\n 464→// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n 465→// archived data (e.g., during resampling). This ensures the cached archive data remains\n 466→// immutable while allowing per-request transformations.\n 467→func deepCopy(source schema.JobData) schema.JobData {\n 468→\tresult := make(schema.JobData, len(source))\n 469→\n 470→\tfor metricName, scopeMap := range source {\n 471→\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n 472→\n 473→\t\tfor scope, jobMetric := range scopeMap {\n 474→\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n 475→\t\t}\n 476→\t}\n 477→\n 478→\treturn result\n 479→}\n 480→\n 481→func copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n 482→\tdst := &schema.JobMetric{\n 483→\t\tTimestep: src.Timestep,\n 484→\t\tUnit: src.Unit,\n 485→\t\tSeries: make([]schema.Series, len(src.Series)),\n 486→\t}\n 487→\n 488→\tfor i := range src.Series {\n 489→\t\tdst.Series[i] = copySeries(&src.Series[i])\n 490→\t}\n 491→\n 492→\tif src.StatisticsSeries != nil {\n 493→\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n 494→\t}\n 495→\n 496→\treturn dst\n 497→}\n 498→\n 499→func copySeries(src *schema.Series) schema.Series {\n 500→\tdst := schema.Series{\n 501→\t\tHostname: src.Hostname,\n 502→\t\tID: src.ID,\n 503→\t\tStatistics: src.Statistics,\n 504→\t\tData: make([]schema.Float, len(src.Data)),\n 505→\t}\n 506→\n 507→\tcopy(dst.Data, src.Data)\n 508→\treturn dst\n 509→}\n 510→\n 511→func copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n 512→\tdst := &schema.StatsSeries{\n 513→\t\tMin: make([]schema.Float, len(src.Min)),\n 514→\t\tMean: make([]schema.Float, len(src.Mean)),\n 515→\t\tMedian: make([]schema.Float, len(src.Median)),\n 516→\t\tMax: make([]schema.Float, len(src.Max)),\n 517→\t}\n 518→\n 519→\tcopy(dst.Min, src.Min)\n 520→\tcopy(dst.Mean, src.Mean)\n 521→\tcopy(dst.Median, src.Median)\n 522→\tcopy(dst.Max, src.Max)\n 523→\n 524→\tif len(src.Percentiles) > 0 {\n 525→\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n 526→\t\tfor percentile, values := range src.Percentiles {\n 527→\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n 528→\t\t\tcopy(dst.Percentiles[percentile], values)\n 529→\t\t}\n 530→\t}\n 531→\n 532→\treturn dst\n 533→}\n 534→"}]},"uuid":"6f15dd05-08bd-4884-a480-bfaa9eff2e5e","timestamp":"2026-03-19T19:46:22.534Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d\",\n\t\t*job.ID, job.State, metrics, scopes, resolution)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","numLines":534,"startLine":1,"totalLines":534}},"sourceToolAssistantUUID":"a90bf017-7c03-49f2-87af-00728605cf55","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"6918159d-98ed-47a5-9717-1c88e0e885f6","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016TT8yhDP5XWDkAPTfeLBva","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package metricstore provides an efficient in-memory time-series metric storage system\n 7→// with support for hierarchical data organization, checkpointing, and archiving.\n 8→//\n 9→// The package organizes metrics in a tree structure (cluster → host → component) and\n 10→// provides concurrent read/write access to metric data with configurable aggregation strategies.\n 11→// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n 12→// and enforcing retention policies.\n 13→//\n 14→// Key features:\n 15→// - In-memory metric storage with configurable retention\n 16→// - Hierarchical data organization (selectors)\n 17→// - Concurrent checkpoint/archive workers\n 18→// - Support for sum and average aggregation\n 19→// - NATS integration for metric ingestion\n 20→package metricstore\n 21→\n 22→import (\n 23→\t\"bytes\"\n 24→\t\"context\"\n 25→\t\"encoding/json\"\n 26→\t\"errors\"\n 27→\t\"fmt\"\n 28→\t\"runtime\"\n 29→\t\"runtime/debug\"\n 30→\t\"slices\"\n 31→\t\"sync\"\n 32→\t\"time\"\n 33→\n 34→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n 35→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 36→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n 37→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 38→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n 39→)\n 40→\n 41→// GlobalState holds the global state for the metric store with thread-safe access.\n 42→type GlobalState struct {\n 43→\tmu sync.RWMutex\n 44→\tlastRetentionTime int64\n 45→\tselectorsExcluded bool\n 46→}\n 47→\n 48→var (\n 49→\tsingleton sync.Once\n 50→\tmsInstance *MemoryStore\n 51→\t// shutdownFunc stores the context cancellation function created in Init\n 52→\t// and is called during Shutdown to cancel all background goroutines\n 53→\tshutdownFunc context.CancelFunc\n 54→\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n 55→\t// Create a global instance\n 56→\tstate = &GlobalState{}\n 57→)\n 58→\n 59→// NodeProvider provides information about nodes currently in use by running jobs.\n 60→//\n 61→// This interface allows metricstore to query job information without directly\n 62→// depending on the repository package, breaking the import cycle.\n 63→//\n 64→// Implementations should return nodes that are actively processing jobs started\n 65→// before the given timestamp. These nodes will be excluded from retention-based\n 66→// garbage collection to prevent data loss for jobs that are still running or\n 67→// recently completed.\n 68→type NodeProvider interface {\n 69→\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n 70→\t// that are currently in use by jobs that started before the given timestamp.\n 71→\t//\n 72→\t// Parameters:\n 73→\t// - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n 74→\t//\n 75→\t// Returns:\n 76→\t// - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n 77→\t// - Error if the query fails\n 78→\tGetUsedNodes(ts int64) (map[string][]string, error)\n 79→}\n 80→\n 81→// Metric represents a single metric data point to be written to the store.\n 82→type Metric struct {\n 83→\tName string\n 84→\tValue schema.Float\n 85→\t// MetricConfig contains frequency and aggregation settings for this metric.\n 86→\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n 87→\tMetricConfig MetricConfig\n 88→}\n 89→\n 90→// MemoryStore is the main in-memory time-series metric storage implementation.\n 91→//\n 92→// It organizes metrics in a hierarchical tree structure where each level represents\n 93→// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n 94→// store multiple metrics as time-series buffers.\n 95→//\n 96→// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n 97→// All public methods are safe for concurrent use.\n 98→type MemoryStore struct {\n 99→\tMetrics map[string]MetricConfig\n 100→\troot Level\n 101→\tnodeProvider NodeProvider\n 102→}\n 103→\n 104→// Init initializes the metric store from configuration and starts background workers.\n 105→//\n 106→// This function must be called exactly once before any other metricstore operations.\n 107→// It performs the following initialization steps:\n 108→// 1. Validates and decodes the metric store configuration\n 109→// 2. Configures worker pool size (defaults to NumCPU/2+1, max 10)\n 110→// 3. Loads metric configurations from all registered clusters\n 111→// 4. Restores checkpoints within the retention window\n 112→// 5. Starts background workers for retention, checkpointing, archiving, and monitoring\n 113→// 6. Optionally subscribes to NATS for real-time metric ingestion\n 114→//\n 115→// Parameters:\n 116→// - rawConfig: JSON configuration for the metric store (see MetricStoreConfig)\n 117→// - wg: WaitGroup that will be incremented for each background goroutine started\n 118→//\n 119→// The function will call cclog.Fatal on critical errors during initialization.\n 120→// Use Shutdown() to cleanly stop all background workers started by Init().\n 121→//\n 122→// Note: Signal handling must be implemented by the caller. Call Shutdown() when\n 123→// receiving termination signals to ensure checkpoint data is persisted.\n 124→func Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\n 125→\tstartupTime := time.Now()\n 126→\n 127→\tif rawConfig != nil {\n 128→\t\tconfig.Validate(configSchema, rawConfig)\n 129→\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n 130→\t\tdec.DisallowUnknownFields()\n 131→\t\tif err := dec.Decode(&Keys); err != nil {\n 132→\t\t\tcclog.Abortf(\"[METRICSTORE]> Metric Store Config Init: Could not decode config file '%s'.\\nError: %s\\n\", rawConfig, err.Error())\n 133→\t\t}\n 134→\t}\n 135→\n 136→\t// Set NumWorkers from config or use default\n 137→\tif Keys.NumWorkers <= 0 {\n 138→\t\tKeys.NumWorkers = min(runtime.NumCPU()/2+1, DefaultMaxWorkers)\n 139→\t}\n 140→\tcclog.Debugf(\"[METRICSTORE]> Using %d workers for checkpoint/archive operations\\n\", Keys.NumWorkers)\n 141→\n 142→\t// Pass the config.MetricStoreKeys\n 143→\tInitMetrics(metrics)\n 144→\n 145→\tms := GetMemoryStore()\n 146→\n 147→\td, err := time.ParseDuration(Keys.RetentionInMemory)\n 148→\tif err != nil {\n 149→\t\tcclog.Fatal(err)\n 150→\t}\n 151→\n 152→\trestoreFrom := startupTime.Add(-d)\n 153→\tcclog.Infof(\"[METRICSTORE]> Loading checkpoints newer than %s\\n\", restoreFrom.Format(time.RFC3339))\n 154→\tfiles, err := ms.FromCheckpointFiles(Keys.Checkpoints.RootDir, restoreFrom.Unix())\n 155→\tloadedData := ms.SizeInBytes() / 1024 / 1024 // In MB\n 156→\tif err != nil {\n 157→\t\tcclog.Fatalf(\"[METRICSTORE]> Loading checkpoints failed: %s\\n\", err.Error())\n 158→\t} else {\n 159→\t\tcclog.Infof(\"[METRICSTORE]> Checkpoints loaded (%d files, %d MB, that took %fs)\\n\", files, loadedData, time.Since(startupTime).Seconds())\n 160→\t}\n 161→\n 162→\t// Try to use less memory by forcing a GC run here and then\n 163→\t// lowering the target percentage. The default of 100 means\n 164→\t// that only once the ratio of new allocations execeds the\n 165→\t// previously active heap, a GC is triggered.\n 166→\t// Forcing a GC here will set the \"previously active heap\"\n 167→\t// to a minumum.\n 168→\t// runtime.GC()\n 169→\n 170→\tctx, shutdown := context.WithCancel(context.Background())\n 171→\n 172→\tRetention(wg, ctx)\n 173→\tCheckpointing(wg, ctx)\n 174→\tCleanUp(wg, ctx)\n 175→\tWALStaging(wg, ctx)\n 176→\tMemoryUsageTracker(wg, ctx)\n 177→\n 178→\t// Note: Signal handling has been removed from this function.\n 179→\t// The caller is responsible for handling shutdown signals and calling\n 180→\t// the shutdown() function when appropriate.\n 181→\t// Store the shutdown function for later use by Shutdown()\n 182→\tshutdownFuncMu.Lock()\n 183→\tshutdownFunc = shutdown\n 184→\tshutdownFuncMu.Unlock()\n 185→\n 186→\tif Keys.Subscriptions != nil {\n 187→\t\twg.Go(func() {\n 188→\t\t\tif err := ReceiveNats(ms, Keys.NumWorkers, ctx); err != nil {\n 189→\t\t\t\tcclog.Fatal(err)\n 190→\t\t\t}\n 191→\t\t})\n 192→\t}\n 193→}\n 194→\n 195→// InitMetrics initializes the singleton MemoryStore instance with the given metric configurations.\n 196→//\n 197→// This function must be called before GetMemoryStore() and can only be called once due to\n 198→// the singleton pattern. It assigns each metric an internal offset for efficient buffer indexing.\n 199→//\n 200→// Parameters:\n 201→// - metrics: Map of metric names to their configurations (frequency and aggregation strategy)\n 202→//\n 203→// Panics if any metric has Frequency == 0, which indicates an invalid configuration.\n 204→//\n 205→// After this call, the global msInstance is ready for use via GetMemoryStore().\n 206→func InitMetrics(metrics map[string]MetricConfig) {\n 207→\tsingleton.Do(func() {\n 208→\t\toffset := 0\n 209→\t\tfor key, cfg := range metrics {\n 210→\t\t\tif cfg.Frequency == 0 {\n 211→\t\t\t\tpanic(\"[METRICSTORE]> invalid frequency\")\n 212→\t\t\t}\n 213→\n 214→\t\t\tmetrics[key] = MetricConfig{\n 215→\t\t\t\tFrequency: cfg.Frequency,\n 216→\t\t\t\tAggregation: cfg.Aggregation,\n 217→\t\t\t\toffset: offset,\n 218→\t\t\t}\n 219→\t\t\toffset += 1\n 220→\t\t}\n 221→\n 222→\t\tmsInstance = &MemoryStore{\n 223→\t\t\troot: Level{\n 224→\t\t\t\tmetrics: make([]*buffer, len(metrics)),\n 225→\t\t\t\tchildren: make(map[string]*Level),\n 226→\t\t\t},\n 227→\t\t\tMetrics: metrics,\n 228→\t\t}\n 229→\t})\n 230→}\n 231→\n 232→// GetMemoryStore returns the singleton MemoryStore instance.\n 233→//\n 234→// Returns the initialized MemoryStore singleton. Calls cclog.Fatal if InitMetrics() was not called first.\n 235→//\n 236→// This function is safe for concurrent use after initialization.\n 237→func GetMemoryStore() *MemoryStore {\n 238→\tif msInstance == nil {\n 239→\t\tcclog.Warnf(\"[METRICSTORE]> MemoryStore not initialized!\")\n 240→\t}\n 241→\n 242→\treturn msInstance\n 243→}\n 244→\n 245→func (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\n 246→\tif metric, ok := ms.Metrics[metricName]; ok {\n 247→\t\treturn metric.Frequency, nil\n 248→\t}\n 249→\treturn 0, fmt.Errorf(\"[METRICSTORE]> metric %s not found\", metricName)\n 250→}\n 251→\n 252→// SetNodeProvider sets the NodeProvider implementation for the MemoryStore.\n 253→// This must be called during initialization to provide job state information\n 254→// for selective buffer retention during Free operations.\n 255→// If not set, the Free function will fall back to freeing all buffers.\n 256→func (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\n 257→\tms.nodeProvider = provider\n 258→}\n 259→\n 260→// Shutdown performs a graceful shutdown of the metric store.\n 261→//\n 262→// This function cancels all background goroutines started by Init() and writes\n 263→// a final checkpoint to disk before returning. It should be called when the\n 264→// application receives a termination signal.\n 265→//\n 266→// The function will:\n 267→// 1. Cancel the context to stop all background workers\n 268→// 2. Close the WAL messages channel if using WAL format\n 269→// 3. Write a final checkpoint to preserve in-memory data\n 270→// 4. Log any errors encountered during shutdown\n 271→//\n 272→// Note: This function blocks until the final checkpoint is written.\n 273→func Shutdown() {\n 274→\tshutdownFuncMu.Lock()\n 275→\tdefer shutdownFuncMu.Unlock()\n 276→\tif shutdownFunc != nil {\n 277→\t\tshutdownFunc()\n 278→\t}\n 279→\n 280→\tif Keys.Checkpoints.FileFormat == \"wal\" {\n 281→\t\tfor _, ch := range walShardChs {\n 282→\t\t\tclose(ch)\n 283→\t\t}\n 284→\t}\n 285→\n 286→\tcclog.Infof(\"[METRICSTORE]> Writing to '%s'...\\n\", Keys.Checkpoints.RootDir)\n 287→\tvar files int\n 288→\tvar err error\n 289→\n 290→\tms := GetMemoryStore()\n 291→\n 292→\tlastCheckpointMu.Lock()\n 293→\tfrom := lastCheckpoint\n 294→\tlastCheckpointMu.Unlock()\n 295→\n 296→\tif Keys.Checkpoints.FileFormat == \"wal\" {\n 297→\t\tvar hostDirs []string\n 298→\t\tfiles, hostDirs, err = ms.ToCheckpointWAL(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n 299→\t\tif err == nil {\n 300→\t\t\tRotateWALFilesAfterShutdown(hostDirs)\n 301→\t\t}\n 302→\t} else {\n 303→\t\tfiles, err = ms.ToCheckpoint(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n 304→\t}\n 305→\n 306→\tif err != nil {\n 307→\t\tcclog.Errorf(\"[METRICSTORE]> Writing checkpoint failed: %s\\n\", err.Error())\n 308→\t}\n 309→\tcclog.Infof(\"[METRICSTORE]> Done! (%d files written)\\n\", files)\n 310→}\n 311→\n 312→// Retention starts a background goroutine that periodically frees old metric data.\n 313→//\n 314→// This worker runs at half the retention interval and calls Free() to remove buffers\n 315→// older than the configured retention time. It respects the NodeProvider to preserve\n 316→// data for nodes with active jobs.\n 317→//\n 318→// Parameters:\n 319→// - wg: WaitGroup to signal completion when context is cancelled\n 320→// - ctx: Context for cancellation signal\n 321→//\n 322→// The goroutine exits when ctx is cancelled.\n 323→func Retention(wg *sync.WaitGroup, ctx context.Context) {\n 324→\tms := GetMemoryStore()\n 325→\n 326→\twg.Go(func() {\n 327→\t\td, err := time.ParseDuration(Keys.RetentionInMemory)\n 328→\t\tif err != nil {\n 329→\t\t\tcclog.Fatal(err)\n 330→\t\t}\n 331→\t\tif d <= 0 {\n 332→\t\t\treturn\n 333→\t\t}\n 334→\n 335→\t\ttickInterval := d / 2\n 336→\t\tif tickInterval <= 0 {\n 337→\t\t\treturn\n 338→\t\t}\n 339→\t\tticker := time.NewTicker(tickInterval)\n 340→\t\tdefer ticker.Stop()\n 341→\n 342→\t\tfor {\n 343→\t\t\tselect {\n 344→\t\t\tcase <-ctx.Done():\n 345→\t\t\t\treturn\n 346→\t\t\tcase <-ticker.C:\n 347→\t\t\t\tstate.mu.Lock()\n 348→\n 349→\t\t\t\tt := time.Now().Add(-d)\n 350→\n 351→\t\t\t\tstate.lastRetentionTime = t.Unix()\n 352→\n 353→\t\t\t\tcclog.Infof(\"[METRICSTORE]> start freeing buffers (older than %s)...\\n\", t.Format(time.RFC3339))\n 354→\n 355→\t\t\t\tfreed, err := Free(ms, t)\n 356→\t\t\t\tif err != nil {\n 357→\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> freeing up buffers failed: %s\\n\", err.Error())\n 358→\t\t\t\t} else {\n 359→\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d buffers freed\\n\", freed)\n 360→\t\t\t\t}\n 361→\n 362→\t\t\t\tstate.mu.Unlock()\n 363→\n 364→\t\t\t\t// Clean up the buffer pool\n 365→\t\t\t\tbufferPool.Clean(state.lastRetentionTime)\n 366→\t\t\t}\n 367→\t\t}\n 368→\t})\n 369→}\n 370→\n 371→// MemoryUsageTracker starts a background goroutine that monitors memory usage.\n 372→//\n 373→// This worker checks actual process memory usage (via runtime.MemStats) periodically\n 374→// and force-frees buffers if memory exceeds the configured cap. It uses FreeOSMemory()\n 375→// to return memory to the OS after freeing buffers, avoiding aggressive GC that causes\n 376→// performance issues.\n 377→//\n 378→// The tracker logs both actual memory usage (heap allocated) and metric data size for\n 379→// visibility into memory overhead from Go runtime structures and allocations.\n 380→//\n 381→// Parameters:\n 382→// - wg: WaitGroup to signal completion when context is cancelled\n 383→// - ctx: Context for cancellation signal\n 384→//\n 385→// The goroutine exits when ctx is cancelled.\n 386→func MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\n 387→\tms := GetMemoryStore()\n 388→\n 389→\twg.Go(func() {\n 390→\t\tnormalInterval := DefaultMemoryUsageTrackerInterval\n 391→\t\tfastInterval := 30 * time.Second\n 392→\n 393→\t\tif normalInterval <= 0 {\n 394→\t\t\treturn\n 395→\t\t}\n 396→\n 397→\t\tticker := time.NewTicker(normalInterval)\n 398→\t\tdefer ticker.Stop()\n 399→\t\tcurrentInterval := normalInterval\n 400→\n 401→\t\tfor {\n 402→\t\t\tselect {\n 403→\t\t\tcase <-ctx.Done():\n 404→\t\t\t\treturn\n 405→\t\t\tcase <-ticker.C:\n 406→\t\t\t\tvar mem runtime.MemStats\n 407→\t\t\t\truntime.ReadMemStats(&mem)\n 408→\t\t\t\tactualMemoryGB := float64(mem.Alloc) / 1e9\n 409→\t\t\t\tmetricDataGB := ms.SizeInGB()\n 410→\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory usage: %.2f GB actual (%.2f GB metric data)\", actualMemoryGB, metricDataGB)\n 411→\n 412→\t\t\t\tfreedExcluded := 0\n 413→\t\t\t\tfreedEmergency := 0\n 414→\t\t\t\tvar err error\n 415→\n 416→\t\t\t\tstate.mu.RLock()\n 417→\t\t\t\tlastRetention := state.lastRetentionTime\n 418→\t\t\t\tselectorsExcluded := state.selectorsExcluded\n 419→\t\t\t\tstate.mu.RUnlock()\n 420→\n 421→\t\t\t\tif lastRetention != 0 && selectorsExcluded {\n 422→\t\t\t\t\tfreedExcluded, err = ms.Free(nil, lastRetention)\n 423→\t\t\t\t\tif err != nil {\n 424→\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing the excluded buffers: %s\", err)\n 425→\t\t\t\t\t}\n 426→\n 427→\t\t\t\t\tif freedExcluded > 0 {\n 428→\t\t\t\t\t\tdebug.FreeOSMemory()\n 429→\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d excluded buffers force-freed\", freedExcluded)\n 430→\t\t\t\t\t}\n 431→\t\t\t\t}\n 432→\n 433→\t\t\t\truntime.ReadMemStats(&mem)\n 434→\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n 435→\n 436→\t\t\t\tif actualMemoryGB > float64(Keys.MemoryCap) {\n 437→\t\t\t\t\tcclog.Warnf(\"[METRICSTORE]> memory usage %.2f GB exceeds cap %d GB, starting emergency buffer freeing\", actualMemoryGB, Keys.MemoryCap)\n 438→\n 439→\t\t\t\t\t// Use progressive time-based Free with increasing threshold\n 440→\t\t\t\t\t// instead of ForceFree loop — fewer tree traversals, more effective\n 441→\t\t\t\t\td, parseErr := time.ParseDuration(Keys.RetentionInMemory)\n 442→\t\t\t\t\tif parseErr != nil {\n 443→\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> cannot parse retention duration: %s\", parseErr)\n 444→\t\t\t\t\t} else {\n 445→\t\t\t\t\t\tthresholds := []float64{0.75, 0.5, 0.25}\n 446→\t\t\t\t\t\tfor _, fraction := range thresholds {\n 447→\t\t\t\t\t\t\tthreshold := time.Now().Add(-time.Duration(float64(d) * fraction))\n 448→\t\t\t\t\t\t\tfreed, freeErr := ms.Free(nil, threshold.Unix())\n 449→\t\t\t\t\t\t\tif freeErr != nil {\n 450→\t\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while freeing buffers at %.0f%% retention: %s\", fraction*100, freeErr)\n 451→\t\t\t\t\t\t\t}\n 452→\t\t\t\t\t\t\tfreedEmergency += freed\n 453→\n 454→\t\t\t\t\t\t\tbufferPool.Clear()\n 455→\t\t\t\t\t\t\truntime.GC()\n 456→\t\t\t\t\t\t\truntime.ReadMemStats(&mem)\n 457→\t\t\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n 458→\n 459→\t\t\t\t\t\t\tif actualMemoryGB < float64(Keys.MemoryCap) {\n 460→\t\t\t\t\t\t\t\tbreak\n 461→\t\t\t\t\t\t\t}\n 462→\t\t\t\t\t\t}\n 463→\t\t\t\t\t}\n 464→\n 465→\t\t\t\t\tbufferPool.Clear()\n 466→\t\t\t\t\tdebug.FreeOSMemory()\n 467→\n 468→\t\t\t\t\truntime.ReadMemStats(&mem)\n 469→\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n 470→\n 471→\t\t\t\t\tif actualMemoryGB >= float64(Keys.MemoryCap) {\n 472→\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> after emergency frees (%d buffers), memory usage %.2f GB still at/above cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n 473→\t\t\t\t\t} else {\n 474→\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> emergency freeing complete: %d buffers freed, memory now %.2f GB\", freedEmergency, actualMemoryGB)\n 475→\t\t\t\t\t}\n 476→\t\t\t\t}\n 477→\n 478→\t\t\t\t// Adaptive ticker: check more frequently when memory is high\n 479→\t\t\t\tmemoryRatio := actualMemoryGB / float64(Keys.MemoryCap)\n 480→\t\t\t\tif memoryRatio > 0.8 && currentInterval != fastInterval {\n 481→\t\t\t\t\tticker.Reset(fastInterval)\n 482→\t\t\t\t\tcurrentInterval = fastInterval\n 483→\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to fast check interval (30s)\", memoryRatio*100)\n 484→\t\t\t\t} else if memoryRatio <= 0.8 && currentInterval != normalInterval {\n 485→\t\t\t\t\tticker.Reset(normalInterval)\n 486→\t\t\t\t\tcurrentInterval = normalInterval\n 487→\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to normal check interval\", memoryRatio*100)\n 488→\t\t\t\t}\n 489→\t\t\t}\n 490→\t\t}\n 491→\t})\n 492→}\n 493→\n 494→// Free removes metric data older than the given time while preserving data for active nodes.\n 495→//\n 496→// This function implements intelligent retention by consulting the NodeProvider (if configured)\n 497→// to determine which nodes are currently in use by running jobs. Data for these nodes is\n 498→// preserved even if older than the retention time.\n 499→//\n 500→// Parameters:\n 501→// - ms: The MemoryStore instance\n 502→// - t: Time threshold - buffers with data older than this will be freed\n 503→//\n 504→// Returns:\n 505→// - Number of buffers freed\n 506→// - Error if NodeProvider query fails\n 507→//\n 508→// Behavior:\n 509→// - If no NodeProvider is set: frees all buffers older than t\n 510→// - If NodeProvider returns empty map: frees all buffers older than t\n 511→// - Otherwise: preserves buffers for nodes returned by GetUsedNodes(), frees others\n 512→func Free(ms *MemoryStore, t time.Time) (int, error) {\n 513→\t// If no NodeProvider is configured, free all buffers older than t\n 514→\tif ms.nodeProvider == nil {\n 515→\t\treturn ms.Free(nil, t.Unix())\n 516→\t}\n 517→\n 518→\texcludeSelectors, err := ms.nodeProvider.GetUsedNodes(t.Unix())\n 519→\tif err != nil {\n 520→\t\treturn 0, err\n 521→\t}\n 522→\n 523→\tswitch lenMap := len(excludeSelectors); lenMap {\n 524→\n 525→\t// If the length of the map returned by GetUsedNodes() is 0,\n 526→\t// then use default Free method with nil selector\n 527→\tcase 0:\n 528→\t\tstate.selectorsExcluded = false\n 529→\t\treturn ms.Free(nil, t.Unix())\n 530→\n 531→\t// Else formulate selectors, exclude those from the map\n 532→\t// and free the rest of the selectors\n 533→\tdefault:\n 534→\t\tstate.selectorsExcluded = true\n 535→\t\tselectors := GetSelectors(ms, excludeSelectors)\n 536→\t\treturn FreeSelected(ms, selectors, t)\n 537→\t}\n 538→}\n 539→\n 540→// FreeSelected frees buffers for specific selectors while preserving others.\n 541→//\n 542→// This function is used when we want to retain some specific nodes beyond the retention time.\n 543→// It iterates through the provided selectors and frees their associated buffers.\n 544→//\n 545→// Parameters:\n 546→// - ms: The MemoryStore instance\n 547→// - selectors: List of selector paths to free (e.g., [[\"cluster1\", \"node1\"], [\"cluster2\", \"node2\"]])\n 548→// - t: Time threshold for freeing buffers\n 549→//\n 550→// Returns the total number of buffers freed and any error encountered.\n 551→func FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\n 552→\tfreed := 0\n 553→\n 554→\tfor _, selector := range selectors {\n 555→\n 556→\t\tfreedBuffers, err := ms.Free(selector, t.Unix())\n 557→\t\tif err != nil {\n 558→\t\t\tcclog.Errorf(\"error while freeing selected buffers: %#v\", err)\n 559→\t\t}\n 560→\t\tfreed += freedBuffers\n 561→\n 562→\t}\n 563→\n 564→\treturn freed, nil\n 565→}\n 566→\n 567→// GetSelectors returns all selectors at depth 2 (cluster/node level) that are NOT in the exclusion map.\n 568→//\n 569→// This function generates a list of selectors whose buffers should be freed by excluding\n 570→// selectors that correspond to nodes currently in use by running jobs.\n 571→//\n 572→// Parameters:\n 573→// - ms: The MemoryStore instance\n 574→// - excludeSelectors: Map of cluster names to node hostnames that should NOT be freed\n 575→//\n 576→// Returns a list of selectors ([]string paths) that can be safely freed.\n 577→//\n 578→// Example:\n 579→//\n 580→//\tIf the tree has paths [\"emmy\", \"node001\"] and [\"emmy\", \"node002\"],\n 581→//\tand excludeSelectors contains {\"emmy\": [\"node001\"]},\n 582→//\tthen only [[\"emmy\", \"node002\"]] is returned.\n 583→func GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\n 584→\tallSelectors := ms.GetPaths(2)\n 585→\n 586→\tfilteredSelectors := make([][]string, 0, len(allSelectors))\n 587→\n 588→\tfor _, path := range allSelectors {\n 589→\t\tif len(path) < 2 {\n 590→\t\t\tcontinue\n 591→\t\t}\n 592→\n 593→\t\tkey := path[0] // The \"Key\" (Level 1)\n 594→\t\tvalue := path[1] // The \"Value\" (Level 2)\n 595→\n 596→\t\texclude := false\n 597→\n 598→\t\t// Check if the key exists in our exclusion map\n 599→\t\tif excludedValues, exists := excludeSelectors[key]; exists {\n 600→\t\t\t// The key exists, now check if the specific value is in the exclusion list\n 601→\t\t\tif slices.Contains(excludedValues, value) {\n 602→\t\t\t\texclude = true\n 603→\t\t\t}\n 604→\t\t}\n 605→\n 606→\t\tif !exclude {\n 607→\t\t\tfilteredSelectors = append(filteredSelectors, path)\n 608→\t\t}\n 609→\t}\n 610→\n 611→\treturn filteredSelectors\n 612→}\n 613→\n 614→// GetPaths returns a list of lists (paths) to the specified depth.\n 615→func (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\n 616→\tvar results [][]string\n 617→\n 618→\t// Start recursion. Initial path is empty.\n 619→\t// We treat Root as depth 0.\n 620→\tms.root.collectPaths(0, targetDepth, []string{}, &results)\n 621→\n 622→\treturn results\n 623→}\n 624→\n 625→// Write all values in `metrics` to the level specified by `selector` for time `ts`.\n 626→// Look at `findLevelOrCreate` for how selectors work.\n 627→func (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\n 628→\tvar ok bool\n 629→\tfor i, metric := range metrics {\n 630→\t\tif metric.MetricConfig.Frequency == 0 {\n 631→\t\t\tmetric.MetricConfig, ok = m.Metrics[metric.Name]\n 632→\t\t\tif !ok {\n 633→\t\t\t\tcclog.Debugf(\"[METRICSTORE]> Unknown metric '%s' in Write() - skipping\", metric.Name)\n 634→\t\t\t\tmetric.MetricConfig.Frequency = 0\n 635→\t\t\t}\n 636→\t\t\tmetrics[i] = metric\n 637→\t\t}\n 638→\t}\n 639→\n 640→\treturn m.WriteToLevel(&m.root, selector, ts, metrics)\n 641→}\n 642→\n 643→func (m *MemoryStore) GetLevel(selector []string) *Level {\n 644→\treturn m.root.findLevelOrCreate(selector, len(m.Metrics))\n 645→}\n 646→\n 647→// WriteToLevel assumes that `minfo` in `metrics` is filled in\n 648→func (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n 649→\tl = l.findLevelOrCreate(selector, len(m.Metrics))\n 650→\tl.lock.Lock()\n 651→\tdefer l.lock.Unlock()\n 652→\n 653→\tfor _, metric := range metrics {\n 654→\t\tif metric.MetricConfig.Frequency == 0 {\n 655→\t\t\tcontinue\n 656→\t\t}\n 657→\n 658→\t\tb := l.metrics[metric.MetricConfig.offset]\n 659→\t\tif b == nil {\n 660→\t\t\t// First write to this metric and level\n 661→\t\t\tb = newBuffer(ts, metric.MetricConfig.Frequency)\n 662→\t\t\tl.metrics[metric.MetricConfig.offset] = b\n 663→\t\t}\n 664→\n 665→\t\tnb, err := b.write(ts, metric.Value)\n 666→\t\tif err != nil {\n 667→\t\t\treturn err\n 668→\t\t}\n 669→\n 670→\t\t// Last write created a new buffer...\n 671→\t\tif b != nb {\n 672→\t\t\tl.metrics[metric.MetricConfig.offset] = nb\n 673→\t\t}\n 674→\t}\n 675→\treturn nil\n 676→}\n 677→\n 678→// Read returns all values for metric `metric` from `from` to `to` for the selected level(s).\n 679→// If the level does not hold the metric itself, the data will be aggregated recursively from the children.\n 680→// The second and third return value are the actual from/to for the data. Those can be different from\n 681→// the range asked for if no data was available.\n 682→func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {\n 683→\tif from > to {\n 684→\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")\n 685→\t}\n 686→\n 687→\tminfo, ok := m.Metrics[metric]\n 688→\tif !ok {\n 689→\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> unknown metric: \" + metric)\n 690→\t}\n 691→\n 692→\tn, data := 0, make([]schema.Float, (to-from)/minfo.Frequency+1)\n 693→\n 694→\terr := m.root.findBuffers(selector, minfo.offset, func(b *buffer) error {\n 695→\t\tcdata, cfrom, cto, err := b.read(from, to, data)\n 696→\t\tif err != nil {\n 697→\t\t\treturn err\n 698→\t\t}\n 699→\n 700→\t\tif n == 0 {\n 701→\t\t\tfrom, to = cfrom, cto\n 702→\t\t} else if from != cfrom || to != cto || len(data) != len(cdata) {\n 703→\t\t\tmissingfront, missingback := int((from-cfrom)/minfo.Frequency), int((to-cto)/minfo.Frequency)\n 704→\t\t\tif missingfront != 0 {\n 705→\t\t\t\treturn ErrDataDoesNotAlign\n 706→\t\t\t}\n 707→\n 708→\t\t\tnewlen := len(cdata) - missingback\n 709→\t\t\tif newlen < 1 {\n 710→\t\t\t\treturn ErrDataDoesNotAlign\n 711→\t\t\t}\n 712→\t\t\tcdata = cdata[0:newlen]\n 713→\t\t\tif len(cdata) != len(data) {\n 714→\t\t\t\treturn ErrDataDoesNotAlign\n 715→\t\t\t}\n 716→\n 717→\t\t\tfrom, to = cfrom, cto\n 718→\t\t}\n 719→\n 720→\t\tdata = cdata\n 721→\t\tn += 1\n 722→\t\treturn nil\n 723→\t})\n 724→\n 725→\tif err != nil {\n 726→\t\treturn nil, 0, 0, 0, err\n 727→\t} else if n == 0 {\n 728→\t\treturn nil, 0, 0, 0, ErrNoHostOrMetric\n 729→\t} else if n > 1 {\n 730→\t\tif minfo.Aggregation == AvgAggregation {\n 731→\t\t\tnormalize := 1. / schema.Float(n)\n 732→\t\t\tfor i := 0; i < len(data); i++ {\n 733→\t\t\t\tdata[i] *= normalize\n 734→\t\t\t}\n 735→\t\t} else if minfo.Aggregation != SumAggregation {\n 736→\t\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid aggregation\")\n 737→\t\t}\n 738→\t}\n 739→\n 740→\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n 741→\tif err != nil {\n 742→\t\treturn nil, 0, 0, 0, err\n 743→\t}\n 744→\n 745→\treturn data, from, to, resolution, nil\n 746→}\n 747→\n 748→// Free releases all buffers for the selected level and all its children that\n 749→// contain only values older than `t`.\n 750→func (m *MemoryStore) Free(selector []string, t int64) (int, error) {\n 751→\treturn m.GetLevel(selector).free(t)\n 752→}\n 753→\n 754→// ForceFree unconditionally removes the oldest buffer from each metric chain.\n 755→func (m *MemoryStore) ForceFree() (int, error) {\n 756→\treturn m.GetLevel(nil).forceFree()\n 757→}\n 758→\n 759→func (m *MemoryStore) FreeAll() error {\n 760→\tm.root.lock.Lock()\n 761→\tdefer m.root.lock.Unlock()\n 762→\n 763→\tfor k := range m.root.children {\n 764→\t\tdelete(m.root.children, k)\n 765→\t}\n 766→\n 767→\treturn nil\n 768→}\n 769→\n 770→func (m *MemoryStore) SizeInBytes() int64 {\n 771→\treturn m.root.sizeInBytes()\n 772→}\n 773→\n 774→func (m *MemoryStore) SizeInGB() float64 {\n 775→\treturn float64(m.root.sizeInBytes()) / 1e9\n 776→}\n 777→\n 778→// ListChildren , given a selector, returns a list of all children of the level\n 779→// selected.\n 780→func (m *MemoryStore) ListChildren(selector []string) []string {\n 781→\tlvl := &m.root\n 782→\tfor lvl != nil && len(selector) != 0 {\n 783→\t\tlvl.lock.RLock()\n 784→\t\tnext := lvl.children[selector[0]]\n 785→\t\tlvl.lock.RUnlock()\n 786→\t\tlvl = next\n 787→\t\tselector = selector[1:]\n 788→\t}\n 789→\n 790→\tif lvl == nil {\n 791→\t\treturn nil\n 792→\t}\n 793→\n 794→\tlvl.lock.RLock()\n 795→\tdefer lvl.lock.RUnlock()\n 796→\n 797→\tchildren := make([]string, 0, len(lvl.children))\n 798→\tfor child := range lvl.children {\n 799→\t\tchildren = append(children, child)\n 800→\t}\n 801→\n 802→\treturn children\n 803→}\n 804→"}]},"uuid":"179fcf19-aa73-454a-b1e6-ad500a5f692f","timestamp":"2026-03-19T19:46:23.887Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/metricstore.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricstore provides an efficient in-memory time-series metric storage system\n// with support for hierarchical data organization, checkpointing, and archiving.\n//\n// The package organizes metrics in a tree structure (cluster → host → component) and\n// provides concurrent read/write access to metric data with configurable aggregation strategies.\n// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n// and enforcing retention policies.\n//\n// Key features:\n// - In-memory metric storage with configurable retention\n// - Hierarchical data organization (selectors)\n// - Concurrent checkpoint/archive workers\n// - Support for sum and average aggregation\n// - NATS integration for metric ingestion\npackage metricstore\n\nimport (\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"runtime\"\n\t\"runtime/debug\"\n\t\"slices\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// GlobalState holds the global state for the metric store with thread-safe access.\ntype GlobalState struct {\n\tmu sync.RWMutex\n\tlastRetentionTime int64\n\tselectorsExcluded bool\n}\n\nvar (\n\tsingleton sync.Once\n\tmsInstance *MemoryStore\n\t// shutdownFunc stores the context cancellation function created in Init\n\t// and is called during Shutdown to cancel all background goroutines\n\tshutdownFunc context.CancelFunc\n\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n\t// Create a global instance\n\tstate = &GlobalState{}\n)\n\n// NodeProvider provides information about nodes currently in use by running jobs.\n//\n// This interface allows metricstore to query job information without directly\n// depending on the repository package, breaking the import cycle.\n//\n// Implementations should return nodes that are actively processing jobs started\n// before the given timestamp. These nodes will be excluded from retention-based\n// garbage collection to prevent data loss for jobs that are still running or\n// recently completed.\ntype NodeProvider interface {\n\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n\t// that are currently in use by jobs that started before the given timestamp.\n\t//\n\t// Parameters:\n\t// - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n\t//\n\t// Returns:\n\t// - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n\t// - Error if the query fails\n\tGetUsedNodes(ts int64) (map[string][]string, error)\n}\n\n// Metric represents a single metric data point to be written to the store.\ntype Metric struct {\n\tName string\n\tValue schema.Float\n\t// MetricConfig contains frequency and aggregation settings for this metric.\n\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n\tMetricConfig MetricConfig\n}\n\n// MemoryStore is the main in-memory time-series metric storage implementation.\n//\n// It organizes metrics in a hierarchical tree structure where each level represents\n// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n// store multiple metrics as time-series buffers.\n//\n// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n// All public methods are safe for concurrent use.\ntype MemoryStore struct {\n\tMetrics map[string]MetricConfig\n\troot Level\n\tnodeProvider NodeProvider\n}\n\n// Init initializes the metric store from configuration and starts background workers.\n//\n// This function must be called exactly once before any other metricstore operations.\n// It performs the following initialization steps:\n// 1. Validates and decodes the metric store configuration\n// 2. Configures worker pool size (defaults to NumCPU/2+1, max 10)\n// 3. Loads metric configurations from all registered clusters\n// 4. Restores checkpoints within the retention window\n// 5. Starts background workers for retention, checkpointing, archiving, and monitoring\n// 6. Optionally subscribes to NATS for real-time metric ingestion\n//\n// Parameters:\n// - rawConfig: JSON configuration for the metric store (see MetricStoreConfig)\n// - wg: WaitGroup that will be incremented for each background goroutine started\n//\n// The function will call cclog.Fatal on critical errors during initialization.\n// Use Shutdown() to cleanly stop all background workers started by Init().\n//\n// Note: Signal handling must be implemented by the caller. Call Shutdown() when\n// receiving termination signals to ensure checkpoint data is persisted.\nfunc Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\n\tstartupTime := time.Now()\n\n\tif rawConfig != nil {\n\t\tconfig.Validate(configSchema, rawConfig)\n\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n\t\tdec.DisallowUnknownFields()\n\t\tif err := dec.Decode(&Keys); err != nil {\n\t\t\tcclog.Abortf(\"[METRICSTORE]> Metric Store Config Init: Could not decode config file '%s'.\\nError: %s\\n\", rawConfig, err.Error())\n\t\t}\n\t}\n\n\t// Set NumWorkers from config or use default\n\tif Keys.NumWorkers <= 0 {\n\t\tKeys.NumWorkers = min(runtime.NumCPU()/2+1, DefaultMaxWorkers)\n\t}\n\tcclog.Debugf(\"[METRICSTORE]> Using %d workers for checkpoint/archive operations\\n\", Keys.NumWorkers)\n\n\t// Pass the config.MetricStoreKeys\n\tInitMetrics(metrics)\n\n\tms := GetMemoryStore()\n\n\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\tif err != nil {\n\t\tcclog.Fatal(err)\n\t}\n\n\trestoreFrom := startupTime.Add(-d)\n\tcclog.Infof(\"[METRICSTORE]> Loading checkpoints newer than %s\\n\", restoreFrom.Format(time.RFC3339))\n\tfiles, err := ms.FromCheckpointFiles(Keys.Checkpoints.RootDir, restoreFrom.Unix())\n\tloadedData := ms.SizeInBytes() / 1024 / 1024 // In MB\n\tif err != nil {\n\t\tcclog.Fatalf(\"[METRICSTORE]> Loading checkpoints failed: %s\\n\", err.Error())\n\t} else {\n\t\tcclog.Infof(\"[METRICSTORE]> Checkpoints loaded (%d files, %d MB, that took %fs)\\n\", files, loadedData, time.Since(startupTime).Seconds())\n\t}\n\n\t// Try to use less memory by forcing a GC run here and then\n\t// lowering the target percentage. The default of 100 means\n\t// that only once the ratio of new allocations execeds the\n\t// previously active heap, a GC is triggered.\n\t// Forcing a GC here will set the \"previously active heap\"\n\t// to a minumum.\n\t// runtime.GC()\n\n\tctx, shutdown := context.WithCancel(context.Background())\n\n\tRetention(wg, ctx)\n\tCheckpointing(wg, ctx)\n\tCleanUp(wg, ctx)\n\tWALStaging(wg, ctx)\n\tMemoryUsageTracker(wg, ctx)\n\n\t// Note: Signal handling has been removed from this function.\n\t// The caller is responsible for handling shutdown signals and calling\n\t// the shutdown() function when appropriate.\n\t// Store the shutdown function for later use by Shutdown()\n\tshutdownFuncMu.Lock()\n\tshutdownFunc = shutdown\n\tshutdownFuncMu.Unlock()\n\n\tif Keys.Subscriptions != nil {\n\t\twg.Go(func() {\n\t\t\tif err := ReceiveNats(ms, Keys.NumWorkers, ctx); err != nil {\n\t\t\t\tcclog.Fatal(err)\n\t\t\t}\n\t\t})\n\t}\n}\n\n// InitMetrics initializes the singleton MemoryStore instance with the given metric configurations.\n//\n// This function must be called before GetMemoryStore() and can only be called once due to\n// the singleton pattern. It assigns each metric an internal offset for efficient buffer indexing.\n//\n// Parameters:\n// - metrics: Map of metric names to their configurations (frequency and aggregation strategy)\n//\n// Panics if any metric has Frequency == 0, which indicates an invalid configuration.\n//\n// After this call, the global msInstance is ready for use via GetMemoryStore().\nfunc InitMetrics(metrics map[string]MetricConfig) {\n\tsingleton.Do(func() {\n\t\toffset := 0\n\t\tfor key, cfg := range metrics {\n\t\t\tif cfg.Frequency == 0 {\n\t\t\t\tpanic(\"[METRICSTORE]> invalid frequency\")\n\t\t\t}\n\n\t\t\tmetrics[key] = MetricConfig{\n\t\t\t\tFrequency: cfg.Frequency,\n\t\t\t\tAggregation: cfg.Aggregation,\n\t\t\t\toffset: offset,\n\t\t\t}\n\t\t\toffset += 1\n\t\t}\n\n\t\tmsInstance = &MemoryStore{\n\t\t\troot: Level{\n\t\t\t\tmetrics: make([]*buffer, len(metrics)),\n\t\t\t\tchildren: make(map[string]*Level),\n\t\t\t},\n\t\t\tMetrics: metrics,\n\t\t}\n\t})\n}\n\n// GetMemoryStore returns the singleton MemoryStore instance.\n//\n// Returns the initialized MemoryStore singleton. Calls cclog.Fatal if InitMetrics() was not called first.\n//\n// This function is safe for concurrent use after initialization.\nfunc GetMemoryStore() *MemoryStore {\n\tif msInstance == nil {\n\t\tcclog.Warnf(\"[METRICSTORE]> MemoryStore not initialized!\")\n\t}\n\n\treturn msInstance\n}\n\nfunc (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\n\tif metric, ok := ms.Metrics[metricName]; ok {\n\t\treturn metric.Frequency, nil\n\t}\n\treturn 0, fmt.Errorf(\"[METRICSTORE]> metric %s not found\", metricName)\n}\n\n// SetNodeProvider sets the NodeProvider implementation for the MemoryStore.\n// This must be called during initialization to provide job state information\n// for selective buffer retention during Free operations.\n// If not set, the Free function will fall back to freeing all buffers.\nfunc (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\n\tms.nodeProvider = provider\n}\n\n// Shutdown performs a graceful shutdown of the metric store.\n//\n// This function cancels all background goroutines started by Init() and writes\n// a final checkpoint to disk before returning. It should be called when the\n// application receives a termination signal.\n//\n// The function will:\n// 1. Cancel the context to stop all background workers\n// 2. Close the WAL messages channel if using WAL format\n// 3. Write a final checkpoint to preserve in-memory data\n// 4. Log any errors encountered during shutdown\n//\n// Note: This function blocks until the final checkpoint is written.\nfunc Shutdown() {\n\tshutdownFuncMu.Lock()\n\tdefer shutdownFuncMu.Unlock()\n\tif shutdownFunc != nil {\n\t\tshutdownFunc()\n\t}\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tfor _, ch := range walShardChs {\n\t\t\tclose(ch)\n\t\t}\n\t}\n\n\tcclog.Infof(\"[METRICSTORE]> Writing to '%s'...\\n\", Keys.Checkpoints.RootDir)\n\tvar files int\n\tvar err error\n\n\tms := GetMemoryStore()\n\n\tlastCheckpointMu.Lock()\n\tfrom := lastCheckpoint\n\tlastCheckpointMu.Unlock()\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tvar hostDirs []string\n\t\tfiles, hostDirs, err = ms.ToCheckpointWAL(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t\tif err == nil {\n\t\t\tRotateWALFilesAfterShutdown(hostDirs)\n\t\t}\n\t} else {\n\t\tfiles, err = ms.ToCheckpoint(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t}\n\n\tif err != nil {\n\t\tcclog.Errorf(\"[METRICSTORE]> Writing checkpoint failed: %s\\n\", err.Error())\n\t}\n\tcclog.Infof(\"[METRICSTORE]> Done! (%d files written)\\n\", files)\n}\n\n// Retention starts a background goroutine that periodically frees old metric data.\n//\n// This worker runs at half the retention interval and calls Free() to remove buffers\n// older than the configured retention time. It respects the NodeProvider to preserve\n// data for nodes with active jobs.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc Retention(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\t\tif err != nil {\n\t\t\tcclog.Fatal(err)\n\t\t}\n\t\tif d <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\ttickInterval := d / 2\n\t\tif tickInterval <= 0 {\n\t\t\treturn\n\t\t}\n\t\tticker := time.NewTicker(tickInterval)\n\t\tdefer ticker.Stop()\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tstate.mu.Lock()\n\n\t\t\t\tt := time.Now().Add(-d)\n\n\t\t\t\tstate.lastRetentionTime = t.Unix()\n\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> start freeing buffers (older than %s)...\\n\", t.Format(time.RFC3339))\n\n\t\t\t\tfreed, err := Free(ms, t)\n\t\t\t\tif err != nil {\n\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> freeing up buffers failed: %s\\n\", err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d buffers freed\\n\", freed)\n\t\t\t\t}\n\n\t\t\t\tstate.mu.Unlock()\n\n\t\t\t\t// Clean up the buffer pool\n\t\t\t\tbufferPool.Clean(state.lastRetentionTime)\n\t\t\t}\n\t\t}\n\t})\n}\n\n// MemoryUsageTracker starts a background goroutine that monitors memory usage.\n//\n// This worker checks actual process memory usage (via runtime.MemStats) periodically\n// and force-frees buffers if memory exceeds the configured cap. It uses FreeOSMemory()\n// to return memory to the OS after freeing buffers, avoiding aggressive GC that causes\n// performance issues.\n//\n// The tracker logs both actual memory usage (heap allocated) and metric data size for\n// visibility into memory overhead from Go runtime structures and allocations.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\tnormalInterval := DefaultMemoryUsageTrackerInterval\n\t\tfastInterval := 30 * time.Second\n\n\t\tif normalInterval <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\tticker := time.NewTicker(normalInterval)\n\t\tdefer ticker.Stop()\n\t\tcurrentInterval := normalInterval\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tvar mem runtime.MemStats\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB := float64(mem.Alloc) / 1e9\n\t\t\t\tmetricDataGB := ms.SizeInGB()\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory usage: %.2f GB actual (%.2f GB metric data)\", actualMemoryGB, metricDataGB)\n\n\t\t\t\tfreedExcluded := 0\n\t\t\t\tfreedEmergency := 0\n\t\t\t\tvar err error\n\n\t\t\t\tstate.mu.RLock()\n\t\t\t\tlastRetention := state.lastRetentionTime\n\t\t\t\tselectorsExcluded := state.selectorsExcluded\n\t\t\t\tstate.mu.RUnlock()\n\n\t\t\t\tif lastRetention != 0 && selectorsExcluded {\n\t\t\t\t\tfreedExcluded, err = ms.Free(nil, lastRetention)\n\t\t\t\t\tif err != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing the excluded buffers: %s\", err)\n\t\t\t\t\t}\n\n\t\t\t\t\tif freedExcluded > 0 {\n\t\t\t\t\t\tdebug.FreeOSMemory()\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d excluded buffers force-freed\", freedExcluded)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\tif actualMemoryGB > float64(Keys.MemoryCap) {\n\t\t\t\t\tcclog.Warnf(\"[METRICSTORE]> memory usage %.2f GB exceeds cap %d GB, starting emergency buffer freeing\", actualMemoryGB, Keys.MemoryCap)\n\n\t\t\t\t\t// Use progressive time-based Free with increasing threshold\n\t\t\t\t\t// instead of ForceFree loop — fewer tree traversals, more effective\n\t\t\t\t\td, parseErr := time.ParseDuration(Keys.RetentionInMemory)\n\t\t\t\t\tif parseErr != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> cannot parse retention duration: %s\", parseErr)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tthresholds := []float64{0.75, 0.5, 0.25}\n\t\t\t\t\t\tfor _, fraction := range thresholds {\n\t\t\t\t\t\t\tthreshold := time.Now().Add(-time.Duration(float64(d) * fraction))\n\t\t\t\t\t\t\tfreed, freeErr := ms.Free(nil, threshold.Unix())\n\t\t\t\t\t\t\tif freeErr != nil {\n\t\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while freeing buffers at %.0f%% retention: %s\", fraction*100, freeErr)\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tfreedEmergency += freed\n\n\t\t\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\t\t\truntime.GC()\n\t\t\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\t\t\tif actualMemoryGB < float64(Keys.MemoryCap) {\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\tdebug.FreeOSMemory()\n\n\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\tif actualMemoryGB >= float64(Keys.MemoryCap) {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> after emergency frees (%d buffers), memory usage %.2f GB still at/above cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> emergency freeing complete: %d buffers freed, memory now %.2f GB\", freedEmergency, actualMemoryGB)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Adaptive ticker: check more frequently when memory is high\n\t\t\t\tmemoryRatio := actualMemoryGB / float64(Keys.MemoryCap)\n\t\t\t\tif memoryRatio > 0.8 && currentInterval != fastInterval {\n\t\t\t\t\tticker.Reset(fastInterval)\n\t\t\t\t\tcurrentInterval = fastInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to fast check interval (30s)\", memoryRatio*100)\n\t\t\t\t} else if memoryRatio <= 0.8 && currentInterval != normalInterval {\n\t\t\t\t\tticker.Reset(normalInterval)\n\t\t\t\t\tcurrentInterval = normalInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to normal check interval\", memoryRatio*100)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t})\n}\n\n// Free removes metric data older than the given time while preserving data for active nodes.\n//\n// This function implements intelligent retention by consulting the NodeProvider (if configured)\n// to determine which nodes are currently in use by running jobs. Data for these nodes is\n// preserved even if older than the retention time.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - t: Time threshold - buffers with data older than this will be freed\n//\n// Returns:\n// - Number of buffers freed\n// - Error if NodeProvider query fails\n//\n// Behavior:\n// - If no NodeProvider is set: frees all buffers older than t\n// - If NodeProvider returns empty map: frees all buffers older than t\n// - Otherwise: preserves buffers for nodes returned by GetUsedNodes(), frees others\nfunc Free(ms *MemoryStore, t time.Time) (int, error) {\n\t// If no NodeProvider is configured, free all buffers older than t\n\tif ms.nodeProvider == nil {\n\t\treturn ms.Free(nil, t.Unix())\n\t}\n\n\texcludeSelectors, err := ms.nodeProvider.GetUsedNodes(t.Unix())\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tswitch lenMap := len(excludeSelectors); lenMap {\n\n\t// If the length of the map returned by GetUsedNodes() is 0,\n\t// then use default Free method with nil selector\n\tcase 0:\n\t\tstate.selectorsExcluded = false\n\t\treturn ms.Free(nil, t.Unix())\n\n\t// Else formulate selectors, exclude those from the map\n\t// and free the rest of the selectors\n\tdefault:\n\t\tstate.selectorsExcluded = true\n\t\tselectors := GetSelectors(ms, excludeSelectors)\n\t\treturn FreeSelected(ms, selectors, t)\n\t}\n}\n\n// FreeSelected frees buffers for specific selectors while preserving others.\n//\n// This function is used when we want to retain some specific nodes beyond the retention time.\n// It iterates through the provided selectors and frees their associated buffers.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - selectors: List of selector paths to free (e.g., [[\"cluster1\", \"node1\"], [\"cluster2\", \"node2\"]])\n// - t: Time threshold for freeing buffers\n//\n// Returns the total number of buffers freed and any error encountered.\nfunc FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\n\tfreed := 0\n\n\tfor _, selector := range selectors {\n\n\t\tfreedBuffers, err := ms.Free(selector, t.Unix())\n\t\tif err != nil {\n\t\t\tcclog.Errorf(\"error while freeing selected buffers: %#v\", err)\n\t\t}\n\t\tfreed += freedBuffers\n\n\t}\n\n\treturn freed, nil\n}\n\n// GetSelectors returns all selectors at depth 2 (cluster/node level) that are NOT in the exclusion map.\n//\n// This function generates a list of selectors whose buffers should be freed by excluding\n// selectors that correspond to nodes currently in use by running jobs.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - excludeSelectors: Map of cluster names to node hostnames that should NOT be freed\n//\n// Returns a list of selectors ([]string paths) that can be safely freed.\n//\n// Example:\n//\n//\tIf the tree has paths [\"emmy\", \"node001\"] and [\"emmy\", \"node002\"],\n//\tand excludeSelectors contains {\"emmy\": [\"node001\"]},\n//\tthen only [[\"emmy\", \"node002\"]] is returned.\nfunc GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\n\tallSelectors := ms.GetPaths(2)\n\n\tfilteredSelectors := make([][]string, 0, len(allSelectors))\n\n\tfor _, path := range allSelectors {\n\t\tif len(path) < 2 {\n\t\t\tcontinue\n\t\t}\n\n\t\tkey := path[0] // The \"Key\" (Level 1)\n\t\tvalue := path[1] // The \"Value\" (Level 2)\n\n\t\texclude := false\n\n\t\t// Check if the key exists in our exclusion map\n\t\tif excludedValues, exists := excludeSelectors[key]; exists {\n\t\t\t// The key exists, now check if the specific value is in the exclusion list\n\t\t\tif slices.Contains(excludedValues, value) {\n\t\t\t\texclude = true\n\t\t\t}\n\t\t}\n\n\t\tif !exclude {\n\t\t\tfilteredSelectors = append(filteredSelectors, path)\n\t\t}\n\t}\n\n\treturn filteredSelectors\n}\n\n// GetPaths returns a list of lists (paths) to the specified depth.\nfunc (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\n\tvar results [][]string\n\n\t// Start recursion. Initial path is empty.\n\t// We treat Root as depth 0.\n\tms.root.collectPaths(0, targetDepth, []string{}, &results)\n\n\treturn results\n}\n\n// Write all values in `metrics` to the level specified by `selector` for time `ts`.\n// Look at `findLevelOrCreate` for how selectors work.\nfunc (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\n\tvar ok bool\n\tfor i, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tmetric.MetricConfig, ok = m.Metrics[metric.Name]\n\t\t\tif !ok {\n\t\t\t\tcclog.Debugf(\"[METRICSTORE]> Unknown metric '%s' in Write() - skipping\", metric.Name)\n\t\t\t\tmetric.MetricConfig.Frequency = 0\n\t\t\t}\n\t\t\tmetrics[i] = metric\n\t\t}\n\t}\n\n\treturn m.WriteToLevel(&m.root, selector, ts, metrics)\n}\n\nfunc (m *MemoryStore) GetLevel(selector []string) *Level {\n\treturn m.root.findLevelOrCreate(selector, len(m.Metrics))\n}\n\n// WriteToLevel assumes that `minfo` in `metrics` is filled in\nfunc (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n\tl = l.findLevelOrCreate(selector, len(m.Metrics))\n\tl.lock.Lock()\n\tdefer l.lock.Unlock()\n\n\tfor _, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tcontinue\n\t\t}\n\n\t\tb := l.metrics[metric.MetricConfig.offset]\n\t\tif b == nil {\n\t\t\t// First write to this metric and level\n\t\t\tb = newBuffer(ts, metric.MetricConfig.Frequency)\n\t\t\tl.metrics[metric.MetricConfig.offset] = b\n\t\t}\n\n\t\tnb, err := b.write(ts, metric.Value)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\t// Last write created a new buffer...\n\t\tif b != nb {\n\t\t\tl.metrics[metric.MetricConfig.offset] = nb\n\t\t}\n\t}\n\treturn nil\n}\n\n// Read returns all values for metric `metric` from `from` to `to` for the selected level(s).\n// If the level does not hold the metric itself, the data will be aggregated recursively from the children.\n// The second and third return value are the actual from/to for the data. Those can be different from\n// the range asked for if no data was available.\nfunc (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {\n\tif from > to {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")\n\t}\n\n\tminfo, ok := m.Metrics[metric]\n\tif !ok {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> unknown metric: \" + metric)\n\t}\n\n\tn, data := 0, make([]schema.Float, (to-from)/minfo.Frequency+1)\n\n\terr := m.root.findBuffers(selector, minfo.offset, func(b *buffer) error {\n\t\tcdata, cfrom, cto, err := b.read(from, to, data)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tif n == 0 {\n\t\t\tfrom, to = cfrom, cto\n\t\t} else if from != cfrom || to != cto || len(data) != len(cdata) {\n\t\t\tmissingfront, missingback := int((from-cfrom)/minfo.Frequency), int((to-cto)/minfo.Frequency)\n\t\t\tif missingfront != 0 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tnewlen := len(cdata) - missingback\n\t\t\tif newlen < 1 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\t\t\tcdata = cdata[0:newlen]\n\t\t\tif len(cdata) != len(data) {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tfrom, to = cfrom, cto\n\t\t}\n\n\t\tdata = cdata\n\t\tn += 1\n\t\treturn nil\n\t})\n\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t} else if n == 0 {\n\t\treturn nil, 0, 0, 0, ErrNoHostOrMetric\n\t} else if n > 1 {\n\t\tif minfo.Aggregation == AvgAggregation {\n\t\t\tnormalize := 1. / schema.Float(n)\n\t\t\tfor i := 0; i < len(data); i++ {\n\t\t\t\tdata[i] *= normalize\n\t\t\t}\n\t\t} else if minfo.Aggregation != SumAggregation {\n\t\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid aggregation\")\n\t\t}\n\t}\n\n\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t}\n\n\treturn data, from, to, resolution, nil\n}\n\n// Free releases all buffers for the selected level and all its children that\n// contain only values older than `t`.\nfunc (m *MemoryStore) Free(selector []string, t int64) (int, error) {\n\treturn m.GetLevel(selector).free(t)\n}\n\n// ForceFree unconditionally removes the oldest buffer from each metric chain.\nfunc (m *MemoryStore) ForceFree() (int, error) {\n\treturn m.GetLevel(nil).forceFree()\n}\n\nfunc (m *MemoryStore) FreeAll() error {\n\tm.root.lock.Lock()\n\tdefer m.root.lock.Unlock()\n\n\tfor k := range m.root.children {\n\t\tdelete(m.root.children, k)\n\t}\n\n\treturn nil\n}\n\nfunc (m *MemoryStore) SizeInBytes() int64 {\n\treturn m.root.sizeInBytes()\n}\n\nfunc (m *MemoryStore) SizeInGB() float64 {\n\treturn float64(m.root.sizeInBytes()) / 1e9\n}\n\n// ListChildren , given a selector, returns a list of all children of the level\n// selected.\nfunc (m *MemoryStore) ListChildren(selector []string) []string {\n\tlvl := &m.root\n\tfor lvl != nil && len(selector) != 0 {\n\t\tlvl.lock.RLock()\n\t\tnext := lvl.children[selector[0]]\n\t\tlvl.lock.RUnlock()\n\t\tlvl = next\n\t\tselector = selector[1:]\n\t}\n\n\tif lvl == nil {\n\t\treturn nil\n\t}\n\n\tlvl.lock.RLock()\n\tdefer lvl.lock.RUnlock()\n\n\tchildren := make([]string, 0, len(lvl.children))\n\tfor child := range lvl.children {\n\t\tchildren = append(children, child)\n\t}\n\n\treturn children\n}\n","numLines":804,"startLine":1,"totalLines":804}},"sourceToolAssistantUUID":"6918159d-98ed-47a5-9717-1c88e0e885f6","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"f5f70f75-e3f0-4a2e-a1f6-8070a59f0611","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019bNBWigLiGdX8oKM2pnvE2","type":"tool_result","content":" 1→module github.com/ClusterCockpit/cc-backend\n 2→\n 3→go 1.25.0\n 4→\n 5→tool (\n 6→\tgithub.com/99designs/gqlgen\n 7→\tgithub.com/swaggo/swag/cmd/swag\n 8→)\n 9→\n 10→require (\n 11→\tgithub.com/99designs/gqlgen v0.17.88\n 12→\tgithub.com/ClusterCockpit/cc-lib/v2 v2.9.1\n 13→\tgithub.com/ClusterCockpit/cc-line-protocol/v2 v2.4.0\n 14→\tgithub.com/Masterminds/squirrel v1.5.4\n 15→\tgithub.com/aws/aws-sdk-go-v2 v1.41.3\n 16→\tgithub.com/aws/aws-sdk-go-v2/config v1.32.11\n 17→\tgithub.com/aws/aws-sdk-go-v2/credentials v1.19.11\n 18→\tgithub.com/aws/aws-sdk-go-v2/service/s3 v1.97.0\n 19→\tgithub.com/coreos/go-oidc/v3 v3.17.0\n 20→\tgithub.com/expr-lang/expr v1.17.8\n 21→\tgithub.com/go-chi/chi/v5 v5.2.5\n 22→\tgithub.com/go-chi/cors v1.2.2\n 23→\tgithub.com/go-co-op/gocron/v2 v2.19.1\n 24→\tgithub.com/go-ldap/ldap/v3 v3.4.12\n 25→\tgithub.com/golang-jwt/jwt/v5 v5.3.1\n 26→\tgithub.com/golang-migrate/migrate/v4 v4.19.1\n 27→\tgithub.com/google/gops v0.3.29\n 28→\tgithub.com/gorilla/sessions v1.4.0\n 29→\tgithub.com/jmoiron/sqlx v1.4.0\n 30→\tgithub.com/joho/godotenv v1.5.1\n 31→\tgithub.com/mattn/go-sqlite3 v1.14.34\n 32→\tgithub.com/parquet-go/parquet-go v0.29.0\n 33→\tgithub.com/qustavo/sqlhooks/v2 v2.1.0\n 34→\tgithub.com/santhosh-tekuri/jsonschema/v5 v5.3.1\n 35→\tgithub.com/stretchr/testify v1.11.1\n 36→\tgithub.com/swaggo/http-swagger v1.3.4\n 37→\tgithub.com/swaggo/swag v1.16.6\n 38→\tgithub.com/vektah/gqlparser/v2 v2.5.32\n 39→\tgolang.org/x/crypto v0.49.0\n 40→\tgolang.org/x/oauth2 v0.36.0\n 41→\tgolang.org/x/sync v0.20.0\n 42→\tgolang.org/x/time v0.15.0\n 43→)\n 44→\n 45→require (\n 46→\tgithub.com/Azure/go-ntlmssp v0.1.0 // indirect\n 47→\tgithub.com/KyleBanks/depth v1.2.1 // indirect\n 48→\tgithub.com/agnivade/levenshtein v1.2.1 // indirect\n 49→\tgithub.com/andybalholm/brotli v1.2.0 // indirect\n 50→\tgithub.com/apapsch/go-jsonmerge/v2 v2.0.0 // indirect\n 51→\tgithub.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.6 // indirect\n 52→\tgithub.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.19 // indirect\n 53→\tgithub.com/aws/aws-sdk-go-v2/internal/configsources v1.4.19 // indirect\n 54→\tgithub.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.19 // indirect\n 55→\tgithub.com/aws/aws-sdk-go-v2/internal/ini v1.8.5 // indirect\n 56→\tgithub.com/aws/aws-sdk-go-v2/internal/v4a v1.4.20 // indirect\n 57→\tgithub.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.6 // indirect\n 58→\tgithub.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.11 // indirect\n 59→\tgithub.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.19 // indirect\n 60→\tgithub.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.19 // indirect\n 61→\tgithub.com/aws/aws-sdk-go-v2/service/signin v1.0.7 // indirect\n 62→\tgithub.com/aws/aws-sdk-go-v2/service/sso v1.30.12 // indirect\n 63→\tgithub.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.16 // indirect\n 64→\tgithub.com/aws/aws-sdk-go-v2/service/sts v1.41.8 // indirect\n 65→\tgithub.com/aws/smithy-go v1.24.2 // indirect\n 66→\tgithub.com/cpuguy83/go-md2man/v2 v2.0.7 // indirect\n 67→\tgithub.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect\n 68→\tgithub.com/fsnotify/fsnotify v1.9.0 // indirect\n 69→\tgithub.com/go-asn1-ber/asn1-ber v1.5.8-0.20250403174932-29230038a667 // indirect\n 70→\tgithub.com/go-jose/go-jose/v4 v4.1.3 // indirect\n 71→\tgithub.com/go-openapi/jsonpointer v0.22.5 // indirect\n 72→\tgithub.com/go-openapi/jsonreference v0.21.5 // indirect\n 73→\tgithub.com/go-openapi/spec v0.22.4 // indirect\n 74→\tgithub.com/go-openapi/swag/conv v0.25.5 // indirect\n 75→\tgithub.com/go-openapi/swag/jsonname v0.25.5 // indirect\n 76→\tgithub.com/go-openapi/swag/jsonutils v0.25.5 // indirect\n 77→\tgithub.com/go-openapi/swag/loading v0.25.5 // indirect\n 78→\tgithub.com/go-openapi/swag/stringutils v0.25.5 // indirect\n 79→\tgithub.com/go-openapi/swag/typeutils v0.25.5 // indirect\n 80→\tgithub.com/go-openapi/swag/yamlutils v0.25.5 // indirect\n 81→\tgithub.com/go-viper/mapstructure/v2 v2.5.0 // indirect\n 82→\tgithub.com/goccy/go-yaml v1.19.2 // indirect\n 83→\tgithub.com/google/uuid v1.6.0 // indirect\n 84→\tgithub.com/gorilla/securecookie v1.1.2 // indirect\n 85→\tgithub.com/gorilla/websocket v1.5.3 // indirect\n 86→\tgithub.com/hashicorp/golang-lru/v2 v2.0.7 // indirect\n 87→\tgithub.com/influxdata/influxdb-client-go/v2 v2.14.0 // indirect\n 88→\tgithub.com/influxdata/line-protocol v0.0.0-20210922203350-b1ad95c89adf // indirect\n 89→\tgithub.com/jonboulle/clockwork v0.5.0 // indirect\n 90→\tgithub.com/klauspost/compress v1.18.4 // indirect\n 91→\tgithub.com/kr/pretty v0.3.1 // indirect\n 92→\tgithub.com/lann/builder v0.0.0-20180802200727-47ae307949d0 // indirect\n 93→\tgithub.com/lann/ps v0.0.0-20150810152359-62de8c46ede0 // indirect\n 94→\tgithub.com/nats-io/nats.go v1.49.0 // indirect\n 95→\tgithub.com/nats-io/nkeys v0.4.15 // indirect\n 96→\tgithub.com/nats-io/nuid v1.0.1 // indirect\n 97→\tgithub.com/oapi-codegen/runtime v1.2.0 // indirect\n 98→\tgithub.com/parquet-go/bitpack v1.0.0 // indirect\n 99→\tgithub.com/parquet-go/jsonlite v1.4.0 // indirect\n 100→\tgithub.com/pierrec/lz4/v4 v4.1.26 // indirect\n 101→\tgithub.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect\n 102→\tgithub.com/robfig/cron/v3 v3.0.1 // indirect\n 103→\tgithub.com/rogpeppe/go-internal v1.10.0 // indirect\n 104→\tgithub.com/russross/blackfriday/v2 v2.1.0 // indirect\n 105→\tgithub.com/sosodev/duration v1.4.0 // indirect\n 106→\tgithub.com/stmcginnis/gofish v0.21.4 // indirect\n 107→\tgithub.com/stretchr/objx v0.5.2 // indirect\n 108→\tgithub.com/swaggo/files v1.0.1 // indirect\n 109→\tgithub.com/twpayne/go-geom v1.6.1 // indirect\n 110→\tgithub.com/urfave/cli/v2 v2.27.7 // indirect\n 111→\tgithub.com/urfave/cli/v3 v3.7.0 // indirect\n 112→\tgithub.com/xrash/smetrics v0.0.0-20250705151800-55b8f293f342 // indirect\n 113→\tgo.yaml.in/yaml/v2 v2.4.3 // indirect\n 114→\tgo.yaml.in/yaml/v3 v3.0.4 // indirect\n 115→\tgolang.org/x/mod v0.34.0 // indirect\n 116→\tgolang.org/x/net v0.52.0 // indirect\n 117→\tgolang.org/x/sys v0.42.0 // indirect\n 118→\tgolang.org/x/text v0.35.0 // indirect\n 119→\tgolang.org/x/tools v0.43.0 // indirect\n 120→\tgoogle.golang.org/protobuf v1.36.11 // indirect\n 121→\tgopkg.in/yaml.v3 v3.0.1 // indirect\n 122→\tsigs.k8s.io/yaml v1.6.0 // indirect\n 123→)\n 124→"}]},"uuid":"dc0ddff5-c73d-4daf-8a79-92776d512e94","timestamp":"2026-03-19T19:46:24.682Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/go.mod","content":"module github.com/ClusterCockpit/cc-backend\n\ngo 1.25.0\n\ntool (\n\tgithub.com/99designs/gqlgen\n\tgithub.com/swaggo/swag/cmd/swag\n)\n\nrequire (\n\tgithub.com/99designs/gqlgen v0.17.88\n\tgithub.com/ClusterCockpit/cc-lib/v2 v2.9.1\n\tgithub.com/ClusterCockpit/cc-line-protocol/v2 v2.4.0\n\tgithub.com/Masterminds/squirrel v1.5.4\n\tgithub.com/aws/aws-sdk-go-v2 v1.41.3\n\tgithub.com/aws/aws-sdk-go-v2/config v1.32.11\n\tgithub.com/aws/aws-sdk-go-v2/credentials v1.19.11\n\tgithub.com/aws/aws-sdk-go-v2/service/s3 v1.97.0\n\tgithub.com/coreos/go-oidc/v3 v3.17.0\n\tgithub.com/expr-lang/expr v1.17.8\n\tgithub.com/go-chi/chi/v5 v5.2.5\n\tgithub.com/go-chi/cors v1.2.2\n\tgithub.com/go-co-op/gocron/v2 v2.19.1\n\tgithub.com/go-ldap/ldap/v3 v3.4.12\n\tgithub.com/golang-jwt/jwt/v5 v5.3.1\n\tgithub.com/golang-migrate/migrate/v4 v4.19.1\n\tgithub.com/google/gops v0.3.29\n\tgithub.com/gorilla/sessions v1.4.0\n\tgithub.com/jmoiron/sqlx v1.4.0\n\tgithub.com/joho/godotenv v1.5.1\n\tgithub.com/mattn/go-sqlite3 v1.14.34\n\tgithub.com/parquet-go/parquet-go v0.29.0\n\tgithub.com/qustavo/sqlhooks/v2 v2.1.0\n\tgithub.com/santhosh-tekuri/jsonschema/v5 v5.3.1\n\tgithub.com/stretchr/testify v1.11.1\n\tgithub.com/swaggo/http-swagger v1.3.4\n\tgithub.com/swaggo/swag v1.16.6\n\tgithub.com/vektah/gqlparser/v2 v2.5.32\n\tgolang.org/x/crypto v0.49.0\n\tgolang.org/x/oauth2 v0.36.0\n\tgolang.org/x/sync v0.20.0\n\tgolang.org/x/time v0.15.0\n)\n\nrequire (\n\tgithub.com/Azure/go-ntlmssp v0.1.0 // indirect\n\tgithub.com/KyleBanks/depth v1.2.1 // indirect\n\tgithub.com/agnivade/levenshtein v1.2.1 // indirect\n\tgithub.com/andybalholm/brotli v1.2.0 // indirect\n\tgithub.com/apapsch/go-jsonmerge/v2 v2.0.0 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.6 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/configsources v1.4.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/ini v1.8.5 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/v4a v1.4.20 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.6 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.11 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/signin v1.0.7 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/sso v1.30.12 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.16 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/sts v1.41.8 // indirect\n\tgithub.com/aws/smithy-go v1.24.2 // indirect\n\tgithub.com/cpuguy83/go-md2man/v2 v2.0.7 // indirect\n\tgithub.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect\n\tgithub.com/fsnotify/fsnotify v1.9.0 // indirect\n\tgithub.com/go-asn1-ber/asn1-ber v1.5.8-0.20250403174932-29230038a667 // indirect\n\tgithub.com/go-jose/go-jose/v4 v4.1.3 // indirect\n\tgithub.com/go-openapi/jsonpointer v0.22.5 // indirect\n\tgithub.com/go-openapi/jsonreference v0.21.5 // indirect\n\tgithub.com/go-openapi/spec v0.22.4 // indirect\n\tgithub.com/go-openapi/swag/conv v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/jsonname v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/jsonutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/loading v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/stringutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/typeutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/yamlutils v0.25.5 // indirect\n\tgithub.com/go-viper/mapstructure/v2 v2.5.0 // indirect\n\tgithub.com/goccy/go-yaml v1.19.2 // indirect\n\tgithub.com/google/uuid v1.6.0 // indirect\n\tgithub.com/gorilla/securecookie v1.1.2 // indirect\n\tgithub.com/gorilla/websocket v1.5.3 // indirect\n\tgithub.com/hashicorp/golang-lru/v2 v2.0.7 // indirect\n\tgithub.com/influxdata/influxdb-client-go/v2 v2.14.0 // indirect\n\tgithub.com/influxdata/line-protocol v0.0.0-20210922203350-b1ad95c89adf // indirect\n\tgithub.com/jonboulle/clockwork v0.5.0 // indirect\n\tgithub.com/klauspost/compress v1.18.4 // indirect\n\tgithub.com/kr/pretty v0.3.1 // indirect\n\tgithub.com/lann/builder v0.0.0-20180802200727-47ae307949d0 // indirect\n\tgithub.com/lann/ps v0.0.0-20150810152359-62de8c46ede0 // indirect\n\tgithub.com/nats-io/nats.go v1.49.0 // indirect\n\tgithub.com/nats-io/nkeys v0.4.15 // indirect\n\tgithub.com/nats-io/nuid v1.0.1 // indirect\n\tgithub.com/oapi-codegen/runtime v1.2.0 // indirect\n\tgithub.com/parquet-go/bitpack v1.0.0 // indirect\n\tgithub.com/parquet-go/jsonlite v1.4.0 // indirect\n\tgithub.com/pierrec/lz4/v4 v4.1.26 // indirect\n\tgithub.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect\n\tgithub.com/robfig/cron/v3 v3.0.1 // indirect\n\tgithub.com/rogpeppe/go-internal v1.10.0 // indirect\n\tgithub.com/russross/blackfriday/v2 v2.1.0 // indirect\n\tgithub.com/sosodev/duration v1.4.0 // indirect\n\tgithub.com/stmcginnis/gofish v0.21.4 // indirect\n\tgithub.com/stretchr/objx v0.5.2 // indirect\n\tgithub.com/swaggo/files v1.0.1 // indirect\n\tgithub.com/twpayne/go-geom v1.6.1 // indirect\n\tgithub.com/urfave/cli/v2 v2.27.7 // indirect\n\tgithub.com/urfave/cli/v3 v3.7.0 // indirect\n\tgithub.com/xrash/smetrics v0.0.0-20250705151800-55b8f293f342 // indirect\n\tgo.yaml.in/yaml/v2 v2.4.3 // indirect\n\tgo.yaml.in/yaml/v3 v3.0.4 // indirect\n\tgolang.org/x/mod v0.34.0 // indirect\n\tgolang.org/x/net v0.52.0 // indirect\n\tgolang.org/x/sys v0.42.0 // indirect\n\tgolang.org/x/text v0.35.0 // indirect\n\tgolang.org/x/tools v0.43.0 // indirect\n\tgoogle.golang.org/protobuf v1.36.11 // indirect\n\tgopkg.in/yaml.v3 v3.0.1 // indirect\n\tsigs.k8s.io/yaml v1.6.0 // indirect\n)\n","numLines":124,"startLine":1,"totalLines":124}},"sourceToolAssistantUUID":"f5f70f75-e3f0-4a2e-a1f6-8070a59f0611","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"dc0ddff5-c73d-4daf-8a79-92776d512e94","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_016pZH3KTZGFHzsUeP15nDrN","toolUseID":"toolu_016pZH3KTZGFHzsUeP15nDrN","timestamp":"2026-03-19T19:46:24.751Z","uuid":"9e5c8cd3-7f9c-4fa6-ab39-e1f35119bacf","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"5734c09b-ab74-4c3b-965d-149fbfd45518","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016pZH3KTZGFHzsUeP15nDrN","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// This file implements high-level query functions for loading job metric data\n 7→// with automatic scope transformation and aggregation.\n 8→//\n 9→// Key Concepts:\n 10→//\n 11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n 12→// - HWThread: Per hardware thread\n 13→// - Core: Per CPU core\n 14→// - Socket: Per CPU socket\n 15→// - MemoryDomain: Per memory domain (NUMA)\n 16→// - Accelerator: Per GPU/accelerator\n 17→// - Node: Per compute node\n 18→//\n 19→// Scope Transformation: The buildQueries functions transform between native scope\n 20→// and requested scope by:\n 21→// - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n 22→// - Rejecting requests for finer granularity than available\n 23→// - Handling special cases (e.g., Accelerator metrics)\n 24→//\n 25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n 26→// based on cluster topology and job resources.\n 27→package metricstore\n 28→\n 29→import (\n 30→\t\"context\"\n 31→\t\"fmt\"\n 32→\t\"strings\"\n 33→\t\"time\"\n 34→\n 35→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n 36→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 37→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 38→)\n 39→\n 40→type InternalMetricStore struct{}\n 41→\n 42→var MetricStoreHandle *InternalMetricStore\n 43→\n 44→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n 45→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n 46→\tnodes []string, metrics []string,\n 47→) (map[string]HealthCheckResult, error) {\n 48→\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n 49→}\n 50→\n 51→// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n 52→// When set to a non-nil function, LoadData will call this function instead of the default implementation.\n 53→var TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n 54→\n 55→// LoadData loads metric data for a specific job with automatic scope transformation.\n 56→//\n 57→// This is the primary function for retrieving job metric data. It handles:\n 58→// - Building queries with scope transformation via buildQueries\n 59→// - Fetching data from the metric store\n 60→// - Organizing results by metric and scope\n 61→// - Converting NaN statistics to 0 for JSON compatibility\n 62→// - Partial error handling (returns data for successful queries even if some fail)\n 63→//\n 64→// Parameters:\n 65→// - job: Job metadata including cluster, resources, and time range\n 66→// - metrics: List of metric names to load\n 67→// - scopes: Requested metric scopes (will be transformed to match native scopes)\n 68→// - ctx: Context for cancellation (currently unused but reserved for future use)\n 69→// - resolution: Data resolution in seconds (0 for native resolution)\n 70→//\n 71→// Returns:\n 72→// - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n 73→// - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n 74→//\n 75→// Example:\n 76→//\n 77→//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\n 78→func (ccms *InternalMetricStore) LoadData(\n 79→\tjob *schema.Job,\n 80→\tmetrics []string,\n 81→\tscopes []schema.MetricScope,\n 82→\tctx context.Context,\n 83→\tresolution int,\n 84→) (schema.JobData, error) {\n 85→\tif TestLoadDataCallback != nil {\n 86→\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n 87→\t}\n 88→\n 89→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n 90→\tif err != nil {\n 91→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n 92→\t\treturn nil, err\n 93→\t}\n 94→\n 95→\t// Verify assignment is correct - log any inconsistencies for debugging\n 96→\tif len(queries) != len(assignedScope) {\n 97→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n 98→\t\t\tlen(queries), len(assignedScope))\n 99→\t}\n 100→\n 101→\treq := APIQueryRequest{\n 102→\t\tCluster: job.Cluster,\n 103→\t\tFrom: job.StartTime,\n 104→\t\tTo: job.StartTime + int64(job.Duration),\n 105→\t\tQueries: queries,\n 106→\t\tWithStats: true,\n 107→\t\tWithData: true,\n 108→\t}\n 109→\n 110→\tresBody, err := FetchData(req)\n 111→\tif err != nil {\n 112→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 113→\t\treturn nil, err\n 114→\t}\n 115→\n 116→\tvar errors []string\n 117→\tjobData := make(schema.JobData)\n 118→\n 119→\t// Add safety check for potential index out of range errors\n 120→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n 121→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n 122→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n 123→\t\tif len(resBody.Results) > len(req.Queries) {\n 124→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n 125→\t\t}\n 126→\t\tif len(assignedScope) > len(req.Queries) {\n 127→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n 128→\t\t}\n 129→\t}\n 130→\n 131→\tfor i, row := range resBody.Results {\n 132→\t\tquery := req.Queries[i]\n 133→\t\tmetric := query.Metric\n 134→\t\tscope := assignedScope[i]\n 135→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n 136→\n 137→\t\tif mc == nil {\n 138→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n 139→\t\t\tcontinue\n 140→\t\t}\n 141→\n 142→\t\tif _, ok := jobData[metric]; !ok {\n 143→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n 144→\t\t}\n 145→\n 146→\t\tres := mc.Timestep\n 147→\t\tif len(row) > 0 {\n 148→\t\t\tres = int(row[0].Resolution)\n 149→\t\t}\n 150→\n 151→\t\tjobMetric, ok := jobData[metric][scope]\n 152→\t\tif !ok {\n 153→\t\t\tjobMetric = &schema.JobMetric{\n 154→\t\t\t\tUnit: mc.Unit,\n 155→\t\t\t\tTimestep: res,\n 156→\t\t\t\tSeries: make([]schema.Series, 0),\n 157→\t\t\t}\n 158→\t\t\tjobData[metric][scope] = jobMetric\n 159→\t\t}\n 160→\n 161→\t\tfor ndx, res := range row {\n 162→\t\t\tif res.Error != nil {\n 163→\t\t\t\t/* Build list for \"partial errors\", if any */\n 164→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n 165→\t\t\t\tcontinue\n 166→\t\t\t}\n 167→\n 168→\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n 169→\n 170→\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n 171→\n 172→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n 173→\t\t\t\tHostname: query.Hostname,\n 174→\t\t\t\tID: id,\n 175→\t\t\t\tStatistics: schema.MetricStatistics{\n 176→\t\t\t\t\tAvg: float64(res.Avg),\n 177→\t\t\t\t\tMin: float64(res.Min),\n 178→\t\t\t\t\tMax: float64(res.Max),\n 179→\t\t\t\t},\n 180→\t\t\t\tData: res.Data,\n 181→\t\t\t})\n 182→\t\t}\n 183→\n 184→\t\t// So that one can later check len(jobData):\n 185→\t\tif len(jobMetric.Series) == 0 {\n 186→\t\t\tdelete(jobData[metric], scope)\n 187→\t\t\tif len(jobData[metric]) == 0 {\n 188→\t\t\t\tdelete(jobData, metric)\n 189→\t\t\t}\n 190→\t\t}\n 191→\t}\n 192→\n 193→\tif len(errors) != 0 {\n 194→\t\t/* Returns list for \"partial errors\" */\n 195→\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n 196→\t}\n 197→\treturn jobData, nil\n 198→}\n 199→\n 200→// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n 201→//\n 202→// This function implements the core scope transformation logic, handling all combinations of\n 203→// native metric scopes and requested scopes. It uses the cluster topology to determine which\n 204→// hardware IDs to include in each query.\n 205→//\n 206→// Scope Transformation Rules:\n 207→// - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n 208→// - If native scope < requested scope: Returns error (cannot increase granularity)\n 209→// - Special handling for Accelerator scope (independent of CPU hierarchy)\n 210→//\n 211→// The function generates one or more APIQuery per (metric, scope, host) combination:\n 212→// - For non-aggregated queries: One query with all relevant IDs\n 213→// - For aggregated queries: May generate multiple queries (e.g., one per socket/core)\n 214→//\n 215→// Parameters:\n 216→// - job: Job metadata including cluster, subcluster, and resource allocation\n 217→// - metrics: List of metrics to query\n 218→// - scopes: Requested scopes for each metric\n 219→// - resolution: Data resolution in seconds\n 220→//\n 221→// Returns:\n 222→// - []APIQuery: List of queries to execute\n 223→// - []schema.MetricScope: Assigned scope for each query (after transformation)\n 224→// - error: Returns error if topology lookup fails or unhandled scope combination encountered\n 225→func buildQueries(\n 226→\tjob *schema.Job,\n 227→\tmetrics []string,\n 228→\tscopes []schema.MetricScope,\n 229→\tresolution int64,\n 230→) ([]APIQuery, []schema.MetricScope, error) {\n 231→\tif len(job.Resources) == 0 {\n 232→\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > no resources allocated for job %d\", job.JobID)\n 233→\t}\n 234→\n 235→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n 236→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n 237→\n 238→\tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n 239→\tif scerr != nil {\n 240→\t\treturn nil, nil, scerr\n 241→\t}\n 242→\ttopology := subcluster.Topology\n 243→\n 244→\tfor _, metric := range metrics {\n 245→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n 246→\t\tif mc == nil {\n 247→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, job.Cluster)\n 248→\t\t\tcontinue\n 249→\t\t}\n 250→\n 251→\t\t// Skip if metric is removed for subcluster\n 252→\t\tif len(mc.SubClusters) != 0 && IsMetricRemovedForSubCluster(mc, job.SubCluster) {\n 253→\t\t\tcontinue\n 254→\t\t}\n 255→\n 256→\t\t// Avoid duplicates...\n 257→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n 258→\n 259→\tscopesLoop:\n 260→\t\tfor _, requestedScope := range scopes {\n 261→\t\t\tnativeScope := mc.Scope\n 262→\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n 263→\t\t\t\tcontinue\n 264→\t\t\t}\n 265→\n 266→\t\t\tscope := nativeScope.Max(requestedScope)\n 267→\t\t\tfor _, s := range handledScopes {\n 268→\t\t\t\tif scope == s {\n 269→\t\t\t\t\tcontinue scopesLoop\n 270→\t\t\t\t}\n 271→\t\t\t}\n 272→\t\t\thandledScopes = append(handledScopes, scope)\n 273→\n 274→\t\t\tfor _, host := range job.Resources {\n 275→\t\t\t\thwthreads := host.HWThreads\n 276→\t\t\t\tif hwthreads == nil {\n 277→\t\t\t\t\thwthreads = topology.Node\n 278→\t\t\t\t}\n 279→\n 280→\t\t\t\tscopeResults, ok := BuildScopeQueries(\n 281→\t\t\t\t\tnativeScope, requestedScope,\n 282→\t\t\t\t\tmetric, host.Hostname,\n 283→\t\t\t\t\t&topology, hwthreads, host.Accelerators,\n 284→\t\t\t\t)\n 285→\n 286→\t\t\t\tif !ok {\n 287→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n 288→\t\t\t\t}\n 289→\n 290→\t\t\t\tfor _, sr := range scopeResults {\n 291→\t\t\t\t\tqueries = append(queries, APIQuery{\n 292→\t\t\t\t\t\tMetric: sr.Metric,\n 293→\t\t\t\t\t\tHostname: sr.Hostname,\n 294→\t\t\t\t\t\tAggregate: sr.Aggregate,\n 295→\t\t\t\t\t\tType: sr.Type,\n 296→\t\t\t\t\t\tTypeIds: sr.TypeIds,\n 297→\t\t\t\t\t\tResolution: resolution,\n 298→\t\t\t\t\t})\n 299→\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n 300→\t\t\t\t}\n 301→\t\t\t}\n 302→\t\t}\n 303→\t}\n 304→\n 305→\treturn queries, assignedScope, nil\n 306→}\n 307→\n 308→// LoadStats loads only metric statistics (avg/min/max) for a job at node scope.\n 309→//\n 310→// This is an optimized version of LoadData that fetches only statistics without\n 311→// time-series data, reducing bandwidth and memory usage. Always queries at node scope.\n 312→//\n 313→// Parameters:\n 314→// - job: Job metadata\n 315→// - metrics: List of metric names\n 316→// - ctx: Context (currently unused)\n 317→//\n 318→// Returns:\n 319→// - Map of metric → hostname → statistics\n 320→// - Error on query building or fetching failure\n 321→func (ccms *InternalMetricStore) LoadStats(\n 322→\tjob *schema.Job,\n 323→\tmetrics []string,\n 324→\tctx context.Context,\n 325→) (map[string]map[string]schema.MetricStatistics, error) {\n 326→\t// TODO(#166): Add scope parameter for analysis view accelerator normalization\n 327→\tqueries, _, err := buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0)\n 328→\tif err != nil {\n 329→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n 330→\t\treturn nil, err\n 331→\t}\n 332→\n 333→\treq := APIQueryRequest{\n 334→\t\tCluster: job.Cluster,\n 335→\t\tFrom: job.StartTime,\n 336→\t\tTo: job.StartTime + int64(job.Duration),\n 337→\t\tQueries: queries,\n 338→\t\tWithStats: true,\n 339→\t\tWithData: false,\n 340→\t}\n 341→\n 342→\tresBody, err := FetchData(req)\n 343→\tif err != nil {\n 344→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 345→\t\treturn nil, err\n 346→\t}\n 347→\n 348→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n 349→\tfor i, res := range resBody.Results {\n 350→\t\tif i >= len(req.Queries) {\n 351→\t\t\tcclog.Warnf(\"LoadStats: result index %d exceeds queries length %d\", i, len(req.Queries))\n 352→\t\t\tbreak\n 353→\t\t}\n 354→\t\tif len(res) == 0 {\n 355→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n 356→\t\t\tcontinue\n 357→\t\t}\n 358→\t\tquery := req.Queries[i]\n 359→\t\tmetric := query.Metric\n 360→\t\tdata := res[0]\n 361→\t\tif data.Error != nil {\n 362→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n 363→\t\t\tcontinue\n 364→\t\t}\n 365→\n 366→\t\tmetricdata, ok := stats[metric]\n 367→\t\tif !ok {\n 368→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n 369→\t\t\tstats[metric] = metricdata\n 370→\t\t}\n 371→\n 372→\t\tif data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n 373→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n 374→\t\t\tcontinue\n 375→\t\t}\n 376→\n 377→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n 378→\t\t\tAvg: float64(data.Avg),\n 379→\t\t\tMin: float64(data.Min),\n 380→\t\t\tMax: float64(data.Max),\n 381→\t\t}\n 382→\t}\n 383→\n 384→\treturn stats, nil\n 385→}\n 386→\n 387→// LoadScopedStats loads metric statistics for a job with scope-aware grouping.\n 388→//\n 389→// Similar to LoadStats but supports multiple scopes and returns statistics grouped\n 390→// by scope with hardware IDs (e.g., per-core, per-socket statistics).\n 391→//\n 392→// Parameters:\n 393→// - job: Job metadata\n 394→// - metrics: List of metric names\n 395→// - scopes: Requested metric scopes\n 396→// - ctx: Context (currently unused)\n 397→//\n 398→// Returns:\n 399→// - ScopedJobStats: Map of metric → scope → []ScopedStats (with hostname and ID)\n 400→// - Error or partial error listing failed queries\n 401→func (ccms *InternalMetricStore) LoadScopedStats(\n 402→\tjob *schema.Job,\n 403→\tmetrics []string,\n 404→\tscopes []schema.MetricScope,\n 405→\tctx context.Context,\n 406→) (schema.ScopedJobStats, error) {\n 407→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, 0)\n 408→\tif err != nil {\n 409→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n 410→\t\treturn nil, err\n 411→\t}\n 412→\n 413→\treq := APIQueryRequest{\n 414→\t\tCluster: job.Cluster,\n 415→\t\tFrom: job.StartTime,\n 416→\t\tTo: job.StartTime + int64(job.Duration),\n 417→\t\tQueries: queries,\n 418→\t\tWithStats: true,\n 419→\t\tWithData: false,\n 420→\t}\n 421→\n 422→\tresBody, err := FetchData(req)\n 423→\tif err != nil {\n 424→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 425→\t\treturn nil, err\n 426→\t}\n 427→\n 428→\tvar errors []string\n 429→\tscopedJobStats := make(schema.ScopedJobStats)\n 430→\n 431→\tfor i, row := range resBody.Results {\n 432→\t\tif len(row) == 0 {\n 433→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n 434→\t\t\tcontinue\n 435→\t\t}\n 436→\t\tquery := req.Queries[i]\n 437→\t\tmetric := query.Metric\n 438→\t\tscope := assignedScope[i]\n 439→\n 440→\t\tif _, ok := scopedJobStats[metric]; !ok {\n 441→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n 442→\t\t}\n 443→\n 444→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n 445→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n 446→\t\t}\n 447→\n 448→\t\tfor ndx, res := range row {\n 449→\t\t\tif res.Error != nil {\n 450→\t\t\t\t/* Build list for \"partial errors\", if any */\n 451→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n 452→\t\t\t\tcontinue\n 453→\t\t\t}\n 454→\n 455→\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n 456→\n 457→\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n 458→\n 459→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n 460→\t\t\t\tHostname: query.Hostname,\n 461→\t\t\t\tID: id,\n 462→\t\t\t\tData: &schema.MetricStatistics{\n 463→\t\t\t\t\tAvg: float64(res.Avg),\n 464→\t\t\t\t\tMin: float64(res.Min),\n 465→\t\t\t\t\tMax: float64(res.Max),\n 466→\t\t\t\t},\n 467→\t\t\t})\n 468→\t\t}\n 469→\n 470→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n 471→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n 472→\t\t\tdelete(scopedJobStats[metric], scope)\n 473→\t\t\tif len(scopedJobStats[metric]) == 0 {\n 474→\t\t\t\tdelete(scopedJobStats, metric)\n 475→\t\t\t}\n 476→\t\t}\n 477→\t}\n 478→\n 479→\tif len(errors) != 0 {\n 480→\t\t/* Returns list for \"partial errors\" */\n 481→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n 482→\t}\n 483→\treturn scopedJobStats, nil\n 484→}\n 485→\n 486→// LoadNodeData loads metric data for specific nodes in a cluster over a time range.\n 487→//\n 488→// Unlike LoadData which operates on job resources, this function queries arbitrary nodes\n 489→// directly. Useful for system monitoring and node status views.\n 490→//\n 491→// Parameters:\n 492→// - cluster: Cluster name\n 493→// - metrics: List of metric names\n 494→// - nodes: List of node hostnames (nil = all nodes in cluster via ForAllNodes)\n 495→// - scopes: Requested metric scopes (currently unused - always node scope)\n 496→// - from, to: Time range\n 497→// - ctx: Context (currently unused)\n 498→//\n 499→// Returns:\n 500→// - Map of hostname → metric → []JobMetric\n 501→// - Error or partial error listing failed queries\n 502→func (ccms *InternalMetricStore) LoadNodeData(\n 503→\tcluster string,\n 504→\tmetrics, nodes []string,\n 505→\tscopes []schema.MetricScope,\n 506→\tfrom, to time.Time,\n 507→\tctx context.Context,\n 508→) (map[string]map[string][]*schema.JobMetric, error) {\n 509→\treq := APIQueryRequest{\n 510→\t\tCluster: cluster,\n 511→\t\tFrom: from.Unix(),\n 512→\t\tTo: to.Unix(),\n 513→\t\tWithStats: true,\n 514→\t\tWithData: true,\n 515→\t}\n 516→\n 517→\tif nodes == nil {\n 518→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n 519→\t} else {\n 520→\t\tfor _, node := range nodes {\n 521→\t\t\tfor _, metric := range metrics {\n 522→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n 523→\t\t\t\t\tHostname: node,\n 524→\t\t\t\t\tMetric: metric,\n 525→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n 526→\t\t\t\t})\n 527→\t\t\t}\n 528→\t\t}\n 529→\t}\n 530→\n 531→\tresBody, err := FetchData(req)\n 532→\tif err != nil {\n 533→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 534→\t\treturn nil, err\n 535→\t}\n 536→\n 537→\tvar errors []string\n 538→\tdata := make(map[string]map[string][]*schema.JobMetric)\n 539→\tfor i, res := range resBody.Results {\n 540→\t\tif len(res) == 0 {\n 541→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n 542→\t\t\tcontinue\n 543→\t\t}\n 544→\n 545→\t\tvar query APIQuery\n 546→\t\tif resBody.Queries != nil {\n 547→\t\t\tquery = resBody.Queries[i]\n 548→\t\t} else {\n 549→\t\t\tquery = req.Queries[i]\n 550→\t\t}\n 551→\n 552→\t\tmetric := query.Metric\n 553→\t\tqdata := res[0]\n 554→\t\tif qdata.Error != nil {\n 555→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n 556→\t\t\tcontinue\n 557→\t\t}\n 558→\n 559→\t\tmc := archive.GetMetricConfig(cluster, metric)\n 560→\t\tif mc == nil {\n 561→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n 562→\t\t\tcontinue\n 563→\t\t}\n 564→\n 565→\t\tSanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n 566→\n 567→\t\thostdata, ok := data[query.Hostname]\n 568→\t\tif !ok {\n 569→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n 570→\t\t\tdata[query.Hostname] = hostdata\n 571→\t\t}\n 572→\n 573→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n 574→\t\t\tUnit: mc.Unit,\n 575→\t\t\tTimestep: mc.Timestep,\n 576→\t\t\tSeries: []schema.Series{\n 577→\t\t\t\t{\n 578→\t\t\t\t\tHostname: query.Hostname,\n 579→\t\t\t\t\tData: qdata.Data,\n 580→\t\t\t\t\tStatistics: schema.MetricStatistics{\n 581→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n 582→\t\t\t\t\t\tMin: float64(qdata.Min),\n 583→\t\t\t\t\t\tMax: float64(qdata.Max),\n 584→\t\t\t\t\t},\n 585→\t\t\t\t},\n 586→\t\t\t},\n 587→\t\t})\n 588→\t}\n 589→\n 590→\tif len(errors) != 0 {\n 591→\t\t/* Returns list of \"partial errors\" */\n 592→\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n 593→\t}\n 594→\n 595→\treturn data, nil\n 596→}\n 597→\n 598→// LoadNodeListData loads metric data for a list of nodes with full scope transformation support.\n 599→//\n 600→// This is the most flexible node data loading function, supporting arbitrary scopes and\n 601→// resolution. Uses buildNodeQueries for proper scope transformation based on topology.\n 602→//\n 603→// Parameters:\n 604→// - cluster: Cluster name\n 605→// - subCluster: SubCluster name (empty string to infer from node names)\n 606→// - nodes: List of node hostnames\n 607→// - metrics: List of metric names\n 608→// - scopes: Requested metric scopes\n 609→// - resolution: Data resolution in seconds\n 610→// - from, to: Time range\n 611→// - ctx: Context (currently unused)\n 612→//\n 613→// Returns:\n 614→// - Map of hostname → JobData (metric → scope → JobMetric)\n 615→// - Error or partial error listing failed queries\n 616→func (ccms *InternalMetricStore) LoadNodeListData(\n 617→\tcluster, subCluster string,\n 618→\tnodes []string,\n 619→\tmetrics []string,\n 620→\tscopes []schema.MetricScope,\n 621→\tresolution int,\n 622→\tfrom, to time.Time,\n 623→\tctx context.Context,\n 624→) (map[string]schema.JobData, error) {\n 625→\t// Note: Order of node data is not guaranteed after this point\n 626→\tqueries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))\n 627→\tif err != nil {\n 628→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCLuster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n 629→\t\treturn nil, err\n 630→\t}\n 631→\n 632→\t// Verify assignment is correct - log any inconsistencies for debugging\n 633→\tif len(queries) != len(assignedScope) {\n 634→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n 635→\t\t\tlen(queries), len(assignedScope))\n 636→\t}\n 637→\n 638→\treq := APIQueryRequest{\n 639→\t\tCluster: cluster,\n 640→\t\tQueries: queries,\n 641→\t\tFrom: from.Unix(),\n 642→\t\tTo: to.Unix(),\n 643→\t\tWithStats: true,\n 644→\t\tWithData: true,\n 645→\t}\n 646→\n 647→\tresBody, err := FetchData(req)\n 648→\tif err != nil {\n 649→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 650→\t\treturn nil, err\n 651→\t}\n 652→\n 653→\tvar errors []string\n 654→\tdata := make(map[string]schema.JobData)\n 655→\n 656→\t// Add safety check for index out of range issues\n 657→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n 658→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n 659→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n 660→\t\tif len(resBody.Results) > len(req.Queries) {\n 661→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n 662→\t\t}\n 663→\t\tif len(assignedScope) > len(req.Queries) {\n 664→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n 665→\t\t}\n 666→\t}\n 667→\n 668→\tfor i, row := range resBody.Results {\n 669→\t\tvar query APIQuery\n 670→\t\tif resBody.Queries != nil {\n 671→\t\t\tif i < len(resBody.Queries) {\n 672→\t\t\t\tquery = resBody.Queries[i]\n 673→\t\t\t} else {\n 674→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n 675→\t\t\t\t\ti, len(resBody.Queries))\n 676→\t\t\t\tcontinue\n 677→\t\t\t}\n 678→\t\t} else {\n 679→\t\t\tquery = req.Queries[i]\n 680→\t\t}\n 681→\n 682→\t\tmetric := query.Metric\n 683→\t\tscope := assignedScope[i]\n 684→\t\tmc := archive.GetMetricConfig(cluster, metric)\n 685→\t\tif mc == nil {\n 686→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n 687→\t\t\tcontinue\n 688→\t\t}\n 689→\n 690→\t\tres := mc.Timestep\n 691→\t\tif len(row) > 0 {\n 692→\t\t\tres = int(row[0].Resolution)\n 693→\t\t}\n 694→\n 695→\t\t// Init Nested Map Data Structures If Not Found\n 696→\t\thostData, ok := data[query.Hostname]\n 697→\t\tif !ok {\n 698→\t\t\thostData = make(schema.JobData)\n 699→\t\t\tdata[query.Hostname] = hostData\n 700→\t\t}\n 701→\n 702→\t\tmetricData, ok := hostData[metric]\n 703→\t\tif !ok {\n 704→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n 705→\t\t\tdata[query.Hostname][metric] = metricData\n 706→\t\t}\n 707→\n 708→\t\tscopeData, ok := metricData[scope]\n 709→\t\tif !ok {\n 710→\t\t\tscopeData = &schema.JobMetric{\n 711→\t\t\t\tUnit: mc.Unit,\n 712→\t\t\t\tTimestep: res,\n 713→\t\t\t\tSeries: make([]schema.Series, 0),\n 714→\t\t\t}\n 715→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n 716→\t\t}\n 717→\n 718→\t\tfor ndx, res := range row {\n 719→\t\t\tif res.Error != nil {\n 720→\t\t\t\t/* Build list for \"partial errors\", if any */\n 721→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n 722→\t\t\t\tcontinue\n 723→\t\t\t}\n 724→\n 725→\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n 726→\n 727→\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n 728→\n 729→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n 730→\t\t\t\tHostname: query.Hostname,\n 731→\t\t\t\tID: id,\n 732→\t\t\t\tStatistics: schema.MetricStatistics{\n 733→\t\t\t\t\tAvg: float64(res.Avg),\n 734→\t\t\t\t\tMin: float64(res.Min),\n 735→\t\t\t\t\tMax: float64(res.Max),\n 736→\t\t\t\t},\n 737→\t\t\t\tData: res.Data,\n 738→\t\t\t})\n 739→\t\t}\n 740→\t}\n 741→\n 742→\tif len(errors) != 0 {\n 743→\t\t/* Returns list of \"partial errors\" */\n 744→\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n 745→\t}\n 746→\n 747→\treturn data, nil\n 748→}\n 749→\n 750→// buildNodeQueries constructs APIQuery structures for node-based queries with scope transformation.\n 751→//\n 752→// Similar to buildQueries but operates on node lists rather than job resources.\n 753→// Supports dynamic subcluster lookup when subCluster parameter is empty.\n 754→//\n 755→// Parameters:\n 756→// - cluster: Cluster name\n 757→// - subCluster: SubCluster name (empty = infer from node hostnames)\n 758→// - nodes: List of node hostnames\n 759→// - metrics: List of metric names\n 760→// - scopes: Requested metric scopes\n 761→// - resolution: Data resolution in seconds\n 762→//\n 763→// Returns:\n 764→// - []APIQuery: List of queries to execute\n 765→// - []schema.MetricScope: Assigned scope for each query\n 766→// - error: Returns error if topology lookup fails or unhandled scope combination\n 767→func buildNodeQueries(\n 768→\tcluster string,\n 769→\tsubCluster string,\n 770→\tnodes []string,\n 771→\tmetrics []string,\n 772→\tscopes []schema.MetricScope,\n 773→\tresolution int64,\n 774→) ([]APIQuery, []schema.MetricScope, error) {\n 775→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n 776→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n 777→\n 778→\t// Get Topol before loop if subCluster given\n 779→\tvar subClusterTopol *schema.SubCluster\n 780→\tvar scterr error\n 781→\tif subCluster != \"\" {\n 782→\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subCluster)\n 783→\t\tif scterr != nil {\n 784→\t\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", cluster, subCluster, scterr.Error())\n 785→\t\t\treturn nil, nil, scterr\n 786→\t\t}\n 787→\t}\n 788→\n 789→\tfor _, metric := range metrics {\n 790→\t\tmc := archive.GetMetricConfig(cluster, metric)\n 791→\t\tif mc == nil {\n 792→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n 793→\t\t\tcontinue\n 794→\t\t}\n 795→\n 796→\t\t// Skip if metric is removed for subcluster\n 797→\t\tif mc.SubClusters != nil && IsMetricRemovedForSubCluster(mc, subCluster) {\n 798→\t\t\tcontinue\n 799→\t\t}\n 800→\n 801→\t\t// Avoid duplicates...\n 802→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n 803→\n 804→\tnodeScopesLoop:\n 805→\t\tfor _, requestedScope := range scopes {\n 806→\t\t\tnativeScope := mc.Scope\n 807→\n 808→\t\t\tscope := nativeScope.Max(requestedScope)\n 809→\t\t\tfor _, s := range handledScopes {\n 810→\t\t\t\tif scope == s {\n 811→\t\t\t\t\tcontinue nodeScopesLoop\n 812→\t\t\t\t}\n 813→\t\t\t}\n 814→\t\t\thandledScopes = append(handledScopes, scope)\n 815→\n 816→\t\t\tfor _, hostname := range nodes {\n 817→\n 818→\t\t\t\t// If no subCluster given, get it by node\n 819→\t\t\t\tif subCluster == \"\" {\n 820→\t\t\t\t\tsubClusterName, scnerr := archive.GetSubClusterByNode(cluster, hostname)\n 821→\t\t\t\t\tif scnerr != nil {\n 822→\t\t\t\t\t\treturn nil, nil, scnerr\n 823→\t\t\t\t\t}\n 824→\t\t\t\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subClusterName)\n 825→\t\t\t\t\tif scterr != nil {\n 826→\t\t\t\t\t\treturn nil, nil, scterr\n 827→\t\t\t\t\t}\n 828→\t\t\t\t}\n 829→\n 830→\t\t\t\t// Always full node hwthread id list, no partial queries expected\n 831→\t\t\t\ttopology := subClusterTopol.Topology\n 832→\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n 833→\n 834→\t\t\t\t// Moved check here if metric matches hardware specs\n 835→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n 836→\t\t\t\t\tcontinue\n 837→\t\t\t\t}\n 838→\n 839→\t\t\t\tscopeResults, ok := BuildScopeQueries(\n 840→\t\t\t\t\tnativeScope, requestedScope,\n 841→\t\t\t\t\tmetric, hostname,\n 842→\t\t\t\t\t&topology, topology.Node, acceleratorIds,\n 843→\t\t\t\t)\n 844→\n 845→\t\t\t\tif !ok {\n 846→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n 847→\t\t\t\t}\n 848→\n 849→\t\t\t\tfor _, sr := range scopeResults {\n 850→\t\t\t\t\tqueries = append(queries, APIQuery{\n 851→\t\t\t\t\t\tMetric: sr.Metric,\n 852→\t\t\t\t\t\tHostname: sr.Hostname,\n 853→\t\t\t\t\t\tAggregate: sr.Aggregate,\n 854→\t\t\t\t\t\tType: sr.Type,\n 855→\t\t\t\t\t\tTypeIds: sr.TypeIds,\n 856→\t\t\t\t\t\tResolution: resolution,\n 857→\t\t\t\t\t})\n 858→\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n 859→\t\t\t\t}\n 860→\t\t\t}\n 861→\t\t}\n 862→\t}\n 863→\n 864→\treturn queries, assignedScope, nil\n 865→}\n 866→"}]},"uuid":"ca9f8c53-bbf7-41b7-9853-7d2a7ca1d363","timestamp":"2026-03-19T19:46:24.751Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/query.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file implements high-level query functions for loading job metric data\n// with automatic scope transformation and aggregation.\n//\n// Key Concepts:\n//\n// Metric Scopes: Metrics are collected at different granularities (native scope):\n// - HWThread: Per hardware thread\n// - Core: Per CPU core\n// - Socket: Per CPU socket\n// - MemoryDomain: Per memory domain (NUMA)\n// - Accelerator: Per GPU/accelerator\n// - Node: Per compute node\n//\n// Scope Transformation: The buildQueries functions transform between native scope\n// and requested scope by:\n// - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n// - Rejecting requests for finer granularity than available\n// - Handling special cases (e.g., Accelerator metrics)\n//\n// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n// based on cluster topology and job resources.\npackage metricstore\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\ntype InternalMetricStore struct{}\n\nvar MetricStoreHandle *InternalMetricStore\n\n// HealthCheck delegates to the internal MemoryStore's HealthCheck.\nfunc (ccms *InternalMetricStore) HealthCheck(cluster string,\n\tnodes []string, metrics []string,\n) (map[string]HealthCheckResult, error) {\n\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n}\n\n// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n// When set to a non-nil function, LoadData will call this function instead of the default implementation.\nvar TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n\n// LoadData loads metric data for a specific job with automatic scope transformation.\n//\n// This is the primary function for retrieving job metric data. It handles:\n// - Building queries with scope transformation via buildQueries\n// - Fetching data from the metric store\n// - Organizing results by metric and scope\n// - Converting NaN statistics to 0 for JSON compatibility\n// - Partial error handling (returns data for successful queries even if some fail)\n//\n// Parameters:\n// - job: Job metadata including cluster, resources, and time range\n// - metrics: List of metric names to load\n// - scopes: Requested metric scopes (will be transformed to match native scopes)\n// - ctx: Context for cancellation (currently unused but reserved for future use)\n// - resolution: Data resolution in seconds (0 for native resolution)\n//\n// Returns:\n// - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n// - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n//\n// Example:\n//\n//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\nfunc (ccms *InternalMetricStore) LoadData(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tif TestLoadDataCallback != nil {\n\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n\t}\n\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tjobData := make(schema.JobData)\n\n\t// Add safety check for potential index out of range errors\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := jobData[metric]; !ok {\n\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\tjobMetric, ok := jobData[metric][scope]\n\t\tif !ok {\n\t\t\tjobMetric = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tjobData[metric][scope] = jobMetric\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(jobData):\n\t\tif len(jobMetric.Series) == 0 {\n\t\t\tdelete(jobData[metric], scope)\n\t\t\tif len(jobData[metric]) == 0 {\n\t\t\t\tdelete(jobData, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn jobData, nil\n}\n\n// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n//\n// This function implements the core scope transformation logic, handling all combinations of\n// native metric scopes and requested scopes. It uses the cluster topology to determine which\n// hardware IDs to include in each query.\n//\n// Scope Transformation Rules:\n// - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n// - If native scope < requested scope: Returns error (cannot increase granularity)\n// - Special handling for Accelerator scope (independent of CPU hierarchy)\n//\n// The function generates one or more APIQuery per (metric, scope, host) combination:\n// - For non-aggregated queries: One query with all relevant IDs\n// - For aggregated queries: May generate multiple queries (e.g., one per socket/core)\n//\n// Parameters:\n// - job: Job metadata including cluster, subcluster, and resource allocation\n// - metrics: List of metrics to query\n// - scopes: Requested scopes for each metric\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query (after transformation)\n// - error: Returns error if topology lookup fails or unhandled scope combination encountered\nfunc buildQueries(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tif len(job.Resources) == 0 {\n\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > no resources allocated for job %d\", job.JobID)\n\t}\n\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n\n\tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n\tif scerr != nil {\n\t\treturn nil, nil, scerr\n\t}\n\ttopology := subcluster.Topology\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif len(mc.SubClusters) != 0 && IsMetricRemovedForSubCluster(mc, job.SubCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tscopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue scopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, host := range job.Resources {\n\t\t\t\thwthreads := host.HWThreads\n\t\t\t\tif hwthreads == nil {\n\t\t\t\t\thwthreads = topology.Node\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, host.Hostname,\n\t\t\t\t\t&topology, hwthreads, host.Accelerators,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n\n// LoadStats loads only metric statistics (avg/min/max) for a job at node scope.\n//\n// This is an optimized version of LoadData that fetches only statistics without\n// time-series data, reducing bandwidth and memory usage. Always queries at node scope.\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of metric → hostname → statistics\n// - Error on query building or fetching failure\nfunc (ccms *InternalMetricStore) LoadStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error) {\n\t// TODO(#166): Add scope parameter for analysis view accelerator normalization\n\tqueries, _, err := buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n\tfor i, res := range resBody.Results {\n\t\tif i >= len(req.Queries) {\n\t\t\tcclog.Warnf(\"LoadStats: result index %d exceeds queries length %d\", i, len(req.Queries))\n\t\t\tbreak\n\t\t}\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tdata := res[0]\n\t\tif data.Error != nil {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata, ok := stats[metric]\n\t\tif !ok {\n\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n\t\t\tstats[metric] = metricdata\n\t\t}\n\n\t\tif data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n\t\t\tAvg: float64(data.Avg),\n\t\t\tMin: float64(data.Min),\n\t\t\tMax: float64(data.Max),\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// LoadScopedStats loads metric statistics for a job with scope-aware grouping.\n//\n// Similar to LoadStats but supports multiple scopes and returns statistics grouped\n// by scope with hardware IDs (e.g., per-core, per-socket statistics).\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - ScopedJobStats: Map of metric → scope → []ScopedStats (with hostname and ID)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadScopedStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tscopedJobStats := make(schema.ScopedJobStats)\n\n\tfor i, row := range resBody.Results {\n\t\tif len(row) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\n\t\tif _, ok := scopedJobStats[metric]; !ok {\n\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n\t\t}\n\n\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tData: &schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n\t\tif len(scopedJobStats[metric][scope]) == 0 {\n\t\t\tdelete(scopedJobStats[metric], scope)\n\t\t\tif len(scopedJobStats[metric]) == 0 {\n\t\t\t\tdelete(scopedJobStats, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn scopedJobStats, nil\n}\n\n// LoadNodeData loads metric data for specific nodes in a cluster over a time range.\n//\n// Unlike LoadData which operates on job resources, this function queries arbitrary nodes\n// directly. Useful for system monitoring and node status views.\n//\n// Parameters:\n// - cluster: Cluster name\n// - metrics: List of metric names\n// - nodes: List of node hostnames (nil = all nodes in cluster via ForAllNodes)\n// - scopes: Requested metric scopes (currently unused - always node scope)\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → metric → []JobMetric\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tif nodes == nil {\n\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n\t} else {\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range metrics {\n\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n\t\t\t\t\tHostname: node,\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]map[string][]*schema.JobMetric)\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tquery = resBody.Queries[i]\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tqdata := res[0]\n\t\tif qdata.Error != nil {\n\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n\t\t\tcontinue\n\t\t}\n\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tSanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n\n\t\thostdata, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostdata = make(map[string][]*schema.JobMetric)\n\t\t\tdata[query.Hostname] = hostdata\n\t\t}\n\n\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n\t\t\tUnit: mc.Unit,\n\t\t\tTimestep: mc.Timestep,\n\t\t\tSeries: []schema.Series{\n\t\t\t\t{\n\t\t\t\t\tHostname: query.Hostname,\n\t\t\t\t\tData: qdata.Data,\n\t\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\t\tAvg: float64(qdata.Avg),\n\t\t\t\t\t\tMin: float64(qdata.Min),\n\t\t\t\t\t\tMax: float64(qdata.Max),\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t})\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData loads metric data for a list of nodes with full scope transformation support.\n//\n// This is the most flexible node data loading function, supporting arbitrary scopes and\n// resolution. Uses buildNodeQueries for proper scope transformation based on topology.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty string to infer from node names)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → JobData (metric → scope → JobMetric)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\t// Note: Order of node data is not guaranteed after this point\n\tqueries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCLuster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]schema.JobData)\n\n\t// Add safety check for index out of range issues\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tif i < len(resBody.Queries) {\n\t\t\t\tquery = resBody.Queries[i]\n\t\t\t} else {\n\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n\t\t\t\t\ti, len(resBody.Queries))\n\t\t\t\tcontinue\n\t\t\t}\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\t// Init Nested Map Data Structures If Not Found\n\t\thostData, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostData = make(schema.JobData)\n\t\t\tdata[query.Hostname] = hostData\n\t\t}\n\n\t\tmetricData, ok := hostData[metric]\n\t\tif !ok {\n\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\tdata[query.Hostname][metric] = metricData\n\t\t}\n\n\t\tscopeData, ok := metricData[scope]\n\t\tif !ok {\n\t\t\tscopeData = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tdata[query.Hostname][metric][scope] = scopeData\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// buildNodeQueries constructs APIQuery structures for node-based queries with scope transformation.\n//\n// Similar to buildQueries but operates on node lists rather than job resources.\n// Supports dynamic subcluster lookup when subCluster parameter is empty.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty = infer from node hostnames)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query\n// - error: Returns error if topology lookup fails or unhandled scope combination\nfunc buildNodeQueries(\n\tcluster string,\n\tsubCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n\n\t// Get Topol before loop if subCluster given\n\tvar subClusterTopol *schema.SubCluster\n\tvar scterr error\n\tif subCluster != \"\" {\n\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subCluster)\n\t\tif scterr != nil {\n\t\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", cluster, subCluster, scterr.Error())\n\t\t\treturn nil, nil, scterr\n\t\t}\n\t}\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif mc.SubClusters != nil && IsMetricRemovedForSubCluster(mc, subCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tnodeScopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue nodeScopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, hostname := range nodes {\n\n\t\t\t\t// If no subCluster given, get it by node\n\t\t\t\tif subCluster == \"\" {\n\t\t\t\t\tsubClusterName, scnerr := archive.GetSubClusterByNode(cluster, hostname)\n\t\t\t\t\tif scnerr != nil {\n\t\t\t\t\t\treturn nil, nil, scnerr\n\t\t\t\t\t}\n\t\t\t\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subClusterName)\n\t\t\t\t\tif scterr != nil {\n\t\t\t\t\t\treturn nil, nil, scterr\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Always full node hwthread id list, no partial queries expected\n\t\t\t\ttopology := subClusterTopol.Topology\n\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n\n\t\t\t\t// Moved check here if metric matches hardware specs\n\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, hostname,\n\t\t\t\t\t&topology, topology.Node, acceleratorIds,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n","numLines":866,"startLine":1,"totalLines":866}},"sourceToolAssistantUUID":"5734c09b-ab74-4c3b-965d-149fbfd45518","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"a586e7f3-9ea4-4a17-a90e-b33fd167ab5c","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01WkhD9E3Lp248Ruckttm4nN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016yTnXvj6e5hD8eEe9LQ4cz","name":"Read","input":{"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","offset":490,"limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3100,"cache_creation_input_tokens":46269,"cache_read_input_tokens":19950,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":46269},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD1ozkXXbh5C28tctsje","type":"assistant","uuid":"1cba72d7-c5b0-4c87-8cb2-6703aba0ec61","timestamp":"2026-03-19T19:46:31.486Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"1cba72d7-c5b0-4c87-8cb2-6703aba0ec61","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016yTnXvj6e5hD8eEe9LQ4cz","type":"tool_result","content":" 490→\n 491→\tif user := repository.GetUserFromContext(ctx); user != nil &&\n 492→\t\tjob.User != user.Username &&\n 493→\t\tuser.HasNotRoles([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n 494→\t\treturn nil, errors.New(\"you are not allowed to see this job\")\n 495→\t}\n 496→\n 497→\treturn job, nil\n 498→}\n 499→\n 500→// JobMetrics is the resolver for the jobMetrics field.\n 501→func (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int) ([]*model.JobMetricWithName, error) {\n 502→\tif resolution == nil { // Load from Config\n 503→\t\tif config.Keys.EnableResampling != nil {\n 504→\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n 505→\t\t\tresolution = &defaultRes\n 506→\t\t} else { // Set 0 (Loads configured metric timestep)\n 507→\t\t\tdefaultRes := 0\n 508→\t\t\tresolution = &defaultRes\n 509→\t\t}\n 510→\t}\n 511→\n 512→\tjob, err := r.Query().Job(ctx, id)\n 513→\tif err != nil {\n 514→\t\tcclog.Warn(\"Error while querying job for metrics\")\n 515→\t\treturn nil, err\n 516→\t}\n 517→\n 518→\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n 519→\tif err != nil {"}]},"uuid":"59b18486-454c-4588-89c2-64b79ef97ec8","timestamp":"2026-03-19T19:46:31.493Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","content":"\n\tif user := repository.GetUserFromContext(ctx); user != nil &&\n\t\tjob.User != user.Username &&\n\t\tuser.HasNotRoles([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\treturn nil, errors.New(\"you are not allowed to see this job\")\n\t}\n\n\treturn job, nil\n}\n\n// JobMetrics is the resolver for the jobMetrics field.\nfunc (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int) ([]*model.JobMetricWithName, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying job for metrics\")\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {","numLines":30,"startLine":490,"totalLines":1077}},"sourceToolAssistantUUID":"1cba72d7-c5b0-4c87-8cb2-6703aba0ec61","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"59b18486-454c-4588-89c2-64b79ef97ec8","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01WkhD9E3Lp248Ruckttm4nN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L9E2oSReHuM8he3szqc9u5","name":"Read","input":{"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","offset":860,"limit":40},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3100,"cache_creation_input_tokens":46269,"cache_read_input_tokens":19950,"output_tokens":336,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":46269,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD1ozkXXbh5C28tctsje","type":"assistant","uuid":"f2b2f767-4ee1-4402-904c-473a0195e095","timestamp":"2026-03-19T19:46:32.220Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"f2b2f767-4ee1-4402-904c-473a0195e095","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01L9E2oSReHuM8he3szqc9u5","toolUseID":"toolu_01L9E2oSReHuM8he3szqc9u5","timestamp":"2026-03-19T19:46:32.231Z","uuid":"1af454d4-119a-402c-b111-6deab56a9c62","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"f2b2f767-4ee1-4402-904c-473a0195e095","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L9E2oSReHuM8he3szqc9u5","type":"tool_result","content":" 860→\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n 861→\t\t\t\t\tName: metric,\n 862→\t\t\t\t\tScope: schema.MetricScopeNode,\n 863→\t\t\t\t\tMetric: scopedMetric,\n 864→\t\t\t\t})\n 865→\t\t\t}\n 866→\t\t}\n 867→\n 868→\t\tnodeMetrics = append(nodeMetrics, host)\n 869→\t}\n 870→\n 871→\treturn nodeMetrics, nil\n 872→}\n 873→\n 874→// NodeMetricsList is the resolver for the nodeMetricsList field.\n 875→func (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int) (*model.NodesResultList, error) {\n 876→\tif resolution == nil { // Load from Config\n 877→\t\tif config.Keys.EnableResampling != nil {\n 878→\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n 879→\t\t\tresolution = &defaultRes\n 880→\t\t} else { // Set 0 (Loads configured metric timestep)\n 881→\t\t\tdefaultRes := 0\n 882→\t\t\tresolution = &defaultRes\n 883→\t\t}\n 884→\t}\n 885→\n 886→\tuser := repository.GetUserFromContext(ctx)\n 887→\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n 888→\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n 889→\t}\n 890→\n 891→\tnodeRepo := repository.GetNodeRepository()\n 892→\t// nodes -> array hostname\n 893→\tnodes, nodeStateMap, metricHealthMap, countNodes, hasNextPage, nerr := nodeRepo.GetNodesForList(ctx, cluster, subCluster, stateFilter, nodeFilter, page)\n 894→\tif nerr != nil {\n 895→\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n 896→\t}\n 897→\n 898→\tif metrics == nil {\n 899→\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {"}]},"uuid":"b658cfae-754d-46c8-932e-2c3e2afd8004","timestamp":"2026-03-19T19:46:32.230Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","content":"\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: schema.MetricScopeNode,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetrics = append(nodeMetrics, host)\n\t}\n\n\treturn nodeMetrics, nil\n}\n\n// NodeMetricsList is the resolver for the nodeMetricsList field.\nfunc (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int) (*model.NodesResultList, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\t// nodes -> array hostname\n\tnodes, nodeStateMap, metricHealthMap, countNodes, hasNextPage, nerr := nodeRepo.GetNodesForList(ctx, cluster, subCluster, stateFilter, nodeFilter, page)\n\tif nerr != nil {\n\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {","numLines":40,"startLine":860,"totalLines":1077}},"sourceToolAssistantUUID":"f2b2f767-4ee1-4402-904c-473a0195e095","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"b658cfae-754d-46c8-932e-2c3e2afd8004","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01JajsTe7QVrdH6q2hq7poRX","type":"message","role":"assistant","content":[{"type":"text","text":"Let me see more of the NodeMetricsList resolver to find where LoadNodeListData is called."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4956,"cache_read_input_tokens":66219,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4956},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD1pYdvB66TRGoMJQLi2","type":"assistant","uuid":"eb7a4a8c-c4bd-4243-9d79-7560aea296c2","timestamp":"2026-03-19T19:46:35.924Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"eb7a4a8c-c4bd-4243-9d79-7560aea296c2","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01JajsTe7QVrdH6q2hq7poRX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LjZNakoGom5UaRGqQajaK3","name":"Read","input":{"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","offset":898,"limit":40},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4956,"cache_read_input_tokens":66219,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4956},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD1pYdvB66TRGoMJQLi2","type":"assistant","uuid":"d818f944-32aa-458a-a104-d54319be0638","timestamp":"2026-03-19T19:46:36.816Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d818f944-32aa-458a-a104-d54319be0638","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01LjZNakoGom5UaRGqQajaK3","toolUseID":"toolu_01LjZNakoGom5UaRGqQajaK3","timestamp":"2026-03-19T19:46:36.837Z","uuid":"0a9669d5-b30f-4be5-9c23-43ce384f521d","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d818f944-32aa-458a-a104-d54319be0638","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LjZNakoGom5UaRGqQajaK3","type":"tool_result","content":" 898→\tif metrics == nil {\n 899→\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n 900→\t\t\tmetrics = append(metrics, mc.Name)\n 901→\t\t}\n 902→\t}\n 903→\n 904→\t// data -> map hostname:jobdata\n 905→\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n 906→\tif err != nil {\n 907→\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n 908→\t\treturn nil, err\n 909→\t}\n 910→\n 911→\tnodeMetricsList := make([]*model.NodeMetrics, 0, len(data))\n 912→\tfor _, hostname := range nodes {\n 913→\t\thost := &model.NodeMetrics{\n 914→\t\t\tHost: hostname,\n 915→\t\t\tNodeState: nodeStateMap[hostname],\n 916→\t\t\tMetricHealth: metricHealthMap[hostname],\n 917→\t\t\tMetrics: make([]*model.JobMetricWithName, 0),\n 918→\t\t}\n 919→\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n 920→\t\tif err != nil {\n 921→\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n 922→\t\t}\n 923→\n 924→\t\tfor metric, scopedMetrics := range data[hostname] {\n 925→\t\t\tfor scope, scopedMetric := range scopedMetrics {\n 926→\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n 927→\t\t\t\t\tName: metric,\n 928→\t\t\t\t\tScope: scope,\n 929→\t\t\t\t\tMetric: scopedMetric,\n 930→\t\t\t\t})\n 931→\t\t\t}\n 932→\t\t}\n 933→\n 934→\t\tnodeMetricsList = append(nodeMetricsList, host)\n 935→\t}\n 936→\n 937→\tnodeMetricsListResult := &model.NodesResultList{"}]},"uuid":"aece1d4c-3f1e-4eb1-a62d-a3dc89999aec","timestamp":"2026-03-19T19:46:36.836Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","content":"\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}\n\n\tnodeMetricsList := make([]*model.NodeMetrics, 0, len(data))\n\tfor _, hostname := range nodes {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost: hostname,\n\t\t\tNodeState: nodeStateMap[hostname],\n\t\t\tMetricHealth: metricHealthMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range data[hostname] {\n\t\t\tfor scope, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: scope,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetricsList = append(nodeMetricsList, host)\n\t}\n\n\tnodeMetricsListResult := &model.NodesResultList{","numLines":40,"startLine":898,"totalLines":1077}},"sourceToolAssistantUUID":"d818f944-32aa-458a-a104-d54319be0638","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"1d1c61e2-83f4-4868-9773-b6a174944761","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JvT8m9rJKTVqSCCX8t5kQh","type":"tool_result","content":" 1→// Code generated by github.com/99designs/gqlgen, DO NOT EDIT.\n 2→\n 3→package model\n 4→\n 5→import (\n 6→\t\"bytes\"\n 7→\t\"fmt\"\n 8→\t\"io\"\n 9→\t\"strconv\"\n 10→\t\"time\"\n 11→\n 12→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n 13→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 14→)\n 15→\n 16→type ClusterMetricWithName struct {\n 17→\tName string `json:\"name\"`\n 18→\tUnit *schema.Unit `json:\"unit,omitempty\"`\n 19→\tTimestep int `json:\"timestep\"`\n 20→\tData []schema.Float `json:\"data\"`\n 21→}\n 22→\n 23→type ClusterMetrics struct {\n 24→\tNodeCount int `json:\"nodeCount\"`\n 25→\tMetrics []*ClusterMetricWithName `json:\"metrics\"`\n 26→}\n 27→\n 28→type Count struct {\n 29→\tName string `json:\"name\"`\n 30→\tCount int `json:\"count\"`\n 31→}\n 32→\n 33→type EnergyFootprintValue struct {\n 34→\tHardware string `json:\"hardware\"`\n 35→\tMetric string `json:\"metric\"`\n 36→\tValue float64 `json:\"value\"`\n 37→}\n 38→\n 39→type FloatRange struct {\n 40→\tFrom float64 `json:\"from\"`\n 41→\tTo float64 `json:\"to\"`\n 42→}\n 43→\n 44→type FootprintValue struct {\n 45→\tName string `json:\"name\"`\n 46→\tStat string `json:\"stat\"`\n 47→\tValue float64 `json:\"value\"`\n 48→}\n 49→\n 50→type Footprints struct {"}]},"uuid":"93a03b09-5b0d-4b6f-8cfd-f98673d23de1","timestamp":"2026-03-19T19:46:37.309Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/model/models_gen.go","content":"// Code generated by github.com/99designs/gqlgen, DO NOT EDIT.\n\npackage model\n\nimport (\n\t\"bytes\"\n\t\"fmt\"\n\t\"io\"\n\t\"strconv\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\ntype ClusterMetricWithName struct {\n\tName string `json:\"name\"`\n\tUnit *schema.Unit `json:\"unit,omitempty\"`\n\tTimestep int `json:\"timestep\"`\n\tData []schema.Float `json:\"data\"`\n}\n\ntype ClusterMetrics struct {\n\tNodeCount int `json:\"nodeCount\"`\n\tMetrics []*ClusterMetricWithName `json:\"metrics\"`\n}\n\ntype Count struct {\n\tName string `json:\"name\"`\n\tCount int `json:\"count\"`\n}\n\ntype EnergyFootprintValue struct {\n\tHardware string `json:\"hardware\"`\n\tMetric string `json:\"metric\"`\n\tValue float64 `json:\"value\"`\n}\n\ntype FloatRange struct {\n\tFrom float64 `json:\"from\"`\n\tTo float64 `json:\"to\"`\n}\n\ntype FootprintValue struct {\n\tName string `json:\"name\"`\n\tStat string `json:\"stat\"`\n\tValue float64 `json:\"value\"`\n}\n\ntype Footprints struct {","numLines":50,"startLine":1,"totalLines":454}},"sourceToolAssistantUUID":"1d1c61e2-83f4-4868-9773-b6a174944761","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"de22bf09-c7bb-4a09-8355-54c673785223","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01EWBGj7FkJjyBkNpRcR98Ht","type":"message","role":"assistant","content":[{"type":"text","text":"Good. Now I have the full picture. Let me implement this in stages. I'll start with Part 1 (cc-lib changes) and Part 2 (cc-backend changes) in parallel where possible.\n\n## Part 1: cc-lib changes"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":385,"cache_read_input_tokens":72493,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":385},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD1qMcBAXXy7RcM5DxLU","type":"assistant","uuid":"67ea9f45-d54b-477b-a319-45690b1e5a26","timestamp":"2026-03-19T19:46:47.806Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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NaN values are skipped;\n// if all values in a bucket are NaN, the output for that bucket is NaN.\n//\n// Parameters:\n// - data: input time-series data points\n// - oldFrequency: original sampling frequency\n// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n//\n// Returns:\n// - Averaged data slice\n// - Actual frequency used\n// - Error if newFrequency is not a multiple of oldFrequency\nfunc AverageResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n\tnewDataLength, step, err := validateFrequency(len(data), oldFrequency, newFrequency)\n\tif err != nil {\n\t\treturn nil, 0, err\n\t}\n\tif newDataLength == -1 {\n\t\treturn data, oldFrequency, nil\n\t}\n\n\tnewData := make([]schema.Float, newDataLength)\n\tfor i := range newDataLength {\n\t\tsum := 0.0\n\t\tcount := 0\n\t\tbase := i * step\n\t\tfor j := range step {\n\t\t\tv := float64(data[base+j])\n\t\t\tif !math.IsNaN(v) {\n\t\t\t\tsum += v\n\t\t\t\tcount++\n\t\t\t}\n\t\t}\n\t\tif count == 0 {\n\t\t\tnewData[i] = schema.NaN\n\t\t} else {\n\t\t\tnewData[i] = schema.Float(sum / float64(count))\n\t\t}\n\t}\n\n\treturn newData, newFrequency, nil\n}\n\nfunc SetMinimumRequiredPoints(setVal int) {\n\tMinimumRequiredPoints = setVal\n}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-lib.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package resampler provides time-series data downsampling algorithms.\n//\n// This package implements two downsampling strategies for reducing the number\n// of data points in time-series data while preserving important characteristics:\n//\n// - SimpleResampler: A fast, straightforward algorithm that selects every nth point\n// - LargestTriangleThreeBucket (LTTB): A perceptually-aware algorithm that preserves\n// visual characteristics by selecting points that maximize the area of triangles\n// formed with neighboring points\n//\n// Both algorithms are designed to work with schema.Float data and handle NaN values\n// appropriately. They require that the new sampling frequency is a multiple of the\n// old frequency.\n//\n// References:\n// - LTTB Algorithm: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n// - Implementation adapted from: https://github.com/haoel/downsampling\npackage resampler\n\nimport (\n\t\"fmt\"\n\t\"math\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// MinimumRequiredPoints is the default number of points required to trigger resampling.\n// If the data has fewer points, the original time series will be returned without resampling.\nvar MinimumRequiredPoints int = 1000\n\n// calculateTriangleArea computes the area of a triangle defined by three points.\n//\n// The area is calculated using the cross product formula:\n// Area = 0.5 * |((paX - pcX) * (pbY - paY)) - ((paX - pbX) * (pcY - paY))|\n//\n// This is used by the LTTB algorithm to determine which points preserve the\n// most visual information when downsampling.\nfunc calculateTriangleArea(paX, paY, pbX, pbY, pcX, pcY schema.Float) float64 {\n\tarea := ((paX-pcX)*(pbY-paY) - (paX-pbX)*(pcY-paY)) * 0.5\n\treturn math.Abs(float64(area))\n}\n\n// calculateAverageDataPoint computes the average point from a slice of data points.\n//\n// Parameters:\n// - points: slice of Y values to average\n// - xStart: starting X coordinate for the points\n//\n// Returns:\n// - avgX: average X coordinate\n// - avgY: average Y value, or NaN if any input point is NaN\n//\n// This function is used by LTTB to find the centroid of points in a bucket,\n// which is then used to calculate triangle areas for point selection.\nfunc calculateAverageDataPoint(points []schema.Float, xStart int64) (avgX schema.Float, avgY schema.Float) {\n\thasNaN := false\n\tfor _, point := range points {\n\t\tavgX += schema.Float(xStart)\n\t\tavgY += point\n\t\txStart++\n\t\tif math.IsNaN(float64(point)) {\n\t\t\thasNaN = true\n\t\t}\n\t}\n\n\tl := schema.Float(len(points))\n\tavgX /= l\n\tavgY /= l\n\n\tif hasNaN {\n\t\treturn avgX, schema.NaN\n\t}\n\treturn avgX, avgY\n}\n\n// SimpleResampler performs simple downsampling by selecting every nth point.\n//\n// This is the fastest downsampling method but may miss important features in the data.\n// It works by calculating a step size (newFrequency / oldFrequency) and selecting\n// every step-th point from the original data.\n//\n// Parameters:\n// - data: input time-series data points\n// - oldFrequency: original sampling frequency (points per time unit)\n// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n//\n// Returns:\n// - Downsampled data slice\n// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n// - Error if newFrequency is not a multiple of oldFrequency\n//\n// The function returns the original data unchanged if:\n// - Either frequency is 0\n// - newFrequency <= oldFrequency (no downsampling needed)\n// - The resulting data would have fewer than 1 point\n// - The original data has fewer than 100 points\n// - The downsampled data would have the same or more points than the original\n//\n// Example:\n//\n//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}\n//\tdownsampled, freq, err := SimpleResampler(data, 1, 2)\n//\t// Returns: [1.0, 3.0, 5.0], 2, nil\nfunc SimpleResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n\t// checks if the frequencies are valid or not.\n\tnewDataLength, step, err := validateFrequency(len(data), oldFrequency, newFrequency)\n\tif err != nil {\n\t\treturn nil, 0, err\n\t}\n\tif newDataLength == -1 {\n\t\treturn data, oldFrequency, nil\n\t}\n\n\tnewData := make([]schema.Float, newDataLength)\n\tfor i := range newDataLength {\n\t\tnewData[i] = data[i*step]\n\t}\n\n\treturn newData, newFrequency, nil\n}\n\nfunc validateFrequency(lenData int, oldFrequency, newFrequency int64) (int, int, error) {\n\t// Validate inputs and check if downsampling is needed\n\tif oldFrequency == 0 || newFrequency == 0 || newFrequency <= oldFrequency {\n\t\treturn -1, 0, nil\n\t}\n\n\t// Ensure new frequency is a multiple of old frequency\n\tif newFrequency%oldFrequency != 0 {\n\t\treturn -1, 0, fmt.Errorf(\"new sampling frequency (%d) must be a multiple of old frequency (%d)\", newFrequency, oldFrequency)\n\t}\n\n\tstep := int(newFrequency / oldFrequency)\n\tnewDataLength := lenData / step\n\n\t// Don't downsample if result would be trivial or counterproductive\n\tif (newDataLength == 0) || (lenData < MinimumRequiredPoints) || (newDataLength >= lenData) {\n\t\treturn -1, 0, nil\n\t}\n\n\treturn newDataLength, step, nil\n}\n\n// LargestTriangleThreeBucket (LTTB) performs perceptually-aware downsampling.\n//\n// LTTB is a downsampling algorithm that preserves the visual characteristics of\n// time-series data by selecting points that form the largest triangles with their\n// neighbors. This ensures that important peaks, valleys, and trends are retained\n// even when significantly reducing the number of points.\n//\n// Algorithm Overview:\n// 1. The data is divided into buckets (except first and last points which are always kept)\n// 2. For each bucket, the algorithm selects the point that forms the largest triangle\n// with the previous selected point and the average of the next bucket\n// 3. This maximizes the visual area and preserves important features\n//\n// Time Complexity: O(n) where n is the number of input points\n// Space Complexity: O(m) where m is the number of output points\n//\n// Parameters:\n// - data: input time-series data points\n// - oldFrequency: original sampling frequency (points per time unit)\n// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)\n//\n// Returns:\n// - Downsampled data slice\n// - Actual frequency used (may be oldFrequency if downsampling wasn't performed)\n// - Error if newFrequency is not a multiple of oldFrequency\n//\n// The function returns the original data unchanged if:\n// - Either frequency is 0\n// - newFrequency <= oldFrequency (no downsampling needed)\n// - The resulting data would have fewer than 1 point\n// - The original data has fewer than 100 points\n// - The downsampled data would have the same or more points than the original\n//\n// NaN Handling:\n// The algorithm properly handles NaN values and preserves them in the output when\n// they represent the maximum area point in a bucket.\n//\n// Example:\n//\n//\tdata := []schema.Float{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}\n//\tdownsampled, freq, err := LargestTriangleThreeBucket(data, 1, 2)\n//\t// Returns downsampled data preserving visual characteristics\n//\n// References:\n// - Original paper: https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf\n// - Adapted from: https://github.com/haoel/downsampling/blob/master/core/lttb.go\nfunc LargestTriangleThreeBucket(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {\n\t// checks if the frequencies are valid or not.\n\tnewDataLength, _, err := validateFrequency(len(data), oldFrequency, newFrequency)\n\tif err != nil {\n\t\treturn nil, 0, err\n\t}\n\tif newDataLength == -1 {\n\t\treturn data, oldFrequency, nil\n\t}\n\n\tnewData := make([]schema.Float, 0, newDataLength)\n\n\t// Bucket size. Leave room for start and end data points\n\tbucketSize := float64(len(data)-2) / float64(newDataLength-2)\n\n\t// Always add the first point\n\tnewData = append(newData, data[0])\n\n\t// Bucket pointers:\n\t// - bucketLow: current bucket's start\n\t// - bucketMiddle: current bucket's end (also next bucket's start)\n\t// - bucketHigh: next bucket's end\n\tbucketLow := 1\n\tbucketMiddle := int(math.Floor(bucketSize)) + 1\n\n\tvar prevMaxAreaPoint int\n\n\t// Process each bucket (excluding first and last points)\n\tfor i := 0; i < newDataLength-2; i++ {\n\t\tbucketHigh := int(math.Floor(float64(i+2)*bucketSize)) + 1\n\t\tif bucketHigh >= len(data)-1 {\n\t\t\tbucketHigh = len(data) - 2\n\t\t}\n\n\t\t// Calculate average point for next bucket (point c in triangle)\n\t\tavgPointX, avgPointY := calculateAverageDataPoint(data[bucketMiddle:bucketHigh+1], int64(bucketMiddle))\n\n\t\t// Get the range for current bucket\n\t\tcurrBucketStart := bucketLow\n\t\tcurrBucketEnd := bucketMiddle\n\n\t\t// Point a (previously selected point)\n\t\tpointX := prevMaxAreaPoint\n\t\tpointY := data[prevMaxAreaPoint]\n\n\t\tmaxArea := -1.0\n\t\tvar maxAreaPoint int\n\t\tflag_ := 0\n\n\t\t// Find the point in current bucket that forms the largest triangle\n\t\tfor ; currBucketStart < currBucketEnd; currBucketStart++ {\n\t\t\tarea := calculateTriangleArea(\n\t\t\t\tschema.Float(pointX), pointY,\n\t\t\t\tavgPointX, avgPointY,\n\t\t\t\tschema.Float(currBucketStart), data[currBucketStart],\n\t\t\t)\n\n\t\t\tif area > maxArea || math.IsNaN(area) {\n\t\t\t\tmaxArea = area\n\t\t\t\tmaxAreaPoint = currBucketStart\n\t\t\t}\n\t\t\t// if math.IsNaN(float64(avgPointY)) {\n\t\t\t// \tflag_ = 1\n\t\t\t// }\n\t\t}\n\n\t\t// Add the point with maximum area from this bucket\n\t\tif flag_ == 1 {\n\t\t\tnewData = append(newData, schema.NaN) // Pick this point from the bucket\n\t\t} else {\n\t\t\tnewData = append(newData, data[maxAreaPoint]) // Pick this point from the bucket\n\t\t}\n\n\t\tprevMaxAreaPoint = maxAreaPoint\n\n\t\t// Move to the next bucket\n\t\tbucketLow = bucketMiddle\n\t\tbucketMiddle = bucketHigh\n\t}\n\n\t// Always add the last point\n\tnewData = append(newData, data[len(data)-1])\n\n\treturn newData, newFrequency, nil\n}\n\nfunc SetMinimumRequiredPoints(setVal int) {\n\tMinimumRequiredPoints = setVal\n}\n","structuredPatch":[{"oldStart":278,"oldLines":6,"newStart":278,"newLines":71,"lines":[" return newData, newFrequency, nil"," }"," ","+// ResamplerFunc is the signature shared by all resampler algorithms.","+type ResamplerFunc func(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)","+","+// GetResampler returns the resampler function for the given name.","+// Valid names: \"lttb\" (default), \"average\", \"simple\". Empty string returns LTTB.","+func GetResampler(name string) (ResamplerFunc, error) {","+ switch name {","+ case \"\", \"lttb\":","+ return LargestTriangleThreeBucket, nil","+ case \"average\":","+ return AverageResampler, nil","+ case \"simple\":","+ return SimpleResampler, nil","+ default:","+ return nil, fmt.Errorf(\"unknown resampler algorithm: %s\", name)","+ }","+}","+","+// AverageResampler performs RRDTool-style average consolidation by dividing data into","+// fixed-size buckets and computing the arithmetic mean of all valid points in each bucket.","+//","+// This produces scientifically accurate averages over time intervals, unlike LTTB which","+// preserves visual shape, or SimpleResampler which decimates. NaN values are skipped;","+// if all values in a bucket are NaN, the output for that bucket is NaN.","+//","+// Parameters:","+// - data: input time-series data points","+// - oldFrequency: original sampling frequency","+// - newFrequency: target sampling frequency (must be a multiple of oldFrequency)","+//","+// Returns:","+// - Averaged data slice","+// - Actual frequency used","+// - Error if newFrequency is not a multiple of oldFrequency","+func AverageResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error) {","+ newDataLength, step, err := validateFrequency(len(data), oldFrequency, newFrequency)","+ if err != nil {","+ return nil, 0, err","+ }","+ if newDataLength == -1 {","+ return data, oldFrequency, nil","+ }","+","+ newData := make([]schema.Float, newDataLength)","+ for i := range newDataLength {","+ sum := 0.0","+ count := 0","+ base := i * step","+ for j := range step {","+ v := float64(data[base+j])","+ if !math.IsNaN(v) {","+ sum += v","+ count++","+ }","+ }","+ if count == 0 {","+ newData[i] = schema.NaN","+ } else {","+ newData[i] = schema.Float(sum / float64(count))","+ }","+ }","+","+ return newData, newFrequency, nil","+}","+"," func SetMinimumRequiredPoints(setVal int) {"," MinimumRequiredPoints = setVal"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"71c7ee6d-60c2-425d-ba31-0f0e8ad7b87f","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"00d5b8ed-9ed4-4296-870a-3814aef66cb4","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01VkFHqjxHKFMpU81JzbRVYA","type":"message","role":"assistant","content":[{"type":"text","text":"Now add tests to the test file:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":970,"cache_read_input_tokens":72878,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":970},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD1ryYRgjZEbmv4HvCy6","type":"assistant","uuid":"1ec0c3c4-0baf-4ddc-b7eb-f1e14566a76c","timestamp":"2026-03-19T19:47:08.346Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"1ec0c3c4-0baf-4ddc-b7eb-f1e14566a76c","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01VkFHqjxHKFMpU81JzbRVYA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FVbjg3sfhPnBh514UT7bYK","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-lib/resampler/resampler_test.go","old_string":"// Benchmark tests\n\nfunc BenchmarkSimpleResampler(b *testing.B) {","new_string":"// TestAverageResampler tests the AverageResampler function\nfunc TestAverageResampler(t *testing.T) {\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t\tcheckValues bool\n\t\texpectedData []schema.Float\n\t}{\n\t\t{\n\t\t\tname: \"Basic averaging step=2\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := AverageResampler(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"AverageResampler() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"AverageResampler() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"AverageResampler() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"AverageResampler() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\tif tt.checkValues && tt.expectedData != nil {\n\t\t\t\tfor i := range result {\n\t\t\t\t\tif math.Abs(float64(result[i]-tt.expectedData[i])) > 1e-10 {\n\t\t\t\t\t\tt.Errorf(\"AverageResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestAverageResamplerValues tests exact output values of AverageResampler\nfunc TestAverageResamplerValues(t *testing.T) {\n\tSetMinimumRequiredPoints(3)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\t// [1,2,3,4,5,6] step=2 -> [1.5, 3.5, 5.5]\n\tdata := []schema.Float{1, 2, 3, 4, 5, 6}\n\tresult, freq, err := AverageResampler(data, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif freq != 2 {\n\t\tt.Errorf(\"frequency = %v, want 2\", freq)\n\t}\n\texpected := []schema.Float{1.5, 3.5, 5.5}\n\tif len(result) != len(expected) {\n\t\tt.Fatalf(\"result length = %v, want %v\", len(result), len(expected))\n\t}\n\tfor i := range expected {\n\t\tif math.Abs(float64(result[i]-expected[i])) > 1e-10 {\n\t\t\tt.Errorf(\"result[%d] = %v, want %v\", i, result[i], expected[i])\n\t\t}\n\t}\n}\n\n// TestAverageResamplerNaN tests NaN handling in AverageResampler\nfunc TestAverageResamplerNaN(t *testing.T) {\n\tSetMinimumRequiredPoints(3)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\t// NaN skipping: [1, NaN, 3, 4] step=2 -> [1.0, 3.5]\n\tdata := []schema.Float{1, schema.NaN, 3, 4}\n\tresult, _, err := AverageResampler(data, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif len(result) != 2 {\n\t\tt.Fatalf(\"result length = %v, want 2\", len(result))\n\t}\n\tif math.Abs(float64(result[0]-1.0)) > 1e-10 {\n\t\tt.Errorf(\"result[0] = %v, want 1.0\", result[0])\n\t}\n\tif math.Abs(float64(result[1]-3.5)) > 1e-10 {\n\t\tt.Errorf(\"result[1] = %v, want 3.5\", result[1])\n\t}\n\n\t// All-NaN bucket: [NaN, NaN, 3, 4] step=2 -> [NaN, 3.5]\n\tdata2 := []schema.Float{schema.NaN, schema.NaN, 3, 4}\n\tresult2, _, err := AverageResampler(data2, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif !math.IsNaN(float64(result2[0])) {\n\t\tt.Errorf(\"result[0] = %v, want NaN\", result2[0])\n\t}\n\tif math.Abs(float64(result2[1]-3.5)) > 1e-10 {\n\t\tt.Errorf(\"result[1] = %v, want 3.5\", result2[1])\n\t}\n}\n\n// TestGetResampler tests the GetResampler lookup function\nfunc TestGetResampler(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\texpectErr bool\n\t}{\n\t\t{\"\", false},\n\t\t{\"lttb\", false},\n\t\t{\"average\", false},\n\t\t{\"simple\", false},\n\t\t{\"unknown\", true},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(\"name=\"+tt.name, func(t *testing.T) {\n\t\t\tfn, err := GetResampler(tt.name)\n\t\t\tif tt.expectErr {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Error(\"expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"unexpected error: %v\", err)\n\t\t\t}\n\t\t\tif fn == nil {\n\t\t\t\tt.Error(\"expected non-nil function\")\n\t\t\t}\n\t\t})\n\t}\n}\n\n// Benchmark tests\n\nfunc BenchmarkAverageResampler(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = AverageResampler(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkSimpleResampler(b *testing.B) 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{"parentUuid":"d5c79bf5-d7a9-478d-b0ef-a3be64f614df","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FVbjg3sfhPnBh514UT7bYK","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-lib/resampler/resampler_test.go has been updated successfully."}]},"uuid":"0f96a191-1004-45d0-accb-73df3923a1ee","timestamp":"2026-03-19T19:47:31.519Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-lib/resampler/resampler_test.go","oldString":"// Benchmark tests\n\nfunc BenchmarkSimpleResampler(b *testing.B) {","newString":"// TestAverageResampler tests the AverageResampler function\nfunc TestAverageResampler(t *testing.T) {\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t\tcheckValues bool\n\t\texpectedData []schema.Float\n\t}{\n\t\t{\n\t\t\tname: \"Basic averaging step=2\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := AverageResampler(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"AverageResampler() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"AverageResampler() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"AverageResampler() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"AverageResampler() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\tif tt.checkValues && tt.expectedData != nil {\n\t\t\t\tfor i := range result {\n\t\t\t\t\tif math.Abs(float64(result[i]-tt.expectedData[i])) > 1e-10 {\n\t\t\t\t\t\tt.Errorf(\"AverageResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestAverageResamplerValues tests exact output values of AverageResampler\nfunc TestAverageResamplerValues(t *testing.T) {\n\tSetMinimumRequiredPoints(3)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\t// [1,2,3,4,5,6] step=2 -> [1.5, 3.5, 5.5]\n\tdata := []schema.Float{1, 2, 3, 4, 5, 6}\n\tresult, freq, err := AverageResampler(data, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif freq != 2 {\n\t\tt.Errorf(\"frequency = %v, want 2\", freq)\n\t}\n\texpected := []schema.Float{1.5, 3.5, 5.5}\n\tif len(result) != len(expected) {\n\t\tt.Fatalf(\"result length = %v, want %v\", len(result), len(expected))\n\t}\n\tfor i := range expected {\n\t\tif math.Abs(float64(result[i]-expected[i])) > 1e-10 {\n\t\t\tt.Errorf(\"result[%d] = %v, want %v\", i, result[i], expected[i])\n\t\t}\n\t}\n}\n\n// TestAverageResamplerNaN tests NaN handling in AverageResampler\nfunc TestAverageResamplerNaN(t *testing.T) {\n\tSetMinimumRequiredPoints(3)\n\tdefer SetMinimumRequiredPoints(1000)\n\n\t// NaN skipping: [1, NaN, 3, 4] step=2 -> [1.0, 3.5]\n\tdata := []schema.Float{1, schema.NaN, 3, 4}\n\tresult, _, err := AverageResampler(data, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif len(result) != 2 {\n\t\tt.Fatalf(\"result length = %v, want 2\", len(result))\n\t}\n\tif math.Abs(float64(result[0]-1.0)) > 1e-10 {\n\t\tt.Errorf(\"result[0] = %v, want 1.0\", result[0])\n\t}\n\tif math.Abs(float64(result[1]-3.5)) > 1e-10 {\n\t\tt.Errorf(\"result[1] = %v, want 3.5\", result[1])\n\t}\n\n\t// All-NaN bucket: [NaN, NaN, 3, 4] step=2 -> [NaN, 3.5]\n\tdata2 := []schema.Float{schema.NaN, schema.NaN, 3, 4}\n\tresult2, _, err := AverageResampler(data2, 1, 2)\n\tif err != nil {\n\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t}\n\tif !math.IsNaN(float64(result2[0])) {\n\t\tt.Errorf(\"result[0] = %v, want NaN\", result2[0])\n\t}\n\tif math.Abs(float64(result2[1]-3.5)) > 1e-10 {\n\t\tt.Errorf(\"result[1] = %v, want 3.5\", result2[1])\n\t}\n}\n\n// TestGetResampler tests the GetResampler lookup function\nfunc TestGetResampler(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\texpectErr bool\n\t}{\n\t\t{\"\", false},\n\t\t{\"lttb\", false},\n\t\t{\"average\", false},\n\t\t{\"simple\", false},\n\t\t{\"unknown\", true},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(\"name=\"+tt.name, func(t *testing.T) {\n\t\t\tfn, err := GetResampler(tt.name)\n\t\t\tif tt.expectErr {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Error(\"expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"unexpected error: %v\", err)\n\t\t\t}\n\t\t\tif fn == nil {\n\t\t\t\tt.Error(\"expected non-nil function\")\n\t\t\t}\n\t\t})\n\t}\n}\n\n// Benchmark tests\n\nfunc BenchmarkAverageResampler(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = AverageResampler(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkSimpleResampler(b *testing.B) {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-lib.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\npackage resampler\n\nimport (\n\t\"math\"\n\t\"testing\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// TestCalculateTriangleArea tests the triangle area calculation helper function\nfunc TestCalculateTriangleArea(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\tpaX, paY schema.Float\n\t\tpbX, pbY schema.Float\n\t\tpcX, pcY schema.Float\n\t\texpected float64\n\t}{\n\t\t{\n\t\t\tname: \"Right triangle\",\n\t\t\tpaX: 0, paY: 0,\n\t\t\tpbX: 0, pbY: 3,\n\t\t\tpcX: 4, pcY: 0,\n\t\t\texpected: 6.0,\n\t\t},\n\t\t{\n\t\t\tname: \"Collinear points (zero area)\",\n\t\t\tpaX: 0, paY: 0,\n\t\t\tpbX: 1, pbY: 1,\n\t\t\tpcX: 2, pcY: 2,\n\t\t\texpected: 0.0,\n\t\t},\n\t\t{\n\t\t\tname: \"Negative coordinates\",\n\t\t\tpaX: -1, paY: -1,\n\t\t\tpbX: -1, pbY: 2,\n\t\t\tpcX: 3, pcY: -1,\n\t\t\texpected: 6.0,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult := calculateTriangleArea(tt.paX, tt.paY, tt.pbX, tt.pbY, tt.pcX, tt.pcY)\n\t\t\tif math.Abs(result-tt.expected) > 1e-10 {\n\t\t\t\tt.Errorf(\"calculateTriangleArea() = %v, want %v\", result, tt.expected)\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestCalculateAverageDataPoint tests the average point calculation helper function\nfunc TestCalculateAverageDataPoint(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\tpoints []schema.Float\n\t\txStart int64\n\t\texpectedX schema.Float\n\t\texpectedY schema.Float\n\t\texpectNaN bool\n\t}{\n\t\t{\n\t\t\tname: \"Simple average\",\n\t\t\tpoints: []schema.Float{1.0, 2.0, 3.0},\n\t\t\txStart: 0,\n\t\t\texpectedX: 1.0,\n\t\t\texpectedY: 2.0,\n\t\t\texpectNaN: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Single point\",\n\t\t\tpoints: []schema.Float{5.0},\n\t\t\txStart: 10,\n\t\t\texpectedX: 10.0,\n\t\t\texpectedY: 5.0,\n\t\t\texpectNaN: false,\n\t\t},\n\t\t{\n\t\t\tname: \"With NaN value\",\n\t\t\tpoints: []schema.Float{1.0, schema.NaN, 3.0},\n\t\t\txStart: 0,\n\t\t\texpectedX: 1.0,\n\t\t\texpectNaN: true,\n\t\t},\n\t\t{\n\t\t\tname: \"All NaN values\",\n\t\t\tpoints: []schema.Float{schema.NaN, schema.NaN},\n\t\t\txStart: 0,\n\t\t\texpectedX: 0.5,\n\t\t\texpectNaN: true,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tavgX, avgY := calculateAverageDataPoint(tt.points, tt.xStart)\n\t\t\tif math.Abs(float64(avgX-tt.expectedX)) > 1e-10 {\n\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgX = %v, want %v\", avgX, tt.expectedX)\n\t\t\t}\n\t\t\tif tt.expectNaN {\n\t\t\t\tif !math.IsNaN(float64(avgY)) {\n\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY should be NaN, got %v\", avgY)\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif math.Abs(float64(avgY-tt.expectedY)) > 1e-10 {\n\t\t\t\t\tt.Errorf(\"calculateAverageDataPoint() avgY = %v, want %v\", avgY, tt.expectedY)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestSimpleResampler tests the SimpleResampler function\nfunc TestSimpleResampler(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t\tcheckValues bool\n\t\texpectedData []schema.Float\n\t}{\n\t\t{\n\t\t\tname: \"Normal downsampling\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Exact values check\",\n\t\t\tdata: []schema.Float{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 10,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t\tcheckValues: false, // Too small, won't downsample\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t\texpectError: false,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := SimpleResampler(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"SimpleResampler() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"SimpleResampler() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"SimpleResampler() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"SimpleResampler() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\tif tt.checkValues && tt.expectedData != nil {\n\t\t\t\tfor i := range result {\n\t\t\t\t\tif result[i] != tt.expectedData[i] {\n\t\t\t\t\t\tt.Errorf(\"SimpleResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestLargestTriangleThreeBucket tests the LTTB algorithm\nfunc TestLargestTriangleThreeBucket(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\ttests := []struct {\n\t\tname string\n\t\tdata []schema.Float\n\t\toldFrequency int64\n\t\tnewFrequency int64\n\t\texpectedLen int\n\t\texpectedFreq int64\n\t\texpectError bool\n\t}{\n\t\t{\n\t\t\tname: \"Normal downsampling\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Sine wave pattern\",\n\t\t\tdata: makeSineWave(500, 10),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 5,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 5,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"No downsampling needed (new <= old)\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 2,\n\t\t\tnewFrequency: 1,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero old frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 0,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 0,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Zero new frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 0,\n\t\t\texpectedLen: 200,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Non-multiple frequency\",\n\t\t\tdata: makeTestData(200, 1.0),\n\t\t\toldFrequency: 3,\n\t\t\tnewFrequency: 7,\n\t\t\texpectError: true,\n\t\t},\n\t\t{\n\t\t\tname: \"Small dataset (< 100 points)\",\n\t\t\tdata: makeTestData(50, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 50,\n\t\t\texpectedFreq: 1,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"With NaN values\",\n\t\t\tdata: makeTestDataWithNaN(200),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 2,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 2,\n\t\t\texpectError: false,\n\t\t},\n\t\t{\n\t\t\tname: \"Large downsampling factor\",\n\t\t\tdata: makeTestData(1000, 1.0),\n\t\t\toldFrequency: 1,\n\t\t\tnewFrequency: 10,\n\t\t\texpectedLen: 100,\n\t\t\texpectedFreq: 10,\n\t\t\texpectError: false,\n\t\t},\n\t}\n\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tresult, freq, err := LargestTriangleThreeBucket(tt.data, tt.oldFrequency, tt.newFrequency)\n\n\t\t\tif tt.expectError {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() expected error, got nil\")\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif err != nil {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() unexpected error: %v\", err)\n\t\t\t\treturn\n\t\t\t}\n\n\t\t\tif len(result) != tt.expectedLen {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() result length = %v, want %v\", len(result), tt.expectedLen)\n\t\t\t}\n\n\t\t\tif freq != tt.expectedFreq {\n\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() frequency = %v, want %v\", freq, tt.expectedFreq)\n\t\t\t}\n\n\t\t\t// Verify first and last points are preserved\n\t\t\tif len(result) > 0 && len(tt.data) > 0 {\n\t\t\t\t// Handle NaN comparison specially since NaN != NaN\n\t\t\t\tif math.IsNaN(float64(tt.data[0])) {\n\t\t\t\t\tif !math.IsNaN(float64(result[0])) {\n\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want NaN\", result[0])\n\t\t\t\t\t}\n\t\t\t\t} else if result[0] != tt.data[0] {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() first point not preserved: got %v, want %v\", result[0], tt.data[0])\n\t\t\t\t}\n\n\t\t\t\tif math.IsNaN(float64(tt.data[len(tt.data)-1])) {\n\t\t\t\t\tif !math.IsNaN(float64(result[len(result)-1])) {\n\t\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want NaN\", result[len(result)-1])\n\t\t\t\t\t}\n\t\t\t\t} else if result[len(result)-1] != tt.data[len(tt.data)-1] {\n\t\t\t\t\tt.Errorf(\"LargestTriangleThreeBucket() last point not preserved: got %v, want %v\", result[len(result)-1], tt.data[len(tt.data)-1])\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\n// TestLTTBPreservesFeatures tests that LTTB preserves important features better than simple resampling\nfunc TestLTTBPreservesFeatures(t *testing.T) {\n\t// Set minimum required points to 100 for testing\n\tSetMinimumRequiredPoints(100)\n\tdefer SetMinimumRequiredPoints(1000) // Restore default after test\n\n\t// Create a dataset with a spike\n\tdata := make([]schema.Float, 200)\n\tfor i := range data {\n\t\tif i == 100 {\n\t\t\tdata[i] = 100.0 // Spike\n\t\t} else {\n\t\t\tdata[i] = 1.0\n\t\t}\n\t}\n\n\tlttbResult, _, _ := LargestTriangleThreeBucket(data, 1, 2)\n\n\t// LTTB should be more likely to preserve the spike\n\tlttbHasSpike := false\n\n\tfor _, v := range lttbResult {\n\t\tif v > 50.0 {\n\t\t\tlttbHasSpike = true\n\t\t\tbreak\n\t\t}\n\t}\n\n\t// This test is probabilistic, but LTTB should generally preserve the spike\n\tif !lttbHasSpike {\n\t\tt.Log(\"Warning: LTTB did not preserve spike (may happen occasionally)\")\n\t}\n}\n\n// Helper functions for test data generation\n\n// makeTestData creates a simple linear test dataset\nfunc makeTestData(size int, increment schema.Float) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tdata[i] = schema.Float(i) * increment\n\t}\n\treturn data\n}\n\n// makeSineWave creates a sine wave test dataset\nfunc makeSineWave(size int, periods float64) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tangle := 2 * math.Pi * periods * float64(i) / float64(size)\n\t\tdata[i] = schema.Float(math.Sin(angle))\n\t}\n\treturn data\n}\n\n// makeTestDataWithNaN creates test data with some NaN values\nfunc makeTestDataWithNaN(size int) []schema.Float {\n\tdata := make([]schema.Float, size)\n\tfor i := range data {\n\t\tif i%20 == 0 {\n\t\t\tdata[i] = schema.NaN\n\t\t} else {\n\t\t\tdata[i] = schema.Float(i)\n\t\t}\n\t}\n\treturn data\n}\n\n// Benchmark tests\n\nfunc BenchmarkSimpleResampler(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = SimpleResampler(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkLargestTriangleThreeBucket(b *testing.B) {\n\tdata := makeTestData(10000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = LargestTriangleThreeBucket(data, 1, 10)\n\t}\n}\n\nfunc BenchmarkLTTBLargeDataset(b *testing.B) {\n\tdata := makeTestData(100000, 1.0)\n\tb.ResetTimer()\n\tfor i := 0; i < b.N; i++ {\n\t\t_, _, _ = LargestTriangleThreeBucket(data, 1, 100)\n\t}\n}\n","structuredPatch":[{"oldStart":450,"oldLines":8,"newStart":450,"newLines":216,"lines":[" return data"," }"," ","+// TestAverageResampler tests the AverageResampler function","+func TestAverageResampler(t *testing.T) {","+ SetMinimumRequiredPoints(100)","+ defer SetMinimumRequiredPoints(1000)","+","+ tests := []struct {","+ name string","+ data []schema.Float","+ oldFrequency int64","+ newFrequency int64","+ expectedLen int","+ expectedFreq int64","+ expectError bool","+ checkValues bool","+ expectedData []schema.Float","+ }{","+ {","+ name: \"Basic averaging step=2\",","+ data: makeTestData(200, 1.0),","+ oldFrequency: 1,","+ newFrequency: 2,","+ expectedLen: 100,","+ expectedFreq: 2,","+ },","+ {","+ name: \"No downsampling needed (new <= old)\",","+ data: makeTestData(200, 1.0),","+ oldFrequency: 2,","+ newFrequency: 1,","+ expectedLen: 200,","+ expectedFreq: 2,","+ },","+ {","+ name: \"Zero old frequency\",","+ data: makeTestData(200, 1.0),","+ oldFrequency: 0,","+ newFrequency: 2,","+ expectedLen: 200,","+ expectedFreq: 0,","+ },","+ {","+ name: \"Zero new frequency\",","+ data: makeTestData(200, 1.0),","+ oldFrequency: 1,","+ newFrequency: 0,","+ expectedLen: 200,","+ expectedFreq: 1,","+ },","+ {","+ name: \"Non-multiple frequency\",","+ data: makeTestData(200, 1.0),","+ oldFrequency: 3,","+ newFrequency: 7,","+ expectError: true,","+ },","+ {","+ name: \"Small dataset (< 100 points)\",","+ data: makeTestData(50, 1.0),","+ oldFrequency: 1,","+ newFrequency: 2,","+ expectedLen: 50,","+ expectedFreq: 1,","+ },","+ {","+ name: \"Large downsampling factor\",","+ data: makeTestData(1000, 1.0),","+ oldFrequency: 1,","+ newFrequency: 10,","+ expectedLen: 100,","+ expectedFreq: 10,","+ },","+ }","+","+ for _, tt := range tests {","+ t.Run(tt.name, func(t *testing.T) {","+ result, freq, err := AverageResampler(tt.data, tt.oldFrequency, tt.newFrequency)","+","+ if tt.expectError {","+ if err == nil {","+ t.Errorf(\"AverageResampler() expected error, got nil\")","+ }","+ return","+ }","+","+ if err != nil {","+ t.Errorf(\"AverageResampler() unexpected error: %v\", err)","+ return","+ }","+","+ if len(result) != tt.expectedLen {","+ t.Errorf(\"AverageResampler() result length = %v, want %v\", len(result), tt.expectedLen)","+ }","+","+ if freq != tt.expectedFreq {","+ t.Errorf(\"AverageResampler() frequency = %v, want %v\", freq, tt.expectedFreq)","+ }","+","+ if tt.checkValues && tt.expectedData != nil {","+ for i := range result {","+ if math.Abs(float64(result[i]-tt.expectedData[i])) > 1e-10 {","+ t.Errorf(\"AverageResampler() result[%d] = %v, want %v\", i, result[i], tt.expectedData[i])","+ }","+ }","+ }","+ })","+ }","+}","+","+// TestAverageResamplerValues tests exact output values of AverageResampler","+func TestAverageResamplerValues(t *testing.T) {","+ SetMinimumRequiredPoints(3)","+ defer SetMinimumRequiredPoints(1000)","+","+ // [1,2,3,4,5,6] step=2 -> [1.5, 3.5, 5.5]","+ data := []schema.Float{1, 2, 3, 4, 5, 6}","+ result, freq, err := AverageResampler(data, 1, 2)","+ if err != nil {","+ t.Fatalf(\"unexpected error: %v\", err)","+ }","+ if freq != 2 {","+ t.Errorf(\"frequency = %v, want 2\", freq)","+ }","+ expected := []schema.Float{1.5, 3.5, 5.5}","+ if len(result) != len(expected) {","+ t.Fatalf(\"result length = %v, want %v\", len(result), len(expected))","+ }","+ for i := range expected {","+ if math.Abs(float64(result[i]-expected[i])) > 1e-10 {","+ t.Errorf(\"result[%d] = %v, want %v\", i, result[i], expected[i])","+ }","+ }","+}","+","+// TestAverageResamplerNaN tests NaN handling in AverageResampler","+func TestAverageResamplerNaN(t *testing.T) {","+ SetMinimumRequiredPoints(3)","+ defer SetMinimumRequiredPoints(1000)","+","+ // NaN skipping: [1, NaN, 3, 4] step=2 -> [1.0, 3.5]","+ data := []schema.Float{1, schema.NaN, 3, 4}","+ result, _, err := AverageResampler(data, 1, 2)","+ if err != nil {","+ t.Fatalf(\"unexpected error: %v\", err)","+ }","+ if len(result) != 2 {","+ t.Fatalf(\"result length = %v, want 2\", len(result))","+ }","+ if math.Abs(float64(result[0]-1.0)) > 1e-10 {","+ t.Errorf(\"result[0] = %v, want 1.0\", result[0])","+ }","+ if math.Abs(float64(result[1]-3.5)) > 1e-10 {","+ t.Errorf(\"result[1] = %v, want 3.5\", result[1])","+ }","+","+ // All-NaN bucket: [NaN, NaN, 3, 4] step=2 -> [NaN, 3.5]","+ data2 := []schema.Float{schema.NaN, schema.NaN, 3, 4}","+ result2, _, err := AverageResampler(data2, 1, 2)","+ if err != nil {","+ t.Fatalf(\"unexpected error: %v\", err)","+ }","+ if !math.IsNaN(float64(result2[0])) {","+ t.Errorf(\"result[0] = %v, want NaN\", result2[0])","+ }","+ if math.Abs(float64(result2[1]-3.5)) > 1e-10 {","+ t.Errorf(\"result[1] = %v, want 3.5\", result2[1])","+ }","+}","+","+// TestGetResampler tests the GetResampler lookup function","+func TestGetResampler(t *testing.T) {","+ tests := []struct {","+ name string","+ expectErr bool","+ }{","+ {\"\", false},","+ {\"lttb\", false},","+ {\"average\", false},","+ {\"simple\", false},","+ {\"unknown\", true},","+ }","+","+ for _, tt := range tests {","+ t.Run(\"name=\"+tt.name, func(t *testing.T) {","+ fn, err := GetResampler(tt.name)","+ if tt.expectErr {","+ if err == nil {","+ t.Error(\"expected error, got nil\")","+ }","+ return","+ }","+ if err != nil {","+ t.Errorf(\"unexpected error: %v\", err)","+ }","+ if fn == nil {","+ t.Error(\"expected non-nil function\")","+ }","+ })","+ }","+}","+"," // Benchmark tests"," ","+func BenchmarkAverageResampler(b *testing.B) {","+ data := makeTestData(10000, 1.0)","+ b.ResetTimer()","+ for i := 0; i < b.N; i++ {","+ _, _, _ = AverageResampler(data, 1, 10)","+ }","+}","+"," func BenchmarkSimpleResampler(b *testing.B) {"," data := makeTestData(10000, 1.0)"," b.ResetTimer()"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"d5c79bf5-d7a9-478d-b0ef-a3be64f614df","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"f312fe55-8f77-48b6-8f15-9c028ea497db","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01BVkeNXnUbmW9Cws8iUq6Xy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H9o75FP87ksoUy6Ur1jdP3","name":"Bash","input":{"command":"cd /Users/jan/prg/CC/cc-lib && go test ./resampler/... -v -count=1 2>&1 | tail -60","description":"Run resampler tests in cc-lib","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2655,"cache_read_input_tokens":73848,"output_tokens":141,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2655,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD1tvUSXHQAX7whxUN4P","type":"assistant","uuid":"f6fe2c70-6950-403c-9162-a00c7dcda6a5","timestamp":"2026-03-19T19:47:37.086Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"f6fe2c70-6950-403c-9162-a00c7dcda6a5","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H9o75FP87ksoUy6Ur1jdP3","type":"tool_result","content":" --- PASS: TestSimpleResampler/Non-multiple_frequency (0.00s)\n --- PASS: TestSimpleResampler/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestSimpleResampler/Exact_values_check (0.00s)\n --- PASS: TestSimpleResampler/Large_downsampling_factor (0.00s)\n=== RUN TestLargestTriangleThreeBucket\n=== RUN TestLargestTriangleThreeBucket/Normal_downsampling\n=== RUN TestLargestTriangleThreeBucket/Sine_wave_pattern\n=== RUN TestLargestTriangleThreeBucket/No_downsampling_needed_(new_<=_old)\n=== RUN TestLargestTriangleThreeBucket/Zero_old_frequency\n=== RUN TestLargestTriangleThreeBucket/Zero_new_frequency\n=== RUN TestLargestTriangleThreeBucket/Non-multiple_frequency\n=== RUN TestLargestTriangleThreeBucket/Small_dataset_(<_100_points)\n=== RUN TestLargestTriangleThreeBucket/With_NaN_values\n=== RUN TestLargestTriangleThreeBucket/Large_downsampling_factor\n--- PASS: TestLargestTriangleThreeBucket (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Normal_downsampling (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Sine_wave_pattern (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/No_downsampling_needed_(new_<=_old) (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Zero_old_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Zero_new_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Non-multiple_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/With_NaN_values (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Large_downsampling_factor (0.00s)\n=== RUN TestLTTBPreservesFeatures\n--- PASS: TestLTTBPreservesFeatures (0.00s)\n=== RUN TestAverageResampler\n=== RUN TestAverageResampler/Basic_averaging_step=2\n=== RUN TestAverageResampler/No_downsampling_needed_(new_<=_old)\n=== RUN TestAverageResampler/Zero_old_frequency\n=== RUN TestAverageResampler/Zero_new_frequency\n=== RUN TestAverageResampler/Non-multiple_frequency\n=== RUN TestAverageResampler/Small_dataset_(<_100_points)\n=== RUN TestAverageResampler/Large_downsampling_factor\n--- PASS: TestAverageResampler (0.00s)\n --- PASS: TestAverageResampler/Basic_averaging_step=2 (0.00s)\n --- PASS: TestAverageResampler/No_downsampling_needed_(new_<=_old) (0.00s)\n --- PASS: TestAverageResampler/Zero_old_frequency (0.00s)\n --- PASS: TestAverageResampler/Zero_new_frequency (0.00s)\n --- PASS: TestAverageResampler/Non-multiple_frequency (0.00s)\n --- PASS: TestAverageResampler/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestAverageResampler/Large_downsampling_factor (0.00s)\n=== RUN TestAverageResamplerValues\n--- PASS: TestAverageResamplerValues (0.00s)\n=== RUN TestAverageResamplerNaN\n--- PASS: TestAverageResamplerNaN (0.00s)\n=== RUN TestGetResampler\n=== RUN TestGetResampler/name=\n=== RUN TestGetResampler/name=lttb\n=== RUN TestGetResampler/name=average\n=== RUN TestGetResampler/name=simple\n=== RUN TestGetResampler/name=unknown\n--- PASS: TestGetResampler (0.00s)\n --- PASS: TestGetResampler/name= (0.00s)\n --- PASS: TestGetResampler/name=lttb (0.00s)\n --- PASS: TestGetResampler/name=average (0.00s)\n --- PASS: TestGetResampler/name=simple (0.00s)\n --- PASS: TestGetResampler/name=unknown (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-lib/v2/resampler\t0.421s\nShell cwd was reset to /Users/jan/tmp/cc-backend","is_error":false}]},"uuid":"765d5aa8-47b4-448a-8cf0-f9db8079670f","timestamp":"2026-03-19T19:48:58.228Z","toolUseResult":{"stdout":" --- PASS: TestSimpleResampler/Non-multiple_frequency (0.00s)\n --- PASS: TestSimpleResampler/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestSimpleResampler/Exact_values_check (0.00s)\n --- PASS: TestSimpleResampler/Large_downsampling_factor (0.00s)\n=== RUN TestLargestTriangleThreeBucket\n=== RUN TestLargestTriangleThreeBucket/Normal_downsampling\n=== RUN TestLargestTriangleThreeBucket/Sine_wave_pattern\n=== RUN TestLargestTriangleThreeBucket/No_downsampling_needed_(new_<=_old)\n=== RUN TestLargestTriangleThreeBucket/Zero_old_frequency\n=== RUN TestLargestTriangleThreeBucket/Zero_new_frequency\n=== RUN TestLargestTriangleThreeBucket/Non-multiple_frequency\n=== RUN TestLargestTriangleThreeBucket/Small_dataset_(<_100_points)\n=== RUN TestLargestTriangleThreeBucket/With_NaN_values\n=== RUN TestLargestTriangleThreeBucket/Large_downsampling_factor\n--- PASS: TestLargestTriangleThreeBucket (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Normal_downsampling (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Sine_wave_pattern (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/No_downsampling_needed_(new_<=_old) (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Zero_old_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Zero_new_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Non-multiple_frequency (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/With_NaN_values (0.00s)\n --- PASS: TestLargestTriangleThreeBucket/Large_downsampling_factor (0.00s)\n=== RUN TestLTTBPreservesFeatures\n--- PASS: TestLTTBPreservesFeatures (0.00s)\n=== RUN TestAverageResampler\n=== RUN TestAverageResampler/Basic_averaging_step=2\n=== RUN TestAverageResampler/No_downsampling_needed_(new_<=_old)\n=== RUN TestAverageResampler/Zero_old_frequency\n=== RUN TestAverageResampler/Zero_new_frequency\n=== RUN TestAverageResampler/Non-multiple_frequency\n=== RUN TestAverageResampler/Small_dataset_(<_100_points)\n=== RUN TestAverageResampler/Large_downsampling_factor\n--- PASS: TestAverageResampler (0.00s)\n --- PASS: TestAverageResampler/Basic_averaging_step=2 (0.00s)\n --- PASS: TestAverageResampler/No_downsampling_needed_(new_<=_old) (0.00s)\n --- PASS: TestAverageResampler/Zero_old_frequency (0.00s)\n --- PASS: TestAverageResampler/Zero_new_frequency (0.00s)\n --- PASS: TestAverageResampler/Non-multiple_frequency (0.00s)\n --- PASS: TestAverageResampler/Small_dataset_(<_100_points) (0.00s)\n --- PASS: TestAverageResampler/Large_downsampling_factor (0.00s)\n=== RUN TestAverageResamplerValues\n--- PASS: TestAverageResamplerValues (0.00s)\n=== RUN TestAverageResamplerNaN\n--- PASS: TestAverageResamplerNaN (0.00s)\n=== RUN TestGetResampler\n=== RUN TestGetResampler/name=\n=== RUN TestGetResampler/name=lttb\n=== RUN TestGetResampler/name=average\n=== RUN TestGetResampler/name=simple\n=== RUN TestGetResampler/name=unknown\n--- PASS: TestGetResampler (0.00s)\n --- PASS: TestGetResampler/name= (0.00s)\n --- PASS: TestGetResampler/name=lttb (0.00s)\n --- PASS: TestGetResampler/name=average (0.00s)\n --- PASS: TestGetResampler/name=simple (0.00s)\n --- PASS: 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v0.17.88\n\tgithub.com/ClusterCockpit/cc-lib/v2 v2.9.1\n\tgithub.com/ClusterCockpit/cc-line-protocol/v2 v2.4.0\n\tgithub.com/Masterminds/squirrel v1.5.4\n\tgithub.com/aws/aws-sdk-go-v2 v1.41.3\n\tgithub.com/aws/aws-sdk-go-v2/config v1.32.11\n\tgithub.com/aws/aws-sdk-go-v2/credentials v1.19.11\n\tgithub.com/aws/aws-sdk-go-v2/service/s3 v1.97.0\n\tgithub.com/coreos/go-oidc/v3 v3.17.0\n\tgithub.com/expr-lang/expr v1.17.8\n\tgithub.com/go-chi/chi/v5 v5.2.5\n\tgithub.com/go-chi/cors v1.2.2\n\tgithub.com/go-co-op/gocron/v2 v2.19.1\n\tgithub.com/go-ldap/ldap/v3 v3.4.12\n\tgithub.com/golang-jwt/jwt/v5 v5.3.1\n\tgithub.com/golang-migrate/migrate/v4 v4.19.1\n\tgithub.com/google/gops v0.3.29\n\tgithub.com/gorilla/sessions v1.4.0\n\tgithub.com/jmoiron/sqlx v1.4.0\n\tgithub.com/joho/godotenv v1.5.1\n\tgithub.com/mattn/go-sqlite3 v1.14.34\n\tgithub.com/parquet-go/parquet-go v0.29.0\n\tgithub.com/qustavo/sqlhooks/v2 v2.1.0\n\tgithub.com/santhosh-tekuri/jsonschema/v5 v5.3.1\n\tgithub.com/stretchr/testify v1.11.1\n\tgithub.com/swaggo/http-swagger v1.3.4\n\tgithub.com/swaggo/swag v1.16.6\n\tgithub.com/vektah/gqlparser/v2 v2.5.32\n\tgolang.org/x/crypto v0.49.0\n\tgolang.org/x/oauth2 v0.36.0\n\tgolang.org/x/sync v0.20.0\n\tgolang.org/x/time v0.15.0\n)\n\nrequire (\n\tgithub.com/Azure/go-ntlmssp v0.1.0 // indirect\n\tgithub.com/KyleBanks/depth v1.2.1 // indirect\n\tgithub.com/agnivade/levenshtein v1.2.1 // indirect\n\tgithub.com/andybalholm/brotli v1.2.0 // indirect\n\tgithub.com/apapsch/go-jsonmerge/v2 v2.0.0 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.6 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/configsources v1.4.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/ini v1.8.5 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/v4a v1.4.20 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.6 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.11 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/signin v1.0.7 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/sso v1.30.12 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.16 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/sts v1.41.8 // indirect\n\tgithub.com/aws/smithy-go v1.24.2 // indirect\n\tgithub.com/cpuguy83/go-md2man/v2 v2.0.7 // indirect\n\tgithub.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect\n\tgithub.com/fsnotify/fsnotify v1.9.0 // indirect\n\tgithub.com/go-asn1-ber/asn1-ber v1.5.8-0.20250403174932-29230038a667 // indirect\n\tgithub.com/go-jose/go-jose/v4 v4.1.3 // indirect\n\tgithub.com/go-openapi/jsonpointer v0.22.5 // indirect\n\tgithub.com/go-openapi/jsonreference v0.21.5 // indirect\n\tgithub.com/go-openapi/spec v0.22.4 // indirect\n\tgithub.com/go-openapi/swag/conv v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/jsonname v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/jsonutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/loading v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/stringutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/typeutils v0.25.5 // indirect\n\tgithub.com/go-openapi/swag/yamlutils v0.25.5 // indirect\n\tgithub.com/go-viper/mapstructure/v2 v2.5.0 // indirect\n\tgithub.com/goccy/go-yaml v1.19.2 // indirect\n\tgithub.com/google/uuid v1.6.0 // indirect\n\tgithub.com/gorilla/securecookie v1.1.2 // indirect\n\tgithub.com/gorilla/websocket v1.5.3 // indirect\n\tgithub.com/hashicorp/golang-lru/v2 v2.0.7 // indirect\n\tgithub.com/influxdata/influxdb-client-go/v2 v2.14.0 // indirect\n\tgithub.com/influxdata/line-protocol v0.0.0-20210922203350-b1ad95c89adf // indirect\n\tgithub.com/jonboulle/clockwork v0.5.0 // indirect\n\tgithub.com/klauspost/compress v1.18.4 // indirect\n\tgithub.com/kr/pretty v0.3.1 // indirect\n\tgithub.com/lann/builder v0.0.0-20180802200727-47ae307949d0 // indirect\n\tgithub.com/lann/ps v0.0.0-20150810152359-62de8c46ede0 // indirect\n\tgithub.com/nats-io/nats.go v1.49.0 // indirect\n\tgithub.com/nats-io/nkeys v0.4.15 // indirect\n\tgithub.com/nats-io/nuid v1.0.1 // indirect\n\tgithub.com/oapi-codegen/runtime v1.2.0 // indirect\n\tgithub.com/parquet-go/bitpack v1.0.0 // indirect\n\tgithub.com/parquet-go/jsonlite v1.4.0 // indirect\n\tgithub.com/pierrec/lz4/v4 v4.1.26 // indirect\n\tgithub.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect\n\tgithub.com/robfig/cron/v3 v3.0.1 // indirect\n\tgithub.com/rogpeppe/go-internal v1.10.0 // indirect\n\tgithub.com/russross/blackfriday/v2 v2.1.0 // indirect\n\tgithub.com/sosodev/duration v1.4.0 // indirect\n\tgithub.com/stmcginnis/gofish v0.21.4 // indirect\n\tgithub.com/stretchr/objx v0.5.2 // indirect\n\tgithub.com/swaggo/files v1.0.1 // indirect\n\tgithub.com/twpayne/go-geom v1.6.1 // indirect\n\tgithub.com/urfave/cli/v2 v2.27.7 // indirect\n\tgithub.com/urfave/cli/v3 v3.7.0 // indirect\n\tgithub.com/xrash/smetrics v0.0.0-20250705151800-55b8f293f342 // indirect\n\tgo.yaml.in/yaml/v2 v2.4.3 // indirect\n\tgo.yaml.in/yaml/v3 v3.0.4 // indirect\n\tgolang.org/x/mod v0.34.0 // indirect\n\tgolang.org/x/net v0.52.0 // indirect\n\tgolang.org/x/sys v0.42.0 // indirect\n\tgolang.org/x/text v0.35.0 // indirect\n\tgolang.org/x/tools v0.43.0 // indirect\n\tgoogle.golang.org/protobuf v1.36.11 // indirect\n\tgopkg.in/yaml.v3 v3.0.1 // indirect\n\tsigs.k8s.io/yaml v1.6.0 // indirect\n)\n","structuredPatch":[{"oldStart":7,"oldLines":6,"newStart":7,"newLines":8,"lines":[" github.com/swaggo/swag/cmd/swag"," )"," ","+replace 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# Slurm partitions\n subClusters: [SubCluster!]! # Hardware partitions/subclusters\n}\n\ntype SubCluster {\n name: String!\n nodes: String!\n numberOfNodes: Int!\n processorType: String!\n socketsPerNode: Int!\n coresPerSocket: Int!\n threadsPerCore: Int!\n flopRateScalar: MetricValue!\n flopRateSimd: MetricValue!\n memoryBandwidth: MetricValue!\n topology: Topology!\n metricConfig: [MetricConfig!]!\n footprint: [String!]!\n}\n\ntype FootprintValue {\n name: String!\n stat: String!\n value: Float!\n}\n\ntype EnergyFootprintValue {\n hardware: String!\n metric: String!\n value: Float!\n}\n\ntype MetricValue {\n name: String\n unit: Unit!\n value: Float!\n}\n\ntype Topology {\n node: [Int!]\n socket: [[Int!]!]\n memoryDomain: [[Int!]!]\n die: [[Int!]!]\n core: [[Int!]!]\n accelerators: [Accelerator!]\n}\n\ntype Accelerator {\n id: String!\n type: String!\n model: String!\n}\n\ntype SubClusterConfig {\n name: String!\n peak: Float\n normal: Float\n caution: Float\n alert: Float\n remove: Boolean\n}\n\ntype MetricConfig {\n name: String!\n unit: Unit!\n scope: MetricScope!\n aggregation: String!\n timestep: Int!\n peak: Float!\n normal: Float\n caution: Float!\n alert: Float!\n lowerIsBetter: Boolean\n subClusters: [SubClusterConfig!]!\n}\n\ntype Tag {\n id: ID!\n type: String!\n name: String!\n scope: String!\n}\n\ntype Resource {\n hostname: String!\n hwthreads: [Int!]\n accelerators: [String!]\n configuration: String\n}\n\ntype JobMetricWithName {\n name: String!\n scope: MetricScope!\n metric: JobMetric!\n}\n\ntype ClusterMetricWithName {\n name: String!\n unit: Unit\n timestep: Int!\n data: [NullableFloat!]!\n}\n\ntype JobMetric {\n unit: Unit\n timestep: Int!\n series: [Series!]\n statisticsSeries: StatsSeries\n}\n\ntype Series {\n hostname: String!\n id: String\n statistics: MetricStatistics\n data: [NullableFloat!]!\n}\n\ntype StatsSeries {\n mean: [NullableFloat!]!\n median: [NullableFloat!]!\n min: [NullableFloat!]!\n max: [NullableFloat!]!\n}\n\ntype NamedStatsWithScope {\n name: String!\n scope: MetricScope!\n stats: [ScopedStats!]!\n}\n\ntype ScopedStats {\n hostname: String!\n id: String\n data: MetricStatistics!\n}\n\ntype JobStats {\n id: Int!\n jobId: String!\n startTime: Int!\n duration: Int!\n cluster: String!\n subCluster: String!\n numNodes: Int!\n numHWThreads: Int\n numAccelerators: Int\n stats: [NamedStats!]!\n}\n\ntype NamedStats {\n name: String!\n data: MetricStatistics!\n}\n\ntype Unit {\n base: String!\n prefix: String\n}\n\ntype MetricStatistics {\n avg: Float!\n min: Float!\n max: Float!\n}\n\ntype MetricFootprints {\n metric: String!\n data: [NullableFloat!]!\n}\n\ntype Footprints {\n timeWeights: TimeWeights!\n metrics: [MetricFootprints!]!\n}\n\ntype TimeWeights {\n nodeHours: [NullableFloat!]!\n accHours: [NullableFloat!]!\n coreHours: [NullableFloat!]!\n}\n\nenum Aggregate {\n USER\n PROJECT\n CLUSTER\n SUBCLUSTER\n}\nenum SortByAggregate {\n TOTALWALLTIME\n TOTALJOBS\n TOTALUSERS\n TOTALNODES\n TOTALNODEHOURS\n TOTALCORES\n TOTALCOREHOURS\n TOTALACCS\n TOTALACCHOURS\n}\n\ntype NodeMetrics {\n host: String!\n nodeState: String!\n metricHealth: String!\n subCluster: String!\n metrics: [JobMetricWithName!]!\n}\n\ntype ClusterMetrics {\n nodeCount: Int!\n metrics: [ClusterMetricWithName!]!\n}\n\ntype NodesResultList {\n items: [NodeMetrics!]!\n offset: Int\n limit: Int\n count: Int\n totalNodes: Int\n hasNextPage: Boolean\n}\n\ntype ClusterSupport {\n cluster: String!\n subClusters: [String!]!\n}\n\ntype GlobalMetricListItem {\n name: String!\n unit: Unit!\n scope: MetricScope!\n footprint: String\n availability: [ClusterSupport!]!\n}\n\ntype Count {\n name: String!\n count: Int!\n}\n\ntype User {\n username: String!\n name: String!\n email: String!\n}\n\ninput MetricStatItem {\n metricName: String!\n range: FloatRange!\n}\n\ntype Query {\n clusters: [Cluster!]! # List of all clusters\n tags: [Tag!]! # List of all tags\n globalMetrics: [GlobalMetricListItem!]!\n\n user(username: String!): User\n allocatedNodes(cluster: String!): [Count!]!\n\n ## Node Queries New\n node(id: ID!): Node\n nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n\n job(id: ID!): Job\n jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n ): [JobMetricWithName!]!\n\n jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n\n scopedJobStats(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n ): [NamedStatsWithScope!]!\n\n jobs(\n filter: [JobFilter!]\n page: PageRequest\n order: OrderByInput\n ): JobResultList!\n\n jobsStatistics(\n filter: [JobFilter!]\n metrics: [String!]\n page: PageRequest\n sortBy: SortByAggregate\n groupBy: Aggregate\n numDurationBins: String\n numMetricBins: Int\n ): [JobsStatistics!]!\n\n jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n\n rooflineHeatmap(\n filter: [JobFilter!]!\n rows: Int!\n cols: Int!\n minX: Float!\n minY: Float!\n maxX: Float!\n maxY: Float!\n ): [[Float!]!]!\n\n nodeMetrics(\n cluster: String!\n nodes: [String!]\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n ): [NodeMetrics!]!\n\n nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n ): NodesResultList!\n\n clusterMetrics(\n cluster: String!\n metrics: [String!]\n from: Time!\n to: Time!\n ): ClusterMetrics!\n}\n\ntype Mutation {\n createTag(type: String!, name: String!, scope: String!): Tag!\n deleteTag(id: ID!): ID!\n addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagFromList(tagIds: [ID!]!): [Int!]!\n\n updateConfiguration(name: String!, value: String!): String\n}\n\ntype IntRangeOutput {\n from: Int!\n to: Int!\n}\ntype TimeRangeOutput {\n range: String\n from: Time!\n to: Time!\n}\n\ninput NodeFilter {\n hostname: StringInput\n cluster: StringInput\n subCluster: StringInput\n schedulerState: SchedulerState\n healthState: MonitoringState\n timeStart: Int\n}\n\ninput JobFilter {\n tags: [ID!]\n dbId: [ID!]\n jobId: StringInput\n arrayJobId: Int\n user: StringInput\n project: StringInput\n jobName: StringInput\n cluster: StringInput\n subCluster: StringInput\n partition: StringInput\n duration: IntRange\n energy: FloatRange\n\n numNodes: IntRange\n numAccelerators: IntRange\n numHWThreads: IntRange\n\n startTime: TimeRange\n state: [JobState!]\n metricStats: [MetricStatItem!]\n shared: String\n schedule: String\n node: StringInput\n}\n\ninput OrderByInput {\n field: String!\n type: String!\n order: SortDirectionEnum! = ASC\n}\n\nenum SortDirectionEnum {\n DESC\n ASC\n}\n\ninput StringInput {\n eq: String\n neq: String\n contains: String\n startsWith: String\n endsWith: String\n in: [String!]\n}\n\ninput IntRange {\n from: Int!\n to: Int!\n}\ninput TimeRange {\n range: String\n from: Time\n to: Time\n}\n\ninput FloatRange {\n from: Float!\n to: Float!\n}\n\ntype NodeStateResultList {\n items: [Node!]!\n count: Int\n}\n\ntype JobResultList {\n items: [Job!]!\n offset: Int\n limit: Int\n count: Int\n hasNextPage: Boolean\n}\n\ntype JobLinkResultList {\n listQuery: String\n items: [JobLink!]!\n count: Int\n}\n\ntype HistoPoint {\n count: Int!\n value: Int!\n}\n\ntype MetricHistoPoints {\n metric: String!\n unit: String!\n stat: String\n data: [MetricHistoPoint!]\n}\n\ntype MetricHistoPoint {\n bin: Int\n count: Int!\n min: Int\n max: Int\n}\n\ntype JobsStatistics {\n id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n name: String! # if User-Statistics: Given Name of Account (ID) Owner\n totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n totalJobs: Int! # Number of jobs\n runningJobs: Int! # Number of running jobs\n shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n totalNodes: Int! # Sum of the nodes of all matched jobs\n totalNodeHours: Int! # Sum of the node hours of all matched jobs\n totalCores: Int! # Sum of the cores of all matched jobs\n totalCoreHours: Int! # Sum of the core hours of all matched jobs\n totalAccs: Int! # Sum of the accs of all matched jobs\n totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n histNumAccs: [HistoPoint!]! # value: number of accs, count: number of jobs with that number of accs\n histMetrics: [MetricHistoPoints!]! # metric: metricname, data array of histopoints: value: metric average bin, count: number of jobs with that metric average\n}\n\ninput PageRequest {\n itemsPerPage: Int!\n page: Int!\n}\n","structuredPatch":[{"oldStart":250,"oldLines":6,"newStart":250,"newLines":12,"lines":[" coreHours: [NullableFloat!]!"," }"," ","+enum ResampleAlgo {","+ LTTB","+ AVERAGE","+ SIMPLE","+}","+"," enum Aggregate {"," USER"," PROJECT"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"f2c845fb-df82-4b9a-806b-1911786624ca","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"127ff9fa-bda4-4e5a-8a9b-5626d63bfd3d","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H7o7S2abbC6kiwSdNj4i8u","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/api/schema.graphqls has been updated successfully."}]},"uuid":"d740a71d-69a7-483e-94cd-9ee070c2bcda","timestamp":"2026-03-19T19:49:14.421Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/api/schema.graphqls","oldString":" jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n ): [JobMetricWithName!]!","newString":" jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n resampleAlgo: ResampleAlgo\n ): [JobMetricWithName!]!","originalFile":"scalar Time\nscalar Any\n\nscalar NullableFloat\nscalar MetricScope\nscalar JobState\nscalar SchedulerState\nscalar MonitoringState\n\ntype Node {\n id: ID!\n hostname: String!\n cluster: String!\n subCluster: String!\n jobsRunning: Int!\n cpusAllocated: Int\n memoryAllocated: Int\n gpusAllocated: Int\n schedulerState: SchedulerState!\n healthState: MonitoringState!\n metaData: Any\n healthData: Any\n}\n\ntype NodeStates {\n state: String!\n count: Int!\n}\n\ntype NodeStatesTimed {\n state: String!\n counts: [Int!]!\n times: [Int!]!\n}\n\ntype Job {\n id: ID!\n jobId: Int!\n user: String!\n project: String!\n cluster: String!\n subCluster: String!\n startTime: Time!\n duration: Int!\n walltime: Int!\n numNodes: Int!\n numHWThreads: Int!\n numAcc: Int!\n energy: Float!\n SMT: Int!\n shared: String!\n partition: String!\n arrayJobId: Int!\n monitoringStatus: Int!\n state: JobState!\n tags: [Tag!]!\n resources: [Resource!]!\n concurrentJobs: JobLinkResultList\n footprint: [FootprintValue]\n energyFootprint: [EnergyFootprintValue]\n metaData: Any\n userData: User\n}\n\ntype JobLink {\n id: ID!\n jobId: Int!\n}\n\ntype Cluster {\n name: String!\n partitions: [String!]! # Slurm partitions\n subClusters: [SubCluster!]! # Hardware partitions/subclusters\n}\n\ntype SubCluster {\n name: String!\n nodes: String!\n numberOfNodes: Int!\n processorType: String!\n socketsPerNode: Int!\n coresPerSocket: Int!\n threadsPerCore: Int!\n flopRateScalar: MetricValue!\n flopRateSimd: MetricValue!\n memoryBandwidth: MetricValue!\n topology: Topology!\n metricConfig: [MetricConfig!]!\n footprint: [String!]!\n}\n\ntype FootprintValue {\n name: String!\n stat: String!\n value: Float!\n}\n\ntype EnergyFootprintValue {\n hardware: String!\n metric: String!\n value: Float!\n}\n\ntype MetricValue {\n name: String\n unit: Unit!\n value: Float!\n}\n\ntype Topology {\n node: [Int!]\n socket: [[Int!]!]\n memoryDomain: [[Int!]!]\n die: [[Int!]!]\n core: [[Int!]!]\n accelerators: [Accelerator!]\n}\n\ntype Accelerator {\n id: String!\n type: String!\n model: String!\n}\n\ntype SubClusterConfig {\n name: String!\n peak: Float\n normal: Float\n caution: Float\n alert: Float\n remove: Boolean\n}\n\ntype MetricConfig {\n name: String!\n unit: Unit!\n scope: MetricScope!\n aggregation: String!\n timestep: Int!\n peak: Float!\n normal: Float\n caution: Float!\n alert: Float!\n lowerIsBetter: Boolean\n subClusters: [SubClusterConfig!]!\n}\n\ntype Tag {\n id: ID!\n type: String!\n name: String!\n scope: String!\n}\n\ntype Resource {\n hostname: String!\n hwthreads: [Int!]\n accelerators: [String!]\n configuration: String\n}\n\ntype JobMetricWithName {\n name: String!\n scope: MetricScope!\n metric: JobMetric!\n}\n\ntype ClusterMetricWithName {\n name: String!\n unit: Unit\n timestep: Int!\n data: [NullableFloat!]!\n}\n\ntype JobMetric {\n unit: Unit\n timestep: Int!\n series: [Series!]\n statisticsSeries: StatsSeries\n}\n\ntype Series {\n hostname: String!\n id: String\n statistics: MetricStatistics\n data: [NullableFloat!]!\n}\n\ntype StatsSeries {\n mean: [NullableFloat!]!\n median: [NullableFloat!]!\n min: [NullableFloat!]!\n max: [NullableFloat!]!\n}\n\ntype NamedStatsWithScope {\n name: String!\n scope: MetricScope!\n stats: [ScopedStats!]!\n}\n\ntype ScopedStats {\n hostname: String!\n id: String\n data: MetricStatistics!\n}\n\ntype JobStats {\n id: Int!\n jobId: String!\n startTime: Int!\n duration: Int!\n cluster: String!\n subCluster: String!\n numNodes: Int!\n numHWThreads: Int\n numAccelerators: Int\n stats: [NamedStats!]!\n}\n\ntype NamedStats {\n name: String!\n data: MetricStatistics!\n}\n\ntype Unit {\n base: String!\n prefix: String\n}\n\ntype MetricStatistics {\n avg: Float!\n min: Float!\n max: Float!\n}\n\ntype MetricFootprints {\n metric: String!\n data: [NullableFloat!]!\n}\n\ntype Footprints {\n timeWeights: TimeWeights!\n metrics: [MetricFootprints!]!\n}\n\ntype TimeWeights {\n nodeHours: [NullableFloat!]!\n accHours: [NullableFloat!]!\n coreHours: [NullableFloat!]!\n}\n\nenum ResampleAlgo {\n LTTB\n AVERAGE\n SIMPLE\n}\n\nenum Aggregate {\n USER\n PROJECT\n CLUSTER\n SUBCLUSTER\n}\nenum SortByAggregate {\n TOTALWALLTIME\n TOTALJOBS\n TOTALUSERS\n TOTALNODES\n TOTALNODEHOURS\n TOTALCORES\n TOTALCOREHOURS\n TOTALACCS\n TOTALACCHOURS\n}\n\ntype NodeMetrics {\n host: String!\n nodeState: String!\n metricHealth: String!\n subCluster: String!\n metrics: [JobMetricWithName!]!\n}\n\ntype ClusterMetrics {\n nodeCount: Int!\n metrics: [ClusterMetricWithName!]!\n}\n\ntype NodesResultList {\n items: [NodeMetrics!]!\n offset: Int\n limit: Int\n count: Int\n totalNodes: Int\n hasNextPage: Boolean\n}\n\ntype ClusterSupport {\n cluster: String!\n subClusters: [String!]!\n}\n\ntype GlobalMetricListItem {\n name: String!\n unit: Unit!\n scope: MetricScope!\n footprint: String\n availability: [ClusterSupport!]!\n}\n\ntype Count {\n name: String!\n count: Int!\n}\n\ntype User {\n username: String!\n name: String!\n email: String!\n}\n\ninput MetricStatItem {\n metricName: String!\n range: FloatRange!\n}\n\ntype Query {\n clusters: [Cluster!]! # List of all clusters\n tags: [Tag!]! # List of all tags\n globalMetrics: [GlobalMetricListItem!]!\n\n user(username: String!): User\n allocatedNodes(cluster: String!): [Count!]!\n\n ## Node Queries New\n node(id: ID!): Node\n nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n\n job(id: ID!): Job\n jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n ): [JobMetricWithName!]!\n\n jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n\n scopedJobStats(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n ): [NamedStatsWithScope!]!\n\n jobs(\n filter: [JobFilter!]\n page: PageRequest\n order: OrderByInput\n ): JobResultList!\n\n jobsStatistics(\n filter: [JobFilter!]\n metrics: [String!]\n page: PageRequest\n sortBy: SortByAggregate\n groupBy: Aggregate\n numDurationBins: String\n numMetricBins: Int\n ): [JobsStatistics!]!\n\n jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n\n rooflineHeatmap(\n filter: [JobFilter!]!\n rows: Int!\n cols: Int!\n minX: Float!\n minY: Float!\n maxX: Float!\n maxY: Float!\n ): [[Float!]!]!\n\n nodeMetrics(\n cluster: String!\n nodes: [String!]\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n ): [NodeMetrics!]!\n\n nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n ): NodesResultList!\n\n clusterMetrics(\n cluster: String!\n metrics: [String!]\n from: Time!\n to: Time!\n ): ClusterMetrics!\n}\n\ntype Mutation {\n createTag(type: String!, name: String!, scope: String!): Tag!\n deleteTag(id: ID!): ID!\n addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagFromList(tagIds: [ID!]!): [Int!]!\n\n updateConfiguration(name: String!, value: String!): String\n}\n\ntype IntRangeOutput {\n from: Int!\n to: Int!\n}\ntype TimeRangeOutput {\n range: String\n from: Time!\n to: Time!\n}\n\ninput NodeFilter {\n hostname: StringInput\n cluster: StringInput\n subCluster: StringInput\n schedulerState: SchedulerState\n healthState: MonitoringState\n timeStart: Int\n}\n\ninput JobFilter {\n tags: [ID!]\n dbId: [ID!]\n jobId: StringInput\n arrayJobId: Int\n user: StringInput\n project: StringInput\n jobName: StringInput\n cluster: StringInput\n subCluster: StringInput\n partition: StringInput\n duration: IntRange\n energy: FloatRange\n\n numNodes: IntRange\n numAccelerators: IntRange\n numHWThreads: IntRange\n\n startTime: TimeRange\n state: [JobState!]\n metricStats: [MetricStatItem!]\n shared: String\n schedule: String\n node: StringInput\n}\n\ninput OrderByInput {\n field: String!\n type: String!\n order: SortDirectionEnum! = ASC\n}\n\nenum SortDirectionEnum {\n DESC\n ASC\n}\n\ninput StringInput {\n eq: String\n neq: String\n contains: String\n startsWith: String\n endsWith: String\n in: [String!]\n}\n\ninput IntRange {\n from: Int!\n to: Int!\n}\ninput TimeRange {\n range: String\n from: Time\n to: Time\n}\n\ninput FloatRange {\n from: Float!\n to: Float!\n}\n\ntype NodeStateResultList {\n items: [Node!]!\n count: Int\n}\n\ntype JobResultList {\n items: [Job!]!\n offset: Int\n limit: Int\n count: Int\n hasNextPage: Boolean\n}\n\ntype JobLinkResultList {\n listQuery: String\n items: [JobLink!]!\n count: Int\n}\n\ntype HistoPoint {\n count: Int!\n value: Int!\n}\n\ntype MetricHistoPoints {\n metric: String!\n unit: String!\n stat: String\n data: [MetricHistoPoint!]\n}\n\ntype MetricHistoPoint {\n bin: Int\n count: Int!\n min: Int\n max: Int\n}\n\ntype JobsStatistics {\n id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n name: String! # if User-Statistics: Given Name of Account (ID) Owner\n totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n totalJobs: Int! # Number of jobs\n runningJobs: Int! # Number of running jobs\n shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n totalNodes: Int! # Sum of the nodes of all matched jobs\n totalNodeHours: Int! # Sum of the node hours of all matched jobs\n totalCores: Int! # Sum of the cores of all matched jobs\n totalCoreHours: Int! # Sum of the core hours of all matched jobs\n totalAccs: Int! # Sum of the accs of all matched jobs\n totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n histNumAccs: [HistoPoint!]! 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{"parentUuid":"9a3078e8-1920-46d2-8e7a-e10e4ad189df","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GVDts1dt2VqT8cmwtwW9Ch","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/api/schema.graphqls has been updated successfully."}]},"uuid":"6cc704a5-8d62-4fff-b3de-7449a0f73320","timestamp":"2026-03-19T19:49:21.121Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/api/schema.graphqls","oldString":" nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n ): NodesResultList!","newString":" nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n resampleAlgo: ResampleAlgo\n ): NodesResultList!","originalFile":"scalar Time\nscalar Any\n\nscalar NullableFloat\nscalar MetricScope\nscalar JobState\nscalar SchedulerState\nscalar MonitoringState\n\ntype Node {\n id: ID!\n hostname: String!\n cluster: String!\n subCluster: String!\n jobsRunning: Int!\n cpusAllocated: Int\n memoryAllocated: Int\n gpusAllocated: Int\n schedulerState: SchedulerState!\n healthState: MonitoringState!\n metaData: Any\n healthData: Any\n}\n\ntype NodeStates {\n state: String!\n count: Int!\n}\n\ntype NodeStatesTimed {\n state: String!\n counts: [Int!]!\n times: [Int!]!\n}\n\ntype Job {\n id: ID!\n jobId: Int!\n user: String!\n project: String!\n cluster: String!\n subCluster: String!\n startTime: Time!\n duration: Int!\n walltime: Int!\n numNodes: Int!\n numHWThreads: Int!\n numAcc: Int!\n energy: Float!\n SMT: Int!\n shared: String!\n partition: String!\n arrayJobId: Int!\n monitoringStatus: Int!\n state: JobState!\n tags: [Tag!]!\n resources: [Resource!]!\n concurrentJobs: JobLinkResultList\n footprint: [FootprintValue]\n energyFootprint: [EnergyFootprintValue]\n metaData: Any\n userData: User\n}\n\ntype JobLink {\n id: ID!\n jobId: Int!\n}\n\ntype Cluster {\n name: String!\n partitions: [String!]! # Slurm partitions\n subClusters: [SubCluster!]! # Hardware partitions/subclusters\n}\n\ntype SubCluster {\n name: String!\n nodes: String!\n numberOfNodes: Int!\n processorType: String!\n socketsPerNode: Int!\n coresPerSocket: Int!\n threadsPerCore: Int!\n flopRateScalar: MetricValue!\n flopRateSimd: MetricValue!\n memoryBandwidth: MetricValue!\n topology: Topology!\n metricConfig: [MetricConfig!]!\n footprint: [String!]!\n}\n\ntype FootprintValue {\n name: String!\n stat: String!\n value: Float!\n}\n\ntype EnergyFootprintValue {\n hardware: String!\n metric: String!\n value: Float!\n}\n\ntype MetricValue {\n name: String\n unit: Unit!\n value: Float!\n}\n\ntype Topology {\n node: [Int!]\n socket: [[Int!]!]\n memoryDomain: [[Int!]!]\n die: [[Int!]!]\n core: [[Int!]!]\n accelerators: [Accelerator!]\n}\n\ntype Accelerator {\n id: String!\n type: String!\n model: String!\n}\n\ntype SubClusterConfig {\n name: String!\n peak: Float\n normal: Float\n caution: Float\n alert: Float\n remove: Boolean\n}\n\ntype MetricConfig {\n name: String!\n unit: Unit!\n scope: MetricScope!\n aggregation: String!\n timestep: Int!\n peak: Float!\n normal: Float\n caution: Float!\n alert: Float!\n lowerIsBetter: Boolean\n subClusters: [SubClusterConfig!]!\n}\n\ntype Tag {\n id: ID!\n type: String!\n name: String!\n scope: String!\n}\n\ntype Resource {\n hostname: String!\n hwthreads: [Int!]\n accelerators: [String!]\n configuration: String\n}\n\ntype JobMetricWithName {\n name: String!\n scope: MetricScope!\n metric: JobMetric!\n}\n\ntype ClusterMetricWithName {\n name: String!\n unit: Unit\n timestep: Int!\n data: [NullableFloat!]!\n}\n\ntype JobMetric {\n unit: Unit\n timestep: Int!\n series: [Series!]\n statisticsSeries: StatsSeries\n}\n\ntype Series {\n hostname: String!\n id: String\n statistics: MetricStatistics\n data: [NullableFloat!]!\n}\n\ntype StatsSeries {\n mean: [NullableFloat!]!\n median: [NullableFloat!]!\n min: [NullableFloat!]!\n max: [NullableFloat!]!\n}\n\ntype NamedStatsWithScope {\n name: String!\n scope: MetricScope!\n stats: [ScopedStats!]!\n}\n\ntype ScopedStats {\n hostname: String!\n id: String\n data: MetricStatistics!\n}\n\ntype JobStats {\n id: Int!\n jobId: String!\n startTime: Int!\n duration: Int!\n cluster: String!\n subCluster: String!\n numNodes: Int!\n numHWThreads: Int\n numAccelerators: Int\n stats: [NamedStats!]!\n}\n\ntype NamedStats {\n name: String!\n data: MetricStatistics!\n}\n\ntype Unit {\n base: String!\n prefix: String\n}\n\ntype MetricStatistics {\n avg: Float!\n min: Float!\n max: Float!\n}\n\ntype MetricFootprints {\n metric: String!\n data: [NullableFloat!]!\n}\n\ntype Footprints {\n timeWeights: TimeWeights!\n metrics: [MetricFootprints!]!\n}\n\ntype TimeWeights {\n nodeHours: [NullableFloat!]!\n accHours: [NullableFloat!]!\n coreHours: [NullableFloat!]!\n}\n\nenum ResampleAlgo {\n LTTB\n AVERAGE\n SIMPLE\n}\n\nenum Aggregate {\n USER\n PROJECT\n CLUSTER\n SUBCLUSTER\n}\nenum SortByAggregate {\n TOTALWALLTIME\n TOTALJOBS\n TOTALUSERS\n TOTALNODES\n TOTALNODEHOURS\n TOTALCORES\n TOTALCOREHOURS\n TOTALACCS\n TOTALACCHOURS\n}\n\ntype NodeMetrics {\n host: String!\n nodeState: String!\n metricHealth: String!\n subCluster: String!\n metrics: [JobMetricWithName!]!\n}\n\ntype ClusterMetrics {\n nodeCount: Int!\n metrics: [ClusterMetricWithName!]!\n}\n\ntype NodesResultList {\n items: [NodeMetrics!]!\n offset: Int\n limit: Int\n count: Int\n totalNodes: Int\n hasNextPage: Boolean\n}\n\ntype ClusterSupport {\n cluster: String!\n subClusters: [String!]!\n}\n\ntype GlobalMetricListItem {\n name: String!\n unit: Unit!\n scope: MetricScope!\n footprint: String\n availability: [ClusterSupport!]!\n}\n\ntype Count {\n name: String!\n count: Int!\n}\n\ntype User {\n username: String!\n name: String!\n email: String!\n}\n\ninput MetricStatItem {\n metricName: String!\n range: FloatRange!\n}\n\ntype Query {\n clusters: [Cluster!]! # List of all clusters\n tags: [Tag!]! # List of all tags\n globalMetrics: [GlobalMetricListItem!]!\n\n user(username: String!): User\n allocatedNodes(cluster: String!): [Count!]!\n\n ## Node Queries New\n node(id: ID!): Node\n nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n\n job(id: ID!): Job\n jobMetrics(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n resolution: Int\n resampleAlgo: ResampleAlgo\n ): [JobMetricWithName!]!\n\n jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n\n scopedJobStats(\n id: ID!\n metrics: [String!]\n scopes: [MetricScope!]\n ): [NamedStatsWithScope!]!\n\n jobs(\n filter: [JobFilter!]\n page: PageRequest\n order: OrderByInput\n ): JobResultList!\n\n jobsStatistics(\n filter: [JobFilter!]\n metrics: [String!]\n page: PageRequest\n sortBy: SortByAggregate\n groupBy: Aggregate\n numDurationBins: String\n numMetricBins: Int\n ): [JobsStatistics!]!\n\n jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n\n rooflineHeatmap(\n filter: [JobFilter!]!\n rows: Int!\n cols: Int!\n minX: Float!\n minY: Float!\n maxX: Float!\n maxY: Float!\n ): [[Float!]!]!\n\n nodeMetrics(\n cluster: String!\n nodes: [String!]\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n ): [NodeMetrics!]!\n\n nodeMetricsList(\n cluster: String!\n subCluster: String!\n stateFilter: String!\n nodeFilter: String!\n scopes: [MetricScope!]\n metrics: [String!]\n from: Time!\n to: Time!\n page: PageRequest\n resolution: Int\n ): NodesResultList!\n\n clusterMetrics(\n cluster: String!\n metrics: [String!]\n from: Time!\n to: Time!\n ): ClusterMetrics!\n}\n\ntype Mutation {\n createTag(type: String!, name: String!, scope: String!): Tag!\n deleteTag(id: ID!): ID!\n addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n removeTagFromList(tagIds: [ID!]!): [Int!]!\n\n updateConfiguration(name: String!, value: String!): String\n}\n\ntype IntRangeOutput {\n from: Int!\n to: Int!\n}\ntype TimeRangeOutput {\n range: String\n from: Time!\n to: Time!\n}\n\ninput NodeFilter {\n hostname: StringInput\n cluster: StringInput\n subCluster: StringInput\n schedulerState: SchedulerState\n healthState: MonitoringState\n timeStart: Int\n}\n\ninput JobFilter {\n tags: [ID!]\n dbId: [ID!]\n jobId: StringInput\n arrayJobId: Int\n user: StringInput\n project: StringInput\n jobName: StringInput\n cluster: StringInput\n subCluster: StringInput\n partition: StringInput\n duration: IntRange\n energy: FloatRange\n\n numNodes: IntRange\n numAccelerators: IntRange\n numHWThreads: IntRange\n\n startTime: TimeRange\n state: [JobState!]\n metricStats: [MetricStatItem!]\n shared: String\n schedule: String\n node: StringInput\n}\n\ninput OrderByInput {\n field: String!\n type: String!\n order: SortDirectionEnum! = ASC\n}\n\nenum SortDirectionEnum {\n DESC\n ASC\n}\n\ninput StringInput {\n eq: String\n neq: String\n contains: String\n startsWith: String\n endsWith: String\n in: [String!]\n}\n\ninput IntRange {\n from: Int!\n to: Int!\n}\ninput TimeRange {\n range: String\n from: Time\n to: Time\n}\n\ninput FloatRange {\n from: Float!\n to: Float!\n}\n\ntype NodeStateResultList {\n items: [Node!]!\n count: Int\n}\n\ntype JobResultList {\n items: [Job!]!\n offset: Int\n limit: Int\n count: Int\n hasNextPage: Boolean\n}\n\ntype JobLinkResultList {\n listQuery: String\n items: [JobLink!]!\n count: Int\n}\n\ntype HistoPoint {\n count: Int!\n value: Int!\n}\n\ntype MetricHistoPoints {\n metric: String!\n unit: String!\n stat: String\n data: [MetricHistoPoint!]\n}\n\ntype MetricHistoPoint {\n bin: Int\n count: Int!\n min: Int\n max: Int\n}\n\ntype JobsStatistics {\n id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n name: String! # if User-Statistics: Given Name of Account (ID) Owner\n totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n totalJobs: Int! # Number of jobs\n runningJobs: Int! # Number of running jobs\n shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n totalNodes: Int! # Sum of the nodes of all matched jobs\n totalNodeHours: Int! # Sum of the node hours of all matched jobs\n totalCores: Int! # Sum of the cores of all matched jobs\n totalCoreHours: Int! # Sum of the core hours of all matched jobs\n totalAccs: Int! # Sum of the accs of all matched jobs\n totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n histNumAccs: [HistoPoint!]! # value: number of accs, count: number of jobs with that number of accs\n histMetrics: [MetricHistoPoints!]! # metric: metricname, data array of histopoints: value: metric average bin, count: number of jobs with that metric average\n}\n\ninput PageRequest {\n itemsPerPage: Int!\n page: Int!\n}\n","structuredPatch":[{"oldStart":406,"oldLines":6,"newStart":406,"newLines":7,"lines":[" to: Time!"," page: PageRequest"," resolution: Int","+ resampleAlgo: ResampleAlgo"," ): NodesResultList!"," "," clusterMetrics("]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"9a3078e8-1920-46d2-8e7a-e10e4ad189df","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"6cc704a5-8d62-4fff-b3de-7449a0f73320","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01PHTDBx9LRqEiYq8o7pBQB9","type":"message","role":"assistant","content":[{"type":"text","text":"## Part 2b: Regenerate GraphQL code"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":342,"cache_read_input_tokens":78956,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":342},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD22zfvXbjtifohPy4nX","type":"assistant","uuid":"9a22b490-9dd3-486a-944f-a178ed23b959","timestamp":"2026-03-19T19:49:24.257Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"9a22b490-9dd3-486a-944f-a178ed23b959","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01PHTDBx9LRqEiYq8o7pBQB9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012kc9GbffMrNyXjHBEfi1SX","name":"Bash","input":{"command":"make graphql 2>&1","description":"Regenerate GraphQL code","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":342,"cache_read_input_tokens":78956,"output_tokens":123,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":342,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD22zfvXbjtifohPy4nX","type":"assistant","uuid":"adc207ce-7b4f-4187-9bb2-6a4cb2979fb2","timestamp":"2026-03-19T19:49:25.114Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"adc207ce-7b4f-4187-9bb2-6a4cb2979fb2","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012kc9GbffMrNyXjHBEfi1SX","type":"tool_result","content":"===> GENERATE graphql","is_error":false}]},"uuid":"2f058943-a6eb-4b49-bb3c-aaa9db2bf8d0","timestamp":"2026-03-19T19:49:37.281Z","toolUseResult":{"stdout":"===> GENERATE graphql","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"adc207ce-7b4f-4187-9bb2-6a4cb2979fb2","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"2f058943-a6eb-4b49-bb3c-aaa9db2bf8d0","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01DhQziuNTykJTKoThoHhWiY","type":"message","role":"assistant","content":[{"type":"text","text":"Good, `make graphql` succeeded. Now let me check what the generated code looks like for the new parameter:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":240,"cache_read_input_tokens":79298,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":240},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD24BiULnrsKvR1AHmUG","type":"assistant","uuid":"d87d29da-39b9-4443-a7d6-54527e109000","timestamp":"2026-03-19T19:49:40.559Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d87d29da-39b9-4443-a7d6-54527e109000","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01DhQziuNTykJTKoThoHhWiY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DBCxQfsNMisgtFd7BjcPUH","name":"Grep","input":{"pattern":"ResampleAlgo","path":"/Users/jan/tmp/cc-backend/internal/graph","output_mode":"content"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":240,"cache_read_input_tokens":79298,"output_tokens":129,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":240,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD24BiULnrsKvR1AHmUG","type":"assistant","uuid":"fe8e646d-3130-4a48-8b71-cb81e60ab948","timestamp":"2026-03-19T19:49:41.326Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"fe8e646d-3130-4a48-8b71-cb81e60ab948","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_01DBCxQfsNMisgtFd7BjcPUH","toolUseID":"toolu_01DBCxQfsNMisgtFd7BjcPUH","timestamp":"2026-03-19T19:49:41.351Z","uuid":"2b64c64d-25a8-4c4b-972d-c62f64f51ef4","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"fe8e646d-3130-4a48-8b71-cb81e60ab948","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DBCxQfsNMisgtFd7BjcPUH","type":"tool_result","content":"internal/graph/schema.resolvers.go:501:func (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\ninternal/graph/schema.resolvers.go:875:func (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error) {\ninternal/graph/model/models_gen.go:331:type ResampleAlgo string\ninternal/graph/model/models_gen.go:334:\tResampleAlgoLttb ResampleAlgo = \"LTTB\"\ninternal/graph/model/models_gen.go:335:\tResampleAlgoAverage ResampleAlgo = \"AVERAGE\"\ninternal/graph/model/models_gen.go:336:\tResampleAlgoSimple ResampleAlgo = \"SIMPLE\"\ninternal/graph/model/models_gen.go:339:var AllResampleAlgo = []ResampleAlgo{\ninternal/graph/model/models_gen.go:340:\tResampleAlgoLttb,\ninternal/graph/model/models_gen.go:341:\tResampleAlgoAverage,\ninternal/graph/model/models_gen.go:342:\tResampleAlgoSimple,\ninternal/graph/model/models_gen.go:345:func (e ResampleAlgo) IsValid() bool {\ninternal/graph/model/models_gen.go:347:\tcase ResampleAlgoLttb, ResampleAlgoAverage, ResampleAlgoSimple:\ninternal/graph/model/models_gen.go:353:func (e ResampleAlgo) String() string {\ninternal/graph/model/models_gen.go:357:func (e *ResampleAlgo) UnmarshalGQL(v any) error {\ninternal/graph/model/models_gen.go:363:\t*e = ResampleAlgo(str)\ninternal/graph/model/models_gen.go:365:\t\treturn fmt.Errorf(\"%s is not a valid ResampleAlgo\", str)\ninternal/graph/model/models_gen.go:370:func (e ResampleAlgo) MarshalGQL(w io.Writer) {\ninternal/graph/model/models_gen.go:374:func (e *ResampleAlgo) UnmarshalJSON(b []byte) error {\ninternal/graph/model/models_gen.go:382:func (e ResampleAlgo) MarshalJSON() ([]byte, error) {\ninternal/graph/generated/generated.go:329:\t\tJobMetrics func(childComplexity int, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) int\ninternal/graph/generated/generated.go:337:\t\tNodeMetricsList func(childComplexity int, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) int\ninternal/graph/generated/generated.go:485:\tJobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error)\ninternal/graph/generated/generated.go:494:\tNodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error)\ninternal/graph/generated/generated.go:1668:\t\treturn e.ComplexityRoot.Query.JobMetrics(childComplexity, args[\"id\"].(string), args[\"metrics\"].([]string), args[\"scopes\"].([]schema.MetricScope), args[\"resolution\"].(*int), args[\"resampleAlgo\"].(*model.ResampleAlgo)), true\ninternal/graph/generated/generated.go:1756:\t\treturn e.ComplexityRoot.Query.NodeMetricsList(childComplexity, args[\"cluster\"].(string), args[\"subCluster\"].(string), args[\"stateFilter\"].(string), args[\"nodeFilter\"].(string), args[\"scopes\"].([]schema.MetricScope), args[\"metrics\"].([]string), args[\"from\"].(time.Time), args[\"to\"].(time.Time), args[\"page\"].(*model.PageRequest), args[\"resolution\"].(*int), args[\"resampleAlgo\"].(*model.ResampleAlgo)), true\ninternal/graph/generated/generated.go:2527:enum ResampleAlgo {\ninternal/graph/generated/generated.go:2623: resampleAlgo: ResampleAlgo\ninternal/graph/generated/generated.go:2683: resampleAlgo: ResampleAlgo\ninternal/graph/generated/generated.go:3017:\targ4, err := graphql.ProcessArgField(ctx, rawArgs, \"resampleAlgo\", ec.unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo)\ninternal/graph/generated/generated.go:3199:\targ10, err := graphql.ProcessArgField(ctx, rawArgs, \"resampleAlgo\", ec.unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo)\ninternal/graph/generated/generated.go:9457:\t\t\treturn ec.Resolvers.Query().JobMetrics(ctx, fc.Args[\"id\"].(string), fc.Args[\"metrics\"].([]string), fc.Args[\"scopes\"].([]schema.MetricScope), fc.Args[\"resolution\"].(*int), fc.Args[\"resampleAlgo\"].(*model.ResampleAlgo))\ninternal/graph/generated/generated.go:9938:\t\t\treturn ec.Resolvers.Query().NodeMetricsList(ctx, fc.Args[\"cluster\"].(string), fc.Args[\"subCluster\"].(string), fc.Args[\"stateFilter\"].(string), fc.Args[\"nodeFilter\"].(string), fc.Args[\"scopes\"].([]schema.MetricScope), fc.Args[\"metrics\"].([]string), fc.Args[\"from\"].(time.Time), fc.Args[\"to\"].(time.Time), fc.Args[\"page\"].(*model.PageRequest), fc.Args[\"resolution\"].(*int), fc.Args[\"resampleAlgo\"].(*model.ResampleAlgo))\ninternal/graph/generated/generated.go:19693:func (ec *executionContext) unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo(ctx context.Context, v any) (*model.ResampleAlgo, error) {\ninternal/graph/generated/generated.go:19697:\tvar res = new(model.ResampleAlgo)\ninternal/graph/generated/generated.go:19702:func (ec *executionContext) marshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo(ctx context.Context, sel ast.SelectionSet, v *model.ResampleAlgo) graphql.Marshaler {"}]},"uuid":"32d58324-b391-4673-96b6-996bd4a662ee","timestamp":"2026-03-19T19:49:41.351Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"internal/graph/schema.resolvers.go:501:func (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\ninternal/graph/schema.resolvers.go:875:func (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error) {\ninternal/graph/model/models_gen.go:331:type ResampleAlgo string\ninternal/graph/model/models_gen.go:334:\tResampleAlgoLttb ResampleAlgo = \"LTTB\"\ninternal/graph/model/models_gen.go:335:\tResampleAlgoAverage ResampleAlgo = \"AVERAGE\"\ninternal/graph/model/models_gen.go:336:\tResampleAlgoSimple ResampleAlgo = \"SIMPLE\"\ninternal/graph/model/models_gen.go:339:var AllResampleAlgo = []ResampleAlgo{\ninternal/graph/model/models_gen.go:340:\tResampleAlgoLttb,\ninternal/graph/model/models_gen.go:341:\tResampleAlgoAverage,\ninternal/graph/model/models_gen.go:342:\tResampleAlgoSimple,\ninternal/graph/model/models_gen.go:345:func (e ResampleAlgo) IsValid() bool {\ninternal/graph/model/models_gen.go:347:\tcase ResampleAlgoLttb, ResampleAlgoAverage, ResampleAlgoSimple:\ninternal/graph/model/models_gen.go:353:func (e ResampleAlgo) String() string {\ninternal/graph/model/models_gen.go:357:func (e *ResampleAlgo) UnmarshalGQL(v any) error {\ninternal/graph/model/models_gen.go:363:\t*e = ResampleAlgo(str)\ninternal/graph/model/models_gen.go:365:\t\treturn fmt.Errorf(\"%s is not a valid ResampleAlgo\", str)\ninternal/graph/model/models_gen.go:370:func (e ResampleAlgo) MarshalGQL(w io.Writer) {\ninternal/graph/model/models_gen.go:374:func (e *ResampleAlgo) UnmarshalJSON(b []byte) error {\ninternal/graph/model/models_gen.go:382:func (e ResampleAlgo) MarshalJSON() ([]byte, error) {\ninternal/graph/generated/generated.go:329:\t\tJobMetrics func(childComplexity int, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) int\ninternal/graph/generated/generated.go:337:\t\tNodeMetricsList func(childComplexity int, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) int\ninternal/graph/generated/generated.go:485:\tJobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error)\ninternal/graph/generated/generated.go:494:\tNodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error)\ninternal/graph/generated/generated.go:1668:\t\treturn e.ComplexityRoot.Query.JobMetrics(childComplexity, args[\"id\"].(string), args[\"metrics\"].([]string), args[\"scopes\"].([]schema.MetricScope), args[\"resolution\"].(*int), args[\"resampleAlgo\"].(*model.ResampleAlgo)), true\ninternal/graph/generated/generated.go:1756:\t\treturn e.ComplexityRoot.Query.NodeMetricsList(childComplexity, args[\"cluster\"].(string), args[\"subCluster\"].(string), args[\"stateFilter\"].(string), args[\"nodeFilter\"].(string), args[\"scopes\"].([]schema.MetricScope), args[\"metrics\"].([]string), args[\"from\"].(time.Time), args[\"to\"].(time.Time), args[\"page\"].(*model.PageRequest), args[\"resolution\"].(*int), args[\"resampleAlgo\"].(*model.ResampleAlgo)), true\ninternal/graph/generated/generated.go:2527:enum ResampleAlgo {\ninternal/graph/generated/generated.go:2623: resampleAlgo: ResampleAlgo\ninternal/graph/generated/generated.go:2683: resampleAlgo: ResampleAlgo\ninternal/graph/generated/generated.go:3017:\targ4, err := graphql.ProcessArgField(ctx, rawArgs, \"resampleAlgo\", ec.unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo)\ninternal/graph/generated/generated.go:3199:\targ10, err := graphql.ProcessArgField(ctx, rawArgs, \"resampleAlgo\", ec.unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo)\ninternal/graph/generated/generated.go:9457:\t\t\treturn ec.Resolvers.Query().JobMetrics(ctx, fc.Args[\"id\"].(string), fc.Args[\"metrics\"].([]string), fc.Args[\"scopes\"].([]schema.MetricScope), fc.Args[\"resolution\"].(*int), fc.Args[\"resampleAlgo\"].(*model.ResampleAlgo))\ninternal/graph/generated/generated.go:9938:\t\t\treturn ec.Resolvers.Query().NodeMetricsList(ctx, fc.Args[\"cluster\"].(string), fc.Args[\"subCluster\"].(string), fc.Args[\"stateFilter\"].(string), fc.Args[\"nodeFilter\"].(string), fc.Args[\"scopes\"].([]schema.MetricScope), fc.Args[\"metrics\"].([]string), fc.Args[\"from\"].(time.Time), fc.Args[\"to\"].(time.Time), fc.Args[\"page\"].(*model.PageRequest), fc.Args[\"resolution\"].(*int), fc.Args[\"resampleAlgo\"].(*model.ResampleAlgo))\ninternal/graph/generated/generated.go:19693:func (ec *executionContext) unmarshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo(ctx context.Context, v any) (*model.ResampleAlgo, error) {\ninternal/graph/generated/generated.go:19697:\tvar res = new(model.ResampleAlgo)\ninternal/graph/generated/generated.go:19702:func (ec *executionContext) marshalOResampleAlgo2ᚖgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋinternalᚋgraphᚋmodelᚐResampleAlgo(ctx context.Context, sel ast.SelectionSet, v *model.ResampleAlgo) graphql.Marshaler {","numLines":35},"sourceToolAssistantUUID":"fe8e646d-3130-4a48-8b71-cb81e60ab948","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"32d58324-b391-4673-96b6-996bd4a662ee","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_019QymyuXoMtUByfqkQARqsP","type":"message","role":"assistant","content":[{"type":"text","text":"The code generation worked. The resolvers already have the new `resampleAlgo *model.ResampleAlgo` parameter. Now I need to:\n\n1. Update the resolvers to pass the algorithm through\n2. Update `LoadData` and `LoadNodeListData` in dataLoader.go\n3. Update `MemoryStore.Read` in metricstore.go\n4. Update `LoadNodeListData` in query.go\n5. Update cache key\n\nLet me create a helper to convert the enum to string, then thread it through. Let me read the current resolver code more carefully:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2128,"cache_read_input_tokens":79538,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2128},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD24V7yYBLdo2x5HbzNN","type":"assistant","uuid":"70163587-35ec-4f70-8dbf-504fbed5a4aa","timestamp":"2026-03-19T19:49:47.930Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"70163587-35ec-4f70-8dbf-504fbed5a4aa","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_019QymyuXoMtUByfqkQARqsP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KUPcbufnB5bLz7KE4hFvhG","name":"Read","input":{"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","offset":500,"limit":50},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2128,"cache_read_input_tokens":79538,"output_tokens":238,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2128,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD24V7yYBLdo2x5HbzNN","type":"assistant","uuid":"7c8cb975-46c6-4381-b8e5-753b7f235ee8","timestamp":"2026-03-19T19:49:48.495Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"7c8cb975-46c6-4381-b8e5-753b7f235ee8","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01KUPcbufnB5bLz7KE4hFvhG","toolUseID":"toolu_01KUPcbufnB5bLz7KE4hFvhG","timestamp":"2026-03-19T19:49:48.508Z","uuid":"460d842c-c9a9-4d6e-963b-cf510941b16b","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"7c8cb975-46c6-4381-b8e5-753b7f235ee8","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KUPcbufnB5bLz7KE4hFvhG","type":"tool_result","content":" 500→// JobMetrics is the resolver for the jobMetrics field.\n 501→func (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\n 502→\tif resolution == nil { // Load from Config\n 503→\t\tif config.Keys.EnableResampling != nil {\n 504→\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n 505→\t\t\tresolution = &defaultRes\n 506→\t\t} else { // Set 0 (Loads configured metric timestep)\n 507→\t\t\tdefaultRes := 0\n 508→\t\t\tresolution = &defaultRes\n 509→\t\t}\n 510→\t}\n 511→\n 512→\tjob, err := r.Query().Job(ctx, id)\n 513→\tif err != nil {\n 514→\t\tcclog.Warn(\"Error while querying job for metrics\")\n 515→\t\treturn nil, err\n 516→\t}\n 517→\n 518→\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n 519→\tif err != nil {\n 520→\t\tcclog.Warn(\"Error while loading job data\")\n 521→\t\treturn nil, err\n 522→\t}\n 523→\n 524→\tres := []*model.JobMetricWithName{}\n 525→\tfor name, md := range data {\n 526→\t\tfor scope, metric := range md {\n 527→\t\t\tres = append(res, &model.JobMetricWithName{\n 528→\t\t\t\tName: name,\n 529→\t\t\t\tScope: scope,\n 530→\t\t\t\tMetric: metric,\n 531→\t\t\t})\n 532→\t\t}\n 533→\t}\n 534→\n 535→\treturn res, err\n 536→}\n 537→\n 538→// JobStats is the resolver for the jobStats field.\n 539→func (r *queryResolver) JobStats(ctx context.Context, id string, metrics []string) ([]*model.NamedStats, error) {\n 540→\tjob, err := r.Query().Job(ctx, id)\n 541→\tif err != nil {\n 542→\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n 543→\t\treturn nil, err\n 544→\t}\n 545→\n 546→\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n 547→\tif err != nil {\n 548→\t\tcclog.Warnf(\"Error while loading jobStats data for job id %s\", id)\n 549→\t\treturn nil, err"}]},"uuid":"ca216301-42d8-4095-ae46-ae99876cf51f","timestamp":"2026-03-19T19:49:48.508Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","content":"// JobMetrics is the resolver for the jobMetrics field.\nfunc (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying job for metrics\")\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &model.JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, err\n}\n\n// JobStats is the resolver for the jobStats field.\nfunc (r *queryResolver) JobStats(ctx context.Context, id string, metrics []string) ([]*model.NamedStats, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading jobStats data for job id %s\", id)\n\t\treturn nil, err","numLines":50,"startLine":500,"totalLines":1077}},"sourceToolAssistantUUID":"7c8cb975-46c6-4381-b8e5-753b7f235ee8","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"ca216301-42d8-4095-ae46-ae99876cf51f","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Tk3ApYdNSvpo6fLQBDL2qF","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me update all the files. I'll convert the enum to a lowercase string for the resampler lookup.\n\n## Part 2c: Update resolvers"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":931,"cache_read_input_tokens":81666,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":931},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD251pw4diF2drau5PZk","type":"assistant","uuid":"9e0034c9-4e05-427a-baf0-aa0991a3fe5f","timestamp":"2026-03-19T19:49:52.224Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"9e0034c9-4e05-427a-baf0-aa0991a3fe5f","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Tk3ApYdNSvpo6fLQBDL2qF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CXpPa7GqGsEUjrBaFogC7g","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","old_string":"\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}","new_string":"\talgoName := \"\"\n\tif resampleAlgo != nil {\n\t\talgoName = strings.ToLower(resampleAlgo.String())\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution, algoName)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":931,"cache_read_input_tokens":81666,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":931},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD251pw4diF2drau5PZk","type":"assistant","uuid":"c84bd7d5-2ecb-4bed-9b4f-bd62686d4716","timestamp":"2026-03-19T19:49:54.697Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"c84bd7d5-2ecb-4bed-9b4f-bd62686d4716","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01CXpPa7GqGsEUjrBaFogC7g","toolUseID":"toolu_01CXpPa7GqGsEUjrBaFogC7g","timestamp":"2026-03-19T19:49:54.711Z","uuid":"e2315aff-61a9-4df2-8491-fcb299412f83","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"c84bd7d5-2ecb-4bed-9b4f-bd62686d4716","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CXpPa7GqGsEUjrBaFogC7g","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go has been updated successfully."}]},"uuid":"1e5770e8-63cd-4d29-9080-8c96cb42d093","timestamp":"2026-03-19T19:49:54.711Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","oldString":"\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}","newString":"\talgoName := \"\"\n\tif resampleAlgo != nil {\n\t\talgoName = strings.ToLower(resampleAlgo.String())\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution, algoName)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}","originalFile":"package graph\n\n// This file will be automatically regenerated based on the schema, any resolver\n// implementations\n// will be copied through when generating and any unknown code will be moved to the end.\n// Code generated by github.com/99designs/gqlgen version v0.17.88\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\t\"regexp\"\n\t\"slices\"\n\t\"strconv\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/generated\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\tccunit \"github.com/ClusterCockpit/cc-lib/v2/ccUnits\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// Partitions is the resolver for the partitions field.\nfunc (r *clusterResolver) Partitions(ctx context.Context, obj *schema.Cluster) ([]string, error) {\n\treturn r.Repo.Partitions(obj.Name)\n}\n\n// StartTime is the resolver for the startTime field.\nfunc (r *jobResolver) StartTime(ctx context.Context, obj *schema.Job) (*time.Time, error) {\n\ttimestamp := time.Unix(obj.StartTime, 0)\n\treturn &timestamp, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *jobResolver) Tags(ctx context.Context, obj *schema.Job) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), obj.ID)\n}\n\n// ConcurrentJobs is the resolver for the concurrentJobs field.\nfunc (r *jobResolver) ConcurrentJobs(ctx context.Context, obj *schema.Job) (*model.JobLinkResultList, error) {\n\t// FIXME: Make the hardcoded duration configurable\n\tif obj.Shared != \"none\" && obj.Duration > 600 {\n\t\treturn r.Repo.FindConcurrentJobs(ctx, obj)\n\t}\n\n\treturn nil, nil\n}\n\n// Footprint is the resolver for the footprint field.\nfunc (r *jobResolver) Footprint(ctx context.Context, obj *schema.Job) ([]*model.FootprintValue, error) {\n\trawFootprint, err := r.Repo.FetchFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.FootprintValue{}\n\tfor name, value := range rawFootprint {\n\n\t\tparts := strings.Split(name, \"_\")\n\t\tstatPart := parts[len(parts)-1]\n\t\tnameParts := parts[:len(parts)-1]\n\n\t\tres = append(res, &model.FootprintValue{\n\t\t\tName: strings.Join(nameParts, \"_\"),\n\t\t\tStat: statPart,\n\t\t\tValue: value,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// EnergyFootprint is the resolver for the energyFootprint field.\nfunc (r *jobResolver) EnergyFootprint(ctx context.Context, obj *schema.Job) ([]*model.EnergyFootprintValue, error) {\n\trawEnergyFootprint, err := r.Repo.FetchEnergyFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job energy footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.EnergyFootprintValue{}\n\tfor name, value := range rawEnergyFootprint {\n\t\t// Suboptimal: Nearly hardcoded metric name expectations\n\t\tmatchCPU := regexp.MustCompile(`cpu|Cpu|CPU`)\n\t\tmatchAcc := regexp.MustCompile(`acc|Acc|ACC`)\n\t\tmatchMem := regexp.MustCompile(`mem|Mem|MEM`)\n\t\tmatchCore := regexp.MustCompile(`core|Core|CORE`)\n\n\t\thwType := \"\"\n\t\tswitch test := name; { // NOtice ';' for var declaration\n\t\tcase matchCPU.MatchString(test):\n\t\t\thwType = \"CPU\"\n\t\tcase matchAcc.MatchString(test):\n\t\t\thwType = \"Accelerator\"\n\t\tcase matchMem.MatchString(test):\n\t\t\thwType = \"Memory\"\n\t\tcase matchCore.MatchString(test):\n\t\t\thwType = \"Core\"\n\t\tdefault:\n\t\t\thwType = \"Other\"\n\t\t}\n\n\t\tres = append(res, &model.EnergyFootprintValue{\n\t\t\tHardware: hwType,\n\t\t\tMetric: name,\n\t\t\tValue: value,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *jobResolver) MetaData(ctx context.Context, obj *schema.Job) (any, error) {\n\treturn r.Repo.FetchMetadata(obj)\n}\n\n// UserData is the resolver for the userData field.\nfunc (r *jobResolver) UserData(ctx context.Context, obj *schema.Job) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, obj.User)\n}\n\n// Name is the resolver for the name field.\nfunc (r *metricValueResolver) Name(ctx context.Context, obj *schema.MetricValue) (*string, error) {\n\tpanic(fmt.Errorf(\"not implemented: Name - name\"))\n}\n\n// CreateTag is the resolver for the createTag field.\nfunc (r *mutationResolver) CreateTag(ctx context.Context, typeArg string, name string, scope string) (*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\tif user.HasRole(schema.RoleAdmin) && scope == \"admin\" ||\n\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && scope == \"global\" ||\n\t\tuser.Username == scope {\n\t\t// Create in DB\n\t\tid, err := r.Repo.CreateTag(typeArg, name, scope)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while creating tag\")\n\t\t\treturn nil, err\n\t\t}\n\t\treturn &schema.Tag{ID: id, Type: typeArg, Name: name, Scope: scope}, nil\n\t} else {\n\t\tcclog.Warnf(\"Not authorized to create tag with scope: %s\", scope)\n\t\treturn nil, fmt.Errorf(\"not authorized to create tag with scope: %s\", scope)\n\t}\n}\n\n// DeleteTag is the resolver for the deleteTag field.\nfunc (r *mutationResolver) DeleteTag(ctx context.Context, id string) (string, error) {\n\t// This Uses ID string <-> ID string, removeTagFromList uses []string <-> []int\n\tpanic(fmt.Errorf(\"not implemented: DeleteTag - deleteTag\"))\n}\n\n// AddTagsToJob is the resolver for the addTagsToJob field.\nfunc (r *mutationResolver) AddTagsToJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while adding tag to job\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Add to Job\n\t\t\tif tags, err = r.Repo.AddTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while adding tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to add tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to add tag: %d\", tid)\n\t\t}\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagsFromJob is the resolver for the removeTagsFromJob field.\nfunc (r *mutationResolver) RemoveTagsFromJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Remove from Job\n\t\t\tif tags, err = r.Repo.RemoveTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagFromList is the resolver for the removeTagFromList field.\nfunc (r *mutationResolver) RemoveTagFromList(ctx context.Context, tagIds []string) ([]int, error) {\n\t// Needs Contextuser\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\ttags := []int{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id for removal\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && (tscope == \"global\" || tscope == \"admin\") || user.Username == tscope {\n\t\t\t// Remove from DB\n\t\t\tif err = r.Repo.RemoveTagByID(tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t} else {\n\t\t\t\ttags = append(tags, int(tid))\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\t}\n\treturn tags, nil\n}\n\n// UpdateConfiguration is the resolver for the updateConfiguration field.\nfunc (r *mutationResolver) UpdateConfiguration(ctx context.Context, name string, value string) (*string, error) {\n\tif err := repository.GetUserCfgRepo().UpdateConfig(name, value, repository.GetUserFromContext(ctx)); err != nil {\n\t\tcclog.Warn(\"Error while updating user config\")\n\t\treturn nil, err\n\t}\n\n\treturn nil, nil\n}\n\n// ID is the resolver for the id field.\nfunc (r *nodeResolver) ID(ctx context.Context, obj *schema.Node) (string, error) {\n\tpanic(fmt.Errorf(\"not implemented: ID - id\"))\n}\n\n// SchedulerState is the resolver for the schedulerState field.\nfunc (r *nodeResolver) SchedulerState(ctx context.Context, obj *schema.Node) (schema.SchedulerState, error) {\n\tif obj.NodeState != \"\" {\n\t\treturn obj.NodeState, nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no SchedulerState (NodeState) on node object\")\n\t}\n}\n\n// HealthState is the resolver for the healthState field.\nfunc (r *nodeResolver) HealthState(ctx context.Context, obj *schema.Node) (string, error) {\n\tif obj.HealthState != \"\" {\n\t\treturn string(obj.HealthState), nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no HealthState (NodeState) on node object\")\n\t}\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *nodeResolver) MetaData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.MetaData != nil {\n\t\treturn obj.MetaData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no MetaData (NodeState) on node object\")\n\t\temptyMeta := make(map[string]string, 0)\n\t\treturn emptyMeta, nil\n\t}\n}\n\n// HealthData is the resolver for the healthData field.\nfunc (r *nodeResolver) HealthData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.HealthData != nil {\n\t\treturn obj.HealthData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no HealthData (NodeState) on node object\")\n\t\temptyHealth := make(map[string][]string, 0)\n\t\treturn emptyHealth, nil\n\t}\n}\n\n// Clusters is the resolver for the clusters field.\nfunc (r *queryResolver) Clusters(ctx context.Context) ([]*schema.Cluster, error) {\n\treturn archive.Clusters, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *queryResolver) Tags(ctx context.Context) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), nil)\n}\n\n// GlobalMetrics is the resolver for the globalMetrics field.\nfunc (r *queryResolver) GlobalMetrics(ctx context.Context) ([]*schema.GlobalMetricListItem, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\n\tif user != nil {\n\t\tif user.HasRole(schema.RoleUser) || user.HasRole(schema.RoleManager) {\n\t\t\treturn archive.GlobalUserMetricList, nil\n\t\t}\n\t}\n\n\treturn archive.GlobalMetricList, nil\n}\n\n// User is the resolver for the user field.\nfunc (r *queryResolver) User(ctx context.Context, username string) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, username)\n}\n\n// AllocatedNodes is the resolver for the allocatedNodes field.\nfunc (r *queryResolver) AllocatedNodes(ctx context.Context, cluster string) ([]*model.Count, error) {\n\tdata, err := r.Repo.AllocatedNodes(cluster)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching allocated nodes\")\n\t\treturn nil, err\n\t}\n\n\tcounts := make([]*model.Count, 0, len(data))\n\tfor subcluster, hosts := range data {\n\t\tcounts = append(counts, &model.Count{\n\t\t\tName: subcluster,\n\t\t\tCount: len(hosts),\n\t\t})\n\t}\n\n\treturn counts, nil\n}\n\n// Node is the resolver for the node field.\nfunc (r *queryResolver) Node(ctx context.Context, id string) (*schema.Node, error) {\n\trepo := repository.GetNodeRepository()\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\treturn repo.GetNodeByID(numericID, false)\n}\n\n// Nodes is the resolver for the nodes field.\nfunc (r *queryResolver) Nodes(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodes(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodesWithMeta is the resolver for the nodesWithMeta field.\nfunc (r *queryResolver) NodesWithMeta(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\t// Why Extra Handler? -> graphql.CollectAllFields(ctx) only returns toplevel fields (i.e.: items, count), and not subfields like item.metaData\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodesWithMeta(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodeStates is the resolver for the nodeStates field.\nfunc (r *queryResolver) NodeStates(ctx context.Context, filter []*model.NodeFilter) ([]*model.NodeStates, error) {\n\trepo := repository.GetNodeRepository()\n\n\tstateCounts, serr := repo.CountStates(ctx, filter, \"node_state\")\n\tif serr != nil {\n\t\tcclog.Warnf(\"Error while counting nodeStates: %s\", serr.Error())\n\t\treturn nil, serr\n\t}\n\n\thealthCounts, herr := repo.CountStates(ctx, filter, \"health_state\")\n\tif herr != nil {\n\t\tcclog.Warnf(\"Error while counting healthStates: %s\", herr.Error())\n\t\treturn nil, herr\n\t}\n\n\tallCounts := append(stateCounts, healthCounts...)\n\n\treturn allCounts, nil\n}\n\n// NodeStatesTimed is the resolver for the nodeStatesTimed field.\nfunc (r *queryResolver) NodeStatesTimed(ctx context.Context, filter []*model.NodeFilter, typeArg string) ([]*model.NodeStatesTimed, error) {\n\trepo := repository.GetNodeRepository()\n\n\tif typeArg == \"node\" {\n\t\tstateCounts, serr := repo.CountStatesTimed(ctx, filter, \"node_state\")\n\t\tif serr != nil {\n\t\t\tcclog.Warnf(\"Error while counting nodeStates in time: %s\", serr.Error())\n\t\t\treturn nil, serr\n\t\t}\n\t\treturn stateCounts, nil\n\t}\n\n\tif typeArg == \"health\" {\n\t\thealthCounts, herr := repo.CountStatesTimed(ctx, filter, \"health_state\")\n\t\tif herr != nil {\n\t\t\tcclog.Warnf(\"Error while counting healthStates in time: %s\", herr.Error())\n\t\t\treturn nil, herr\n\t\t}\n\t\treturn healthCounts, nil\n\t}\n\n\treturn nil, errors.New(\"unknown Node State Query Type\")\n}\n\n// Job is the resolver for the job field.\nfunc (r *queryResolver) Job(ctx context.Context, id string) (*schema.Job, error) {\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\tjob, err := r.Repo.FindByID(ctx, numericID)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while finding job by id\")\n\t\treturn nil, err\n\t}\n\n\tif user := repository.GetUserFromContext(ctx); user != nil &&\n\t\tjob.User != user.Username &&\n\t\tuser.HasNotRoles([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\treturn nil, errors.New(\"you are not allowed to see this job\")\n\t}\n\n\treturn job, nil\n}\n\n// JobMetrics is the resolver for the jobMetrics field.\nfunc (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying job for metrics\")\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &model.JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, err\n}\n\n// JobStats is the resolver for the jobStats field.\nfunc (r *queryResolver) JobStats(ctx context.Context, id string, metrics []string) ([]*model.NamedStats, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading jobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := []*model.NamedStats{}\n\tfor name, md := range data {\n\t\tres = append(res, &model.NamedStats{\n\t\t\tName: name,\n\t\t\tData: &md,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// ScopedJobStats is the resolver for the scopedJobStats field.\nfunc (r *queryResolver) ScopedJobStats(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope) ([]*model.NamedStatsWithScope, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadScopedJobStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading scopedJobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := make([]*model.NamedStatsWithScope, 0)\n\tfor name, scoped := range data {\n\t\tfor scope, stats := range scoped {\n\n\t\t\tmdlStats := make([]*model.ScopedStats, 0)\n\t\t\tfor _, stat := range stats {\n\t\t\t\tmdlStats = append(mdlStats, &model.ScopedStats{\n\t\t\t\t\tHostname: stat.Hostname,\n\t\t\t\t\tID: stat.ID,\n\t\t\t\t\tData: stat.Data,\n\t\t\t\t})\n\t\t\t}\n\n\t\t\tres = append(res, &model.NamedStatsWithScope{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tStats: mdlStats,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, nil\n}\n\n// Jobs is the resolver for the jobs field.\nfunc (r *queryResolver) Jobs(ctx context.Context, filter []*model.JobFilter, page *model.PageRequest, order *model.OrderByInput) (*model.JobResultList, error) {\n\tif page == nil {\n\t\tpage = &model.PageRequest{\n\t\t\tItemsPerPage: 50,\n\t\t\tPage: 1,\n\t\t}\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, page, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs\")\n\t\treturn nil, err\n\t}\n\n\tcount, err := r.Repo.CountJobs(ctx, filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while counting jobs\")\n\t\treturn nil, err\n\t}\n\n\t// Note: Even if App-Default 'config.Keys.UiDefaults[\"job_list_usePaging\"]' is set, always return hasNextPage boolean.\n\t// Users can decide in frontend to use continuous scroll, even if app-default is paging!\n\t// Skip if page.ItemsPerPage == -1 (\"Load All\" -> No Next Page required, Status Dashboards)\n\t/*\n\t Example Page 4 @ 10 IpP : Does item 41 exist?\n\t Minimal Page 41 @ 1 IpP : If len(result) is 1, Page 5 @ 10 IpP exists.\n\t*/\n\thasNextPage := false\n\tif page.ItemsPerPage != -1 {\n\t\tnextPage := &model.PageRequest{\n\t\t\tItemsPerPage: 1,\n\t\t\tPage: ((page.Page * page.ItemsPerPage) + 1),\n\t\t}\n\t\tnextJobs, err := r.Repo.QueryJobs(ctx, filter, nextPage, order)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while querying next jobs\")\n\t\t\treturn nil, err\n\t\t}\n\t\thasNextPage = len(nextJobs) == 1\n\t}\n\n\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n}\n\n// JobsStatistics is the resolver for the jobsStatistics field.\nfunc (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n\tvar err error\n\tvar stats []*model.JobsStatistics\n\n\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\t// Build requested fields map for selective column computation\n\tstatsFields := []string{\n\t\t\"totalJobs\", \"totalUsers\", \"totalWalltime\", \"totalNodes\", \"totalCores\",\n\t\t\"totalAccs\", \"totalNodeHours\", \"totalCoreHours\", \"totalAccHours\", \"runningJobs\", \"shortJobs\",\n\t}\n\treqFields := make(map[string]bool, len(statsFields))\n\tfetchedMainStats := false\n\tfor _, f := range statsFields {\n\t\tif requireField(ctx, f) {\n\t\t\treqFields[f] = true\n\t\t\tif f != \"runningJobs\" && f != \"shortJobs\" {\n\t\t\t\tfetchedMainStats = true\n\t\t\t}\n\t\t}\n\t}\n\n\tif fetchedMainStats {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter, reqFields)\n\t\t} else {\n\t\t\tstartGrouped := time.Now()\n\t\t\t// Use request-scoped cache: multiple aliases with same (filter, groupBy)\n\t\t\t// but different sortBy/page hit the DB only once.\n\t\t\tif cache := getStatsGroupCache(ctx); cache != nil {\n\t\t\t\tkey := statsCacheKey(filter, groupBy, reqFields)\n\t\t\t\tvar allStats []*model.JobsStatistics\n\t\t\t\tallStats, err = cache.getOrCompute(key, func() ([]*model.JobsStatistics, error) {\n\t\t\t\t\treturn r.Repo.JobsStatsGrouped(ctx, filter, nil, nil, groupBy, reqFields)\n\t\t\t\t})\n\t\t\t\tif err == nil {\n\t\t\t\t\tstats = sortAndPageStats(allStats, sortBy, page)\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n\t\t\t}\n\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n\t// Only run separate count queries if main stats were not fetched.\n\tif !fetchedMainStats {\n\t\tif groupBy != nil {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t\t}\n\t\t} else {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n\n\t\tif numDurationBins == nil {\n\t\t\tnumDurationBins = &defaultDurationBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\tif requireField(ctx, \"histMetrics\") {\n\n\t\tif numMetricBins == nil {\n\t\t\tnumMetricBins = &defaultMetricBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// JobsMetricStats is the resolver for the jobsMetricStats field.\nfunc (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n\t// No Paging, Fixed Order by StartTime ASC\n\torder := &model.OrderByInput{\n\t\tField: \"startTime\",\n\t\tType: \"col\",\n\t\tOrder: \"ASC\",\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobStats{}\n\tfor _, job := range jobs {\n\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading comparison jobStats data for job id %d\", job.JobID)\n\t\t\tcontinue\n\t\t\t// return nil, err\n\t\t}\n\n\t\tsres := []*model.NamedStats{}\n\t\tfor name, md := range data {\n\t\t\tsres = append(sres, &model.NamedStats{\n\t\t\t\tName: name,\n\t\t\t\tData: &md,\n\t\t\t})\n\t\t}\n\n\t\tnumThreadsInt := int(job.NumHWThreads)\n\t\tnumAccsInt := int(job.NumAcc)\n\t\tres = append(res, &model.JobStats{\n\t\t\tID: int(*job.ID),\n\t\t\tJobID: strconv.Itoa(int(job.JobID)),\n\t\t\tStartTime: int(job.StartTime),\n\t\t\tDuration: int(job.Duration),\n\t\t\tCluster: job.Cluster,\n\t\t\tSubCluster: job.SubCluster,\n\t\t\tNumNodes: int(job.NumNodes),\n\t\t\tNumHWThreads: &numThreadsInt,\n\t\t\tNumAccelerators: &numAccsInt,\n\t\t\tStats: sres,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// JobsFootprints is the resolver for the jobsFootprints field.\nfunc (r *queryResolver) JobsFootprints(ctx context.Context, filter []*model.JobFilter, metrics []string) (*model.Footprints, error) {\n\t// NOTE: Legacy Naming! This resolver is for normalized histograms in analysis view only - *Not* related to DB \"footprint\" column!\n\treturn r.jobsFootprints(ctx, filter, metrics)\n}\n\n// RooflineHeatmap is the resolver for the rooflineHeatmap field.\nfunc (r *queryResolver) RooflineHeatmap(ctx context.Context, filter []*model.JobFilter, rows int, cols int, minX float64, minY float64, maxX float64, maxY float64) ([][]float64, error) {\n\treturn r.rooflineHeatmap(ctx, filter, rows, cols, minX, minY, maxX, maxY)\n}\n\n// NodeMetrics is the resolver for the nodeMetrics field.\nfunc (r *queryResolver) NodeMetrics(ctx context.Context, cluster string, nodes []string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time) ([]*model.NodeMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tdefaultMetrics := make([]string, 0)\n\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\tdefaultMetrics = append(defaultMetrics, mc.Name)\n\t}\n\tif metrics == nil {\n\t\tmetrics = defaultMetrics\n\t} else {\n\t\tmetrics = slices.DeleteFunc(metrics, func(metric string) bool {\n\t\t\treturn !slices.Contains(defaultMetrics, metric) // Remove undefined metrics.\n\t\t})\n\t}\n\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\tnodeStateMap, metricHealthMap, _ := nodeRepo.MapNodes(cluster)\n\n\tnodeMetrics := make([]*model.NodeMetrics, 0, len(data))\n\tfor hostname, metrics := range data {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost: hostname,\n\t\t\tNodeState: nodeStateMap[hostname],\n\t\t\tMetricHealth: metricHealthMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0, len(metrics)*len(scopes)),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: schema.MetricScopeNode,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetrics = append(nodeMetrics, host)\n\t}\n\n\treturn nodeMetrics, nil\n}\n\n// NodeMetricsList is the resolver for the nodeMetricsList field.\nfunc (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\t// nodes -> array hostname\n\tnodes, nodeStateMap, metricHealthMap, countNodes, hasNextPage, nerr := nodeRepo.GetNodesForList(ctx, cluster, subCluster, stateFilter, nodeFilter, page)\n\tif nerr != nil {\n\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}\n\n\tnodeMetricsList := make([]*model.NodeMetrics, 0, len(data))\n\tfor _, hostname := range nodes {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost: hostname,\n\t\t\tNodeState: nodeStateMap[hostname],\n\t\t\tMetricHealth: metricHealthMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range data[hostname] {\n\t\t\tfor scope, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: scope,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetricsList = append(nodeMetricsList, host)\n\t}\n\n\tnodeMetricsListResult := &model.NodesResultList{\n\t\tItems: nodeMetricsList,\n\t\t// TotalNodes depends on sum of nodes grouped on latest timestamp, see repo/node.go:357\n\t\tTotalNodes: &countNodes,\n\t\tHasNextPage: &hasNextPage,\n\t}\n\n\treturn nodeMetricsListResult, nil\n}\n\n// ClusterMetrics is the resolver for the clusterMetrics field.\nfunc (r *queryResolver) ClusterMetrics(ctx context.Context, cluster string, metrics []string, from time.Time, to time.Time) (*model.ClusterMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// 'nodes' == nil -> Defaults to all nodes of cluster for existing query workflow\n\tscopes := []schema.MetricScope{\"node\"}\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nil, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tclusterMetricData := make([]*model.ClusterMetricWithName, 0)\n\tclusterMetrics := model.ClusterMetrics{NodeCount: 0, Metrics: clusterMetricData}\n\n\tcollectorTimestep := make(map[string]int)\n\tcollectorUnit := make(map[string]schema.Unit)\n\tcollectorData := make(map[string][]schema.Float)\n\n\tfor _, metrics := range data {\n\t\tclusterMetrics.NodeCount += 1\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\t// Collect Info Once\n\t\t\t\t_, okTimestep := collectorTimestep[metric]\n\t\t\t\tif !okTimestep {\n\t\t\t\t\tcollectorTimestep[metric] = scopedMetric.Timestep\n\t\t\t\t}\n\t\t\t\t_, okUnit := collectorUnit[metric]\n\t\t\t\tif !okUnit {\n\t\t\t\t\tcollectorUnit[metric] = scopedMetric.Unit\n\t\t\t\t}\n\t\t\t\t// Collect Data\n\t\t\t\tfor _, ser := range scopedMetric.Series {\n\t\t\t\t\t_, okData := collectorData[metric]\n\t\t\t\t\t// Init With Datasize > 0\n\t\t\t\t\tif !okData && len(ser.Data) != 0 {\n\t\t\t\t\t\tcollectorData[metric] = make([]schema.Float, len(ser.Data))\n\t\t\t\t\t} else if !okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip init: no data -> %s at %s; size %d\", metric, ser.Hostname, len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t\t// Sum if init'd and matching size\n\t\t\t\t\tif okData && len(ser.Data) == len(collectorData[metric]) {\n\t\t\t\t\t\tfor i, val := range ser.Data {\n\t\t\t\t\t\t\tif val.IsNaN() {\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcollectorData[metric][i] += val\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} else if okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip sum: data diff -> %s at %s; want size %d, have size %d\", metric, ser.Hostname, len(collectorData[metric]), len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tfor metricName, data := range collectorData {\n\t\t// use ccUnits for backend normalization to \"Tera\"\n\t\tp_old := ccunit.NewPrefix(collectorUnit[metricName].Prefix)\n\t\tp_new := ccunit.NewPrefix(\"T\")\n\t\tconvFunc := ccunit.GetPrefixPrefixFactor(p_old, p_new)\n\t\tu_new := schema.Unit{Prefix: p_new.Prefix(), Base: collectorUnit[metricName].Base}\n\n\t\troundedData := make([]schema.Float, 0)\n\t\tfor _, v_old := range data {\n\t\t\tv_new := math.Round(convFunc(float64(v_old)).(float64)*100.0) / 100.0\n\t\t\troundedData = append(roundedData, schema.Float(v_new))\n\t\t}\n\n\t\tcm := model.ClusterMetricWithName{\n\t\t\tName: metricName,\n\t\t\tUnit: &u_new,\n\t\t\tTimestep: collectorTimestep[metricName],\n\t\t\tData: roundedData,\n\t\t}\n\n\t\tclusterMetrics.Metrics = append(clusterMetrics.Metrics, &cm)\n\t}\n\n\treturn &clusterMetrics, nil\n}\n\n// NumberOfNodes is the resolver for the numberOfNodes field.\nfunc (r *subClusterResolver) NumberOfNodes(ctx context.Context, obj *schema.SubCluster) (int, error) {\n\tnodeList, err := archive.ParseNodeList(obj.Nodes)\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\treturn nodeList.NodeCount(), nil\n}\n\n// Cluster returns generated.ClusterResolver implementation.\nfunc (r *Resolver) Cluster() generated.ClusterResolver { return &clusterResolver{r} }\n\n// Job returns generated.JobResolver implementation.\nfunc (r *Resolver) Job() generated.JobResolver { return &jobResolver{r} }\n\n// MetricValue returns generated.MetricValueResolver implementation.\nfunc (r *Resolver) MetricValue() generated.MetricValueResolver { return &metricValueResolver{r} }\n\n// Mutation returns generated.MutationResolver implementation.\nfunc (r *Resolver) Mutation() generated.MutationResolver { return &mutationResolver{r} }\n\n// Node returns generated.NodeResolver implementation.\nfunc (r *Resolver) Node() generated.NodeResolver { return &nodeResolver{r} }\n\n// Query returns generated.QueryResolver implementation.\nfunc (r *Resolver) Query() generated.QueryResolver { return &queryResolver{r} }\n\n// SubCluster returns generated.SubClusterResolver implementation.\nfunc (r *Resolver) SubCluster() generated.SubClusterResolver { return &subClusterResolver{r} }\n\ntype clusterResolver struct{ *Resolver }\ntype jobResolver struct{ *Resolver }\ntype metricValueResolver struct{ *Resolver }\ntype mutationResolver struct{ *Resolver }\ntype nodeResolver struct{ *Resolver }\ntype queryResolver struct{ *Resolver }\ntype subClusterResolver struct{ *Resolver }\n","structuredPatch":[{"oldStart":515,"oldLines":7,"newStart":515,"newLines":12,"lines":[" return nil, err"," }"," ","- data, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)","+ algoName := \"\"","+ if resampleAlgo != nil {","+ algoName = strings.ToLower(resampleAlgo.String())","+ }","+","+ data, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution, algoName)"," if err != nil {"," cclog.Warn(\"Error while loading job data\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c84bd7d5-2ecb-4bed-9b4f-bd62686d4716","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"ac0918ac-1f65-4b58-b085-f262e212eb32","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013GcHozGSS5c196FA7rQf1o","type":"tool_result","content":" 900→\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n 901→\t}\n 902→\n 903→\tif metrics == nil {\n 904→\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n 905→\t\t\tmetrics = append(metrics, mc.Name)\n 906→\t\t}\n 907→\t}\n 908→\n 909→\t// data -> map hostname:jobdata\n 910→\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n 911→\tif err != nil {\n 912→\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n 913→\t\treturn nil, err\n 914→\t}"}]},"uuid":"c5fe55f7-532b-48e7-89ef-1a023b6ad9d4","timestamp":"2026-03-19T19:50:02.784Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","content":"\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}","numLines":15,"startLine":900,"totalLines":1082}},"sourceToolAssistantUUID":"ac0918ac-1f65-4b58-b085-f262e212eb32","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"c5fe55f7-532b-48e7-89ef-1a023b6ad9d4","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01VuUBZHdWyTb9Lq7xBx6ruN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019PCJNmDZPGTzEa9Tb9SRZy","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","old_string":"\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}","new_string":"\talgoName := \"\"\n\tif resampleAlgo != nil {\n\t\talgoName = strings.ToLower(resampleAlgo.String())\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx, algoName)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":450,"cache_read_input_tokens":83322,"output_tokens":341,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":450,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD264uFWbkBD8cjy9Kgc","type":"assistant","uuid":"7799d214-fcfd-4cfa-b2f7-982b2be6f7f9","timestamp":"2026-03-19T19:50:07.802Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"7799d214-fcfd-4cfa-b2f7-982b2be6f7f9","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_019PCJNmDZPGTzEa9Tb9SRZy","toolUseID":"toolu_019PCJNmDZPGTzEa9Tb9SRZy","timestamp":"2026-03-19T19:50:07.833Z","uuid":"bb8ef21d-97c9-4907-8cb8-ff41b4db4b25","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"7799d214-fcfd-4cfa-b2f7-982b2be6f7f9","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019PCJNmDZPGTzEa9Tb9SRZy","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go has been updated successfully."}]},"uuid":"e3f8c9de-33d9-4dc7-bca0-954692aa531d","timestamp":"2026-03-19T19:50:07.832Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/schema.resolvers.go","oldString":"\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}","newString":"\talgoName := \"\"\n\tif resampleAlgo != nil {\n\t\talgoName = strings.ToLower(resampleAlgo.String())\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx, algoName)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}","originalFile":"package graph\n\n// This file will be automatically regenerated based on the schema, any resolver\n// implementations\n// will be copied through when generating and any unknown code will be moved to the end.\n// Code generated by github.com/99designs/gqlgen version v0.17.88\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\t\"regexp\"\n\t\"slices\"\n\t\"strconv\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/generated\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\tccunit \"github.com/ClusterCockpit/cc-lib/v2/ccUnits\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// Partitions is the resolver for the partitions field.\nfunc (r *clusterResolver) Partitions(ctx context.Context, obj *schema.Cluster) ([]string, error) {\n\treturn r.Repo.Partitions(obj.Name)\n}\n\n// StartTime is the resolver for the startTime field.\nfunc (r *jobResolver) StartTime(ctx context.Context, obj *schema.Job) (*time.Time, error) {\n\ttimestamp := time.Unix(obj.StartTime, 0)\n\treturn &timestamp, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *jobResolver) Tags(ctx context.Context, obj *schema.Job) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), obj.ID)\n}\n\n// ConcurrentJobs is the resolver for the concurrentJobs field.\nfunc (r *jobResolver) ConcurrentJobs(ctx context.Context, obj *schema.Job) (*model.JobLinkResultList, error) {\n\t// FIXME: Make the hardcoded duration configurable\n\tif obj.Shared != \"none\" && obj.Duration > 600 {\n\t\treturn r.Repo.FindConcurrentJobs(ctx, obj)\n\t}\n\n\treturn nil, nil\n}\n\n// Footprint is the resolver for the footprint field.\nfunc (r *jobResolver) Footprint(ctx context.Context, obj *schema.Job) ([]*model.FootprintValue, error) {\n\trawFootprint, err := r.Repo.FetchFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.FootprintValue{}\n\tfor name, value := range rawFootprint {\n\n\t\tparts := strings.Split(name, \"_\")\n\t\tstatPart := parts[len(parts)-1]\n\t\tnameParts := parts[:len(parts)-1]\n\n\t\tres = append(res, &model.FootprintValue{\n\t\t\tName: strings.Join(nameParts, \"_\"),\n\t\t\tStat: statPart,\n\t\t\tValue: value,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// EnergyFootprint is the resolver for the energyFootprint field.\nfunc (r *jobResolver) EnergyFootprint(ctx context.Context, obj *schema.Job) ([]*model.EnergyFootprintValue, error) {\n\trawEnergyFootprint, err := r.Repo.FetchEnergyFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job energy footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.EnergyFootprintValue{}\n\tfor name, value := range rawEnergyFootprint {\n\t\t// Suboptimal: Nearly hardcoded metric name expectations\n\t\tmatchCPU := regexp.MustCompile(`cpu|Cpu|CPU`)\n\t\tmatchAcc := regexp.MustCompile(`acc|Acc|ACC`)\n\t\tmatchMem := regexp.MustCompile(`mem|Mem|MEM`)\n\t\tmatchCore := regexp.MustCompile(`core|Core|CORE`)\n\n\t\thwType := \"\"\n\t\tswitch test := name; { // NOtice ';' for var declaration\n\t\tcase matchCPU.MatchString(test):\n\t\t\thwType = \"CPU\"\n\t\tcase matchAcc.MatchString(test):\n\t\t\thwType = \"Accelerator\"\n\t\tcase matchMem.MatchString(test):\n\t\t\thwType = \"Memory\"\n\t\tcase matchCore.MatchString(test):\n\t\t\thwType = \"Core\"\n\t\tdefault:\n\t\t\thwType = \"Other\"\n\t\t}\n\n\t\tres = append(res, &model.EnergyFootprintValue{\n\t\t\tHardware: hwType,\n\t\t\tMetric: name,\n\t\t\tValue: value,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *jobResolver) MetaData(ctx context.Context, obj *schema.Job) (any, error) {\n\treturn r.Repo.FetchMetadata(obj)\n}\n\n// UserData is the resolver for the userData field.\nfunc (r *jobResolver) UserData(ctx context.Context, obj *schema.Job) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, obj.User)\n}\n\n// Name is the resolver for the name field.\nfunc (r *metricValueResolver) Name(ctx context.Context, obj *schema.MetricValue) (*string, error) {\n\tpanic(fmt.Errorf(\"not implemented: Name - name\"))\n}\n\n// CreateTag is the resolver for the createTag field.\nfunc (r *mutationResolver) CreateTag(ctx context.Context, typeArg string, name string, scope string) (*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\tif user.HasRole(schema.RoleAdmin) && scope == \"admin\" ||\n\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && scope == \"global\" ||\n\t\tuser.Username == scope {\n\t\t// Create in DB\n\t\tid, err := r.Repo.CreateTag(typeArg, name, scope)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while creating tag\")\n\t\t\treturn nil, err\n\t\t}\n\t\treturn &schema.Tag{ID: id, Type: typeArg, Name: name, Scope: scope}, nil\n\t} else {\n\t\tcclog.Warnf(\"Not authorized to create tag with scope: %s\", scope)\n\t\treturn nil, fmt.Errorf(\"not authorized to create tag with scope: %s\", scope)\n\t}\n}\n\n// DeleteTag is the resolver for the deleteTag field.\nfunc (r *mutationResolver) DeleteTag(ctx context.Context, id string) (string, error) {\n\t// This Uses ID string <-> ID string, removeTagFromList uses []string <-> []int\n\tpanic(fmt.Errorf(\"not implemented: DeleteTag - deleteTag\"))\n}\n\n// AddTagsToJob is the resolver for the addTagsToJob field.\nfunc (r *mutationResolver) AddTagsToJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while adding tag to job\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Add to Job\n\t\t\tif tags, err = r.Repo.AddTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while adding tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to add tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to add tag: %d\", tid)\n\t\t}\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagsFromJob is the resolver for the removeTagsFromJob field.\nfunc (r *mutationResolver) RemoveTagsFromJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Remove from Job\n\t\t\tif tags, err = r.Repo.RemoveTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagFromList is the resolver for the removeTagFromList field.\nfunc (r *mutationResolver) RemoveTagFromList(ctx context.Context, tagIds []string) ([]int, error) {\n\t// Needs Contextuser\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\ttags := []int{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id for removal\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && (tscope == \"global\" || tscope == \"admin\") || user.Username == tscope {\n\t\t\t// Remove from DB\n\t\t\tif err = r.Repo.RemoveTagByID(tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t} else {\n\t\t\t\ttags = append(tags, int(tid))\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\t}\n\treturn tags, nil\n}\n\n// UpdateConfiguration is the resolver for the updateConfiguration field.\nfunc (r *mutationResolver) UpdateConfiguration(ctx context.Context, name string, value string) (*string, error) {\n\tif err := repository.GetUserCfgRepo().UpdateConfig(name, value, repository.GetUserFromContext(ctx)); err != nil {\n\t\tcclog.Warn(\"Error while updating user config\")\n\t\treturn nil, err\n\t}\n\n\treturn nil, nil\n}\n\n// ID is the resolver for the id field.\nfunc (r *nodeResolver) ID(ctx context.Context, obj *schema.Node) (string, error) {\n\tpanic(fmt.Errorf(\"not implemented: ID - id\"))\n}\n\n// SchedulerState is the resolver for the schedulerState field.\nfunc (r *nodeResolver) SchedulerState(ctx context.Context, obj *schema.Node) (schema.SchedulerState, error) {\n\tif obj.NodeState != \"\" {\n\t\treturn obj.NodeState, nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no SchedulerState (NodeState) on node object\")\n\t}\n}\n\n// HealthState is the resolver for the healthState field.\nfunc (r *nodeResolver) HealthState(ctx context.Context, obj *schema.Node) (string, error) {\n\tif obj.HealthState != \"\" {\n\t\treturn string(obj.HealthState), nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no HealthState (NodeState) on node object\")\n\t}\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *nodeResolver) MetaData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.MetaData != nil {\n\t\treturn obj.MetaData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no MetaData (NodeState) on node object\")\n\t\temptyMeta := make(map[string]string, 0)\n\t\treturn emptyMeta, nil\n\t}\n}\n\n// HealthData is the resolver for the healthData field.\nfunc (r *nodeResolver) HealthData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.HealthData != nil {\n\t\treturn obj.HealthData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no HealthData (NodeState) on node object\")\n\t\temptyHealth := make(map[string][]string, 0)\n\t\treturn emptyHealth, nil\n\t}\n}\n\n// Clusters is the resolver for the clusters field.\nfunc (r *queryResolver) Clusters(ctx context.Context) ([]*schema.Cluster, error) {\n\treturn archive.Clusters, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *queryResolver) Tags(ctx context.Context) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), nil)\n}\n\n// GlobalMetrics is the resolver for the globalMetrics field.\nfunc (r *queryResolver) GlobalMetrics(ctx context.Context) ([]*schema.GlobalMetricListItem, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\n\tif user != nil {\n\t\tif user.HasRole(schema.RoleUser) || user.HasRole(schema.RoleManager) {\n\t\t\treturn archive.GlobalUserMetricList, nil\n\t\t}\n\t}\n\n\treturn archive.GlobalMetricList, nil\n}\n\n// User is the resolver for the user field.\nfunc (r *queryResolver) User(ctx context.Context, username string) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, username)\n}\n\n// AllocatedNodes is the resolver for the allocatedNodes field.\nfunc (r *queryResolver) AllocatedNodes(ctx context.Context, cluster string) ([]*model.Count, error) {\n\tdata, err := r.Repo.AllocatedNodes(cluster)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching allocated nodes\")\n\t\treturn nil, err\n\t}\n\n\tcounts := make([]*model.Count, 0, len(data))\n\tfor subcluster, hosts := range data {\n\t\tcounts = append(counts, &model.Count{\n\t\t\tName: subcluster,\n\t\t\tCount: len(hosts),\n\t\t})\n\t}\n\n\treturn counts, nil\n}\n\n// Node is the resolver for the node field.\nfunc (r *queryResolver) Node(ctx context.Context, id string) (*schema.Node, error) {\n\trepo := repository.GetNodeRepository()\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\treturn repo.GetNodeByID(numericID, false)\n}\n\n// Nodes is the resolver for the nodes field.\nfunc (r *queryResolver) Nodes(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodes(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodesWithMeta is the resolver for the nodesWithMeta field.\nfunc (r *queryResolver) NodesWithMeta(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\t// Why Extra Handler? -> graphql.CollectAllFields(ctx) only returns toplevel fields (i.e.: items, count), and not subfields like item.metaData\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodesWithMeta(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodeStates is the resolver for the nodeStates field.\nfunc (r *queryResolver) NodeStates(ctx context.Context, filter []*model.NodeFilter) ([]*model.NodeStates, error) {\n\trepo := repository.GetNodeRepository()\n\n\tstateCounts, serr := repo.CountStates(ctx, filter, \"node_state\")\n\tif serr != nil {\n\t\tcclog.Warnf(\"Error while counting nodeStates: %s\", serr.Error())\n\t\treturn nil, serr\n\t}\n\n\thealthCounts, herr := repo.CountStates(ctx, filter, \"health_state\")\n\tif herr != nil {\n\t\tcclog.Warnf(\"Error while counting healthStates: %s\", herr.Error())\n\t\treturn nil, herr\n\t}\n\n\tallCounts := append(stateCounts, healthCounts...)\n\n\treturn allCounts, nil\n}\n\n// NodeStatesTimed is the resolver for the nodeStatesTimed field.\nfunc (r *queryResolver) NodeStatesTimed(ctx context.Context, filter []*model.NodeFilter, typeArg string) ([]*model.NodeStatesTimed, error) {\n\trepo := repository.GetNodeRepository()\n\n\tif typeArg == \"node\" {\n\t\tstateCounts, serr := repo.CountStatesTimed(ctx, filter, \"node_state\")\n\t\tif serr != nil {\n\t\t\tcclog.Warnf(\"Error while counting nodeStates in time: %s\", serr.Error())\n\t\t\treturn nil, serr\n\t\t}\n\t\treturn stateCounts, nil\n\t}\n\n\tif typeArg == \"health\" {\n\t\thealthCounts, herr := repo.CountStatesTimed(ctx, filter, \"health_state\")\n\t\tif herr != nil {\n\t\t\tcclog.Warnf(\"Error while counting healthStates in time: %s\", herr.Error())\n\t\t\treturn nil, herr\n\t\t}\n\t\treturn healthCounts, nil\n\t}\n\n\treturn nil, errors.New(\"unknown Node State Query Type\")\n}\n\n// Job is the resolver for the job field.\nfunc (r *queryResolver) Job(ctx context.Context, id string) (*schema.Job, error) {\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\tjob, err := r.Repo.FindByID(ctx, numericID)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while finding job by id\")\n\t\treturn nil, err\n\t}\n\n\tif user := repository.GetUserFromContext(ctx); user != nil &&\n\t\tjob.User != user.Username &&\n\t\tuser.HasNotRoles([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\treturn nil, errors.New(\"you are not allowed to see this job\")\n\t}\n\n\treturn job, nil\n}\n\n// JobMetrics is the resolver for the jobMetrics field.\nfunc (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int, resampleAlgo *model.ResampleAlgo) ([]*model.JobMetricWithName, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying job for metrics\")\n\t\treturn nil, err\n\t}\n\n\talgoName := \"\"\n\tif resampleAlgo != nil {\n\t\talgoName = strings.ToLower(resampleAlgo.String())\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution, algoName)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &model.JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, err\n}\n\n// JobStats is the resolver for the jobStats field.\nfunc (r *queryResolver) JobStats(ctx context.Context, id string, metrics []string) ([]*model.NamedStats, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading jobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := []*model.NamedStats{}\n\tfor name, md := range data {\n\t\tres = append(res, &model.NamedStats{\n\t\t\tName: name,\n\t\t\tData: &md,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// ScopedJobStats is the resolver for the scopedJobStats field.\nfunc (r *queryResolver) ScopedJobStats(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope) ([]*model.NamedStatsWithScope, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadScopedJobStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading scopedJobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := make([]*model.NamedStatsWithScope, 0)\n\tfor name, scoped := range data {\n\t\tfor scope, stats := range scoped {\n\n\t\t\tmdlStats := make([]*model.ScopedStats, 0)\n\t\t\tfor _, stat := range stats {\n\t\t\t\tmdlStats = append(mdlStats, &model.ScopedStats{\n\t\t\t\t\tHostname: stat.Hostname,\n\t\t\t\t\tID: stat.ID,\n\t\t\t\t\tData: stat.Data,\n\t\t\t\t})\n\t\t\t}\n\n\t\t\tres = append(res, &model.NamedStatsWithScope{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tStats: mdlStats,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, nil\n}\n\n// Jobs is the resolver for the jobs field.\nfunc (r *queryResolver) Jobs(ctx context.Context, filter []*model.JobFilter, page *model.PageRequest, order *model.OrderByInput) (*model.JobResultList, error) {\n\tif page == nil {\n\t\tpage = &model.PageRequest{\n\t\t\tItemsPerPage: 50,\n\t\t\tPage: 1,\n\t\t}\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, page, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs\")\n\t\treturn nil, err\n\t}\n\n\tcount, err := r.Repo.CountJobs(ctx, filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while counting jobs\")\n\t\treturn nil, err\n\t}\n\n\t// Note: Even if App-Default 'config.Keys.UiDefaults[\"job_list_usePaging\"]' is set, always return hasNextPage boolean.\n\t// Users can decide in frontend to use continuous scroll, even if app-default is paging!\n\t// Skip if page.ItemsPerPage == -1 (\"Load All\" -> No Next Page required, Status Dashboards)\n\t/*\n\t Example Page 4 @ 10 IpP : Does item 41 exist?\n\t Minimal Page 41 @ 1 IpP : If len(result) is 1, Page 5 @ 10 IpP exists.\n\t*/\n\thasNextPage := false\n\tif page.ItemsPerPage != -1 {\n\t\tnextPage := &model.PageRequest{\n\t\t\tItemsPerPage: 1,\n\t\t\tPage: ((page.Page * page.ItemsPerPage) + 1),\n\t\t}\n\t\tnextJobs, err := r.Repo.QueryJobs(ctx, filter, nextPage, order)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while querying next jobs\")\n\t\t\treturn nil, err\n\t\t}\n\t\thasNextPage = len(nextJobs) == 1\n\t}\n\n\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n}\n\n// JobsStatistics is the resolver for the jobsStatistics field.\nfunc (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n\tvar err error\n\tvar stats []*model.JobsStatistics\n\n\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\t// Build requested fields map for selective column computation\n\tstatsFields := []string{\n\t\t\"totalJobs\", \"totalUsers\", \"totalWalltime\", \"totalNodes\", \"totalCores\",\n\t\t\"totalAccs\", \"totalNodeHours\", \"totalCoreHours\", \"totalAccHours\", \"runningJobs\", \"shortJobs\",\n\t}\n\treqFields := make(map[string]bool, len(statsFields))\n\tfetchedMainStats := false\n\tfor _, f := range statsFields {\n\t\tif requireField(ctx, f) {\n\t\t\treqFields[f] = true\n\t\t\tif f != \"runningJobs\" && f != \"shortJobs\" {\n\t\t\t\tfetchedMainStats = true\n\t\t\t}\n\t\t}\n\t}\n\n\tif fetchedMainStats {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter, reqFields)\n\t\t} else {\n\t\t\tstartGrouped := time.Now()\n\t\t\t// Use request-scoped cache: multiple aliases with same (filter, groupBy)\n\t\t\t// but different sortBy/page hit the DB only once.\n\t\t\tif cache := getStatsGroupCache(ctx); cache != nil {\n\t\t\t\tkey := statsCacheKey(filter, groupBy, reqFields)\n\t\t\t\tvar allStats []*model.JobsStatistics\n\t\t\t\tallStats, err = cache.getOrCompute(key, func() ([]*model.JobsStatistics, error) {\n\t\t\t\t\treturn r.Repo.JobsStatsGrouped(ctx, filter, nil, nil, groupBy, reqFields)\n\t\t\t\t})\n\t\t\t\tif err == nil {\n\t\t\t\t\tstats = sortAndPageStats(allStats, sortBy, page)\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n\t\t\t}\n\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n\t// Only run separate count queries if main stats were not fetched.\n\tif !fetchedMainStats {\n\t\tif groupBy != nil {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t\t}\n\t\t} else {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n\n\t\tif numDurationBins == nil {\n\t\t\tnumDurationBins = &defaultDurationBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\tif requireField(ctx, \"histMetrics\") {\n\n\t\tif numMetricBins == nil {\n\t\t\tnumMetricBins = &defaultMetricBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// JobsMetricStats is the resolver for the jobsMetricStats field.\nfunc (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n\t// No Paging, Fixed Order by StartTime ASC\n\torder := &model.OrderByInput{\n\t\tField: \"startTime\",\n\t\tType: \"col\",\n\t\tOrder: \"ASC\",\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobStats{}\n\tfor _, job := range jobs {\n\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading comparison jobStats data for job id %d\", job.JobID)\n\t\t\tcontinue\n\t\t\t// return nil, err\n\t\t}\n\n\t\tsres := []*model.NamedStats{}\n\t\tfor name, md := range data {\n\t\t\tsres = append(sres, &model.NamedStats{\n\t\t\t\tName: name,\n\t\t\t\tData: &md,\n\t\t\t})\n\t\t}\n\n\t\tnumThreadsInt := int(job.NumHWThreads)\n\t\tnumAccsInt := int(job.NumAcc)\n\t\tres = append(res, &model.JobStats{\n\t\t\tID: int(*job.ID),\n\t\t\tJobID: strconv.Itoa(int(job.JobID)),\n\t\t\tStartTime: int(job.StartTime),\n\t\t\tDuration: int(job.Duration),\n\t\t\tCluster: job.Cluster,\n\t\t\tSubCluster: job.SubCluster,\n\t\t\tNumNodes: int(job.NumNodes),\n\t\t\tNumHWThreads: &numThreadsInt,\n\t\t\tNumAccelerators: &numAccsInt,\n\t\t\tStats: sres,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// JobsFootprints is the resolver for the jobsFootprints field.\nfunc (r *queryResolver) JobsFootprints(ctx context.Context, filter []*model.JobFilter, metrics []string) (*model.Footprints, error) {\n\t// NOTE: Legacy Naming! This resolver is for normalized histograms in analysis view only - *Not* related to DB \"footprint\" column!\n\treturn r.jobsFootprints(ctx, filter, metrics)\n}\n\n// RooflineHeatmap is the resolver for the rooflineHeatmap field.\nfunc (r *queryResolver) RooflineHeatmap(ctx context.Context, filter []*model.JobFilter, rows int, cols int, minX float64, minY float64, maxX float64, maxY float64) ([][]float64, error) {\n\treturn r.rooflineHeatmap(ctx, filter, rows, cols, minX, minY, maxX, maxY)\n}\n\n// NodeMetrics is the resolver for the nodeMetrics field.\nfunc (r *queryResolver) NodeMetrics(ctx context.Context, cluster string, nodes []string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time) ([]*model.NodeMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tdefaultMetrics := make([]string, 0)\n\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\tdefaultMetrics = append(defaultMetrics, mc.Name)\n\t}\n\tif metrics == nil {\n\t\tmetrics = defaultMetrics\n\t} else {\n\t\tmetrics = slices.DeleteFunc(metrics, func(metric string) bool {\n\t\t\treturn !slices.Contains(defaultMetrics, metric) // Remove undefined metrics.\n\t\t})\n\t}\n\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\tnodeStateMap, metricHealthMap, _ := nodeRepo.MapNodes(cluster)\n\n\tnodeMetrics := make([]*model.NodeMetrics, 0, len(data))\n\tfor hostname, metrics := range data {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost: hostname,\n\t\t\tNodeState: nodeStateMap[hostname],\n\t\t\tMetricHealth: metricHealthMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0, len(metrics)*len(scopes)),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: schema.MetricScopeNode,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetrics = append(nodeMetrics, host)\n\t}\n\n\treturn nodeMetrics, nil\n}\n\n// NodeMetricsList is the resolver for the nodeMetricsList field.\nfunc (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\t// nodes -> array hostname\n\tnodes, nodeStateMap, metricHealthMap, countNodes, hasNextPage, nerr := nodeRepo.GetNodesForList(ctx, cluster, subCluster, stateFilter, nodeFilter, page)\n\tif nerr != nil {\n\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}\n\n\tnodeMetricsList := make([]*model.NodeMetrics, 0, len(data))\n\tfor _, hostname := range nodes {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost: hostname,\n\t\t\tNodeState: nodeStateMap[hostname],\n\t\t\tMetricHealth: metricHealthMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range data[hostname] {\n\t\t\tfor scope, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName: metric,\n\t\t\t\t\tScope: scope,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetricsList = append(nodeMetricsList, host)\n\t}\n\n\tnodeMetricsListResult := &model.NodesResultList{\n\t\tItems: nodeMetricsList,\n\t\t// TotalNodes depends on sum of nodes grouped on latest timestamp, see repo/node.go:357\n\t\tTotalNodes: &countNodes,\n\t\tHasNextPage: &hasNextPage,\n\t}\n\n\treturn nodeMetricsListResult, nil\n}\n\n// ClusterMetrics is the resolver for the clusterMetrics field.\nfunc (r *queryResolver) ClusterMetrics(ctx context.Context, cluster string, metrics []string, from time.Time, to time.Time) (*model.ClusterMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// 'nodes' == nil -> Defaults to all nodes of cluster for existing query workflow\n\tscopes := []schema.MetricScope{\"node\"}\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nil, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tclusterMetricData := make([]*model.ClusterMetricWithName, 0)\n\tclusterMetrics := model.ClusterMetrics{NodeCount: 0, Metrics: clusterMetricData}\n\n\tcollectorTimestep := make(map[string]int)\n\tcollectorUnit := make(map[string]schema.Unit)\n\tcollectorData := make(map[string][]schema.Float)\n\n\tfor _, metrics := range data {\n\t\tclusterMetrics.NodeCount += 1\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\t// Collect Info Once\n\t\t\t\t_, okTimestep := collectorTimestep[metric]\n\t\t\t\tif !okTimestep {\n\t\t\t\t\tcollectorTimestep[metric] = scopedMetric.Timestep\n\t\t\t\t}\n\t\t\t\t_, okUnit := collectorUnit[metric]\n\t\t\t\tif !okUnit {\n\t\t\t\t\tcollectorUnit[metric] = scopedMetric.Unit\n\t\t\t\t}\n\t\t\t\t// Collect Data\n\t\t\t\tfor _, ser := range scopedMetric.Series {\n\t\t\t\t\t_, okData := collectorData[metric]\n\t\t\t\t\t// Init With Datasize > 0\n\t\t\t\t\tif !okData && len(ser.Data) != 0 {\n\t\t\t\t\t\tcollectorData[metric] = make([]schema.Float, len(ser.Data))\n\t\t\t\t\t} else if !okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip init: no data -> %s at %s; size %d\", metric, ser.Hostname, len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t\t// Sum if init'd and matching size\n\t\t\t\t\tif okData && len(ser.Data) == len(collectorData[metric]) {\n\t\t\t\t\t\tfor i, val := range ser.Data {\n\t\t\t\t\t\t\tif val.IsNaN() {\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcollectorData[metric][i] += val\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} else if okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip sum: data diff -> %s at %s; want size %d, have size %d\", metric, ser.Hostname, len(collectorData[metric]), len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tfor metricName, data := range collectorData {\n\t\t// use ccUnits for backend normalization to \"Tera\"\n\t\tp_old := ccunit.NewPrefix(collectorUnit[metricName].Prefix)\n\t\tp_new := ccunit.NewPrefix(\"T\")\n\t\tconvFunc := ccunit.GetPrefixPrefixFactor(p_old, p_new)\n\t\tu_new := schema.Unit{Prefix: p_new.Prefix(), Base: collectorUnit[metricName].Base}\n\n\t\troundedData := make([]schema.Float, 0)\n\t\tfor _, v_old := range data {\n\t\t\tv_new := math.Round(convFunc(float64(v_old)).(float64)*100.0) / 100.0\n\t\t\troundedData = append(roundedData, schema.Float(v_new))\n\t\t}\n\n\t\tcm := model.ClusterMetricWithName{\n\t\t\tName: metricName,\n\t\t\tUnit: &u_new,\n\t\t\tTimestep: collectorTimestep[metricName],\n\t\t\tData: roundedData,\n\t\t}\n\n\t\tclusterMetrics.Metrics = append(clusterMetrics.Metrics, &cm)\n\t}\n\n\treturn &clusterMetrics, nil\n}\n\n// NumberOfNodes is the resolver for the numberOfNodes field.\nfunc (r *subClusterResolver) NumberOfNodes(ctx context.Context, obj *schema.SubCluster) (int, error) {\n\tnodeList, err := archive.ParseNodeList(obj.Nodes)\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\treturn nodeList.NodeCount(), nil\n}\n\n// Cluster returns generated.ClusterResolver implementation.\nfunc (r *Resolver) Cluster() generated.ClusterResolver { return &clusterResolver{r} }\n\n// Job returns generated.JobResolver implementation.\nfunc (r *Resolver) Job() generated.JobResolver { return &jobResolver{r} }\n\n// MetricValue returns generated.MetricValueResolver implementation.\nfunc (r *Resolver) MetricValue() generated.MetricValueResolver { return &metricValueResolver{r} }\n\n// Mutation returns generated.MutationResolver implementation.\nfunc (r *Resolver) Mutation() generated.MutationResolver { return &mutationResolver{r} }\n\n// Node returns generated.NodeResolver implementation.\nfunc (r *Resolver) Node() generated.NodeResolver { return &nodeResolver{r} }\n\n// Query returns generated.QueryResolver implementation.\nfunc (r *Resolver) Query() generated.QueryResolver { return &queryResolver{r} }\n\n// SubCluster returns generated.SubClusterResolver implementation.\nfunc (r *Resolver) SubCluster() generated.SubClusterResolver { return &subClusterResolver{r} }\n\ntype clusterResolver struct{ *Resolver }\ntype jobResolver struct{ *Resolver }\ntype metricValueResolver struct{ *Resolver }\ntype mutationResolver struct{ *Resolver }\ntype nodeResolver struct{ *Resolver }\ntype queryResolver struct{ *Resolver }\ntype subClusterResolver struct{ *Resolver }\n","structuredPatch":[{"oldStart":906,"oldLines":8,"newStart":906,"newLines":13,"lines":[" }"," }"," ","+ algoName := \"\"","+ if resampleAlgo != nil {","+ algoName = strings.ToLower(resampleAlgo.String())","+ }","+"," // data -> map hostname:jobdata","- data, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)","+ data, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx, algoName)"," if err != nil {"," cclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"7799d214-fcfd-4cfa-b2f7-982b2be6f7f9","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"c5542b64-984a-4945-ae03-7940738ae91d","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ErJjsA1pkhUugipnd7umjP","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go has been updated successfully."}]},"uuid":"ae5ee62b-0a3a-4add-8812-9c5ad6b1b147","timestamp":"2026-03-19T19:50:14.068Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","oldString":"func cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d\",\n\t\t*job.ID, job.State, metrics, scopes, resolution)\n}","newString":"func cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tresampleAlgo string,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",\n\t\t*job.ID, job.State, metrics, scopes, resolution, resampleAlgo)\n}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d\",\n\t\t*job.ID, job.State, metrics, scopes, resolution)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","structuredPatch":[{"oldStart":62,"oldLines":9,"newStart":62,"newLines":10,"lines":[" metrics []string,"," scopes []schema.MetricScope,"," resolution int,","+ resampleAlgo string,"," ) string {","- return fmt.Sprintf(\"%d(%s):[%v],[%v]-%d\",","- *job.ID, job.State, metrics, scopes, resolution)","+ return fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",","+ *job.ID, job.State, metrics, scopes, resolution, resampleAlgo)"," }"," "," // LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c5542b64-984a-4945-ae03-7940738ae91d","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"193a383d-3ad5-4b52-8544-b83aff171f32","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HwXDMWVSdZKGYjHgBA7mth","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go has been updated successfully."}]},"uuid":"6dc21b17-64c3-400d-b7a9-9d3ef3af22ee","timestamp":"2026-03-19T19:50:20.955Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","oldString":"func LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {","newString":"func LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n\tresampleAlgo string,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution, resampleAlgo), func() (_ any, ttl time.Duration, size int) {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tresampleAlgo string,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",\n\t\t*job.ID, job.State, metrics, scopes, resolution, resampleAlgo)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","structuredPatch":[{"oldStart":88,"oldLines":8,"newStart":88,"newLines":9,"lines":[" scopes []schema.MetricScope,"," ctx context.Context,"," resolution int,","+ resampleAlgo string,"," ) (schema.JobData, error) {","- data := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {","+ data := cache.Get(cacheKey(job, metrics, scopes, resolution, resampleAlgo), func() (_ any, ttl time.Duration, size int) {"," var jd schema.JobData"," var err error"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"193a383d-3ad5-4b52-8544-b83aff171f32","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"44b11482-88cf-4431-962c-e637bc5fa821","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01DFSz1WPfg3a7r5fYLV5jAs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KZKmFiKVagLoMj12yWqQKF","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","old_string":"\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}","new_string":"\t\t// Resample archived data to reduce data points to the requested resolution,\n\t\t\t// improving transfer performance and client-side rendering.\n\t\t\tresampleFn, rfErr := resampler.GetResampler(resampleAlgo)\n\t\t\tif rfErr != nil {\n\t\t\t\treturn rfErr, 0, 0\n\t\t\t}\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampleFn(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":480,"cache_read_input_tokens":84671,"output_tokens":538,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":480,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD27QXV8gtY22YvfT15Q","type":"assistant","uuid":"37066a58-e11e-4416-aa57-5cf19aa276c3","timestamp":"2026-03-19T19:50:31.972Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"37066a58-e11e-4416-aa57-5cf19aa276c3","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KZKmFiKVagLoMj12yWqQKF","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go has been updated successfully."}]},"uuid":"a56ab674-4b0d-413d-98b0-1bb373e8dac3","timestamp":"2026-03-19T19:50:31.999Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","oldString":"\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}","newString":"\t\t// Resample archived data to reduce data points to the requested resolution,\n\t\t\t// improving transfer performance and client-side rendering.\n\t\t\tresampleFn, rfErr := resampler.GetResampler(resampleAlgo)\n\t\t\tif rfErr != nil {\n\t\t\t\treturn rfErr, 0, 0\n\t\t\t}\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampleFn(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tresampleAlgo string,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",\n\t\t*job.ID, job.State, metrics, scopes, resolution, resampleAlgo)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n\tresampleAlgo string,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution, resampleAlgo), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points\n\t\t\t// to the requested resolution, improving transfer performance and client-side rendering.\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","structuredPatch":[{"oldStart":138,"oldLines":13,"newStart":138,"newLines":17,"lines":[" "," jd = deepCopy(jdTemp)"," ","- // Resample archived data using Largest Triangle Three Bucket algorithm to reduce data points","- // to the requested resolution, improving transfer performance and client-side rendering.","+ // Resample archived data to reduce data points to the requested resolution,","+ // improving transfer performance and client-side rendering.","+ resampleFn, rfErr := resampler.GetResampler(resampleAlgo)","+ if rfErr != nil {","+ return rfErr, 0, 0","+ }"," for _, v := range jd {"," for _, v_ := range v {"," timestep := int64(0)"," for i := 0; i < len(v_.Series); i += 1 {","- v_.Series[i].Data, timestep, err = resampler.LargestTriangleThreeBucket(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))","+ v_.Series[i].Data, timestep, err = resampleFn(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))"," if err != nil {"," return err, 0, 0"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"37066a58-e11e-4416-aa57-5cf19aa276c3","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"e8825136-6ef3-48fb-a5ea-b566adc518e5","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01SmFgv4z76Q19Lvt2e1RQwa","toolUseID":"toolu_01SmFgv4z76Q19Lvt2e1RQwa","timestamp":"2026-03-19T19:50:43.206Z","uuid":"26b8c33d-beab-4822-a994-70a428d90889","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"e8825136-6ef3-48fb-a5ea-b566adc518e5","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SmFgv4z76Q19Lvt2e1RQwa","type":"tool_result","content":" 300→\t\tresolution = max(resolution, mc.Timestep)\n 301→\t}\n 302→\n 303→\tif r.URL.Query().Get(\"all-metrics\") == \"true\" {\n 304→\t\tdata, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution)\n 305→\t\tif err != nil {\n 306→\t\t\tcclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)\n 307→\t\t\treturn"}]},"uuid":"7777df74-e3e3-466e-aa11-9f8b02a6ef52","timestamp":"2026-03-19T19:50:43.206Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","content":"\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tif r.URL.Query().Get(\"all-metrics\") == \"true\" {\n\t\tdata, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\t\treturn","numLines":8,"startLine":300,"totalLines":1155}},"sourceToolAssistantUUID":"e8825136-6ef3-48fb-a5ea-b566adc518e5","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"3f214149-ac14-4b7c-87e5-cccf980f1a9f","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011KLuCk7pbT7YMMvGngCuxS","type":"tool_result","content":" 396→\tfor _, mc := range metricConfigs {\n 397→\t\tresolution = max(resolution, mc.Timestep)\n 398→\t}\n 399→\n 400→\tdata, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)\n 401→\tif err != nil {\n 402→\t\tcclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)\n 403→\t\treturn"}]},"uuid":"f10b680c-952c-4f9c-9b14-18894366c279","timestamp":"2026-03-19T19:50:43.478Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","content":"\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)\n\tif err != nil {\n\t\tcclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\treturn","numLines":8,"startLine":396,"totalLines":1155}},"sourceToolAssistantUUID":"3f214149-ac14-4b7c-87e5-cccf980f1a9f","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"type":"file-history-snapshot","messageId":"244f3aa8-513f-4ec9-a616-a0243519c008","snapshot":{"messageId":"f6080b4d-410f-4d5f-8683-7ccc8a8d37db","trackedFileBackups":{"/Users/jan/prg/CC/cc-lib/resampler/resampler.go":{"backupFileName":"f91dbcaeedc0ea99@v1","version":1,"backupTime":"2026-03-19T19:47:04.877Z"},"/Users/jan/prg/CC/cc-lib/resampler/resampler_test.go":{"backupFileName":"ecf18c271c51c425@v1","version":1,"backupTime":"2026-03-19T19:47:31.515Z"},"go.mod":{"backupFileName":"295902d9d7ef4a36@v1","version":1,"backupTime":"2026-03-19T19:49:04.337Z"},"api/schema.graphqls":{"backupFileName":"1278404eed7b3071@v1","version":1,"backupTime":"2026-03-19T19:49:09.082Z"},"internal/graph/schema.resolvers.go":{"backupFileName":"ff42fed0123ceae0@v1","version":1,"backupTime":"2026-03-19T19:49:54.708Z"},"internal/metricdispatch/dataLoader.go":{"backupFileName":"bbec81954e2885d1@v1","version":1,"backupTime":"2026-03-19T19:50:14.066Z"},"internal/api/api_test.go":{"backupFileName":"c458aa057859713e@v1","version":1,"backupTime":"2026-03-19T19:50:49.090Z"}},"timestamp":"2026-03-19T19:46:16.256Z"},"isSnapshotUpdate":true}
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{"parentUuid":"244f3aa8-513f-4ec9-a616-a0243519c008","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014D1VMGJsmwPHAiunZXYaS5","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/api/api_test.go has been updated successfully."}]},"uuid":"40d41692-201f-4d05-bfe0-892390bbab27","timestamp":"2026-03-19T19:50:49.093Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/api_test.go","oldString":"data, err := metricdispatch.LoadData(stoppedJob, []string{\"load_one\"}, []schema.MetricScope{schema.MetricScopeNode}, context.Background(), 60)","newString":"data, err := metricdispatch.LoadData(stoppedJob, []string{\"load_one\"}, []schema.MetricScope{schema.MetricScopeNode}, context.Background(), 60, \"\")","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\npackage api_test\n\nimport (\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"net/http/httptest\"\n\t\"os\"\n\t\"path/filepath\"\n\t\"reflect\"\n\t\"strings\"\n\t\"testing\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/api\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/archiver\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/auth\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n\tccconf \"github.com/ClusterCockpit/cc-lib/v2/ccConfig\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/go-chi/chi/v5\"\n\n\t_ \"github.com/mattn/go-sqlite3\"\n)\n\nfunc setup(t *testing.T) *api.RestAPI {\n\trepository.ResetConnection()\n\n\tconst testconfig = `{\n\t\"main\": {\n\t \"addr\": \"0.0.0.0:8080\",\n\t \"validate\": false,\n \"api-allowed-ips\": [\n \"*\"\n ]\n },\n \"metric-store\": {\n \"checkpoints\": {\n \"interval\": \"12h\"\n },\n \"retention-in-memory\": \"48h\",\n \"memory-cap\": 100\n },\n\t\"archive\": {\n\t\t \"kind\": \"file\",\n\t\t \"path\": \"./var/job-archive\"\n\t},\n\t\"auth\": {\n \"jwts\": {\n \"max-age\": \"2m\"\n }\n }\n}`\n\tconst testclusterJSON = `{\n \"name\": \"testcluster\",\n\t\t\"subClusters\": [\n\t\t\t{\n\t\t\t\t\"name\": \"sc1\",\n\t\t\t\t\"nodes\": \"host123,host124,host125\",\n\t\t\t\t\"processorType\": \"Intel Core i7-4770\",\n\t\t\t\t\"socketsPerNode\": 1,\n\t\t\t\t\"coresPerSocket\": 4,\n\t\t\t\t\"threadsPerCore\": 2,\n \"flopRateScalar\": {\n \"unit\": {\n \"prefix\": \"G\",\n \"base\": \"F/s\"\n },\n \"value\": 14\n },\n \"flopRateSimd\": {\n \"unit\": {\n \"prefix\": \"G\",\n \"base\": \"F/s\"\n },\n \"value\": 112\n },\n \"memoryBandwidth\": {\n \"unit\": {\n \"prefix\": \"G\",\n \"base\": \"B/s\"\n },\n \"value\": 24\n },\n \"numberOfNodes\": 70,\n\t\t\t\t\"topology\": {\n\t\t\t\t\t\"node\": [0, 1, 2, 3, 4, 5, 6, 7],\n\t\t\t\t\t\"socket\": [[0, 1, 2, 3, 4, 5, 6, 7]],\n\t\t\t\t\t\"memoryDomain\": [[0, 1, 2, 3, 4, 5, 6, 7]],\n\t\t\t\t\t\"die\": [[0, 1, 2, 3, 4, 5, 6, 7]],\n\t\t\t\t\t\"core\": [[0], [1], [2], [3], [4], [5], [6], [7]]\n\t\t\t\t}\n\t\t\t}\n\t\t],\n\t\t\"metricConfig\": [\n\t\t\t{\n\t\t\t\t\"name\": \"load_one\",\n\t\t\t \"unit\": { \"base\": \"\"},\n\t\t\t\t\"scope\": \"node\",\n\t\t\t\t\"timestep\": 60,\n \"aggregation\": \"avg\",\n\t\t\t\t\"peak\": 8,\n\t\t\t\t\"normal\": 0,\n\t\t\t\t\"caution\": 0,\n\t\t\t\t\"alert\": 0\n\t\t\t}\n\t\t]\n\t}`\n\n\tcclog.Init(\"info\", true)\n\ttmpdir := t.TempDir()\n\tjobarchive := filepath.Join(tmpdir, \"job-archive\")\n\tif err := os.Mkdir(jobarchive, 0o777); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tif err := os.WriteFile(filepath.Join(jobarchive, \"version.txt\"), fmt.Appendf(nil, \"%d\", 3), 0o666); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tif err := os.Mkdir(filepath.Join(jobarchive, \"testcluster\"), 0o777); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tif err := os.WriteFile(filepath.Join(jobarchive, \"testcluster\", \"cluster.json\"), []byte(testclusterJSON), 0o666); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tdbfilepath := filepath.Join(tmpdir, \"test.db\")\n\terr := repository.MigrateDB(dbfilepath)\n\tif err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tcfgFilePath := filepath.Join(tmpdir, \"config.json\")\n\tif err := os.WriteFile(cfgFilePath, []byte(testconfig), 0o666); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\tccconf.Init(cfgFilePath)\n\tmetricstore.MetricStoreHandle = &metricstore.InternalMetricStore{}\n\n\t// Load and check main configuration\n\tif cfg := ccconf.GetPackageConfig(\"main\"); cfg != nil {\n\t\tconfig.Init(cfg)\n\t} else {\n\t\tcclog.Abort(\"Main configuration must be present\")\n\t}\n\tarchiveCfg := fmt.Sprintf(\"{\\\"kind\\\": \\\"file\\\",\\\"path\\\": \\\"%s\\\"}\", jobarchive)\n\n\trepository.Connect(dbfilepath)\n\n\tif err := archive.Init(json.RawMessage(archiveCfg)); err != nil {\n\t\tt.Fatal(err)\n\t}\n\n\t// metricstore initialization removed - it's initialized via callback in tests\n\n\tarchiver.Start(repository.GetJobRepository(), context.Background())\n\n\tif cfg := ccconf.GetPackageConfig(\"auth\"); cfg != nil {\n\t\tauth.Init(&cfg)\n\t} else {\n\t\tcclog.Warn(\"Authentication disabled due to missing configuration\")\n\t\tauth.Init(nil)\n\t}\n\n\tgraph.Init()\n\n\treturn api.New()\n}\n\nfunc cleanup() {\n\tif err := archiver.Shutdown(5 * time.Second); err != nil {\n\t\tcclog.Warnf(\"Archiver shutdown timeout in tests: %v\", err)\n\t}\n}\n\n/*\n* This function starts a job, stops it, and then reads its data from the job-archive.\n* Do not run sub-tests in parallel! Tests should not be run in parallel at all, because\n* at least `setup` modifies global state.\n */\nfunc TestRestApi(t *testing.T) {\n\trestapi := setup(t)\n\tt.Cleanup(cleanup)\n\ttestData := schema.JobData{\n\t\t\"load_one\": map[schema.MetricScope]*schema.JobMetric{\n\t\t\tschema.MetricScopeNode: {\n\t\t\t\tUnit: schema.Unit{Base: \"load\"},\n\t\t\t\tTimestep: 60,\n\t\t\t\tSeries: []schema.Series{\n\t\t\t\t\t{\n\t\t\t\t\t\tHostname: \"host123\",\n\t\t\t\t\t\tStatistics: schema.MetricStatistics{Min: 0.1, Avg: 0.2, Max: 0.3},\n\t\t\t\t\t\tData: []schema.Float{0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3},\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tmetricstore.TestLoadDataCallback = func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error) {\n\t\treturn testData, nil\n\t}\n\n\tr := chi.NewRouter()\n\trestapi.MountAPIRoutes(r)\n\n\tvar TestJobID int64 = 123\n\tTestClusterName := \"testcluster\"\n\tvar TestStartTime int64 = 123456789\n\n\tconst startJobBody string = `{\n \"jobId\": 123,\n\t\t\"user\": \"testuser\",\n\t\t\"project\": \"testproj\",\n\t\t\"cluster\": \"testcluster\",\n\t\t\"partition\": \"default\",\n\t\t\"walltime\": 3600,\n\t\t\"arrayJobId\": 0,\n\t\t\"numNodes\": 1,\n\t\t\"numHwthreads\": 8,\n\t\t\"numAcc\": 0,\n\t\t\"shared\": \"none\",\n\t\t\"monitoringStatus\": 1,\n\t\t\"smt\": 1,\n\t\t\"resources\": [\n\t\t\t{\n\t\t\t\t\"hostname\": \"host123\",\n\t\t\t\t\"hwthreads\": [0, 1, 2, 3, 4, 5, 6, 7]\n\t\t\t}\n\t\t],\n\t\t\"metaData\": { \"jobScript\": \"blablabla...\" },\n\t\t\"startTime\": 123456789\n\t}`\n\n\tconst contextUserKey repository.ContextKey = \"user\"\n\tcontextUserValue := &schema.User{\n\t\tUsername: \"testuser\",\n\t\tProjects: make([]string, 0),\n\t\tRoles: []string{\"user\"},\n\t\tAuthType: 0,\n\t\tAuthSource: 2,\n\t}\n\n\tif ok := t.Run(\"StartJob\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/start_job/\", bytes.NewBuffer([]byte(startJobBody)))\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusCreated {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\t\t// resolver := graph.GetResolverInstance()\n\t\trestapi.JobRepository.SyncJobs()\n\t\tjob, err := restapi.JobRepository.Find(&TestJobID, &TestClusterName, &TestStartTime)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\t// job.Tags, err = resolver.Job().Tags(ctx, job)\n\t\t// if err != nil {\n\t\t// \tt.Fatal(err)\n\t\t// }\n\n\t\tif job.JobID != 123 ||\n\t\t\tjob.User != \"testuser\" ||\n\t\t\tjob.Project != \"testproj\" ||\n\t\t\tjob.Cluster != \"testcluster\" ||\n\t\t\tjob.SubCluster != \"sc1\" ||\n\t\t\tjob.Partition != \"default\" ||\n\t\t\tjob.Walltime != 3600 ||\n\t\t\tjob.ArrayJobID != 0 ||\n\t\t\tjob.NumNodes != 1 ||\n\t\t\tjob.NumHWThreads != 8 ||\n\t\t\tjob.NumAcc != 0 ||\n\t\t\tjob.MonitoringStatus != 1 ||\n\t\t\tjob.SMT != 1 ||\n\t\t\t!reflect.DeepEqual(job.Resources, []*schema.Resource{{Hostname: \"host123\", HWThreads: []int{0, 1, 2, 3, 4, 5, 6, 7}}}) ||\n\t\t\tjob.StartTime != 123456789 {\n\t\t\tt.Fatalf(\"unexpected job properties: %#v\", job)\n\t\t}\n\n\t\t// if len(job.Tags) != 1 || job.Tags[0].Type != \"testTagType\" || job.Tags[0].Name != \"testTagName\" || job.Tags[0].Scope != \"testuser\" {\n\t\t// \tt.Fatalf(\"unexpected tags: %#v\", job.Tags)\n\t\t// }\n\t}); !ok {\n\t\treturn\n\t}\n\n\tconst stopJobBody string = `{\n \"jobId\": 123,\n\t\t\"startTime\": 123456789,\n\t\t\"cluster\": \"testcluster\",\n\n\t\t\"jobState\": \"completed\",\n\t\t\"stopTime\": 123457789\n\t}`\n\n\tvar stoppedJob *schema.Job\n\tif ok := t.Run(\"StopJob\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/stop_job/\", bytes.NewBuffer([]byte(stopJobBody)))\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusOK {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\n\t\t// Archiving happens asynchronously, will be completed in cleanup\n\t\tjob, err := restapi.JobRepository.Find(&TestJobID, &TestClusterName, &TestStartTime)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\tif job.State != schema.JobStateCompleted {\n\t\t\tt.Fatal(\"expected job to be completed\")\n\t\t}\n\n\t\tif job.Duration != (123457789 - 123456789) {\n\t\t\tt.Fatalf(\"unexpected job properties: %#v\", job)\n\t\t}\n\n\t\tjob.MetaData, err = restapi.JobRepository.FetchMetadata(job)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\tif !reflect.DeepEqual(job.MetaData, map[string]string{\"jobScript\": \"blablabla...\"}) {\n\t\t\tt.Fatalf(\"unexpected job.metaData: %#v\", job.MetaData)\n\t\t}\n\n\t\tstoppedJob = job\n\t}); !ok {\n\t\treturn\n\t}\n\n\tt.Run(\"CheckArchive\", func(t *testing.T) {\n\t\tdata, err := metricdispatch.LoadData(stoppedJob, []string{\"load_one\"}, []schema.MetricScope{schema.MetricScopeNode}, context.Background(), 60)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\tif !reflect.DeepEqual(data, testData) {\n\t\t\tt.Fatal(\"unexpected data fetched from archive\")\n\t\t}\n\t})\n\n\tt.Run(\"CheckDoubleStart\", func(t *testing.T) {\n\t\t// Starting a job with the same jobId and cluster should only be allowed if the startTime is far appart!\n\t\tbody := strings.ReplaceAll(startJobBody, `\"startTime\": 123456789`, `\"startTime\": 123456790`)\n\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/start_job/\", bytes.NewBuffer([]byte(body)))\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusUnprocessableEntity {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\t})\n\n\tconst startJobBodyFailed string = `{\n \"jobId\": 12345,\n\t\t\"user\": \"testuser\",\n\t\t\"project\": \"testproj\",\n\t\t\"cluster\": \"testcluster\",\n\t\t\"partition\": \"default\",\n\t\t\"walltime\": 3600,\n\t\t\"numNodes\": 1,\n\t\t\"shared\": \t\"none\",\n\t\t\"monitoringStatus\": 1,\n\t\t\"smt\": 1,\n\t\t\"resources\": [\n\t\t\t{\n\t\t\t\t\"hostname\": \"host123\"\n\t\t\t}\n\t\t],\n\t\t\"startTime\": 12345678\n\t}`\n\n\tok := t.Run(\"StartJobFailed\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/start_job/\", bytes.NewBuffer([]byte(startJobBodyFailed)))\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusCreated {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\t})\n\tif !ok {\n\t\tt.Fatal(\"subtest failed\")\n\t}\n\n\ttime.Sleep(1 * time.Second)\n\trestapi.JobRepository.SyncJobs()\n\n\tconst stopJobBodyFailed string = `{\n \"jobId\": 12345,\n\t\t\"cluster\": \"testcluster\",\n\n\t\t\"jobState\": \"failed\",\n\t\t\"stopTime\": 12355678\n\t}`\n\n\tok = t.Run(\"StopJobFailed\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/stop_job/\", bytes.NewBuffer([]byte(stopJobBodyFailed)))\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusOK {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\n\t\t// Archiving happens asynchronously, will be completed in cleanup\n\t\tjobid, cluster := int64(12345), \"testcluster\"\n\t\tjob, err := restapi.JobRepository.Find(&jobid, &cluster, nil)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\tif job.State != schema.JobStateFailed {\n\t\t\tt.Fatal(\"expected job to be failed\")\n\t\t}\n\t})\n\tif !ok {\n\t\tt.Fatal(\"subtest failed\")\n\t}\n\n\tt.Run(\"GetUsedNodesNoRunning\", func(t *testing.T) {\n\t\tcontextUserValue := &schema.User{\n\t\t\tUsername: \"testuser\",\n\t\t\tProjects: make([]string, 0),\n\t\t\tRoles: []string{\"api\"},\n\t\t\tAuthType: 0,\n\t\t\tAuthSource: 2,\n\t\t}\n\n\t\treq := httptest.NewRequest(http.MethodGet, \"/jobs/used_nodes?ts=123456790\", nil)\n\t\trecorder := httptest.NewRecorder()\n\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tresponse := recorder.Result()\n\t\tif response.StatusCode != http.StatusOK {\n\t\t\tt.Fatal(response.Status, recorder.Body.String())\n\t\t}\n\n\t\tvar result api.GetUsedNodesAPIResponse\n\t\tif err := json.NewDecoder(response.Body).Decode(&result); err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\n\t\tif result.UsedNodes == nil {\n\t\t\tt.Fatal(\"expected usedNodes to be non-nil\")\n\t\t}\n\n\t\tif len(result.UsedNodes) != 0 {\n\t\t\tt.Fatalf(\"expected no used nodes for stopped jobs, got: %v\", result.UsedNodes)\n\t\t}\n\t})\n}\n\n// TestStopJobWithReusedJobId verifies that stopping a recently started job works\n// even when an older job with the same jobId exists in the job table (e.g. with\n// state \"failed\"). This is a regression test for the bug where Find() on the job\n// table would match the old job instead of the new one still in job_cache.\nfunc TestStopJobWithReusedJobId(t *testing.T) {\n\trestapi := setup(t)\n\tt.Cleanup(cleanup)\n\n\ttestData := schema.JobData{\n\t\t\"load_one\": map[schema.MetricScope]*schema.JobMetric{\n\t\t\tschema.MetricScopeNode: {\n\t\t\t\tUnit: schema.Unit{Base: \"load\"},\n\t\t\t\tTimestep: 60,\n\t\t\t\tSeries: []schema.Series{\n\t\t\t\t\t{\n\t\t\t\t\t\tHostname: \"host123\",\n\t\t\t\t\t\tStatistics: schema.MetricStatistics{Min: 0.1, Avg: 0.2, Max: 0.3},\n\t\t\t\t\t\tData: []schema.Float{0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3},\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tmetricstore.TestLoadDataCallback = func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error) {\n\t\treturn testData, nil\n\t}\n\n\tr := chi.NewRouter()\n\trestapi.MountAPIRoutes(r)\n\n\tconst contextUserKey repository.ContextKey = \"user\"\n\tcontextUserValue := &schema.User{\n\t\tUsername: \"testuser\",\n\t\tProjects: make([]string, 0),\n\t\tRoles: []string{\"user\"},\n\t\tAuthType: 0,\n\t\tAuthSource: 2,\n\t}\n\n\t// Step 1: Start the first job (jobId=999)\n\tconst startJobBody1 string = `{\n\t\t\"jobId\": 999,\n\t\t\"user\": \"testuser\",\n\t\t\"project\": \"testproj\",\n\t\t\"cluster\": \"testcluster\",\n\t\t\"partition\": \"default\",\n\t\t\"walltime\": 3600,\n\t\t\"numNodes\": 1,\n\t\t\"numHwthreads\": 8,\n\t\t\"numAcc\": 0,\n\t\t\"shared\": \"none\",\n\t\t\"monitoringStatus\": 1,\n\t\t\"smt\": 1,\n\t\t\"resources\": [{\"hostname\": \"host123\", \"hwthreads\": [0, 1, 2, 3, 4, 5, 6, 7]}],\n\t\t\"startTime\": 200000000\n\t}`\n\n\tif ok := t.Run(\"StartFirstJob\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/start_job/\", bytes.NewBuffer([]byte(startJobBody1)))\n\t\trecorder := httptest.NewRecorder()\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tif recorder.Result().StatusCode != http.StatusCreated {\n\t\t\tt.Fatal(recorder.Result().Status, recorder.Body.String())\n\t\t}\n\t}); !ok {\n\t\treturn\n\t}\n\n\t// Step 2: Sync to move job from cache to job table, then stop it as \"failed\"\n\ttime.Sleep(1 * time.Second)\n\trestapi.JobRepository.SyncJobs()\n\n\tconst stopJobBody1 string = `{\n\t\t\"jobId\": 999,\n\t\t\"startTime\": 200000000,\n\t\t\"cluster\": \"testcluster\",\n\t\t\"jobState\": \"failed\",\n\t\t\"stopTime\": 200001000\n\t}`\n\n\tif ok := t.Run(\"StopFirstJobAsFailed\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/stop_job/\", bytes.NewBuffer([]byte(stopJobBody1)))\n\t\trecorder := httptest.NewRecorder()\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tif recorder.Result().StatusCode != http.StatusOK {\n\t\t\tt.Fatal(recorder.Result().Status, recorder.Body.String())\n\t\t}\n\n\t\tjobid, cluster := int64(999), \"testcluster\"\n\t\tjob, err := restapi.JobRepository.Find(&jobid, &cluster, nil)\n\t\tif err != nil {\n\t\t\tt.Fatal(err)\n\t\t}\n\t\tif job.State != schema.JobStateFailed {\n\t\t\tt.Fatalf(\"expected first job to be failed, got: %s\", job.State)\n\t\t}\n\t}); !ok {\n\t\treturn\n\t}\n\n\t// Wait for archiving to complete\n\ttime.Sleep(1 * time.Second)\n\n\t// Step 3: Start a NEW job with the same jobId=999 but different startTime.\n\t// This job will sit in job_cache (not yet synced).\n\tconst startJobBody2 string = `{\n\t\t\"jobId\": 999,\n\t\t\"user\": \"testuser\",\n\t\t\"project\": \"testproj\",\n\t\t\"cluster\": \"testcluster\",\n\t\t\"partition\": \"default\",\n\t\t\"walltime\": 3600,\n\t\t\"numNodes\": 1,\n\t\t\"numHwthreads\": 8,\n\t\t\"numAcc\": 0,\n\t\t\"shared\": \"none\",\n\t\t\"monitoringStatus\": 1,\n\t\t\"smt\": 1,\n\t\t\"resources\": [{\"hostname\": \"host123\", \"hwthreads\": [0, 1, 2, 3, 4, 5, 6, 7]}],\n\t\t\"startTime\": 300000000\n\t}`\n\n\tif ok := t.Run(\"StartSecondJob\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/start_job/\", bytes.NewBuffer([]byte(startJobBody2)))\n\t\trecorder := httptest.NewRecorder()\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tif recorder.Result().StatusCode != http.StatusCreated {\n\t\t\tt.Fatal(recorder.Result().Status, recorder.Body.String())\n\t\t}\n\t}); !ok {\n\t\treturn\n\t}\n\n\t// Step 4: Stop the second job WITHOUT syncing first.\n\t// Before the fix, this would fail because Find() on the job table would\n\t// match the old failed job (jobId=999) and reject with \"already stopped\".\n\tconst stopJobBody2 string = `{\n\t\t\"jobId\": 999,\n\t\t\"startTime\": 300000000,\n\t\t\"cluster\": \"testcluster\",\n\t\t\"jobState\": \"completed\",\n\t\t\"stopTime\": 300001000\n\t}`\n\n\tt.Run(\"StopSecondJobBeforeSync\", func(t *testing.T) {\n\t\treq := httptest.NewRequest(http.MethodPost, \"/jobs/stop_job/\", bytes.NewBuffer([]byte(stopJobBody2)))\n\t\trecorder := httptest.NewRecorder()\n\t\tctx := context.WithValue(req.Context(), contextUserKey, contextUserValue)\n\t\tr.ServeHTTP(recorder, req.WithContext(ctx))\n\t\tif recorder.Result().StatusCode != http.StatusOK {\n\t\t\tt.Fatalf(\"expected stop to succeed for cached job, got: %s %s\",\n\t\t\t\trecorder.Result().Status, recorder.Body.String())\n\t\t}\n\t})\n}\n","structuredPatch":[{"oldStart":355,"oldLines":7,"newStart":355,"newLines":7,"lines":[" }"," "," t.Run(\"CheckArchive\", func(t *testing.T) {","- data, err := metricdispatch.LoadData(stoppedJob, []string{\"load_one\"}, []schema.MetricScope{schema.MetricScopeNode}, context.Background(), 60)","+ data, err := metricdispatch.LoadData(stoppedJob, []string{\"load_one\"}, []schema.MetricScope{schema.MetricScopeNode}, context.Background(), 60, \"\")"," if err != nil {"," t.Fatal(err)"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"244f3aa8-513f-4ec9-a616-a0243519c008","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"7b30720d-e447-437f-b4f0-35eac4164df4","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DCymm9D7fMFJmRzpw5DbRB","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/api/job.go has been updated successfully."}]},"uuid":"112f2f3e-dd35-460d-9e18-dbda70ae4fb5","timestamp":"2026-03-19T19:50:50.386Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","oldString":"data, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution)","newString":"data, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution, \"\")","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage api\n\nimport (\n\t\"bufio\"\n\t\"database/sql\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"strconv\"\n\t\"strings\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/archiver\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/importer\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/go-chi/chi/v5\"\n)\n\nconst (\n\t// secondsPerDay is the number of seconds in 24 hours.\n\t// Used for duplicate job detection within a day window.\n\tsecondsPerDay = 86400\n)\n\n// StopJobAPIRequest model\ntype StopJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" example:\"123000\"`\n\tCluster *string `json:\"cluster\" example:\"fritz\"`\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"`\n\tState schema.JobState `json:\"jobState\" validate:\"required\" example:\"completed\"`\n\tStopTime int64 `json:\"stopTime\" validate:\"required\" example:\"1649763839\"`\n}\n\n// DeleteJobAPIRequest model\ntype DeleteJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n}\n\n// GetJobsAPIResponse model\ntype GetJobsAPIResponse struct {\n\tJobs []*schema.Job `json:\"jobs\"` // Array of jobs\n\tItems int `json:\"items\"` // Number of jobs returned\n\tPage int `json:\"page\"` // Page id returned\n}\n\n// APITag model\ntype APITag struct {\n\t// Tag Type\n\tType string `json:\"type\" example:\"Debug\"`\n\tName string `json:\"name\" example:\"Testjob\"` // Tag Name\n\tScope string `json:\"scope\" example:\"global\"` // Tag Scope for Frontend Display\n}\n\ntype EditMetaRequest struct {\n\tKey string `json:\"key\" example:\"jobScript\"`\n\tValue string `json:\"value\" example:\"bash script\"`\n}\n\n// JobMetaRequest model\ntype JobMetaRequest struct {\n\tJobId *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n\tPayload EditMetaRequest `json:\"payload\"` // Content to Add to Job Meta_Data\n}\n\ntype TagJobAPIRequest []*APITag\n\ntype GetJobAPIRequest []string\n\ntype GetJobAPIResponse struct {\n\tMeta *schema.Job\n\tData []*JobMetricWithName\n}\n\ntype GetCompleteJobAPIResponse struct {\n\tMeta *schema.Job\n\tData schema.JobData\n}\n\ntype JobMetricWithName struct {\n\tMetric *schema.JobMetric `json:\"metric\"`\n\tName string `json:\"name\"`\n\tScope schema.MetricScope `json:\"scope\"`\n}\n\n// getJobs godoc\n// @summary Lists all jobs\n// @tags Job query\n// @description Get a list of all jobs. Filters can be applied using query parameters.\n// @description Number of results can be limited by page. Results are sorted by descending startTime.\n// @produce json\n// @param state query string false \"Job State\" Enums(running, completed, failed, cancelled, stopped, timeout)\n// @param cluster query string false \"Job Cluster\"\n// @param start-time query string false \"Syntax: '$from-$to', as unix epoch timestamps in seconds\"\n// @param items-per-page query int false \"Items per page (Default: 25)\"\n// @param page query int false \"Page Number (Default: 1)\"\n// @param with-metadata query bool false \"Include metadata (e.g. jobScript) in response\"\n// @success 200 {object} api.GetJobsAPIResponse \"Job array and page info\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 \t\t {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/ [get]\nfunc (api *RestAPI) getJobs(rw http.ResponseWriter, r *http.Request) {\n\twithMetadata := false\n\tfilter := &model.JobFilter{}\n\tpage := &model.PageRequest{ItemsPerPage: 25, Page: 1}\n\torder := &model.OrderByInput{Field: \"startTime\", Type: \"col\", Order: model.SortDirectionEnumDesc}\n\n\tfor key, vals := range r.URL.Query() {\n\t\tswitch key {\n\t\tcase \"project\":\n\t\t\tfilter.Project = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"state\":\n\t\t\tfor _, s := range vals {\n\t\t\t\tstate := schema.JobState(s)\n\t\t\t\tif !state.Valid() {\n\t\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: state\"),\n\t\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\t\treturn\n\t\t\t\t}\n\t\t\t\tfilter.State = append(filter.State, state)\n\t\t\t}\n\t\tcase \"cluster\":\n\t\t\tfilter.Cluster = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"start-time\": // ?startTime=1753707480-1754053139\n\t\t\tst := strings.Split(vals[0], \"-\")\n\t\t\tif len(st) != 2 {\n\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: startTime\"),\n\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tfrom, err := strconv.ParseInt(st[0], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tto, err := strconv.ParseInt(st[1], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tufrom, uto := time.Unix(from, 0), time.Unix(to, 0)\n\t\t\tfilter.StartTime = &config.TimeRange{From: &ufrom, To: &uto}\n\t\tcase \"page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.Page = x\n\t\tcase \"items-per-page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.ItemsPerPage = x\n\t\tcase \"with-metadata\":\n\t\t\twithMetadata = true\n\t\tdefault:\n\t\t\thandleError(fmt.Errorf(\"invalid query parameter: %s\", key),\n\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tjobs, err := api.JobRepository.QueryJobs(r.Context(), []*model.JobFilter{filter}, page, order)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tresults := make([]*schema.Job, 0, len(jobs))\n\tfor _, job := range jobs {\n\t\tif withMetadata {\n\t\t\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\t\tif err != nil {\n\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tif job.MonitoringStatus == schema.MonitoringStatusArchivingSuccessful {\n\t\t\tjob.Statistics, err = archive.GetStatistics(job)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tresults = append(results, job)\n\t}\n\n\tcclog.Debugf(\"/api/jobs: %d jobs returned\", len(results))\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobsAPIResponse{\n\t\tJobs: results,\n\t\tItems: page.ItemsPerPage,\n\t\tPage: page.Page,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getCompleteJobByID godoc\n// @summary Get job meta and optional all metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param all-metrics query bool false \"Include all available metrics\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [get]\nfunc (api *RestAPI) getCompleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id) // Get Job from Repo by ID\n\t} else {\n\t\thandleError(fmt.Errorf(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tvar data schema.JobData\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tif r.URL.Query().Get(\"all-metrics\") == \"true\" {\n\t\tdata, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetCompleteJobAPIResponse{\n\t\tMeta: job,\n\t\tData: data,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getJobByID godoc\n// @summary Get job meta and configurable metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @accept json\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param request body api.GetJobAPIRequest true \"Array of metric names\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [post]\nfunc (api *RestAPI) getJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar metrics GetJobAPIRequest\n\tif err = decode(r.Body, &metrics); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)\n\tif err != nil {\n\t\tcclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\treturn\n\t}\n\n\tres := []*JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobAPIResponse{\n\t\tMeta: job,\n\t\tData: res,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// editMeta godoc\n// @summary Edit meta-data json of job identified by database id\n// @tags Job add and modify\n// @description Edit key value pairs in job metadata json of job specified by database id\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.EditMetaRequest true \"Metadata Key value pair to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/{id} [patch]\nfunc (api *RestAPI) editMeta(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tvar req EditMetaRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Key, req.Value); err != nil {\n\t\thandleError(fmt.Errorf(\"updating metadata failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// editMetaByRequest godoc\n// @summary Edit meta-data json of job identified by request\n// @tags Job add and modify\n// @description Edit key value pairs in metadata json of job specified by jobID, StartTime and Cluster\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param request body api.JobMetaRequest true \"Specifies job and payload to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/ [patch]\nfunc (api *RestAPI) editMetaByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := JobMetaRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will have its meta_data edited) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobId == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// log.Printf(\"loading db job for editMetaByRequest... : JobMetaRequest=%v\", req)\n\tjob, err = api.JobRepository.Find(req.JobId, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Payload.Key, req.Payload.Value); err != nil {\n\t\thttp.Error(rw, err.Error(), http.StatusInternalServerError)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tjson.NewEncoder(rw).Encode(job)\n}\n\n// tagJob godoc\n// @summary Adds one or more tags to a job\n// @tags Job add and modify\n// @description Adds tag(s) to a job specified by DB ID. Name and Type of Tag(s) can be chosen freely.\n// @description Tag Scope for frontend visibility will default to \"global\" if none entered, other options: \"admin\" or specific username.\n// @description If tagged job is already finished: Tag will be written directly to respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to add\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/tag_job/{id} [post]\nfunc (api *RestAPI) tagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, tag := range req {\n\t\ttagID, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), *job.ID, tag.Type, tag.Name, tag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = append(job.Tags, &schema.Tag{\n\t\t\tID: tagID,\n\t\t\tType: tag.Type,\n\t\t\tName: tag.Name,\n\t\t\tScope: tag.Scope,\n\t\t})\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTagJob godoc\n// @summary Removes one or more tags from a job\n// @tags Job add and modify\n// @description Removes tag(s) from a job specified by DB ID. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description If tagged job is already finished: Tag will be removed from respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /jobs/tag_job/{id} [delete]\nfunc (api *RestAPI) removeTagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warnf(\"Cannot delete private tag for job %d: Skip\", job.JobID)\n\t\t\tcontinue\n\t\t}\n\n\t\tremainingTags, err := api.JobRepository.RemoveJobTagByRequest(repository.GetUserFromContext(r.Context()), *job.ID, rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = remainingTags\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTags godoc\n// @summary Removes all tags and job-relations for type:name tuple\n// @tags Tag remove\n// @description Removes tags by type and name. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description Tag wills be removed from respective archive files.\n// @accept json\n// @produce plain\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {string} string \"Success Response\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /tags/ [delete]\nfunc (api *RestAPI) removeTags(rw http.ResponseWriter, r *http.Request) {\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\ttargetCount := len(req)\n\tcurrentCount := 0\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warn(\"Cannot delete private tag: Skip\")\n\t\t\tcontinue\n\t\t}\n\n\t\terr := api.JobRepository.RemoveTagByRequest(rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcurrentCount++\n\t}\n\n\trw.WriteHeader(http.StatusOK)\n\tfmt.Fprintf(rw, \"Deleted Tags from DB: %d successfull of %d requested\\n\", currentCount, targetCount)\n}\n\n// startJob godoc\n// @summary Adds a new job as \"running\"\n// @tags Job add and modify\n// @description Job specified in request body will be saved to database as \"running\" with new DB ID.\n// @description Job specifications follow the 'Job' scheme, API will fail to execute if requirements are not met.\n// @accept json\n// @produce json\n// @param request body schema.Job true \"Job to add\"\n// @success 201 {object} api.DefaultAPIResponse \"Job added successfully\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: The combination of jobId, clusterId and startTime does already exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/start_job/ [post]\nfunc (api *RestAPI) startJob(rw http.ResponseWriter, r *http.Request) {\n\treq := schema.Job{\n\t\tShared: \"none\",\n\t\tMonitoringStatus: schema.MonitoringStatusRunningOrArchiving,\n\t}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tcclog.Debugf(\"REST: %s\", req.GoString())\n\treq.State = schema.JobStateRunning\n\n\tif err := importer.SanityChecks(&req); err != nil {\n\t\thandleError(err, http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// aquire lock to avoid race condition between API calls\n\tvar unlockOnce sync.Once\n\tapi.RepositoryMutex.Lock()\n\tdefer unlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\t// Check if combination of (job_id, cluster_id, start_time) already exists:\n\tjobs, err := api.JobRepository.FindAll(&req.JobID, &req.Cluster, nil)\n\tif err != nil && err != sql.ErrNoRows {\n\t\thandleError(fmt.Errorf(\"checking for duplicate failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\tif err == nil {\n\t\tfor _, job := range jobs {\n\t\t\t// Check if jobs are within the same day (prevent duplicates)\n\t\t\tif (req.StartTime - job.StartTime) < secondsPerDay {\n\t\t\t\thandleError(fmt.Errorf(\"a job with that jobId, cluster and startTime already exists: dbid: %d, jobid: %d\", *job.ID, job.JobID), http.StatusUnprocessableEntity, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\t}\n\n\t// When tags are present, insert directly into the job table so that the\n\t// returned ID can be used with AddTagOrCreate (which queries the job table).\n\t// Jobs without tags use the cache path as before.\n\tvar id int64\n\tif len(req.Tags) > 0 {\n\t\tid, err = api.JobRepository.StartDirect(&req)\n\t} else {\n\t\tid, err = api.JobRepository.Start(&req)\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"insert into database failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\t// unlock here, adding Tags can be async\n\tunlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\tfor _, tag := range req.Tags {\n\t\tif _, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), id, tag.Type, tag.Name, tag.Scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag to new job %d failed: %w\", id, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Infof(\"new job (id: %d): cluster=%s, jobId=%d, user=%s, startTime=%d\", id, req.Cluster, req.JobID, req.User, req.StartTime)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusCreated)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: \"success\",\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// stopJobByRequest godoc\n// @summary Marks job as completed and triggers archiving\n// @tags Job add and modify\n// @description Job to stop is specified by request body. All fields are required in this case.\n// @description Returns full job resource information according to 'Job' scheme.\n// @produce json\n// @param request body api.StopJobAPIRequest true \"All fields required\"\n// @success 200 {object} schema.Job \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: job has already been stopped\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/stop_job/ [post]\nfunc (api *RestAPI) stopJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := StopJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be stopped) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tisCached := false\n\tjob, err = api.JobRepository.FindCached(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\t// Not in cache, try main job table\n\t\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\t\treturn\n\t\t}\n\t} else {\n\t\tisCached = true\n\t}\n\n\tapi.checkAndHandleStopJob(rw, job, req, isCached)\n}\n\n// deleteJobByID godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to remove is specified by database ID. This will not remove the job from the job archive.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/{id} [delete]\nfunc (api *RestAPI) deleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"id\")\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\terr = api.JobRepository.DeleteJobByID(id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %s\", id),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobByRequest godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to delete is specified by request body. All fields are required in this case.\n// @accept json\n// @produce json\n// @param request body api.DeleteJobAPIRequest true \"All fields required\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/ [delete]\nfunc (api *RestAPI) deleteJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := DeleteJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be deleted) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\terr = api.JobRepository.DeleteJobByID(*job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %d\", *job.ID),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobBefore godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Remove all jobs with start time before timestamp. The jobs will not be removed from the job archive.\n// @produce json\n// @param ts path int true \"Unix epoch timestamp\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job_before/{ts} [delete]\n// @param omit-tagged query bool false \"Omit jobs with tags from deletion\"\nfunc (api *RestAPI) deleteJobBefore(rw http.ResponseWriter, r *http.Request) {\n\tvar cnt int\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"ts\")\n\tvar err error\n\tif id != \"\" {\n\t\tts, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for ts: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\t// Check for omit-tagged query parameter\n\t\tomitTagged := \"none\"\n\t\tif omitTaggedStr := r.URL.Query().Get(\"omit-tagged\"); omitTaggedStr != \"\" {\n\t\t\tswitch omitTaggedStr {\n\t\t\tcase \"none\", \"all\", \"user\":\n\t\t\t\tomitTagged = omitTaggedStr\n\t\t\tdefault:\n\t\t\t\thandleError(fmt.Errorf(\"omit-tagged must be one of: none, all, user\"), http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tcnt, err = api.JobRepository.DeleteJobsBefore(ts, omitTagged)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'ts' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting jobs failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted %d jobs\", cnt),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\nfunc (api *RestAPI) checkAndHandleStopJob(rw http.ResponseWriter, job *schema.Job, req StopJobAPIRequest, isCached bool) {\n\t// Sanity checks\n\tif job.State != schema.JobStateRunning {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : job has already been stopped (state is: %s)\", job.JobID, *job.ID, job.Cluster, job.State), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif job.StartTime > req.StopTime {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : stopTime %d must be larger/equal than startTime %d\", job.JobID, *job.ID, job.Cluster, req.StopTime, job.StartTime), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif req.State != \"\" && !req.State.Valid() {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : invalid requested job state: %#v\", job.JobID, *job.ID, job.Cluster, req.State), http.StatusBadRequest, rw)\n\t\treturn\n\t} else if req.State == \"\" {\n\t\treq.State = schema.JobStateCompleted\n\t}\n\n\t// Mark job as stopped in the database (update state and duration)\n\tjob.Duration = int32(req.StopTime - job.StartTime)\n\tjob.State = req.State\n\tapi.JobRepository.Mutex.Lock()\n\tdefer api.JobRepository.Mutex.Unlock()\n\n\t// If the job is still in job_cache, transfer it to the job table first\n\t// so that job.ID always points to the job table for downstream code\n\tif isCached {\n\t\tnewID, err := api.JobRepository.TransferCachedJobToMain(*job.ID)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : transferring cached job failed: %w\", job.JobID, *job.ID, job.Cluster, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcclog.Infof(\"transferred cached job to main table: old id %d -> new id %d (jobId=%d)\", *job.ID, newID, job.JobID)\n\t\tjob.ID = &newID\n\t}\n\n\tif err := api.JobRepository.Stop(*job.ID, job.Duration, job.State, job.MonitoringStatus); err != nil {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : marking job as '%s' (duration: %d) in DB failed: %w\", job.JobID, *job.ID, job.Cluster, job.State, job.Duration, err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tcclog.Infof(\"archiving job... (dbid: %d): cluster=%s, jobId=%d, user=%s, startTime=%d, duration=%d, state=%s\", *job.ID, job.Cluster, job.JobID, job.User, job.StartTime, job.Duration, job.State)\n\n\t// Send a response (with status OK). This means that errors that happen from here on forward\n\t// can *NOT* be communicated to the client. If reading from a MetricDataRepository or\n\t// writing to the filesystem fails, the client will not know.\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n\n\t// Monitoring is disabled...\n\tif job.MonitoringStatus == schema.MonitoringStatusDisabled {\n\t\treturn\n\t}\n\n\t// Trigger async archiving\n\tarchiver.TriggerArchiving(job)\n}\n\nfunc (api *RestAPI) getJobMetrics(rw http.ResponseWriter, r *http.Request) {\n\tid := chi.URLParam(r, \"id\")\n\tmetrics := r.URL.Query()[\"metric\"]\n\tvar scopes []schema.MetricScope\n\tfor _, scope := range r.URL.Query()[\"scope\"] {\n\t\tvar s schema.MetricScope\n\t\tif err := s.UnmarshalGQL(scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"unmarshaling scope failed: %w\", err), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t\tscopes = append(scopes, s)\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\n\ttype Response struct {\n\t\tData *struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t} `json:\"data\"`\n\t\tError *struct {\n\t\t\tMessage string `json:\"message\"`\n\t\t} `json:\"error\"`\n\t}\n\n\tresolver := graph.GetResolverInstance()\n\tdata, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)\n\tif err != nil {\n\t\tif err := json.NewEncoder(rw).Encode(Response{\n\t\t\tError: &struct {\n\t\t\t\tMessage string `json:\"message\"`\n\t\t\t}{Message: err.Error()},\n\t\t}); err != nil {\n\t\t\tcclog.Errorf(\"Failed to encode error response: %v\", err)\n\t\t}\n\t\treturn\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(Response{\n\t\tData: &struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t}{JobMetrics: data},\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// GetUsedNodesAPIResponse model\ntype GetUsedNodesAPIResponse struct {\n\tUsedNodes map[string][]string `json:\"usedNodes\"` // Map of cluster names to lists of used node hostnames\n}\n\n// getUsedNodes godoc\n// @summary Lists used nodes by cluster\n// @tags Job query\n// @description Get a map of cluster names to lists of unique hostnames that are currently in use by running jobs that started before the specified timestamp.\n// @produce json\n// @param ts query int true \"Unix timestamp to filter jobs (jobs with start_time < ts)\"\n// @success 200 {object} api.GetUsedNodesAPIResponse \"Map of cluster names to hostname lists\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/used_nodes [get]\nfunc (api *RestAPI) getUsedNodes(rw http.ResponseWriter, r *http.Request) {\n\tif user := repository.GetUserFromContext(r.Context()); user != nil &&\n\t\t!user.HasRole(schema.RoleAPI) {\n\t\thandleError(fmt.Errorf(\"missing role: %v\", schema.GetRoleString(schema.RoleAPI)), http.StatusForbidden, rw)\n\t\treturn\n\t}\n\n\ttsStr := r.URL.Query().Get(\"ts\")\n\tif tsStr == \"\" {\n\t\thandleError(fmt.Errorf(\"missing required query parameter: ts\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tts, err := strconv.ParseInt(tsStr, 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"invalid timestamp format: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tusedNodes, err := api.JobRepository.GetUsedNodes(ts)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"failed to get used nodes: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tpayload := GetUsedNodesAPIResponse{\n\t\tUsedNodes: usedNodes,\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n","structuredPatch":[{"oldStart":301,"oldLines":7,"newStart":301,"newLines":7,"lines":[" }"," "," if r.URL.Query().Get(\"all-metrics\") == \"true\" {","- data, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution)","+ data, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution, \"\")"," if err != nil {"," cclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)"," return"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"7b30720d-e447-437f-b4f0-35eac4164df4","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"1504dc0b-c24b-42a3-ab0c-3fbba5c4571e","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GHUTJ6aEBpmeQDs4aFLYDJ","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/api/job.go has been updated successfully."}]},"uuid":"c3e35267-33b8-4132-ad14-eed0ce7936d6","timestamp":"2026-03-19T19:50:51.305Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","oldString":"data, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)","newString":"data, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution, \"\")","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage api\n\nimport (\n\t\"bufio\"\n\t\"database/sql\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"strconv\"\n\t\"strings\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/archiver\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/importer\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/go-chi/chi/v5\"\n)\n\nconst (\n\t// secondsPerDay is the number of seconds in 24 hours.\n\t// Used for duplicate job detection within a day window.\n\tsecondsPerDay = 86400\n)\n\n// StopJobAPIRequest model\ntype StopJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" example:\"123000\"`\n\tCluster *string `json:\"cluster\" example:\"fritz\"`\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"`\n\tState schema.JobState `json:\"jobState\" validate:\"required\" example:\"completed\"`\n\tStopTime int64 `json:\"stopTime\" validate:\"required\" example:\"1649763839\"`\n}\n\n// DeleteJobAPIRequest model\ntype DeleteJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n}\n\n// GetJobsAPIResponse model\ntype GetJobsAPIResponse struct {\n\tJobs []*schema.Job `json:\"jobs\"` // Array of jobs\n\tItems int `json:\"items\"` // Number of jobs returned\n\tPage int `json:\"page\"` // Page id returned\n}\n\n// APITag model\ntype APITag struct {\n\t// Tag Type\n\tType string `json:\"type\" example:\"Debug\"`\n\tName string `json:\"name\" example:\"Testjob\"` // Tag Name\n\tScope string `json:\"scope\" example:\"global\"` // Tag Scope for Frontend Display\n}\n\ntype EditMetaRequest struct {\n\tKey string `json:\"key\" example:\"jobScript\"`\n\tValue string `json:\"value\" example:\"bash script\"`\n}\n\n// JobMetaRequest model\ntype JobMetaRequest struct {\n\tJobId *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n\tPayload EditMetaRequest `json:\"payload\"` // Content to Add to Job Meta_Data\n}\n\ntype TagJobAPIRequest []*APITag\n\ntype GetJobAPIRequest []string\n\ntype GetJobAPIResponse struct {\n\tMeta *schema.Job\n\tData []*JobMetricWithName\n}\n\ntype GetCompleteJobAPIResponse struct {\n\tMeta *schema.Job\n\tData schema.JobData\n}\n\ntype JobMetricWithName struct {\n\tMetric *schema.JobMetric `json:\"metric\"`\n\tName string `json:\"name\"`\n\tScope schema.MetricScope `json:\"scope\"`\n}\n\n// getJobs godoc\n// @summary Lists all jobs\n// @tags Job query\n// @description Get a list of all jobs. Filters can be applied using query parameters.\n// @description Number of results can be limited by page. Results are sorted by descending startTime.\n// @produce json\n// @param state query string false \"Job State\" Enums(running, completed, failed, cancelled, stopped, timeout)\n// @param cluster query string false \"Job Cluster\"\n// @param start-time query string false \"Syntax: '$from-$to', as unix epoch timestamps in seconds\"\n// @param items-per-page query int false \"Items per page (Default: 25)\"\n// @param page query int false \"Page Number (Default: 1)\"\n// @param with-metadata query bool false \"Include metadata (e.g. jobScript) in response\"\n// @success 200 {object} api.GetJobsAPIResponse \"Job array and page info\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 \t\t {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/ [get]\nfunc (api *RestAPI) getJobs(rw http.ResponseWriter, r *http.Request) {\n\twithMetadata := false\n\tfilter := &model.JobFilter{}\n\tpage := &model.PageRequest{ItemsPerPage: 25, Page: 1}\n\torder := &model.OrderByInput{Field: \"startTime\", Type: \"col\", Order: model.SortDirectionEnumDesc}\n\n\tfor key, vals := range r.URL.Query() {\n\t\tswitch key {\n\t\tcase \"project\":\n\t\t\tfilter.Project = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"state\":\n\t\t\tfor _, s := range vals {\n\t\t\t\tstate := schema.JobState(s)\n\t\t\t\tif !state.Valid() {\n\t\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: state\"),\n\t\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\t\treturn\n\t\t\t\t}\n\t\t\t\tfilter.State = append(filter.State, state)\n\t\t\t}\n\t\tcase \"cluster\":\n\t\t\tfilter.Cluster = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"start-time\": // ?startTime=1753707480-1754053139\n\t\t\tst := strings.Split(vals[0], \"-\")\n\t\t\tif len(st) != 2 {\n\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: startTime\"),\n\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tfrom, err := strconv.ParseInt(st[0], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tto, err := strconv.ParseInt(st[1], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tufrom, uto := time.Unix(from, 0), time.Unix(to, 0)\n\t\t\tfilter.StartTime = &config.TimeRange{From: &ufrom, To: &uto}\n\t\tcase \"page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.Page = x\n\t\tcase \"items-per-page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.ItemsPerPage = x\n\t\tcase \"with-metadata\":\n\t\t\twithMetadata = true\n\t\tdefault:\n\t\t\thandleError(fmt.Errorf(\"invalid query parameter: %s\", key),\n\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tjobs, err := api.JobRepository.QueryJobs(r.Context(), []*model.JobFilter{filter}, page, order)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tresults := make([]*schema.Job, 0, len(jobs))\n\tfor _, job := range jobs {\n\t\tif withMetadata {\n\t\t\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\t\tif err != nil {\n\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tif job.MonitoringStatus == schema.MonitoringStatusArchivingSuccessful {\n\t\t\tjob.Statistics, err = archive.GetStatistics(job)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tresults = append(results, job)\n\t}\n\n\tcclog.Debugf(\"/api/jobs: %d jobs returned\", len(results))\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobsAPIResponse{\n\t\tJobs: results,\n\t\tItems: page.ItemsPerPage,\n\t\tPage: page.Page,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getCompleteJobByID godoc\n// @summary Get job meta and optional all metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param all-metrics query bool false \"Include all available metrics\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [get]\nfunc (api *RestAPI) getCompleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id) // Get Job from Repo by ID\n\t} else {\n\t\thandleError(fmt.Errorf(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tvar data schema.JobData\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tif r.URL.Query().Get(\"all-metrics\") == \"true\" {\n\t\tdata, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution, \"\")\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetCompleteJobAPIResponse{\n\t\tMeta: job,\n\t\tData: data,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getJobByID godoc\n// @summary Get job meta and configurable metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @accept json\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param request body api.GetJobAPIRequest true \"Array of metric names\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [post]\nfunc (api *RestAPI) getJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar metrics GetJobAPIRequest\n\tif err = decode(r.Body, &metrics); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)\n\tif err != nil {\n\t\tcclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\treturn\n\t}\n\n\tres := []*JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobAPIResponse{\n\t\tMeta: job,\n\t\tData: res,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// editMeta godoc\n// @summary Edit meta-data json of job identified by database id\n// @tags Job add and modify\n// @description Edit key value pairs in job metadata json of job specified by database id\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.EditMetaRequest true \"Metadata Key value pair to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/{id} [patch]\nfunc (api *RestAPI) editMeta(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tvar req EditMetaRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Key, req.Value); err != nil {\n\t\thandleError(fmt.Errorf(\"updating metadata failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// editMetaByRequest godoc\n// @summary Edit meta-data json of job identified by request\n// @tags Job add and modify\n// @description Edit key value pairs in metadata json of job specified by jobID, StartTime and Cluster\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param request body api.JobMetaRequest true \"Specifies job and payload to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/ [patch]\nfunc (api *RestAPI) editMetaByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := JobMetaRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will have its meta_data edited) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobId == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// log.Printf(\"loading db job for editMetaByRequest... : JobMetaRequest=%v\", req)\n\tjob, err = api.JobRepository.Find(req.JobId, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Payload.Key, req.Payload.Value); err != nil {\n\t\thttp.Error(rw, err.Error(), http.StatusInternalServerError)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tjson.NewEncoder(rw).Encode(job)\n}\n\n// tagJob godoc\n// @summary Adds one or more tags to a job\n// @tags Job add and modify\n// @description Adds tag(s) to a job specified by DB ID. Name and Type of Tag(s) can be chosen freely.\n// @description Tag Scope for frontend visibility will default to \"global\" if none entered, other options: \"admin\" or specific username.\n// @description If tagged job is already finished: Tag will be written directly to respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to add\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/tag_job/{id} [post]\nfunc (api *RestAPI) tagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, tag := range req {\n\t\ttagID, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), *job.ID, tag.Type, tag.Name, tag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = append(job.Tags, &schema.Tag{\n\t\t\tID: tagID,\n\t\t\tType: tag.Type,\n\t\t\tName: tag.Name,\n\t\t\tScope: tag.Scope,\n\t\t})\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTagJob godoc\n// @summary Removes one or more tags from a job\n// @tags Job add and modify\n// @description Removes tag(s) from a job specified by DB ID. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description If tagged job is already finished: Tag will be removed from respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /jobs/tag_job/{id} [delete]\nfunc (api *RestAPI) removeTagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warnf(\"Cannot delete private tag for job %d: Skip\", job.JobID)\n\t\t\tcontinue\n\t\t}\n\n\t\tremainingTags, err := api.JobRepository.RemoveJobTagByRequest(repository.GetUserFromContext(r.Context()), *job.ID, rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = remainingTags\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTags godoc\n// @summary Removes all tags and job-relations for type:name tuple\n// @tags Tag remove\n// @description Removes tags by type and name. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description Tag wills be removed from respective archive files.\n// @accept json\n// @produce plain\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {string} string \"Success Response\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /tags/ [delete]\nfunc (api *RestAPI) removeTags(rw http.ResponseWriter, r *http.Request) {\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\ttargetCount := len(req)\n\tcurrentCount := 0\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warn(\"Cannot delete private tag: Skip\")\n\t\t\tcontinue\n\t\t}\n\n\t\terr := api.JobRepository.RemoveTagByRequest(rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcurrentCount++\n\t}\n\n\trw.WriteHeader(http.StatusOK)\n\tfmt.Fprintf(rw, \"Deleted Tags from DB: %d successfull of %d requested\\n\", currentCount, targetCount)\n}\n\n// startJob godoc\n// @summary Adds a new job as \"running\"\n// @tags Job add and modify\n// @description Job specified in request body will be saved to database as \"running\" with new DB ID.\n// @description Job specifications follow the 'Job' scheme, API will fail to execute if requirements are not met.\n// @accept json\n// @produce json\n// @param request body schema.Job true \"Job to add\"\n// @success 201 {object} api.DefaultAPIResponse \"Job added successfully\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: The combination of jobId, clusterId and startTime does already exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/start_job/ [post]\nfunc (api *RestAPI) startJob(rw http.ResponseWriter, r *http.Request) {\n\treq := schema.Job{\n\t\tShared: \"none\",\n\t\tMonitoringStatus: schema.MonitoringStatusRunningOrArchiving,\n\t}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tcclog.Debugf(\"REST: %s\", req.GoString())\n\treq.State = schema.JobStateRunning\n\n\tif err := importer.SanityChecks(&req); err != nil {\n\t\thandleError(err, http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// aquire lock to avoid race condition between API calls\n\tvar unlockOnce sync.Once\n\tapi.RepositoryMutex.Lock()\n\tdefer unlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\t// Check if combination of (job_id, cluster_id, start_time) already exists:\n\tjobs, err := api.JobRepository.FindAll(&req.JobID, &req.Cluster, nil)\n\tif err != nil && err != sql.ErrNoRows {\n\t\thandleError(fmt.Errorf(\"checking for duplicate failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\tif err == nil {\n\t\tfor _, job := range jobs {\n\t\t\t// Check if jobs are within the same day (prevent duplicates)\n\t\t\tif (req.StartTime - job.StartTime) < secondsPerDay {\n\t\t\t\thandleError(fmt.Errorf(\"a job with that jobId, cluster and startTime already exists: dbid: %d, jobid: %d\", *job.ID, job.JobID), http.StatusUnprocessableEntity, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\t}\n\n\t// When tags are present, insert directly into the job table so that the\n\t// returned ID can be used with AddTagOrCreate (which queries the job table).\n\t// Jobs without tags use the cache path as before.\n\tvar id int64\n\tif len(req.Tags) > 0 {\n\t\tid, err = api.JobRepository.StartDirect(&req)\n\t} else {\n\t\tid, err = api.JobRepository.Start(&req)\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"insert into database failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\t// unlock here, adding Tags can be async\n\tunlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\tfor _, tag := range req.Tags {\n\t\tif _, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), id, tag.Type, tag.Name, tag.Scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag to new job %d failed: %w\", id, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Infof(\"new job (id: %d): cluster=%s, jobId=%d, user=%s, startTime=%d\", id, req.Cluster, req.JobID, req.User, req.StartTime)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusCreated)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: \"success\",\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// stopJobByRequest godoc\n// @summary Marks job as completed and triggers archiving\n// @tags Job add and modify\n// @description Job to stop is specified by request body. All fields are required in this case.\n// @description Returns full job resource information according to 'Job' scheme.\n// @produce json\n// @param request body api.StopJobAPIRequest true \"All fields required\"\n// @success 200 {object} schema.Job \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: job has already been stopped\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/stop_job/ [post]\nfunc (api *RestAPI) stopJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := StopJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be stopped) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tisCached := false\n\tjob, err = api.JobRepository.FindCached(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\t// Not in cache, try main job table\n\t\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\t\treturn\n\t\t}\n\t} else {\n\t\tisCached = true\n\t}\n\n\tapi.checkAndHandleStopJob(rw, job, req, isCached)\n}\n\n// deleteJobByID godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to remove is specified by database ID. This will not remove the job from the job archive.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/{id} [delete]\nfunc (api *RestAPI) deleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"id\")\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\terr = api.JobRepository.DeleteJobByID(id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %s\", id),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobByRequest godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to delete is specified by request body. All fields are required in this case.\n// @accept json\n// @produce json\n// @param request body api.DeleteJobAPIRequest true \"All fields required\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/ [delete]\nfunc (api *RestAPI) deleteJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := DeleteJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be deleted) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\terr = api.JobRepository.DeleteJobByID(*job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %d\", *job.ID),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobBefore godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Remove all jobs with start time before timestamp. The jobs will not be removed from the job archive.\n// @produce json\n// @param ts path int true \"Unix epoch timestamp\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job_before/{ts} [delete]\n// @param omit-tagged query bool false \"Omit jobs with tags from deletion\"\nfunc (api *RestAPI) deleteJobBefore(rw http.ResponseWriter, r *http.Request) {\n\tvar cnt int\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"ts\")\n\tvar err error\n\tif id != \"\" {\n\t\tts, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for ts: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\t// Check for omit-tagged query parameter\n\t\tomitTagged := \"none\"\n\t\tif omitTaggedStr := r.URL.Query().Get(\"omit-tagged\"); omitTaggedStr != \"\" {\n\t\t\tswitch omitTaggedStr {\n\t\t\tcase \"none\", \"all\", \"user\":\n\t\t\t\tomitTagged = omitTaggedStr\n\t\t\tdefault:\n\t\t\t\thandleError(fmt.Errorf(\"omit-tagged must be one of: none, all, user\"), http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tcnt, err = api.JobRepository.DeleteJobsBefore(ts, omitTagged)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'ts' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting jobs failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted %d jobs\", cnt),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\nfunc (api *RestAPI) checkAndHandleStopJob(rw http.ResponseWriter, job *schema.Job, req StopJobAPIRequest, isCached bool) {\n\t// Sanity checks\n\tif job.State != schema.JobStateRunning {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : job has already been stopped (state is: %s)\", job.JobID, *job.ID, job.Cluster, job.State), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif job.StartTime > req.StopTime {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : stopTime %d must be larger/equal than startTime %d\", job.JobID, *job.ID, job.Cluster, req.StopTime, job.StartTime), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif req.State != \"\" && !req.State.Valid() {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : invalid requested job state: %#v\", job.JobID, *job.ID, job.Cluster, req.State), http.StatusBadRequest, rw)\n\t\treturn\n\t} else if req.State == \"\" {\n\t\treq.State = schema.JobStateCompleted\n\t}\n\n\t// Mark job as stopped in the database (update state and duration)\n\tjob.Duration = int32(req.StopTime - job.StartTime)\n\tjob.State = req.State\n\tapi.JobRepository.Mutex.Lock()\n\tdefer api.JobRepository.Mutex.Unlock()\n\n\t// If the job is still in job_cache, transfer it to the job table first\n\t// so that job.ID always points to the job table for downstream code\n\tif isCached {\n\t\tnewID, err := api.JobRepository.TransferCachedJobToMain(*job.ID)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : transferring cached job failed: %w\", job.JobID, *job.ID, job.Cluster, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcclog.Infof(\"transferred cached job to main table: old id %d -> new id %d (jobId=%d)\", *job.ID, newID, job.JobID)\n\t\tjob.ID = &newID\n\t}\n\n\tif err := api.JobRepository.Stop(*job.ID, job.Duration, job.State, job.MonitoringStatus); err != nil {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : marking job as '%s' (duration: %d) in DB failed: %w\", job.JobID, *job.ID, job.Cluster, job.State, job.Duration, err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tcclog.Infof(\"archiving job... (dbid: %d): cluster=%s, jobId=%d, user=%s, startTime=%d, duration=%d, state=%s\", *job.ID, job.Cluster, job.JobID, job.User, job.StartTime, job.Duration, job.State)\n\n\t// Send a response (with status OK). This means that errors that happen from here on forward\n\t// can *NOT* be communicated to the client. If reading from a MetricDataRepository or\n\t// writing to the filesystem fails, the client will not know.\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n\n\t// Monitoring is disabled...\n\tif job.MonitoringStatus == schema.MonitoringStatusDisabled {\n\t\treturn\n\t}\n\n\t// Trigger async archiving\n\tarchiver.TriggerArchiving(job)\n}\n\nfunc (api *RestAPI) getJobMetrics(rw http.ResponseWriter, r *http.Request) {\n\tid := chi.URLParam(r, \"id\")\n\tmetrics := r.URL.Query()[\"metric\"]\n\tvar scopes []schema.MetricScope\n\tfor _, scope := range r.URL.Query()[\"scope\"] {\n\t\tvar s schema.MetricScope\n\t\tif err := s.UnmarshalGQL(scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"unmarshaling scope failed: %w\", err), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t\tscopes = append(scopes, s)\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\n\ttype Response struct {\n\t\tData *struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t} `json:\"data\"`\n\t\tError *struct {\n\t\t\tMessage string `json:\"message\"`\n\t\t} `json:\"error\"`\n\t}\n\n\tresolver := graph.GetResolverInstance()\n\tdata, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)\n\tif err != nil {\n\t\tif err := json.NewEncoder(rw).Encode(Response{\n\t\t\tError: &struct {\n\t\t\t\tMessage string `json:\"message\"`\n\t\t\t}{Message: err.Error()},\n\t\t}); err != nil {\n\t\t\tcclog.Errorf(\"Failed to encode error response: %v\", err)\n\t\t}\n\t\treturn\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(Response{\n\t\tData: &struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t}{JobMetrics: data},\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// GetUsedNodesAPIResponse model\ntype GetUsedNodesAPIResponse struct {\n\tUsedNodes map[string][]string `json:\"usedNodes\"` // Map of cluster names to lists of used node hostnames\n}\n\n// getUsedNodes godoc\n// @summary Lists used nodes by cluster\n// @tags Job query\n// @description Get a map of cluster names to lists of unique hostnames that are currently in use by running jobs that started before the specified timestamp.\n// @produce json\n// @param ts query int true \"Unix timestamp to filter jobs (jobs with start_time < ts)\"\n// @success 200 {object} api.GetUsedNodesAPIResponse \"Map of cluster names to hostname lists\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/used_nodes [get]\nfunc (api *RestAPI) getUsedNodes(rw http.ResponseWriter, r *http.Request) {\n\tif user := repository.GetUserFromContext(r.Context()); user != nil &&\n\t\t!user.HasRole(schema.RoleAPI) {\n\t\thandleError(fmt.Errorf(\"missing role: %v\", schema.GetRoleString(schema.RoleAPI)), http.StatusForbidden, rw)\n\t\treturn\n\t}\n\n\ttsStr := r.URL.Query().Get(\"ts\")\n\tif tsStr == \"\" {\n\t\thandleError(fmt.Errorf(\"missing required query parameter: ts\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tts, err := strconv.ParseInt(tsStr, 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"invalid timestamp format: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tusedNodes, err := api.JobRepository.GetUsedNodes(ts)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"failed to get used nodes: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tpayload := GetUsedNodesAPIResponse{\n\t\tUsedNodes: usedNodes,\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n","structuredPatch":[{"oldStart":397,"oldLines":7,"newStart":397,"newLines":7,"lines":[" resolution = max(resolution, mc.Timestep)"," }"," ","- data, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution)","+ data, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution, \"\")"," if err != nil {"," cclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)"," return"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"1504dc0b-c24b-42a3-ab0c-3fbba5c4571e","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage archiver\n\nimport (\n\t\"context\"\n\t\"math\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// ArchiveJob archives a completed job's metric data to the configured archive backend.\n//\n// This function performs the following operations:\n// 1. Loads all metric data for the job from the metric data repository\n// 2. Calculates job-level statistics (avg, min, max) for each metric\n// 3. Stores the job metadata and metric data to the archive backend\n//\n// Metric data is retrieved at the highest available resolution (typically 60s)\n// for the following scopes:\n// - Node scope (always)\n// - Core scope (for jobs with ≤8 nodes, to reduce data volume)\n// - Accelerator scope (if job used accelerators)\n//\n// The function respects context cancellation. If ctx is cancelled (e.g., during\n// shutdown timeout), the operation will be interrupted and return an error.\n//\n// Parameters:\n// - job: The job to archive (must be a completed job)\n// - ctx: Context for cancellation and timeout control\n//\n// Returns:\n// - *schema.Job with populated Statistics field\n// - error if data loading or archiving fails\n//\n// If config.Keys.DisableArchive is true, only job statistics are calculated\n// and returned (no data is written to archive backend).\nfunc ArchiveJob(job *schema.Job, ctx context.Context) (*schema.Job, error) {\n\tallMetrics := make([]string, 0)\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tfor _, mc := range metricConfigs {\n\t\tallMetrics = append(allMetrics, mc.Name)\n\t}\n\n\tscopes := []schema.MetricScope{schema.MetricScopeNode}\n\t// FIXME: Add a config option for this\n\tif job.NumNodes <= 8 {\n\t\t// This will add the native scope if core scope is not available\n\t\tscopes = append(scopes, schema.MetricScopeCore)\n\t}\n\n\tif job.NumAcc > 0 {\n\t\tscopes = append(scopes, schema.MetricScopeAccelerator)\n\t}\n\n\tjobData, err := metricdispatch.LoadData(job, allMetrics, scopes, ctx, 0) // 0 Resulotion-Value retrieves highest res (60s)\n\tif err != nil {\n\t\tcclog.Error(\"Error wile loading job data for archiving\")\n\t\treturn nil, err\n\t}\n\n\tjob.Statistics = make(map[string]schema.JobStatistics)\n\n\tfor metric, data := range jobData {\n\t\tavg, min, max := 0.0, math.MaxFloat32, -math.MaxFloat32\n\t\tnodeData, ok := data[\"node\"]\n\t\tif !ok {\n\t\t\t// This should never happen ?\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, series := range nodeData.Series {\n\t\t\tavg += series.Statistics.Avg\n\t\t\tmin = math.Min(min, series.Statistics.Min)\n\t\t\tmax = math.Max(max, series.Statistics.Max)\n\t\t}\n\n\t\t// Round AVG Result to 2 Digits\n\t\tjob.Statistics[metric] = schema.JobStatistics{\n\t\t\tUnit: schema.Unit{\n\t\t\t\tPrefix: archive.GetMetricConfig(job.Cluster, metric).Unit.Prefix,\n\t\t\t\tBase: archive.GetMetricConfig(job.Cluster, metric).Unit.Base,\n\t\t\t},\n\t\t\tAvg: (math.Round((avg/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: min,\n\t\t\tMax: max,\n\t\t}\n\t}\n\n\treturn job, archive.GetHandle().ImportJob(job, &jobData)\n}\n","structuredPatch":[{"oldStart":59,"oldLines":7,"newStart":59,"newLines":7,"lines":[" scopes = append(scopes, schema.MetricScopeAccelerator)"," }"," ","- jobData, err := metricdispatch.LoadData(job, allMetrics, scopes, ctx, 0) // 0 Resulotion-Value retrieves highest res (60s)","+ jobData, err := metricdispatch.LoadData(job, allMetrics, scopes, ctx, 0, \"\") // 0 Resulotion-Value retrieves highest res (60s)"," if err != nil {"," cclog.Error(\"Error wile loading job data for archiving\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"22e7bb73-7d9c-4589-a1c5-aa4d6b19b8f2","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"8c0f4fb6-de1f-4362-907f-f36503bdcf49","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GLRTxvBDmPo5zFHZq9p3nB","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/graph/util.go has been updated successfully."}]},"uuid":"bc7dab8f-3aae-4e02-b232-76e523af65e3","timestamp":"2026-03-19T19:50:54.146Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/graph/util.go","oldString":"jobdata, err := metricdispatch.LoadData(job, []string{\"flops_any\", \"mem_bw\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 0)","newString":"jobdata, err := metricdispatch.LoadData(job, []string{\"flops_any\", \"mem_bw\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 0, \"\")","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage graph\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"slices\"\n\n\t\"github.com/99designs/gqlgen/graphql\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\nconst MAX_JOBS_FOR_ANALYSIS = 500\n\n// Helper function for the rooflineHeatmap GraphQL query placed here so that schema.resolvers.go is not too full.\nfunc (r *queryResolver) rooflineHeatmap(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\trows int, cols int,\n\tminX float64, minY float64, maxX float64, maxY float64,\n) ([][]float64, error) {\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, &model.PageRequest{Page: 1, ItemsPerPage: MAX_JOBS_FOR_ANALYSIS + 1}, nil)\n\tif err != nil {\n\t\tcclog.Error(\"Error while querying jobs for roofline\")\n\t\treturn nil, err\n\t}\n\tif len(jobs) > MAX_JOBS_FOR_ANALYSIS {\n\t\treturn nil, fmt.Errorf(\"GRAPH/UTIL > too many jobs matched (max: %d)\", MAX_JOBS_FOR_ANALYSIS)\n\t}\n\n\tfcols, frows := float64(cols), float64(rows)\n\tminX, minY, maxX, maxY = math.Log10(minX), math.Log10(minY), math.Log10(maxX), math.Log10(maxY)\n\ttiles := make([][]float64, rows)\n\tfor i := range tiles {\n\t\ttiles[i] = make([]float64, cols)\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\t// metricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\t\t// resolution := 0\n\n\t\t// for _, mc := range metricConfigs {\n\t\t// \tresolution = max(resolution, mc.Timestep)\n\t\t// }\n\n\t\tjobdata, err := metricdispatch.LoadData(job, []string{\"flops_any\", \"mem_bw\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 0)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading roofline metrics for job %d\", *job.ID)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tflops_, membw_ := jobdata[\"flops_any\"], jobdata[\"mem_bw\"]\n\t\tif flops_ == nil && membw_ == nil {\n\t\t\tcclog.Warnf(\"rooflineHeatmap(): 'flops_any' or 'mem_bw' missing for job %d\", *job.ID)\n\t\t\tcontinue\n\t\t\t// return nil, fmt.Errorf(\"GRAPH/UTIL > 'flops_any' or 'mem_bw' missing for job %d\", job.ID)\n\t\t}\n\n\t\tflops, ok1 := flops_[\"node\"]\n\t\tmembw, ok2 := membw_[\"node\"]\n\t\tif !ok1 || !ok2 {\n\t\t\tcclog.Info(\"rooflineHeatmap() query not implemented for where flops_any or mem_bw not available at 'node' level\")\n\t\t\tcontinue\n\t\t\t// TODO/FIXME:\n\t\t\t// return nil, errors.New(\"GRAPH/UTIL > todo: rooflineHeatmap() query not implemented for where flops_any or mem_bw not available at 'node' level\")\n\t\t}\n\n\t\tfor n := 0; n < len(flops.Series); n++ {\n\t\t\tflopsSeries, membwSeries := flops.Series[n], membw.Series[n]\n\t\t\tfor i := 0; i < len(flopsSeries.Data); i++ {\n\t\t\t\tif i >= len(membwSeries.Data) {\n\t\t\t\t\tbreak\n\t\t\t\t}\n\n\t\t\t\tx, y := math.Log10(float64(flopsSeries.Data[i]/membwSeries.Data[i])), math.Log10(float64(flopsSeries.Data[i]))\n\t\t\t\tif math.IsNaN(x) || math.IsNaN(y) || x < minX || x >= maxX || y < minY || y > maxY {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tx, y = math.Floor(((x-minX)/(maxX-minX))*fcols), math.Floor(((y-minY)/(maxY-minY))*frows)\n\t\t\t\tif x < 0 || x >= fcols || y < 0 || y >= frows {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\ttiles[int(y)][int(x)] += 1\n\t\t\t}\n\t\t}\n\t}\n\n\treturn tiles, nil\n}\n\n// Helper function for the jobsFootprints GraphQL query placed here so that schema.resolvers.go is not too full.\nfunc (r *queryResolver) jobsFootprints(ctx context.Context, filter []*model.JobFilter, metrics []string) (*model.Footprints, error) {\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, &model.PageRequest{Page: 1, ItemsPerPage: MAX_JOBS_FOR_ANALYSIS + 1}, nil)\n\tif err != nil {\n\t\tcclog.Error(\"Error while querying jobs for footprint\")\n\t\treturn nil, err\n\t}\n\tif len(jobs) > MAX_JOBS_FOR_ANALYSIS {\n\t\treturn nil, fmt.Errorf(\"GRAPH/UTIL > too many jobs matched (max: %d)\", MAX_JOBS_FOR_ANALYSIS)\n\t}\n\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\ttimeweights := new(model.TimeWeights)\n\ttimeweights.NodeHours = make([]schema.Float, 0, len(jobs))\n\ttimeweights.AccHours = make([]schema.Float, 0, len(jobs))\n\ttimeweights.CoreHours = make([]schema.Float, 0, len(jobs))\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Error(\"Error while loading averages for footprint\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// #166 collect arrays: Null values or no null values?\n\t\ttimeweights.NodeHours = append(timeweights.NodeHours, schema.Float(float64(job.Duration)/60.0*float64(job.NumNodes)))\n\t\tif job.NumAcc > 0 {\n\t\t\ttimeweights.AccHours = append(timeweights.AccHours, schema.Float(float64(job.Duration)/60.0*float64(job.NumAcc)))\n\t\t} else {\n\t\t\ttimeweights.AccHours = append(timeweights.AccHours, schema.Float(1.0))\n\t\t}\n\t\tif job.NumHWThreads > 0 {\n\t\t\ttimeweights.CoreHours = append(timeweights.CoreHours, schema.Float(float64(job.Duration)/60.0*float64(job.NumHWThreads))) // SQLite HWThreads == Cores; numCoresForJob(job)\n\t\t} else {\n\t\t\ttimeweights.CoreHours = append(timeweights.CoreHours, schema.Float(1.0))\n\t\t}\n\t}\n\n\tres := make([]*model.MetricFootprints, len(avgs))\n\tfor i, arr := range avgs {\n\t\tres[i] = &model.MetricFootprints{\n\t\t\tMetric: metrics[i],\n\t\t\tData: arr,\n\t\t}\n\t}\n\n\treturn &model.Footprints{\n\t\tTimeWeights: timeweights,\n\t\tMetrics: res,\n\t}, nil\n}\n\n// func numCoresForJob(job *schema.Job) (numCores int) {\n\n// \tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n// \tif scerr != nil {\n// \t\treturn 1\n// \t}\n\n// \ttotalJobCores := 0\n// \ttopology := subcluster.Topology\n\n// \tfor _, host := range job.Resources {\n// \t\thwthreads := host.HWThreads\n// \t\tif hwthreads == nil {\n// \t\t\thwthreads = topology.Node\n// \t\t}\n\n// \t\thostCores, _ := topology.GetCoresFromHWThreads(hwthreads)\n// \t\ttotalJobCores += len(hostCores)\n// \t}\n\n// \treturn totalJobCores\n// }\n\nfunc requireField(ctx context.Context, name string) bool {\n\tfields := graphql.CollectAllFields(ctx)\n\n\treturn slices.Contains(fields, name)\n}\n","structuredPatch":[{"oldStart":55,"oldLines":7,"newStart":55,"newLines":7,"lines":[" // \tresolution = max(resolution, mc.Timestep)"," // }"," ","- jobdata, err := metricdispatch.LoadData(job, []string{\"flops_any\", \"mem_bw\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 0)","+ jobdata, err := metricdispatch.LoadData(job, []string{\"flops_any\", \"mem_bw\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 0, \"\")"," if err != nil {"," cclog.Warnf(\"Error while loading roofline metrics for job %d\", *job.ID)"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"8c0f4fb6-de1f-4362-907f-f36503bdcf49","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"18278401-8ed6-4275-97c4-30bb786d5dbe","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UztGbU6SaPC6QvT5p9ERAw","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go has been updated successfully."}]},"uuid":"156ba788-7222-4807-ba7e-becbde41513e","timestamp":"2026-03-19T19:51:00.263Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","oldString":"func LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {","newString":"func LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n\tresampleAlgo string,\n) (map[string]schema.JobData, error) {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tresampleAlgo string,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",\n\t\t*job.ID, job.State, metrics, scopes, resolution, resampleAlgo)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n\tresampleAlgo string,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution, resampleAlgo), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data to reduce data points to the requested resolution,\n\t\t\t// improving transfer performance and client-side rendering.\n\t\t\tresampleFn, rfErr := resampler.GetResampler(resampleAlgo)\n\t\t\tif rfErr != nil {\n\t\t\t\treturn rfErr, 0, 0\n\t\t\t}\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampleFn(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","structuredPatch":[{"oldStart":420,"oldLines":6,"newStart":420,"newLines":7,"lines":[" resolution int,"," from, to time.Time,"," ctx context.Context,","+ resampleAlgo string,"," ) (map[string]schema.JobData, error) {"," if metrics == nil {"," for _, m := range archive.GetCluster(cluster).MetricConfig {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"18278401-8ed6-4275-97c4-30bb786d5dbe","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"c9c7b9c5-f54d-4dae-be9d-0ef98a6d41e5","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Da2Jo2XSJHUMr8PpwYTtvZ","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go has been updated successfully."}]},"uuid":"6a4f2567-8dbc-4fa9-855a-6374bd833968","timestamp":"2026-03-19T19:51:04.922Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/metricdispatch/dataLoader.go","oldString":"\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)","newString":"\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx, resampleAlgo)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricdispatch provides a unified interface for loading and caching job metric data.\n//\n// This package serves as a central dispatcher that routes metric data requests to the appropriate\n// backend based on job state. For running jobs, data is fetched from the metric store (e.g., cc-metric-store).\n// For completed jobs, data is retrieved from the file-based job archive.\n//\n// # Key Features\n//\n// - Automatic backend selection based on job state (running vs. archived)\n// - LRU cache for performance optimization (128 MB default cache size)\n// - Data resampling using Largest Triangle Three Bucket algorithm for archived data\n// - Automatic statistics series generation for jobs with many nodes\n// - Support for scoped metrics (node, socket, accelerator, core)\n//\n// # Cache Behavior\n//\n// Cached data has different TTL (time-to-live) values depending on job state:\n// - Running jobs: 2 minutes (data changes frequently)\n// - Completed jobs: 5 hours (data is static)\n//\n// The cache key is based on job ID, state, requested metrics, scopes, and resolution.\n//\n// # Usage\n//\n// The primary entry point is LoadData, which automatically handles both running and archived jobs:\n//\n//\tjobData, err := metricdispatch.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t // Handle error\n//\t}\n//\n// For statistics only, use LoadJobStats, LoadScopedJobStats, or LoadAverages depending on the required format.\npackage metricdispatch\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"math\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// cache is an LRU cache with 128 MB capacity for storing loaded job metric data.\n// The cache reduces load on both the metric store and archive backends.\nvar cache *lrucache.Cache = lrucache.New(128 * 1024 * 1024)\n\n// cacheKey generates a unique cache key for a job's metric data based on job ID, state,\n// requested metrics, scopes, and resolution. Duration and StartTime are intentionally excluded\n// because job.ID is more unique and the cache TTL ensures entries don't persist indefinitely.\nfunc cacheKey(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tresampleAlgo string,\n) string {\n\treturn fmt.Sprintf(\"%d(%s):[%v],[%v]-%d-%s\",\n\t\t*job.ID, job.State, metrics, scopes, resolution, resampleAlgo)\n}\n\n// LoadData retrieves metric data for a job from the appropriate backend (memory store for running jobs,\n// archive for completed jobs) and applies caching, resampling, and statistics generation as needed.\n//\n// For running jobs or when archive is disabled, data is fetched from the metric store.\n// For completed archived jobs, data is loaded from the job archive and resampled if needed.\n//\n// Parameters:\n// - job: The job for which to load metric data\n// - metrics: List of metric names to load (nil loads all metrics for the cluster)\n// - scopes: Metric scopes to include (nil defaults to node scope)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Target number of data points for resampling (only applies to archived data)\n//\n// Returns the loaded job data and any error encountered. For partial errors (some metrics failed),\n// the function returns the successfully loaded data with a warning logged.\nfunc LoadData(job *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n\tresampleAlgo string,\n) (schema.JobData, error) {\n\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution, resampleAlgo), func() (_ any, ttl time.Duration, size int) {\n\t\tvar jd schema.JobData\n\t\tvar err error\n\n\t\tif job.State == schema.JobStateRunning ||\n\t\t\tjob.MonitoringStatus == schema.MonitoringStatusRunningOrArchiving {\n\n\t\t\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tif scopes == nil {\n\t\t\t\tscopes = append(scopes, schema.MetricScopeNode)\n\t\t\t}\n\n\t\t\tif metrics == nil {\n\t\t\t\tcluster := archive.GetCluster(job.Cluster)\n\t\t\t\tfor _, mc := range cluster.MetricConfig {\n\t\t\t\t\tmetrics = append(metrics, mc.Name)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tjd, err = ms.LoadData(job, metrics, scopes, ctx, resolution)\n\t\t\tif err != nil {\n\t\t\t\tif len(jd) != 0 {\n\t\t\t\t\tcclog.Warnf(\"partial error loading metrics from store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Warnf(\"failed to load job data from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\t\treturn err, 0, 0\n\t\t\t\t}\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t} else {\n\t\t\tvar jdTemp schema.JobData\n\t\t\tjdTemp, err = archive.GetHandle().LoadJobData(job)\n\t\t\tif err != nil {\n\t\t\t\tcclog.Warnf(\"failed to load job data from archive for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\t\t\treturn err, 0, 0\n\t\t\t}\n\n\t\t\tjd = deepCopy(jdTemp)\n\n\t\t\t// Resample archived data to reduce data points to the requested resolution,\n\t\t\t// improving transfer performance and client-side rendering.\n\t\t\tresampleFn, rfErr := resampler.GetResampler(resampleAlgo)\n\t\t\tif rfErr != nil {\n\t\t\t\treturn rfErr, 0, 0\n\t\t\t}\n\t\t\tfor _, v := range jd {\n\t\t\t\tfor _, v_ := range v {\n\t\t\t\t\ttimestep := int64(0)\n\t\t\t\t\tfor i := 0; i < len(v_.Series); i += 1 {\n\t\t\t\t\t\tv_.Series[i].Data, timestep, err = resampleFn(v_.Series[i].Data, int64(v_.Timestep), int64(resolution))\n\t\t\t\t\t\tif err != nil {\n\t\t\t\t\t\t\treturn err, 0, 0\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tv_.Timestep = int(timestep)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Filter job data to only include requested metrics and scopes, avoiding unnecessary data transfer.\n\t\t\tif metrics != nil || scopes != nil {\n\t\t\t\tif metrics == nil {\n\t\t\t\t\tmetrics = make([]string, 0, len(jd))\n\t\t\t\t\tfor k := range jd {\n\t\t\t\t\t\tmetrics = append(metrics, k)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tres := schema.JobData{}\n\t\t\t\tfor _, metric := range metrics {\n\t\t\t\t\tif perscope, ok := jd[metric]; ok {\n\t\t\t\t\t\tif len(perscope) > 1 {\n\t\t\t\t\t\t\tsubset := make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\t\t\t\t\tfor _, scope := range scopes {\n\t\t\t\t\t\t\t\tif jm, ok := perscope[scope]; ok {\n\t\t\t\t\t\t\t\t\tsubset[scope] = jm\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif len(subset) > 0 {\n\t\t\t\t\t\t\t\tperscope = subset\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tres[metric] = perscope\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tjd = res\n\t\t\t}\n\t\t\tsize = jd.Size()\n\t\t}\n\n\t\tttl = 5 * time.Hour\n\t\tif job.State == schema.JobStateRunning {\n\t\t\tttl = 2 * time.Minute\n\t\t}\n\n\t\t// Generate statistics series for jobs with many nodes to enable min/median/max graphs\n\t\t// instead of overwhelming the UI with individual node lines. Note that newly calculated\n\t\t// statistics use min/median/max, while archived statistics may use min/mean/max.\n\t\tconst maxSeriesSize int = 8\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\n\t\tnodeScopeRequested := false\n\t\tfor _, scope := range scopes {\n\t\t\tif scope == schema.MetricScopeNode {\n\t\t\t\tnodeScopeRequested = true\n\t\t\t}\n\t\t}\n\n\t\tif nodeScopeRequested {\n\t\t\tjd.AddNodeScope(\"flops_any\")\n\t\t\tjd.AddNodeScope(\"mem_bw\")\n\t\t}\n\n\t\t// Round Resulting Stat Values\n\t\tjd.RoundMetricStats()\n\n\t\treturn jd, ttl, size\n\t})\n\n\tif err, ok := data.(error); ok {\n\t\tcclog.Errorf(\"error in cached dataset for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn data.(schema.JobData), nil\n}\n\n// LoadAverages computes average values for the specified metrics across all nodes of a job.\n// For running jobs, it loads statistics from the metric store. For completed jobs, it uses\n// the pre-calculated averages from the job archive. The results are appended to the data slice.\nfunc LoadAverages(\n\tjob *schema.Job,\n\tmetrics []string,\n\tdata [][]schema.Float,\n\tctx context.Context,\n) error {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadAveragesFromArchive(job, metrics, data) // #166 change also here?\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn err\n\t}\n\n\tfor i, m := range metrics {\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[i] = append(data[i], schema.NaN)\n\t\t\tcontinue\n\t\t}\n\n\t\tsum := 0.0\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t}\n\t\tdata[i] = append(data[i], schema.Float(sum))\n\t}\n\n\treturn nil\n}\n\n// LoadScopedJobStats retrieves job statistics organized by metric scope (node, socket, core, accelerator).\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadScopedJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadScopedStatsFromArchive(job, metrics, scopes)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tscopedStats, err := ms.LoadScopedStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load scoped statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Round Resulting Stat Values\n\tscopedStats.RoundScopedMetricStats()\n\n\treturn scopedStats, nil\n}\n\n// LoadJobStats retrieves aggregated statistics (min/avg/max) for each requested metric across all job nodes.\n// For running jobs, statistics are computed from the metric store. For completed jobs, pre-calculated\n// statistics are loaded from the job archive.\nfunc LoadJobStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]schema.MetricStatistics, error) {\n\tif job.State != schema.JobStateRunning {\n\t\treturn archive.LoadStatsFromArchive(job, metrics)\n\t}\n\n\tms, err := GetMetricDataRepo(job.Cluster, job.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tjob.Cluster, job.SubCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata := make(map[string]schema.MetricStatistics, len(metrics))\n\n\tstats, err := ms.LoadStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"failed to load statistics from metric store for job %d (user: %s, project: %s, cluster: %s-%s): %s\",\n\t\t\tjob.JobID, job.User, job.Project, job.Cluster, job.SubCluster, err.Error())\n\t\treturn data, err\n\t}\n\n\tfor _, m := range metrics {\n\t\tsum, avg, min, max := 0.0, 0.0, 0.0, 0.0\n\t\tnodes, ok := stats[m]\n\t\tif !ok {\n\t\t\tdata[m] = schema.MetricStatistics{Min: min, Avg: avg, Max: max}\n\t\t\tcontinue\n\t\t}\n\n\t\tfor _, node := range nodes {\n\t\t\tsum += node.Avg\n\t\t\tmin = math.Min(min, node.Min)\n\t\t\tmax = math.Max(max, node.Max)\n\t\t}\n\n\t\tdata[m] = schema.MetricStatistics{\n\t\t\tAvg: (math.Round((sum/float64(job.NumNodes))*100) / 100),\n\t\t\tMin: (math.Round(min*100) / 100),\n\t\t\tMax: (math.Round(max*100) / 100),\n\t\t}\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeData retrieves metric data for specific nodes in a cluster within a time range.\n// This is used for node monitoring views and system status pages. Data is always fetched from\n// the metric store (not the archive) since it's for current/recent node status monitoring.\n//\n// Returns a nested map structure: node -> metric -> scoped data.\n// FIXME: Add support for subcluster specific cc-metric-stores\nfunc LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, \"\")\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s: %s\",\n\t\t\tcluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node data from metric store for cluster %s: %s\", cluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node data queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves time-series metric data for multiple nodes within a time range,\n// with optional resampling and automatic statistics generation for large datasets.\n// This is used for comparing multiple nodes or displaying node status over time.\n//\n// Returns a map of node names to their job-like metric data structures.\nfunc LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n\tresampleAlgo string,\n) (map[string]schema.JobData, error) {\n\tif metrics == nil {\n\t\tfor _, m := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, m.Name)\n\t\t}\n\t}\n\n\tms, err := GetMetricDataRepo(cluster, subCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"failed to access metricDataRepo for cluster %s-%s: %s\",\n\t\t\tcluster, subCluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tdata, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)\n\tif err != nil {\n\t\tif len(data) != 0 {\n\t\t\tcclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t} else {\n\t\t\tcclog.Warnf(\"failed to load node list data from metric store for cluster %s, subcluster %s: %s\",\n\t\t\t\tcluster, subCluster, err.Error())\n\t\t\treturn nil, err\n\t\t}\n\t}\n\n\t// Generate statistics series for datasets with many series to improve visualization performance.\n\t// Statistics are calculated as min/median/max.\n\tconst maxSeriesSize int = 8\n\tfor _, jd := range data {\n\t\tfor _, scopes := range jd {\n\t\t\tfor _, jm := range scopes {\n\t\t\t\tif jm.StatisticsSeries != nil || len(jm.Series) < maxSeriesSize {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tjm.AddStatisticsSeries()\n\t\t\t}\n\t\t}\n\t}\n\n\tif data == nil {\n\t\treturn nil, fmt.Errorf(\"metric store for cluster '%s' does not support node list queries\", cluster)\n\t}\n\n\treturn data, nil\n}\n\n// deepCopy creates a deep copy of JobData to prevent cache corruption when modifying\n// archived data (e.g., during resampling). This ensures the cached archive data remains\n// immutable while allowing per-request transformations.\nfunc deepCopy(source schema.JobData) schema.JobData {\n\tresult := make(schema.JobData, len(source))\n\n\tfor metricName, scopeMap := range source {\n\t\tresult[metricName] = make(map[schema.MetricScope]*schema.JobMetric, len(scopeMap))\n\n\t\tfor scope, jobMetric := range scopeMap {\n\t\t\tresult[metricName][scope] = copyJobMetric(jobMetric)\n\t\t}\n\t}\n\n\treturn result\n}\n\nfunc copyJobMetric(src *schema.JobMetric) *schema.JobMetric {\n\tdst := &schema.JobMetric{\n\t\tTimestep: src.Timestep,\n\t\tUnit: src.Unit,\n\t\tSeries: make([]schema.Series, len(src.Series)),\n\t}\n\n\tfor i := range src.Series {\n\t\tdst.Series[i] = copySeries(&src.Series[i])\n\t}\n\n\tif src.StatisticsSeries != nil {\n\t\tdst.StatisticsSeries = copyStatisticsSeries(src.StatisticsSeries)\n\t}\n\n\treturn dst\n}\n\nfunc copySeries(src *schema.Series) schema.Series {\n\tdst := schema.Series{\n\t\tHostname: src.Hostname,\n\t\tID: src.ID,\n\t\tStatistics: src.Statistics,\n\t\tData: make([]schema.Float, len(src.Data)),\n\t}\n\n\tcopy(dst.Data, src.Data)\n\treturn dst\n}\n\nfunc copyStatisticsSeries(src *schema.StatsSeries) *schema.StatsSeries {\n\tdst := &schema.StatsSeries{\n\t\tMin: make([]schema.Float, len(src.Min)),\n\t\tMean: make([]schema.Float, len(src.Mean)),\n\t\tMedian: make([]schema.Float, len(src.Median)),\n\t\tMax: make([]schema.Float, len(src.Max)),\n\t}\n\n\tcopy(dst.Min, src.Min)\n\tcopy(dst.Mean, src.Mean)\n\tcopy(dst.Median, src.Median)\n\tcopy(dst.Max, src.Max)\n\n\tif len(src.Percentiles) > 0 {\n\t\tdst.Percentiles = make(map[int][]schema.Float, len(src.Percentiles))\n\t\tfor percentile, values := range src.Percentiles {\n\t\t\tdst.Percentiles[percentile] = make([]schema.Float, len(values))\n\t\t\tcopy(dst.Percentiles[percentile], values)\n\t\t}\n\t}\n\n\treturn dst\n}\n","structuredPatch":[{"oldStart":435,"oldLines":7,"newStart":435,"newLines":7,"lines":[" return nil, err"," }"," ","- data, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx)","+ data, err := ms.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, resolution, from, to, ctx, resampleAlgo)"," if err != nil {"," if len(data) != 0 {"," cclog.Warnf(\"partial error loading node list data from metric store for cluster %s, subcluster %s: %s\","]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c9c7b9c5-f54d-4dae-be9d-0ef98a6d41e5","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"e4ac98c2-cea7-4430-a3ce-68040c0af1b2","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PhSaA3cP5UbUt1Wf8GH3BC","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/query.go has been updated successfully."}]},"uuid":"69bfa8e5-14d3-4912-995b-f7a94463ec77","timestamp":"2026-03-19T19:51:12.039Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/query.go","oldString":"func (ccms *InternalMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {","newString":"func (ccms *InternalMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n\tresampleAlgo string,\n) (map[string]schema.JobData, error) {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file implements high-level query functions for loading job metric data\n// with automatic scope transformation and aggregation.\n//\n// Key Concepts:\n//\n// Metric Scopes: Metrics are collected at different granularities (native scope):\n// - HWThread: Per hardware thread\n// - Core: Per CPU core\n// - Socket: Per CPU socket\n// - MemoryDomain: Per memory domain (NUMA)\n// - Accelerator: Per GPU/accelerator\n// - Node: Per compute node\n//\n// Scope Transformation: The buildQueries functions transform between native scope\n// and requested scope by:\n// - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n// - Rejecting requests for finer granularity than available\n// - Handling special cases (e.g., Accelerator metrics)\n//\n// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n// based on cluster topology and job resources.\npackage metricstore\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\ntype InternalMetricStore struct{}\n\nvar MetricStoreHandle *InternalMetricStore\n\n// HealthCheck delegates to the internal MemoryStore's HealthCheck.\nfunc (ccms *InternalMetricStore) HealthCheck(cluster string,\n\tnodes []string, metrics []string,\n) (map[string]HealthCheckResult, error) {\n\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n}\n\n// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n// When set to a non-nil function, LoadData will call this function instead of the default implementation.\nvar TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n\n// LoadData loads metric data for a specific job with automatic scope transformation.\n//\n// This is the primary function for retrieving job metric data. It handles:\n// - Building queries with scope transformation via buildQueries\n// - Fetching data from the metric store\n// - Organizing results by metric and scope\n// - Converting NaN statistics to 0 for JSON compatibility\n// - Partial error handling (returns data for successful queries even if some fail)\n//\n// Parameters:\n// - job: Job metadata including cluster, resources, and time range\n// - metrics: List of metric names to load\n// - scopes: Requested metric scopes (will be transformed to match native scopes)\n// - ctx: Context for cancellation (currently unused but reserved for future use)\n// - resolution: Data resolution in seconds (0 for native resolution)\n//\n// Returns:\n// - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n// - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n//\n// Example:\n//\n//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\nfunc (ccms *InternalMetricStore) LoadData(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tif TestLoadDataCallback != nil {\n\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n\t}\n\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tjobData := make(schema.JobData)\n\n\t// Add safety check for potential index out of range errors\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := jobData[metric]; !ok {\n\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\tjobMetric, ok := jobData[metric][scope]\n\t\tif !ok {\n\t\t\tjobMetric = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tjobData[metric][scope] = jobMetric\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(jobData):\n\t\tif len(jobMetric.Series) == 0 {\n\t\t\tdelete(jobData[metric], scope)\n\t\t\tif len(jobData[metric]) == 0 {\n\t\t\t\tdelete(jobData, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn jobData, nil\n}\n\n// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n//\n// This function implements the core scope transformation logic, handling all combinations of\n// native metric scopes and requested scopes. It uses the cluster topology to determine which\n// hardware IDs to include in each query.\n//\n// Scope Transformation Rules:\n// - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n// - If native scope < requested scope: Returns error (cannot increase granularity)\n// - Special handling for Accelerator scope (independent of CPU hierarchy)\n//\n// The function generates one or more APIQuery per (metric, scope, host) combination:\n// - For non-aggregated queries: One query with all relevant IDs\n// - For aggregated queries: May generate multiple queries (e.g., one per socket/core)\n//\n// Parameters:\n// - job: Job metadata including cluster, subcluster, and resource allocation\n// - metrics: List of metrics to query\n// - scopes: Requested scopes for each metric\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query (after transformation)\n// - error: Returns error if topology lookup fails or unhandled scope combination encountered\nfunc buildQueries(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tif len(job.Resources) == 0 {\n\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > no resources allocated for job %d\", job.JobID)\n\t}\n\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n\n\tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n\tif scerr != nil {\n\t\treturn nil, nil, scerr\n\t}\n\ttopology := subcluster.Topology\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif len(mc.SubClusters) != 0 && IsMetricRemovedForSubCluster(mc, job.SubCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tscopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue scopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, host := range job.Resources {\n\t\t\t\thwthreads := host.HWThreads\n\t\t\t\tif hwthreads == nil {\n\t\t\t\t\thwthreads = topology.Node\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, host.Hostname,\n\t\t\t\t\t&topology, hwthreads, host.Accelerators,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n\n// LoadStats loads only metric statistics (avg/min/max) for a job at node scope.\n//\n// This is an optimized version of LoadData that fetches only statistics without\n// time-series data, reducing bandwidth and memory usage. Always queries at node scope.\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of metric → hostname → statistics\n// - Error on query building or fetching failure\nfunc (ccms *InternalMetricStore) LoadStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error) {\n\t// TODO(#166): Add scope parameter for analysis view accelerator normalization\n\tqueries, _, err := buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n\tfor i, res := range resBody.Results {\n\t\tif i >= len(req.Queries) {\n\t\t\tcclog.Warnf(\"LoadStats: result index %d exceeds queries length %d\", i, len(req.Queries))\n\t\t\tbreak\n\t\t}\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tdata := res[0]\n\t\tif data.Error != nil {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata, ok := stats[metric]\n\t\tif !ok {\n\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n\t\t\tstats[metric] = metricdata\n\t\t}\n\n\t\tif data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n\t\t\tAvg: float64(data.Avg),\n\t\t\tMin: float64(data.Min),\n\t\t\tMax: float64(data.Max),\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// LoadScopedStats loads metric statistics for a job with scope-aware grouping.\n//\n// Similar to LoadStats but supports multiple scopes and returns statistics grouped\n// by scope with hardware IDs (e.g., per-core, per-socket statistics).\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - ScopedJobStats: Map of metric → scope → []ScopedStats (with hostname and ID)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadScopedStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tscopedJobStats := make(schema.ScopedJobStats)\n\n\tfor i, row := range resBody.Results {\n\t\tif len(row) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\n\t\tif _, ok := scopedJobStats[metric]; !ok {\n\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n\t\t}\n\n\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tData: &schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n\t\tif len(scopedJobStats[metric][scope]) == 0 {\n\t\t\tdelete(scopedJobStats[metric], scope)\n\t\t\tif len(scopedJobStats[metric]) == 0 {\n\t\t\t\tdelete(scopedJobStats, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn scopedJobStats, nil\n}\n\n// LoadNodeData loads metric data for specific nodes in a cluster over a time range.\n//\n// Unlike LoadData which operates on job resources, this function queries arbitrary nodes\n// directly. Useful for system monitoring and node status views.\n//\n// Parameters:\n// - cluster: Cluster name\n// - metrics: List of metric names\n// - nodes: List of node hostnames (nil = all nodes in cluster via ForAllNodes)\n// - scopes: Requested metric scopes (currently unused - always node scope)\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → metric → []JobMetric\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tif nodes == nil {\n\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n\t} else {\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range metrics {\n\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n\t\t\t\t\tHostname: node,\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]map[string][]*schema.JobMetric)\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tquery = resBody.Queries[i]\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tqdata := res[0]\n\t\tif qdata.Error != nil {\n\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n\t\t\tcontinue\n\t\t}\n\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tSanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n\n\t\thostdata, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostdata = make(map[string][]*schema.JobMetric)\n\t\t\tdata[query.Hostname] = hostdata\n\t\t}\n\n\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n\t\t\tUnit: mc.Unit,\n\t\t\tTimestep: mc.Timestep,\n\t\t\tSeries: []schema.Series{\n\t\t\t\t{\n\t\t\t\t\tHostname: query.Hostname,\n\t\t\t\t\tData: qdata.Data,\n\t\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\t\tAvg: float64(qdata.Avg),\n\t\t\t\t\t\tMin: float64(qdata.Min),\n\t\t\t\t\t\tMax: float64(qdata.Max),\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t})\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData loads metric data for a list of nodes with full scope transformation support.\n//\n// This is the most flexible node data loading function, supporting arbitrary scopes and\n// resolution. Uses buildNodeQueries for proper scope transformation based on topology.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty string to infer from node names)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → JobData (metric → scope → JobMetric)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\t// Note: Order of node data is not guaranteed after this point\n\tqueries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCLuster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]schema.JobData)\n\n\t// Add safety check for index out of range issues\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tif i < len(resBody.Queries) {\n\t\t\t\tquery = resBody.Queries[i]\n\t\t\t} else {\n\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n\t\t\t\t\ti, len(resBody.Queries))\n\t\t\t\tcontinue\n\t\t\t}\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\t// Init Nested Map Data Structures If Not Found\n\t\thostData, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostData = make(schema.JobData)\n\t\t\tdata[query.Hostname] = hostData\n\t\t}\n\n\t\tmetricData, ok := hostData[metric]\n\t\tif !ok {\n\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\tdata[query.Hostname][metric] = metricData\n\t\t}\n\n\t\tscopeData, ok := metricData[scope]\n\t\tif !ok {\n\t\t\tscopeData = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tdata[query.Hostname][metric][scope] = scopeData\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// buildNodeQueries constructs APIQuery structures for node-based queries with scope transformation.\n//\n// Similar to buildQueries but operates on node lists rather than job resources.\n// Supports dynamic subcluster lookup when subCluster parameter is empty.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty = infer from node hostnames)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query\n// - error: Returns error if topology lookup fails or unhandled scope combination\nfunc buildNodeQueries(\n\tcluster string,\n\tsubCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n\n\t// Get Topol before loop if subCluster given\n\tvar subClusterTopol *schema.SubCluster\n\tvar scterr error\n\tif subCluster != \"\" {\n\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subCluster)\n\t\tif scterr != nil {\n\t\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", cluster, subCluster, scterr.Error())\n\t\t\treturn nil, nil, scterr\n\t\t}\n\t}\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif mc.SubClusters != nil && IsMetricRemovedForSubCluster(mc, subCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tnodeScopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue nodeScopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, hostname := range nodes {\n\n\t\t\t\t// If no subCluster given, get it by node\n\t\t\t\tif subCluster == \"\" {\n\t\t\t\t\tsubClusterName, scnerr := archive.GetSubClusterByNode(cluster, hostname)\n\t\t\t\t\tif scnerr != nil {\n\t\t\t\t\t\treturn nil, nil, scnerr\n\t\t\t\t\t}\n\t\t\t\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subClusterName)\n\t\t\t\t\tif scterr != nil {\n\t\t\t\t\t\treturn nil, nil, scterr\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Always full node hwthread id list, no partial queries expected\n\t\t\t\ttopology := subClusterTopol.Topology\n\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n\n\t\t\t\t// Moved check here if metric matches hardware specs\n\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, hostname,\n\t\t\t\t\t&topology, topology.Node, acceleratorIds,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n","structuredPatch":[{"oldStart":621,"oldLines":6,"newStart":621,"newLines":7,"lines":[" resolution int,"," from, to time.Time,"," ctx context.Context,","+ resampleAlgo string,"," ) (map[string]schema.JobData, error) {"," // Note: Order of node data is not guaranteed after this point"," queries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"e4ac98c2-cea7-4430-a3ce-68040c0af1b2","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"69bfa8e5-14d3-4912-995b-f7a94463ec77","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01FRaqMLS5A6J8ZZu4Y3dGJK","type":"message","role":"assistant","content":[{"type":"text","text":"Now I need to check if `LoadNodeListData` in query.go calls `MemoryStore.Read` - let me look at how the resolution is passed. Looking at the query.go code, the resolution is passed through the `APIQuery.Resolution` field and processed in `FetchData`. The actual resampling for node list data happens in `MemoryStore.Read`. Let me trace where `Read` is called for node list data."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":616,"cache_read_input_tokens":89899,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":616},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2BB9w5rZe4GEap4Sjn","type":"assistant","uuid":"41cdad21-b6bc-430e-bdc7-40d4d90e621e","timestamp":"2026-03-19T19:51:17.596Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"41cdad21-b6bc-430e-bdc7-40d4d90e621e","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01FRaqMLS5A6J8ZZu4Y3dGJK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017UU9bDoyPc4BzBcZfDRBYJ","name":"Grep","input":{"pattern":"func FetchData|func.*\\.Read\\(","path":"/Users/jan/tmp/cc-backend/pkg/metricstore","output_mode":"content"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":616,"cache_read_input_tokens":89899,"output_tokens":212,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":616,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2BB9w5rZe4GEap4Sjn","type":"assistant","uuid":"e80ef4ef-d5d7-457b-bfe1-9f8a8e3efbe5","timestamp":"2026-03-19T19:51:18.793Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"e80ef4ef-d5d7-457b-bfe1-9f8a8e3efbe5","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_017UU9bDoyPc4BzBcZfDRBYJ","toolUseID":"toolu_017UU9bDoyPc4BzBcZfDRBYJ","timestamp":"2026-03-19T19:51:18.821Z","uuid":"099f4058-20a6-4311-b678-9a217e1ca82e","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"e80ef4ef-d5d7-457b-bfe1-9f8a8e3efbe5","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017UU9bDoyPc4BzBcZfDRBYJ","type":"tool_result","content":"pkg/metricstore/api.go:182:// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\npkg/metricstore/api.go:197:func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {"}]},"uuid":"8cb36ffd-d6d4-41c0-b95f-15cf1364a284","timestamp":"2026-03-19T19:51:18.821Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"pkg/metricstore/api.go:182:// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\npkg/metricstore/api.go:197:func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {","numLines":2},"sourceToolAssistantUUID":"e80ef4ef-d5d7-457b-bfe1-9f8a8e3efbe5","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"8cb36ffd-d6d4-41c0-b95f-15cf1364a284","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01CqKcMBwrd34kWPf5yycvMf","type":"message","role":"assistant","content":[{"type":"text","text":"There's a `MetricDataRepository` interface I need to update too. Let me find it:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":554,"cache_read_input_tokens":90515,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":554},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2BgN6hot6BwyYWY1FH","type":"assistant","uuid":"bf3ab29e-648c-4eec-bf31-9d753f6e89e7","timestamp":"2026-03-19T19:51:23.244Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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err != nil {\n\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> External Metric Store Config Init: Could not decode config file '%s' Error: %s\", rawConfig, err.Error())\n\t\t}\n\n\t\tif len(configs) == 0 {\n\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> No external metric store configurations found in config file\")\n\t\t}\n\n\t\tfor _, config := range configs {\n\t\t\tmetricDataRepos[config.Scope] = ccms.NewCCMetricStore(config.URL, config.Token)\n\t\t}\n\t}\n\n\treturn nil\n}\n\nfunc GetMetricDataRepo(cluster string, subcluster string) (MetricDataRepository, error) {\n\tvar repo MetricDataRepository\n\tvar ok bool\n\n\tkey := cluster + \"-\" + subcluster\n\trepo, ok = metricDataRepos[key]\n\n\tif !ok {\n\t\trepo, ok = metricDataRepos[cluster]\n\n\t\tif !ok {\n\t\t\trepo, ok = metricDataRepos[\"*\"]\n\n\t\t\tif !ok {\n\t\t\t\tif metricstore.MetricStoreHandle == nil {\n\t\t\t\t\treturn nil, fmt.Errorf(\"[METRICDISPATCH]> no metric data repository configured '%s'\", key)\n\t\t\t\t}\n\n\t\t\t\trepo = metricstore.MetricStoreHandle\n\t\t\t\tcclog.Debugf(\"[METRICDISPATCH]> Using internal metric data repository for '%s'\", key)\n\t\t\t}\n\t\t}\n\t}\n\n\treturn repo, nil\n}\n\n// GetHealthCheckRepo returns the MetricDataRepository for performing health checks on a cluster.\n// It uses the same fallback logic as GetMetricDataRepo: cluster → wildcard → internal.\nfunc GetHealthCheckRepo(cluster string) (MetricDataRepository, error) {\n\treturn GetMetricDataRepo(cluster, \"\")\n}\n","structuredPatch":[{"oldStart":51,"oldLines":7,"newStart":51,"newLines":8,"lines":[" scopes []schema.MetricScope,"," resolution int,"," from, to time.Time,","- ctx context.Context) (map[string]schema.JobData, error)","+ ctx context.Context,","+ resampleAlgo string) (map[string]schema.JobData, error)"," "," // HealthCheck evaluates the monitoring state for a set of nodes against expected metrics."," HealthCheck(cluster string,"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"d0e98556-6eb6-4cc6-b83e-31b5bf5b2ecb","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"2ab2f0d6-9066-457a-801c-3db0b6386169","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WVGkNWpfRm7Gtyxv7Y2TGh","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/metricstore.go has been updated successfully."}]},"uuid":"91ebc5a2-3c79-4b58-b8f9-02714f18ee46","timestamp":"2026-03-19T19:51:38.774Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/metricstore.go","oldString":"func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {","newString":"func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64, resampleAlgo string) ([]schema.Float, int64, int64, int64, error) {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricstore provides an efficient in-memory time-series metric storage system\n// with support for hierarchical data organization, checkpointing, and archiving.\n//\n// The package organizes metrics in a tree structure (cluster → host → component) and\n// provides concurrent read/write access to metric data with configurable aggregation strategies.\n// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n// and enforcing retention policies.\n//\n// Key features:\n// - In-memory metric storage with configurable retention\n// - Hierarchical data organization (selectors)\n// - Concurrent checkpoint/archive workers\n// - Support for sum and average aggregation\n// - NATS integration for metric ingestion\npackage metricstore\n\nimport (\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"runtime\"\n\t\"runtime/debug\"\n\t\"slices\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// GlobalState holds the global state for the metric store with thread-safe access.\ntype GlobalState struct {\n\tmu sync.RWMutex\n\tlastRetentionTime int64\n\tselectorsExcluded bool\n}\n\nvar (\n\tsingleton sync.Once\n\tmsInstance *MemoryStore\n\t// shutdownFunc stores the context cancellation function created in Init\n\t// and is called during Shutdown to cancel all background goroutines\n\tshutdownFunc context.CancelFunc\n\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n\t// Create a global instance\n\tstate = &GlobalState{}\n)\n\n// NodeProvider provides information about nodes currently in use by running jobs.\n//\n// This interface allows metricstore to query job information without directly\n// depending on the repository package, breaking the import cycle.\n//\n// Implementations should return nodes that are actively processing jobs started\n// before the given timestamp. These nodes will be excluded from retention-based\n// garbage collection to prevent data loss for jobs that are still running or\n// recently completed.\ntype NodeProvider interface {\n\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n\t// that are currently in use by jobs that started before the given timestamp.\n\t//\n\t// Parameters:\n\t// - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n\t//\n\t// Returns:\n\t// - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n\t// - Error if the query fails\n\tGetUsedNodes(ts int64) (map[string][]string, error)\n}\n\n// Metric represents a single metric data point to be written to the store.\ntype Metric struct {\n\tName string\n\tValue schema.Float\n\t// MetricConfig contains frequency and aggregation settings for this metric.\n\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n\tMetricConfig MetricConfig\n}\n\n// MemoryStore is the main in-memory time-series metric storage implementation.\n//\n// It organizes metrics in a hierarchical tree structure where each level represents\n// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n// store multiple metrics as time-series buffers.\n//\n// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n// All public methods are safe for concurrent use.\ntype MemoryStore struct {\n\tMetrics map[string]MetricConfig\n\troot Level\n\tnodeProvider NodeProvider\n}\n\n// Init initializes the metric store from configuration and starts background workers.\n//\n// This function must be called exactly once before any other metricstore operations.\n// It performs the following initialization steps:\n// 1. Validates and decodes the metric store configuration\n// 2. Configures worker pool size (defaults to NumCPU/2+1, max 10)\n// 3. Loads metric configurations from all registered clusters\n// 4. Restores checkpoints within the retention window\n// 5. Starts background workers for retention, checkpointing, archiving, and monitoring\n// 6. Optionally subscribes to NATS for real-time metric ingestion\n//\n// Parameters:\n// - rawConfig: JSON configuration for the metric store (see MetricStoreConfig)\n// - wg: WaitGroup that will be incremented for each background goroutine started\n//\n// The function will call cclog.Fatal on critical errors during initialization.\n// Use Shutdown() to cleanly stop all background workers started by Init().\n//\n// Note: Signal handling must be implemented by the caller. Call Shutdown() when\n// receiving termination signals to ensure checkpoint data is persisted.\nfunc Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\n\tstartupTime := time.Now()\n\n\tif rawConfig != nil {\n\t\tconfig.Validate(configSchema, rawConfig)\n\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n\t\tdec.DisallowUnknownFields()\n\t\tif err := dec.Decode(&Keys); err != nil {\n\t\t\tcclog.Abortf(\"[METRICSTORE]> Metric Store Config Init: Could not decode config file '%s'.\\nError: %s\\n\", rawConfig, err.Error())\n\t\t}\n\t}\n\n\t// Set NumWorkers from config or use default\n\tif Keys.NumWorkers <= 0 {\n\t\tKeys.NumWorkers = min(runtime.NumCPU()/2+1, DefaultMaxWorkers)\n\t}\n\tcclog.Debugf(\"[METRICSTORE]> Using %d workers for checkpoint/archive operations\\n\", Keys.NumWorkers)\n\n\t// Pass the config.MetricStoreKeys\n\tInitMetrics(metrics)\n\n\tms := GetMemoryStore()\n\n\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\tif err != nil {\n\t\tcclog.Fatal(err)\n\t}\n\n\trestoreFrom := startupTime.Add(-d)\n\tcclog.Infof(\"[METRICSTORE]> Loading checkpoints newer than %s\\n\", restoreFrom.Format(time.RFC3339))\n\tfiles, err := ms.FromCheckpointFiles(Keys.Checkpoints.RootDir, restoreFrom.Unix())\n\tloadedData := ms.SizeInBytes() / 1024 / 1024 // In MB\n\tif err != nil {\n\t\tcclog.Fatalf(\"[METRICSTORE]> Loading checkpoints failed: %s\\n\", err.Error())\n\t} else {\n\t\tcclog.Infof(\"[METRICSTORE]> Checkpoints loaded (%d files, %d MB, that took %fs)\\n\", files, loadedData, time.Since(startupTime).Seconds())\n\t}\n\n\t// Try to use less memory by forcing a GC run here and then\n\t// lowering the target percentage. The default of 100 means\n\t// that only once the ratio of new allocations execeds the\n\t// previously active heap, a GC is triggered.\n\t// Forcing a GC here will set the \"previously active heap\"\n\t// to a minumum.\n\t// runtime.GC()\n\n\tctx, shutdown := context.WithCancel(context.Background())\n\n\tRetention(wg, ctx)\n\tCheckpointing(wg, ctx)\n\tCleanUp(wg, ctx)\n\tWALStaging(wg, ctx)\n\tMemoryUsageTracker(wg, ctx)\n\n\t// Note: Signal handling has been removed from this function.\n\t// The caller is responsible for handling shutdown signals and calling\n\t// the shutdown() function when appropriate.\n\t// Store the shutdown function for later use by Shutdown()\n\tshutdownFuncMu.Lock()\n\tshutdownFunc = shutdown\n\tshutdownFuncMu.Unlock()\n\n\tif Keys.Subscriptions != nil {\n\t\twg.Go(func() {\n\t\t\tif err := ReceiveNats(ms, Keys.NumWorkers, ctx); err != nil {\n\t\t\t\tcclog.Fatal(err)\n\t\t\t}\n\t\t})\n\t}\n}\n\n// InitMetrics initializes the singleton MemoryStore instance with the given metric configurations.\n//\n// This function must be called before GetMemoryStore() and can only be called once due to\n// the singleton pattern. It assigns each metric an internal offset for efficient buffer indexing.\n//\n// Parameters:\n// - metrics: Map of metric names to their configurations (frequency and aggregation strategy)\n//\n// Panics if any metric has Frequency == 0, which indicates an invalid configuration.\n//\n// After this call, the global msInstance is ready for use via GetMemoryStore().\nfunc InitMetrics(metrics map[string]MetricConfig) {\n\tsingleton.Do(func() {\n\t\toffset := 0\n\t\tfor key, cfg := range metrics {\n\t\t\tif cfg.Frequency == 0 {\n\t\t\t\tpanic(\"[METRICSTORE]> invalid frequency\")\n\t\t\t}\n\n\t\t\tmetrics[key] = MetricConfig{\n\t\t\t\tFrequency: cfg.Frequency,\n\t\t\t\tAggregation: cfg.Aggregation,\n\t\t\t\toffset: offset,\n\t\t\t}\n\t\t\toffset += 1\n\t\t}\n\n\t\tmsInstance = &MemoryStore{\n\t\t\troot: Level{\n\t\t\t\tmetrics: make([]*buffer, len(metrics)),\n\t\t\t\tchildren: make(map[string]*Level),\n\t\t\t},\n\t\t\tMetrics: metrics,\n\t\t}\n\t})\n}\n\n// GetMemoryStore returns the singleton MemoryStore instance.\n//\n// Returns the initialized MemoryStore singleton. Calls cclog.Fatal if InitMetrics() was not called first.\n//\n// This function is safe for concurrent use after initialization.\nfunc GetMemoryStore() *MemoryStore {\n\tif msInstance == nil {\n\t\tcclog.Warnf(\"[METRICSTORE]> MemoryStore not initialized!\")\n\t}\n\n\treturn msInstance\n}\n\nfunc (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\n\tif metric, ok := ms.Metrics[metricName]; ok {\n\t\treturn metric.Frequency, nil\n\t}\n\treturn 0, fmt.Errorf(\"[METRICSTORE]> metric %s not found\", metricName)\n}\n\n// SetNodeProvider sets the NodeProvider implementation for the MemoryStore.\n// This must be called during initialization to provide job state information\n// for selective buffer retention during Free operations.\n// If not set, the Free function will fall back to freeing all buffers.\nfunc (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\n\tms.nodeProvider = provider\n}\n\n// Shutdown performs a graceful shutdown of the metric store.\n//\n// This function cancels all background goroutines started by Init() and writes\n// a final checkpoint to disk before returning. It should be called when the\n// application receives a termination signal.\n//\n// The function will:\n// 1. Cancel the context to stop all background workers\n// 2. Close the WAL messages channel if using WAL format\n// 3. Write a final checkpoint to preserve in-memory data\n// 4. Log any errors encountered during shutdown\n//\n// Note: This function blocks until the final checkpoint is written.\nfunc Shutdown() {\n\tshutdownFuncMu.Lock()\n\tdefer shutdownFuncMu.Unlock()\n\tif shutdownFunc != nil {\n\t\tshutdownFunc()\n\t}\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tfor _, ch := range walShardChs {\n\t\t\tclose(ch)\n\t\t}\n\t}\n\n\tcclog.Infof(\"[METRICSTORE]> Writing to '%s'...\\n\", Keys.Checkpoints.RootDir)\n\tvar files int\n\tvar err error\n\n\tms := GetMemoryStore()\n\n\tlastCheckpointMu.Lock()\n\tfrom := lastCheckpoint\n\tlastCheckpointMu.Unlock()\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tvar hostDirs []string\n\t\tfiles, hostDirs, err = ms.ToCheckpointWAL(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t\tif err == nil {\n\t\t\tRotateWALFilesAfterShutdown(hostDirs)\n\t\t}\n\t} else {\n\t\tfiles, err = ms.ToCheckpoint(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t}\n\n\tif err != nil {\n\t\tcclog.Errorf(\"[METRICSTORE]> Writing checkpoint failed: %s\\n\", err.Error())\n\t}\n\tcclog.Infof(\"[METRICSTORE]> Done! (%d files written)\\n\", files)\n}\n\n// Retention starts a background goroutine that periodically frees old metric data.\n//\n// This worker runs at half the retention interval and calls Free() to remove buffers\n// older than the configured retention time. It respects the NodeProvider to preserve\n// data for nodes with active jobs.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc Retention(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\t\tif err != nil {\n\t\t\tcclog.Fatal(err)\n\t\t}\n\t\tif d <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\ttickInterval := d / 2\n\t\tif tickInterval <= 0 {\n\t\t\treturn\n\t\t}\n\t\tticker := time.NewTicker(tickInterval)\n\t\tdefer ticker.Stop()\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tstate.mu.Lock()\n\n\t\t\t\tt := time.Now().Add(-d)\n\n\t\t\t\tstate.lastRetentionTime = t.Unix()\n\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> start freeing buffers (older than %s)...\\n\", t.Format(time.RFC3339))\n\n\t\t\t\tfreed, err := Free(ms, t)\n\t\t\t\tif err != nil {\n\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> freeing up buffers failed: %s\\n\", err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d buffers freed\\n\", freed)\n\t\t\t\t}\n\n\t\t\t\tstate.mu.Unlock()\n\n\t\t\t\t// Clean up the buffer pool\n\t\t\t\tbufferPool.Clean(state.lastRetentionTime)\n\t\t\t}\n\t\t}\n\t})\n}\n\n// MemoryUsageTracker starts a background goroutine that monitors memory usage.\n//\n// This worker checks actual process memory usage (via runtime.MemStats) periodically\n// and force-frees buffers if memory exceeds the configured cap. It uses FreeOSMemory()\n// to return memory to the OS after freeing buffers, avoiding aggressive GC that causes\n// performance issues.\n//\n// The tracker logs both actual memory usage (heap allocated) and metric data size for\n// visibility into memory overhead from Go runtime structures and allocations.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\tnormalInterval := DefaultMemoryUsageTrackerInterval\n\t\tfastInterval := 30 * time.Second\n\n\t\tif normalInterval <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\tticker := time.NewTicker(normalInterval)\n\t\tdefer ticker.Stop()\n\t\tcurrentInterval := normalInterval\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tvar mem runtime.MemStats\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB := float64(mem.Alloc) / 1e9\n\t\t\t\tmetricDataGB := ms.SizeInGB()\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory usage: %.2f GB actual (%.2f GB metric data)\", actualMemoryGB, metricDataGB)\n\n\t\t\t\tfreedExcluded := 0\n\t\t\t\tfreedEmergency := 0\n\t\t\t\tvar err error\n\n\t\t\t\tstate.mu.RLock()\n\t\t\t\tlastRetention := state.lastRetentionTime\n\t\t\t\tselectorsExcluded := state.selectorsExcluded\n\t\t\t\tstate.mu.RUnlock()\n\n\t\t\t\tif lastRetention != 0 && selectorsExcluded {\n\t\t\t\t\tfreedExcluded, err = ms.Free(nil, lastRetention)\n\t\t\t\t\tif err != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing the excluded buffers: %s\", err)\n\t\t\t\t\t}\n\n\t\t\t\t\tif freedExcluded > 0 {\n\t\t\t\t\t\tdebug.FreeOSMemory()\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d excluded buffers force-freed\", freedExcluded)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\tif actualMemoryGB > float64(Keys.MemoryCap) {\n\t\t\t\t\tcclog.Warnf(\"[METRICSTORE]> memory usage %.2f GB exceeds cap %d GB, starting emergency buffer freeing\", actualMemoryGB, Keys.MemoryCap)\n\n\t\t\t\t\t// Use progressive time-based Free with increasing threshold\n\t\t\t\t\t// instead of ForceFree loop — fewer tree traversals, more effective\n\t\t\t\t\td, parseErr := time.ParseDuration(Keys.RetentionInMemory)\n\t\t\t\t\tif parseErr != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> cannot parse retention duration: %s\", parseErr)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tthresholds := []float64{0.75, 0.5, 0.25}\n\t\t\t\t\t\tfor _, fraction := range thresholds {\n\t\t\t\t\t\t\tthreshold := time.Now().Add(-time.Duration(float64(d) * fraction))\n\t\t\t\t\t\t\tfreed, freeErr := ms.Free(nil, threshold.Unix())\n\t\t\t\t\t\t\tif freeErr != nil {\n\t\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while freeing buffers at %.0f%% retention: %s\", fraction*100, freeErr)\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tfreedEmergency += freed\n\n\t\t\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\t\t\truntime.GC()\n\t\t\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\t\t\tif actualMemoryGB < float64(Keys.MemoryCap) {\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\tdebug.FreeOSMemory()\n\n\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\tif actualMemoryGB >= float64(Keys.MemoryCap) {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> after emergency frees (%d buffers), memory usage %.2f GB still at/above cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> emergency freeing complete: %d buffers freed, memory now %.2f GB\", freedEmergency, actualMemoryGB)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Adaptive ticker: check more frequently when memory is high\n\t\t\t\tmemoryRatio := actualMemoryGB / float64(Keys.MemoryCap)\n\t\t\t\tif memoryRatio > 0.8 && currentInterval != fastInterval {\n\t\t\t\t\tticker.Reset(fastInterval)\n\t\t\t\t\tcurrentInterval = fastInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to fast check interval (30s)\", memoryRatio*100)\n\t\t\t\t} else if memoryRatio <= 0.8 && currentInterval != normalInterval {\n\t\t\t\t\tticker.Reset(normalInterval)\n\t\t\t\t\tcurrentInterval = normalInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to normal check interval\", memoryRatio*100)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t})\n}\n\n// Free removes metric data older than the given time while preserving data for active nodes.\n//\n// This function implements intelligent retention by consulting the NodeProvider (if configured)\n// to determine which nodes are currently in use by running jobs. Data for these nodes is\n// preserved even if older than the retention time.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - t: Time threshold - buffers with data older than this will be freed\n//\n// Returns:\n// - Number of buffers freed\n// - Error if NodeProvider query fails\n//\n// Behavior:\n// - If no NodeProvider is set: frees all buffers older than t\n// - If NodeProvider returns empty map: frees all buffers older than t\n// - Otherwise: preserves buffers for nodes returned by GetUsedNodes(), frees others\nfunc Free(ms *MemoryStore, t time.Time) (int, error) {\n\t// If no NodeProvider is configured, free all buffers older than t\n\tif ms.nodeProvider == nil {\n\t\treturn ms.Free(nil, t.Unix())\n\t}\n\n\texcludeSelectors, err := ms.nodeProvider.GetUsedNodes(t.Unix())\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tswitch lenMap := len(excludeSelectors); lenMap {\n\n\t// If the length of the map returned by GetUsedNodes() is 0,\n\t// then use default Free method with nil selector\n\tcase 0:\n\t\tstate.selectorsExcluded = false\n\t\treturn ms.Free(nil, t.Unix())\n\n\t// Else formulate selectors, exclude those from the map\n\t// and free the rest of the selectors\n\tdefault:\n\t\tstate.selectorsExcluded = true\n\t\tselectors := GetSelectors(ms, excludeSelectors)\n\t\treturn FreeSelected(ms, selectors, t)\n\t}\n}\n\n// FreeSelected frees buffers for specific selectors while preserving others.\n//\n// This function is used when we want to retain some specific nodes beyond the retention time.\n// It iterates through the provided selectors and frees their associated buffers.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - selectors: List of selector paths to free (e.g., [[\"cluster1\", \"node1\"], [\"cluster2\", \"node2\"]])\n// - t: Time threshold for freeing buffers\n//\n// Returns the total number of buffers freed and any error encountered.\nfunc FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\n\tfreed := 0\n\n\tfor _, selector := range selectors {\n\n\t\tfreedBuffers, err := ms.Free(selector, t.Unix())\n\t\tif err != nil {\n\t\t\tcclog.Errorf(\"error while freeing selected buffers: %#v\", err)\n\t\t}\n\t\tfreed += freedBuffers\n\n\t}\n\n\treturn freed, nil\n}\n\n// GetSelectors returns all selectors at depth 2 (cluster/node level) that are NOT in the exclusion map.\n//\n// This function generates a list of selectors whose buffers should be freed by excluding\n// selectors that correspond to nodes currently in use by running jobs.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - excludeSelectors: Map of cluster names to node hostnames that should NOT be freed\n//\n// Returns a list of selectors ([]string paths) that can be safely freed.\n//\n// Example:\n//\n//\tIf the tree has paths [\"emmy\", \"node001\"] and [\"emmy\", \"node002\"],\n//\tand excludeSelectors contains {\"emmy\": [\"node001\"]},\n//\tthen only [[\"emmy\", \"node002\"]] is returned.\nfunc GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\n\tallSelectors := ms.GetPaths(2)\n\n\tfilteredSelectors := make([][]string, 0, len(allSelectors))\n\n\tfor _, path := range allSelectors {\n\t\tif len(path) < 2 {\n\t\t\tcontinue\n\t\t}\n\n\t\tkey := path[0] // The \"Key\" (Level 1)\n\t\tvalue := path[1] // The \"Value\" (Level 2)\n\n\t\texclude := false\n\n\t\t// Check if the key exists in our exclusion map\n\t\tif excludedValues, exists := excludeSelectors[key]; exists {\n\t\t\t// The key exists, now check if the specific value is in the exclusion list\n\t\t\tif slices.Contains(excludedValues, value) {\n\t\t\t\texclude = true\n\t\t\t}\n\t\t}\n\n\t\tif !exclude {\n\t\t\tfilteredSelectors = append(filteredSelectors, path)\n\t\t}\n\t}\n\n\treturn filteredSelectors\n}\n\n// GetPaths returns a list of lists (paths) to the specified depth.\nfunc (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\n\tvar results [][]string\n\n\t// Start recursion. Initial path is empty.\n\t// We treat Root as depth 0.\n\tms.root.collectPaths(0, targetDepth, []string{}, &results)\n\n\treturn results\n}\n\n// Write all values in `metrics` to the level specified by `selector` for time `ts`.\n// Look at `findLevelOrCreate` for how selectors work.\nfunc (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\n\tvar ok bool\n\tfor i, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tmetric.MetricConfig, ok = m.Metrics[metric.Name]\n\t\t\tif !ok {\n\t\t\t\tcclog.Debugf(\"[METRICSTORE]> Unknown metric '%s' in Write() - skipping\", metric.Name)\n\t\t\t\tmetric.MetricConfig.Frequency = 0\n\t\t\t}\n\t\t\tmetrics[i] = metric\n\t\t}\n\t}\n\n\treturn m.WriteToLevel(&m.root, selector, ts, metrics)\n}\n\nfunc (m *MemoryStore) GetLevel(selector []string) *Level {\n\treturn m.root.findLevelOrCreate(selector, len(m.Metrics))\n}\n\n// WriteToLevel assumes that `minfo` in `metrics` is filled in\nfunc (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n\tl = l.findLevelOrCreate(selector, len(m.Metrics))\n\tl.lock.Lock()\n\tdefer l.lock.Unlock()\n\n\tfor _, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tcontinue\n\t\t}\n\n\t\tb := l.metrics[metric.MetricConfig.offset]\n\t\tif b == nil {\n\t\t\t// First write to this metric and level\n\t\t\tb = newBuffer(ts, metric.MetricConfig.Frequency)\n\t\t\tl.metrics[metric.MetricConfig.offset] = b\n\t\t}\n\n\t\tnb, err := b.write(ts, metric.Value)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\t// Last write created a new buffer...\n\t\tif b != nb {\n\t\t\tl.metrics[metric.MetricConfig.offset] = nb\n\t\t}\n\t}\n\treturn nil\n}\n\n// Read returns all values for metric `metric` from `from` to `to` for the selected level(s).\n// If the level does not hold the metric itself, the data will be aggregated recursively from the children.\n// The second and third return value are the actual from/to for the data. Those can be different from\n// the range asked for if no data was available.\nfunc (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {\n\tif from > to {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")\n\t}\n\n\tminfo, ok := m.Metrics[metric]\n\tif !ok {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> unknown metric: \" + metric)\n\t}\n\n\tn, data := 0, make([]schema.Float, (to-from)/minfo.Frequency+1)\n\n\terr := m.root.findBuffers(selector, minfo.offset, func(b *buffer) error {\n\t\tcdata, cfrom, cto, err := b.read(from, to, data)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tif n == 0 {\n\t\t\tfrom, to = cfrom, cto\n\t\t} else if from != cfrom || to != cto || len(data) != len(cdata) {\n\t\t\tmissingfront, missingback := int((from-cfrom)/minfo.Frequency), int((to-cto)/minfo.Frequency)\n\t\t\tif missingfront != 0 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tnewlen := len(cdata) - missingback\n\t\t\tif newlen < 1 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\t\t\tcdata = cdata[0:newlen]\n\t\t\tif len(cdata) != len(data) {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tfrom, to = cfrom, cto\n\t\t}\n\n\t\tdata = cdata\n\t\tn += 1\n\t\treturn nil\n\t})\n\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t} else if n == 0 {\n\t\treturn nil, 0, 0, 0, ErrNoHostOrMetric\n\t} else if n > 1 {\n\t\tif minfo.Aggregation == AvgAggregation {\n\t\t\tnormalize := 1. / schema.Float(n)\n\t\t\tfor i := 0; i < len(data); i++ {\n\t\t\t\tdata[i] *= normalize\n\t\t\t}\n\t\t} else if minfo.Aggregation != SumAggregation {\n\t\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid aggregation\")\n\t\t}\n\t}\n\n\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t}\n\n\treturn data, from, to, resolution, nil\n}\n\n// Free releases all buffers for the selected level and all its children that\n// contain only values older than `t`.\nfunc (m *MemoryStore) Free(selector []string, t int64) (int, error) {\n\treturn m.GetLevel(selector).free(t)\n}\n\n// ForceFree unconditionally removes the oldest buffer from each metric chain.\nfunc (m *MemoryStore) ForceFree() (int, error) {\n\treturn m.GetLevel(nil).forceFree()\n}\n\nfunc (m *MemoryStore) FreeAll() error {\n\tm.root.lock.Lock()\n\tdefer m.root.lock.Unlock()\n\n\tfor k := range m.root.children {\n\t\tdelete(m.root.children, k)\n\t}\n\n\treturn nil\n}\n\nfunc (m *MemoryStore) SizeInBytes() int64 {\n\treturn m.root.sizeInBytes()\n}\n\nfunc (m *MemoryStore) SizeInGB() float64 {\n\treturn float64(m.root.sizeInBytes()) / 1e9\n}\n\n// ListChildren , given a selector, returns a list of all children of the level\n// selected.\nfunc (m *MemoryStore) ListChildren(selector []string) []string {\n\tlvl := &m.root\n\tfor lvl != nil && len(selector) != 0 {\n\t\tlvl.lock.RLock()\n\t\tnext := lvl.children[selector[0]]\n\t\tlvl.lock.RUnlock()\n\t\tlvl = next\n\t\tselector = selector[1:]\n\t}\n\n\tif lvl == nil {\n\t\treturn nil\n\t}\n\n\tlvl.lock.RLock()\n\tdefer lvl.lock.RUnlock()\n\n\tchildren := make([]string, 0, len(lvl.children))\n\tfor child := range lvl.children {\n\t\tchildren = append(children, child)\n\t}\n\n\treturn children\n}\n","structuredPatch":[{"oldStart":679,"oldLines":7,"newStart":679,"newLines":7,"lines":[" // If the level does not hold the metric itself, the data will be aggregated recursively from the children."," // The second and third return value are the actual from/to for the data. Those can be different from"," // the range asked for if no data was available.","-func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {","+func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64, resampleAlgo string) ([]schema.Float, int64, int64, int64, error) {"," if from > to {"," return nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"2ab2f0d6-9066-457a-801c-3db0b6386169","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"7734f61f-4c35-4461-9311-812505b737c3","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017a1jBn33wtq2z8Xs9KE3mD","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/metricstore.go has been updated successfully."}]},"uuid":"4a85e3c5-ed02-4e74-b701-5a9ac0572a0a","timestamp":"2026-03-19T19:51:45.027Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/metricstore.go","oldString":"\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t}","newString":"\tresampleFn, rfErr := resampler.GetResampler(resampleAlgo)\n\tif rfErr != nil {\n\t\treturn nil, 0, 0, 0, rfErr\n\t}\n\tdata, resolution, err = resampleFn(data, minfo.Frequency, resolution)\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricstore provides an efficient in-memory time-series metric storage system\n// with support for hierarchical data organization, checkpointing, and archiving.\n//\n// The package organizes metrics in a tree structure (cluster → host → component) and\n// provides concurrent read/write access to metric data with configurable aggregation strategies.\n// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n// and enforcing retention policies.\n//\n// Key features:\n// - In-memory metric storage with configurable retention\n// - Hierarchical data organization (selectors)\n// - Concurrent checkpoint/archive workers\n// - Support for sum and average aggregation\n// - NATS integration for metric ingestion\npackage metricstore\n\nimport (\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"runtime\"\n\t\"runtime/debug\"\n\t\"slices\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// GlobalState holds the global state for the metric store with thread-safe access.\ntype GlobalState struct {\n\tmu sync.RWMutex\n\tlastRetentionTime int64\n\tselectorsExcluded bool\n}\n\nvar (\n\tsingleton sync.Once\n\tmsInstance *MemoryStore\n\t// shutdownFunc stores the context cancellation function created in Init\n\t// and is called during Shutdown to cancel all background goroutines\n\tshutdownFunc context.CancelFunc\n\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n\t// Create a global instance\n\tstate = &GlobalState{}\n)\n\n// NodeProvider provides information about nodes currently in use by running jobs.\n//\n// This interface allows metricstore to query job information without directly\n// depending on the repository package, breaking the import cycle.\n//\n// Implementations should return nodes that are actively processing jobs started\n// before the given timestamp. These nodes will be excluded from retention-based\n// garbage collection to prevent data loss for jobs that are still running or\n// recently completed.\ntype NodeProvider interface {\n\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n\t// that are currently in use by jobs that started before the given timestamp.\n\t//\n\t// Parameters:\n\t// - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n\t//\n\t// Returns:\n\t// - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n\t// - Error if the query fails\n\tGetUsedNodes(ts int64) (map[string][]string, error)\n}\n\n// Metric represents a single metric data point to be written to the store.\ntype Metric struct {\n\tName string\n\tValue schema.Float\n\t// MetricConfig contains frequency and aggregation settings for this metric.\n\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n\tMetricConfig MetricConfig\n}\n\n// MemoryStore is the main in-memory time-series metric storage implementation.\n//\n// It organizes metrics in a hierarchical tree structure where each level represents\n// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n// store multiple metrics as time-series buffers.\n//\n// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n// All public methods are safe for concurrent use.\ntype MemoryStore struct {\n\tMetrics map[string]MetricConfig\n\troot Level\n\tnodeProvider NodeProvider\n}\n\n// Init initializes the metric store from configuration and starts background workers.\n//\n// This function must be called exactly once before any other metricstore operations.\n// It performs the following initialization steps:\n// 1. Validates and decodes the metric store configuration\n// 2. Configures worker pool size (defaults to NumCPU/2+1, max 10)\n// 3. Loads metric configurations from all registered clusters\n// 4. Restores checkpoints within the retention window\n// 5. Starts background workers for retention, checkpointing, archiving, and monitoring\n// 6. Optionally subscribes to NATS for real-time metric ingestion\n//\n// Parameters:\n// - rawConfig: JSON configuration for the metric store (see MetricStoreConfig)\n// - wg: WaitGroup that will be incremented for each background goroutine started\n//\n// The function will call cclog.Fatal on critical errors during initialization.\n// Use Shutdown() to cleanly stop all background workers started by Init().\n//\n// Note: Signal handling must be implemented by the caller. Call Shutdown() when\n// receiving termination signals to ensure checkpoint data is persisted.\nfunc Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\n\tstartupTime := time.Now()\n\n\tif rawConfig != nil {\n\t\tconfig.Validate(configSchema, rawConfig)\n\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n\t\tdec.DisallowUnknownFields()\n\t\tif err := dec.Decode(&Keys); err != nil {\n\t\t\tcclog.Abortf(\"[METRICSTORE]> Metric Store Config Init: Could not decode config file '%s'.\\nError: %s\\n\", rawConfig, err.Error())\n\t\t}\n\t}\n\n\t// Set NumWorkers from config or use default\n\tif Keys.NumWorkers <= 0 {\n\t\tKeys.NumWorkers = min(runtime.NumCPU()/2+1, DefaultMaxWorkers)\n\t}\n\tcclog.Debugf(\"[METRICSTORE]> Using %d workers for checkpoint/archive operations\\n\", Keys.NumWorkers)\n\n\t// Pass the config.MetricStoreKeys\n\tInitMetrics(metrics)\n\n\tms := GetMemoryStore()\n\n\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\tif err != nil {\n\t\tcclog.Fatal(err)\n\t}\n\n\trestoreFrom := startupTime.Add(-d)\n\tcclog.Infof(\"[METRICSTORE]> Loading checkpoints newer than %s\\n\", restoreFrom.Format(time.RFC3339))\n\tfiles, err := ms.FromCheckpointFiles(Keys.Checkpoints.RootDir, restoreFrom.Unix())\n\tloadedData := ms.SizeInBytes() / 1024 / 1024 // In MB\n\tif err != nil {\n\t\tcclog.Fatalf(\"[METRICSTORE]> Loading checkpoints failed: %s\\n\", err.Error())\n\t} else {\n\t\tcclog.Infof(\"[METRICSTORE]> Checkpoints loaded (%d files, %d MB, that took %fs)\\n\", files, loadedData, time.Since(startupTime).Seconds())\n\t}\n\n\t// Try to use less memory by forcing a GC run here and then\n\t// lowering the target percentage. The default of 100 means\n\t// that only once the ratio of new allocations execeds the\n\t// previously active heap, a GC is triggered.\n\t// Forcing a GC here will set the \"previously active heap\"\n\t// to a minumum.\n\t// runtime.GC()\n\n\tctx, shutdown := context.WithCancel(context.Background())\n\n\tRetention(wg, ctx)\n\tCheckpointing(wg, ctx)\n\tCleanUp(wg, ctx)\n\tWALStaging(wg, ctx)\n\tMemoryUsageTracker(wg, ctx)\n\n\t// Note: Signal handling has been removed from this function.\n\t// The caller is responsible for handling shutdown signals and calling\n\t// the shutdown() function when appropriate.\n\t// Store the shutdown function for later use by Shutdown()\n\tshutdownFuncMu.Lock()\n\tshutdownFunc = shutdown\n\tshutdownFuncMu.Unlock()\n\n\tif Keys.Subscriptions != nil {\n\t\twg.Go(func() {\n\t\t\tif err := ReceiveNats(ms, Keys.NumWorkers, ctx); err != nil {\n\t\t\t\tcclog.Fatal(err)\n\t\t\t}\n\t\t})\n\t}\n}\n\n// InitMetrics initializes the singleton MemoryStore instance with the given metric configurations.\n//\n// This function must be called before GetMemoryStore() and can only be called once due to\n// the singleton pattern. It assigns each metric an internal offset for efficient buffer indexing.\n//\n// Parameters:\n// - metrics: Map of metric names to their configurations (frequency and aggregation strategy)\n//\n// Panics if any metric has Frequency == 0, which indicates an invalid configuration.\n//\n// After this call, the global msInstance is ready for use via GetMemoryStore().\nfunc InitMetrics(metrics map[string]MetricConfig) {\n\tsingleton.Do(func() {\n\t\toffset := 0\n\t\tfor key, cfg := range metrics {\n\t\t\tif cfg.Frequency == 0 {\n\t\t\t\tpanic(\"[METRICSTORE]> invalid frequency\")\n\t\t\t}\n\n\t\t\tmetrics[key] = MetricConfig{\n\t\t\t\tFrequency: cfg.Frequency,\n\t\t\t\tAggregation: cfg.Aggregation,\n\t\t\t\toffset: offset,\n\t\t\t}\n\t\t\toffset += 1\n\t\t}\n\n\t\tmsInstance = &MemoryStore{\n\t\t\troot: Level{\n\t\t\t\tmetrics: make([]*buffer, len(metrics)),\n\t\t\t\tchildren: make(map[string]*Level),\n\t\t\t},\n\t\t\tMetrics: metrics,\n\t\t}\n\t})\n}\n\n// GetMemoryStore returns the singleton MemoryStore instance.\n//\n// Returns the initialized MemoryStore singleton. Calls cclog.Fatal if InitMetrics() was not called first.\n//\n// This function is safe for concurrent use after initialization.\nfunc GetMemoryStore() *MemoryStore {\n\tif msInstance == nil {\n\t\tcclog.Warnf(\"[METRICSTORE]> MemoryStore not initialized!\")\n\t}\n\n\treturn msInstance\n}\n\nfunc (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\n\tif metric, ok := ms.Metrics[metricName]; ok {\n\t\treturn metric.Frequency, nil\n\t}\n\treturn 0, fmt.Errorf(\"[METRICSTORE]> metric %s not found\", metricName)\n}\n\n// SetNodeProvider sets the NodeProvider implementation for the MemoryStore.\n// This must be called during initialization to provide job state information\n// for selective buffer retention during Free operations.\n// If not set, the Free function will fall back to freeing all buffers.\nfunc (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\n\tms.nodeProvider = provider\n}\n\n// Shutdown performs a graceful shutdown of the metric store.\n//\n// This function cancels all background goroutines started by Init() and writes\n// a final checkpoint to disk before returning. It should be called when the\n// application receives a termination signal.\n//\n// The function will:\n// 1. Cancel the context to stop all background workers\n// 2. Close the WAL messages channel if using WAL format\n// 3. Write a final checkpoint to preserve in-memory data\n// 4. Log any errors encountered during shutdown\n//\n// Note: This function blocks until the final checkpoint is written.\nfunc Shutdown() {\n\tshutdownFuncMu.Lock()\n\tdefer shutdownFuncMu.Unlock()\n\tif shutdownFunc != nil {\n\t\tshutdownFunc()\n\t}\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tfor _, ch := range walShardChs {\n\t\t\tclose(ch)\n\t\t}\n\t}\n\n\tcclog.Infof(\"[METRICSTORE]> Writing to '%s'...\\n\", Keys.Checkpoints.RootDir)\n\tvar files int\n\tvar err error\n\n\tms := GetMemoryStore()\n\n\tlastCheckpointMu.Lock()\n\tfrom := lastCheckpoint\n\tlastCheckpointMu.Unlock()\n\n\tif Keys.Checkpoints.FileFormat == \"wal\" {\n\t\tvar hostDirs []string\n\t\tfiles, hostDirs, err = ms.ToCheckpointWAL(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t\tif err == nil {\n\t\t\tRotateWALFilesAfterShutdown(hostDirs)\n\t\t}\n\t} else {\n\t\tfiles, err = ms.ToCheckpoint(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n\t}\n\n\tif err != nil {\n\t\tcclog.Errorf(\"[METRICSTORE]> Writing checkpoint failed: %s\\n\", err.Error())\n\t}\n\tcclog.Infof(\"[METRICSTORE]> Done! (%d files written)\\n\", files)\n}\n\n// Retention starts a background goroutine that periodically frees old metric data.\n//\n// This worker runs at half the retention interval and calls Free() to remove buffers\n// older than the configured retention time. It respects the NodeProvider to preserve\n// data for nodes with active jobs.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc Retention(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\td, err := time.ParseDuration(Keys.RetentionInMemory)\n\t\tif err != nil {\n\t\t\tcclog.Fatal(err)\n\t\t}\n\t\tif d <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\ttickInterval := d / 2\n\t\tif tickInterval <= 0 {\n\t\t\treturn\n\t\t}\n\t\tticker := time.NewTicker(tickInterval)\n\t\tdefer ticker.Stop()\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tstate.mu.Lock()\n\n\t\t\t\tt := time.Now().Add(-d)\n\n\t\t\t\tstate.lastRetentionTime = t.Unix()\n\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> start freeing buffers (older than %s)...\\n\", t.Format(time.RFC3339))\n\n\t\t\t\tfreed, err := Free(ms, t)\n\t\t\t\tif err != nil {\n\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> freeing up buffers failed: %s\\n\", err.Error())\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d buffers freed\\n\", freed)\n\t\t\t\t}\n\n\t\t\t\tstate.mu.Unlock()\n\n\t\t\t\t// Clean up the buffer pool\n\t\t\t\tbufferPool.Clean(state.lastRetentionTime)\n\t\t\t}\n\t\t}\n\t})\n}\n\n// MemoryUsageTracker starts a background goroutine that monitors memory usage.\n//\n// This worker checks actual process memory usage (via runtime.MemStats) periodically\n// and force-frees buffers if memory exceeds the configured cap. It uses FreeOSMemory()\n// to return memory to the OS after freeing buffers, avoiding aggressive GC that causes\n// performance issues.\n//\n// The tracker logs both actual memory usage (heap allocated) and metric data size for\n// visibility into memory overhead from Go runtime structures and allocations.\n//\n// Parameters:\n// - wg: WaitGroup to signal completion when context is cancelled\n// - ctx: Context for cancellation signal\n//\n// The goroutine exits when ctx is cancelled.\nfunc MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\n\tms := GetMemoryStore()\n\n\twg.Go(func() {\n\t\tnormalInterval := DefaultMemoryUsageTrackerInterval\n\t\tfastInterval := 30 * time.Second\n\n\t\tif normalInterval <= 0 {\n\t\t\treturn\n\t\t}\n\n\t\tticker := time.NewTicker(normalInterval)\n\t\tdefer ticker.Stop()\n\t\tcurrentInterval := normalInterval\n\n\t\tfor {\n\t\t\tselect {\n\t\t\tcase <-ctx.Done():\n\t\t\t\treturn\n\t\t\tcase <-ticker.C:\n\t\t\t\tvar mem runtime.MemStats\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB := float64(mem.Alloc) / 1e9\n\t\t\t\tmetricDataGB := ms.SizeInGB()\n\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory usage: %.2f GB actual (%.2f GB metric data)\", actualMemoryGB, metricDataGB)\n\n\t\t\t\tfreedExcluded := 0\n\t\t\t\tfreedEmergency := 0\n\t\t\t\tvar err error\n\n\t\t\t\tstate.mu.RLock()\n\t\t\t\tlastRetention := state.lastRetentionTime\n\t\t\t\tselectorsExcluded := state.selectorsExcluded\n\t\t\t\tstate.mu.RUnlock()\n\n\t\t\t\tif lastRetention != 0 && selectorsExcluded {\n\t\t\t\t\tfreedExcluded, err = ms.Free(nil, lastRetention)\n\t\t\t\t\tif err != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing the excluded buffers: %s\", err)\n\t\t\t\t\t}\n\n\t\t\t\t\tif freedExcluded > 0 {\n\t\t\t\t\t\tdebug.FreeOSMemory()\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d excluded buffers force-freed\", freedExcluded)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\tif actualMemoryGB > float64(Keys.MemoryCap) {\n\t\t\t\t\tcclog.Warnf(\"[METRICSTORE]> memory usage %.2f GB exceeds cap %d GB, starting emergency buffer freeing\", actualMemoryGB, Keys.MemoryCap)\n\n\t\t\t\t\t// Use progressive time-based Free with increasing threshold\n\t\t\t\t\t// instead of ForceFree loop — fewer tree traversals, more effective\n\t\t\t\t\td, parseErr := time.ParseDuration(Keys.RetentionInMemory)\n\t\t\t\t\tif parseErr != nil {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> cannot parse retention duration: %s\", parseErr)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tthresholds := []float64{0.75, 0.5, 0.25}\n\t\t\t\t\t\tfor _, fraction := range thresholds {\n\t\t\t\t\t\t\tthreshold := time.Now().Add(-time.Duration(float64(d) * fraction))\n\t\t\t\t\t\t\tfreed, freeErr := ms.Free(nil, threshold.Unix())\n\t\t\t\t\t\t\tif freeErr != nil {\n\t\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while freeing buffers at %.0f%% retention: %s\", fraction*100, freeErr)\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tfreedEmergency += freed\n\n\t\t\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\t\t\truntime.GC()\n\t\t\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\t\t\tif actualMemoryGB < float64(Keys.MemoryCap) {\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tbufferPool.Clear()\n\t\t\t\t\tdebug.FreeOSMemory()\n\n\t\t\t\t\truntime.ReadMemStats(&mem)\n\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n\n\t\t\t\t\tif actualMemoryGB >= float64(Keys.MemoryCap) {\n\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> after emergency frees (%d buffers), memory usage %.2f GB still at/above cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n\t\t\t\t\t} else {\n\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> emergency freeing complete: %d buffers freed, memory now %.2f GB\", freedEmergency, actualMemoryGB)\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Adaptive ticker: check more frequently when memory is high\n\t\t\t\tmemoryRatio := actualMemoryGB / float64(Keys.MemoryCap)\n\t\t\t\tif memoryRatio > 0.8 && currentInterval != fastInterval {\n\t\t\t\t\tticker.Reset(fastInterval)\n\t\t\t\t\tcurrentInterval = fastInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to fast check interval (30s)\", memoryRatio*100)\n\t\t\t\t} else if memoryRatio <= 0.8 && currentInterval != normalInterval {\n\t\t\t\t\tticker.Reset(normalInterval)\n\t\t\t\t\tcurrentInterval = normalInterval\n\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory at %.0f%% of cap, switching to normal check interval\", memoryRatio*100)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t})\n}\n\n// Free removes metric data older than the given time while preserving data for active nodes.\n//\n// This function implements intelligent retention by consulting the NodeProvider (if configured)\n// to determine which nodes are currently in use by running jobs. Data for these nodes is\n// preserved even if older than the retention time.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - t: Time threshold - buffers with data older than this will be freed\n//\n// Returns:\n// - Number of buffers freed\n// - Error if NodeProvider query fails\n//\n// Behavior:\n// - If no NodeProvider is set: frees all buffers older than t\n// - If NodeProvider returns empty map: frees all buffers older than t\n// - Otherwise: preserves buffers for nodes returned by GetUsedNodes(), frees others\nfunc Free(ms *MemoryStore, t time.Time) (int, error) {\n\t// If no NodeProvider is configured, free all buffers older than t\n\tif ms.nodeProvider == nil {\n\t\treturn ms.Free(nil, t.Unix())\n\t}\n\n\texcludeSelectors, err := ms.nodeProvider.GetUsedNodes(t.Unix())\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tswitch lenMap := len(excludeSelectors); lenMap {\n\n\t// If the length of the map returned by GetUsedNodes() is 0,\n\t// then use default Free method with nil selector\n\tcase 0:\n\t\tstate.selectorsExcluded = false\n\t\treturn ms.Free(nil, t.Unix())\n\n\t// Else formulate selectors, exclude those from the map\n\t// and free the rest of the selectors\n\tdefault:\n\t\tstate.selectorsExcluded = true\n\t\tselectors := GetSelectors(ms, excludeSelectors)\n\t\treturn FreeSelected(ms, selectors, t)\n\t}\n}\n\n// FreeSelected frees buffers for specific selectors while preserving others.\n//\n// This function is used when we want to retain some specific nodes beyond the retention time.\n// It iterates through the provided selectors and frees their associated buffers.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - selectors: List of selector paths to free (e.g., [[\"cluster1\", \"node1\"], [\"cluster2\", \"node2\"]])\n// - t: Time threshold for freeing buffers\n//\n// Returns the total number of buffers freed and any error encountered.\nfunc FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\n\tfreed := 0\n\n\tfor _, selector := range selectors {\n\n\t\tfreedBuffers, err := ms.Free(selector, t.Unix())\n\t\tif err != nil {\n\t\t\tcclog.Errorf(\"error while freeing selected buffers: %#v\", err)\n\t\t}\n\t\tfreed += freedBuffers\n\n\t}\n\n\treturn freed, nil\n}\n\n// GetSelectors returns all selectors at depth 2 (cluster/node level) that are NOT in the exclusion map.\n//\n// This function generates a list of selectors whose buffers should be freed by excluding\n// selectors that correspond to nodes currently in use by running jobs.\n//\n// Parameters:\n// - ms: The MemoryStore instance\n// - excludeSelectors: Map of cluster names to node hostnames that should NOT be freed\n//\n// Returns a list of selectors ([]string paths) that can be safely freed.\n//\n// Example:\n//\n//\tIf the tree has paths [\"emmy\", \"node001\"] and [\"emmy\", \"node002\"],\n//\tand excludeSelectors contains {\"emmy\": [\"node001\"]},\n//\tthen only [[\"emmy\", \"node002\"]] is returned.\nfunc GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\n\tallSelectors := ms.GetPaths(2)\n\n\tfilteredSelectors := make([][]string, 0, len(allSelectors))\n\n\tfor _, path := range allSelectors {\n\t\tif len(path) < 2 {\n\t\t\tcontinue\n\t\t}\n\n\t\tkey := path[0] // The \"Key\" (Level 1)\n\t\tvalue := path[1] // The \"Value\" (Level 2)\n\n\t\texclude := false\n\n\t\t// Check if the key exists in our exclusion map\n\t\tif excludedValues, exists := excludeSelectors[key]; exists {\n\t\t\t// The key exists, now check if the specific value is in the exclusion list\n\t\t\tif slices.Contains(excludedValues, value) {\n\t\t\t\texclude = true\n\t\t\t}\n\t\t}\n\n\t\tif !exclude {\n\t\t\tfilteredSelectors = append(filteredSelectors, path)\n\t\t}\n\t}\n\n\treturn filteredSelectors\n}\n\n// GetPaths returns a list of lists (paths) to the specified depth.\nfunc (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\n\tvar results [][]string\n\n\t// Start recursion. Initial path is empty.\n\t// We treat Root as depth 0.\n\tms.root.collectPaths(0, targetDepth, []string{}, &results)\n\n\treturn results\n}\n\n// Write all values in `metrics` to the level specified by `selector` for time `ts`.\n// Look at `findLevelOrCreate` for how selectors work.\nfunc (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\n\tvar ok bool\n\tfor i, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tmetric.MetricConfig, ok = m.Metrics[metric.Name]\n\t\t\tif !ok {\n\t\t\t\tcclog.Debugf(\"[METRICSTORE]> Unknown metric '%s' in Write() - skipping\", metric.Name)\n\t\t\t\tmetric.MetricConfig.Frequency = 0\n\t\t\t}\n\t\t\tmetrics[i] = metric\n\t\t}\n\t}\n\n\treturn m.WriteToLevel(&m.root, selector, ts, metrics)\n}\n\nfunc (m *MemoryStore) GetLevel(selector []string) *Level {\n\treturn m.root.findLevelOrCreate(selector, len(m.Metrics))\n}\n\n// WriteToLevel assumes that `minfo` in `metrics` is filled in\nfunc (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n\tl = l.findLevelOrCreate(selector, len(m.Metrics))\n\tl.lock.Lock()\n\tdefer l.lock.Unlock()\n\n\tfor _, metric := range metrics {\n\t\tif metric.MetricConfig.Frequency == 0 {\n\t\t\tcontinue\n\t\t}\n\n\t\tb := l.metrics[metric.MetricConfig.offset]\n\t\tif b == nil {\n\t\t\t// First write to this metric and level\n\t\t\tb = newBuffer(ts, metric.MetricConfig.Frequency)\n\t\t\tl.metrics[metric.MetricConfig.offset] = b\n\t\t}\n\n\t\tnb, err := b.write(ts, metric.Value)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\t// Last write created a new buffer...\n\t\tif b != nb {\n\t\t\tl.metrics[metric.MetricConfig.offset] = nb\n\t\t}\n\t}\n\treturn nil\n}\n\n// Read returns all values for metric `metric` from `from` to `to` for the selected level(s).\n// If the level does not hold the metric itself, the data will be aggregated recursively from the children.\n// The second and third return value are the actual from/to for the data. Those can be different from\n// the range asked for if no data was available.\nfunc (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64, resampleAlgo string) ([]schema.Float, int64, int64, int64, error) {\n\tif from > to {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")\n\t}\n\n\tminfo, ok := m.Metrics[metric]\n\tif !ok {\n\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> unknown metric: \" + metric)\n\t}\n\n\tn, data := 0, make([]schema.Float, (to-from)/minfo.Frequency+1)\n\n\terr := m.root.findBuffers(selector, minfo.offset, func(b *buffer) error {\n\t\tcdata, cfrom, cto, err := b.read(from, to, data)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tif n == 0 {\n\t\t\tfrom, to = cfrom, cto\n\t\t} else if from != cfrom || to != cto || len(data) != len(cdata) {\n\t\t\tmissingfront, missingback := int((from-cfrom)/minfo.Frequency), int((to-cto)/minfo.Frequency)\n\t\t\tif missingfront != 0 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tnewlen := len(cdata) - missingback\n\t\t\tif newlen < 1 {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\t\t\tcdata = cdata[0:newlen]\n\t\t\tif len(cdata) != len(data) {\n\t\t\t\treturn ErrDataDoesNotAlign\n\t\t\t}\n\n\t\t\tfrom, to = cfrom, cto\n\t\t}\n\n\t\tdata = cdata\n\t\tn += 1\n\t\treturn nil\n\t})\n\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t} else if n == 0 {\n\t\treturn nil, 0, 0, 0, ErrNoHostOrMetric\n\t} else if n > 1 {\n\t\tif minfo.Aggregation == AvgAggregation {\n\t\t\tnormalize := 1. / schema.Float(n)\n\t\t\tfor i := 0; i < len(data); i++ {\n\t\t\t\tdata[i] *= normalize\n\t\t\t}\n\t\t} else if minfo.Aggregation != SumAggregation {\n\t\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid aggregation\")\n\t\t}\n\t}\n\n\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n\tif err != nil {\n\t\treturn nil, 0, 0, 0, err\n\t}\n\n\treturn data, from, to, resolution, nil\n}\n\n// Free releases all buffers for the selected level and all its children that\n// contain only values older than `t`.\nfunc (m *MemoryStore) Free(selector []string, t int64) (int, error) {\n\treturn m.GetLevel(selector).free(t)\n}\n\n// ForceFree unconditionally removes the oldest buffer from each metric chain.\nfunc (m *MemoryStore) ForceFree() (int, error) {\n\treturn m.GetLevel(nil).forceFree()\n}\n\nfunc (m *MemoryStore) FreeAll() error {\n\tm.root.lock.Lock()\n\tdefer m.root.lock.Unlock()\n\n\tfor k := range m.root.children {\n\t\tdelete(m.root.children, k)\n\t}\n\n\treturn nil\n}\n\nfunc (m *MemoryStore) SizeInBytes() int64 {\n\treturn m.root.sizeInBytes()\n}\n\nfunc (m *MemoryStore) SizeInGB() float64 {\n\treturn float64(m.root.sizeInBytes()) / 1e9\n}\n\n// ListChildren , given a selector, returns a list of all children of the level\n// selected.\nfunc (m *MemoryStore) ListChildren(selector []string) []string {\n\tlvl := &m.root\n\tfor lvl != nil && len(selector) != 0 {\n\t\tlvl.lock.RLock()\n\t\tnext := lvl.children[selector[0]]\n\t\tlvl.lock.RUnlock()\n\t\tlvl = next\n\t\tselector = selector[1:]\n\t}\n\n\tif lvl == nil {\n\t\treturn nil\n\t}\n\n\tlvl.lock.RLock()\n\tdefer lvl.lock.RUnlock()\n\n\tchildren := make([]string, 0, len(lvl.children))\n\tfor child := range lvl.children {\n\t\tchildren = append(children, child)\n\t}\n\n\treturn children\n}\n","structuredPatch":[{"oldStart":737,"oldLines":7,"newStart":737,"newLines":11,"lines":[" }"," }"," ","- data, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)","+ resampleFn, rfErr := resampler.GetResampler(resampleAlgo)","+ if rfErr != nil {","+ return nil, 0, 0, 0, rfErr","+ }","+ data, resolution, err = resampleFn(data, minfo.Frequency, resolution)"," if err != nil {"," return nil, 0, 0, 0, err"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"7734f61f-4c35-4461-9311-812505b737c3","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"ed22ebf7-0e18-4210-8498-d0e86d1dd9d9","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hzq6BbrspRGwz1tej7XvH9","type":"tool_result","content":" 175→// - Individual queries via req.Queries\n 176→// - Batch queries for all nodes via req.ForAllNodes\n 177→// - Hierarchical selector construction (cluster → host → type → subtype)\n 178→// - Optional statistics computation (avg, min, max)\n 179→// - Optional data scaling\n 180→// - Optional data padding with NaN values\n 181→//\n 182→// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n 183→// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n 184→// separate results are returned for each combination.\n 185→//\n 186→// Parameters:\n 187→// - req: The query request containing queries, time range, and options\n 188→//\n 189→// Returns:\n 190→// - APIQueryResponse containing results for each query, or error if validation fails\n 191→//\n 192→// Errors:\n 193→// - ErrInvalidTimeRange if req.From > req.To\n 194→// - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n 195→// - Error if MemoryStore is not initialized\n 196→// - Individual query errors are stored in APIMetricData.Error field\n 197→func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n 198→\tif req.From > req.To {\n 199→\t\treturn nil, ErrInvalidTimeRange\n 200→\t}\n 201→\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n 202→\t\treturn nil, ErrEmptyCluster\n 203→\t}\n 204→\n 205→\treq.WithData = true\n 206→\tms := GetMemoryStore()\n 207→\tif ms == nil {\n 208→\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n 209→\t}\n 210→\n 211→\tresponse := APIQueryResponse{\n 212→\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n 213→\t}\n 214→\tif req.ForAllNodes != nil {\n 215→\t\tnodes := ms.ListChildren([]string{req.Cluster})\n 216→\t\tfor _, node := range nodes {\n 217→\t\t\tfor _, metric := range req.ForAllNodes {\n 218→\t\t\t\tq := APIQuery{\n 219→\t\t\t\t\tMetric: metric,\n 220→\t\t\t\t\tHostname: node,\n 221→\t\t\t\t}\n 222→\t\t\t\treq.Queries = append(req.Queries, q)\n 223→\t\t\t\tresponse.Queries = append(response.Queries, q)\n 224→\t\t\t}\n 225→\t\t}\n 226→\t}\n 227→\n 228→\tfor _, query := range req.Queries {\n 229→\t\tsels := make([]util.Selector, 0, 1)\n 230→\t\tif query.Aggregate || query.Type == nil {\n 231→\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n 232→\t\t\tif query.Type != nil {\n 233→\t\t\t\tif len(query.TypeIds) == 1 {\n 234→\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n 235→\t\t\t\t} else {\n 236→\t\t\t\t\tids := make([]string, len(query.TypeIds))\n 237→\t\t\t\t\tfor i, id := range query.TypeIds {\n 238→\t\t\t\t\t\tids[i] = *query.Type + id\n 239→\t\t\t\t\t}\n 240→\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n 241→\t\t\t\t}\n 242→\n 243→\t\t\t\tif query.SubType != nil {\n 244→\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n 245→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n 246→\t\t\t\t\t} else {\n 247→\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n 248→\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n 249→\t\t\t\t\t\t\tids[i] = *query.SubType + id\n 250→\t\t\t\t\t\t}\n 251→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n 252→\t\t\t\t\t}\n 253→\t\t\t\t}\n 254→\t\t\t}\n 255→\t\t\tsels = append(sels, sel)\n 256→\t\t} else {\n 257→\t\t\tfor _, typeID := range query.TypeIds {\n 258→\t\t\t\tif query.SubType != nil {\n 259→\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n 260→\t\t\t\t\t\tsels = append(sels, util.Selector{\n 261→\t\t\t\t\t\t\t{String: req.Cluster},\n 262→\t\t\t\t\t\t\t{String: query.Hostname},\n 263→\t\t\t\t\t\t\t{String: *query.Type + typeID},\n 264→\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n 265→\t\t\t\t\t\t})\n 266→\t\t\t\t\t}\n 267→\t\t\t\t} else {\n 268→\t\t\t\t\tsels = append(sels, util.Selector{\n 269→\t\t\t\t\t\t{String: req.Cluster},\n 270→\t\t\t\t\t\t{String: query.Hostname},\n 271→\t\t\t\t\t\t{String: *query.Type + typeID},\n 272→\t\t\t\t\t})\n 273→\t\t\t\t}\n 274→\t\t\t}\n 275→\t\t}\n 276→\n 277→\t\tvar err error\n 278→\t\tres := make([]APIMetricData, 0, len(sels))\n 279→\t\tfor _, sel := range sels {\n 280→\t\t\tdata := APIMetricData{}\n 281→\n 282→\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n 283→\t\t\tif err != nil {\n 284→\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n 285→\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n 286→\t\t\t\tif err != ErrNoHostOrMetric {\n 287→\t\t\t\t\tmsg := err.Error()\n 288→\t\t\t\t\tdata.Error = &msg\n 289→\t\t\t\t\tres = append(res, data)\n 290→\t\t\t\t} else {\n 291→\t\t\t\t\tcclog.Debugf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n 292→\t\t\t\t}\n 293→\t\t\t\tcontinue\n 294→\t\t\t}"}]},"uuid":"96d4ab7a-3924-48c2-9857-e45a0a183ff0","timestamp":"2026-03-19T19:51:57.580Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/api.go","content":"// - Individual queries via req.Queries\n// - Batch queries for all nodes via req.ForAllNodes\n// - Hierarchical selector construction (cluster → host → type → subtype)\n// - Optional statistics computation (avg, min, max)\n// - Optional data scaling\n// - Optional data padding with NaN values\n//\n// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n// separate results are returned for each combination.\n//\n// Parameters:\n// - req: The query request containing queries, time range, and options\n//\n// Returns:\n// - APIQueryResponse containing results for each query, or error if validation fails\n//\n// Errors:\n// - ErrInvalidTimeRange if req.From > req.To\n// - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n// - Error if MemoryStore is not initialized\n// - Individual query errors are stored in APIMetricData.Error field\nfunc FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n\tif req.From > req.To {\n\t\treturn nil, ErrInvalidTimeRange\n\t}\n\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n\t\treturn nil, ErrEmptyCluster\n\t}\n\n\treq.WithData = true\n\tms := GetMemoryStore()\n\tif ms == nil {\n\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n\t}\n\n\tresponse := APIQueryResponse{\n\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n\t}\n\tif req.ForAllNodes != nil {\n\t\tnodes := ms.ListChildren([]string{req.Cluster})\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range req.ForAllNodes {\n\t\t\t\tq := APIQuery{\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tHostname: node,\n\t\t\t\t}\n\t\t\t\treq.Queries = append(req.Queries, q)\n\t\t\t\tresponse.Queries = append(response.Queries, q)\n\t\t\t}\n\t\t}\n\t}\n\n\tfor _, query := range req.Queries {\n\t\tsels := make([]util.Selector, 0, 1)\n\t\tif query.Aggregate || query.Type == nil {\n\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n\t\t\tif query.Type != nil {\n\t\t\t\tif len(query.TypeIds) == 1 {\n\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n\t\t\t\t} else {\n\t\t\t\t\tids := make([]string, len(query.TypeIds))\n\t\t\t\t\tfor i, id := range query.TypeIds {\n\t\t\t\t\t\tids[i] = *query.Type + id\n\t\t\t\t\t}\n\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t}\n\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n\t\t\t\t\t} else {\n\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n\t\t\t\t\t\t\tids[i] = *query.SubType + id\n\t\t\t\t\t\t}\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tsels = append(sels, sel)\n\t\t} else {\n\t\t\tfor _, typeID := range query.TypeIds {\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n\t\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n\t\t\t\t\t\t})\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t})\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tvar err error\n\t\tres := make([]APIMetricData, 0, len(sels))\n\t\tfor _, sel := range sels {\n\t\t\tdata := APIMetricData{}\n\n\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n\t\t\tif err != nil {\n\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n\t\t\t\tif err != ErrNoHostOrMetric {\n\t\t\t\t\tmsg := err.Error()\n\t\t\t\t\tdata.Error = &msg\n\t\t\t\t\tres = append(res, data)\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Debugf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n\t\t\t\t}\n\t\t\t\tcontinue\n\t\t\t}","numLines":120,"startLine":175,"totalLines":315}},"sourceToolAssistantUUID":"ed22ebf7-0e18-4210-8498-d0e86d1dd9d9","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"30f67e36-b5e2-485f-8d4e-ba72ca7c7838","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DsnLoxTeqk2LJeiojJa1iz","type":"tool_result","content":" 53→type APIQueryRequest struct {\n 54→\tCluster string `json:\"cluster\"`\n 55→\tQueries []APIQuery `json:\"queries\"`\n 56→\tForAllNodes []string `json:\"for-all-nodes\"`\n 57→\tFrom int64 `json:\"from\"`\n 58→\tTo int64 `json:\"to\"`\n 59→\tWithStats bool `json:\"with-stats\"`\n 60→\tWithData bool `json:\"with-data\"`\n 61→\tWithPadding bool `json:\"with-padding\"`\n 62→}\n 63→\n 64→// APIQueryResponse represents the response to an APIQueryRequest.\n 65→//\n 66→// Results is a 2D array where each outer element corresponds to a query,\n 67→// and each inner element corresponds to a selector within that query\n 68→// (e.g., multiple CPUs or cores).\n 69→type APIQueryResponse struct {\n 70→\tQueries []APIQuery `json:\"queries,omitempty\"`\n 71→\tResults [][]APIMetricData `json:\"results\"`\n 72→}\n 73→\n 74→// APIQuery represents a single metric query with optional hierarchical selectors.\n 75→//\n 76→// The hierarchical selection works as follows:\n 77→// - Hostname: The node to query\n 78→// - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n 79→// - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n 80→//\n 81→// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n 82→// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\n 83→type APIQuery struct {\n 84→\tType *string `json:\"type,omitempty\"`\n 85→\tSubType *string `json:\"subtype,omitempty\"`\n 86→\tMetric string `json:\"metric\"`\n 87→\tHostname string `json:\"host\"`\n 88→\tResolution int64 `json:\"resolution\"`\n 89→\tTypeIds []string `json:\"type-ids,omitempty\"`\n 90→\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n 91→\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n 92→\tAggregate bool `json:\"aggreg\"`\n 93→}\n 94→\n 95→// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n 96→//\n 97→// NaN values in the data are ignored during computation. If all values are NaN,\n 98→// the statistics fields will be set to NaN.\n 99→//\n 100→// TODO: Optimize this, just like the stats endpoint!\n 101→func (data *APIMetricData) AddStats() {\n 102→\tn := 0"}]},"uuid":"cda2a495-b384-4391-9c9d-dcda62569ddf","timestamp":"2026-03-19T19:52:06.785Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/api.go","content":"type APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n}\n\n// APIQueryResponse represents the response to an APIQueryRequest.\n//\n// Results is a 2D array where each outer element corresponds to a query,\n// and each inner element corresponds to a selector within that query\n// (e.g., multiple CPUs or cores).\ntype APIQueryResponse struct {\n\tQueries []APIQuery `json:\"queries,omitempty\"`\n\tResults [][]APIMetricData `json:\"results\"`\n}\n\n// APIQuery represents a single metric query with optional hierarchical selectors.\n//\n// The hierarchical selection works as follows:\n// - Hostname: The node to query\n// - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n// - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n//\n// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\ntype APIQuery struct {\n\tType *string `json:\"type,omitempty\"`\n\tSubType *string `json:\"subtype,omitempty\"`\n\tMetric string `json:\"metric\"`\n\tHostname string `json:\"host\"`\n\tResolution int64 `json:\"resolution\"`\n\tTypeIds []string `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate bool `json:\"aggreg\"`\n}\n\n// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n//\n// NaN values in the data are ignored during computation. If all values are NaN,\n// the statistics fields will be set to NaN.\n//\n// TODO: Optimize this, just like the stats endpoint!\nfunc (data *APIMetricData) AddStats() {\n\tn := 0","numLines":50,"startLine":53,"totalLines":315}},"sourceToolAssistantUUID":"30f67e36-b5e2-485f-8d4e-ba72ca7c7838","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"a5b28cd5-5f09-41e0-b727-63af2e551f43","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01SYX9nMLLXphWUADzDCA9X1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TCsq71KuC47rjTyLu6iwex","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/pkg/metricstore/api.go","old_string":"type APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n}","new_string":"type APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n\tResampleAlgo string `json:\"resample-algo,omitempty\"`\n}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":876,"cache_read_input_tokens":95333,"output_tokens":413,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":876,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2FCySCPPYzoEigXFEn","type":"assistant","uuid":"03da767f-2448-4334-9e9a-4d0106d8fd02","timestamp":"2026-03-19T19:52:13.550Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"03da767f-2448-4334-9e9a-4d0106d8fd02","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TCsq71KuC47rjTyLu6iwex","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/api.go has been updated successfully."}]},"uuid":"bc5695d2-3ab6-4db6-96db-122d5b959e24","timestamp":"2026-03-19T19:52:13.580Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/api.go","oldString":"type APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n}","newString":"type APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n\tResampleAlgo string `json:\"resample-algo,omitempty\"`\n}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains the API types and data fetching logic for querying metric data\n// from the in-memory metric store. It provides structures for building complex queries\n// with support for aggregation, scaling, padding, and statistics computation.\npackage metricstore\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\nvar (\n\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n)\n\n// APIMetricData represents the response data for a single metric query.\n//\n// It contains both the time-series data points and computed statistics (avg, min, max).\n// If an error occurred during data retrieval, the Error field will be set and other\n// fields may be incomplete.\ntype APIMetricData struct {\n\tError *string `json:\"error,omitempty\"`\n\tData schema.FloatArray `json:\"data,omitempty\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tResolution int64 `json:\"resolution\"`\n\tAvg schema.Float `json:\"avg\"`\n\tMin schema.Float `json:\"min\"`\n\tMax schema.Float `json:\"max\"`\n}\n\n// APIQueryRequest represents a batch query request for metric data.\n//\n// It supports two modes of operation:\n// 1. Explicit queries via the Queries field\n// 2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n//\n// The request can be customized with flags to include/exclude statistics, raw data, and padding.\ntype APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n}\n\n// APIQueryResponse represents the response to an APIQueryRequest.\n//\n// Results is a 2D array where each outer element corresponds to a query,\n// and each inner element corresponds to a selector within that query\n// (e.g., multiple CPUs or cores).\ntype APIQueryResponse struct {\n\tQueries []APIQuery `json:\"queries,omitempty\"`\n\tResults [][]APIMetricData `json:\"results\"`\n}\n\n// APIQuery represents a single metric query with optional hierarchical selectors.\n//\n// The hierarchical selection works as follows:\n// - Hostname: The node to query\n// - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n// - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n//\n// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\ntype APIQuery struct {\n\tType *string `json:\"type,omitempty\"`\n\tSubType *string `json:\"subtype,omitempty\"`\n\tMetric string `json:\"metric\"`\n\tHostname string `json:\"host\"`\n\tResolution int64 `json:\"resolution\"`\n\tTypeIds []string `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate bool `json:\"aggreg\"`\n}\n\n// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n//\n// NaN values in the data are ignored during computation. If all values are NaN,\n// the statistics fields will be set to NaN.\n//\n// TODO: Optimize this, just like the stats endpoint!\nfunc (data *APIMetricData) AddStats() {\n\tn := 0\n\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n\tfor _, x := range data.Data {\n\t\tif x.IsNaN() {\n\t\t\tcontinue\n\t\t}\n\n\t\tn += 1\n\t\tsum += float64(x)\n\t\tmin = math.Min(min, float64(x))\n\t\tmax = math.Max(max, float64(x))\n\t}\n\n\tif n > 0 {\n\t\tavg := sum / float64(n)\n\t\tdata.Avg = schema.Float(avg)\n\t\tdata.Min = schema.Float(min)\n\t\tdata.Max = schema.Float(max)\n\t} else {\n\t\tdata.Avg, data.Min, data.Max = schema.NaN, schema.NaN, schema.NaN\n\t}\n}\n\n// ScaleBy multiplies all data points and statistics by the given factor.\n//\n// This is commonly used for unit conversion (e.g., bytes to gigabytes).\n// Scaling by 0 or 1 is a no-op for performance reasons.\nfunc (data *APIMetricData) ScaleBy(f schema.Float) {\n\tif f == 1 {\n\t\treturn\n\t}\n\n\tdata.Avg *= f\n\tdata.Min *= f\n\tdata.Max *= f\n\tfor i := 0; i < len(data.Data); i++ {\n\t\tdata.Data[i] *= f\n\t}\n}\n\n// PadDataWithNull pads the beginning of the data array with NaN values if needed.\n//\n// This ensures that the data aligns with the requested 'from' timestamp, even if\n// the metric store doesn't have data for the earliest time points. This is useful\n// for maintaining consistent array indexing across multiple queries.\n//\n// Parameters:\n// - ms: MemoryStore instance to lookup metric configuration\n// - from: The requested start timestamp\n// - to: The requested end timestamp (unused but kept for API consistency)\n// - metric: The metric name to lookup frequency information\nfunc (data *APIMetricData) PadDataWithNull(ms *MemoryStore, from, to int64, metric string) {\n\tminfo, ok := ms.Metrics[metric]\n\tif !ok {\n\t\treturn\n\t}\n\n\tif (data.From / minfo.Frequency) > (from / minfo.Frequency) {\n\t\tpadfront := int((data.From / minfo.Frequency) - (from / minfo.Frequency))\n\t\tndata := make([]schema.Float, 0, padfront+len(data.Data))\n\t\tfor range padfront {\n\t\t\tndata = append(ndata, schema.NaN)\n\t\t}\n\t\tfor j := 0; j < len(data.Data); j++ {\n\t\t\tndata = append(ndata, data.Data[j])\n\t\t}\n\t\tdata.Data = ndata\n\t}\n}\n\n// FetchData executes a batch metric query request and returns the results.\n//\n// This is the primary API for retrieving metric data from the memory store. It supports:\n// - Individual queries via req.Queries\n// - Batch queries for all nodes via req.ForAllNodes\n// - Hierarchical selector construction (cluster → host → type → subtype)\n// - Optional statistics computation (avg, min, max)\n// - Optional data scaling\n// - Optional data padding with NaN values\n//\n// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n// separate results are returned for each combination.\n//\n// Parameters:\n// - req: The query request containing queries, time range, and options\n//\n// Returns:\n// - APIQueryResponse containing results for each query, or error if validation fails\n//\n// Errors:\n// - ErrInvalidTimeRange if req.From > req.To\n// - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n// - Error if MemoryStore is not initialized\n// - Individual query errors are stored in APIMetricData.Error field\nfunc FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n\tif req.From > req.To {\n\t\treturn nil, ErrInvalidTimeRange\n\t}\n\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n\t\treturn nil, ErrEmptyCluster\n\t}\n\n\treq.WithData = true\n\tms := GetMemoryStore()\n\tif ms == nil {\n\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n\t}\n\n\tresponse := APIQueryResponse{\n\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n\t}\n\tif req.ForAllNodes != nil {\n\t\tnodes := ms.ListChildren([]string{req.Cluster})\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range req.ForAllNodes {\n\t\t\t\tq := APIQuery{\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tHostname: node,\n\t\t\t\t}\n\t\t\t\treq.Queries = append(req.Queries, q)\n\t\t\t\tresponse.Queries = append(response.Queries, q)\n\t\t\t}\n\t\t}\n\t}\n\n\tfor _, query := range req.Queries {\n\t\tsels := make([]util.Selector, 0, 1)\n\t\tif query.Aggregate || query.Type == nil {\n\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n\t\t\tif query.Type != nil {\n\t\t\t\tif len(query.TypeIds) == 1 {\n\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n\t\t\t\t} else {\n\t\t\t\t\tids := make([]string, len(query.TypeIds))\n\t\t\t\t\tfor i, id := range query.TypeIds {\n\t\t\t\t\t\tids[i] = *query.Type + id\n\t\t\t\t\t}\n\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t}\n\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n\t\t\t\t\t} else {\n\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n\t\t\t\t\t\t\tids[i] = *query.SubType + id\n\t\t\t\t\t\t}\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tsels = append(sels, sel)\n\t\t} else {\n\t\t\tfor _, typeID := range query.TypeIds {\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n\t\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n\t\t\t\t\t\t})\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t})\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tvar err error\n\t\tres := make([]APIMetricData, 0, len(sels))\n\t\tfor _, sel := range sels {\n\t\t\tdata := APIMetricData{}\n\n\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n\t\t\tif err != nil {\n\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n\t\t\t\tif err != ErrNoHostOrMetric {\n\t\t\t\t\tmsg := err.Error()\n\t\t\t\t\tdata.Error = &msg\n\t\t\t\t\tres = append(res, data)\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Debugf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n\t\t\t\t}\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tif req.WithStats {\n\t\t\t\tdata.AddStats()\n\t\t\t}\n\t\t\tif query.ScaleFactor != 0 {\n\t\t\t\tdata.ScaleBy(query.ScaleFactor)\n\t\t\t}\n\t\t\tif req.WithPadding {\n\t\t\t\tdata.PadDataWithNull(ms, req.From, req.To, query.Metric)\n\t\t\t}\n\t\t\tif !req.WithData {\n\t\t\t\tdata.Data = nil\n\t\t\t}\n\t\t\tres = append(res, data)\n\t\t}\n\t\tresponse.Results = append(response.Results, res)\n\t}\n\n\treturn &response, nil\n}\n","structuredPatch":[{"oldStart":51,"oldLines":14,"newStart":51,"newLines":15,"lines":[" //"," // The request can be customized with flags to include/exclude statistics, raw data, and padding."," type APIQueryRequest struct {","- Cluster string `json:\"cluster\"`","- Queries []APIQuery `json:\"queries\"`","- ForAllNodes []string `json:\"for-all-nodes\"`","- From int64 `json:\"from\"`","- To int64 `json:\"to\"`","- WithStats bool `json:\"with-stats\"`","- WithData bool `json:\"with-data\"`","- WithPadding bool `json:\"with-padding\"`","+ Cluster string `json:\"cluster\"`","+ Queries []APIQuery `json:\"queries\"`","+ ForAllNodes []string `json:\"for-all-nodes\"`","+ From int64 `json:\"from\"`","+ To int64 `json:\"to\"`","+ WithStats bool `json:\"with-stats\"`","+ WithData bool `json:\"with-data\"`","+ WithPadding bool `json:\"with-padding\"`","+ ResampleAlgo string `json:\"resample-algo,omitempty\"`"," }"," "," // APIQueryResponse represents the response to an APIQueryRequest."]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"03da767f-2448-4334-9e9a-4d0106d8fd02","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"2a104b7e-1f94-4aec-9e93-fc7a05f4f2ef","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FNKud2YBeKsv1CBesr2go5","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/api.go has been updated successfully."}]},"uuid":"4545da62-56b6-44c7-9d27-34f3c7b4d2db","timestamp":"2026-03-19T19:52:19.571Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/api.go","oldString":"\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)","newString":"\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains the API types and data fetching logic for querying metric data\n// from the in-memory metric store. It provides structures for building complex queries\n// with support for aggregation, scaling, padding, and statistics computation.\npackage metricstore\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\nvar (\n\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n)\n\n// APIMetricData represents the response data for a single metric query.\n//\n// It contains both the time-series data points and computed statistics (avg, min, max).\n// If an error occurred during data retrieval, the Error field will be set and other\n// fields may be incomplete.\ntype APIMetricData struct {\n\tError *string `json:\"error,omitempty\"`\n\tData schema.FloatArray `json:\"data,omitempty\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tResolution int64 `json:\"resolution\"`\n\tAvg schema.Float `json:\"avg\"`\n\tMin schema.Float `json:\"min\"`\n\tMax schema.Float `json:\"max\"`\n}\n\n// APIQueryRequest represents a batch query request for metric data.\n//\n// It supports two modes of operation:\n// 1. Explicit queries via the Queries field\n// 2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n//\n// The request can be customized with flags to include/exclude statistics, raw data, and padding.\ntype APIQueryRequest struct {\n\tCluster string `json:\"cluster\"`\n\tQueries []APIQuery `json:\"queries\"`\n\tForAllNodes []string `json:\"for-all-nodes\"`\n\tFrom int64 `json:\"from\"`\n\tTo int64 `json:\"to\"`\n\tWithStats bool `json:\"with-stats\"`\n\tWithData bool `json:\"with-data\"`\n\tWithPadding bool `json:\"with-padding\"`\n\tResampleAlgo string `json:\"resample-algo,omitempty\"`\n}\n\n// APIQueryResponse represents the response to an APIQueryRequest.\n//\n// Results is a 2D array where each outer element corresponds to a query,\n// and each inner element corresponds to a selector within that query\n// (e.g., multiple CPUs or cores).\ntype APIQueryResponse struct {\n\tQueries []APIQuery `json:\"queries,omitempty\"`\n\tResults [][]APIMetricData `json:\"results\"`\n}\n\n// APIQuery represents a single metric query with optional hierarchical selectors.\n//\n// The hierarchical selection works as follows:\n// - Hostname: The node to query\n// - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n// - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n//\n// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\ntype APIQuery struct {\n\tType *string `json:\"type,omitempty\"`\n\tSubType *string `json:\"subtype,omitempty\"`\n\tMetric string `json:\"metric\"`\n\tHostname string `json:\"host\"`\n\tResolution int64 `json:\"resolution\"`\n\tTypeIds []string `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate bool `json:\"aggreg\"`\n}\n\n// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n//\n// NaN values in the data are ignored during computation. If all values are NaN,\n// the statistics fields will be set to NaN.\n//\n// TODO: Optimize this, just like the stats endpoint!\nfunc (data *APIMetricData) AddStats() {\n\tn := 0\n\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n\tfor _, x := range data.Data {\n\t\tif x.IsNaN() {\n\t\t\tcontinue\n\t\t}\n\n\t\tn += 1\n\t\tsum += float64(x)\n\t\tmin = math.Min(min, float64(x))\n\t\tmax = math.Max(max, float64(x))\n\t}\n\n\tif n > 0 {\n\t\tavg := sum / float64(n)\n\t\tdata.Avg = schema.Float(avg)\n\t\tdata.Min = schema.Float(min)\n\t\tdata.Max = schema.Float(max)\n\t} else {\n\t\tdata.Avg, data.Min, data.Max = schema.NaN, schema.NaN, schema.NaN\n\t}\n}\n\n// ScaleBy multiplies all data points and statistics by the given factor.\n//\n// This is commonly used for unit conversion (e.g., bytes to gigabytes).\n// Scaling by 0 or 1 is a no-op for performance reasons.\nfunc (data *APIMetricData) ScaleBy(f schema.Float) {\n\tif f == 1 {\n\t\treturn\n\t}\n\n\tdata.Avg *= f\n\tdata.Min *= f\n\tdata.Max *= f\n\tfor i := 0; i < len(data.Data); i++ {\n\t\tdata.Data[i] *= f\n\t}\n}\n\n// PadDataWithNull pads the beginning of the data array with NaN values if needed.\n//\n// This ensures that the data aligns with the requested 'from' timestamp, even if\n// the metric store doesn't have data for the earliest time points. This is useful\n// for maintaining consistent array indexing across multiple queries.\n//\n// Parameters:\n// - ms: MemoryStore instance to lookup metric configuration\n// - from: The requested start timestamp\n// - to: The requested end timestamp (unused but kept for API consistency)\n// - metric: The metric name to lookup frequency information\nfunc (data *APIMetricData) PadDataWithNull(ms *MemoryStore, from, to int64, metric string) {\n\tminfo, ok := ms.Metrics[metric]\n\tif !ok {\n\t\treturn\n\t}\n\n\tif (data.From / minfo.Frequency) > (from / minfo.Frequency) {\n\t\tpadfront := int((data.From / minfo.Frequency) - (from / minfo.Frequency))\n\t\tndata := make([]schema.Float, 0, padfront+len(data.Data))\n\t\tfor range padfront {\n\t\t\tndata = append(ndata, schema.NaN)\n\t\t}\n\t\tfor j := 0; j < len(data.Data); j++ {\n\t\t\tndata = append(ndata, data.Data[j])\n\t\t}\n\t\tdata.Data = ndata\n\t}\n}\n\n// FetchData executes a batch metric query request and returns the results.\n//\n// This is the primary API for retrieving metric data from the memory store. It supports:\n// - Individual queries via req.Queries\n// - Batch queries for all nodes via req.ForAllNodes\n// - Hierarchical selector construction (cluster → host → type → subtype)\n// - Optional statistics computation (avg, min, max)\n// - Optional data scaling\n// - Optional data padding with NaN values\n//\n// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n// separate results are returned for each combination.\n//\n// Parameters:\n// - req: The query request containing queries, time range, and options\n//\n// Returns:\n// - APIQueryResponse containing results for each query, or error if validation fails\n//\n// Errors:\n// - ErrInvalidTimeRange if req.From > req.To\n// - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n// - Error if MemoryStore is not initialized\n// - Individual query errors are stored in APIMetricData.Error field\nfunc FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n\tif req.From > req.To {\n\t\treturn nil, ErrInvalidTimeRange\n\t}\n\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n\t\treturn nil, ErrEmptyCluster\n\t}\n\n\treq.WithData = true\n\tms := GetMemoryStore()\n\tif ms == nil {\n\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n\t}\n\n\tresponse := APIQueryResponse{\n\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n\t}\n\tif req.ForAllNodes != nil {\n\t\tnodes := ms.ListChildren([]string{req.Cluster})\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range req.ForAllNodes {\n\t\t\t\tq := APIQuery{\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tHostname: node,\n\t\t\t\t}\n\t\t\t\treq.Queries = append(req.Queries, q)\n\t\t\t\tresponse.Queries = append(response.Queries, q)\n\t\t\t}\n\t\t}\n\t}\n\n\tfor _, query := range req.Queries {\n\t\tsels := make([]util.Selector, 0, 1)\n\t\tif query.Aggregate || query.Type == nil {\n\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n\t\t\tif query.Type != nil {\n\t\t\t\tif len(query.TypeIds) == 1 {\n\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n\t\t\t\t} else {\n\t\t\t\t\tids := make([]string, len(query.TypeIds))\n\t\t\t\t\tfor i, id := range query.TypeIds {\n\t\t\t\t\t\tids[i] = *query.Type + id\n\t\t\t\t\t}\n\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t}\n\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n\t\t\t\t\t} else {\n\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n\t\t\t\t\t\t\tids[i] = *query.SubType + id\n\t\t\t\t\t\t}\n\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tsels = append(sels, sel)\n\t\t} else {\n\t\t\tfor _, typeID := range query.TypeIds {\n\t\t\t\tif query.SubType != nil {\n\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n\t\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n\t\t\t\t\t\t})\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tsels = append(sels, util.Selector{\n\t\t\t\t\t\t{String: req.Cluster},\n\t\t\t\t\t\t{String: query.Hostname},\n\t\t\t\t\t\t{String: *query.Type + typeID},\n\t\t\t\t\t})\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tvar err error\n\t\tres := make([]APIMetricData, 0, len(sels))\n\t\tfor _, sel := range sels {\n\t\t\tdata := APIMetricData{}\n\n\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n\t\t\tif err != nil {\n\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n\t\t\t\tif err != ErrNoHostOrMetric {\n\t\t\t\t\tmsg := err.Error()\n\t\t\t\t\tdata.Error = &msg\n\t\t\t\t\tres = append(res, data)\n\t\t\t\t} else {\n\t\t\t\t\tcclog.Debugf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n\t\t\t\t}\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tif req.WithStats {\n\t\t\t\tdata.AddStats()\n\t\t\t}\n\t\t\tif query.ScaleFactor != 0 {\n\t\t\t\tdata.ScaleBy(query.ScaleFactor)\n\t\t\t}\n\t\t\tif req.WithPadding {\n\t\t\t\tdata.PadDataWithNull(ms, req.From, req.To, query.Metric)\n\t\t\t}\n\t\t\tif !req.WithData {\n\t\t\t\tdata.Data = nil\n\t\t\t}\n\t\t\tres = append(res, data)\n\t\t}\n\t\tresponse.Results = append(response.Results, res)\n\t}\n\n\treturn &response, nil\n}\n","structuredPatch":[{"oldStart":280,"oldLines":7,"newStart":280,"newLines":7,"lines":[" for _, sel := range sels {"," data := APIMetricData{}"," ","- data.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)","+ data.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)"," if err != nil {"," // Skip Error If Just Missing Host or Metric, Continue"," // Empty Return For Metric Handled Gracefully By Frontend"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"2a104b7e-1f94-4aec-9e93-fc7a05f4f2ef","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"6b70921f-267f-4b4e-827c-9d4dc5e8e33f","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01F6EL8NR3AAeN3Lv3JXinrU","toolUseID":"toolu_01F6EL8NR3AAeN3Lv3JXinrU","timestamp":"2026-03-19T19:52:24.500Z","uuid":"dcd9e19c-919b-4fdf-a6f6-4971ac639003","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"6b70921f-267f-4b4e-827c-9d4dc5e8e33f","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F6EL8NR3AAeN3Lv3JXinrU","type":"tool_result","content":" 637→\t}\n 638→\n 639→\treq := APIQueryRequest{\n 640→\t\tCluster: cluster,\n 641→\t\tQueries: queries,\n 642→\t\tFrom: from.Unix(),\n 643→\t\tTo: to.Unix(),\n 644→\t\tWithStats: true,\n 645→\t\tWithData: true,\n 646→\t}\n 647→\n 648→\tresBody, err := FetchData(req)\n 649→\tif err != nil {\n 650→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n 651→\t\treturn nil, err"}]},"uuid":"53ad7eaa-bf52-481c-b20f-11550252cbfe","timestamp":"2026-03-19T19:52:24.500Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/query.go","content":"\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err","numLines":15,"startLine":637,"totalLines":867}},"sourceToolAssistantUUID":"6b70921f-267f-4b4e-827c-9d4dc5e8e33f","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"53ad7eaa-bf52-481c-b20f-11550252cbfe","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01R1bop4dKbpBqEW65riT6Tx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RiqqpKmwr3RMVi2PmPTZQk","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/pkg/metricstore/query.go","old_string":"\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err","new_string":"\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t\tResampleAlgo: resampleAlgo,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":306,"cache_read_input_tokens":97145,"output_tokens":354,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":306,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2GWo29NRU1tWmSirmM","type":"assistant","uuid":"2bb0d159-7e13-4961-b04e-0f9814b496be","timestamp":"2026-03-19T19:52:30.637Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"2bb0d159-7e13-4961-b04e-0f9814b496be","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RiqqpKmwr3RMVi2PmPTZQk","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/pkg/metricstore/query.go has been updated successfully."}]},"uuid":"1f29c6e4-58f4-4674-b93b-ded1543396e4","timestamp":"2026-03-19T19:52:30.659Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/pkg/metricstore/query.go","oldString":"\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err","newString":"\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t\tResampleAlgo: resampleAlgo,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file implements high-level query functions for loading job metric data\n// with automatic scope transformation and aggregation.\n//\n// Key Concepts:\n//\n// Metric Scopes: Metrics are collected at different granularities (native scope):\n// - HWThread: Per hardware thread\n// - Core: Per CPU core\n// - Socket: Per CPU socket\n// - MemoryDomain: Per memory domain (NUMA)\n// - Accelerator: Per GPU/accelerator\n// - Node: Per compute node\n//\n// Scope Transformation: The buildQueries functions transform between native scope\n// and requested scope by:\n// - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n// - Rejecting requests for finer granularity than available\n// - Handling special cases (e.g., Accelerator metrics)\n//\n// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n// based on cluster topology and job resources.\npackage metricstore\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\ntype InternalMetricStore struct{}\n\nvar MetricStoreHandle *InternalMetricStore\n\n// HealthCheck delegates to the internal MemoryStore's HealthCheck.\nfunc (ccms *InternalMetricStore) HealthCheck(cluster string,\n\tnodes []string, metrics []string,\n) (map[string]HealthCheckResult, error) {\n\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n}\n\n// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n// When set to a non-nil function, LoadData will call this function instead of the default implementation.\nvar TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n\n// LoadData loads metric data for a specific job with automatic scope transformation.\n//\n// This is the primary function for retrieving job metric data. It handles:\n// - Building queries with scope transformation via buildQueries\n// - Fetching data from the metric store\n// - Organizing results by metric and scope\n// - Converting NaN statistics to 0 for JSON compatibility\n// - Partial error handling (returns data for successful queries even if some fail)\n//\n// Parameters:\n// - job: Job metadata including cluster, resources, and time range\n// - metrics: List of metric names to load\n// - scopes: Requested metric scopes (will be transformed to match native scopes)\n// - ctx: Context for cancellation (currently unused but reserved for future use)\n// - resolution: Data resolution in seconds (0 for native resolution)\n//\n// Returns:\n// - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n// - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n//\n// Example:\n//\n//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\nfunc (ccms *InternalMetricStore) LoadData(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tif TestLoadDataCallback != nil {\n\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n\t}\n\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tjobData := make(schema.JobData)\n\n\t// Add safety check for potential index out of range errors\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := jobData[metric]; !ok {\n\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\tjobMetric, ok := jobData[metric][scope]\n\t\tif !ok {\n\t\t\tjobMetric = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tjobData[metric][scope] = jobMetric\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(jobData):\n\t\tif len(jobMetric.Series) == 0 {\n\t\t\tdelete(jobData[metric], scope)\n\t\t\tif len(jobData[metric]) == 0 {\n\t\t\t\tdelete(jobData, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn jobData, nil\n}\n\n// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n//\n// This function implements the core scope transformation logic, handling all combinations of\n// native metric scopes and requested scopes. It uses the cluster topology to determine which\n// hardware IDs to include in each query.\n//\n// Scope Transformation Rules:\n// - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n// - If native scope < requested scope: Returns error (cannot increase granularity)\n// - Special handling for Accelerator scope (independent of CPU hierarchy)\n//\n// The function generates one or more APIQuery per (metric, scope, host) combination:\n// - For non-aggregated queries: One query with all relevant IDs\n// - For aggregated queries: May generate multiple queries (e.g., one per socket/core)\n//\n// Parameters:\n// - job: Job metadata including cluster, subcluster, and resource allocation\n// - metrics: List of metrics to query\n// - scopes: Requested scopes for each metric\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query (after transformation)\n// - error: Returns error if topology lookup fails or unhandled scope combination encountered\nfunc buildQueries(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tif len(job.Resources) == 0 {\n\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > no resources allocated for job %d\", job.JobID)\n\t}\n\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n\n\tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n\tif scerr != nil {\n\t\treturn nil, nil, scerr\n\t}\n\ttopology := subcluster.Topology\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif len(mc.SubClusters) != 0 && IsMetricRemovedForSubCluster(mc, job.SubCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tscopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue scopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, host := range job.Resources {\n\t\t\t\thwthreads := host.HWThreads\n\t\t\t\tif hwthreads == nil {\n\t\t\t\t\thwthreads = topology.Node\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, host.Hostname,\n\t\t\t\t\t&topology, hwthreads, host.Accelerators,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n\n// LoadStats loads only metric statistics (avg/min/max) for a job at node scope.\n//\n// This is an optimized version of LoadData that fetches only statistics without\n// time-series data, reducing bandwidth and memory usage. Always queries at node scope.\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of metric → hostname → statistics\n// - Error on query building or fetching failure\nfunc (ccms *InternalMetricStore) LoadStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error) {\n\t// TODO(#166): Add scope parameter for analysis view accelerator normalization\n\tqueries, _, err := buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n\tfor i, res := range resBody.Results {\n\t\tif i >= len(req.Queries) {\n\t\t\tcclog.Warnf(\"LoadStats: result index %d exceeds queries length %d\", i, len(req.Queries))\n\t\t\tbreak\n\t\t}\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tdata := res[0]\n\t\tif data.Error != nil {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata, ok := stats[metric]\n\t\tif !ok {\n\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n\t\t\tstats[metric] = metricdata\n\t\t}\n\n\t\tif data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n\t\t\tAvg: float64(data.Avg),\n\t\t\tMin: float64(data.Min),\n\t\t\tMax: float64(data.Max),\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// LoadScopedStats loads metric statistics for a job with scope-aware grouping.\n//\n// Similar to LoadStats but supports multiple scopes and returns statistics grouped\n// by scope with hardware IDs (e.g., per-core, per-socket statistics).\n//\n// Parameters:\n// - job: Job metadata\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - ScopedJobStats: Map of metric → scope → []ScopedStats (with hostname and ID)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadScopedStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tscopedJobStats := make(schema.ScopedJobStats)\n\n\tfor i, row := range resBody.Results {\n\t\tif len(row) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\n\t\tif _, ok := scopedJobStats[metric]; !ok {\n\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n\t\t}\n\n\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tData: &schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n\t\tif len(scopedJobStats[metric][scope]) == 0 {\n\t\t\tdelete(scopedJobStats[metric], scope)\n\t\t\tif len(scopedJobStats[metric]) == 0 {\n\t\t\t\tdelete(scopedJobStats, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn scopedJobStats, nil\n}\n\n// LoadNodeData loads metric data for specific nodes in a cluster over a time range.\n//\n// Unlike LoadData which operates on job resources, this function queries arbitrary nodes\n// directly. Useful for system monitoring and node status views.\n//\n// Parameters:\n// - cluster: Cluster name\n// - metrics: List of metric names\n// - nodes: List of node hostnames (nil = all nodes in cluster via ForAllNodes)\n// - scopes: Requested metric scopes (currently unused - always node scope)\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → metric → []JobMetric\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tif nodes == nil {\n\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n\t} else {\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range metrics {\n\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n\t\t\t\t\tHostname: node,\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]map[string][]*schema.JobMetric)\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tquery = resBody.Queries[i]\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tqdata := res[0]\n\t\tif qdata.Error != nil {\n\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n\t\t\tcontinue\n\t\t}\n\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tSanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n\n\t\thostdata, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostdata = make(map[string][]*schema.JobMetric)\n\t\t\tdata[query.Hostname] = hostdata\n\t\t}\n\n\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n\t\t\tUnit: mc.Unit,\n\t\t\tTimestep: mc.Timestep,\n\t\t\tSeries: []schema.Series{\n\t\t\t\t{\n\t\t\t\t\tHostname: query.Hostname,\n\t\t\t\t\tData: qdata.Data,\n\t\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\t\tAvg: float64(qdata.Avg),\n\t\t\t\t\t\tMin: float64(qdata.Min),\n\t\t\t\t\t\tMax: float64(qdata.Max),\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t})\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData loads metric data for a list of nodes with full scope transformation support.\n//\n// This is the most flexible node data loading function, supporting arbitrary scopes and\n// resolution. Uses buildNodeQueries for proper scope transformation based on topology.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty string to infer from node names)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n// - from, to: Time range\n// - ctx: Context (currently unused)\n//\n// Returns:\n// - Map of hostname → JobData (metric → scope → JobMetric)\n// - Error or partial error listing failed queries\nfunc (ccms *InternalMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n\tresampleAlgo string,\n) (map[string]schema.JobData, error) {\n\t// Note: Order of node data is not guaranteed after this point\n\tqueries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCLuster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := FetchData(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]schema.JobData)\n\n\t// Add safety check for index out of range issues\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tif i < len(resBody.Queries) {\n\t\t\t\tquery = resBody.Queries[i]\n\t\t\t} else {\n\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n\t\t\t\t\ti, len(resBody.Queries))\n\t\t\t\tcontinue\n\t\t\t}\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = int(row[0].Resolution)\n\t\t}\n\n\t\t// Init Nested Map Data Structures If Not Found\n\t\thostData, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostData = make(schema.JobData)\n\t\t\tdata[query.Hostname] = hostData\n\t\t}\n\n\t\tmetricData, ok := hostData[metric]\n\t\tif !ok {\n\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\tdata[query.Hostname][metric] = metricData\n\t\t}\n\n\t\tscopeData, ok := metricData[scope]\n\t\tif !ok {\n\t\t\tscopeData = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tdata[query.Hostname][metric][scope] = scopeData\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tSanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// buildNodeQueries constructs APIQuery structures for node-based queries with scope transformation.\n//\n// Similar to buildQueries but operates on node lists rather than job resources.\n// Supports dynamic subcluster lookup when subCluster parameter is empty.\n//\n// Parameters:\n// - cluster: Cluster name\n// - subCluster: SubCluster name (empty = infer from node hostnames)\n// - nodes: List of node hostnames\n// - metrics: List of metric names\n// - scopes: Requested metric scopes\n// - resolution: Data resolution in seconds\n//\n// Returns:\n// - []APIQuery: List of queries to execute\n// - []schema.MetricScope: Assigned scope for each query\n// - error: Returns error if topology lookup fails or unhandled scope combination\nfunc buildNodeQueries(\n\tcluster string,\n\tsubCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int64,\n) ([]APIQuery, []schema.MetricScope, error) {\n\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n\n\t// Get Topol before loop if subCluster given\n\tvar subClusterTopol *schema.SubCluster\n\tvar scterr error\n\tif subCluster != \"\" {\n\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subCluster)\n\t\tif scterr != nil {\n\t\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", cluster, subCluster, scterr.Error())\n\t\t\treturn nil, nil, scterr\n\t\t}\n\t}\n\n\tfor _, metric := range metrics {\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Skip if metric is removed for subcluster\n\t\tif mc.SubClusters != nil && IsMetricRemovedForSubCluster(mc, subCluster) {\n\t\t\tcontinue\n\t\t}\n\n\t\t// Avoid duplicates...\n\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n\n\tnodeScopesLoop:\n\t\tfor _, requestedScope := range scopes {\n\t\t\tnativeScope := mc.Scope\n\n\t\t\tscope := nativeScope.Max(requestedScope)\n\t\t\tfor _, s := range handledScopes {\n\t\t\t\tif scope == s {\n\t\t\t\t\tcontinue nodeScopesLoop\n\t\t\t\t}\n\t\t\t}\n\t\t\thandledScopes = append(handledScopes, scope)\n\n\t\t\tfor _, hostname := range nodes {\n\n\t\t\t\t// If no subCluster given, get it by node\n\t\t\t\tif subCluster == \"\" {\n\t\t\t\t\tsubClusterName, scnerr := archive.GetSubClusterByNode(cluster, hostname)\n\t\t\t\t\tif scnerr != nil {\n\t\t\t\t\t\treturn nil, nil, scnerr\n\t\t\t\t\t}\n\t\t\t\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subClusterName)\n\t\t\t\t\tif scterr != nil {\n\t\t\t\t\t\treturn nil, nil, scterr\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Always full node hwthread id list, no partial queries expected\n\t\t\t\ttopology := subClusterTopol.Topology\n\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n\n\t\t\t\t// Moved check here if metric matches hardware specs\n\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\n\t\t\t\tscopeResults, ok := BuildScopeQueries(\n\t\t\t\t\tnativeScope, requestedScope,\n\t\t\t\t\tmetric, hostname,\n\t\t\t\t\t&topology, topology.Node, acceleratorIds,\n\t\t\t\t)\n\n\t\t\t\tif !ok {\n\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > unsupported scope transformation: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n\t\t\t\t}\n\n\t\t\t\tfor _, sr := range scopeResults {\n\t\t\t\t\tqueries = append(queries, APIQuery{\n\t\t\t\t\t\tMetric: sr.Metric,\n\t\t\t\t\t\tHostname: sr.Hostname,\n\t\t\t\t\t\tAggregate: sr.Aggregate,\n\t\t\t\t\t\tType: sr.Type,\n\t\t\t\t\t\tTypeIds: sr.TypeIds,\n\t\t\t\t\t\tResolution: resolution,\n\t\t\t\t\t})\n\t\t\t\t\tassignedScope = append(assignedScope, sr.Scope)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn queries, assignedScope, nil\n}\n","structuredPatch":[{"oldStart":637,"oldLines":12,"newStart":637,"newLines":13,"lines":[" }"," "," req := APIQueryRequest{","- Cluster: cluster,","- Queries: queries,","- From: from.Unix(),","- To: to.Unix(),","- WithStats: true,","- WithData: true,","+ Cluster: cluster,","+ Queries: queries,","+ From: from.Unix(),","+ To: to.Unix(),","+ WithStats: true,","+ WithData: true,","+ ResampleAlgo: resampleAlgo,"," }"," "," resBody, err := FetchData(req)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"2bb0d159-7e13-4961-b04e-0f9814b496be","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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This allows processing partial results\n// when some nodes or metrics are temporarily unavailable.\n//\n// # API Versioning\n//\n// The client uses cc-metric-store API v2, which includes support for:\n// - Data resampling for bandwidth optimization\n// - Multi-scope queries in a single request\n// - Aggregation across hardware topology levels\npackage metricstoreclient\n\nimport (\n\t\"bufio\"\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tms \"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n// It manages connection details, authentication, and provides methods for querying metrics.\ntype CCMetricStore struct {\n\tclient http.Client // HTTP client with 10-second timeout\n\tjwt string // JWT Bearer token for authentication\n\turl string // Base URL of cc-metric-store instance\n\tqueryEndpoint string // Full URL to query API endpoint\n\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n}\n\n// APIQueryRequest represents a request to the cc-metric-store query API.\n// It supports both explicit queries and \"for-all-nodes\" bulk queries.\ntype APIQueryRequest struct {\n\tCluster string `json:\"cluster\"` // Target cluster name\n\tQueries []APIQuery `json:\"queries\"` // Explicit list of metric queries\n\tForAllNodes []string `json:\"for-all-nodes\"` // Metrics to query for all nodes\n\tFrom int64 `json:\"from\"` // Start time (Unix timestamp)\n\tTo int64 `json:\"to\"` // End time (Unix timestamp)\n\tWithStats bool `json:\"with-stats\"` // Include min/avg/max statistics\n\tWithData bool `json:\"with-data\"` // Include time series data points\n}\n\n// APIQuery specifies a single metric query with optional scope filtering.\n// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\ntype APIQuery struct {\n\tType *string `json:\"type,omitempty\"` // Scope type (e.g., \"core\", \"socket\")\n\tSubType *string `json:\"subtype,omitempty\"` // Sub-scope type (reserved for future use)\n\tMetric string `json:\"metric\"` // Metric name\n\tHostname string `json:\"host\"` // Target hostname\n\tResolution int `json:\"resolution\"` // Data resolution in seconds (0 = native)\n\tTypeIds []string `json:\"type-ids,omitempty\"` // IDs for the scope type (e.g., core IDs)\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n\tAggregate bool `json:\"aggreg\"` // Aggregate across TypeIds\n}\n\n// APIQueryResponse contains the results from a cc-metric-store query.\n// Results align with the Queries slice by index.\ntype APIQueryResponse struct {\n\tQueries []APIQuery `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n\tResults [][]APIMetricData `json:\"results\"` // Result data, indexed by query\n}\n\n// APIMetricData represents time series data and statistics for a single metric series.\n// Error is set if this particular series failed to load.\ntype APIMetricData struct {\n\tError *string `json:\"error\"` // Error message if query failed\n\tData []schema.Float `json:\"data\"` // Time series data points\n\tFrom int64 `json:\"from\"` // Actual start time of data\n\tTo int64 `json:\"to\"` // Actual end time of data\n\tResolution int `json:\"resolution\"` // Actual resolution of data in seconds\n\tAvg schema.Float `json:\"avg\"` // Average value across time range\n\tMin schema.Float `json:\"min\"` // Minimum value in time range\n\tMax schema.Float `json:\"max\"` // Maximum value in time range\n}\n\n// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\nfunc NewCCMetricStore(url string, token string) *CCMetricStore {\n\treturn &CCMetricStore{\n\t\turl: url,\n\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n\t\tjwt: token,\n\t\tclient: http.Client{\n\t\t\tTimeout: 10 * time.Second,\n\t\t},\n\t\ttopologyCache: make(map[string]*schema.Topology),\n\t}\n}\n\n// doRequest executes an HTTP POST request to the cc-metric-store query API.\n// It handles JSON encoding/decoding, authentication, and API versioning.\n// The request body is automatically closed to prevent resource leaks.\nfunc (ccms *CCMetricStore) doRequest(\n\tctx context.Context,\n\tbody *APIQueryRequest,\n) (*APIQueryResponse, error) {\n\tbuf := &bytes.Buffer{}\n\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tif ccms.jwt != \"\" {\n\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n\t}\n\n\t// versioning the cc-metric-store query API.\n\t// v2 = data with resampling\n\t// v1 = data without resampling\n\tq := req.URL.Query()\n\tq.Add(\"version\", \"v2\")\n\treq.URL.RawQuery = q.Encode()\n\n\tres, err := ccms.client.Do(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tdefer res.Body.Close()\n\n\tif res.StatusCode != http.StatusOK {\n\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n\t}\n\n\tvar resBody APIQueryResponse\n\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn &resBody, nil\n}\n\n// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\nfunc (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n\t\treturn topology, nil\n\t}\n\n\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tccms.topologyCache[cacheKey] = &subcluster.Topology\n\treturn &subcluster.Topology, nil\n}\n\n// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\nfunc (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn ccms.getTopology(cluster, subCluster)\n}\n\n// LoadData retrieves time series data and statistics for the specified job and metrics.\n// It queries data for the job's time range and resources, handling scope transformations automatically.\n//\n// Parameters:\n// - job: Job metadata including cluster, time range, and allocated resources\n// - metrics: List of metric names to retrieve\n// - scopes: Requested metric scopes (node, socket, core, etc.)\n// - ctx: Context for cancellation and timeouts\n// - resolution: Data resolution in seconds (0 for native resolution)\n//\n// Returns JobData organized as: metric -> scope -> series list.\n// Supports partial errors: returns available data even if some queries fail.\nfunc (ccms *CCMetricStore) LoadData(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tjobData := make(schema.JobData)\n\n\t// Add safety check for potential index out of range errors\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := jobData[metric]; !ok {\n\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = row[0].Resolution\n\t\t}\n\n\t\tjobMetric, ok := jobData[metric][scope]\n\t\tif !ok {\n\t\t\tjobMetric = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tjobData[metric][scope] = jobMetric\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ms.ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tms.SanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(jobData):\n\t\tif len(jobMetric.Series) == 0 {\n\t\t\tdelete(jobData[metric], scope)\n\t\t\tif len(jobData[metric]) == 0 {\n\t\t\t\tdelete(jobData, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn jobData, nil\n}\n\n// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n// This is faster than LoadData when only statistical summaries are needed (no time series data).\n//\n// Returns statistics organized as: metric -> hostname -> statistics.\nfunc (ccms *CCMetricStore) LoadStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error) {\n\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n\tfor i, res := range resBody.Results {\n\t\tif i >= len(req.Queries) {\n\t\t\tcclog.Warnf(\"LoadStats: result index %d exceeds queries length %d\", i, len(req.Queries))\n\t\t\tbreak\n\t\t}\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tdata := res[0]\n\t\tif data.Error != nil {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata, ok := stats[metric]\n\t\tif !ok {\n\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n\t\t\tstats[metric] = metricdata\n\t\t}\n\n\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n\t\t\tAvg: float64(data.Avg),\n\t\t\tMin: float64(data.Min),\n\t\t\tMax: float64(data.Max),\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n// Used for the Job-View Statistics Table to display per-scope breakdowns.\n//\n// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\nfunc (ccms *CCMetricStore) LoadScopedStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: job.Cluster,\n\t\tFrom: job.StartTime,\n\t\tTo: job.StartTime + int64(job.Duration),\n\t\tQueries: queries,\n\t\tWithStats: true,\n\t\tWithData: false,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tscopedJobStats := make(schema.ScopedJobStats)\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\n\t\tif _, ok := scopedJobStats[metric]; !ok {\n\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n\t\t}\n\n\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ms.ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tms.SanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tData: &schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n\t\tif len(scopedJobStats[metric][scope]) == 0 {\n\t\t\tdelete(scopedJobStats[metric], scope)\n\t\t\tif len(scopedJobStats[metric]) == 0 {\n\t\t\t\tdelete(scopedJobStats, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn scopedJobStats, nil\n}\n\n// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n// Used for the Systems-View Node-Overview to display real-time node status.\n//\n// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\nfunc (ccms *CCMetricStore) LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tif nodes == nil {\n\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n\t} else {\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range metrics {\n\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n\t\t\t\t\tHostname: node,\n\t\t\t\t\tMetric: metric,\n\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]map[string][]*schema.JobMetric)\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tquery = resBody.Queries[i]\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tqdata := res[0]\n\t\tif qdata.Error != nil {\n\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n\t\t\tcontinue\n\t\t}\n\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tms.SanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n\n\t\thostdata, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostdata = make(map[string][]*schema.JobMetric)\n\t\t\tdata[query.Hostname] = hostdata\n\t\t}\n\n\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n\t\t\tUnit: mc.Unit,\n\t\t\tTimestep: mc.Timestep,\n\t\t\tSeries: []schema.Series{\n\t\t\t\t{\n\t\t\t\t\tHostname: query.Hostname,\n\t\t\t\t\tData: qdata.Data,\n\t\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\t\tAvg: float64(qdata.Avg),\n\t\t\t\t\t\tMin: float64(qdata.Min),\n\t\t\t\t\t\tMax: float64(qdata.Max),\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t})\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n//\n// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n// substring matching on hostnames.\n//\n// Returns:\n// - Node data organized as: hostname -> JobData (metric -> scope -> series)\n// - Total node count (before pagination)\n// - HasNextPage flag indicating if more pages are available\n// - Error (may be partial error with some data returned)\nfunc (ccms *CCMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster: cluster,\n\t\tQueries: queries,\n\t\tFrom: from.Unix(),\n\t\tTo: to.Unix(),\n\t\tWithStats: true,\n\t\tWithData: true,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]schema.JobData)\n\n\t// Add safety check for index out of range issues\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tif i < len(resBody.Queries) {\n\t\t\t\tquery = resBody.Queries[i]\n\t\t\t} else {\n\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n\t\t\t\t\ti, len(resBody.Queries))\n\t\t\t\tcontinue\n\t\t\t}\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = row[0].Resolution\n\t\t}\n\n\t\t// Init Nested Map Data Structures If Not Found\n\t\thostData, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostData = make(schema.JobData)\n\t\t\tdata[query.Hostname] = hostData\n\t\t}\n\n\t\tmetricData, ok := hostData[metric]\n\t\tif !ok {\n\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\tdata[query.Hostname][metric] = metricData\n\t\t}\n\n\t\tscopeData, ok := metricData[scope]\n\t\tif !ok {\n\t\t\tscopeData = &schema.JobMetric{\n\t\t\t\tUnit: mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries: make([]schema.Series, 0),\n\t\t\t}\n\t\t\tdata[query.Hostname][metric][scope] = scopeData\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := ms.ExtractTypeID(query.Type, query.TypeIds, ndx, query.Metric, query.Hostname)\n\n\t\t\tms.SanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID: id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// HealthCheck queries the external cc-metric-store's health check endpoint.\n// It sends a HealthCheckReq as the request body to /api/healthcheck and\n// returns the per-node health check results.\nfunc (ccms *CCMetricStore) HealthCheck(cluster string,\n\tnodes []string, metrics []string,\n) (map[string]ms.HealthCheckResult, error) {\n\treq := ms.HealthCheckReq{\n\t\tCluster: cluster,\n\t\tNodes: nodes,\n\t\tMetricNames: metrics,\n\t}\n\n\tbuf := &bytes.Buffer{}\n\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tif ccms.jwt != \"\" {\n\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n\t}\n\n\tres, err := ccms.client.Do(httpReq)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tdefer res.Body.Close()\n\n\tif res.StatusCode != http.StatusOK {\n\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n\t}\n\n\tvar results map[string]ms.HealthCheckResult\n\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn results, nil\n}\n\n// hasNaNStats returns true if any of the statistics contain NaN values.\nfunc hasNaNStats(avg, min, max schema.Float) bool {\n\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n}\n","structuredPatch":[{"oldStart":617,"oldLines":6,"newStart":617,"newLines":7,"lines":[" resolution int,"," from, to time.Time,"," ctx context.Context,","+ resampleAlgo string,"," ) (map[string]schema.JobData, error) {"," queries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)"," if err != nil {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"83c6c897-0bd8-43ab-8b29-9c3c1b3d9890","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"012cdf8b-d57a-4b1d-a2ee-4bc53efd629e","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Kt2Wb82LxZjaYvdHQuKb4b","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0125Rmiz3CTUGW4SfmFsrXRm","name":"Grep","input":{"pattern":"\\.Read\\(","path":"/Users/jan/tmp/cc-backend/pkg/metricstore","output_mode":"content","-n":true,"glob":"*.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":487,"cache_read_input_tokens":98538,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":487},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2JNRJ3JwraeGkRfab8","type":"assistant","uuid":"ab08ccdc-c954-4e31-a5b8-624d2c81140b","timestamp":"2026-03-19T19:52:54.832Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"ab08ccdc-c954-4e31-a5b8-624d2c81140b","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_0125Rmiz3CTUGW4SfmFsrXRm","toolUseID":"toolu_0125Rmiz3CTUGW4SfmFsrXRm","timestamp":"2026-03-19T19:52:54.869Z","uuid":"8d712fc2-3146-4667-adb8-4e10b7b5faa3","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"ab08ccdc-c954-4e31-a5b8-624d2c81140b","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0125Rmiz3CTUGW4SfmFsrXRm","type":"tool_result","content":"pkg/metricstore/parquetArchive_test.go:107:\tn, err := reader.Read(readRows)\npkg/metricstore/parquetArchive_test.go:231:\tn, err := reader.Read(readRows)\npkg/metricstore/api.go:183:// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\npkg/metricstore/api.go:283:\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)\npkg/metricstore/parquetArchive.go:209:\t\tif err := binary.Read(br, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/parquetArchive.go:216:\t\tif err := binary.Read(br, binary.LittleEndian, &fileFrom); err != nil {\npkg/metricstore/parquetArchive.go:219:\t\tif err := binary.Read(br, binary.LittleEndian, &fileTo); err != nil {\npkg/metricstore/walCheckpoint.go:484:\tif err := binary.Read(r, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/walCheckpoint.go:496:\tif err := binary.Read(r, binary.LittleEndian, &payloadLen); err != nil {\npkg/metricstore/walCheckpoint.go:510:\tif err := binary.Read(r, binary.LittleEndian, &storedCRC); err != nil {\npkg/metricstore/walCheckpoint.go:591:\tif err := binary.Read(br, binary.LittleEndian, &fileMagic); err != nil {\npkg/metricstore/walCheckpoint.go:857:\tif err := binary.Read(br, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/walCheckpoint.go:865:\tif err := binary.Read(br, binary.LittleEndian, &fileFrom); err != nil {\npkg/metricstore/walCheckpoint.go:868:\tif err := binary.Read(br, binary.LittleEndian, &fileTo); err != nil {\npkg/metricstore/walCheckpoint.go:894:\tif err := binary.Read(r, binary.LittleEndian, &numMetrics); err != nil {\npkg/metricstore/walCheckpoint.go:905:\t\tif err := binary.Read(r, binary.LittleEndian, &freq); err != nil {\npkg/metricstore/walCheckpoint.go:908:\t\tif err := binary.Read(r, binary.LittleEndian, &start); err != nil {\npkg/metricstore/walCheckpoint.go:913:\t\tif err := binary.Read(r, binary.LittleEndian, &numValues); err != nil {\npkg/metricstore/walCheckpoint.go:920:\t\t\tif err := binary.Read(r, binary.LittleEndian, &bits); err != nil {\npkg/metricstore/walCheckpoint.go:934:\tif err := binary.Read(r, binary.LittleEndian, &numChildren); err != nil {\npkg/metricstore/walCheckpoint.go:957:\tif err := binary.Read(r, binary.LittleEndian, &sLen); err != nil {"}]},"uuid":"5266c4b8-f7c1-4d5e-b5ee-230a13525de1","timestamp":"2026-03-19T19:52:54.869Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"pkg/metricstore/parquetArchive_test.go:107:\tn, err := reader.Read(readRows)\npkg/metricstore/parquetArchive_test.go:231:\tn, err := reader.Read(readRows)\npkg/metricstore/api.go:183:// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\npkg/metricstore/api.go:283:\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)\npkg/metricstore/parquetArchive.go:209:\t\tif err := binary.Read(br, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/parquetArchive.go:216:\t\tif err := binary.Read(br, binary.LittleEndian, &fileFrom); err != nil {\npkg/metricstore/parquetArchive.go:219:\t\tif err := binary.Read(br, binary.LittleEndian, &fileTo); err != nil {\npkg/metricstore/walCheckpoint.go:484:\tif err := binary.Read(r, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/walCheckpoint.go:496:\tif err := binary.Read(r, binary.LittleEndian, &payloadLen); err != nil {\npkg/metricstore/walCheckpoint.go:510:\tif err := binary.Read(r, binary.LittleEndian, &storedCRC); err != nil {\npkg/metricstore/walCheckpoint.go:591:\tif err := binary.Read(br, binary.LittleEndian, &fileMagic); err != nil {\npkg/metricstore/walCheckpoint.go:857:\tif err := binary.Read(br, binary.LittleEndian, &magic); err != nil {\npkg/metricstore/walCheckpoint.go:865:\tif err := binary.Read(br, binary.LittleEndian, &fileFrom); err != nil {\npkg/metricstore/walCheckpoint.go:868:\tif err := binary.Read(br, binary.LittleEndian, &fileTo); err != nil {\npkg/metricstore/walCheckpoint.go:894:\tif err := binary.Read(r, binary.LittleEndian, &numMetrics); err != nil {\npkg/metricstore/walCheckpoint.go:905:\t\tif err := binary.Read(r, binary.LittleEndian, &freq); err != nil {\npkg/metricstore/walCheckpoint.go:908:\t\tif err := binary.Read(r, binary.LittleEndian, &start); err != nil {\npkg/metricstore/walCheckpoint.go:913:\t\tif err := binary.Read(r, binary.LittleEndian, &numValues); err != nil {\npkg/metricstore/walCheckpoint.go:920:\t\t\tif err := binary.Read(r, binary.LittleEndian, &bits); err != nil {\npkg/metricstore/walCheckpoint.go:934:\tif err := binary.Read(r, binary.LittleEndian, &numChildren); err != nil {\npkg/metricstore/walCheckpoint.go:957:\tif err := binary.Read(r, binary.LittleEndian, &sLen); err != nil {","numLines":21},"sourceToolAssistantUUID":"ab08ccdc-c954-4e31-a5b8-624d2c81140b","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"5266c4b8-f7c1-4d5e-b5ee-230a13525de1","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01FsJFQoZJqoZYLt33VYW3fL","type":"message","role":"assistant","content":[{"type":"text","text":"Only the one in api.go. Now let me check for any test files that might call `MemoryStore.Read`:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1044,"cache_read_input_tokens":99025,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1044},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2Jmdnuqtai3Sh2DuAs","type":"assistant","uuid":"7eb9a416-b66e-42a5-839c-ff3532b6f57f","timestamp":"2026-03-19T19:52:58.601Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"7eb9a416-b66e-42a5-839c-ff3532b6f57f","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01FsJFQoZJqoZYLt33VYW3fL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0148cKtnbGywkbDSSoxkTunL","name":"Grep","input":{"pattern":"MemoryStore.*Read\\(|\\.Read\\(.*metric","path":"/Users/jan/tmp/cc-backend/pkg/metricstore","glob":"*_test.go","output_mode":"content"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1044,"cache_read_input_tokens":99025,"output_tokens":166,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1044,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2Jmdnuqtai3Sh2DuAs","type":"assistant","uuid":"9a8d67b5-6c5f-4b0c-a939-7c62f78c6c64","timestamp":"2026-03-19T19:52:59.247Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"9a8d67b5-6c5f-4b0c-a939-7c62f78c6c64","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_0148cKtnbGywkbDSSoxkTunL","toolUseID":"toolu_0148cKtnbGywkbDSSoxkTunL","timestamp":"2026-03-19T19:52:59.280Z","uuid":"c8e6522f-1bda-4754-859f-5c34b273339c","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"9a8d67b5-6c5f-4b0c-a939-7c62f78c6c64","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0148cKtnbGywkbDSSoxkTunL","type":"tool_result","content":"No matches found"}]},"uuid":"29c46d12-1831-4c3f-8077-7973737ccfc3","timestamp":"2026-03-19T19:52:59.280Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"","numLines":0},"sourceToolAssistantUUID":"9a8d67b5-6c5f-4b0c-a939-7c62f78c6c64","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"29c46d12-1831-4c3f-8077-7973737ccfc3","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01H3pZXCLijcvZsFgYvYz2tG","type":"message","role":"assistant","content":[{"type":"text","text":"Good. Now let me also check the healthcheck.go and any other file that might call Read:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":181,"cache_read_input_tokens":100069,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":181},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2K5Q8RGqRLRUYh6Eyd","type":"assistant","uuid":"d75a4c74-7f3d-4d32-8882-20801fc01807","timestamp":"2026-03-19T19:53:03.209Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d75a4c74-7f3d-4d32-8882-20801fc01807","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01H3pZXCLijcvZsFgYvYz2tG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FNtJKDwbbNmTwifqatCeby","name":"Grep","input":{"pattern":"m\\.Read\\(|ms\\.Read\\(|store\\.Read\\(","path":"/Users/jan/tmp/cc-backend","output_mode":"content","glob":"*.go"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":181,"cache_read_input_tokens":100069,"output_tokens":153,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":181,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2K5Q8RGqRLRUYh6Eyd","type":"assistant","uuid":"6e40c96a-8dda-4c47-88db-5c265bd11f42","timestamp":"2026-03-19T19:53:03.850Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"6e40c96a-8dda-4c47-88db-5c265bd11f42","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_01FNtJKDwbbNmTwifqatCeby","toolUseID":"toolu_01FNtJKDwbbNmTwifqatCeby","timestamp":"2026-03-19T19:53:03.885Z","uuid":"5ce58579-0aa1-4d96-bb6e-6a68ab3a196f","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"6e40c96a-8dda-4c47-88db-5c265bd11f42","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FNtJKDwbbNmTwifqatCeby","type":"tool_result","content":"pkg/metricstore/api.go:283:\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)"}]},"uuid":"4f96f527-42fb-4d58-8560-56f6f55dae13","timestamp":"2026-03-19T19:53:03.885Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"pkg/metricstore/api.go:283:\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution, req.ResampleAlgo)","numLines":1},"sourceToolAssistantUUID":"6e40c96a-8dda-4c47-88db-5c265bd11f42","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"4f96f527-42fb-4d58-8560-56f6f55dae13","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Mf7d9JnSqra6PNvvWPpSqG","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me try building:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":225,"cache_read_input_tokens":100250,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":225},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2KR6YoMEZhLDptv1C5","type":"assistant","uuid":"2c68cad7-9b40-4007-98d2-6046c424d368","timestamp":"2026-03-19T19:53:07.703Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"2c68cad7-9b40-4007-98d2-6046c424d368","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Mf7d9JnSqra6PNvvWPpSqG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011EETmLgEnCMnebwN82KB5G","name":"Bash","input":{"command":"go build ./... 2>&1 | head -30","description":"Build all packages","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":225,"cache_read_input_tokens":100250,"output_tokens":120,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":225,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2KR6YoMEZhLDptv1C5","type":"assistant","uuid":"78181157-af72-4b65-9daa-8108035f5f33","timestamp":"2026-03-19T19:53:08.679Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"78181157-af72-4b65-9daa-8108035f5f33","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011EETmLgEnCMnebwN82KB5G","type":"tool_result","content":"# github.com/ClusterCockpit/cc-backend/internal/api\ninternal/api/job.go:1081:77: not enough arguments in call to resolver.Query().JobMetrics\n\thave (\"context\".Context, string, []string, []schema.MetricScope, nil)\n\twant (\"context\".Context, string, []string, []schema.MetricScope, *int, *model.ResampleAlgo)","is_error":false}]},"uuid":"df5c6afb-d6c0-4e37-8f33-edec49ea0894","timestamp":"2026-03-19T19:53:30.744Z","toolUseResult":{"stdout":"# github.com/ClusterCockpit/cc-backend/internal/api\ninternal/api/job.go:1081:77: not enough arguments in call to resolver.Query().JobMetrics\n\thave (\"context\".Context, string, []string, []schema.MetricScope, nil)\n\twant (\"context\".Context, string, []string, []schema.MetricScope, *int, *model.ResampleAlgo)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"78181157-af72-4b65-9daa-8108035f5f33","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"df5c6afb-d6c0-4e37-8f33-edec49ea0894","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_012t4jCXyduaBNpkbitscw4Y","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011XYmR6PY2fAS4ffnYqAL9j","name":"Read","input":{"file_path":"/Users/jan/tmp/cc-backend/internal/api/job.go","offset":1078,"limit":5},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":216,"cache_read_input_tokens":100475,"output_tokens":105,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":216,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2MPwQSqUZ1PZSxiPMN","type":"assistant","uuid":"d5deedad-8051-4152-afa5-17d7be2b6c97","timestamp":"2026-03-19T19:53:34.645Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d5deedad-8051-4152-afa5-17d7be2b6c97","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_011XYmR6PY2fAS4ffnYqAL9j","toolUseID":"toolu_011XYmR6PY2fAS4ffnYqAL9j","timestamp":"2026-03-19T19:53:34.672Z","uuid":"5eeabd49-f050-4938-a057-5383ffc7bde7","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"d5deedad-8051-4152-afa5-17d7be2b6c97","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011XYmR6PY2fAS4ffnYqAL9j","type":"tool_result","content":" 1078→\t}\n 1079→\n 1080→\tresolver := graph.GetResolverInstance()\n 1081→\tdata, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)\n 1082→\tif err != nil {"}]},"uuid":"60deee9f-44ae-4649-9196-f8dc0e901e20","timestamp":"2026-03-19T19:53:34.671Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","content":"\t}\n\n\tresolver := graph.GetResolverInstance()\n\tdata, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)\n\tif err != nil {","numLines":5,"startLine":1078,"totalLines":1155}},"sourceToolAssistantUUID":"d5deedad-8051-4152-afa5-17d7be2b6c97","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"60deee9f-44ae-4649-9196-f8dc0e901e20","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01CHes8VcLGstyYrNLjRUucC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Tqrep2ywJBTupcDXFVVmZL","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/tmp/cc-backend/internal/api/job.go","old_string":"data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)","new_string":"data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil, nil)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":186,"cache_read_input_tokens":100691,"output_tokens":175,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":186,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2MgkCKBVE5epdtum3s","type":"assistant","uuid":"02ffcac1-f02b-4c4e-8ec6-0f055dd90d57","timestamp":"2026-03-19T19:53:40.373Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"02ffcac1-f02b-4c4e-8ec6-0f055dd90d57","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01Tqrep2ywJBTupcDXFVVmZL","toolUseID":"toolu_01Tqrep2ywJBTupcDXFVVmZL","timestamp":"2026-03-19T19:53:40.401Z","uuid":"e1da41aa-8c61-48f7-a625-3f54d27b07e7","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"02ffcac1-f02b-4c4e-8ec6-0f055dd90d57","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Tqrep2ywJBTupcDXFVVmZL","type":"tool_result","content":"The file /Users/jan/tmp/cc-backend/internal/api/job.go has been updated successfully."}]},"uuid":"bd4c7c61-b75c-4238-8e87-685cbb88a4c8","timestamp":"2026-03-19T19:53:40.401Z","toolUseResult":{"filePath":"/Users/jan/tmp/cc-backend/internal/api/job.go","oldString":"data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)","newString":"data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil, nil)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage api\n\nimport (\n\t\"bufio\"\n\t\"database/sql\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"strconv\"\n\t\"strings\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/archiver\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/importer\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/go-chi/chi/v5\"\n)\n\nconst (\n\t// secondsPerDay is the number of seconds in 24 hours.\n\t// Used for duplicate job detection within a day window.\n\tsecondsPerDay = 86400\n)\n\n// StopJobAPIRequest model\ntype StopJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" example:\"123000\"`\n\tCluster *string `json:\"cluster\" example:\"fritz\"`\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"`\n\tState schema.JobState `json:\"jobState\" validate:\"required\" example:\"completed\"`\n\tStopTime int64 `json:\"stopTime\" validate:\"required\" example:\"1649763839\"`\n}\n\n// DeleteJobAPIRequest model\ntype DeleteJobAPIRequest struct {\n\tJobID *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n}\n\n// GetJobsAPIResponse model\ntype GetJobsAPIResponse struct {\n\tJobs []*schema.Job `json:\"jobs\"` // Array of jobs\n\tItems int `json:\"items\"` // Number of jobs returned\n\tPage int `json:\"page\"` // Page id returned\n}\n\n// APITag model\ntype APITag struct {\n\t// Tag Type\n\tType string `json:\"type\" example:\"Debug\"`\n\tName string `json:\"name\" example:\"Testjob\"` // Tag Name\n\tScope string `json:\"scope\" example:\"global\"` // Tag Scope for Frontend Display\n}\n\ntype EditMetaRequest struct {\n\tKey string `json:\"key\" example:\"jobScript\"`\n\tValue string `json:\"value\" example:\"bash script\"`\n}\n\n// JobMetaRequest model\ntype JobMetaRequest struct {\n\tJobId *int64 `json:\"jobId\" validate:\"required\" example:\"123000\"` // Cluster Job ID of job\n\tCluster *string `json:\"cluster\" example:\"fritz\"` // Cluster of job\n\tStartTime *int64 `json:\"startTime\" example:\"1649723812\"` // Start Time of job as epoch\n\tPayload EditMetaRequest `json:\"payload\"` // Content to Add to Job Meta_Data\n}\n\ntype TagJobAPIRequest []*APITag\n\ntype GetJobAPIRequest []string\n\ntype GetJobAPIResponse struct {\n\tMeta *schema.Job\n\tData []*JobMetricWithName\n}\n\ntype GetCompleteJobAPIResponse struct {\n\tMeta *schema.Job\n\tData schema.JobData\n}\n\ntype JobMetricWithName struct {\n\tMetric *schema.JobMetric `json:\"metric\"`\n\tName string `json:\"name\"`\n\tScope schema.MetricScope `json:\"scope\"`\n}\n\n// getJobs godoc\n// @summary Lists all jobs\n// @tags Job query\n// @description Get a list of all jobs. Filters can be applied using query parameters.\n// @description Number of results can be limited by page. Results are sorted by descending startTime.\n// @produce json\n// @param state query string false \"Job State\" Enums(running, completed, failed, cancelled, stopped, timeout)\n// @param cluster query string false \"Job Cluster\"\n// @param start-time query string false \"Syntax: '$from-$to', as unix epoch timestamps in seconds\"\n// @param items-per-page query int false \"Items per page (Default: 25)\"\n// @param page query int false \"Page Number (Default: 1)\"\n// @param with-metadata query bool false \"Include metadata (e.g. jobScript) in response\"\n// @success 200 {object} api.GetJobsAPIResponse \"Job array and page info\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 \t\t {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/ [get]\nfunc (api *RestAPI) getJobs(rw http.ResponseWriter, r *http.Request) {\n\twithMetadata := false\n\tfilter := &model.JobFilter{}\n\tpage := &model.PageRequest{ItemsPerPage: 25, Page: 1}\n\torder := &model.OrderByInput{Field: \"startTime\", Type: \"col\", Order: model.SortDirectionEnumDesc}\n\n\tfor key, vals := range r.URL.Query() {\n\t\tswitch key {\n\t\tcase \"project\":\n\t\t\tfilter.Project = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"state\":\n\t\t\tfor _, s := range vals {\n\t\t\t\tstate := schema.JobState(s)\n\t\t\t\tif !state.Valid() {\n\t\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: state\"),\n\t\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\t\treturn\n\t\t\t\t}\n\t\t\t\tfilter.State = append(filter.State, state)\n\t\t\t}\n\t\tcase \"cluster\":\n\t\t\tfilter.Cluster = &model.StringInput{Eq: &vals[0]}\n\t\tcase \"start-time\": // ?startTime=1753707480-1754053139\n\t\t\tst := strings.Split(vals[0], \"-\")\n\t\t\tif len(st) != 2 {\n\t\t\t\thandleError(fmt.Errorf(\"invalid query parameter value: startTime\"),\n\t\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tfrom, err := strconv.ParseInt(st[0], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tto, err := strconv.ParseInt(st[1], 10, 64)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tufrom, uto := time.Unix(from, 0), time.Unix(to, 0)\n\t\t\tfilter.StartTime = &config.TimeRange{From: &ufrom, To: &uto}\n\t\tcase \"page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.Page = x\n\t\tcase \"items-per-page\":\n\t\t\tx, err := strconv.Atoi(vals[0])\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t\tpage.ItemsPerPage = x\n\t\tcase \"with-metadata\":\n\t\t\twithMetadata = true\n\t\tdefault:\n\t\t\thandleError(fmt.Errorf(\"invalid query parameter: %s\", key),\n\t\t\t\thttp.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tjobs, err := api.JobRepository.QueryJobs(r.Context(), []*model.JobFilter{filter}, page, order)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tresults := make([]*schema.Job, 0, len(jobs))\n\tfor _, job := range jobs {\n\t\tif withMetadata {\n\t\t\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\t\tif err != nil {\n\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tif job.MonitoringStatus == schema.MonitoringStatusArchivingSuccessful {\n\t\t\tjob.Statistics, err = archive.GetStatistics(job)\n\t\t\tif err != nil {\n\t\t\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tresults = append(results, job)\n\t}\n\n\tcclog.Debugf(\"/api/jobs: %d jobs returned\", len(results))\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobsAPIResponse{\n\t\tJobs: results,\n\t\tItems: page.ItemsPerPage,\n\t\tPage: page.Page,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getCompleteJobByID godoc\n// @summary Get job meta and optional all metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param all-metrics query bool false \"Include all available metrics\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [get]\nfunc (api *RestAPI) getCompleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id) // Get Job from Repo by ID\n\t} else {\n\t\thandleError(fmt.Errorf(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tvar data schema.JobData\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tif r.URL.Query().Get(\"all-metrics\") == \"true\" {\n\t\tdata, err = metricdispatch.LoadData(job, nil, scopes, r.Context(), resolution, \"\")\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"REST: error while loading all-metrics job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetCompleteJobAPIResponse{\n\t\tMeta: job,\n\t\tData: data,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// getJobByID godoc\n// @summary Get job meta and configurable metric data\n// @tags Job query\n// @description Job to get is specified by database ID\n// @description Returns full job resource information according to 'Job' scheme and all metrics according to 'JobData'.\n// @accept json\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @param request body api.GetJobAPIRequest true \"Array of metric names\"\n// @success 200 {object} api.GetJobAPIResponse \"Job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/{id} [post]\nfunc (api *RestAPI) getJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job from db\n\tid := chi.URLParam(r, \"id\")\n\tvar job *schema.Job\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob, err = api.JobRepository.FindByID(r.Context(), id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job with db id %s failed: %w\", id, err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\n\t}\n\tif _, err = api.JobRepository.FetchMetadata(job); err != nil {\n\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar metrics GetJobAPIRequest\n\tif err = decode(r.Body, &metrics); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tvar scopes []schema.MetricScope\n\n\tif job.NumNodes == 1 {\n\t\tscopes = []schema.MetricScope{\"core\"}\n\t} else {\n\t\tscopes = []schema.MetricScope{\"node\"}\n\t}\n\n\tmetricConfigs := archive.GetCluster(job.Cluster).MetricConfig\n\tresolution := 0\n\n\tfor _, mc := range metricConfigs {\n\t\tresolution = max(resolution, mc.Timestep)\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, r.Context(), resolution, \"\")\n\tif err != nil {\n\t\tcclog.Warnf(\"REST: error while loading job data for JobID %d on %s\", job.JobID, job.Cluster)\n\t\treturn\n\t}\n\n\tres := []*JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &JobMetricWithName{\n\t\t\t\tName: name,\n\t\t\t\tScope: scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\tcclog.Debugf(\"/api/job/%s: get job %d\", id, job.JobID)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tbw := bufio.NewWriter(rw)\n\tdefer bw.Flush()\n\n\tpayload := GetJobAPIResponse{\n\t\tMeta: job,\n\t\tData: res,\n\t}\n\n\tif err := json.NewEncoder(bw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n\n// editMeta godoc\n// @summary Edit meta-data json of job identified by database id\n// @tags Job add and modify\n// @description Edit key value pairs in job metadata json of job specified by database id\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.EditMetaRequest true \"Metadata Key value pair to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/{id} [patch]\nfunc (api *RestAPI) editMeta(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tvar req EditMetaRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Key, req.Value); err != nil {\n\t\thandleError(fmt.Errorf(\"updating metadata failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// editMetaByRequest godoc\n// @summary Edit meta-data json of job identified by request\n// @tags Job add and modify\n// @description Edit key value pairs in metadata json of job specified by jobID, StartTime and Cluster\n// @description If a key already exists its content will be overwritten\n// @accept json\n// @produce json\n// @param request body api.JobMetaRequest true \"Specifies job and payload to add or update\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/edit_meta/ [patch]\nfunc (api *RestAPI) editMetaByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := JobMetaRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will have its meta_data edited) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobId == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// log.Printf(\"loading db job for editMetaByRequest... : JobMetaRequest=%v\", req)\n\tjob, err = api.JobRepository.Find(req.JobId, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif err := api.JobRepository.UpdateMetadata(job, req.Payload.Key, req.Payload.Value); err != nil {\n\t\thttp.Error(rw, err.Error(), http.StatusInternalServerError)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tjson.NewEncoder(rw).Encode(job)\n}\n\n// tagJob godoc\n// @summary Adds one or more tags to a job\n// @tags Job add and modify\n// @description Adds tag(s) to a job specified by DB ID. Name and Type of Tag(s) can be chosen freely.\n// @description Tag Scope for frontend visibility will default to \"global\" if none entered, other options: \"admin\" or specific username.\n// @description If tagged job is already finished: Tag will be written directly to respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to add\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/tag_job/{id} [post]\nfunc (api *RestAPI) tagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, tag := range req {\n\t\ttagID, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), *job.ID, tag.Type, tag.Name, tag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = append(job.Tags, &schema.Tag{\n\t\t\tID: tagID,\n\t\t\tType: tag.Type,\n\t\t\tName: tag.Name,\n\t\t\tScope: tag.Scope,\n\t\t})\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTagJob godoc\n// @summary Removes one or more tags from a job\n// @tags Job add and modify\n// @description Removes tag(s) from a job specified by DB ID. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description If tagged job is already finished: Tag will be removed from respective archive files.\n// @accept json\n// @produce json\n// @param id path int true \"Job Database ID\"\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {object} schema.Job \"Updated job resource\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /jobs/tag_job/{id} [delete]\nfunc (api *RestAPI) removeTagJob(rw http.ResponseWriter, r *http.Request) {\n\tid, err := strconv.ParseInt(chi.URLParam(r, \"id\"), 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"parsing job ID failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err := api.JobRepository.FindByID(r.Context(), id)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\treturn\n\t}\n\n\tjob.Tags, err = api.JobRepository.GetTags(repository.GetUserFromContext(r.Context()), job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"getting tags failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warnf(\"Cannot delete private tag for job %d: Skip\", job.JobID)\n\t\t\tcontinue\n\t\t}\n\n\t\tremainingTags, err := api.JobRepository.RemoveJobTagByRequest(repository.GetUserFromContext(r.Context()), *job.ID, rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\n\t\tjob.Tags = remainingTags\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n}\n\n// removeTags godoc\n// @summary Removes all tags and job-relations for type:name tuple\n// @tags Tag remove\n// @description Removes tags by type and name. Name and Type of Tag(s) must match.\n// @description Tag Scope is required for matching, options: \"global\", \"admin\". Private tags can not be deleted via API.\n// @description Tag wills be removed from respective archive files.\n// @accept json\n// @produce plain\n// @param request body api.TagJobAPIRequest true \"Array of tag-objects to remove\"\n// @success 200 {string} string \"Success Response\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 404 {object} api.ErrorResponse \"Job or tag does not exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /tags/ [delete]\nfunc (api *RestAPI) removeTags(rw http.ResponseWriter, r *http.Request) {\n\tvar req TagJobAPIRequest\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"decoding request failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\ttargetCount := len(req)\n\tcurrentCount := 0\n\tfor _, rtag := range req {\n\t\t// Only Global and Admin Tags\n\t\tif rtag.Scope != \"global\" && rtag.Scope != \"admin\" {\n\t\t\tcclog.Warn(\"Cannot delete private tag: Skip\")\n\t\t\tcontinue\n\t\t}\n\n\t\terr := api.JobRepository.RemoveTagByRequest(rtag.Type, rtag.Name, rtag.Scope)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"removing tag failed: %w\", err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcurrentCount++\n\t}\n\n\trw.WriteHeader(http.StatusOK)\n\tfmt.Fprintf(rw, \"Deleted Tags from DB: %d successfull of %d requested\\n\", currentCount, targetCount)\n}\n\n// startJob godoc\n// @summary Adds a new job as \"running\"\n// @tags Job add and modify\n// @description Job specified in request body will be saved to database as \"running\" with new DB ID.\n// @description Job specifications follow the 'Job' scheme, API will fail to execute if requirements are not met.\n// @accept json\n// @produce json\n// @param request body schema.Job true \"Job to add\"\n// @success 201 {object} api.DefaultAPIResponse \"Job added successfully\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: The combination of jobId, clusterId and startTime does already exist\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/start_job/ [post]\nfunc (api *RestAPI) startJob(rw http.ResponseWriter, r *http.Request) {\n\treq := schema.Job{\n\t\tShared: \"none\",\n\t\tMonitoringStatus: schema.MonitoringStatusRunningOrArchiving,\n\t}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tcclog.Debugf(\"REST: %s\", req.GoString())\n\treq.State = schema.JobStateRunning\n\n\tif err := importer.SanityChecks(&req); err != nil {\n\t\thandleError(err, http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// aquire lock to avoid race condition between API calls\n\tvar unlockOnce sync.Once\n\tapi.RepositoryMutex.Lock()\n\tdefer unlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\t// Check if combination of (job_id, cluster_id, start_time) already exists:\n\tjobs, err := api.JobRepository.FindAll(&req.JobID, &req.Cluster, nil)\n\tif err != nil && err != sql.ErrNoRows {\n\t\thandleError(fmt.Errorf(\"checking for duplicate failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\tif err == nil {\n\t\tfor _, job := range jobs {\n\t\t\t// Check if jobs are within the same day (prevent duplicates)\n\t\t\tif (req.StartTime - job.StartTime) < secondsPerDay {\n\t\t\t\thandleError(fmt.Errorf(\"a job with that jobId, cluster and startTime already exists: dbid: %d, jobid: %d\", *job.ID, job.JobID), http.StatusUnprocessableEntity, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\t}\n\n\t// When tags are present, insert directly into the job table so that the\n\t// returned ID can be used with AddTagOrCreate (which queries the job table).\n\t// Jobs without tags use the cache path as before.\n\tvar id int64\n\tif len(req.Tags) > 0 {\n\t\tid, err = api.JobRepository.StartDirect(&req)\n\t} else {\n\t\tid, err = api.JobRepository.Start(&req)\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"insert into database failed: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\t// unlock here, adding Tags can be async\n\tunlockOnce.Do(api.RepositoryMutex.Unlock)\n\n\tfor _, tag := range req.Tags {\n\t\tif _, err := api.JobRepository.AddTagOrCreate(repository.GetUserFromContext(r.Context()), id, tag.Type, tag.Name, tag.Scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"adding tag to new job %d failed: %w\", id, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t}\n\n\tcclog.Infof(\"new job (id: %d): cluster=%s, jobId=%d, user=%s, startTime=%d\", id, req.Cluster, req.JobID, req.User, req.StartTime)\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusCreated)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: \"success\",\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// stopJobByRequest godoc\n// @summary Marks job as completed and triggers archiving\n// @tags Job add and modify\n// @description Job to stop is specified by request body. All fields are required in this case.\n// @description Returns full job resource information according to 'Job' scheme.\n// @produce json\n// @param request body api.StopJobAPIRequest true \"All fields required\"\n// @success 200 {object} schema.Job \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: job has already been stopped\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/stop_job/ [post]\nfunc (api *RestAPI) stopJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := StopJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be stopped) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tisCached := false\n\tjob, err = api.JobRepository.FindCached(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\t// Not in cache, try main job table\n\t\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusNotFound, rw)\n\t\t\treturn\n\t\t}\n\t} else {\n\t\tisCached = true\n\t}\n\n\tapi.checkAndHandleStopJob(rw, job, req, isCached)\n}\n\n// deleteJobByID godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to remove is specified by database ID. This will not remove the job from the job archive.\n// @produce json\n// @param id path int true \"Database ID of Job\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/{id} [delete]\nfunc (api *RestAPI) deleteJobByID(rw http.ResponseWriter, r *http.Request) {\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"id\")\n\tvar err error\n\tif id != \"\" {\n\t\tid, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for id: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\terr = api.JobRepository.DeleteJobByID(id)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'id' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %s\", id),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobByRequest godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Job to delete is specified by request body. All fields are required in this case.\n// @accept json\n// @produce json\n// @param request body api.DeleteJobAPIRequest true \"All fields required\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job/ [delete]\nfunc (api *RestAPI) deleteJobByRequest(rw http.ResponseWriter, r *http.Request) {\n\t// Parse request body\n\treq := DeleteJobAPIRequest{}\n\tif err := decode(r.Body, &req); err != nil {\n\t\thandleError(fmt.Errorf(\"parsing request body failed: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\t// Fetch job (that will be deleted) from db\n\tvar job *schema.Job\n\tvar err error\n\tif req.JobID == nil {\n\t\thandleError(errors.New(\"the field 'jobId' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tjob, err = api.JobRepository.Find(req.JobID, req.Cluster, req.StartTime)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"finding job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\terr = api.JobRepository.DeleteJobByID(*job.ID)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting job failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted job %d\", *job.ID),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// deleteJobBefore godoc\n// @summary Remove a job from the sql database\n// @tags Job remove\n// @description Remove all jobs with start time before timestamp. The jobs will not be removed from the job archive.\n// @produce json\n// @param ts path int true \"Unix epoch timestamp\"\n// @success 200 {object} api.DefaultAPIResponse \"Success message\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 404 {object} api.ErrorResponse \"Resource not found\"\n// @failure 422 {object} api.ErrorResponse \"Unprocessable Entity: finding job failed: sql: no rows in result set\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/delete_job_before/{ts} [delete]\n// @param omit-tagged query bool false \"Omit jobs with tags from deletion\"\nfunc (api *RestAPI) deleteJobBefore(rw http.ResponseWriter, r *http.Request) {\n\tvar cnt int\n\t// Fetch job (that will be stopped) from db\n\tid := chi.URLParam(r, \"ts\")\n\tvar err error\n\tif id != \"\" {\n\t\tts, e := strconv.ParseInt(id, 10, 64)\n\t\tif e != nil {\n\t\t\thandleError(fmt.Errorf(\"integer expected in path for ts: %w\", e), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\n\t\t// Check for omit-tagged query parameter\n\t\tomitTagged := \"none\"\n\t\tif omitTaggedStr := r.URL.Query().Get(\"omit-tagged\"); omitTaggedStr != \"\" {\n\t\t\tswitch omitTaggedStr {\n\t\t\tcase \"none\", \"all\", \"user\":\n\t\t\t\tomitTagged = omitTaggedStr\n\t\t\tdefault:\n\t\t\t\thandleError(fmt.Errorf(\"omit-tagged must be one of: none, all, user\"), http.StatusBadRequest, rw)\n\t\t\t\treturn\n\t\t\t}\n\t\t}\n\n\t\tcnt, err = api.JobRepository.DeleteJobsBefore(ts, omitTagged)\n\t} else {\n\t\thandleError(errors.New(\"the parameter 'ts' is required\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"deleting jobs failed: %w\", err), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(DefaultAPIResponse{\n\t\tMessage: fmt.Sprintf(\"Successfully deleted %d jobs\", cnt),\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\nfunc (api *RestAPI) checkAndHandleStopJob(rw http.ResponseWriter, job *schema.Job, req StopJobAPIRequest, isCached bool) {\n\t// Sanity checks\n\tif job.State != schema.JobStateRunning {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : job has already been stopped (state is: %s)\", job.JobID, *job.ID, job.Cluster, job.State), http.StatusUnprocessableEntity, rw)\n\t\treturn\n\t}\n\n\tif job.StartTime > req.StopTime {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : stopTime %d must be larger/equal than startTime %d\", job.JobID, *job.ID, job.Cluster, req.StopTime, job.StartTime), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tif req.State != \"\" && !req.State.Valid() {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : invalid requested job state: %#v\", job.JobID, *job.ID, job.Cluster, req.State), http.StatusBadRequest, rw)\n\t\treturn\n\t} else if req.State == \"\" {\n\t\treq.State = schema.JobStateCompleted\n\t}\n\n\t// Mark job as stopped in the database (update state and duration)\n\tjob.Duration = int32(req.StopTime - job.StartTime)\n\tjob.State = req.State\n\tapi.JobRepository.Mutex.Lock()\n\tdefer api.JobRepository.Mutex.Unlock()\n\n\t// If the job is still in job_cache, transfer it to the job table first\n\t// so that job.ID always points to the job table for downstream code\n\tif isCached {\n\t\tnewID, err := api.JobRepository.TransferCachedJobToMain(*job.ID)\n\t\tif err != nil {\n\t\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : transferring cached job failed: %w\", job.JobID, *job.ID, job.Cluster, err), http.StatusInternalServerError, rw)\n\t\t\treturn\n\t\t}\n\t\tcclog.Infof(\"transferred cached job to main table: old id %d -> new id %d (jobId=%d)\", *job.ID, newID, job.JobID)\n\t\tjob.ID = &newID\n\t}\n\n\tif err := api.JobRepository.Stop(*job.ID, job.Duration, job.State, job.MonitoringStatus); err != nil {\n\t\thandleError(fmt.Errorf(\"jobId %d (id %d) on %s : marking job as '%s' (duration: %d) in DB failed: %w\", job.JobID, *job.ID, job.Cluster, job.State, job.Duration, err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\tcclog.Infof(\"archiving job... (dbid: %d): cluster=%s, jobId=%d, user=%s, startTime=%d, duration=%d, state=%s\", *job.ID, job.Cluster, job.JobID, job.User, job.StartTime, job.Duration, job.State)\n\n\t// Send a response (with status OK). This means that errors that happen from here on forward\n\t// can *NOT* be communicated to the client. If reading from a MetricDataRepository or\n\t// writing to the filesystem fails, the client will not know.\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\tif err := json.NewEncoder(rw).Encode(job); err != nil {\n\t\tcclog.Errorf(\"Failed to encode job response: %v\", err)\n\t}\n\n\t// Monitoring is disabled...\n\tif job.MonitoringStatus == schema.MonitoringStatusDisabled {\n\t\treturn\n\t}\n\n\t// Trigger async archiving\n\tarchiver.TriggerArchiving(job)\n}\n\nfunc (api *RestAPI) getJobMetrics(rw http.ResponseWriter, r *http.Request) {\n\tid := chi.URLParam(r, \"id\")\n\tmetrics := r.URL.Query()[\"metric\"]\n\tvar scopes []schema.MetricScope\n\tfor _, scope := range r.URL.Query()[\"scope\"] {\n\t\tvar s schema.MetricScope\n\t\tif err := s.UnmarshalGQL(scope); err != nil {\n\t\t\thandleError(fmt.Errorf(\"unmarshaling scope failed: %w\", err), http.StatusBadRequest, rw)\n\t\t\treturn\n\t\t}\n\t\tscopes = append(scopes, s)\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\trw.WriteHeader(http.StatusOK)\n\n\ttype Response struct {\n\t\tData *struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t} `json:\"data\"`\n\t\tError *struct {\n\t\t\tMessage string `json:\"message\"`\n\t\t} `json:\"error\"`\n\t}\n\n\tresolver := graph.GetResolverInstance()\n\tdata, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)\n\tif err != nil {\n\t\tif err := json.NewEncoder(rw).Encode(Response{\n\t\t\tError: &struct {\n\t\t\t\tMessage string `json:\"message\"`\n\t\t\t}{Message: err.Error()},\n\t\t}); err != nil {\n\t\t\tcclog.Errorf(\"Failed to encode error response: %v\", err)\n\t\t}\n\t\treturn\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(Response{\n\t\tData: &struct {\n\t\t\tJobMetrics []*model.JobMetricWithName `json:\"jobMetrics\"`\n\t\t}{JobMetrics: data},\n\t}); err != nil {\n\t\tcclog.Errorf(\"Failed to encode response: %v\", err)\n\t}\n}\n\n// GetUsedNodesAPIResponse model\ntype GetUsedNodesAPIResponse struct {\n\tUsedNodes map[string][]string `json:\"usedNodes\"` // Map of cluster names to lists of used node hostnames\n}\n\n// getUsedNodes godoc\n// @summary Lists used nodes by cluster\n// @tags Job query\n// @description Get a map of cluster names to lists of unique hostnames that are currently in use by running jobs that started before the specified timestamp.\n// @produce json\n// @param ts query int true \"Unix timestamp to filter jobs (jobs with start_time < ts)\"\n// @success 200 {object} api.GetUsedNodesAPIResponse \"Map of cluster names to hostname lists\"\n// @failure 400 {object} api.ErrorResponse \"Bad Request\"\n// @failure 401 {object} api.ErrorResponse \"Unauthorized\"\n// @failure 403 {object} api.ErrorResponse \"Forbidden\"\n// @failure 500 {object} api.ErrorResponse \"Internal Server Error\"\n// @security ApiKeyAuth\n// @router /api/jobs/used_nodes [get]\nfunc (api *RestAPI) getUsedNodes(rw http.ResponseWriter, r *http.Request) {\n\tif user := repository.GetUserFromContext(r.Context()); user != nil &&\n\t\t!user.HasRole(schema.RoleAPI) {\n\t\thandleError(fmt.Errorf(\"missing role: %v\", schema.GetRoleString(schema.RoleAPI)), http.StatusForbidden, rw)\n\t\treturn\n\t}\n\n\ttsStr := r.URL.Query().Get(\"ts\")\n\tif tsStr == \"\" {\n\t\thandleError(fmt.Errorf(\"missing required query parameter: ts\"), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tts, err := strconv.ParseInt(tsStr, 10, 64)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"invalid timestamp format: %w\", err), http.StatusBadRequest, rw)\n\t\treturn\n\t}\n\n\tusedNodes, err := api.JobRepository.GetUsedNodes(ts)\n\tif err != nil {\n\t\thandleError(fmt.Errorf(\"failed to get used nodes: %w\", err), http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n\n\trw.Header().Add(\"Content-Type\", \"application/json\")\n\tpayload := GetUsedNodesAPIResponse{\n\t\tUsedNodes: usedNodes,\n\t}\n\n\tif err := json.NewEncoder(rw).Encode(payload); err != nil {\n\t\thandleError(err, http.StatusInternalServerError, rw)\n\t\treturn\n\t}\n}\n","structuredPatch":[{"oldStart":1078,"oldLines":7,"newStart":1078,"newLines":7,"lines":[" }"," "," resolver := graph.GetResolverInstance()","- data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil)","+ data, err := resolver.Query().JobMetrics(r.Context(), id, metrics, scopes, nil, nil)"," if err != nil {"," if err := json.NewEncoder(rw).Encode(Response{"," Error: &struct {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"02ffcac1-f02b-4c4e-8ec6-0f055dd90d57","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"bd4c7c61-b75c-4238-8e87-685cbb88a4c8","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01M8bWXvb6NRUdoBJVsT4H2s","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0192SSu27U95gQS7nDaKJrYR","name":"Bash","input":{"command":"go build ./... 2>&1 | head -30","description":"Build all packages","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":322,"cache_read_input_tokens":100877,"output_tokens":101,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":322,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2N7MHcFbCpSb64WPeP","type":"assistant","uuid":"6ae637c4-11ed-4cbf-b5cf-db4b081a9ab8","timestamp":"2026-03-19T19:53:43.431Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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{"parentUuid":"d729dd31-c4c4-4bb0-9e20-d52a2c3355a8","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01QyadHtTmqbn4jh1c1j3g5d","type":"message","role":"assistant","content":[{"type":"text","text":"Build succeeds. Now let me run the tests:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":239,"cache_read_input_tokens":101199,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":239},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2NhRQFU4YxGMxGmcc3","type":"assistant","uuid":"b699bb5c-7cb3-47c6-94c9-790d33012a6b","timestamp":"2026-03-19T19:53:52.055Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"b699bb5c-7cb3-47c6-94c9-790d33012a6b","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01QyadHtTmqbn4jh1c1j3g5d","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014zmpV7dSBzrpwHi6eRgs47","name":"Bash","input":{"command":"make test 2>&1 | tail -30","description":"Run all tests","timeout":300000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":239,"cache_read_input_tokens":101199,"output_tokens":111,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":239,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2NhRQFU4YxGMxGmcc3","type":"assistant","uuid":"145cde6a-ddd8-4acd-bc29-a982316d9cb5","timestamp":"2026-03-19T19:53:52.190Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"145cde6a-ddd8-4acd-bc29-a982316d9cb5","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014zmpV7dSBzrpwHi6eRgs47","type":"tool_result","content":"===> TESTING\n? \tgithub.com/ClusterCockpit/cc-backend/cmd/cc-backend\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/api\t6.856s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/archiver\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/auth\t0.894s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/config\t1.165s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph/generated\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph/model\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/importer\t1.587s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/metricdispatch\t1.902s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/metricstoreclient\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t2.533s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/routerConfig\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/tagger\t2.372s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/taskmanager\t2.403s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/archive\t2.905s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/archive/parquet\t2.670s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/metricstore\t2.588s\nok \tgithub.com/ClusterCockpit/cc-backend/tools/archive-manager\t4.323s\n? \tgithub.com/ClusterCockpit/cc-backend/tools/archive-migration\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/tools/convert-pem-pubkey\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/tools/gen-keypair\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/web\t2.054s","is_error":false}]},"uuid":"5bf769b0-cf26-4899-a3f5-f1fff3c18edf","timestamp":"2026-03-19T19:54:11.078Z","toolUseResult":{"stdout":"===> TESTING\n? \tgithub.com/ClusterCockpit/cc-backend/cmd/cc-backend\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/api\t6.856s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/archiver\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/auth\t0.894s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/config\t1.165s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph/generated\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/internal/graph/model\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/importer\t1.587s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/metricdispatch\t1.902s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/metricstoreclient\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t2.533s\n? \tgithub.com/ClusterCockpit/cc-backend/internal/routerConfig\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/internal/tagger\t2.372s\nok \tgithub.com/ClusterCockpit/cc-backend/internal/taskmanager\t2.403s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/archive\t2.905s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/archive/parquet\t2.670s\nok \tgithub.com/ClusterCockpit/cc-backend/pkg/metricstore\t2.588s\nok \tgithub.com/ClusterCockpit/cc-backend/tools/archive-manager\t4.323s\n? \tgithub.com/ClusterCockpit/cc-backend/tools/archive-migration\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/tools/convert-pem-pubkey\t[no test files]\n? \tgithub.com/ClusterCockpit/cc-backend/tools/gen-keypair\t[no test files]\nok \tgithub.com/ClusterCockpit/cc-backend/web\t2.054s","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"145cde6a-ddd8-4acd-bc29-a982316d9cb5","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"5bf769b0-cf26-4899-a3f5-f1fff3c18edf","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Ur3itYhqAxTzz4cNT4mH4M","type":"message","role":"assistant","content":[{"type":"text","text":"All tests pass. Let me verify the full build works too:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":774,"cache_read_input_tokens":101438,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":774},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZD2QNNyq1CLENL3Ybxf1","type":"assistant","uuid":"121f0d7e-bc08-4201-b622-c2d9f6136fea","timestamp":"2026-03-19T19:54:14.581Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"121f0d7e-bc08-4201-b622-c2d9f6136fea","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Ur3itYhqAxTzz4cNT4mH4M","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DgwbXqFbyQe6ExyfLDLySH","name":"Bash","input":{"command":"make 2>&1 | tail -10","description":"Full build (frontend + backend)","timeout":300000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":774,"cache_read_input_tokens":101438,"output_tokens":115,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":774,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2QNNyq1CLENL3Ybxf1","type":"assistant","uuid":"54d79fbe-34cb-493d-bf88-bf5236c0fe81","timestamp":"2026-03-19T19:54:15.223Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"54d79fbe-34cb-493d-bf88-bf5236c0fe81","isSidechain":false,"promptId":"1c46e1b2-ade5-44be-90d9-e93109c02833","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DgwbXqFbyQe6ExyfLDLySH","type":"tool_result","content":"\u001b[32mcreated \u001b[1mpublic/build/config.js\u001b[22m in \u001b[1m2.4s\u001b[22m\u001b[39m\n\u001b[36m\n\u001b[1msrc/logs.entrypoint.js\u001b[22m → \u001b[1mpublic/build/logs.js\u001b[22m...\u001b[39m\n\u001b[1m\u001b[33m(!) Circular dependencies\u001b[39m\u001b[22m\nnode_modules/svelte/src/internal/client/runtime.js -> node_modules/svelte/src/internal/client/reactivity/effects.js -> node_modules/svelte/src/internal/client/runtime.js\nnode_modules/svelte/src/internal/client/dom/operations.js -> node_modules/svelte/src/internal/client/dom/hydration.js -> node_modules/svelte/src/internal/client/dom/operations.js\nnode_modules/svelte/src/internal/client/runtime.js -> node_modules/svelte/src/internal/client/reactivity/effects.js -> node_modules/svelte/src/internal/client/dom/operations.js -> node_modules/svelte/src/internal/client/dev/equality.js -> node_modules/svelte/src/internal/client/proxy.js -> node_modules/svelte/src/internal/client/runtime.js\n...and 36 more\n\u001b[32mcreated \u001b[1mpublic/build/logs.js\u001b[22m in \u001b[1m1.2s\u001b[22m\u001b[39m\n===> BUILD cc-backend","is_error":false}]},"uuid":"5bfde1ed-26c7-4eae-b1c1-7745dca07ff1","timestamp":"2026-03-19T19:55:12.027Z","toolUseResult":{"stdout":"\u001b[32mcreated \u001b[1mpublic/build/config.js\u001b[22m in \u001b[1m2.4s\u001b[22m\u001b[39m\n\u001b[36m\n\u001b[1msrc/logs.entrypoint.js\u001b[22m → \u001b[1mpublic/build/logs.js\u001b[22m...\u001b[39m\n\u001b[1m\u001b[33m(!) Circular dependencies\u001b[39m\u001b[22m\nnode_modules/svelte/src/internal/client/runtime.js -> node_modules/svelte/src/internal/client/reactivity/effects.js -> node_modules/svelte/src/internal/client/runtime.js\nnode_modules/svelte/src/internal/client/dom/operations.js -> node_modules/svelte/src/internal/client/dom/hydration.js -> node_modules/svelte/src/internal/client/dom/operations.js\nnode_modules/svelte/src/internal/client/runtime.js -> node_modules/svelte/src/internal/client/reactivity/effects.js -> node_modules/svelte/src/internal/client/dom/operations.js -> node_modules/svelte/src/internal/client/dev/equality.js -> node_modules/svelte/src/internal/client/proxy.js -> node_modules/svelte/src/internal/client/runtime.js\n...and 36 more\n\u001b[32mcreated \u001b[1mpublic/build/logs.js\u001b[22m in \u001b[1m1.2s\u001b[22m\u001b[39m\n===> BUILD cc-backend","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"54d79fbe-34cb-493d-bf88-bf5236c0fe81","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"5bfde1ed-26c7-4eae-b1c1-7745dca07ff1","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Fxfi9P56m5nMLunJNV6Tgg","type":"message","role":"assistant","content":[{"type":"text","text":"Everything builds and tests pass. Let me do a quick summary of all changes made:\n\n## Summary\n\n### cc-lib (`/Users/jan/prg/CC/cc-lib/`)\n\n**`resampler/resampler.go`**:\n- Added `ResamplerFunc` type alias for the shared resampler function signature\n- Added `GetResampler(name string)` lookup function returning the resampler for \"lttb\", \"average\", \"simple\", or \"\" (defaults to LTTB)\n- Added `AverageResampler()` — RRDTool-style AVERAGE CF that divides data into fixed buckets and computes arithmetic mean, skipping NaN values (all-NaN bucket → NaN output)\n\n**`resampler/resampler_test.go`**:\n- Added `TestAverageResampler` — table-driven tests covering early-exit conditions, error cases\n- Added `TestAverageResamplerValues` — exact value verification (`[1,2,3,4,5,6]` step=2 → `[1.5, 3.5, 5.5]`)\n- Added `TestAverageResamplerNaN` — NaN skipping and all-NaN bucket tests\n- Added `TestGetResampler` — lookup function tests for all valid names + unknown\n- Added `BenchmarkAverageResampler`\n\n### cc-backend (`/Users/jan/tmp/cc-backend/`)\n\n**`go.mod`**: Added `replace` directive for local cc-lib\n\n**`api/schema.graphqls`**: Added `ResampleAlgo` enum (LTTB, AVERAGE, SIMPLE) and `resampleAlgo` parameter to `jobMetrics` and `nodeMetricsList` queries\n\n**`internal/graph/generated/`**: Regenerated via `make graphql`\n\n**`internal/graph/schema.resolvers.go`**: Both resolvers convert the enum to lowercase string and pass to data loaders\n\n**`internal/metricdispatch/dataLoader.go`**: \n- `cacheKey()` includes algorithm name\n- `LoadData()` accepts `resampleAlgo string`, uses `resampler.GetResampler()` instead of hardcoded LTTB\n- `LoadNodeListData()` accepts and passes `resampleAlgo` through\n\n**`internal/metricdispatch/metricdata.go`**: Updated `MetricDataRepository` interface\n\n**`pkg/metricstore/api.go`**: Added `ResampleAlgo` field to `APIQueryRequest`, passed to `MemoryStore.Read()`\n\n**`pkg/metricstore/metricstore.go`**: `Read()` accepts `resampleAlgo string`, uses `resampler.GetResampler()` instead of hardcoded LTTB\n\n**`pkg/metricstore/query.go`**: `LoadNodeListData()` threads `resampleAlgo` into `APIQueryRequest`\n\n**`internal/metricstoreclient/cc-metric-store.go`**: Updated signature for interface compliance\n\n**`internal/api/job.go`**, **`internal/api/api_test.go`**, **`internal/archiver/archiver.go`**, **`internal/graph/util.go`**: Updated existing callers to pass `\"\"` (default LTTB behavior)"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":486,"cache_read_input_tokens":102212,"output_tokens":793,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":486,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZD2Us12hdBjYawzKGzut","type":"assistant","uuid":"f0e2fd0d-926e-4317-bc61-4d93e89c8c61","timestamp":"2026-03-19T19:55:29.774Z","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
{"parentUuid":"f0e2fd0d-926e-4317-bc61-4d93e89c8c61","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"Stop","hookName":"Stop","command":"entire hooks claude-code stop"},"parentToolUseID":"d3617ba7-0f42-4fb6-af88-690ecb9f670f","toolUseID":"d3617ba7-0f42-4fb6-af88-690ecb9f670f","timestamp":"2026-03-19T19:55:29.802Z","uuid":"b01735fc-dd54-40cf-af04-21750dda6cda","userType":"external","entrypoint":"cli","cwd":"/Users/jan/tmp/cc-backend","sessionId":"260a4a9e-d060-4d86-982b-1bf5959b9b70","version":"2.1.78","gitBranch":"hotfix","slug":"curious-strolling-acorn"}
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