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Merge remote session logs

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This commit is contained in:
Jan Eitzinger
2026-03-20 09:19:29 +01:00
parent dc4e0d2c25 2afa16903c
commit 21d49d9c5c
157 changed files with 6975 additions and 0 deletions
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Correction: If a node is idle + down then mark it as down
---
Change for both the REST and NATS API that the healthstate update is only performed if a node is not in down state. Do mark those nodes as healthy

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Extend CLAUDE.md and add remarks that on any significant change als o sideeffects and all call paths have to be checked. Also add that this is an application that deals with large amounts of data and should focus on maximum throughput.

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# Session Context
## User Prompts
### Prompt 1
Implement the following plan:
# Plan: Improve GetUser logging
## Context
`GetUser` in `internal/repository/user.go` (line 75) logs a `Warn` for every query error, including the common `sql.ErrNoRows` case (user not found). Two problems:
1. `sql.ErrNoRows` is a normal, expected condition — many callers check for it explicitly. It should not produce a warning.
2. The log message **omits the actual error**: `"Error while querying user '%v' from database"` gives no clue what went wrong for re...

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Implement the following plan:
# Plan: Improve GetUser logging
## Context
`GetUser` in `internal/repository/user.go` (line 75) logs a `Warn` for every query error, including the common `sql.ErrNoRows` case (user not found). Two problems:
1. `sql.ErrNoRows` is a normal, expected condition — many callers check for it explicitly. It should not produce a warning.
2. The log message **omits the actual error**: `"Error while querying user '%v' from database"` gives no clue what went wrong for real failures.
This is the same pattern just fixed in `scanJob` (job.go). The established approach: suppress `sql.ErrNoRows`, add `runtime.Caller(1)` context and include `err` in real-error logs.
## Approach
Modify the `Scan` error block in `GetUser` (`internal/repository/user.go`, lines 75-79):
```go
if err := sq.Select(...).QueryRow().Scan(...); err != nil {
if err != sql.ErrNoRows {
_, file, line, _ := runtime.Caller(1)
cclog.Warnf("Error while querying user '%v' from database (%s:%d): %v",
username, filepath.Base(file), line, err)
}
return nil, err
}
```
Add `"path/filepath"` and `"runtime"` to imports (not currently present in user.go).
## Critical File
- `internal/repository/user.go` — only file to change (lines ~7-24 for imports, ~75-79 for error block)
## Verification
1. `go build ./...` — must compile cleanly
2. `go test ./internal/repository/...` — existing tests must pass
If 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-CC-cc-backend/7916ffa0-cf9e-4cb7-a75f-8a1db33c75bd.jsonl

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Add more context information to the log message at line 424

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Check if there are points missing in the ReleaseNotes for the upcoming v1.5.2 release
---
Add the points as suggested

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After those changes the application blocks during startup in ReceiveNats. Ivestigate issue and fix it.

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Also updaten the documentation and config schema to reflect the new options

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Are there other opportunities to reduce the insert pressure on the db using transactions or other techniques?
---
Update the Database config section in the README to reflect the new setting

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# Session Context
## User Prompts
### Prompt 1
Implement the following plan:
# Fix: Memory Escalation in flattenCheckpointFile (68GB+)
## Context
Production gops shows `flattenCheckpointFile` allocating 68GB+ (74.89% of memory). The archiving pipeline accumulates ALL metric data from ALL hosts into a single `[]ParquetMetricRow` slice before writing to Parquet. For large HPC clusters this is catastrophic. Additionally, the `SortingWriterConfig` in the parquet writer buffers everything again internally.
## Root Cause
Two-layer unbounde...
### Prompt 2
Are the any other cases with memory spikes using the Parquet Writer, e.g. in the nodestate retention?
### Prompt 3
[Request interrupted by user for tool use]

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Implement the following plan:
# Fix: Memory Escalation in flattenCheckpointFile (68GB+)
## Context
Production gops shows `flattenCheckpointFile` allocating 68GB+ (74.89% of memory). The archiving pipeline accumulates ALL metric data from ALL hosts into a single `[]ParquetMetricRow` slice before writing to Parquet. For large HPC clusters this is catastrophic. Additionally, the `SortingWriterConfig` in the parquet writer buffers everything again internally.
## Root Cause
Two-layer unbounded accumulation:
1. **`archive.go:239-242`**: `allRows = append(allRows, r.rows...)` merges every host's rows into one giant slice
2. **`parquetArchive.go:108-116`**: `SortingWriterConfig` creates a sorting writer that buffers ALL rows until `Close()`
3. **`parquetArchive.go:199`**: `var rows []ParquetMetricRow` starts at zero capacity, grows via append doubling
Peak memory = (all hosts' rows) + (sorting writer copy) + (append overhead) = ~3x raw data size.
## Fix: Stream per-host to parquet writer
Instead of accumulating all rows, write each host's data as a separate row group.
### Step 1: Add streaming parquet writer (`parquetArchive.go`)
Replace `writeParquetArchive(filename, rows)` with a struct that supports incremental writes:
```go
type parquetArchiveWriter struct {
writer *pq.GenericWriter[ParquetMetricRow]
bw *bufio.Writer
f *os.File
count int
}
func newParquetArchiveWriter(filename string) (*parquetArchiveWriter, error)
func (w *parquetArchiveWriter) WriteHostRows(rows []ParquetMetricRow) error // Write + Flush (creates row group)
func (w *parquetArchiveWriter) Close() error
```
- **Remove `SortingWriterConfig`** - no global sort buffer
- Sort each host's rows in-place with `sort.Slice` before writing (cheap: single host data)
- Each `Flush()` creates a separate row group per host
### Step 2: Add row count estimation (`parquetArchive.go`)
```go
func estimateRowCount(cf *CheckpointFile) int
```
Pre-allocate `rows` slice in `archiveCheckpointsToParquet` to avoid append doubling per host.
### Step 3: Restructure `archiveCheckpoints` (`archive.go`)
Change from:
```
workers → channel → accumulate allRows → writeParquetArchive(allRows)
```
To:
```
open writer → workers → channel → for each host: sort rows, writer.WriteHostRows(rows) → close writer
```
- Only one host's rows in memory at a time
- Track `files`/`dir` for deletion separately (don't retain rows)
- Check `writer.count > 0` instead of `len(allRows) == 0`
### Step 4: Update test (`parquetArchive_test.go`)
- `TestParquetArchiveRoundtrip`: use new streaming writer API
- Keep `archiveCheckpointsToParquet` returning rows (it's per-host, manageable size)
## Files to Modify
- **`pkg/metricstore/parquetArchive.go`**: Add `parquetArchiveWriter`, `estimateRowCount`; remove `writeParquetArchive`; add `"sort"` import
- **`pkg/metricstore/archive.go`**: Restructure `archiveCheckpoints` to stream
- **`pkg/metricstore/parquetArchive_test.go`**: Update roundtrip test
## Memory Impact
- **Before**: All hosts in memory (~40GB for 256 nodes) + sorting buffer (~40GB) = 68GB+
- **After**: One host at a time (~16MB) + parquet page buffer (~1MB) = ~17MB peak
## Sorting Tradeoff
The output changes from one globally-sorted row group to N row groups (one per host), each internally sorted by (metric, timestamp). This is actually better for ClusterCockpit's per-host query patterns (enables row group skipping).
## Verification
```bash
go test -v ./pkg/metricstore/...
```
Also verify with `go vet ./pkg/metricstore/...` for correctness.
If 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-CC-cc-backend/71340843-de3d-4e83-9dcb-2fc130c50e0d.jsonl
---
Are the any other cases with memory spikes using the Parquet Writer, e.g. in the nodestate retention?
---
[Request interrupted by user for tool use]

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---
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Does a similar problem occur on the metricstore REST API path?
---
How many os level threads are used in this application? It is currently configurable?

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Are there other opportunities to reduce the insert pressure on the db using transactions or other techniques?
---
Update the Database config section in the README to reflect the new setting

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# Session Context
## User Prompts
### Prompt 1
Implement the following plan:
# Plan: Improve scanJob logging
## Context
`scanJob` in `internal/repository/job.go` (line 162) logs a `Warn` for every scan error, including the very common `sql.ErrNoRows` case. This produces noisy, unhelpful log lines like:
```
WARN Error while scanning rows (Job): sql: no rows in result set
```
Two problems:
1. `sql.ErrNoRows` is a normal, expected condition (callers are documented to check for it). It should not produce a warning.
2. When a real scan er...

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Implement the following plan:
# Plan: Improve scanJob logging
## Context
`scanJob` in `internal/repository/job.go` (line 162) logs a `Warn` for every scan error, including the very common `sql.ErrNoRows` case. This produces noisy, unhelpful log lines like:
```
WARN Error while scanning rows (Job): sql: no rows in result set
```
Two problems:
1. `sql.ErrNoRows` is a normal, expected condition (callers are documented to check for it). It should not produce a warning.
2. When a real scan error does occur, there's no call-site context — you can't tell which of the ~10 callers triggered it.
The codebase already has a precedent for using `runtime.Caller(1)` in `internal/api/rest.go:188` to include file/line in log messages.
## Approach
Modify `scanJob` in `internal/repository/job.go`:
1. **Skip logging for `sql.ErrNoRows`** — return the error silently. All callers already handle this case themselves.
2. **Add caller context** for real scan errors using `runtime.Caller(1)` — include `file:line` in the warning message.
```go
import (
"database/sql"
"path/filepath"
"runtime"
// existing imports ...
)
func scanJob(row interface{ Scan(...any) error }) (*schema.Job, error) {
job := &schema.Job{}
if err := row.Scan(...); err != nil {
if err != sql.ErrNoRows {
_, file, line, _ := runtime.Caller(1)
cclog.Warnf("Error while scanning rows (Job) (%s:%d): %v", filepath.Base(file), line, err)
}
return nil, err
}
// ... rest unchanged
}
```
## Critical File
- `internal/repository/job.go` — only file to change (lines 154–184)
## Imports to check
`database/sql` is likely already imported (used elsewhere in the package). `runtime` and `path/filepath` — check if already present; add if not.
## Verification
1. `go build ./...` — must compile cleanly
2. `go test ./internal/repository/...` — existing tests must pass
3. Manually: trigger a lookup for a non-existent job ID; confirm no warning is logged
4. Manually (or via test): force a real scan error; confirm warning includes `job.go:<line>`
If 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-CC-cc-backend/b03f52bf-e58e-45a2-8b95-af846938ee2c.jsonl

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# Session Context
## User Prompts
### Prompt 1
Implement the following plan:
# Plan: Add RRDTool-style Average Consolidation Function to Resampler
## Context
The current downsampler in `cc-lib/v2/resampler` offers two algorithms:
- **LTTB** (LargestTriangleThreeBucket): Perceptually-aware — picks points that preserve visual shape (peaks/valleys). Used at all call sites.
- **SimpleResampler**: Decimation — picks every nth point. Fast but lossy.
Neither produces scientifically accurate averages over time intervals. RRDTool's **AVERAGE C...

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Implement the following plan:
# Plan: Add RRDTool-style Average Consolidation Function to Resampler
## Context
The current downsampler in `cc-lib/v2/resampler` offers two algorithms:
- **LTTB** (LargestTriangleThreeBucket): Perceptually-aware — picks points that preserve visual shape (peaks/valleys). Used at all call sites.
- **SimpleResampler**: Decimation — picks every nth point. Fast but lossy.
Neither produces scientifically accurate averages over time intervals. RRDTool's **AVERAGE Consolidation Function (CF)** divides data into fixed-size buckets and computes the arithmetic mean of all points in each bucket. This is the standard approach for monitoring data where the true mean per interval matters more than visual fidelity (e.g., for statistics, SLA reporting, capacity planning).
The goal: add an `AverageResampler` to cc-lib, then expose it as a per-query option in cc-backend's GraphQL API so the frontend/caller can choose the algorithm.
---
## Part 1: cc-lib changes (`/Users/jan/prg/CC/cc-lib/`)
### 1a. Add `AverageResampler` to `resampler/resampler.go`
New exported function with the same signature as existing resamplers:
```go
func AverageResampler(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)
```
**Algorithm (RRDTool AVERAGE CF):**
1. Call `validateFrequency()` for early-exit checks (reuses existing validation).
2. Compute `step = newFrequency / oldFrequency` (points per bucket).
3. For each bucket of `step` consecutive points, compute arithmetic mean skipping NaN values. If all values in a bucket are NaN, output NaN for that bucket.
4. Return averaged data, `newFrequency`, nil.
**NaN handling**: Skip NaN values and average only valid points (consistent with RRDTool behavior and `AddStats()` in `pkg/metricstore/api.go`). This differs from LTTB which propagates NaN if *any* bucket point is NaN.
### 1b. Add `ResamplerFunc` type and lookup
Add a function type and string-based lookup to make algorithm selection easy for consumers:
```go
// ResamplerFunc is the signature shared by all resampler algorithms.
type ResamplerFunc func(data []schema.Float, oldFrequency int64, newFrequency int64) ([]schema.Float, int64, error)
// GetResampler returns the resampler function for the given name.
// Valid names: "lttb" (default), "average", "simple". Empty string returns LTTB.
func GetResampler(name string) (ResamplerFunc, error)
```
### 1c. Add tests to `resampler/resampler_test.go`
Following the existing test patterns (table-driven, same helper functions):
- Basic averaging: `[1,2,3,4,5,6]` step=2 → `[1.5, 3.5, 5.5]`
- NaN skipping within buckets
- All-NaN bucket → NaN output
- Early-exit conditions (same set as SimpleResampler/LTTB tests)
- Frequency validation errors
- Benchmark: `BenchmarkAverageResampler`
---
## Part 2: cc-backend changes (`/Users/jan/tmp/cc-backend/`)
### 2a. Add `replace` directive to `go.mod`
```
replace github.com/ClusterCockpit/cc-lib/v2 => ../../prg/CC/cc-lib
```
### 2b. Add GraphQL enum and query parameter
In `api/schema.graphqls`, add enum and parameter to both queries that accept `resolution`:
```graphql
enum ResampleAlgo {
LTTB
AVERAGE
SIMPLE
}
```
Add `resampleAlgo: ResampleAlgo` parameter to:
- `jobMetrics(id: ID!, metrics: [String!], scopes: [MetricScope!], resolution: Int, resampleAlgo: ResampleAlgo): [JobMetricWithName!]!`
- `nodeMetricsList(..., resolution: Int, resampleAlgo: ResampleAlgo): NodesResultList!`
Then run `make graphql` to regenerate.
### 2c. Update resolvers to pass algorithm through
**`internal/graph/schema.resolvers.go`**:
`JobMetrics` resolver (line ~501): accept new `resampleAlgo *model.ResampleAlgo` parameter (auto-generated by gqlgen), convert to string, pass to `LoadData`.
`NodeMetricsList` resolver (line ~875): same treatment, pass to `LoadNodeListData`.
### 2d. Thread algorithm through data loading
**`internal/metricdispatch/dataLoader.go`**:
- `LoadData()` (line 85): add `resampleAlgo string` parameter. Use `resampler.GetResampler(resampleAlgo)` to get the function, call it instead of hardcoded `resampler.LargestTriangleThreeBucket` at line 145.
- `LoadNodeListData()` (line 409): add `resampleAlgo string` parameter, pass through to `InternalMetricStore.LoadNodeListData`.
**`pkg/metricstore/query.go`**:
- `InternalMetricStore.LoadNodeListData()` (line 616): add `resampleAlgo string` to signature, include in `APIQueryRequest` or pass to `MemoryStore.Read`.
**`pkg/metricstore/metricstore.go`**:
- `MemoryStore.Read()` (line ~682): add `resampleAlgo string` parameter. At line 740, use `resampler.GetResampler(resampleAlgo)` instead of hardcoded LTTB.
### 2e. Update cache key
**`internal/metricdispatch/dataLoader.go`**: The `cacheKey()` function must include the algorithm name so different algorithms don't serve stale cached results.
---
## Files to modify
| File | Repo | Change |
|------|------|--------|
| `resampler/resampler.go` | cc-lib | Add `AverageResampler`, `ResamplerFunc`, `GetResampler` |
| `resampler/resampler_test.go` | cc-lib | Add tests + benchmark |
| `go.mod` | cc-backend | Add `replace` directive |
| `api/schema.graphqls` | cc-backend | Add `ResampleAlgo` enum, update query params |
| `internal/graph/generated/` | cc-backend | Regenerated (`make graphql`) |
| `internal/graph/schema.resolvers.go` | cc-backend | Pass algorithm to data loaders |
| `internal/metricdispatch/dataLoader.go` | cc-backend | Thread algorithm to resampler calls, update cache key |
| `pkg/metricstore/query.go` | cc-backend | Thread algorithm through `LoadNodeListData` |
| `pkg/metricstore/metricstore.go` | cc-backend | Thread algorithm through `Read()`, use `GetResampler` |
## Verification
1. `cd /Users/jan/prg/CC/cc-lib && go test ./resampler/...` — new + existing tests pass
2. `cd /Users/jan/tmp/cc-backend && make graphql` — GraphQL code regenerates cleanly
3. `make` — full build succeeds
4. `make test` — all existing tests pass
5. Manual: GraphQL playground query with `resampleAlgo: AVERAGE` vs default LTTB — average produces smoother curves without artificial peak preservation
If you need specific details from before exiting plan mode (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/jan/.claude/projects/-Users-jan-tmp-cc-backend/190ae749-6a19-47a7-82da-dba5a0e7402d.jsonl

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Analyze the changes from the last release and add a description of the upcoming release.

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