From 4f081ec9ce5b2945d9c74937b0b32b3cebe82f70 Mon Sep 17 00:00:00 2001 From: Jan Eitzinger Date: Mon, 16 Mar 2026 08:55:57 +0100 Subject: [PATCH] Checkpoint: afef27e07ec9 Entire-Session: 7e6b850e-d9e5-48ec-8e24-07c1b3960faa Entire-Strategy: manual-commit Entire-Agent: Claude Code Ephemeral-branch: entire/973ca87-e3b0c4 --- af/ef27e07ec9/0/content_hash.txt | 1 + af/ef27e07ec9/0/full.jsonl | 96 ++++++++++++++++++++++++++++++++ af/ef27e07ec9/0/metadata.json | 34 +++++++++++ af/ef27e07ec9/0/prompt.txt | 5 ++ af/ef27e07ec9/metadata.json | 26 +++++++++ 5 files changed, 162 insertions(+) create mode 100644 af/ef27e07ec9/0/content_hash.txt create mode 100644 af/ef27e07ec9/0/full.jsonl create mode 100644 af/ef27e07ec9/0/metadata.json create mode 100644 af/ef27e07ec9/0/prompt.txt create mode 100644 af/ef27e07ec9/metadata.json diff --git a/af/ef27e07ec9/0/content_hash.txt b/af/ef27e07ec9/0/content_hash.txt new file mode 100644 index 00000000..41b687cc --- /dev/null +++ b/af/ef27e07ec9/0/content_hash.txt @@ -0,0 +1 @@ +sha256:9f1faf03181ff89e0053bef8ee7ce4e3fb8ae5a97effdf5ede653edfd26dc4d2 \ No newline at end of file diff --git a/af/ef27e07ec9/0/full.jsonl b/af/ef27e07ec9/0/full.jsonl new file mode 100644 index 00000000..46ae90b0 --- /dev/null +++ b/af/ef27e07ec9/0/full.jsonl @@ -0,0 +1,96 @@ +{"type":"file-history-snapshot","messageId":"70123311-64f4-4233-82d7-196082d4535a","snapshot":{"messageId":"70123311-64f4-4233-82d7-196082d4535a","trackedFileBackups":{},"timestamp":"2026-03-16T07:45:38.749Z"},"isSnapshotUpdate":false} +{"parentUuid":null,"isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"SessionStart","hookName":"SessionStart:clear","command":"entire hooks claude-code session-start"},"parentToolUseID":"7300ffcf-f59d-4528-9225-4cc290aaf8d0","toolUseID":"7300ffcf-f59d-4528-9225-4cc290aaf8d0","timestamp":"2026-03-16T07:45:38.698Z","uuid":"f281ec6b-6994-4533-8dd7-e1f1e49cb6c4","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"f281ec6b-6994-4533-8dd7-e1f1e49cb6c4","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data\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-prg-ClusterCockpit-cc-backend/0075d51a-8bd8-430a-9f74-fa37f9252c52.jsonl"},"uuid":"70123311-64f4-4233-82d7-196082d4535a","timestamp":"2026-03-16T07:45:38.527Z","planContent":"# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data\n","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} 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295→\t\tUnit: jm.Unit,\n 296→\t\tTimestep: jm.Timestep,\n 297→\t\tSeries: make([]Series, 0, len(hosts)),\n 298→\t}\n 299→\tfor hostname, series := range hosts {\n 300→\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n 301→\t\tfor _, series := range series {\n 302→\t\t\tsum += series.Statistics.Avg\n 303→\t\t\tmin = math.Min(min, series.Statistics.Min)\n 304→\t\t\tmax = math.Max(max, series.Statistics.Max)\n 305→\t\t}\n 306→\n 307→\t\tn, m := 0, len(jm.Series[0].Data)\n 308→\t\tfor _, series := range jm.Series {\n 309→\t\t\tif len(series.Data) > n {\n 310→\t\t\t\tn = len(series.Data)\n 311→\t\t\t}\n 312→\t\t\tif len(series.Data) < m {\n 313→\t\t\t\tm = len(series.Data)\n 314→\t\t\t}\n 315→\t\t}\n 316→\n 317→\t\ti, data := 0, make([]Float, len(series[0].Data))\n 318→\t\tfor ; i < m; i++ {\n 319→\t\t\tx := Float(0.0)\n 320→\t\t\tfor _, series := range jm.Series {\n 321→\t\t\t\tx += series.Data[i]\n 322→\t\t\t}\n 323→\t\t\tdata[i] = x\n 324→\t\t}\n 325→\n 326→\t\tfor ; i < n; i++ {\n 327→\t\t\tdata[i] = NaN\n 328→\t\t}\n 329→\n 330→\t\tnodeJm.Series = append(nodeJm.Series, Series{\n 331→\t\t\tHostname: hostname,\n 332→\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n 333→\t\t\tData: data,\n 334→\t\t})\n 335→\t}\n 336→\n 337→\tscopes[MetricScopeNode] = nodeJm\n 338→\treturn true\n 339→}"}]},"uuid":"1afd1419-b90c-441c-9e4d-43c2f2e0f620","timestamp":"2026-03-16T07:45:58.640Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","content":"\tfor _, series := range jm.Series {\n\t\thosts[series.Hostname] = append(hosts[series.Hostname], series)\n\t}\n\n\tnodeJm := &JobMetric{\n\t\tUnit: jm.Unit,\n\t\tTimestep: jm.Timestep,\n\t\tSeries: make([]Series, 0, len(hosts)),\n\t}\n\tfor hostname, series := range hosts {\n\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n\t\tfor _, series := range series {\n\t\t\tsum += 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\tn, m := 0, len(jm.Series[0].Data)\n\t\tfor _, series := range jm.Series {\n\t\t\tif len(series.Data) > n {\n\t\t\t\tn = len(series.Data)\n\t\t\t}\n\t\t\tif len(series.Data) < m {\n\t\t\t\tm = len(series.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, len(series[0].Data))\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, series := range jm.Series {\n\t\t\t\tx += series.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}\n\n\t\tfor ; i < n; i++ {\n\t\t\tdata[i] = NaN\n\t\t}\n\n\t\tnodeJm.Series = append(nodeJm.Series, Series{\n\t\t\tHostname: hostname,\n\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n\t\t\tData: data,\n\t\t})\n\t}\n\n\tscopes[MetricScopeNode] = nodeJm\n\treturn true\n}","numLines":50,"startLine":290,"totalLines":425}},"sourceToolAssistantUUID":"653f9be7-69b9-4654-96f9-d28def7ea1de","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"1afd1419-b90c-441c-9e4d-43c2f2e0f620","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01GpiZz2GnfbrsD3BbdaVFZ5","type":"message","role":"assistant","content":[{"type":"text","text":"Now I can see the exact buggy code. Let me apply the fix."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":731,"cache_read_input_tokens":19857,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":731},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PSs7qUjdfsnigQUMyA","type":"assistant","uuid":"49bb930a-b197-425b-baff-afcdf7ff0fbb","timestamp":"2026-03-16T07:46:04.107Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"49bb930a-b197-425b-baff-afcdf7ff0fbb","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01GpiZz2GnfbrsD3BbdaVFZ5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NUvFnYGfRkhKFejkP5ve9X","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","old_string":"\t\tn, m := 0, len(jm.Series[0].Data)\n\t\tfor _, series := range jm.Series {\n\t\t\tif len(series.Data) > n {\n\t\t\t\tn = len(series.Data)\n\t\t\t}\n\t\t\tif len(series.Data) < m {\n\t\t\t\tm = len(series.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, len(series[0].Data))\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, series := range jm.Series {\n\t\t\t\tx += series.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}","new_string":"\t\tn, m := 0, len(series[0].Data)\n\t\tfor _, s := range series {\n\t\t\tif len(s.Data) > n {\n\t\t\t\tn = len(s.Data)\n\t\t\t}\n\t\t\tif len(s.Data) < m {\n\t\t\t\tm = len(s.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, n)\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, s := range series {\n\t\t\t\tx += s.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":731,"cache_read_input_tokens":19857,"output_tokens":437,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":731,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6PSs7qUjdfsnigQUMyA","type":"assistant","uuid":"be38eeaf-eac2-4c54-badc-ff31e775b096","timestamp":"2026-03-16T07:46:07.117Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"type":"file-history-snapshot","messageId":"be38eeaf-eac2-4c54-badc-ff31e775b096","snapshot":{"messageId":"70123311-64f4-4233-82d7-196082d4535a","trackedFileBackups":{"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go":{"backupFileName":"c727158a22a91f47@v1","version":1,"backupTime":"2026-03-16T07:46:11.834Z"}},"timestamp":"2026-03-16T07:45:38.749Z"},"isSnapshotUpdate":true} +{"parentUuid":"be38eeaf-eac2-4c54-badc-ff31e775b096","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01NUvFnYGfRkhKFejkP5ve9X","toolUseID":"toolu_01NUvFnYGfRkhKFejkP5ve9X","timestamp":"2026-03-16T07:46:11.837Z","uuid":"29376a79-85f4-4bf0-a7c3-0f4859b2e2b9","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"be38eeaf-eac2-4c54-badc-ff31e775b096","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NUvFnYGfRkhKFejkP5ve9X","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go has been updated successfully."}]},"uuid":"aec1a4cb-bb21-40e2-8688-ea6135ccd4f3","timestamp":"2026-03-16T07:46:11.837Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","oldString":"\t\tn, m := 0, len(jm.Series[0].Data)\n\t\tfor _, series := range jm.Series {\n\t\t\tif len(series.Data) > n {\n\t\t\t\tn = len(series.Data)\n\t\t\t}\n\t\t\tif len(series.Data) < m {\n\t\t\t\tm = len(series.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, len(series[0].Data))\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, series := range jm.Series {\n\t\t\t\tx += series.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}","newString":"\t\tn, m := 0, len(series[0].Data)\n\t\tfor _, s := range series {\n\t\t\tif len(s.Data) > n {\n\t\t\t\tn = len(s.Data)\n\t\t\t}\n\t\t\tif len(s.Data) < m {\n\t\t\t\tm = len(s.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, n)\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, s := range series {\n\t\t\t\tx += s.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}","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\npackage schema\n\nimport (\n\t\"fmt\"\n\t\"io\"\n\t\"math\"\n\t\"sort\"\n\t\"unsafe\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// JobData maps metric names to their data organized by scope.\n// Structure: map[metricName]map[scope]*JobMetric\n//\n// For example: jobData[\"cpu_load\"][MetricScopeNode] contains node-level CPU load data.\n// This structure allows efficient lookup of metrics at different hierarchical levels.\ntype JobData map[string]map[MetricScope]*JobMetric\n\n// ScopedJobStats maps metric names to statistical summaries organized by scope.\n// Structure: map[metricName]map[scope][]*ScopedStats\n//\n// Used to store pre-computed statistics without the full time series data,\n// reducing memory footprint when only aggregated values are needed.\ntype ScopedJobStats map[string]map[MetricScope][]*ScopedStats\n\n// JobMetric contains time series data and statistics for a single metric.\n//\n// The Series field holds time series data from individual nodes/hardware components,\n// while StatisticsSeries provides aggregated statistics across all series over time.\ntype JobMetric struct {\n\tStatisticsSeries *StatsSeries `json:\"statisticsSeries,omitempty\"` // Aggregated statistics over time\n\tUnit Unit `json:\"unit\"` // Unit of measurement\n\tSeries []Series `json:\"series\"` // Individual time series data\n\tTimestep int `json:\"timestep\"` // Sampling interval in seconds\n}\n\n// Series represents a single time series of metric measurements.\n//\n// Each series corresponds to one source (e.g., one node, one core) identified by Hostname and optional ID.\n// The Data field contains the time-ordered measurements, and Statistics provides min/avg/max summaries.\ntype Series struct {\n\tID *string `json:\"id,omitempty\"` // Optional ID (e.g., core ID, GPU ID)\n\tHostname string `json:\"hostname\"` // Source hostname\n\tData []Float `json:\"data\"` // Time series measurements\n\tStatistics MetricStatistics `json:\"statistics\"` // Statistical summary (min/avg/max)\n}\n\n// ScopedStats contains statistical summaries for a specific scope (e.g., one node, one socket).\n// Used when full time series data isn't needed, only the aggregated statistics.\ntype ScopedStats struct {\n\tHostname string `json:\"hostname\"` // Source hostname\n\tID *string `json:\"id,omitempty\"` // Optional scope ID\n\tData *MetricStatistics `json:\"data\"` // Statistical summary\n}\n\n// MetricStatistics holds statistical summary values for metric data.\n// Provides the common statistical aggregations used throughout ClusterCockpit.\ntype MetricStatistics struct {\n\tAvg float64 `json:\"avg\"` // Average/mean value\n\tMin float64 `json:\"min\"` // Minimum value\n\tMax float64 `json:\"max\"` // Maximum value\n}\n\n// StatsSeries contains aggregated statistics across multiple time series over time.\n//\n// Instead of storing individual series, this provides statistical summaries at each time step.\n// For example, at time t, Mean[t] is the average value across all series at that time.\n// Percentiles provides specified percentile values at each time step.\ntype StatsSeries struct {\n\tPercentiles map[int][]Float `json:\"percentiles,omitempty\"` // Percentile values over time (e.g., 10th, 50th, 90th)\n\tMean []Float `json:\"mean\"` // Mean values over time\n\tMedian []Float `json:\"median\"` // Median values over time\n\tMin []Float `json:\"min\"` // Minimum values over time\n\tMax []Float `json:\"max\"` // Maximum values over time\n}\n\n// MetricScope defines the hierarchical level at which a metric is measured.\n//\n// Scopes form a hierarchy from coarse-grained (node) to fine-grained (hwthread/accelerator):\n//\n//\tnode > socket > memoryDomain > core > hwthread\n//\taccelerator is a special scope at the same level as hwthread\n//\n// The scopePrecedence map defines numeric ordering for scope comparisons,\n// which is used when aggregating metrics across different scopes.\ntype MetricScope string\n\nconst (\n\tMetricScopeInvalid MetricScope = \"invalid_scope\"\n\n\tMetricScopeNode MetricScope = \"node\"\n\tMetricScopeSocket MetricScope = \"socket\"\n\tMetricScopeMemoryDomain MetricScope = \"memoryDomain\"\n\tMetricScopeCore MetricScope = \"core\"\n\tMetricScopeHWThread MetricScope = \"hwthread\"\n\n\t// TODO: Add Job and Application scopes\n\n\tMetricScopeAccelerator MetricScope = \"accelerator\"\n)\n\nvar metricScopeGranularity map[MetricScope]int = map[MetricScope]int{\n\tMetricScopeNode: 10,\n\tMetricScopeSocket: 5,\n\tMetricScopeMemoryDomain: 4,\n\tMetricScopeCore: 3,\n\tMetricScopeHWThread: 2,\n\t/* Special-Case Accelerator\n\t * -> No conversion possible if native scope is HWTHREAD\n\t * -> Therefore needs to be less than HWTREAD, else max() would return unhandled case\n\t * -> If nativeScope is accelerator, accelerator metrics return correctly\n\t */\n\tMetricScopeAccelerator: 1,\n\n\tMetricScopeInvalid: -1,\n}\n\nfunc (e *MetricScope) LT(other MetricScope) bool {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\treturn a < b\n}\n\nfunc (e *MetricScope) LTE(other MetricScope) bool {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\treturn a <= b\n}\n\nfunc (e *MetricScope) Max(other MetricScope) MetricScope {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\tif a > b {\n\t\treturn *e\n\t}\n\treturn other\n}\n\nfunc (e *MetricScope) UnmarshalGQL(v any) error {\n\tstr, ok := v.(string)\n\tif !ok {\n\t\treturn fmt.Errorf(\"SCHEMA/METRICS > enums must be strings\")\n\t}\n\n\t*e = MetricScope(str)\n\tif !e.Valid() {\n\t\treturn fmt.Errorf(\"SCHEMA/METRICS > %s is not a valid MetricScope\", str)\n\t}\n\treturn nil\n}\n\nfunc (e MetricScope) MarshalGQL(w io.Writer) {\n\tfmt.Fprintf(w, \"\\\"%s\\\"\", e)\n}\n\nfunc (e MetricScope) Valid() bool {\n\tgran, ok := metricScopeGranularity[e]\n\treturn ok && gran > 0\n}\n\nfunc (jd *JobData) Size() int {\n\tn := 128\n\tfor _, scopes := range *jd {\n\t\tfor _, metric := range scopes {\n\t\t\tif metric.StatisticsSeries != nil {\n\t\t\t\tn += len(metric.StatisticsSeries.Max)\n\t\t\t\tn += len(metric.StatisticsSeries.Mean)\n\t\t\t\tn += len(metric.StatisticsSeries.Median)\n\t\t\t\tn += len(metric.StatisticsSeries.Min)\n\t\t\t}\n\n\t\t\tfor _, series := range metric.Series {\n\t\t\t\tn += len(series.Data)\n\t\t\t}\n\t\t}\n\t}\n\treturn n * int(unsafe.Sizeof(Float(0)))\n}\n\nconst smooth bool = false\n\nfunc (jm *JobMetric) AddStatisticsSeries() {\n\tif jm.StatisticsSeries != nil || len(jm.Series) < 4 {\n\t\treturn\n\t}\n\n\tn, m := 0, len(jm.Series[0].Data)\n\tfor _, series := range jm.Series {\n\t\tif len(series.Data) > n {\n\t\t\tn = len(series.Data)\n\t\t}\n\t\tif len(series.Data) < m {\n\t\t\tm = len(series.Data)\n\t\t}\n\t}\n\n\t// mean := make([]Float, n)\n\tmin, median, max := make([]Float, n), make([]Float, n), make([]Float, n)\n\ti := 0\n\tfor ; i < m; i++ {\n\t\tseriesCount := len(jm.Series)\n\t\t// ssum := 0.0\n\t\tsmin, smed, smax := math.MaxFloat32, make([]float64, seriesCount), -math.MaxFloat32\n\t\tnotnan := 0\n\t\tfor j := range seriesCount {\n\t\t\tx := float64(jm.Series[j].Data[i])\n\t\t\tif math.IsNaN(x) {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tnotnan += 1\n\t\t\t// ssum += x\n\t\t\tsmed[j] = x\n\t\t\tsmin = math.Min(smin, x)\n\t\t\tsmax = math.Max(smax, x)\n\t\t}\n\n\t\tif notnan < 3 {\n\t\t\tmin[i] = NaN\n\t\t\t// mean[i] = NaN\n\t\t\tmedian[i] = NaN\n\t\t\tmax[i] = NaN\n\t\t} else {\n\t\t\tmin[i] = Float(smin)\n\t\t\t// mean[i] = Float(ssum / float64(notnan))\n\t\t\tmax[i] = Float(smax)\n\n\t\t\tmedianRaw, err := util.Median(smed)\n\t\t\tif err != nil {\n\t\t\t\tmedian[i] = NaN\n\t\t\t} else {\n\t\t\t\tmedian[i] = Float(medianRaw)\n\t\t\t}\n\t\t}\n\t}\n\n\tfor ; i < n; i++ {\n\t\tmin[i] = NaN\n\t\t// mean[i] = NaN\n\t\tmedian[i] = NaN\n\t\tmax[i] = NaN\n\t}\n\n\tif smooth {\n\t\tfor i := 2; i < len(median)-2; i++ {\n\t\t\tif min[i].IsNaN() {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tmin[i] = (min[i-2] + min[i-1] + min[i] + min[i+1] + min[i+2]) / 5\n\t\t\tmax[i] = (max[i-2] + max[i-1] + max[i] + max[i+1] + max[i+2]) / 5\n\t\t\t// mean[i] = (mean[i-2] + mean[i-1] + mean[i] + mean[i+1] + mean[i+2]) / 5\n\t\t\t// Reduce Median further\n\t\t\tsmoothRaw := []float64{float64(median[i-2]), float64(median[i-1]), float64(median[i]), float64(median[i+1]), float64(median[i+2])}\n\t\t\tsmoothMedian, err := util.Median(smoothRaw)\n\t\t\tif err != nil {\n\t\t\t\tmedian[i] = NaN\n\t\t\t} else {\n\t\t\t\tmedian[i] = Float(smoothMedian)\n\t\t\t}\n\t\t}\n\t}\n\n\tjm.StatisticsSeries = &StatsSeries{Median: median, Min: min, Max: max} // Mean: mean\n}\n\nfunc (jd *JobData) AddNodeScope(metric string) bool {\n\tscopes, ok := (*jd)[metric]\n\tif !ok {\n\t\treturn false\n\t}\n\n\tmaxScope := MetricScopeInvalid\n\tfor scope := range scopes {\n\t\tmaxScope = maxScope.Max(scope)\n\t}\n\n\tif maxScope == MetricScopeInvalid || maxScope == MetricScopeNode {\n\t\treturn false\n\t}\n\n\tjm := scopes[maxScope]\n\thosts := make(map[string][]Series, 32)\n\tfor _, series := range jm.Series {\n\t\thosts[series.Hostname] = append(hosts[series.Hostname], series)\n\t}\n\n\tnodeJm := &JobMetric{\n\t\tUnit: jm.Unit,\n\t\tTimestep: jm.Timestep,\n\t\tSeries: make([]Series, 0, len(hosts)),\n\t}\n\tfor hostname, series := range hosts {\n\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n\t\tfor _, series := range series {\n\t\t\tsum += 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\tn, m := 0, len(jm.Series[0].Data)\n\t\tfor _, series := range jm.Series {\n\t\t\tif len(series.Data) > n {\n\t\t\t\tn = len(series.Data)\n\t\t\t}\n\t\t\tif len(series.Data) < m {\n\t\t\t\tm = len(series.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, len(series[0].Data))\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, series := range jm.Series {\n\t\t\t\tx += series.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}\n\n\t\tfor ; i < n; i++ {\n\t\t\tdata[i] = NaN\n\t\t}\n\n\t\tnodeJm.Series = append(nodeJm.Series, Series{\n\t\t\tHostname: hostname,\n\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n\t\t\tData: data,\n\t\t})\n\t}\n\n\tscopes[MetricScopeNode] = nodeJm\n\treturn true\n}\n\nfunc (jd *JobData) RoundMetricStats() {\n\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n\tfor _, scopes := range *jd {\n\t\tfor _, jm := range scopes {\n\t\t\tfor index := range jm.Series {\n\t\t\t\tjm.Series[index].Statistics = MetricStatistics{\n\t\t\t\t\tAvg: (math.Round(jm.Series[index].Statistics.Avg*100) / 100),\n\t\t\t\t\tMin: (math.Round(jm.Series[index].Statistics.Min*100) / 100),\n\t\t\t\t\tMax: (math.Round(jm.Series[index].Statistics.Max*100) / 100),\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunc (sjs *ScopedJobStats) RoundScopedMetricStats() {\n\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n\tfor _, scopes := range *sjs {\n\t\tfor _, stats := range scopes {\n\t\t\tfor index := range stats {\n\t\t\t\troundedStats := MetricStatistics{\n\t\t\t\t\tAvg: (math.Round(stats[index].Data.Avg*100) / 100),\n\t\t\t\t\tMin: (math.Round(stats[index].Data.Min*100) / 100),\n\t\t\t\t\tMax: (math.Round(stats[index].Data.Max*100) / 100),\n\t\t\t\t}\n\t\t\t\tstats[index].Data = &roundedStats\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunc (jm *JobMetric) AddPercentiles(ps []int) bool {\n\tif jm.StatisticsSeries == nil {\n\t\tjm.AddStatisticsSeries()\n\t}\n\n\tif len(jm.Series) < 3 {\n\t\treturn false\n\t}\n\n\tif jm.StatisticsSeries.Percentiles == nil {\n\t\tjm.StatisticsSeries.Percentiles = make(map[int][]Float, len(ps))\n\t}\n\n\tn := 0\n\tfor _, series := range jm.Series {\n\t\tif len(series.Data) > n {\n\t\t\tn = len(series.Data)\n\t\t}\n\t}\n\n\tdata := make([][]float64, n)\n\tfor i := range n {\n\t\tvals := make([]float64, 0, len(jm.Series))\n\t\tfor _, series := range jm.Series {\n\t\t\tif i < len(series.Data) {\n\t\t\t\tvals = append(vals, float64(series.Data[i]))\n\t\t\t}\n\t\t}\n\n\t\tsort.Float64s(vals)\n\t\tdata[i] = vals\n\t}\n\n\tfor _, p := range ps {\n\t\tif p < 1 || p > 99 {\n\t\t\tpanic(\"SCHEMA/METRICS > invalid percentile\")\n\t\t}\n\n\t\tif _, ok := jm.StatisticsSeries.Percentiles[p]; ok {\n\t\t\tcontinue\n\t\t}\n\n\t\tpercentiles := make([]Float, n)\n\t\tfor i := range n {\n\t\t\tsorted := data[i]\n\t\t\tpercentiles[i] = Float(sorted[(len(sorted)*p)/100])\n\t\t}\n\n\t\tjm.StatisticsSeries.Percentiles[p] = percentiles\n\t}\n\n\treturn true\n}\n","structuredPatch":[{"oldStart":304,"oldLines":21,"newStart":304,"newLines":21,"lines":[" max = math.Max(max, series.Statistics.Max)"," }"," ","- n, m := 0, len(jm.Series[0].Data)","- for _, series := range jm.Series {","- if len(series.Data) > n {","- n = len(series.Data)","+ n, m := 0, len(series[0].Data)","+ for _, s := range series {","+ if len(s.Data) > n {","+ n = len(s.Data)"," }","- if len(series.Data) < m {","- m = len(series.Data)","+ if len(s.Data) < m {","+ m = len(s.Data)"," }"," }"," ","- i, data := 0, make([]Float, len(series[0].Data))","+ i, data := 0, make([]Float, n)"," for ; i < m; i++ {"," x := Float(0.0)","- for _, series := range jm.Series {","- x += series.Data[i]","+ for _, s := range series {","+ x += s.Data[i]"," }"," data[i] = x"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"be38eeaf-eac2-4c54-badc-ff31e775b096","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"aec1a4cb-bb21-40e2-8688-ea6135ccd4f3","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Sju9oLnwgsYi1BGhTc4rsP","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me verify the fix looks correct and check if there are existing tests."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":497,"cache_read_input_tokens":20588,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":497},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PTqYMsHSp63ziUPA6a","type":"assistant","uuid":"d58c8ef6-45f6-4b2a-ad0d-8f2b003e515c","timestamp":"2026-03-16T07:46:16.294Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"d58c8ef6-45f6-4b2a-ad0d-8f2b003e515c","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Sju9oLnwgsYi1BGhTc4rsP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QtJ3Ce4NygxVt79aP98Pj4","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","offset":306,"limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":497,"cache_read_input_tokens":20588,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":497},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PTqYMsHSp63ziUPA6a","type":"assistant","uuid":"76b3228d-095c-4d67-9dbb-52803152778c","timestamp":"2026-03-16T07:46:16.710Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"76b3228d-095c-4d67-9dbb-52803152778c","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01QtJ3Ce4NygxVt79aP98Pj4","toolUseID":"toolu_01QtJ3Ce4NygxVt79aP98Pj4","timestamp":"2026-03-16T07:46:16.740Z","uuid":"e6f5405b-242d-42f5-8fc0-1946449fbe22","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"76b3228d-095c-4d67-9dbb-52803152778c","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QtJ3Ce4NygxVt79aP98Pj4","type":"tool_result","content":" 306→\n 307→\t\tn, m := 0, len(series[0].Data)\n 308→\t\tfor _, s := range series {\n 309→\t\t\tif len(s.Data) > n {\n 310→\t\t\t\tn = len(s.Data)\n 311→\t\t\t}\n 312→\t\t\tif len(s.Data) < m {\n 313→\t\t\t\tm = len(s.Data)\n 314→\t\t\t}\n 315→\t\t}\n 316→\n 317→\t\ti, data := 0, make([]Float, n)\n 318→\t\tfor ; i < m; i++ {\n 319→\t\t\tx := Float(0.0)\n 320→\t\t\tfor _, s := range series {\n 321→\t\t\t\tx += s.Data[i]\n 322→\t\t\t}\n 323→\t\t\tdata[i] = x\n 324→\t\t}\n 325→\n 326→\t\tfor ; i < n; i++ {\n 327→\t\t\tdata[i] = NaN\n 328→\t\t}\n 329→\n 330→\t\tnodeJm.Series = append(nodeJm.Series, Series{\n 331→\t\t\tHostname: hostname,\n 332→\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n 333→\t\t\tData: data,\n 334→\t\t})\n 335→\t}"}]},"uuid":"9c608de6-632a-4609-9bc2-6253abda8c1f","timestamp":"2026-03-16T07:46:16.739Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","content":"\n\t\tn, m := 0, len(series[0].Data)\n\t\tfor _, s := range series {\n\t\t\tif len(s.Data) > n {\n\t\t\t\tn = len(s.Data)\n\t\t\t}\n\t\t\tif len(s.Data) < m {\n\t\t\t\tm = len(s.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, n)\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, s := range series {\n\t\t\t\tx += s.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}\n\n\t\tfor ; i < n; i++ {\n\t\t\tdata[i] = NaN\n\t\t}\n\n\t\tnodeJm.Series = append(nodeJm.Series, Series{\n\t\t\tHostname: hostname,\n\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n\t\t\tData: data,\n\t\t})\n\t}","numLines":30,"startLine":306,"totalLines":425}},"sourceToolAssistantUUID":"76b3228d-095c-4d67-9dbb-52803152778c","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"9c608de6-632a-4609-9bc2-6253abda8c1f","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Sju9oLnwgsYi1BGhTc4rsP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AkcqtMqgR31wtnETAcDK7U","name":"Glob","input":{"pattern":"**/cc-lib/schema/*_test.go","path":"/Users/jan/prg/ClusterCockpit"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":497,"cache_read_input_tokens":20588,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":497},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PTqYMsHSp63ziUPA6a","type":"assistant","uuid":"5152db45-e24e-408d-9424-446e40603962","timestamp":"2026-03-16T07:46:17.212Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"5152db45-e24e-408d-9424-446e40603962","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Glob","command":"callback"},"parentToolUseID":"toolu_01AkcqtMqgR31wtnETAcDK7U","toolUseID":"toolu_01AkcqtMqgR31wtnETAcDK7U","timestamp":"2026-03-16T07:46:33.234Z","uuid":"5ab83f34-d10a-46ab-8eda-83635d29fed6","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"5152db45-e24e-408d-9424-446e40603962","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AkcqtMqgR31wtnETAcDK7U","type":"tool_result","content":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/user_test.go\n/Users/jan/prg/ClusterCockpit/cc-lib/schema/validate_test.go\n/Users/jan/prg/ClusterCockpit/cc-lib/schema/float_test.go"}]},"uuid":"71e04280-0681-4079-9796-877449b89f3f","timestamp":"2026-03-16T07:46:33.234Z","toolUseResult":{"filenames":["/Users/jan/prg/ClusterCockpit/cc-lib/schema/user_test.go","/Users/jan/prg/ClusterCockpit/cc-lib/schema/validate_test.go","/Users/jan/prg/ClusterCockpit/cc-lib/schema/float_test.go"],"durationMs":319,"numFiles":3,"truncated":false},"sourceToolAssistantUUID":"5152db45-e24e-408d-9424-446e40603962","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"71e04280-0681-4079-9796-877449b89f3f","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01YR3kMMGwcrfLJEEXRUf8uw","type":"message","role":"assistant","content":[{"type":"text","text":"The diagnostics are just the LSP having trouble with multi-file types — those are defined elsewhere in the package. The fix looks correct. Now let me write a unit test.\n\nLet me check the existing types to write a proper test."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":965,"cache_read_input_tokens":21085,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":965},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PVR1YP5ubVHoiAgXfv","type":"assistant","uuid":"777309a1-1539-4cad-b482-ce3ee38e0994","timestamp":"2026-03-16T07:46:37.910Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"777309a1-1539-4cad-b482-ce3ee38e0994","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01YR3kMMGwcrfLJEEXRUf8uw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015Gj9GgBiw1KRFnrzk7dmEA","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","offset":1,"limit":60},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":965,"cache_read_input_tokens":21085,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":965},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PVR1YP5ubVHoiAgXfv","type":"assistant","uuid":"9d00f0b1-193b-410f-91a2-879caffb1986","timestamp":"2026-03-16T07:46:38.383Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"9d00f0b1-193b-410f-91a2-879caffb1986","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_015Gj9GgBiw1KRFnrzk7dmEA","toolUseID":"toolu_015Gj9GgBiw1KRFnrzk7dmEA","timestamp":"2026-03-16T07:46:38.391Z","uuid":"70d03270-eb97-449a-bd7c-f50d648e9a9e","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"9d00f0b1-193b-410f-91a2-879caffb1986","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015Gj9GgBiw1KRFnrzk7dmEA","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 schema\n 7→\n 8→import (\n 9→\t\"fmt\"\n 10→\t\"io\"\n 11→\t\"math\"\n 12→\t\"sort\"\n 13→\t\"unsafe\"\n 14→\n 15→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n 16→)\n 17→\n 18→// JobData maps metric names to their data organized by scope.\n 19→// Structure: map[metricName]map[scope]*JobMetric\n 20→//\n 21→// For example: jobData[\"cpu_load\"][MetricScopeNode] contains node-level CPU load data.\n 22→// This structure allows efficient lookup of metrics at different hierarchical levels.\n 23→type JobData map[string]map[MetricScope]*JobMetric\n 24→\n 25→// ScopedJobStats maps metric names to statistical summaries organized by scope.\n 26→// Structure: map[metricName]map[scope][]*ScopedStats\n 27→//\n 28→// Used to store pre-computed statistics without the full time series data,\n 29→// reducing memory footprint when only aggregated values are needed.\n 30→type ScopedJobStats map[string]map[MetricScope][]*ScopedStats\n 31→\n 32→// JobMetric contains time series data and statistics for a single metric.\n 33→//\n 34→// The Series field holds time series data from individual nodes/hardware components,\n 35→// while StatisticsSeries provides aggregated statistics across all series over time.\n 36→type JobMetric struct {\n 37→\tStatisticsSeries *StatsSeries `json:\"statisticsSeries,omitempty\"` // Aggregated statistics over time\n 38→\tUnit Unit `json:\"unit\"` // Unit of measurement\n 39→\tSeries []Series `json:\"series\"` // Individual time series data\n 40→\tTimestep int `json:\"timestep\"` // Sampling interval in seconds\n 41→}\n 42→\n 43→// Series represents a single time series of metric measurements.\n 44→//\n 45→// Each series corresponds to one source (e.g., one node, one core) identified by Hostname and optional ID.\n 46→// The Data field contains the time-ordered measurements, and Statistics provides min/avg/max summaries.\n 47→type Series struct {\n 48→\tID *string `json:\"id,omitempty\"` // Optional ID (e.g., core ID, GPU ID)\n 49→\tHostname string `json:\"hostname\"` // Source hostname\n 50→\tData []Float `json:\"data\"` // Time series measurements\n 51→\tStatistics MetricStatistics `json:\"statistics\"` // Statistical summary (min/avg/max)\n 52→}\n 53→\n 54→// ScopedStats contains statistical summaries for a specific scope (e.g., one node, one socket).\n 55→// Used when full time series data isn't needed, only the aggregated statistics.\n 56→type ScopedStats struct {\n 57→\tHostname string `json:\"hostname\"` // Source hostname\n 58→\tID *string `json:\"id,omitempty\"` // Optional scope ID\n 59→\tData *MetricStatistics `json:\"data\"` // Statistical summary\n 60→}"}]},"uuid":"b3aaba50-a0ce-4da3-aeeb-45487a367040","timestamp":"2026-03-16T07:46:38.391Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.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\npackage schema\n\nimport (\n\t\"fmt\"\n\t\"io\"\n\t\"math\"\n\t\"sort\"\n\t\"unsafe\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// JobData maps metric names to their data organized by scope.\n// Structure: map[metricName]map[scope]*JobMetric\n//\n// For example: jobData[\"cpu_load\"][MetricScopeNode] contains node-level CPU load data.\n// This structure allows efficient lookup of metrics at different hierarchical levels.\ntype JobData map[string]map[MetricScope]*JobMetric\n\n// ScopedJobStats maps metric names to statistical summaries organized by scope.\n// Structure: map[metricName]map[scope][]*ScopedStats\n//\n// Used to store pre-computed statistics without the full time series data,\n// reducing memory footprint when only aggregated values are needed.\ntype ScopedJobStats map[string]map[MetricScope][]*ScopedStats\n\n// JobMetric contains time series data and statistics for a single metric.\n//\n// The Series field holds time series data from individual nodes/hardware components,\n// while StatisticsSeries provides aggregated statistics across all series over time.\ntype JobMetric struct {\n\tStatisticsSeries *StatsSeries `json:\"statisticsSeries,omitempty\"` // Aggregated statistics over time\n\tUnit Unit `json:\"unit\"` // Unit of measurement\n\tSeries []Series `json:\"series\"` // Individual time series data\n\tTimestep int `json:\"timestep\"` // Sampling interval in seconds\n}\n\n// Series represents a single time series of metric measurements.\n//\n// Each series corresponds to one source (e.g., one node, one core) identified by Hostname and optional ID.\n// The Data field contains the time-ordered measurements, and Statistics provides min/avg/max summaries.\ntype Series struct {\n\tID *string `json:\"id,omitempty\"` // Optional ID (e.g., core ID, GPU ID)\n\tHostname string `json:\"hostname\"` // Source hostname\n\tData []Float `json:\"data\"` // Time series measurements\n\tStatistics MetricStatistics `json:\"statistics\"` // Statistical summary (min/avg/max)\n}\n\n// ScopedStats contains statistical summaries for a specific scope (e.g., one node, one socket).\n// Used when full time series data isn't needed, only the aggregated statistics.\ntype ScopedStats struct {\n\tHostname string `json:\"hostname\"` // Source hostname\n\tID *string `json:\"id,omitempty\"` // Optional scope ID\n\tData *MetricStatistics `json:\"data\"` // Statistical summary\n}","numLines":60,"startLine":1,"totalLines":425}},"sourceToolAssistantUUID":"9d00f0b1-193b-410f-91a2-879caffb1986","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} 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`json:\"max\"` // Maximum value\n 68→}\n 69→\n 70→// StatsSeries contains aggregated statistics across multiple time series over time.\n 71→//\n 72→// Instead of storing individual series, this provides statistical summaries at each time step.\n 73→// For example, at time t, Mean[t] is the average value across all series at that time."}]},"uuid":"ee134bd3-21a4-4174-bd9b-4040630c1f91","timestamp":"2026-03-16T07:46:49.614Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go","content":"type MetricStatistics struct {\n\tAvg float64 `json:\"avg\"` // Average/mean value\n\tMin float64 `json:\"min\"` // Minimum value\n\tMax float64 `json:\"max\"` // Maximum value\n}\n\n// StatsSeries contains aggregated statistics across multiple time series over time.\n//\n// Instead of storing individual series, this provides statistical summaries at each time step.\n// For example, at time t, Mean[t] is the average value across all series at that 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but in metric data it's common to have missing\n20:// or invalid measurements that should be represented as null in JSON. This type allows NaN values\n25:// - NaN values marshal to JSON null\n26:// - JSON null unmarshals to NaN\n39:\tNaN Float = Float(math.NaN())\n43:func (f Float) IsNaN() bool {\n44:\treturn math.IsNaN(float64(f))\n51:// ConvertToFloat converts a regular float64 to a Float, treating -1.0 as a sentinel for NaN.\n55:\t\treturn NaN\n62:// NaN values are serialized as JSON null. Regular float values are\n65:\tif f.IsNaN() {\n73:// JSON null is deserialized as NaN. Numeric strings are parsed as float64\n78:\t\t*f = NaN\n103:// NaN will be serialized to `null`.\n105:\tif f.IsNaN() {\n114:// encoding/json. NaN data points are serialized as null. Only used via\n139:\t\tif s.Data[i].IsNaN() {\n150:// NaN values in the Float slice will remain as NaN in the float64 slice.\n174:// It efficiently marshals a slice of Float values, converting NaN to null.\n183:\t\tif fa[i].IsNaN() {"}]},"uuid":"24cb098e-731d-40d7-929d-3e4592ee1454","timestamp":"2026-03-16T07:46:50.252Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"17:// Float is a custom float64 type with special handling for NaN values in JSON and GraphQL serialization.\n19:// Standard Go encoding/json treats NaN as an error, but in metric data it's common to have missing\n20:// or invalid measurements that should be represented as null in JSON. This type allows NaN values\n25:// - NaN values marshal to JSON null\n26:// - JSON null unmarshals to NaN\n39:\tNaN Float = Float(math.NaN())\n43:func (f Float) IsNaN() bool {\n44:\treturn math.IsNaN(float64(f))\n51:// ConvertToFloat converts a regular float64 to a Float, treating -1.0 as a sentinel for NaN.\n55:\t\treturn NaN\n62:// NaN values are serialized as JSON null. Regular float values are\n65:\tif f.IsNaN() {\n73:// JSON null is deserialized as NaN. Numeric strings are parsed as float64\n78:\t\t*f = NaN\n103:// NaN will be serialized to `null`.\n105:\tif f.IsNaN() {\n114:// encoding/json. NaN data points are serialized as null. Only used via\n139:\t\tif s.Data[i].IsNaN() {\n150:// NaN values in the Float slice will remain as NaN in the float64 slice.\n174:// It efficiently marshals a slice of Float values, converting NaN to null.\n183:\t\tif fa[i].IsNaN() {","numLines":21},"sourceToolAssistantUUID":"65428607-1874-44b0-8816-679bf74ce4cb","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"24cb098e-731d-40d7-929d-3e4592ee1454","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_017UaxmJuc1zrMovoyBTPv75","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have all the info needed for the test. Let me write it."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":776,"cache_read_input_tokens":24457,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":776},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PWfmaVbBAkJaJfwn22","type":"assistant","uuid":"2adba59c-d830-4920-b49f-a6465ff9da0b","timestamp":"2026-03-16T07:46:53.471Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"type":"file-history-snapshot","messageId":"2486f437-cbbf-46b6-84cb-ba1c60e206ed","snapshot":{"messageId":"70123311-64f4-4233-82d7-196082d4535a","trackedFileBackups":{"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go":{"backupFileName":"c727158a22a91f47@v1","version":1,"backupTime":"2026-03-16T07:46:11.834Z"},"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics_test.go":{"backupFileName":null,"version":1,"backupTime":"2026-03-16T07:47:10.318Z"}},"timestamp":"2026-03-16T07:45:38.749Z"},"isSnapshotUpdate":true} +{"parentUuid":"2adba59c-d830-4920-b49f-a6465ff9da0b","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_017UaxmJuc1zrMovoyBTPv75","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011TuQwzpxYhyjw45S3wkG6L","name":"Write","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics_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.\n\npackage schema\n\nimport (\n\t\"testing\"\n)\n\nfunc TestAddNodeScope(t *testing.T) {\n\t// Two hosts with core-level data of different lengths.\n\t// host1 has 2 cores with 3 data points each.\n\t// host2 has 2 cores with 5 data points each.\n\tjd := JobData{\n\t\t\"flops_any\": {\n\t\t\tMetricScopeCore: &JobMetric{\n\t\t\t\tUnit: Unit{Base: \"F/s\"},\n\t\t\t\tTimestep: 10,\n\t\t\t\tSeries: []Series{\n\t\t\t\t\t{Hostname: \"host1\", Data: []Float{1, 2, 3}, Statistics: MetricStatistics{Min: 1, Avg: 2, Max: 3}},\n\t\t\t\t\t{Hostname: \"host1\", Data: []Float{4, 5, 6}, Statistics: MetricStatistics{Min: 4, Avg: 5, Max: 6}},\n\t\t\t\t\t{Hostname: \"host2\", Data: []Float{10, 20, 30, 40, 50}, Statistics: MetricStatistics{Min: 10, Avg: 30, Max: 50}},\n\t\t\t\t\t{Hostname: \"host2\", Data: []Float{100, 200, 300, 400, 500}, Statistics: MetricStatistics{Min: 100, Avg: 300, Max: 500}},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tok := jd.AddNodeScope(\"flops_any\")\n\tif !ok {\n\t\tt.Fatal(\"AddNodeScope returned false\")\n\t}\n\n\tnodeMetric, exists := jd[\"flops_any\"][MetricScopeNode]\n\tif !exists {\n\t\tt.Fatal(\"node scope not created\")\n\t}\n\n\tif len(nodeMetric.Series) != 2 {\n\t\tt.Fatalf(\"expected 2 node series, got %d\", len(nodeMetric.Series))\n\t}\n\n\t// Build a map for deterministic checking (range over map is random order).\n\tbyHost := make(map[string]Series)\n\tfor _, s := range nodeMetric.Series {\n\t\tbyHost[s.Hostname] = s\n\t}\n\n\t// host1: sum of cores = [1+4, 2+5, 3+6] = [5, 7, 9], length 3\n\th1 := byHost[\"host1\"]\n\tif len(h1.Data) != 3 {\n\t\tt.Fatalf(\"host1: expected 3 data points, got %d\", len(h1.Data))\n\t}\n\texpectH1 := []Float{5, 7, 9}\n\tfor i, v := range expectH1 {\n\t\tif h1.Data[i] != v {\n\t\t\tt.Errorf(\"host1 data[%d]: expected %v, got %v\", i, v, h1.Data[i])\n\t\t}\n\t}\n\n\t// host2: sum of cores = [110, 220, 330, 440, 550], length 5\n\th2 := byHost[\"host2\"]\n\tif len(h2.Data) != 5 {\n\t\tt.Fatalf(\"host2: expected 5 data points, got %d\", len(h2.Data))\n\t}\n\texpectH2 := []Float{110, 220, 330, 440, 550}\n\tfor i, v := range expectH2 {\n\t\tif h2.Data[i] != v {\n\t\t\tt.Errorf(\"host2 data[%d]: expected %v, got %v\", i, v, h2.Data[i])\n\t\t}\n\t}\n}\n\nfunc TestAddNodeScopeUnevenCores(t *testing.T) {\n\t// Same host, cores with different data lengths.\n\tjd := JobData{\n\t\t\"mem_bw\": {\n\t\t\tMetricScopeCore: &JobMetric{\n\t\t\t\tUnit: Unit{Base: \"B/s\"},\n\t\t\t\tTimestep: 10,\n\t\t\t\tSeries: []Series{\n\t\t\t\t\t{Hostname: \"node1\", Data: []Float{1, 2, 3}, Statistics: MetricStatistics{Min: 1, Avg: 2, Max: 3}},\n\t\t\t\t\t{Hostname: \"node1\", Data: []Float{10, 20, 30, 40, 50}, Statistics: MetricStatistics{Min: 10, Avg: 30, Max: 50}},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tok := jd.AddNodeScope(\"mem_bw\")\n\tif !ok {\n\t\tt.Fatal(\"AddNodeScope returned false\")\n\t}\n\n\tnodeMetric := jd[\"mem_bw\"][MetricScopeNode]\n\tif len(nodeMetric.Series) != 1 {\n\t\tt.Fatalf(\"expected 1 node series, got %d\", len(nodeMetric.Series))\n\t}\n\n\ts := nodeMetric.Series[0]\n\t// n=5 (max length), m=3 (min length)\n\t// data[0..2] = sum, data[3..4] = NaN\n\tif len(s.Data) != 5 {\n\t\tt.Fatalf(\"expected 5 data points, got %d\", len(s.Data))\n\t}\n\n\texpect := []Float{11, 22, 33}\n\tfor i, v := range expect {\n\t\tif s.Data[i] != v {\n\t\t\tt.Errorf(\"data[%d]: expected %v, got %v\", i, v, s.Data[i])\n\t\t}\n\t}\n\n\t// Remaining should be NaN\n\tfor i := 3; i < 5; i++ {\n\t\tif !s.Data[i].IsNaN() {\n\t\t\tt.Errorf(\"data[%d]: expected NaN, got %v\", i, s.Data[i])\n\t\t}\n\t}\n}\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":776,"cache_read_input_tokens":24457,"output_tokens":1627,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":776,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6PWfmaVbBAkJaJfwn22","type":"assistant","uuid":"2486f437-cbbf-46b6-84cb-ba1c60e206ed","timestamp":"2026-03-16T07:47:10.307Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"2486f437-cbbf-46b6-84cb-ba1c60e206ed","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Write","command":"callback"},"parentToolUseID":"toolu_011TuQwzpxYhyjw45S3wkG6L","toolUseID":"toolu_011TuQwzpxYhyjw45S3wkG6L","timestamp":"2026-03-16T07:47:10.319Z","uuid":"eca6868f-98c1-4e38-89ac-f2405eb6f06e","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"2486f437-cbbf-46b6-84cb-ba1c60e206ed","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011TuQwzpxYhyjw45S3wkG6L","type":"tool_result","content":"File created successfully at: /Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics_test.go"}]},"uuid":"1817f84b-6fa7-4a30-8a9d-4ef751d1675c","timestamp":"2026-03-16T07:47:10.319Z","toolUseResult":{"type":"create","filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics_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.\n\npackage schema\n\nimport (\n\t\"testing\"\n)\n\nfunc TestAddNodeScope(t *testing.T) {\n\t// Two hosts with core-level data of different lengths.\n\t// host1 has 2 cores with 3 data points each.\n\t// host2 has 2 cores with 5 data points each.\n\tjd := JobData{\n\t\t\"flops_any\": {\n\t\t\tMetricScopeCore: &JobMetric{\n\t\t\t\tUnit: Unit{Base: \"F/s\"},\n\t\t\t\tTimestep: 10,\n\t\t\t\tSeries: []Series{\n\t\t\t\t\t{Hostname: \"host1\", Data: []Float{1, 2, 3}, Statistics: MetricStatistics{Min: 1, Avg: 2, Max: 3}},\n\t\t\t\t\t{Hostname: \"host1\", Data: []Float{4, 5, 6}, Statistics: MetricStatistics{Min: 4, Avg: 5, Max: 6}},\n\t\t\t\t\t{Hostname: \"host2\", Data: []Float{10, 20, 30, 40, 50}, Statistics: MetricStatistics{Min: 10, Avg: 30, Max: 50}},\n\t\t\t\t\t{Hostname: \"host2\", Data: []Float{100, 200, 300, 400, 500}, Statistics: MetricStatistics{Min: 100, Avg: 300, Max: 500}},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tok := jd.AddNodeScope(\"flops_any\")\n\tif !ok {\n\t\tt.Fatal(\"AddNodeScope returned false\")\n\t}\n\n\tnodeMetric, exists := jd[\"flops_any\"][MetricScopeNode]\n\tif !exists {\n\t\tt.Fatal(\"node scope not created\")\n\t}\n\n\tif len(nodeMetric.Series) != 2 {\n\t\tt.Fatalf(\"expected 2 node series, got %d\", len(nodeMetric.Series))\n\t}\n\n\t// Build a map for deterministic checking (range over map is random order).\n\tbyHost := make(map[string]Series)\n\tfor _, s := range nodeMetric.Series {\n\t\tbyHost[s.Hostname] = s\n\t}\n\n\t// host1: sum of cores = [1+4, 2+5, 3+6] = [5, 7, 9], length 3\n\th1 := byHost[\"host1\"]\n\tif len(h1.Data) != 3 {\n\t\tt.Fatalf(\"host1: expected 3 data points, got %d\", len(h1.Data))\n\t}\n\texpectH1 := []Float{5, 7, 9}\n\tfor i, v := range expectH1 {\n\t\tif h1.Data[i] != v {\n\t\t\tt.Errorf(\"host1 data[%d]: expected %v, got %v\", i, v, h1.Data[i])\n\t\t}\n\t}\n\n\t// host2: sum of cores = [110, 220, 330, 440, 550], length 5\n\th2 := byHost[\"host2\"]\n\tif len(h2.Data) != 5 {\n\t\tt.Fatalf(\"host2: expected 5 data points, got %d\", len(h2.Data))\n\t}\n\texpectH2 := []Float{110, 220, 330, 440, 550}\n\tfor i, v := range expectH2 {\n\t\tif h2.Data[i] != v {\n\t\t\tt.Errorf(\"host2 data[%d]: expected %v, got %v\", i, v, h2.Data[i])\n\t\t}\n\t}\n}\n\nfunc TestAddNodeScopeUnevenCores(t *testing.T) {\n\t// Same host, cores with different data lengths.\n\tjd := JobData{\n\t\t\"mem_bw\": {\n\t\t\tMetricScopeCore: &JobMetric{\n\t\t\t\tUnit: Unit{Base: \"B/s\"},\n\t\t\t\tTimestep: 10,\n\t\t\t\tSeries: []Series{\n\t\t\t\t\t{Hostname: \"node1\", Data: []Float{1, 2, 3}, Statistics: MetricStatistics{Min: 1, Avg: 2, Max: 3}},\n\t\t\t\t\t{Hostname: \"node1\", Data: []Float{10, 20, 30, 40, 50}, Statistics: MetricStatistics{Min: 10, Avg: 30, Max: 50}},\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\tok := jd.AddNodeScope(\"mem_bw\")\n\tif !ok {\n\t\tt.Fatal(\"AddNodeScope returned false\")\n\t}\n\n\tnodeMetric := jd[\"mem_bw\"][MetricScopeNode]\n\tif len(nodeMetric.Series) != 1 {\n\t\tt.Fatalf(\"expected 1 node series, got %d\", len(nodeMetric.Series))\n\t}\n\n\ts := nodeMetric.Series[0]\n\t// n=5 (max length), m=3 (min length)\n\t// data[0..2] = sum, data[3..4] = NaN\n\tif len(s.Data) != 5 {\n\t\tt.Fatalf(\"expected 5 data points, got %d\", len(s.Data))\n\t}\n\n\texpect := []Float{11, 22, 33}\n\tfor i, v := range expect {\n\t\tif s.Data[i] != v {\n\t\t\tt.Errorf(\"data[%d]: expected %v, got %v\", i, v, s.Data[i])\n\t\t}\n\t}\n\n\t// Remaining should be NaN\n\tfor i := 3; i < 5; i++ {\n\t\tif !s.Data[i].IsNaN() {\n\t\t\tt.Errorf(\"data[%d]: expected NaN, got %v\", i, s.Data[i])\n\t\t}\n\t}\n}\n","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"2486f437-cbbf-46b6-84cb-ba1c60e206ed","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"1817f84b-6fa7-4a30-8a9d-4ef751d1675c","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Hs54QHuvpRzQUFfqY9wum2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RwSC8xCi5HFefojmAekU36","name":"Bash","input":{"command":"cd /Users/jan/prg/ClusterCockpit/cc-lib && go test ./schema/ -run \"TestAddNodeScope\" -v 2>&1","description":"Run the new unit tests"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1669,"cache_read_input_tokens":25233,"output_tokens":110,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1669,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6PY9aRzFWaLW5jCfCfc","type":"assistant","uuid":"6f035d94-d16c-4643-bb0b-b57906a5e923","timestamp":"2026-03-16T07:47:13.701Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"6f035d94-d16c-4643-bb0b-b57906a5e923","isSidechain":false,"promptId":"6996e93a-15e5-4605-839c-80588e7ae3fa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RwSC8xCi5HFefojmAekU36","type":"tool_result","content":"=== RUN TestAddNodeScope\n--- PASS: TestAddNodeScope (0.00s)\n=== RUN TestAddNodeScopeUnevenCores\n--- PASS: TestAddNodeScopeUnevenCores (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-lib/v2/schema\t0.528s\nShell cwd was reset to /Users/jan/prg/ClusterCockpit/cc-backend","is_error":false}]},"uuid":"d763af7b-48ee-4920-adc5-49cdc769841e","timestamp":"2026-03-16T07:47:15.561Z","toolUseResult":{"stdout":"=== RUN TestAddNodeScope\n--- PASS: TestAddNodeScope (0.00s)\n=== RUN TestAddNodeScopeUnevenCores\n--- PASS: TestAddNodeScopeUnevenCores (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-lib/v2/schema\t0.528s","stderr":"\nShell cwd was reset to /Users/jan/prg/ClusterCockpit/cc-backend","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"6f035d94-d16c-4643-bb0b-b57906a5e923","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"d763af7b-48ee-4920-adc5-49cdc769841e","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01SepaF5vECMZHaSSpdvyvYh","type":"message","role":"assistant","content":[{"type":"text","text":"Both tests pass. Here's a summary of what was done:\n\n**Fix applied in cc-lib** (`schema/metrics.go:307-324`):\n\n1. **`n`/`m` now computed from local `series`** (current hostname's sub-node series) instead of `jm.Series` (all hostnames) — fixes the crash where `data` was undersized\n2. **`data` sized to `n`** (max length for this host) instead of `len(series[0].Data)` — prevents index-out-of-range panic\n3. **Summation iterates local `series`** instead of `jm.Series` — fixes the logic bug where each node got the sum of ALL hosts' cores\n\n**Tests added** (`schema/metrics_test.go`):\n- `TestAddNodeScope` — two hosts with different data lengths (3 vs 5 points), verifies correct per-host aggregation and no panic\n- `TestAddNodeScopeUnevenCores` — single host with cores of different lengths, verifies NaN padding for shorter series\n\nYou'll need to commit and release this in cc-lib, then bump the dependency in cc-backend's 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+{"parentUuid":"0fb7f73e-1387-43fd-ab26-b4984c90b67f","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01W59LgvCnmZQFhfSZHv5NXH","toolUseID":"toolu_01W59LgvCnmZQFhfSZHv5NXH","timestamp":"2026-03-16T07:49:27.783Z","uuid":"1f96e77c-23c8-41e0-87dc-8cc03140cc44","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"0fb7f73e-1387-43fd-ab26-b4984c90b67f","isSidechain":false,"promptId":"807decf9-6fba-46ef-86b3-8a871a0dbbdf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W59LgvCnmZQFhfSZHv5NXH","type":"tool_result","content":" 1→# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n 2→\n 3→## Context\n 4→\n 5→The archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n 6→\n 7→## Root Cause (in cc-lib)\n 8→\n 9→`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n 10→\n 11→### Bug 1 — The crash (line 327)\n 12→- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n 13→- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n 14→- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n 15→\n 16→### Bug 2 — Logic error (lines 318-323)\n 17→- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n 18→- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n 19→\n 20→## Fix (in cc-lib `schema/metrics.go`)\n 21→\n 22→Replace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n 23→\n 24→```go\n 25→// Before (BUGGY):\n 26→n, m := 0, len(jm.Series[0].Data)\n 27→for _, series := range jm.Series {\n 28→ ...\n 29→}\n 30→i, data := 0, make([]Float, len(series[0].Data))\n 31→for ; i < m; i++ {\n 32→ x := Float(0.0)\n 33→ for _, series := range jm.Series {\n 34→ x += series.Data[i]\n 35→ }\n 36→ data[i] = x\n 37→}\n 38→\n 39→// After (FIXED):\n 40→n, m := 0, len(series[0].Data)\n 41→for _, s := range series {\n 42→ if len(s.Data) > n {\n 43→ n = len(s.Data)\n 44→ }\n 45→ if len(s.Data) < m {\n 46→ m = len(s.Data)\n 47→ }\n 48→}\n 49→i, data := 0, make([]Float, n)\n 50→for ; i < m; i++ {\n 51→ x := Float(0.0)\n 52→ for _, s := range series {\n 53→ x += s.Data[i]\n 54→ }\n 55→ data[i] = x\n 56→}\n 57→```\n 58→\n 59→Key changes:\n 60→1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n 61→2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n 62→3. Summation iterates local `series`, not `jm.Series`\n 63→\n 64→## Where to Apply\n 65→\n 66→The fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n 67→\n 68→1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n 69→2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n 70→\n 71→**Recommended: Option 1** — fix cc-lib.\n 72→\n 73→## Verification\n 74→\n 75→- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n 76→- Integration: archive a job with mixed-length metric data\n 77→"}]},"uuid":"fbb5031b-9cae-4f33-a976-57cb6c6e3f07","timestamp":"2026-03-16T07:49:27.782Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/.claude/plans/frolicking-splashing-kay.md","content":"# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data\n","numLines":77,"startLine":1,"totalLines":77}},"sourceToolAssistantUUID":"0fb7f73e-1387-43fd-ab26-b4984c90b67f","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"fbb5031b-9cae-4f33-a976-57cb6c6e3f07","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01MQm3ERwmnc8HCpRAqUAuBT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LeqFraVSCHSDfCxoQCHora","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1684,"cache_read_input_tokens":27381,"output_tokens":130,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1684,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6Pi5ZN4ezod52KNdwjY","type":"assistant","uuid":"48b771a8-5d70-44dd-8fe1-7b15d8f84512","timestamp":"2026-03-16T07:49:28.160Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"48b771a8-5d70-44dd-8fe1-7b15d8f84512","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01LeqFraVSCHSDfCxoQCHora","toolUseID":"toolu_01LeqFraVSCHSDfCxoQCHora","timestamp":"2026-03-16T07:49:28.166Z","uuid":"0afbe1cb-9a6a-407f-9670-b2e3fad324f8","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"48b771a8-5d70-44dd-8fe1-7b15d8f84512","isSidechain":false,"promptId":"807decf9-6fba-46ef-86b3-8a871a0dbbdf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LeqFraVSCHSDfCxoQCHora","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 schema\n 7→\n 8→import (\n 9→\t\"fmt\"\n 10→\t\"io\"\n 11→\t\"math\"\n 12→\t\"sort\"\n 13→\t\"unsafe\"\n 14→\n 15→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n 16→)\n 17→\n 18→// JobData maps metric names to their data organized by scope.\n 19→// Structure: map[metricName]map[scope]*JobMetric\n 20→//\n 21→// For example: jobData[\"cpu_load\"][MetricScopeNode] contains node-level CPU load data.\n 22→// This structure allows efficient lookup of metrics at different hierarchical levels.\n 23→type JobData map[string]map[MetricScope]*JobMetric\n 24→\n 25→// ScopedJobStats maps metric names to statistical summaries organized by scope.\n 26→// Structure: map[metricName]map[scope][]*ScopedStats\n 27→//\n 28→// Used to store pre-computed statistics without the full time series data,\n 29→// reducing memory footprint when only aggregated values are needed.\n 30→type ScopedJobStats map[string]map[MetricScope][]*ScopedStats\n 31→\n 32→// JobMetric contains time series data and statistics for a single metric.\n 33→//\n 34→// The Series field holds time series data from individual nodes/hardware components,\n 35→// while StatisticsSeries provides aggregated statistics across all series over time.\n 36→type JobMetric struct {\n 37→\tStatisticsSeries *StatsSeries `json:\"statisticsSeries,omitempty\"` // Aggregated statistics over time\n 38→\tUnit Unit `json:\"unit\"` // Unit of measurement\n 39→\tSeries []Series `json:\"series\"` // Individual time series data\n 40→\tTimestep int `json:\"timestep\"` // Sampling interval in seconds\n 41→}\n 42→\n 43→// Series represents a single time series of metric measurements.\n 44→//\n 45→// Each series corresponds to one source (e.g., one node, one core) identified by Hostname and optional ID.\n 46→// The Data field contains the time-ordered measurements, and Statistics provides min/avg/max summaries.\n 47→type Series struct {\n 48→\tID *string `json:\"id,omitempty\"` // Optional ID (e.g., core ID, GPU ID)\n 49→\tHostname string `json:\"hostname\"` // Source hostname\n 50→\tData []Float `json:\"data\"` // Time series measurements\n 51→\tStatistics MetricStatistics `json:\"statistics\"` // Statistical summary (min/avg/max)\n 52→}\n 53→\n 54→// ScopedStats contains statistical summaries for a specific scope (e.g., one node, one socket).\n 55→// Used when full time series data isn't needed, only the aggregated statistics.\n 56→type ScopedStats struct {\n 57→\tHostname string `json:\"hostname\"` // Source hostname\n 58→\tID *string `json:\"id,omitempty\"` // Optional scope ID\n 59→\tData *MetricStatistics `json:\"data\"` // Statistical summary\n 60→}\n 61→\n 62→// MetricStatistics holds statistical summary values for metric data.\n 63→// Provides the common statistical aggregations used throughout ClusterCockpit.\n 64→type MetricStatistics struct {\n 65→\tAvg float64 `json:\"avg\"` // Average/mean value\n 66→\tMin float64 `json:\"min\"` // Minimum value\n 67→\tMax float64 `json:\"max\"` // Maximum value\n 68→}\n 69→\n 70→// StatsSeries contains aggregated statistics across multiple time series over time.\n 71→//\n 72→// Instead of storing individual series, this provides statistical summaries at each time step.\n 73→// For example, at time t, Mean[t] is the average value across all series at that time.\n 74→// Percentiles provides specified percentile values at each time step.\n 75→type StatsSeries struct {\n 76→\tPercentiles map[int][]Float `json:\"percentiles,omitempty\"` // Percentile values over time (e.g., 10th, 50th, 90th)\n 77→\tMean []Float `json:\"mean\"` // Mean values over time\n 78→\tMedian []Float `json:\"median\"` // Median values over time\n 79→\tMin []Float `json:\"min\"` // Minimum values over time\n 80→\tMax []Float `json:\"max\"` // Maximum values over time\n 81→}\n 82→\n 83→// MetricScope defines the hierarchical level at which a metric is measured.\n 84→//\n 85→// Scopes form a hierarchy from coarse-grained (node) to fine-grained (hwthread/accelerator):\n 86→//\n 87→//\tnode > socket > memoryDomain > core > hwthread\n 88→//\taccelerator is a special scope at the same level as hwthread\n 89→//\n 90→// The scopePrecedence map defines numeric ordering for scope comparisons,\n 91→// which is used when aggregating metrics across different scopes.\n 92→type MetricScope string\n 93→\n 94→const (\n 95→\tMetricScopeInvalid MetricScope = \"invalid_scope\"\n 96→\n 97→\tMetricScopeNode MetricScope = \"node\"\n 98→\tMetricScopeSocket MetricScope = \"socket\"\n 99→\tMetricScopeMemoryDomain MetricScope = \"memoryDomain\"\n 100→\tMetricScopeCore MetricScope = \"core\"\n 101→\tMetricScopeHWThread MetricScope = \"hwthread\"\n 102→\n 103→\t// TODO: Add Job and Application scopes\n 104→\n 105→\tMetricScopeAccelerator MetricScope = \"accelerator\"\n 106→)\n 107→\n 108→var metricScopeGranularity map[MetricScope]int = map[MetricScope]int{\n 109→\tMetricScopeNode: 10,\n 110→\tMetricScopeSocket: 5,\n 111→\tMetricScopeMemoryDomain: 4,\n 112→\tMetricScopeCore: 3,\n 113→\tMetricScopeHWThread: 2,\n 114→\t/* Special-Case Accelerator\n 115→\t * -> No conversion possible if native scope is HWTHREAD\n 116→\t * -> Therefore needs to be less than HWTREAD, else max() would return unhandled case\n 117→\t * -> If nativeScope is accelerator, accelerator metrics return correctly\n 118→\t */\n 119→\tMetricScopeAccelerator: 1,\n 120→\n 121→\tMetricScopeInvalid: -1,\n 122→}\n 123→\n 124→func (e *MetricScope) LT(other MetricScope) bool {\n 125→\ta := metricScopeGranularity[*e]\n 126→\tb := metricScopeGranularity[other]\n 127→\treturn a < b\n 128→}\n 129→\n 130→func (e *MetricScope) LTE(other MetricScope) bool {\n 131→\ta := metricScopeGranularity[*e]\n 132→\tb := metricScopeGranularity[other]\n 133→\treturn a <= b\n 134→}\n 135→\n 136→func (e *MetricScope) Max(other MetricScope) MetricScope {\n 137→\ta := metricScopeGranularity[*e]\n 138→\tb := metricScopeGranularity[other]\n 139→\tif a > b {\n 140→\t\treturn *e\n 141→\t}\n 142→\treturn other\n 143→}\n 144→\n 145→func (e *MetricScope) UnmarshalGQL(v any) error {\n 146→\tstr, ok := v.(string)\n 147→\tif !ok {\n 148→\t\treturn fmt.Errorf(\"SCHEMA/METRICS > enums must be strings\")\n 149→\t}\n 150→\n 151→\t*e = MetricScope(str)\n 152→\tif !e.Valid() {\n 153→\t\treturn fmt.Errorf(\"SCHEMA/METRICS > %s is not a valid MetricScope\", str)\n 154→\t}\n 155→\treturn nil\n 156→}\n 157→\n 158→func (e MetricScope) MarshalGQL(w io.Writer) {\n 159→\tfmt.Fprintf(w, \"\\\"%s\\\"\", e)\n 160→}\n 161→\n 162→func (e MetricScope) Valid() bool {\n 163→\tgran, ok := metricScopeGranularity[e]\n 164→\treturn ok && gran > 0\n 165→}\n 166→\n 167→func (jd *JobData) Size() int {\n 168→\tn := 128\n 169→\tfor _, scopes := range *jd {\n 170→\t\tfor _, metric := range scopes {\n 171→\t\t\tif metric.StatisticsSeries != nil {\n 172→\t\t\t\tn += len(metric.StatisticsSeries.Max)\n 173→\t\t\t\tn += len(metric.StatisticsSeries.Mean)\n 174→\t\t\t\tn += len(metric.StatisticsSeries.Median)\n 175→\t\t\t\tn += len(metric.StatisticsSeries.Min)\n 176→\t\t\t}\n 177→\n 178→\t\t\tfor _, series := range metric.Series {\n 179→\t\t\t\tn += len(series.Data)\n 180→\t\t\t}\n 181→\t\t}\n 182→\t}\n 183→\treturn n * int(unsafe.Sizeof(Float(0)))\n 184→}\n 185→\n 186→const smooth bool = false\n 187→\n 188→func (jm *JobMetric) AddStatisticsSeries() {\n 189→\tif jm.StatisticsSeries != nil || len(jm.Series) < 4 {\n 190→\t\treturn\n 191→\t}\n 192→\n 193→\tn, m := 0, len(jm.Series[0].Data)\n 194→\tfor _, series := range jm.Series {\n 195→\t\tif len(series.Data) > n {\n 196→\t\t\tn = len(series.Data)\n 197→\t\t}\n 198→\t\tif len(series.Data) < m {\n 199→\t\t\tm = len(series.Data)\n 200→\t\t}\n 201→\t}\n 202→\n 203→\t// mean := make([]Float, n)\n 204→\tmin, median, max := make([]Float, n), make([]Float, n), make([]Float, n)\n 205→\ti := 0\n 206→\tfor ; i < m; i++ {\n 207→\t\tseriesCount := len(jm.Series)\n 208→\t\t// ssum := 0.0\n 209→\t\tsmin, smed, smax := math.MaxFloat32, make([]float64, seriesCount), -math.MaxFloat32\n 210→\t\tnotnan := 0\n 211→\t\tfor j := range seriesCount {\n 212→\t\t\tx := float64(jm.Series[j].Data[i])\n 213→\t\t\tif math.IsNaN(x) {\n 214→\t\t\t\tcontinue\n 215→\t\t\t}\n 216→\n 217→\t\t\tnotnan += 1\n 218→\t\t\t// ssum += x\n 219→\t\t\tsmed[j] = x\n 220→\t\t\tsmin = math.Min(smin, x)\n 221→\t\t\tsmax = math.Max(smax, x)\n 222→\t\t}\n 223→\n 224→\t\tif notnan < 3 {\n 225→\t\t\tmin[i] = NaN\n 226→\t\t\t// mean[i] = NaN\n 227→\t\t\tmedian[i] = NaN\n 228→\t\t\tmax[i] = NaN\n 229→\t\t} else {\n 230→\t\t\tmin[i] = Float(smin)\n 231→\t\t\t// mean[i] = Float(ssum / float64(notnan))\n 232→\t\t\tmax[i] = Float(smax)\n 233→\n 234→\t\t\tmedianRaw, err := util.Median(smed)\n 235→\t\t\tif err != nil {\n 236→\t\t\t\tmedian[i] = NaN\n 237→\t\t\t} else {\n 238→\t\t\t\tmedian[i] = Float(medianRaw)\n 239→\t\t\t}\n 240→\t\t}\n 241→\t}\n 242→\n 243→\tfor ; i < n; i++ {\n 244→\t\tmin[i] = NaN\n 245→\t\t// mean[i] = NaN\n 246→\t\tmedian[i] = NaN\n 247→\t\tmax[i] = NaN\n 248→\t}\n 249→\n 250→\tif smooth {\n 251→\t\tfor i := 2; i < len(median)-2; i++ {\n 252→\t\t\tif min[i].IsNaN() {\n 253→\t\t\t\tcontinue\n 254→\t\t\t}\n 255→\n 256→\t\t\tmin[i] = (min[i-2] + min[i-1] + min[i] + min[i+1] + min[i+2]) / 5\n 257→\t\t\tmax[i] = (max[i-2] + max[i-1] + max[i] + max[i+1] + max[i+2]) / 5\n 258→\t\t\t// mean[i] = (mean[i-2] + mean[i-1] + mean[i] + mean[i+1] + mean[i+2]) / 5\n 259→\t\t\t// Reduce Median further\n 260→\t\t\tsmoothRaw := []float64{float64(median[i-2]), float64(median[i-1]), float64(median[i]), float64(median[i+1]), float64(median[i+2])}\n 261→\t\t\tsmoothMedian, err := util.Median(smoothRaw)\n 262→\t\t\tif err != nil {\n 263→\t\t\t\tmedian[i] = NaN\n 264→\t\t\t} else {\n 265→\t\t\t\tmedian[i] = Float(smoothMedian)\n 266→\t\t\t}\n 267→\t\t}\n 268→\t}\n 269→\n 270→\tjm.StatisticsSeries = &StatsSeries{Median: median, Min: min, Max: max} // Mean: mean\n 271→}\n 272→\n 273→func (jd *JobData) AddNodeScope(metric string) bool {\n 274→\tscopes, ok := (*jd)[metric]\n 275→\tif !ok {\n 276→\t\treturn false\n 277→\t}\n 278→\n 279→\tmaxScope := MetricScopeInvalid\n 280→\tfor scope := range scopes {\n 281→\t\tmaxScope = maxScope.Max(scope)\n 282→\t}\n 283→\n 284→\tif maxScope == MetricScopeInvalid || maxScope == MetricScopeNode {\n 285→\t\treturn false\n 286→\t}\n 287→\n 288→\tjm := scopes[maxScope]\n 289→\thosts := make(map[string][]Series, 32)\n 290→\tfor _, series := range jm.Series {\n 291→\t\thosts[series.Hostname] = append(hosts[series.Hostname], series)\n 292→\t}\n 293→\n 294→\tnodeJm := &JobMetric{\n 295→\t\tUnit: jm.Unit,\n 296→\t\tTimestep: jm.Timestep,\n 297→\t\tSeries: make([]Series, 0, len(hosts)),\n 298→\t}\n 299→\tfor hostname, series := range hosts {\n 300→\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n 301→\t\tfor _, series := range series {\n 302→\t\t\tsum += series.Statistics.Avg\n 303→\t\t\tmin = math.Min(min, series.Statistics.Min)\n 304→\t\t\tmax = math.Max(max, series.Statistics.Max)\n 305→\t\t}\n 306→\n 307→\t\tn, m := 0, len(series[0].Data)\n 308→\t\tfor _, s := range series {\n 309→\t\t\tif len(s.Data) > n {\n 310→\t\t\t\tn = len(s.Data)\n 311→\t\t\t}\n 312→\t\t\tif len(s.Data) < m {\n 313→\t\t\t\tm = len(s.Data)\n 314→\t\t\t}\n 315→\t\t}\n 316→\n 317→\t\ti, data := 0, make([]Float, n)\n 318→\t\tfor ; i < m; i++ {\n 319→\t\t\tx := Float(0.0)\n 320→\t\t\tfor _, s := range series {\n 321→\t\t\t\tx += s.Data[i]\n 322→\t\t\t}\n 323→\t\t\tdata[i] = x\n 324→\t\t}\n 325→\n 326→\t\tfor ; i < n; i++ {\n 327→\t\t\tdata[i] = NaN\n 328→\t\t}\n 329→\n 330→\t\tnodeJm.Series = append(nodeJm.Series, Series{\n 331→\t\t\tHostname: hostname,\n 332→\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n 333→\t\t\tData: data,\n 334→\t\t})\n 335→\t}\n 336→\n 337→\tscopes[MetricScopeNode] = nodeJm\n 338→\treturn true\n 339→}\n 340→\n 341→func (jd *JobData) RoundMetricStats() {\n 342→\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n 343→\tfor _, scopes := range *jd {\n 344→\t\tfor _, jm := range scopes {\n 345→\t\t\tfor index := range jm.Series {\n 346→\t\t\t\tjm.Series[index].Statistics = MetricStatistics{\n 347→\t\t\t\t\tAvg: (math.Round(jm.Series[index].Statistics.Avg*100) / 100),\n 348→\t\t\t\t\tMin: (math.Round(jm.Series[index].Statistics.Min*100) / 100),\n 349→\t\t\t\t\tMax: (math.Round(jm.Series[index].Statistics.Max*100) / 100),\n 350→\t\t\t\t}\n 351→\t\t\t}\n 352→\t\t}\n 353→\t}\n 354→}\n 355→\n 356→func (sjs *ScopedJobStats) RoundScopedMetricStats() {\n 357→\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n 358→\tfor _, scopes := range *sjs {\n 359→\t\tfor _, stats := range scopes {\n 360→\t\t\tfor index := range stats {\n 361→\t\t\t\troundedStats := MetricStatistics{\n 362→\t\t\t\t\tAvg: (math.Round(stats[index].Data.Avg*100) / 100),\n 363→\t\t\t\t\tMin: (math.Round(stats[index].Data.Min*100) / 100),\n 364→\t\t\t\t\tMax: (math.Round(stats[index].Data.Max*100) / 100),\n 365→\t\t\t\t}\n 366→\t\t\t\tstats[index].Data = &roundedStats\n 367→\t\t\t}\n 368→\t\t}\n 369→\t}\n 370→}\n 371→\n 372→func (jm *JobMetric) AddPercentiles(ps []int) bool {\n 373→\tif jm.StatisticsSeries == nil {\n 374→\t\tjm.AddStatisticsSeries()\n 375→\t}\n 376→\n 377→\tif len(jm.Series) < 3 {\n 378→\t\treturn false\n 379→\t}\n 380→\n 381→\tif jm.StatisticsSeries.Percentiles == nil {\n 382→\t\tjm.StatisticsSeries.Percentiles = make(map[int][]Float, len(ps))\n 383→\t}\n 384→\n 385→\tn := 0\n 386→\tfor _, series := range jm.Series {\n 387→\t\tif len(series.Data) > n {\n 388→\t\t\tn = len(series.Data)\n 389→\t\t}\n 390→\t}\n 391→\n 392→\tdata := make([][]float64, n)\n 393→\tfor i := range n {\n 394→\t\tvals := make([]float64, 0, len(jm.Series))\n 395→\t\tfor _, series := range jm.Series {\n 396→\t\t\tif i < len(series.Data) {\n 397→\t\t\t\tvals = append(vals, float64(series.Data[i]))\n 398→\t\t\t}\n 399→\t\t}\n 400→\n 401→\t\tsort.Float64s(vals)\n 402→\t\tdata[i] = vals\n 403→\t}\n 404→\n 405→\tfor _, p := range ps {\n 406→\t\tif p < 1 || p > 99 {\n 407→\t\t\tpanic(\"SCHEMA/METRICS > invalid percentile\")\n 408→\t\t}\n 409→\n 410→\t\tif _, ok := jm.StatisticsSeries.Percentiles[p]; ok {\n 411→\t\t\tcontinue\n 412→\t\t}\n 413→\n 414→\t\tpercentiles := make([]Float, n)\n 415→\t\tfor i := range n {\n 416→\t\t\tsorted := data[i]\n 417→\t\t\tpercentiles[i] = Float(sorted[(len(sorted)*p)/100])\n 418→\t\t}\n 419→\n 420→\t\tjm.StatisticsSeries.Percentiles[p] = percentiles\n 421→\t}\n 422→\n 423→\treturn true\n 424→}\n 425→"}]},"uuid":"36179299-d2a4-49b1-ac80-e8ca1a8a61c0","timestamp":"2026-03-16T07:49:28.166Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.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\npackage schema\n\nimport (\n\t\"fmt\"\n\t\"io\"\n\t\"math\"\n\t\"sort\"\n\t\"unsafe\"\n\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\n// JobData maps metric names to their data organized by scope.\n// Structure: map[metricName]map[scope]*JobMetric\n//\n// For example: jobData[\"cpu_load\"][MetricScopeNode] contains node-level CPU load data.\n// This structure allows efficient lookup of metrics at different hierarchical levels.\ntype JobData map[string]map[MetricScope]*JobMetric\n\n// ScopedJobStats maps metric names to statistical summaries organized by scope.\n// Structure: map[metricName]map[scope][]*ScopedStats\n//\n// Used to store pre-computed statistics without the full time series data,\n// reducing memory footprint when only aggregated values are needed.\ntype ScopedJobStats map[string]map[MetricScope][]*ScopedStats\n\n// JobMetric contains time series data and statistics for a single metric.\n//\n// The Series field holds time series data from individual nodes/hardware components,\n// while StatisticsSeries provides aggregated statistics across all series over time.\ntype JobMetric struct {\n\tStatisticsSeries *StatsSeries `json:\"statisticsSeries,omitempty\"` // Aggregated statistics over time\n\tUnit Unit `json:\"unit\"` // Unit of measurement\n\tSeries []Series `json:\"series\"` // Individual time series data\n\tTimestep int `json:\"timestep\"` // Sampling interval in seconds\n}\n\n// Series represents a single time series of metric measurements.\n//\n// Each series corresponds to one source (e.g., one node, one core) identified by Hostname and optional ID.\n// The Data field contains the time-ordered measurements, and Statistics provides min/avg/max summaries.\ntype Series struct {\n\tID *string `json:\"id,omitempty\"` // Optional ID (e.g., core ID, GPU ID)\n\tHostname string `json:\"hostname\"` // Source hostname\n\tData []Float `json:\"data\"` // Time series measurements\n\tStatistics MetricStatistics `json:\"statistics\"` // Statistical summary (min/avg/max)\n}\n\n// ScopedStats contains statistical summaries for a specific scope (e.g., one node, one socket).\n// Used when full time series data isn't needed, only the aggregated statistics.\ntype ScopedStats struct {\n\tHostname string `json:\"hostname\"` // Source hostname\n\tID *string `json:\"id,omitempty\"` // Optional scope ID\n\tData *MetricStatistics `json:\"data\"` // Statistical summary\n}\n\n// MetricStatistics holds statistical summary values for metric data.\n// Provides the common statistical aggregations used throughout ClusterCockpit.\ntype MetricStatistics struct {\n\tAvg float64 `json:\"avg\"` // Average/mean value\n\tMin float64 `json:\"min\"` // Minimum value\n\tMax float64 `json:\"max\"` // Maximum value\n}\n\n// StatsSeries contains aggregated statistics across multiple time series over time.\n//\n// Instead of storing individual series, this provides statistical summaries at each time step.\n// For example, at time t, Mean[t] is the average value across all series at that time.\n// Percentiles provides specified percentile values at each time step.\ntype StatsSeries struct {\n\tPercentiles map[int][]Float `json:\"percentiles,omitempty\"` // Percentile values over time (e.g., 10th, 50th, 90th)\n\tMean []Float `json:\"mean\"` // Mean values over time\n\tMedian []Float `json:\"median\"` // Median values over time\n\tMin []Float `json:\"min\"` // Minimum values over time\n\tMax []Float `json:\"max\"` // Maximum values over time\n}\n\n// MetricScope defines the hierarchical level at which a metric is measured.\n//\n// Scopes form a hierarchy from coarse-grained (node) to fine-grained (hwthread/accelerator):\n//\n//\tnode > socket > memoryDomain > core > hwthread\n//\taccelerator is a special scope at the same level as hwthread\n//\n// The scopePrecedence map defines numeric ordering for scope comparisons,\n// which is used when aggregating metrics across different scopes.\ntype MetricScope string\n\nconst (\n\tMetricScopeInvalid MetricScope = \"invalid_scope\"\n\n\tMetricScopeNode MetricScope = \"node\"\n\tMetricScopeSocket MetricScope = \"socket\"\n\tMetricScopeMemoryDomain MetricScope = \"memoryDomain\"\n\tMetricScopeCore MetricScope = \"core\"\n\tMetricScopeHWThread MetricScope = \"hwthread\"\n\n\t// TODO: Add Job and Application scopes\n\n\tMetricScopeAccelerator MetricScope = \"accelerator\"\n)\n\nvar metricScopeGranularity map[MetricScope]int = map[MetricScope]int{\n\tMetricScopeNode: 10,\n\tMetricScopeSocket: 5,\n\tMetricScopeMemoryDomain: 4,\n\tMetricScopeCore: 3,\n\tMetricScopeHWThread: 2,\n\t/* Special-Case Accelerator\n\t * -> No conversion possible if native scope is HWTHREAD\n\t * -> Therefore needs to be less than HWTREAD, else max() would return unhandled case\n\t * -> If nativeScope is accelerator, accelerator metrics return correctly\n\t */\n\tMetricScopeAccelerator: 1,\n\n\tMetricScopeInvalid: -1,\n}\n\nfunc (e *MetricScope) LT(other MetricScope) bool {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\treturn a < b\n}\n\nfunc (e *MetricScope) LTE(other MetricScope) bool {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\treturn a <= b\n}\n\nfunc (e *MetricScope) Max(other MetricScope) MetricScope {\n\ta := metricScopeGranularity[*e]\n\tb := metricScopeGranularity[other]\n\tif a > b {\n\t\treturn *e\n\t}\n\treturn other\n}\n\nfunc (e *MetricScope) UnmarshalGQL(v any) error {\n\tstr, ok := v.(string)\n\tif !ok {\n\t\treturn fmt.Errorf(\"SCHEMA/METRICS > enums must be strings\")\n\t}\n\n\t*e = MetricScope(str)\n\tif !e.Valid() {\n\t\treturn fmt.Errorf(\"SCHEMA/METRICS > %s is not a valid MetricScope\", str)\n\t}\n\treturn nil\n}\n\nfunc (e MetricScope) MarshalGQL(w io.Writer) {\n\tfmt.Fprintf(w, \"\\\"%s\\\"\", e)\n}\n\nfunc (e MetricScope) Valid() bool {\n\tgran, ok := metricScopeGranularity[e]\n\treturn ok && gran > 0\n}\n\nfunc (jd *JobData) Size() int {\n\tn := 128\n\tfor _, scopes := range *jd {\n\t\tfor _, metric := range scopes {\n\t\t\tif metric.StatisticsSeries != nil {\n\t\t\t\tn += len(metric.StatisticsSeries.Max)\n\t\t\t\tn += len(metric.StatisticsSeries.Mean)\n\t\t\t\tn += len(metric.StatisticsSeries.Median)\n\t\t\t\tn += len(metric.StatisticsSeries.Min)\n\t\t\t}\n\n\t\t\tfor _, series := range metric.Series {\n\t\t\t\tn += len(series.Data)\n\t\t\t}\n\t\t}\n\t}\n\treturn n * int(unsafe.Sizeof(Float(0)))\n}\n\nconst smooth bool = false\n\nfunc (jm *JobMetric) AddStatisticsSeries() {\n\tif jm.StatisticsSeries != nil || len(jm.Series) < 4 {\n\t\treturn\n\t}\n\n\tn, m := 0, len(jm.Series[0].Data)\n\tfor _, series := range jm.Series {\n\t\tif len(series.Data) > n {\n\t\t\tn = len(series.Data)\n\t\t}\n\t\tif len(series.Data) < m {\n\t\t\tm = len(series.Data)\n\t\t}\n\t}\n\n\t// mean := make([]Float, n)\n\tmin, median, max := make([]Float, n), make([]Float, n), make([]Float, n)\n\ti := 0\n\tfor ; i < m; i++ {\n\t\tseriesCount := len(jm.Series)\n\t\t// ssum := 0.0\n\t\tsmin, smed, smax := math.MaxFloat32, make([]float64, seriesCount), -math.MaxFloat32\n\t\tnotnan := 0\n\t\tfor j := range seriesCount {\n\t\t\tx := float64(jm.Series[j].Data[i])\n\t\t\tif math.IsNaN(x) {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tnotnan += 1\n\t\t\t// ssum += x\n\t\t\tsmed[j] = x\n\t\t\tsmin = math.Min(smin, x)\n\t\t\tsmax = math.Max(smax, x)\n\t\t}\n\n\t\tif notnan < 3 {\n\t\t\tmin[i] = NaN\n\t\t\t// mean[i] = NaN\n\t\t\tmedian[i] = NaN\n\t\t\tmax[i] = NaN\n\t\t} else {\n\t\t\tmin[i] = Float(smin)\n\t\t\t// mean[i] = Float(ssum / float64(notnan))\n\t\t\tmax[i] = Float(smax)\n\n\t\t\tmedianRaw, err := util.Median(smed)\n\t\t\tif err != nil {\n\t\t\t\tmedian[i] = NaN\n\t\t\t} else {\n\t\t\t\tmedian[i] = Float(medianRaw)\n\t\t\t}\n\t\t}\n\t}\n\n\tfor ; i < n; i++ {\n\t\tmin[i] = NaN\n\t\t// mean[i] = NaN\n\t\tmedian[i] = NaN\n\t\tmax[i] = NaN\n\t}\n\n\tif smooth {\n\t\tfor i := 2; i < len(median)-2; i++ {\n\t\t\tif min[i].IsNaN() {\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tmin[i] = (min[i-2] + min[i-1] + min[i] + min[i+1] + min[i+2]) / 5\n\t\t\tmax[i] = (max[i-2] + max[i-1] + max[i] + max[i+1] + max[i+2]) / 5\n\t\t\t// mean[i] = (mean[i-2] + mean[i-1] + mean[i] + mean[i+1] + mean[i+2]) / 5\n\t\t\t// Reduce Median further\n\t\t\tsmoothRaw := []float64{float64(median[i-2]), float64(median[i-1]), float64(median[i]), float64(median[i+1]), float64(median[i+2])}\n\t\t\tsmoothMedian, err := util.Median(smoothRaw)\n\t\t\tif err != nil {\n\t\t\t\tmedian[i] = NaN\n\t\t\t} else {\n\t\t\t\tmedian[i] = Float(smoothMedian)\n\t\t\t}\n\t\t}\n\t}\n\n\tjm.StatisticsSeries = &StatsSeries{Median: median, Min: min, Max: max} // Mean: mean\n}\n\nfunc (jd *JobData) AddNodeScope(metric string) bool {\n\tscopes, ok := (*jd)[metric]\n\tif !ok {\n\t\treturn false\n\t}\n\n\tmaxScope := MetricScopeInvalid\n\tfor scope := range scopes {\n\t\tmaxScope = maxScope.Max(scope)\n\t}\n\n\tif maxScope == MetricScopeInvalid || maxScope == MetricScopeNode {\n\t\treturn false\n\t}\n\n\tjm := scopes[maxScope]\n\thosts := make(map[string][]Series, 32)\n\tfor _, series := range jm.Series {\n\t\thosts[series.Hostname] = append(hosts[series.Hostname], series)\n\t}\n\n\tnodeJm := &JobMetric{\n\t\tUnit: jm.Unit,\n\t\tTimestep: jm.Timestep,\n\t\tSeries: make([]Series, 0, len(hosts)),\n\t}\n\tfor hostname, series := range hosts {\n\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n\t\tfor _, series := range series {\n\t\t\tsum += 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\tn, m := 0, len(series[0].Data)\n\t\tfor _, s := range series {\n\t\t\tif len(s.Data) > n {\n\t\t\t\tn = len(s.Data)\n\t\t\t}\n\t\t\tif len(s.Data) < m {\n\t\t\t\tm = len(s.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, n)\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, s := range series {\n\t\t\t\tx += s.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}\n\n\t\tfor ; i < n; i++ {\n\t\t\tdata[i] = NaN\n\t\t}\n\n\t\tnodeJm.Series = append(nodeJm.Series, Series{\n\t\t\tHostname: hostname,\n\t\t\tStatistics: MetricStatistics{Min: min, Avg: sum / float64(len(series)), Max: max},\n\t\t\tData: data,\n\t\t})\n\t}\n\n\tscopes[MetricScopeNode] = nodeJm\n\treturn true\n}\n\nfunc (jd *JobData) RoundMetricStats() {\n\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n\tfor _, scopes := range *jd {\n\t\tfor _, jm := range scopes {\n\t\t\tfor index := range jm.Series {\n\t\t\t\tjm.Series[index].Statistics = MetricStatistics{\n\t\t\t\t\tAvg: (math.Round(jm.Series[index].Statistics.Avg*100) / 100),\n\t\t\t\t\tMin: (math.Round(jm.Series[index].Statistics.Min*100) / 100),\n\t\t\t\t\tMax: (math.Round(jm.Series[index].Statistics.Max*100) / 100),\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunc (sjs *ScopedJobStats) RoundScopedMetricStats() {\n\t// TODO: Make Digit-Precision Configurable? (Currently: Fixed to 2 Digits)\n\tfor _, scopes := range *sjs {\n\t\tfor _, stats := range scopes {\n\t\t\tfor index := range stats {\n\t\t\t\troundedStats := MetricStatistics{\n\t\t\t\t\tAvg: (math.Round(stats[index].Data.Avg*100) / 100),\n\t\t\t\t\tMin: (math.Round(stats[index].Data.Min*100) / 100),\n\t\t\t\t\tMax: (math.Round(stats[index].Data.Max*100) / 100),\n\t\t\t\t}\n\t\t\t\tstats[index].Data = &roundedStats\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunc (jm *JobMetric) AddPercentiles(ps []int) bool {\n\tif jm.StatisticsSeries == nil {\n\t\tjm.AddStatisticsSeries()\n\t}\n\n\tif len(jm.Series) < 3 {\n\t\treturn false\n\t}\n\n\tif jm.StatisticsSeries.Percentiles == nil {\n\t\tjm.StatisticsSeries.Percentiles = make(map[int][]Float, len(ps))\n\t}\n\n\tn := 0\n\tfor _, series := range jm.Series {\n\t\tif len(series.Data) > n {\n\t\t\tn = len(series.Data)\n\t\t}\n\t}\n\n\tdata := make([][]float64, n)\n\tfor i := range n {\n\t\tvals := make([]float64, 0, len(jm.Series))\n\t\tfor _, series := range jm.Series {\n\t\t\tif i < len(series.Data) {\n\t\t\t\tvals = append(vals, float64(series.Data[i]))\n\t\t\t}\n\t\t}\n\n\t\tsort.Float64s(vals)\n\t\tdata[i] = vals\n\t}\n\n\tfor _, p := range ps {\n\t\tif p < 1 || p > 99 {\n\t\t\tpanic(\"SCHEMA/METRICS > invalid percentile\")\n\t\t}\n\n\t\tif _, ok := jm.StatisticsSeries.Percentiles[p]; ok {\n\t\t\tcontinue\n\t\t}\n\n\t\tpercentiles := make([]Float, n)\n\t\tfor i := range n {\n\t\t\tsorted := data[i]\n\t\t\tpercentiles[i] = Float(sorted[(len(sorted)*p)/100])\n\t\t}\n\n\t\tjm.StatisticsSeries.Percentiles[p] = percentiles\n\t}\n\n\treturn true\n}\n","numLines":425,"startLine":1,"totalLines":425}},"sourceToolAssistantUUID":"48b771a8-5d70-44dd-8fe1-7b15d8f84512","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} 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+{"parentUuid":"ac9c042a-a52a-4774-b073-2212448fc8a6","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_019uBcgDXqr5449cicsbYMoS","type":"message","role":"assistant","content":[{"type":"text","text":"I've read the full file. Let me analyze each function for similar bugs and update the plan.\n\nThe file has 5 functions that operate on series data. Let me check them systematically:\n\n**1. `AddStatisticsSeries()` (lines 188-271)** — Computes min/median/max across ALL series at each timestep. Uses `jm.Series` throughout, which is **correct** here because it intentionally aggregates across all series (not per-host). It properly computes `n` (max) and `m` (min) from `jm.Series`, sizes arrays to `n`, and only accesses `Data[i]` when `i < m`. Safe.\n\n**2. `AddNodeScope()` (lines 273-339)** — Already fixed.\n\n**3. `RoundMetricStats()` (lines 341-354)** — Only touches `Statistics` structs, no data array indexing. Safe.\n\n**4. `RoundScopedMetricStats()` (lines 356-370)** — Same, only statistics. Safe.\n\n**5. `AddPercentiles()` (lines 372-424)** — Computes percentiles across ALL series. Uses `jm.Series` which is correct (not per-host). Has explicit bounds check at line 396: `if i < len(series.Data)`. Safe.\n\nLet me update the plan with these findings."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":7046,"cache_read_input_tokens":29065,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":7046},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PiJxWpQ4pof75DbX3p","type":"assistant","uuid":"b6f1afc2-2d05-403f-87b8-a7991688747a","timestamp":"2026-03-16T07:50:01.518Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"type":"file-history-snapshot","messageId":"16b0a9ea-b80c-4434-9e1b-56d28663a39a","snapshot":{"messageId":"d1037be4-80a8-478c-9f3d-2842d151775e","trackedFileBackups":{"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics.go":{"backupFileName":"c727158a22a91f47@v2","version":2,"backupTime":"2026-03-16T07:49:25.037Z"},"/Users/jan/prg/ClusterCockpit/cc-lib/schema/metrics_test.go":{"backupFileName":"21a440652376e992@v2","version":2,"backupTime":"2026-03-16T07:49:25.038Z"},"/Users/jan/.claude/plans/frolicking-splashing-kay.md":{"backupFileName":"afcfd36c1fe573d7@v1","version":1,"backupTime":"2026-03-16T07:50:32.363Z"}},"timestamp":"2026-03-16T07:49:25.037Z"},"isSnapshotUpdate":true} +{"parentUuid":"b6f1afc2-2d05-403f-87b8-a7991688747a","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_019uBcgDXqr5449cicsbYMoS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018d9zfSiTDZFa2vXWP5jb7P","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/.claude/plans/frolicking-splashing-kay.md","old_string":"# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data","new_string":"# Analysis: Similar Bugs in cc-lib `schema/metrics.go`\n\n## Context\n\nAfter fixing the `AddNodeScope` panic (index out of range due to using `jm.Series` instead of local `series`), we're checking if the same class of bug exists elsewhere in the file.\n\n## Already Fixed: `AddNodeScope` (lines 273-339)\n\nThe fix is already applied — `n`/`m` and summation now use local `series` (per-host) instead of `jm.Series` (all hosts). Tests pass.\n\n## Audit of All Functions in `metrics.go`\n\n### `AddStatisticsSeries()` (lines 188-271) — **NO BUG**\n- Computes min/median/max **across ALL series** at each timestep — using `jm.Series` is **intentionally correct**\n- `n`/`m` computed from `jm.Series` (line 193-201): correct, since stats span all series\n- Data access `jm.Series[j].Data[i]` (line 212): guarded by `i < m` where `m` is the minimum series length — safe\n- NaN padding loop (line 243): `i` goes from `m` to `n`, arrays sized to `n` — safe\n- Entry guard (line 189): `len(jm.Series) < 4` prevents `Series[0]` panic\n\n### `AddPercentiles()` (lines 372-424) — **NO BUG**\n- Also operates on ALL series (percentiles across the full set) — `jm.Series` correct\n- Explicit bounds check at line 396: `if i < len(series.Data)` — safe\n- `sorted` at line 417 is never empty because `n` = max series length, so at least one series contributes data at every index\n\n### `RoundMetricStats()` (lines 341-354) — **NO BUG**\n- Only modifies `Statistics` structs (Min/Avg/Max), no data array indexing\n\n### `RoundScopedMetricStats()` (lines 356-370) — **NO BUG**\n- Same pattern, only touches `Data` field which is a `*MetricStatistics`, not a slice\n\n## Conclusion\n\n**`AddNodeScope` was the only function with this bug class.** The other functions either:\n1. Intentionally operate on ALL series (not per-host), so `jm.Series` is correct\n2. Have proper bounds checks before data access\n3. Don't index into data arrays at all\n\nNo further changes needed in this file."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":7046,"cache_read_input_tokens":29065,"output_tokens":3342,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":7046,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6PiJxWpQ4pof75DbX3p","type":"assistant","uuid":"16b0a9ea-b80c-4434-9e1b-56d28663a39a","timestamp":"2026-03-16T07:50:32.343Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"16b0a9ea-b80c-4434-9e1b-56d28663a39a","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_018d9zfSiTDZFa2vXWP5jb7P","toolUseID":"toolu_018d9zfSiTDZFa2vXWP5jb7P","timestamp":"2026-03-16T07:50:32.366Z","uuid":"09c57dc9-e279-49e0-85c7-1dc0388108c0","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"16b0a9ea-b80c-4434-9e1b-56d28663a39a","isSidechain":false,"promptId":"807decf9-6fba-46ef-86b3-8a871a0dbbdf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018d9zfSiTDZFa2vXWP5jb7P","type":"tool_result","content":"The file /Users/jan/.claude/plans/frolicking-splashing-kay.md has been updated successfully."}]},"uuid":"fa4ce5d6-b36e-4838-b659-a8e300c34dbc","timestamp":"2026-03-16T07:50:32.366Z","toolUseResult":{"filePath":"/Users/jan/.claude/plans/frolicking-splashing-kay.md","oldString":"# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data","newString":"# Analysis: Similar Bugs in cc-lib `schema/metrics.go`\n\n## Context\n\nAfter fixing the `AddNodeScope` panic (index out of range due to using `jm.Series` instead of local `series`), we're checking if the same class of bug exists elsewhere in the file.\n\n## Already Fixed: `AddNodeScope` (lines 273-339)\n\nThe fix is already applied — `n`/`m` and summation now use local `series` (per-host) instead of `jm.Series` (all hosts). Tests pass.\n\n## Audit of All Functions in `metrics.go`\n\n### `AddStatisticsSeries()` (lines 188-271) — **NO BUG**\n- Computes min/median/max **across ALL series** at each timestep — using `jm.Series` is **intentionally correct**\n- `n`/`m` computed from `jm.Series` (line 193-201): correct, since stats span all series\n- Data access `jm.Series[j].Data[i]` (line 212): guarded by `i < m` where `m` is the minimum series length — safe\n- NaN padding loop (line 243): `i` goes from `m` to `n`, arrays sized to `n` — safe\n- Entry guard (line 189): `len(jm.Series) < 4` prevents `Series[0]` panic\n\n### `AddPercentiles()` (lines 372-424) — **NO BUG**\n- Also operates on ALL series (percentiles across the full set) — `jm.Series` correct\n- Explicit bounds check at line 396: `if i < len(series.Data)` — safe\n- `sorted` at line 417 is never empty because `n` = max series length, so at least one series contributes data at every index\n\n### `RoundMetricStats()` (lines 341-354) — **NO BUG**\n- Only modifies `Statistics` structs (Min/Avg/Max), no data array indexing\n\n### `RoundScopedMetricStats()` (lines 356-370) — **NO BUG**\n- Same pattern, only touches `Data` field which is a `*MetricStatistics`, not a slice\n\n## Conclusion\n\n**`AddNodeScope` was the only function with this bug class.** The other functions either:\n1. Intentionally operate on ALL series (not per-host), so `jm.Series` is correct\n2. Have proper bounds checks before data access\n3. Don't index into data arrays at all\n\nNo further changes needed in this file.","originalFile":"# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)\n\n## Context\n\nThe archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).\n\n## Root Cause (in cc-lib)\n\n`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:\n\n### Bug 1 — The crash (line 327)\n- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)\n- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname\n- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data\n\n### Bug 2 — Logic error (lines 318-323)\n- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)\n- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics\n\n## Fix (in cc-lib `schema/metrics.go`)\n\nReplace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):\n\n```go\n// Before (BUGGY):\nn, m := 0, len(jm.Series[0].Data)\nfor _, series := range jm.Series {\n ...\n}\ni, data := 0, make([]Float, len(series[0].Data))\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, series := range jm.Series {\n x += series.Data[i]\n }\n data[i] = x\n}\n\n// After (FIXED):\nn, m := 0, len(series[0].Data)\nfor _, s := range series {\n if len(s.Data) > n {\n n = len(s.Data)\n }\n if len(s.Data) < m {\n m = len(s.Data)\n }\n}\ni, data := 0, make([]Float, n)\nfor ; i < m; i++ {\n x := Float(0.0)\n for _, s := range series {\n x += s.Data[i]\n }\n data[i] = x\n}\n```\n\nKey changes:\n1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`\n2. `data` sized to `n` (max length for this host), not `series[0].Data` length\n3. Summation iterates local `series`, not `jm.Series`\n\n## Where to Apply\n\nThe fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:\n\n1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend\n2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug\n\n**Recommended: Option 1** — fix cc-lib.\n\n## Verification\n\n- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation\n- Integration: archive a job with mixed-length metric data\n","structuredPatch":[{"oldStart":1,"oldLines":76,"newStart":1,"newLines":38,"lines":["-# Fix: Panic in `AddNodeScope` (cc-lib v2.8.2)","+# Analysis: Similar Bugs in cc-lib `schema/metrics.go`"," "," ## Context"," ","-The archiver crashes with `panic: runtime error: index out of range [4] with length 4` at `schema/metrics.go:327` in `AddNodeScope`. This happens during job archiving when `LoadData` calls `jd.AddNodeScope(\"flops_any\")` or `jd.AddNodeScope(\"mem_bw\")` (dataLoader.go:214-215).","+After fixing the `AddNodeScope` panic (index out of range due to using `jm.Series` instead of local `series`), we're checking if the same class of bug exists elsewhere in the file."," ","-## Root Cause (in cc-lib)","+## Already Fixed: `AddNodeScope` (lines 273-339)"," ","-`AddNodeScope` aggregates sub-node metrics (e.g., per-core) into node-level values. It has **two bugs**, both in `schema/metrics.go:299-328`:","+The fix is already applied — `n`/`m` and summation now use local `series` (per-host) instead of `jm.Series` (all hosts). Tests pass."," ","-### Bug 1 — The crash (line 327)","-- Line 317: `data` is allocated with `len(series[0].Data)` — the current hostname's first sub-series length (e.g., 4)","-- Lines 307-315: `n` (max data length) is computed across **ALL** `jm.Series` (all hostnames), not just the current hostname","-- Lines 326-328: The loop `for ; i < n; i++ { data[i] = NaN }` writes past `data`'s bounds when another hostname has longer data","+## Audit of All Functions in `metrics.go`"," ","-### Bug 2 — Logic error (lines 318-323)","-- The summation loop iterates `jm.Series` (ALL hostnames' sub-node series) instead of the local `series` (current hostname only)","-- This means each node-level value is the sum across ALL hosts, not just that host's sub-node metrics","+### `AddStatisticsSeries()` (lines 188-271) — **NO BUG**","+- Computes min/median/max **across ALL series** at each timestep — using `jm.Series` is **intentionally correct**","+- `n`/`m` computed from `jm.Series` (line 193-201): correct, since stats span all series","+- Data access `jm.Series[j].Data[i]` (line 212): guarded by `i < m` where `m` is the minimum series length — safe","+- NaN padding loop (line 243): `i` goes from `m` to `n`, arrays sized to `n` — safe","+- Entry guard (line 189): `len(jm.Series) < 4` prevents `Series[0]` panic"," ","-## Fix (in cc-lib `schema/metrics.go`)","+### `AddPercentiles()` (lines 372-424) — **NO BUG**","+- Also operates on ALL series (percentiles across the full set) — `jm.Series` correct","+- Explicit bounds check at line 396: `if i < len(series.Data)` — safe","+- `sorted` at line 417 is never empty because `n` = max series length, so at least one series contributes data at every index"," ","-Replace lines 307-324 to use the local `series` variable (current hostname) instead of `jm.Series` (all hostnames):","+### `RoundMetricStats()` (lines 341-354) — **NO BUG**","+- Only modifies `Statistics` structs (Min/Avg/Max), no data array indexing"," ","-```go","-// Before (BUGGY):","-n, m := 0, len(jm.Series[0].Data)","-for _, series := range jm.Series {","- ...","-}","-i, data := 0, make([]Float, len(series[0].Data))","-for ; i < m; i++ {","- x := Float(0.0)","- for _, series := range jm.Series {","- x += series.Data[i]","- }","- data[i] = x","-}","+### `RoundScopedMetricStats()` (lines 356-370) — **NO BUG**","+- Same pattern, only touches `Data` field which is a `*MetricStatistics`, not a slice"," ","-// After (FIXED):","-n, m := 0, len(series[0].Data)","-for _, s := range series {","- if len(s.Data) > n {","- n = len(s.Data)","- }","- if len(s.Data) < m {","- m = len(s.Data)","- }","-}","-i, data := 0, make([]Float, n)","-for ; i < m; i++ {","- x := Float(0.0)","- for _, s := range series {","- x += s.Data[i]","- }","- data[i] = x","-}","-```","+## Conclusion"," ","-Key changes:","-1. `n`/`m` computed from local `series` (current hostname), not `jm.Series`","-2. `data` sized to `n` (max length for this host), not `series[0].Data` length","-3. Summation iterates local `series`, not `jm.Series`","+**`AddNodeScope` was the only function with this bug class.** The other functions either:","+1. Intentionally operate on ALL series (not per-host), so `jm.Series` is correct","+2. Have proper bounds checks before data access","+3. Don't index into data arrays at all"," ","-## Where to Apply","-","-The fix is in **cc-lib** (`schema/metrics.go:307-324`), not cc-backend. Options:","-","-1. **Fix in cc-lib** — update `AddNodeScope` and release a new version, then bump the dependency in cc-backend","-2. **Workaround in cc-backend** — add a defensive wrapper in `dataLoader.go` before calling `AddNodeScope` (e.g., check series lengths), but this only masks the logic bug","-","-**Recommended: Option 1** — fix cc-lib.","-","-## Verification","-","-- Unit test: create a `JobData` with sub-node metrics where different hosts have different data lengths, call `AddNodeScope`, verify no panic and correct aggregation","-- Integration: archive a job with mixed-length metric data","+No further changes needed in this file."]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"16b0a9ea-b80c-4434-9e1b-56d28663a39a","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"fa4ce5d6-b36e-4838-b659-a8e300c34dbc","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Hrf3vt2Jaz5LfBDagohYd6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C5ZEJwPkoGYJ4d9R537T2n","name":"ToolSearch","input":{"query":"select:ExitPlanMode","max_results":1},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3380,"cache_read_input_tokens":36111,"output_tokens":78,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3380,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6Po3VjDRk2XqLbDaFFm","type":"assistant","uuid":"a2fde27e-7029-4a0c-80c3-8e4eb0aeb559","timestamp":"2026-03-16T07:50:37.504Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"a2fde27e-7029-4a0c-80c3-8e4eb0aeb559","isSidechain":false,"promptId":"807decf9-6fba-46ef-86b3-8a871a0dbbdf","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01C5ZEJwPkoGYJ4d9R537T2n","content":[{"type":"tool_reference","tool_name":"ExitPlanMode"}]}]},"uuid":"88579f1e-a956-4171-aaf0-9932bdc28032","timestamp":"2026-03-16T07:50:37.505Z","toolUseResult":{"matches":["ExitPlanMode"],"query":"select:ExitPlanMode","total_deferred_tools":21},"sourceToolAssistantUUID":"a2fde27e-7029-4a0c-80c3-8e4eb0aeb559","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"88579f1e-a956-4171-aaf0-9932bdc28032","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Xh72nGaD3rZBc8pRV3i6s9","type":"message","role":"assistant","content":[{"type":"text","text":"\n\n**`AddNodeScope` was the only function with this bug class.** The other 4 functions in the file are clean:\n\n- **`AddStatisticsSeries`** and **`AddPercentiles`** — intentionally operate on ALL series (not per-host), so using `jm.Series` is correct. Both have proper bounds checks.\n- **`RoundMetricStats`** and **`RoundScopedMetricStats`** — only touch `Statistics` structs, no data array indexing at all.\n\nNo further code changes needed. The fix we already applied is the only instance."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":831,"cache_read_input_tokens":39491,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":831},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6PoRre1vnFctaMrLNHE","type":"assistant","uuid":"98cbf045-32c9-41fb-be29-d589221bc98b","timestamp":"2026-03-16T07:50:42.551Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"98cbf045-32c9-41fb-be29-d589221bc98b","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Xh72nGaD3rZBc8pRV3i6s9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UTNnfL7UyL3WT49aEaV1nm","name":"ExitPlanMode","input":{"plan":"# Analysis: Similar Bugs in cc-lib `schema/metrics.go`\n\n## Context\n\nAfter fixing the `AddNodeScope` panic (index out of range due to using `jm.Series` instead of local `series`), we're checking if the same class of bug exists elsewhere in the file.\n\n## Already Fixed: `AddNodeScope` (lines 273-339)\n\nThe fix is already applied — `n`/`m` and summation now use local `series` (per-host) instead of `jm.Series` (all hosts). Tests pass.\n\n## Audit of All Functions in `metrics.go`\n\n### `AddStatisticsSeries()` (lines 188-271) — **NO BUG**\n- Computes min/median/max **across ALL series** at each timestep — using `jm.Series` is **intentionally correct**\n- `n`/`m` computed from `jm.Series` (line 193-201): correct, since stats span all series\n- Data access `jm.Series[j].Data[i]` (line 212): guarded by `i < m` where `m` is the minimum series length — safe\n- NaN padding loop (line 243): `i` goes from `m` to `n`, arrays sized to `n` — safe\n- Entry guard (line 189): `len(jm.Series) < 4` prevents `Series[0]` panic\n\n### `AddPercentiles()` (lines 372-424) — **NO BUG**\n- Also operates on ALL series (percentiles across the full set) — `jm.Series` correct\n- Explicit bounds check at line 396: `if i < len(series.Data)` — safe\n- `sorted` at line 417 is never empty because `n` = max series length, so at least one series contributes data at every index\n\n### `RoundMetricStats()` (lines 341-354) — **NO BUG**\n- Only modifies `Statistics` structs (Min/Avg/Max), no data array indexing\n\n### `RoundScopedMetricStats()` (lines 356-370) — **NO BUG**\n- Same pattern, only touches `Data` field which is a `*MetricStatistics`, not a slice\n\n## Conclusion\n\n**`AddNodeScope` was the only function with this bug class.** The other functions either:\n1. Intentionally operate on ALL series (not per-host), so `jm.Series` is correct\n2. Have proper bounds checks before data access\n3. Don't index into data arrays at all\n\nNo further changes needed in this file.\n","planFilePath":"/Users/jan/.claude/plans/frolicking-splashing-kay.md"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":831,"cache_read_input_tokens":39491,"output_tokens":177,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":831,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6PoRre1vnFctaMrLNHE","type":"assistant","uuid":"5b1b7dff-ba3c-4e86-b5d8-6bf88d8b5753","timestamp":"2026-03-16T07:50:42.552Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"5b1b7dff-ba3c-4e86-b5d8-6bf88d8b5753","isSidechain":false,"promptId":"807decf9-6fba-46ef-86b3-8a871a0dbbdf","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"The user doesn't want to proceed with this tool use. 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Let me check if that version has the fix."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":410,"cache_read_input_tokens":40604,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":410},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ6Q7hZxBC768dcLtXxM8","type":"assistant","uuid":"c392c961-3096-4476-a530-99a478dcdae9","timestamp":"2026-03-16T07:54:48.944Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"c392c961-3096-4476-a530-99a478dcdae9","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01CRWp5gv2ap6264Jwv4PZiU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011Ru8vEjurh3zj2Vekx2Ywg","name":"Read","input":{"file_path":"/Users/jan/go/pkg/mod/github.com/!cluster!cockpit/cc-lib/v2@v2.9.0/schema/metrics.go","offset":299,"limit":30},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":410,"cache_read_input_tokens":40604,"output_tokens":166,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":410,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6Q7hZxBC768dcLtXxM8","type":"assistant","uuid":"5437916f-a676-4980-a747-1d775bb741c5","timestamp":"2026-03-16T07:54:50.092Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"5437916f-a676-4980-a747-1d775bb741c5","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_011Ru8vEjurh3zj2Vekx2Ywg","toolUseID":"toolu_011Ru8vEjurh3zj2Vekx2Ywg","timestamp":"2026-03-16T07:54:50.115Z","uuid":"6733c3e5-b4fb-44f5-9478-f475ede597d6","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"5437916f-a676-4980-a747-1d775bb741c5","isSidechain":false,"promptId":"5694affd-e4e4-4aa8-9aaf-626d500c32ce","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011Ru8vEjurh3zj2Vekx2Ywg","type":"tool_result","content":" 299→\tfor hostname, series := range hosts {\n 300→\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n 301→\t\tfor _, series := range series {\n 302→\t\t\tsum += series.Statistics.Avg\n 303→\t\t\tmin = math.Min(min, series.Statistics.Min)\n 304→\t\t\tmax = math.Max(max, series.Statistics.Max)\n 305→\t\t}\n 306→\n 307→\t\tn, m := 0, len(series[0].Data)\n 308→\t\tfor _, s := range series {\n 309→\t\t\tif len(s.Data) > n {\n 310→\t\t\t\tn = len(s.Data)\n 311→\t\t\t}\n 312→\t\t\tif len(s.Data) < m {\n 313→\t\t\t\tm = len(s.Data)\n 314→\t\t\t}\n 315→\t\t}\n 316→\n 317→\t\ti, data := 0, make([]Float, n)\n 318→\t\tfor ; i < m; i++ {\n 319→\t\t\tx := Float(0.0)\n 320→\t\t\tfor _, s := range series {\n 321→\t\t\t\tx += s.Data[i]\n 322→\t\t\t}\n 323→\t\t\tdata[i] = x\n 324→\t\t}\n 325→\n 326→\t\tfor ; i < n; i++ {\n 327→\t\t\tdata[i] = NaN\n 328→\t\t}"}]},"uuid":"6b407c89-0728-42c0-b831-d180049087d6","timestamp":"2026-03-16T07:54:50.115Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/go/pkg/mod/github.com/!cluster!cockpit/cc-lib/v2@v2.9.0/schema/metrics.go","content":"\tfor hostname, series := range hosts {\n\t\tmin, sum, max := math.MaxFloat32, 0.0, -math.MaxFloat32\n\t\tfor _, series := range series {\n\t\t\tsum += 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\tn, m := 0, len(series[0].Data)\n\t\tfor _, s := range series {\n\t\t\tif len(s.Data) > n {\n\t\t\t\tn = len(s.Data)\n\t\t\t}\n\t\t\tif len(s.Data) < m {\n\t\t\t\tm = len(s.Data)\n\t\t\t}\n\t\t}\n\n\t\ti, data := 0, make([]Float, n)\n\t\tfor ; i < m; i++ {\n\t\t\tx := Float(0.0)\n\t\t\tfor _, s := range series {\n\t\t\t\tx += s.Data[i]\n\t\t\t}\n\t\t\tdata[i] = x\n\t\t}\n\n\t\tfor ; i < n; i++ {\n\t\t\tdata[i] = NaN\n\t\t}","numLines":30,"startLine":299,"totalLines":425}},"sourceToolAssistantUUID":"5437916f-a676-4980-a747-1d775bb741c5","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"6b407c89-0728-42c0-b831-d180049087d6","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01G9C7F5rXuv1KvefaWraN1i","type":"message","role":"assistant","content":[{"type":"text","text":"**Yes, the fix is already included in cc-lib v2.9.0** which cc-backend currently uses. The code at lines 307-324 matches the fixed version:\n\n- Line 307: `n, m := 0, len(series[0].Data)` — uses local `series` (not `jm.Series`)\n- Line 308: `for _, s := range series` — iterates local `series`\n- Line 317: `make([]Float, n)` — sized to `n` (max length)\n- Line 320: `for _, s := range series` — sums local `series`\n\nNo action needed — cc-backend's dependency is already on the fixed version."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1034,"cache_read_input_tokens":40604,"output_tokens":164,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1034,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ6Q83fveUYe9bKUeXKJP","type":"assistant","uuid":"ca03dac8-c338-4649-8199-37adb8760ec9","timestamp":"2026-03-16T07:54:54.772Z","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"ca03dac8-c338-4649-8199-37adb8760ec9","isSidechain":false,"type":"progress","data":{"type":"hook_progress","hookEvent":"Stop","hookName":"Stop","command":"entire hooks claude-code stop"},"parentToolUseID":"1d5fc5f1-d32a-456f-86e8-1c4f5860981d","toolUseID":"1d5fc5f1-d32a-456f-86e8-1c4f5860981d","timestamp":"2026-03-16T07:54:54.795Z","uuid":"c4447e70-3d00-4502-bd8e-537eef40bf62","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} +{"parentUuid":"c4447e70-3d00-4502-bd8e-537eef40bf62","isSidechain":false,"type":"system","subtype":"stop_hook_summary","hookCount":1,"hookInfos":[{"command":"entire hooks claude-code stop","durationMs":443}],"hookErrors":[],"preventedContinuation":false,"stopReason":"","hasOutput":false,"level":"suggestion","timestamp":"2026-03-16T07:54:55.238Z","uuid":"d7c9f2da-b7ac-438b-aa7d-908c86d931c2","toolUseID":"1d5fc5f1-d32a-456f-86e8-1c4f5860981d","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"7e6b850e-d9e5-48ec-8e24-07c1b3960faa","version":"2.1.76","gitBranch":"hotfix","slug":"frolicking-splashing-kay"} diff --git a/af/ef27e07ec9/0/metadata.json b/af/ef27e07ec9/0/metadata.json new file mode 100644 index 00000000..273c36bc --- /dev/null +++ b/af/ef27e07ec9/0/metadata.json @@ -0,0 +1,34 @@ +{ + "cli_version": "0.5.0", + "checkpoint_id": "afef27e07ec9", + "session_id": "7e6b850e-d9e5-48ec-8e24-07c1b3960faa", + "strategy": "manual-commit", + "created_at": "2026-03-16T07:55:57.189589Z", + "branch": "hotfix", + "checkpoints_count": 2, + "files_touched": [ + "go.mod", + "go.sum" + ], + "agent": "Claude Code", + "turn_id": "50f042f4abfc", + "token_usage": { + "input_tokens": 25, + "cache_creation_tokens": 27775, + "cache_read_tokens": 470840, + "output_tokens": 7980, + "api_call_count": 17 + }, + "session_metrics": { + "turn_count": 2 + }, + "initial_attribution": { + "calculated_at": "2026-03-16T07:55:57.14185Z", + "agent_lines": 0, + "human_added": 2, + "human_modified": 1, + "human_removed": 0, + "total_committed": 2, + "agent_percentage": 0 + } +} diff --git a/af/ef27e07ec9/0/prompt.txt b/af/ef27e07ec9/0/prompt.txt new file mode 100644 index 00000000..f3b17d8b --- /dev/null +++ b/af/ef27e07ec9/0/prompt.txt @@ -0,0 +1,5 @@ +Analyse if similar errors exist in cc-lib metrics.go + +--- + +Check if the previous bug is fixed no in the current cc-lib used \ No newline at end of file diff --git a/af/ef27e07ec9/metadata.json b/af/ef27e07ec9/metadata.json new file mode 100644 index 00000000..9a704126 --- /dev/null +++ b/af/ef27e07ec9/metadata.json @@ -0,0 +1,26 @@ +{ + "cli_version": "0.5.0", + "checkpoint_id": "afef27e07ec9", + "strategy": "manual-commit", + "branch": "hotfix", + "checkpoints_count": 2, + "files_touched": [ + "go.mod", + "go.sum" + ], + "sessions": [ + { + "metadata": "/af/ef27e07ec9/0/metadata.json", + "transcript": "/af/ef27e07ec9/0/full.jsonl", + "content_hash": "/af/ef27e07ec9/0/content_hash.txt", + "prompt": "/af/ef27e07ec9/0/prompt.txt" + } + ], + "token_usage": { + "input_tokens": 25, + "cache_creation_tokens": 27775, + "cache_read_tokens": 470840, + "output_tokens": 7980, + "api_call_count": 17 + } +}