From 10f88fa8eefa74e86fc81fc48aee0e7565abeeff Mon Sep 17 00:00:00 2001 From: Jan Eitzinger Date: Wed, 11 Mar 2026 06:14:07 +0100 Subject: [PATCH] Checkpoint: e368e6d8abf3 Entire-Session: 520afa6a-6a70-437b-96c1-35c40ed3ec48 Entire-Strategy: manual-commit Entire-Agent: Claude Code Ephemeral-branch: entire/301e590-e3b0c4 --- e3/68e6d8abf3/1/content_hash.txt | 1 + e3/68e6d8abf3/1/context.md | 24 ++++ e3/68e6d8abf3/1/full.jsonl | 213 +++++++++++++++++++++++++++++++ e3/68e6d8abf3/1/metadata.json | 34 +++++ e3/68e6d8abf3/1/prompt.txt | 136 ++++++++++++++++++++ e3/68e6d8abf3/metadata.json | 19 ++- 6 files changed, 421 insertions(+), 6 deletions(-) create mode 100644 e3/68e6d8abf3/1/content_hash.txt create mode 100644 e3/68e6d8abf3/1/context.md create mode 100644 e3/68e6d8abf3/1/full.jsonl create mode 100644 e3/68e6d8abf3/1/metadata.json create mode 100644 e3/68e6d8abf3/1/prompt.txt diff --git a/e3/68e6d8abf3/1/content_hash.txt b/e3/68e6d8abf3/1/content_hash.txt new file mode 100644 index 00000000..3927e843 --- /dev/null +++ b/e3/68e6d8abf3/1/content_hash.txt @@ -0,0 +1 @@ +sha256:f187013ac2acf6db7e6f13db2bfe1ab2c10050fe3d8ffd3d41122449dcf54b3c \ No newline at end of file diff --git a/e3/68e6d8abf3/1/context.md b/e3/68e6d8abf3/1/context.md new file mode 100644 index 00000000..5292445e --- /dev/null +++ b/e3/68e6d8abf3/1/context.md @@ -0,0 +1,24 @@ +# Session Context + +## User Prompts + +### Prompt 1 + +Implement the following plan: + +# Fix SQLite Memory Not Released After Query Timeout + +## Context + +On the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration. + +## Root Cause Analysis + +### 1. `_cache_size=1000000000` is effectively unlimited (~4TB) + +**File:** `i... + +### Prompt 2 + +Our server has 512GB main memory. Does it make sense to make cache_size and soft_heap_limit configurable to make use of the main memory capacity? + diff --git a/e3/68e6d8abf3/1/full.jsonl b/e3/68e6d8abf3/1/full.jsonl new file mode 100644 index 00000000..2e0dfeed --- /dev/null +++ b/e3/68e6d8abf3/1/full.jsonl @@ -0,0 +1,213 @@ +{"type":"file-history-snapshot","messageId":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","snapshot":{"messageId":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","trackedFileBackups":{},"timestamp":"2026-03-11T05:01:36.839Z"},"isSnapshotUpdate":false} +{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"SessionStart","hookName":"SessionStart:clear","command":"entire hooks claude-code session-start"},"parentToolUseID":"2207cb14-e661-4342-b025-c6ee4f8c28a4","toolUseID":"2207cb14-e661-4342-b025-c6ee4f8c28a4","timestamp":"2026-03-11T05:01:36.797Z","uuid":"a8b0b488-3227-469c-b496-934922e9f367"} +{"parentUuid":"a8b0b488-3227-469c-b496-934922e9f367","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Fix SQLite Memory Not Released After Query Timeout\n\n## Context\n\nOn the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`\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-CC-cc-backend/c31c699a-f492-48f7-bcf0-35d3ceeac243.jsonl"},"uuid":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","timestamp":"2026-03-11T05:01:34.044Z","planContent":"# Fix SQLite Memory Not Released After Query Timeout\n\n## Context\n\nOn the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`\n"} +{"parentUuid":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EoICCkYICxgCKkBHgN2UeZvCwmGQLUT1lci1RmXS8GvFO6QBbR0lTwucDfKJuxDQuAs0c5iaIbFwKKwuw2UPqq/nDgNaNrcpYcpHEgw8CBV0zTgUoz64jMAaDBbkztA7pDku4Xic7iIwL1kuLcdfEMbYki/6ssZFVP2WAMSg9KVIJVChV0ubqHzjC2BKqhlI126oGnx1XnCJKmqjQ4iBv8+LhF4RfOZpfVfzeg+D8aGuPl79aJ7dWgzL9PkGD+CVqmpYcM0s+dsh8azEbyNAsDJ05jTyW7KxYngoq7k36ohFz/UoF57opUDcMXPfZlRbKYkMLZY/cpdAWIzh7jmagVNfqfDuGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"57154933-cba3-43d3-b80e-2848f9685fe7","timestamp":"2026-03-11T05:01:39.634Z"} +{"parentUuid":"57154933-cba3-43d3-b80e-2848f9685fe7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BVouxo65S9Ysq9RgPFEKPu","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"45a469f0-4916-43d0-8046-d2d200e01fbc","timestamp":"2026-03-11T05:01:40.051Z"} +{"parentUuid":"45a469f0-4916-43d0-8046-d2d200e01fbc","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01BVouxo65S9Ysq9RgPFEKPu","toolUseID":"toolu_01BVouxo65S9Ysq9RgPFEKPu","timestamp":"2026-03-11T05:01:40.077Z","uuid":"96e3441e-29d9-4b04-bbe7-65ce3b14da71"} +{"parentUuid":"45a469f0-4916-43d0-8046-d2d200e01fbc","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BVouxo65S9Ysq9RgPFEKPu","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"database/sql\"\n 10→\t\"fmt\"\n 11→\t\"net/url\"\n 12→\t\"sync\"\n 13→\t\"time\"\n 14→\n 15→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 16→\t\"github.com/jmoiron/sqlx\"\n 17→\t\"github.com/mattn/go-sqlite3\"\n 18→\t\"github.com/qustavo/sqlhooks/v2\"\n 19→)\n 20→\n 21→var (\n 22→\tdbConnOnce sync.Once\n 23→\tdbConnInstance *DBConnection\n 24→)\n 25→\n 26→type DBConnection struct {\n 27→\tDB *sqlx.DB\n 28→\tDriver string\n 29→}\n 30→\n 31→type DatabaseOptions struct {\n 32→\tURL string\n 33→\tMaxOpenConnections int\n 34→\tMaxIdleConnections int\n 35→\tConnectionMaxLifetime time.Duration\n 36→\tConnectionMaxIdleTime time.Duration\n 37→}\n 38→\n 39→func setupSqlite(db *sql.DB) error {\n 40→\tpragmas := []string{\n 41→\t\t\"temp_store = memory\",\n 42→\t}\n 43→\n 44→\tfor _, pragma := range pragmas {\n 45→\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n 46→\t\tif err != nil {\n 47→\t\t\treturn err\n 48→\t\t}\n 49→\t}\n 50→\n 51→\t// Update query planner statistics so SQLite picks optimal indexes.\n 52→\t// Without this, SQLite guesses row distributions and often chooses wrong\n 53→\t// indexes for queries with IN clauses + ORDER BY, causing full table sorts\n 54→\t// in temp B-trees instead of using covering indexes.\n 55→\tif _, err := db.Exec(\"ANALYZE\"); err != nil {\n 56→\t\tcclog.Warnf(\"Failed to run ANALYZE: %v\", err)\n 57→\t}\n 58→\n 59→\treturn nil\n 60→}\n 61→\n 62→func Connect(db string) {\n 63→\tvar err error\n 64→\tvar dbHandle *sqlx.DB\n 65→\n 66→\tdbConnOnce.Do(func() {\n 67→\t\topts := DatabaseOptions{\n 68→\t\t\tURL: db,\n 69→\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n 70→\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n 71→\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n 72→\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n 73→\t\t}\n 74→\n 75→\t\t// TODO: Have separate DB handles for Writes and Reads\n 76→\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n 77→\t\tconnectionURLParams := make(url.Values)\n 78→\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n 79→\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n 80→\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n 81→\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n 82→\t\tconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n 83→\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n 84→\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n 85→\n 86→\t\tif cclog.Loglevel() == \"debug\" {\n 87→\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n 88→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n 89→\t\t} else {\n 90→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n 91→\t\t}\n 92→\n 93→\t\tif err != nil {\n 94→\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n 95→\t\t}\n 96→\n 97→\t\terr = setupSqlite(dbHandle.DB)\n 98→\t\tif err != nil {\n 99→\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n 100→\t\t}\n 101→\n 102→\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n 103→\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n 104→\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n 105→\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n 106→\n 107→\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n 108→\t\terr = checkDBVersion(dbHandle.DB)\n 109→\t\tif err != nil {\n 110→\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n 111→\t\t}\n 112→\t})\n 113→}\n 114→\n 115→func GetConnection() *DBConnection {\n 116→\tif dbConnInstance == nil {\n 117→\t\tcclog.Fatalf(\"Database connection not initialized!\")\n 118→\t}\n 119→\n 120→\treturn dbConnInstance\n 121→}\n 122→\n 123→// ResetConnection closes the current database connection and resets the connection state.\n 124→// This function is intended for testing purposes only to allow test isolation.\n 125→func ResetConnection() error {\n 126→\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n 127→\t\tif err := dbConnInstance.DB.Close(); err != nil {\n 128→\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n 129→\t\t}\n 130→\t}\n 131→\n 132→\tdbConnInstance = nil\n 133→\tdbConnOnce = sync.Once{}\n 134→\tjobRepoInstance = nil\n 135→\tjobRepoOnce = sync.Once{}\n 136→\tnodeRepoInstance = nil\n 137→\tnodeRepoOnce = sync.Once{}\n 138→\tuserRepoInstance = nil\n 139→\tuserRepoOnce = sync.Once{}\n 140→\tuserCfgRepoInstance = nil\n 141→\tuserCfgRepoOnce = sync.Once{}\n 142→\n 143→\treturn nil\n 144→}\n 145→"}]},"uuid":"d0285193-a0e9-4d8a-a884-387675f0fb93","timestamp":"2026-03-11T05:01:40.076Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"fmt\"\n\t\"net/url\"\n\t\"sync\"\n\t\"time\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/jmoiron/sqlx\"\n\t\"github.com/mattn/go-sqlite3\"\n\t\"github.com/qustavo/sqlhooks/v2\"\n)\n\nvar (\n\tdbConnOnce sync.Once\n\tdbConnInstance *DBConnection\n)\n\ntype DBConnection struct {\n\tDB *sqlx.DB\n\tDriver string\n}\n\ntype DatabaseOptions struct {\n\tURL string\n\tMaxOpenConnections int\n\tMaxIdleConnections int\n\tConnectionMaxLifetime time.Duration\n\tConnectionMaxIdleTime time.Duration\n}\n\nfunc setupSqlite(db *sql.DB) error {\n\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t}\n\n\tfor _, pragma := range pragmas {\n\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\t}\n\n\t// Update query planner statistics so SQLite picks optimal indexes.\n\t// Without this, SQLite guesses row distributions and often chooses wrong\n\t// indexes for queries with IN clauses + ORDER BY, causing full table sorts\n\t// in temp B-trees instead of using covering indexes.\n\tif _, err := db.Exec(\"ANALYZE\"); err != nil {\n\t\tcclog.Warnf(\"Failed to run ANALYZE: %v\", err)\n\t}\n\n\treturn nil\n}\n\nfunc Connect(db string) {\n\tvar err error\n\tvar dbHandle *sqlx.DB\n\n\tdbConnOnce.Do(func() {\n\t\topts := DatabaseOptions{\n\t\t\tURL: db,\n\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n\t\t}\n\n\t\t// TODO: Have separate DB handles for Writes and Reads\n\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n\t\tconnectionURLParams := make(url.Values)\n\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n\t\tconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n\n\t\tif cclog.Loglevel() == \"debug\" {\n\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n\t\t} else {\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n\t\t}\n\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\terr = setupSqlite(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n\n\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n\t\terr = checkDBVersion(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n\t\t}\n\t})\n}\n\nfunc GetConnection() *DBConnection {\n\tif dbConnInstance == nil {\n\t\tcclog.Fatalf(\"Database connection not initialized!\")\n\t}\n\n\treturn dbConnInstance\n}\n\n// ResetConnection closes the current database connection and resets the connection state.\n// This function is intended for testing purposes only to allow test isolation.\nfunc ResetConnection() error {\n\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n\t\tif err := dbConnInstance.DB.Close(); err != nil {\n\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n\t\t}\n\t}\n\n\tdbConnInstance = nil\n\tdbConnOnce = sync.Once{}\n\tjobRepoInstance = nil\n\tjobRepoOnce = sync.Once{}\n\tnodeRepoInstance = nil\n\tnodeRepoOnce = sync.Once{}\n\tuserRepoInstance = nil\n\tuserRepoOnce = sync.Once{}\n\tuserCfgRepoInstance = nil\n\tuserCfgRepoOnce = sync.Once{}\n\n\treturn nil\n}\n","numLines":145,"startLine":1,"totalLines":145}},"sourceToolAssistantUUID":"45a469f0-4916-43d0-8046-d2d200e01fbc"} +{"parentUuid":"d0285193-a0e9-4d8a-a884-387675f0fb93","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017Ej4fS9A7Hz7CNiz4C6Nr8","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/config.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"d7c67007-e407-41c7-bfc5-fdfc54f55bd1","timestamp":"2026-03-11T05:01:40.569Z"} +{"parentUuid":"d7c67007-e407-41c7-bfc5-fdfc54f55bd1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_017Ej4fS9A7Hz7CNiz4C6Nr8","toolUseID":"toolu_017Ej4fS9A7Hz7CNiz4C6Nr8","timestamp":"2026-03-11T05:01:40.582Z","uuid":"ac62f583-4aa3-4aad-8e3b-7b7669ad05fb"} +{"parentUuid":"d7c67007-e407-41c7-bfc5-fdfc54f55bd1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017Ej4fS9A7Hz7CNiz4C6Nr8","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import \"time\"\n 9→\n 10→// RepositoryConfig holds configuration for repository operations.\n 11→// All fields have sensible defaults, so this configuration is optional.\n 12→type RepositoryConfig struct {\n 13→\t// CacheSize is the LRU cache size in bytes for job metadata and energy footprints.\n 14→\t// Default: 1MB (1024 * 1024 bytes)\n 15→\tCacheSize int\n 16→\n 17→\t// MaxOpenConnections is the maximum number of open database connections.\n 18→\t// Default: 4\n 19→\tMaxOpenConnections int\n 20→\n 21→\t// MaxIdleConnections is the maximum number of idle database connections.\n 22→\t// Default: 4\n 23→\tMaxIdleConnections int\n 24→\n 25→\t// ConnectionMaxLifetime is the maximum amount of time a connection may be reused.\n 26→\t// Default: 1 hour\n 27→\tConnectionMaxLifetime time.Duration\n 28→\n 29→\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n 30→\t// Default: 1 hour\n 31→\tConnectionMaxIdleTime time.Duration\n 32→\n 33→\t// MinRunningJobDuration is the minimum duration in seconds for a job to be\n 34→\t// considered in \"running jobs\" queries. This filters out very short jobs.\n 35→\t// Default: 600 seconds (10 minutes)\n 36→\tMinRunningJobDuration int\n 37→}\n 38→\n 39→// DefaultConfig returns the default repository configuration.\n 40→// These values are optimized for typical deployments.\n 41→func DefaultConfig() *RepositoryConfig {\n 42→\treturn &RepositoryConfig{\n 43→\t\tCacheSize: 1 * 1024 * 1024, // 1MB\n 44→\t\tMaxOpenConnections: 4,\n 45→\t\tMaxIdleConnections: 4,\n 46→\t\tConnectionMaxLifetime: time.Hour,\n 47→\t\tConnectionMaxIdleTime: time.Hour,\n 48→\t\tMinRunningJobDuration: 600, // 10 minutes\n 49→\t}\n 50→}\n 51→\n 52→// repoConfig is the package-level configuration instance.\n 53→// It is initialized with defaults and can be overridden via SetConfig.\n 54→var repoConfig *RepositoryConfig = DefaultConfig()\n 55→\n 56→// SetConfig sets the repository configuration.\n 57→// This must be called before any repository initialization (Connect, GetJobRepository, etc.).\n 58→// If not called, default values from DefaultConfig() are used.\n 59→func SetConfig(cfg *RepositoryConfig) {\n 60→\tif cfg != nil {\n 61→\t\trepoConfig = cfg\n 62→\t}\n 63→}\n 64→\n 65→// GetConfig returns the current repository configuration.\n 66→func GetConfig() *RepositoryConfig {\n 67→\treturn repoConfig\n 68→}\n 69→"}]},"uuid":"1817a21e-bd43-49d0-9563-4af344c9dc65","timestamp":"2026-03-11T05:01:40.582Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/config.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport \"time\"\n\n// RepositoryConfig holds configuration for repository operations.\n// All fields have sensible defaults, so this configuration is optional.\ntype RepositoryConfig struct {\n\t// CacheSize is the LRU cache size in bytes for job metadata and energy footprints.\n\t// Default: 1MB (1024 * 1024 bytes)\n\tCacheSize int\n\n\t// MaxOpenConnections is the maximum number of open database connections.\n\t// Default: 4\n\tMaxOpenConnections int\n\n\t// MaxIdleConnections is the maximum number of idle database connections.\n\t// Default: 4\n\tMaxIdleConnections int\n\n\t// ConnectionMaxLifetime is the maximum amount of time a connection may be reused.\n\t// Default: 1 hour\n\tConnectionMaxLifetime time.Duration\n\n\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n\t// Default: 1 hour\n\tConnectionMaxIdleTime time.Duration\n\n\t// MinRunningJobDuration is the minimum duration in seconds for a job to be\n\t// considered in \"running jobs\" queries. This filters out very short jobs.\n\t// Default: 600 seconds (10 minutes)\n\tMinRunningJobDuration int\n}\n\n// DefaultConfig returns the default repository configuration.\n// These values are optimized for typical deployments.\nfunc DefaultConfig() *RepositoryConfig {\n\treturn &RepositoryConfig{\n\t\tCacheSize: 1 * 1024 * 1024, // 1MB\n\t\tMaxOpenConnections: 4,\n\t\tMaxIdleConnections: 4,\n\t\tConnectionMaxLifetime: time.Hour,\n\t\tConnectionMaxIdleTime: time.Hour,\n\t\tMinRunningJobDuration: 600, // 10 minutes\n\t}\n}\n\n// repoConfig is the package-level configuration instance.\n// It is initialized with defaults and can be overridden via SetConfig.\nvar repoConfig *RepositoryConfig = DefaultConfig()\n\n// SetConfig sets the repository configuration.\n// This must be called before any repository initialization (Connect, GetJobRepository, etc.).\n// If not called, default values from DefaultConfig() are used.\nfunc SetConfig(cfg *RepositoryConfig) {\n\tif cfg != nil {\n\t\trepoConfig = cfg\n\t}\n}\n\n// GetConfig returns the current repository configuration.\nfunc GetConfig() *RepositoryConfig {\n\treturn repoConfig\n}\n","numLines":69,"startLine":1,"totalLines":69}},"sourceToolAssistantUUID":"d7c67007-e407-41c7-bfc5-fdfc54f55bd1"} +{"parentUuid":"1817a21e-bd43-49d0-9563-4af344c9dc65","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fw84CDZrpzjetNq5ePJK35","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/jobQuery.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"52c1fca4-c9ba-4347-b01c-aa175486d4eb","timestamp":"2026-03-11T05:01:41.076Z"} +{"parentUuid":"52c1fca4-c9ba-4347-b01c-aa175486d4eb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01Fw84CDZrpzjetNq5ePJK35","toolUseID":"toolu_01Fw84CDZrpzjetNq5ePJK35","timestamp":"2026-03-11T05:01:41.091Z","uuid":"e3a8cbf9-396e-4b09-96cb-a440a3adf8ea"} +{"parentUuid":"52c1fca4-c9ba-4347-b01c-aa175486d4eb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Fw84CDZrpzjetNq5ePJK35","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package repository provides job query functionality with filtering, pagination,\n 7→// and security controls. This file contains the main query builders and security\n 8→// checks for job retrieval operations.\n 9→package repository\n 10→\n 11→import (\n 12→\t\"context\"\n 13→\t\"errors\"\n 14→\t\"fmt\"\n 15→\t\"regexp\"\n 16→\t\"strings\"\n 17→\t\"time\"\n 18→\n 19→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n 20→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n 21→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 22→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 23→\tsq \"github.com/Masterminds/squirrel\"\n 24→)\n 25→\n 26→const (\n 27→\t// Default initial capacity for job result slices\n 28→\tdefaultJobsCapacity = 50\n 29→)\n 30→\n 31→// QueryJobs retrieves jobs from the database with optional filtering, pagination,\n 32→// and sorting. Security controls are automatically applied based on the user context.\n 33→//\n 34→// Parameters:\n 35→// - ctx: Context containing user authentication information\n 36→// - filters: Optional job filters (cluster, state, user, time ranges, etc.)\n 37→// - page: Optional pagination parameters (page number and items per page)\n 38→// - order: Optional sorting specification (column or footprint field)\n 39→//\n 40→// Returns a slice of jobs matching the criteria, or an error if the query fails.\n 41→// The function enforces role-based access control through SecurityCheck.\n 42→func (r *JobRepository) QueryJobs(\n 43→\tctx context.Context,\n 44→\tfilters []*model.JobFilter,\n 45→\tpage *model.PageRequest,\n 46→\torder *model.OrderByInput,\n 47→) ([]*schema.Job, error) {\n 48→\tquery, qerr := SecurityCheck(ctx, sq.Select(jobColumns...).From(\"job\"))\n 49→\tif qerr != nil {\n 50→\t\treturn nil, qerr\n 51→\t}\n 52→\n 53→\tif order != nil {\n 54→\t\tfield := toSnakeCase(order.Field)\n 55→\t\tif order.Type == \"col\" {\n 56→\t\t\tswitch order.Order {\n 57→\t\t\tcase model.SortDirectionEnumAsc:\n 58→\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s ASC\", field))\n 59→\t\t\tcase model.SortDirectionEnumDesc:\n 60→\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s DESC\", field))\n 61→\t\t\tdefault:\n 62→\t\t\t\treturn nil, errors.New(\"invalid sorting order for column\")\n 63→\t\t\t}\n 64→\t\t} else {\n 65→\t\t\t// Order by footprint JSON field values\n 66→\t\t\tquery = query.Where(\"JSON_VALID(footprint)\")\n 67→\t\t\tswitch order.Order {\n 68→\t\t\tcase model.SortDirectionEnumAsc:\n 69→\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") ASC\", field))\n 70→\t\t\tcase model.SortDirectionEnumDesc:\n 71→\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") DESC\", field))\n 72→\t\t\tdefault:\n 73→\t\t\t\treturn nil, errors.New(\"invalid sorting order for footprint\")\n 74→\t\t\t}\n 75→\t\t}\n 76→\t}\n 77→\n 78→\tif page != nil && page.ItemsPerPage != -1 {\n 79→\t\tlimit := uint64(page.ItemsPerPage)\n 80→\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n 81→\t}\n 82→\n 83→\tfor _, f := range filters {\n 84→\t\tquery = BuildWhereClause(f, query)\n 85→\t}\n 86→\n 87→\trows, err := query.RunWith(r.stmtCache).Query()\n 88→\tif err != nil {\n 89→\t\tqueryString, queryVars, _ := query.ToSql()\n 90→\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n 91→\t}\n 92→\tdefer rows.Close()\n 93→\n 94→\tjobs := make([]*schema.Job, 0, defaultJobsCapacity)\n 95→\tfor rows.Next() {\n 96→\t\tjob, err := scanJob(rows)\n 97→\t\tif err != nil {\n 98→\t\t\tcclog.Warnf(\"Error scanning job row: %v\", err)\n 99→\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n 100→\t\t}\n 101→\t\tjobs = append(jobs, job)\n 102→\t}\n 103→\n 104→\tif err := rows.Err(); err != nil {\n 105→\t\treturn nil, fmt.Errorf(\"error iterating job rows: %w\", err)\n 106→\t}\n 107→\n 108→\treturn jobs, nil\n 109→}\n 110→\n 111→// CountJobs returns the total number of jobs matching the given filters.\n 112→// Security controls are automatically applied based on the user context.\n 113→// Uses DISTINCT count to handle tag filters correctly (jobs may appear multiple\n 114→// times when joined with the tag table).\n 115→func (r *JobRepository) CountJobs(\n 116→\tctx context.Context,\n 117→\tfilters []*model.JobFilter,\n 118→) (int, error) {\n 119→\tquery, qerr := SecurityCheck(ctx, sq.Select(\"count(DISTINCT job.id)\").From(\"job\"))\n 120→\tif qerr != nil {\n 121→\t\treturn 0, qerr\n 122→\t}\n 123→\n 124→\tfor _, f := range filters {\n 125→\t\tquery = BuildWhereClause(f, query)\n 126→\t}\n 127→\n 128→\tvar count int\n 129→\tif err := query.RunWith(r.DB).Scan(&count); err != nil {\n 130→\t\treturn 0, fmt.Errorf(\"failed to count jobs: %w\", err)\n 131→\t}\n 132→\n 133→\treturn count, nil\n 134→}\n 135→\n 136→// SecurityCheckWithUser applies role-based access control filters to a job query\n 137→// based on the provided user's roles and permissions.\n 138→//\n 139→// Access rules by role:\n 140→// - API role (exclusive): Full access to all jobs\n 141→// - Admin/Support roles: Full access to all jobs\n 142→// - Manager role: Access to jobs in managed projects plus own jobs\n 143→// - User role: Access only to own jobs\n 144→//\n 145→// Returns an error if the user is nil or has no recognized roles.\n 146→func SecurityCheckWithUser(user *schema.User, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n 147→\tif user == nil {\n 148→\t\tvar qnil sq.SelectBuilder\n 149→\t\treturn qnil, fmt.Errorf(\"user context is nil\")\n 150→\t}\n 151→\n 152→\tswitch {\n 153→\tcase len(user.Roles) == 1 && user.HasRole(schema.RoleAPI):\n 154→\t\treturn query, nil\n 155→\tcase user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}):\n 156→\t\treturn query, nil\n 157→\tcase user.HasRole(schema.RoleManager):\n 158→\t\tif len(user.Projects) != 0 {\n 159→\t\t\treturn query.Where(sq.Or{sq.Eq{\"job.project\": user.Projects}, sq.Eq{\"job.hpc_user\": user.Username}}), nil\n 160→\t\t}\n 161→\t\tcclog.Debugf(\"Manager '%s' has no assigned projects, restricting to personal jobs\", user.Username)\n 162→\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n 163→\tcase user.HasRole(schema.RoleUser):\n 164→\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n 165→\tdefault:\n 166→\t\tvar qnil sq.SelectBuilder\n 167→\t\treturn qnil, fmt.Errorf(\"user has no or unknown roles\")\n 168→\t}\n 169→}\n 170→\n 171→// SecurityCheck extracts the user from the context and applies role-based access\n 172→// control filters to the query. This is a convenience wrapper around SecurityCheckWithUser.\n 173→func SecurityCheck(ctx context.Context, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n 174→\tuser := GetUserFromContext(ctx)\n 175→\treturn SecurityCheckWithUser(user, query)\n 176→}\n 177→\n 178→// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n 179→// them to the query. Supports filtering by job properties (cluster, state, user),\n 180→// time ranges, resource usage, tags, and JSON field searches in meta_data,\n 181→// footprint, and resources columns.\n 182→func BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n 183→\t// Primary Key\n 184→\tif filter.DbID != nil {\n 185→\t\tdbIDs := make([]string, len(filter.DbID))\n 186→\t\tcopy(dbIDs, filter.DbID)\n 187→\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n 188→\t}\n 189→\t// Explicit indices\n 190→\tif filter.Cluster != nil {\n 191→\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n 192→\t}\n 193→\tif filter.SubCluster != nil {\n 194→\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n 195→\t}\n 196→\tif filter.Partition != nil {\n 197→\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n 198→\t}\n 199→\tif filter.State != nil {\n 200→\t\tstates := make([]string, len(filter.State))\n 201→\t\tfor i, val := range filter.State {\n 202→\t\t\tstates[i] = string(val)\n 203→\t\t}\n 204→\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n 205→\t}\n 206→\tif filter.Shared != nil {\n 207→\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n 208→\t}\n 209→\tif filter.Project != nil {\n 210→\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n 211→\t}\n 212→\tif filter.User != nil {\n 213→\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n 214→\t}\n 215→\tif filter.NumNodes != nil {\n 216→\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n 217→\t}\n 218→\tif filter.NumAccelerators != nil {\n 219→\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n 220→\t}\n 221→\tif filter.NumHWThreads != nil {\n 222→\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n 223→\t}\n 224→\tif filter.ArrayJobID != nil {\n 225→\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n 226→\t}\n 227→\tif filter.StartTime != nil {\n 228→\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n 229→\t}\n 230→\tif filter.Duration != nil {\n 231→\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n 232→\t}\n 233→\tif filter.Energy != nil {\n 234→\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n 235→\t}\n 236→\t// Indices on Tag Table\n 237→\tif filter.Tags != nil {\n 238→\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n 239→\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n 240→\t}\n 241→\t// No explicit Indices\n 242→\tif filter.JobID != nil {\n 243→\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n 244→\t}\n 245→\t// Queries Within JSONs\n 246→\tif filter.MetricStats != nil {\n 247→\t\tfor _, ms := range filter.MetricStats {\n 248→\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n 249→\t\t}\n 250→\t}\n 251→\tif filter.Node != nil {\n 252→\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n 253→\t}\n 254→\tif filter.JobName != nil {\n 255→\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n 256→\t}\n 257→\tif filter.Schedule != nil {\n 258→\t\tinteractiveJobname := \"interactive\"\n 259→\t\tswitch *filter.Schedule {\n 260→\t\tcase \"interactive\":\n 261→\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n 262→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n 263→\t\tcase \"batch\":\n 264→\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n 265→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n 266→\t\t}\n 267→\t}\n 268→\n 269→\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n 270→\tif filter.MinRunningFor != nil {\n 271→\t\tnow := time.Now().Unix()\n 272→\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n 273→\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n 274→\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n 275→\t}\n 276→\treturn query\n 277→}\n 278→\n 279→// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\n 280→func buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n 281→\tif cond.From != 1 && cond.To != 0 {\n 282→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 283→\t} else if cond.From != 1 && cond.To == 0 {\n 284→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 285→\t} else if cond.From == 1 && cond.To != 0 {\n 286→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 287→\t} else {\n 288→\t\treturn query\n 289→\t}\n 290→}\n 291→\n 292→// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\n 293→func buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n 294→\tif cond.From != 1.0 && cond.To != 0.0 {\n 295→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 296→\t} else if cond.From != 1.0 && cond.To == 0.0 {\n 297→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 298→\t} else if cond.From == 1.0 && cond.To != 0.0 {\n 299→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 300→\t} else {\n 301→\t\treturn query\n 302→\t}\n 303→}\n 304→\n 305→// buildTimeCondition creates time range filters supporting absolute timestamps,\n 306→// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n 307→// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\n 308→func buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n 309→\tif cond.From != nil && cond.To != nil {\n 310→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n 311→\t} else if cond.From != nil {\n 312→\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n 313→\t} else if cond.To != nil {\n 314→\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n 315→\t} else if cond.Range != \"\" {\n 316→\t\tnow := time.Now().Unix()\n 317→\t\tvar then int64\n 318→\t\tswitch cond.Range {\n 319→\t\tcase \"last6h\":\n 320→\t\t\tthen = now - (60 * 60 * 6)\n 321→\t\tcase \"last24h\":\n 322→\t\t\tthen = now - (60 * 60 * 24)\n 323→\t\tcase \"last7d\":\n 324→\t\t\tthen = now - (60 * 60 * 24 * 7)\n 325→\t\tcase \"last30d\":\n 326→\t\t\tthen = now - (60 * 60 * 24 * 30)\n 327→\t\tdefault:\n 328→\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n 329→\t\t\treturn query\n 330→\t\t}\n 331→\t\treturn query.Where(field+\" >= ?\", then)\n 332→\t} else {\n 333→\t\treturn query\n 334→\t}\n 335→}\n 336→\n 337→// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\n 338→func buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n 339→\tquery = query.Where(\"JSON_VALID(footprint)\")\n 340→\tif cond.From != 1.0 && cond.To != 0.0 {\n 341→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n 342→\t} else if cond.From != 1.0 && cond.To == 0.0 {\n 343→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n 344→\t} else if cond.From == 1.0 && cond.To != 0.0 {\n 345→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n 346→\t} else {\n 347→\t\treturn query\n 348→\t}\n 349→}\n 350→\n 351→// buildStringCondition creates filters for string fields supporting equality,\n 352→// inequality, prefix, suffix, substring, and IN list matching.\n 353→func buildStringCondition(field string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n 354→\tif cond.Eq != nil {\n 355→\t\treturn query.Where(field+\" = ?\", *cond.Eq)\n 356→\t}\n 357→\tif cond.Neq != nil {\n 358→\t\treturn query.Where(field+\" != ?\", *cond.Neq)\n 359→\t}\n 360→\tif cond.StartsWith != nil {\n 361→\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n 362→\t}\n 363→\tif cond.EndsWith != nil {\n 364→\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n 365→\t}\n 366→\tif cond.Contains != nil {\n 367→\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n 368→\t}\n 369→\tif cond.In != nil {\n 370→\t\tqueryElements := make([]string, len(cond.In))\n 371→\t\tcopy(queryElements, cond.In)\n 372→\t\treturn query.Where(sq.Or{sq.Eq{field: queryElements}})\n 373→\t}\n 374→\treturn query\n 375→}\n 376→\n 377→// buildMetaJSONCondition creates filters on fields within the meta_data JSON column.\n 378→func buildMetaJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n 379→\tquery = query.Where(\"JSON_VALID(meta_data)\")\n 380→\tif cond.Eq != nil {\n 381→\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") = ?\", *cond.Eq)\n 382→\t}\n 383→\tif cond.Neq != nil {\n 384→\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") != ?\", *cond.Neq)\n 385→\t}\n 386→\tif cond.StartsWith != nil {\n 387→\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n 388→\t}\n 389→\tif cond.EndsWith != nil {\n 390→\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n 391→\t}\n 392→\tif cond.Contains != nil {\n 393→\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n 394→\t}\n 395→\treturn query\n 396→}\n 397→\n 398→// buildResourceJSONCondition creates filters on fields within the resources JSON array column.\n 399→// Uses json_each to search within array elements.\n 400→func buildResourceJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n 401→\tquery = query.Where(\"JSON_VALID(resources)\")\n 402→\tif cond.Eq != nil {\n 403→\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") = ?)\", *cond.Eq)\n 404→\t}\n 405→\tif cond.Neq != nil { // Currently Unused\n 406→\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") != ?)\", *cond.Neq)\n 407→\t}\n 408→\tif cond.StartsWith != nil { // Currently Unused\n 409→\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\")) LIKE ?)\", fmt.Sprint(*cond.StartsWith, \"%\"))\n 410→\t}\n 411→\tif cond.EndsWith != nil { // Currently Unused\n 412→\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.EndsWith))\n 413→\t}\n 414→\tif cond.Contains != nil {\n 415→\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n 416→\t}\n 417→\treturn query\n 418→}\n 419→\n 420→var (\n 421→\tmatchFirstCap = regexp.MustCompile(\"(.)([A-Z][a-z]+)\")\n 422→\tmatchAllCap = regexp.MustCompile(\"([a-z0-9])([A-Z])\")\n 423→)\n 424→\n 425→// toSnakeCase converts camelCase strings to snake_case for SQL column names.\n 426→// Includes security checks to prevent SQL injection attempts.\n 427→// Panics if potentially dangerous characters are detected.\n 428→func toSnakeCase(str string) string {\n 429→\tfor _, c := range str {\n 430→\t\tif c == '\\'' || c == '\\\\' || c == '\"' || c == ';' || c == '-' || c == ' ' {\n 431→\t\t\tcclog.Panicf(\"toSnakeCase: potentially dangerous character detected in input: %q\", str)\n 432→\t\t}\n 433→\t}\n 434→\n 435→\tsnake := matchFirstCap.ReplaceAllString(str, \"${1}_${2}\")\n 436→\tsnake = matchAllCap.ReplaceAllString(snake, \"${1}_${2}\")\n 437→\treturn strings.ToLower(snake)\n 438→}\n 439→"}]},"uuid":"6b9cfc21-268a-46ac-8d27-14a25063cbc0","timestamp":"2026-03-11T05:01:41.090Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/jobQuery.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package repository provides job query functionality with filtering, pagination,\n// and security controls. This file contains the main query builders and security\n// checks for job retrieval operations.\npackage repository\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"regexp\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\nconst (\n\t// Default initial capacity for job result slices\n\tdefaultJobsCapacity = 50\n)\n\n// QueryJobs retrieves jobs from the database with optional filtering, pagination,\n// and sorting. Security controls are automatically applied based on the user context.\n//\n// Parameters:\n// - ctx: Context containing user authentication information\n// - filters: Optional job filters (cluster, state, user, time ranges, etc.)\n// - page: Optional pagination parameters (page number and items per page)\n// - order: Optional sorting specification (column or footprint field)\n//\n// Returns a slice of jobs matching the criteria, or an error if the query fails.\n// The function enforces role-based access control through SecurityCheck.\nfunc (r *JobRepository) QueryJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n\tpage *model.PageRequest,\n\torder *model.OrderByInput,\n) ([]*schema.Job, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(jobColumns...).From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tif order != nil {\n\t\tfield := toSnakeCase(order.Field)\n\t\tif order.Type == \"col\" {\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for column\")\n\t\t\t}\n\t\t} else {\n\t\t\t// Order by footprint JSON field values\n\t\t\tquery = query.Where(\"JSON_VALID(footprint)\")\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for footprint\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tqueryString, queryVars, _ := query.ToSql()\n\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, defaultJobsCapacity)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error scanning job row: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, fmt.Errorf(\"error iterating job rows: %w\", err)\n\t}\n\n\treturn jobs, nil\n}\n\n// CountJobs returns the total number of jobs matching the given filters.\n// Security controls are automatically applied based on the user context.\n// Uses DISTINCT count to handle tag filters correctly (jobs may appear multiple\n// times when joined with the tag table).\nfunc (r *JobRepository) CountJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n) (int, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"count(DISTINCT job.id)\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn 0, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\tvar count int\n\tif err := query.RunWith(r.DB).Scan(&count); err != nil {\n\t\treturn 0, fmt.Errorf(\"failed to count jobs: %w\", err)\n\t}\n\n\treturn count, nil\n}\n\n// SecurityCheckWithUser applies role-based access control filters to a job query\n// based on the provided user's roles and permissions.\n//\n// Access rules by role:\n// - API role (exclusive): Full access to all jobs\n// - Admin/Support roles: Full access to all jobs\n// - Manager role: Access to jobs in managed projects plus own jobs\n// - User role: Access only to own jobs\n//\n// Returns an error if the user is nil or has no recognized roles.\nfunc SecurityCheckWithUser(user *schema.User, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tif user == nil {\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user context is nil\")\n\t}\n\n\tswitch {\n\tcase len(user.Roles) == 1 && user.HasRole(schema.RoleAPI):\n\t\treturn query, nil\n\tcase user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}):\n\t\treturn query, nil\n\tcase user.HasRole(schema.RoleManager):\n\t\tif len(user.Projects) != 0 {\n\t\t\treturn query.Where(sq.Or{sq.Eq{\"job.project\": user.Projects}, sq.Eq{\"job.hpc_user\": user.Username}}), nil\n\t\t}\n\t\tcclog.Debugf(\"Manager '%s' has no assigned projects, restricting to personal jobs\", user.Username)\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tcase user.HasRole(schema.RoleUser):\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tdefault:\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user has no or unknown roles\")\n\t}\n}\n\n// SecurityCheck extracts the user from the context and applies role-based access\n// control filters to the query. This is a convenience wrapper around SecurityCheckWithUser.\nfunc SecurityCheck(ctx context.Context, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tuser := GetUserFromContext(ctx)\n\treturn SecurityCheckWithUser(user, query)\n}\n\n// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n// them to the query. Supports filtering by job properties (cluster, state, user),\n// time ranges, resource usage, tags, and JSON field searches in meta_data,\n// footprint, and resources columns.\nfunc BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n\t// Primary Key\n\tif filter.DbID != nil {\n\t\tdbIDs := make([]string, len(filter.DbID))\n\t\tcopy(dbIDs, filter.DbID)\n\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n\t}\n\t// Explicit indices\n\tif filter.Cluster != nil {\n\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n\t}\n\tif filter.SubCluster != nil {\n\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n\t}\n\tif filter.Partition != nil {\n\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n\t}\n\tif filter.State != nil {\n\t\tstates := make([]string, len(filter.State))\n\t\tfor i, val := range filter.State {\n\t\t\tstates[i] = string(val)\n\t\t}\n\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n\t}\n\tif filter.Shared != nil {\n\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n\t}\n\tif filter.Project != nil {\n\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n\t}\n\tif filter.User != nil {\n\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n\t}\n\tif filter.NumNodes != nil {\n\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n\t}\n\tif filter.NumAccelerators != nil {\n\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n\t}\n\tif filter.NumHWThreads != nil {\n\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n\t}\n\tif filter.ArrayJobID != nil {\n\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n\t}\n\tif filter.StartTime != nil {\n\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n\t}\n\tif filter.Duration != nil {\n\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n\t}\n\tif filter.Energy != nil {\n\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n\t}\n\t// Indices on Tag Table\n\tif filter.Tags != nil {\n\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n\t}\n\t// No explicit Indices\n\tif filter.JobID != nil {\n\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n\t}\n\t// Queries Within JSONs\n\tif filter.MetricStats != nil {\n\t\tfor _, ms := range filter.MetricStats {\n\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n\t\t}\n\t}\n\tif filter.Node != nil {\n\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n\t}\n\tif filter.JobName != nil {\n\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n\t}\n\tif filter.Schedule != nil {\n\t\tinteractiveJobname := \"interactive\"\n\t\tswitch *filter.Schedule {\n\t\tcase \"interactive\":\n\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n\t\tcase \"batch\":\n\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n\t\t}\n\t}\n\n\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n\tif filter.MinRunningFor != nil {\n\t\tnow := time.Now().Unix()\n\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n\t}\n\treturn query\n}\n\n// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\nfunc buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1 && cond.To == 0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\nfunc buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildTimeCondition creates time range filters supporting absolute timestamps,\n// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\nfunc buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != nil && cond.To != nil {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n\t} else if cond.From != nil {\n\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n\t} else if cond.To != nil {\n\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n\t} else if cond.Range != \"\" {\n\t\tnow := time.Now().Unix()\n\t\tvar then int64\n\t\tswitch cond.Range {\n\t\tcase \"last6h\":\n\t\t\tthen = now - (60 * 60 * 6)\n\t\tcase \"last24h\":\n\t\t\tthen = now - (60 * 60 * 24)\n\t\tcase \"last7d\":\n\t\t\tthen = now - (60 * 60 * 24 * 7)\n\t\tcase \"last30d\":\n\t\t\tthen = now - (60 * 60 * 24 * 30)\n\t\tdefault:\n\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n\t\t\treturn query\n\t\t}\n\t\treturn query.Where(field+\" >= ?\", then)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\nfunc buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(footprint)\")\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildStringCondition creates filters for string fields supporting equality,\n// inequality, prefix, suffix, substring, and IN list matching.\nfunc buildStringCondition(field string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.Eq != nil {\n\t\treturn query.Where(field+\" = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(field+\" != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\tif cond.In != nil {\n\t\tqueryElements := make([]string, len(cond.In))\n\t\tcopy(queryElements, cond.In)\n\t\treturn query.Where(sq.Or{sq.Eq{field: queryElements}})\n\t}\n\treturn query\n}\n\n// buildMetaJSONCondition creates filters on fields within the meta_data JSON column.\nfunc buildMetaJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(meta_data)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\n// buildResourceJSONCondition creates filters on fields within the resources JSON array column.\n// Uses json_each to search within array elements.\nfunc buildResourceJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(resources)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") = ?)\", *cond.Eq)\n\t}\n\tif cond.Neq != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") != ?)\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\")) LIKE ?)\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\nvar (\n\tmatchFirstCap = regexp.MustCompile(\"(.)([A-Z][a-z]+)\")\n\tmatchAllCap = regexp.MustCompile(\"([a-z0-9])([A-Z])\")\n)\n\n// toSnakeCase converts camelCase strings to snake_case for SQL column names.\n// Includes security checks to prevent SQL injection attempts.\n// Panics if potentially dangerous characters are detected.\nfunc toSnakeCase(str string) string {\n\tfor _, c := range str {\n\t\tif c == '\\'' || c == '\\\\' || c == '\"' || c == ';' || c == '-' || c == ' ' {\n\t\t\tcclog.Panicf(\"toSnakeCase: potentially dangerous character detected in input: %q\", str)\n\t\t}\n\t}\n\n\tsnake := matchFirstCap.ReplaceAllString(str, \"${1}_${2}\")\n\tsnake = matchAllCap.ReplaceAllString(snake, \"${1}_${2}\")\n\treturn strings.ToLower(snake)\n}\n","numLines":439,"startLine":1,"totalLines":439}},"sourceToolAssistantUUID":"52c1fca4-c9ba-4347-b01c-aa175486d4eb"} +{"parentUuid":"6b9cfc21-268a-46ac-8d27-14a25063cbc0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MvmrS6HRyPbR6VcxjnKEHf","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"5b4bc6bd-552c-4979-bcde-bc6e1e726842","timestamp":"2026-03-11T05:01:41.587Z"} +{"parentUuid":"5b4bc6bd-552c-4979-bcde-bc6e1e726842","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01MvmrS6HRyPbR6VcxjnKEHf","toolUseID":"toolu_01MvmrS6HRyPbR6VcxjnKEHf","timestamp":"2026-03-11T05:01:41.829Z","uuid":"24af1880-3691-4ab9-b72f-c29559f0dce8"} +{"parentUuid":"5b4bc6bd-552c-4979-bcde-bc6e1e726842","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MvmrS6HRyPbR6VcxjnKEHf","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// This file contains job statistics and histogram generation functionality for the JobRepository.\n 7→//\n 8→// # Job Statistics\n 9→//\n 10→// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n 11→// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n 12→// - Overall (JobsStats): Single aggregate across all matching jobs\n 13→// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n 14→// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n 15→//\n 16→// All statistics methods support filtering via JobFilter and respect security contexts.\n 17→//\n 18→// # Histograms\n 19→//\n 20→// Histogram methods generate distribution data for visualization:\n 21→// - Duration, nodes, cores, accelerators (AddHistograms)\n 22→// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n 23→//\n 24→// Histograms use intelligent binning:\n 25→// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n 26→// - Resources: Natural value-based bins\n 27→// - Metrics: Normalized to peak values with configurable bin counts\n 28→//\n 29→// # Running vs. Completed Jobs\n 30→//\n 31→// Statistics handle running jobs specially:\n 32→// - Duration calculated as (now - start_time) for running jobs\n 33→// - Metric histograms for running jobs load data from metric backend instead of footprint\n 34→// - Job state filtering distinguishes running/completed jobs\n 35→//\n 36→// # Performance Considerations\n 37→//\n 38→// - All queries use prepared statements via stmtCache\n 39→// - Complex aggregations use SQL for efficiency\n 40→// - Histogram pre-initialization ensures consistent bin ranges\n 41→// - Metric histogram queries limited to 5000 jobs for running job analysis\n 42→\n 43→package repository\n 44→\n 45→import (\n 46→\t\"context\"\n 47→\t\"database/sql\"\n 48→\t\"fmt\"\n 49→\t\"time\"\n 50→\n 51→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n 52→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n 53→\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n 54→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n 55→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 56→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 57→\tsq \"github.com/Masterminds/squirrel\"\n 58→)\n 59→\n 60→// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n 61→// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n 62→// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\n 63→var groupBy2column = map[model.Aggregate]string{\n 64→\tmodel.AggregateUser: \"job.hpc_user\",\n 65→\tmodel.AggregateProject: \"job.project\",\n 66→\tmodel.AggregateCluster: \"job.cluster\",\n 67→\tmodel.AggregateSubcluster: \"job.subcluster\",\n 68→}\n 69→\n 70→// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n 71→// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n 72→// Column names match the AS aliases used in buildStatsQuery.\n 73→var sortBy2column = map[model.SortByAggregate]string{\n 74→\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n 75→\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n 76→\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n 77→\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n 78→\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n 79→\tmodel.SortByAggregateTotalcores: \"totalCores\",\n 80→\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n 81→\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n 82→\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n 83→}\n 84→\n 85→// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n 86→//\n 87→// Parameters:\n 88→// - filter: Job filters to apply (cluster, user, time range, etc.)\n 89→// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n 90→// - col: Column name to GROUP BY; empty string for total count without grouping\n 91→//\n 92→// Returns a SelectBuilder that produces either:\n 93→// - Single count: COUNT(job.id) when col is empty\n 94→// - Grouped counts: col, COUNT(job.id) when col is specified\n 95→//\n 96→// The kind parameter enables counting specific job categories:\n 97→// - \"running\": Only jobs with job_state = 'running'\n 98→// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n 99→// - empty: All jobs matching filters\n 100→func (r *JobRepository) buildCountQuery(\n 101→\tfilter []*model.JobFilter,\n 102→\tkind string,\n 103→\tcol string,\n 104→) sq.SelectBuilder {\n 105→\tvar query sq.SelectBuilder\n 106→\n 107→\tif col != \"\" {\n 108→\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n 109→\t} else {\n 110→\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n 111→\t}\n 112→\n 113→\tswitch kind {\n 114→\tcase \"running\":\n 115→\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n 116→\tcase \"short\":\n 117→\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n 118→\t}\n 119→\n 120→\tfor _, f := range filter {\n 121→\t\tquery = BuildWhereClause(f, query)\n 122→\t}\n 123→\n 124→\treturn query\n 125→}\n 126→\n 127→// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n 128→//\n 129→// Parameters:\n 130→// - filter: Job filters to apply (cluster, user, time range, etc.)\n 131→// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n 132→//\n 133→// Returns a SelectBuilder that produces comprehensive statistics:\n 134→// - totalJobs: Count of jobs\n 135→// - totalUsers: Count of distinct users (always 0 when grouping by user)\n 136→// - totalWalltime: Sum of job durations in hours\n 137→// - totalNodes: Sum of nodes used across all jobs\n 138→// - totalNodeHours: Sum of (duration × num_nodes) in hours\n 139→// - totalCores: Sum of hardware threads used across all jobs\n 140→// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n 141→// - totalAccs: Sum of accelerators used across all jobs\n 142→// - totalAccHours: Sum of (duration × num_acc) in hours\n 143→//\n 144→// Special handling:\n 145→// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n 146→// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n 147→// - All time values converted from seconds to hours (÷ 3600) and rounded\n 148→func (r *JobRepository) buildStatsQuery(\n 149→\tfilter []*model.JobFilter,\n 150→\tcol string,\n 151→) sq.SelectBuilder {\n 152→\tvar query sq.SelectBuilder\n 153→\n 154→\tif col != \"\" {\n 155→\t\tquery = sq.Select(\n 156→\t\t\tcol,\n 157→\t\t\t\"name\",\n 158→\t\t\t\"COUNT(job.id) as totalJobs\",\n 159→\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n 160→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n 161→\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n 162→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n 163→\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n 164→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n 165→\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n 166→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n 167→\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n 168→\t} else {\n 169→\t\tquery = sq.Select(\n 170→\t\t\t\"COUNT(job.id) as totalJobs\",\n 171→\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n 172→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n 173→\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n 174→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n 175→\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n 176→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n 177→\t\t\t`CAST(SUM(job.num_acc) as int)`,\n 178→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n 179→\t\t).From(\"job\")\n 180→\t}\n 181→\n 182→\tfor _, f := range filter {\n 183→\t\tquery = BuildWhereClause(f, query)\n 184→\t}\n 185→\n 186→\treturn query\n 187→}\n 188→\n 189→// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n 190→//\n 191→// This is the primary method for generating aggregated statistics views in the UI, providing\n 192→// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n 193→//\n 194→// Parameters:\n 195→// - ctx: Context for security checks and cancellation\n 196→// - filter: Filters to apply (time range, cluster, job state, etc.)\n 197→// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n 198→// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n 199→// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n 200→//\n 201→// Returns a slice of JobsStatistics, one per group, with:\n 202→// - ID: The group identifier (username, project name, cluster name, etc.)\n 203→// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n 204→// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n 205→//\n 206→// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n 207→// Performance: Results are sorted in SQL and pagination applied before scanning rows.\n 208→func (r *JobRepository) JobsStatsGrouped(\n 209→\tctx context.Context,\n 210→\tfilter []*model.JobFilter,\n 211→\tpage *model.PageRequest,\n 212→\tsortBy *model.SortByAggregate,\n 213→\tgroupBy *model.Aggregate,\n 214→) ([]*model.JobsStatistics, error) {\n 215→\tstart := time.Now()\n 216→\tcol := groupBy2column[*groupBy]\n 217→\tquery := r.buildStatsQuery(filter, col)\n 218→\n 219→\tquery, err := SecurityCheck(ctx, query)\n 220→\tif err != nil {\n 221→\t\treturn nil, err\n 222→\t}\n 223→\n 224→\tif sortBy != nil {\n 225→\t\tsortBy := sortBy2column[*sortBy]\n 226→\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n 227→\t}\n 228→\tif page != nil && page.ItemsPerPage != -1 {\n 229→\t\tlimit := uint64(page.ItemsPerPage)\n 230→\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n 231→\t}\n 232→\n 233→\trows, err := query.RunWith(r.DB).Query()\n 234→\tif err != nil {\n 235→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 236→\t\treturn nil, err\n 237→\t}\n 238→\tdefer rows.Close()\n 239→\n 240→\tstats := make([]*model.JobsStatistics, 0, 100)\n 241→\n 242→\tfor rows.Next() {\n 243→\t\tvar id sql.NullString\n 244→\t\tvar name sql.NullString\n 245→\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n 246→\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n 247→\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n 248→\t\t\treturn nil, err\n 249→\t\t}\n 250→\n 251→\t\tif id.Valid {\n 252→\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n 253→\t\t\tvar personName string\n 254→\n 255→\t\t\tif name.Valid {\n 256→\t\t\t\tpersonName = name.String\n 257→\t\t\t}\n 258→\n 259→\t\t\tif jobs.Valid {\n 260→\t\t\t\ttotalJobs = int(jobs.Int64)\n 261→\t\t\t}\n 262→\n 263→\t\t\tif users.Valid {\n 264→\t\t\t\ttotalUsers = int(users.Int64)\n 265→\t\t\t}\n 266→\n 267→\t\t\tif walltime.Valid {\n 268→\t\t\t\ttotalWalltime = int(walltime.Int64)\n 269→\t\t\t}\n 270→\n 271→\t\t\tif nodes.Valid {\n 272→\t\t\t\ttotalNodes = int(nodes.Int64)\n 273→\t\t\t}\n 274→\t\t\tif cores.Valid {\n 275→\t\t\t\ttotalCores = int(cores.Int64)\n 276→\t\t\t}\n 277→\t\t\tif accs.Valid {\n 278→\t\t\t\ttotalAccs = int(accs.Int64)\n 279→\t\t\t}\n 280→\n 281→\t\t\tif nodeHours.Valid {\n 282→\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n 283→\t\t\t}\n 284→\t\t\tif coreHours.Valid {\n 285→\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n 286→\t\t\t}\n 287→\t\t\tif accHours.Valid {\n 288→\t\t\t\ttotalAccHours = int(accHours.Int64)\n 289→\t\t\t}\n 290→\n 291→\t\t\tif col == \"job.hpc_user\" {\n 292→\t\t\t\t// name := r.getUserName(ctx, id.String)\n 293→\t\t\t\tstats = append(stats,\n 294→\t\t\t\t\t&model.JobsStatistics{\n 295→\t\t\t\t\t\tID: id.String,\n 296→\t\t\t\t\t\tName: personName,\n 297→\t\t\t\t\t\tTotalJobs: totalJobs,\n 298→\t\t\t\t\t\tTotalWalltime: totalWalltime,\n 299→\t\t\t\t\t\tTotalNodes: totalNodes,\n 300→\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n 301→\t\t\t\t\t\tTotalCores: totalCores,\n 302→\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n 303→\t\t\t\t\t\tTotalAccs: totalAccs,\n 304→\t\t\t\t\t\tTotalAccHours: totalAccHours,\n 305→\t\t\t\t\t})\n 306→\t\t\t} else {\n 307→\t\t\t\tstats = append(stats,\n 308→\t\t\t\t\t&model.JobsStatistics{\n 309→\t\t\t\t\t\tID: id.String,\n 310→\t\t\t\t\t\tTotalJobs: totalJobs,\n 311→\t\t\t\t\t\tTotalUsers: totalUsers,\n 312→\t\t\t\t\t\tTotalWalltime: totalWalltime,\n 313→\t\t\t\t\t\tTotalNodes: totalNodes,\n 314→\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n 315→\t\t\t\t\t\tTotalCores: totalCores,\n 316→\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n 317→\t\t\t\t\t\tTotalAccs: totalAccs,\n 318→\t\t\t\t\t\tTotalAccHours: totalAccHours,\n 319→\t\t\t\t\t})\n 320→\t\t\t}\n 321→\t\t}\n 322→\t}\n 323→\n 324→\tif err := rows.Err(); err != nil {\n 325→\t\treturn nil, err\n 326→\t}\n 327→\n 328→\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n 329→\treturn stats, nil\n 330→}\n 331→\n 332→// JobsStats computes overall job statistics across all matching jobs without grouping.\n 333→//\n 334→// This method provides a single aggregate view of job metrics, useful for dashboard\n 335→// summaries and overall system utilization reports.\n 336→//\n 337→// Parameters:\n 338→// - ctx: Context for security checks and cancellation\n 339→// - filter: Filters to apply (time range, cluster, job state, etc.)\n 340→//\n 341→// Returns a single-element slice containing aggregate statistics:\n 342→// - totalJobs, totalUsers, totalWalltime\n 343→// - totalNodeHours, totalCoreHours, totalAccHours\n 344→//\n 345→// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n 346→// Security checks are applied via SecurityCheck to respect user access levels.\n 347→func (r *JobRepository) JobsStats(\n 348→\tctx context.Context,\n 349→\tfilter []*model.JobFilter,\n 350→) ([]*model.JobsStatistics, error) {\n 351→\tstart := time.Now()\n 352→\tquery := r.buildStatsQuery(filter, \"\")\n 353→\tquery, err := SecurityCheck(ctx, query)\n 354→\tif err != nil {\n 355→\t\treturn nil, err\n 356→\t}\n 357→\n 358→\trow := query.RunWith(r.DB).QueryRow()\n 359→\tstats := make([]*model.JobsStatistics, 0, 1)\n 360→\n 361→\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n 362→\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n 363→\t\tcclog.Warn(\"Error while scanning rows\")\n 364→\t\treturn nil, err\n 365→\t}\n 366→\n 367→\tif jobs.Valid {\n 368→\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n 369→\n 370→\t\tif nodeHours.Valid {\n 371→\t\t\ttotalNodeHours = int(nodeHours.Int64)\n 372→\t\t}\n 373→\t\tif coreHours.Valid {\n 374→\t\t\ttotalCoreHours = int(coreHours.Int64)\n 375→\t\t}\n 376→\t\tif accHours.Valid {\n 377→\t\t\ttotalAccHours = int(accHours.Int64)\n 378→\t\t}\n 379→\t\tstats = append(stats,\n 380→\t\t\t&model.JobsStatistics{\n 381→\t\t\t\tTotalJobs: int(jobs.Int64),\n 382→\t\t\t\tTotalUsers: int(users.Int64),\n 383→\t\t\t\tTotalWalltime: int(walltime.Int64),\n 384→\t\t\t\tTotalNodeHours: totalNodeHours,\n 385→\t\t\t\tTotalCoreHours: totalCoreHours,\n 386→\t\t\t\tTotalAccHours: totalAccHours,\n 387→\t\t\t})\n 388→\t}\n 389→\n 390→\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n 391→\treturn stats, nil\n 392→}\n 393→\n 394→// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n 395→// Returns 0.0 if the metric is not found or statType is invalid.\n 396→//\n 397→// Parameters:\n 398→// - job: Job struct with populated Statistics field\n 399→// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n 400→// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n 401→//\n 402→// Returns the requested statistic value or 0.0 if not found.\n 403→func LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n 404→\tif stats, ok := job.Statistics[metric]; ok {\n 405→\t\tswitch statType {\n 406→\t\tcase \"avg\":\n 407→\t\t\treturn stats.Avg\n 408→\t\tcase \"max\":\n 409→\t\t\treturn stats.Max\n 410→\t\tcase \"min\":\n 411→\t\t\treturn stats.Min\n 412→\t\tdefault:\n 413→\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n 414→\t\t}\n 415→\t}\n 416→\n 417→\treturn 0.0\n 418→}\n 419→\n 420→// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n 421→//\n 422→// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n 423→// avoiding the overhead of calculating walltime and resource usage metrics.\n 424→//\n 425→// Parameters:\n 426→// - ctx: Context for security checks\n 427→// - filter: Filters to apply\n 428→// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n 429→//\n 430→// Returns JobsStatistics with only ID and TotalJobs populated for each group.\n 431→func (r *JobRepository) JobCountGrouped(\n 432→\tctx context.Context,\n 433→\tfilter []*model.JobFilter,\n 434→\tgroupBy *model.Aggregate,\n 435→) ([]*model.JobsStatistics, error) {\n 436→\tstart := time.Now()\n 437→\tcol := groupBy2column[*groupBy]\n 438→\tquery := r.buildCountQuery(filter, \"\", col)\n 439→\tquery, err := SecurityCheck(ctx, query)\n 440→\tif err != nil {\n 441→\t\treturn nil, err\n 442→\t}\n 443→\trows, err := query.RunWith(r.DB).Query()\n 444→\tif err != nil {\n 445→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 446→\t\treturn nil, err\n 447→\t}\n 448→\tdefer rows.Close()\n 449→\n 450→\tstats := make([]*model.JobsStatistics, 0, 100)\n 451→\n 452→\tfor rows.Next() {\n 453→\t\tvar id sql.NullString\n 454→\t\tvar cnt sql.NullInt64\n 455→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n 456→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 457→\t\t\treturn nil, err\n 458→\t\t}\n 459→\t\tif id.Valid {\n 460→\t\t\tstats = append(stats,\n 461→\t\t\t\t&model.JobsStatistics{\n 462→\t\t\t\t\tID: id.String,\n 463→\t\t\t\t\tTotalJobs: int(cnt.Int64),\n 464→\t\t\t\t})\n 465→\t\t}\n 466→\t}\n 467→\n 468→\tif err := rows.Err(); err != nil {\n 469→\t\treturn nil, err\n 470→\t}\n 471→\n 472→\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n 473→\treturn stats, nil\n 474→}\n 475→\n 476→// AddJobCountGrouped augments existing statistics with additional job counts by category.\n 477→//\n 478→// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n 479→// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n 480→//\n 481→// Parameters:\n 482→// - ctx: Context for security checks\n 483→// - filter: Filters to apply\n 484→// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n 485→// - stats: Existing statistics to augment (modified in-place by ID matching)\n 486→// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n 487→//\n 488→// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n 489→// Groups without matching jobs will have 0 for the added field.\n 490→func (r *JobRepository) AddJobCountGrouped(\n 491→\tctx context.Context,\n 492→\tfilter []*model.JobFilter,\n 493→\tgroupBy *model.Aggregate,\n 494→\tstats []*model.JobsStatistics,\n 495→\tkind string,\n 496→) ([]*model.JobsStatistics, error) {\n 497→\tstart := time.Now()\n 498→\tcol := groupBy2column[*groupBy]\n 499→\tquery := r.buildCountQuery(filter, kind, col)\n 500→\tquery, err := SecurityCheck(ctx, query)\n 501→\tif err != nil {\n 502→\t\treturn nil, err\n 503→\t}\n 504→\trows, err := query.RunWith(r.DB).Query()\n 505→\tif err != nil {\n 506→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 507→\t\treturn nil, err\n 508→\t}\n 509→\tdefer rows.Close()\n 510→\n 511→\tcounts := make(map[string]int)\n 512→\n 513→\tfor rows.Next() {\n 514→\t\tvar id sql.NullString\n 515→\t\tvar cnt sql.NullInt64\n 516→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n 517→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 518→\t\t\treturn nil, err\n 519→\t\t}\n 520→\t\tif id.Valid {\n 521→\t\t\tcounts[id.String] = int(cnt.Int64)\n 522→\t\t}\n 523→\t}\n 524→\n 525→\tif err := rows.Err(); err != nil {\n 526→\t\treturn nil, err\n 527→\t}\n 528→\n 529→\tswitch kind {\n 530→\tcase \"running\":\n 531→\t\tfor _, s := range stats {\n 532→\t\t\ts.RunningJobs = counts[s.ID]\n 533→\t\t}\n 534→\tcase \"short\":\n 535→\t\tfor _, s := range stats {\n 536→\t\t\ts.ShortJobs = counts[s.ID]\n 537→\t\t}\n 538→\t}\n 539→\n 540→\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n 541→\treturn stats, nil\n 542→}\n 543→\n 544→// AddJobCount augments existing overall statistics with additional job counts by category.\n 545→//\n 546→// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n 547→// to all statistics entries (typically just one).\n 548→//\n 549→// Parameters:\n 550→// - ctx: Context for security checks\n 551→// - filter: Filters to apply\n 552→// - stats: Existing statistics to augment (modified in-place)\n 553→// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n 554→//\n 555→// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\n 556→func (r *JobRepository) AddJobCount(\n 557→\tctx context.Context,\n 558→\tfilter []*model.JobFilter,\n 559→\tstats []*model.JobsStatistics,\n 560→\tkind string,\n 561→) ([]*model.JobsStatistics, error) {\n 562→\tstart := time.Now()\n 563→\tquery := r.buildCountQuery(filter, kind, \"\")\n 564→\tquery, err := SecurityCheck(ctx, query)\n 565→\tif err != nil {\n 566→\t\treturn nil, err\n 567→\t}\n 568→\tvar cnt sql.NullInt64\n 569→\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n 570→\t\tcclog.Warn(\"Error while querying DB for job count\")\n 571→\t\treturn nil, err\n 572→\t}\n 573→\n 574→\tcount := int(cnt.Int64)\n 575→\n 576→\tswitch kind {\n 577→\tcase \"running\":\n 578→\t\tfor _, s := range stats {\n 579→\t\t\ts.RunningJobs = count\n 580→\t\t}\n 581→\tcase \"short\":\n 582→\t\tfor _, s := range stats {\n 583→\t\t\ts.ShortJobs = count\n 584→\t\t}\n 585→\t}\n 586→\n 587→\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n 588→\treturn stats, nil\n 589→}\n 590→\n 591→// AddHistograms augments statistics with distribution histograms for job properties.\n 592→//\n 593→// Generates histogram data for visualization of job duration, node count, core count,\n 594→// and accelerator count distributions. Duration histogram uses intelligent binning based\n 595→// on the requested resolution.\n 596→//\n 597→// Parameters:\n 598→// - ctx: Context for security checks\n 599→// - filter: Filters to apply to jobs included in histograms\n 600→// - stat: Statistics struct to augment (modified in-place)\n 601→// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n 602→//\n 603→// Populates these fields in stat:\n 604→// - HistDuration: Job duration distribution (zero-padded bins)\n 605→// - HistNumNodes: Node count distribution\n 606→// - HistNumCores: Core (hwthread) count distribution\n 607→// - HistNumAccs: Accelerator count distribution\n 608→//\n 609→// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n 610→// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\n 611→func (r *JobRepository) AddHistograms(\n 612→\tctx context.Context,\n 613→\tfilter []*model.JobFilter,\n 614→\tstat *model.JobsStatistics,\n 615→\tdurationBins *string,\n 616→) (*model.JobsStatistics, error) {\n 617→\tstart := time.Now()\n 618→\n 619→\tvar targetBinCount int\n 620→\tvar targetBinSize int\n 621→\tswitch *durationBins {\n 622→\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n 623→\t\ttargetBinCount = 60\n 624→\t\ttargetBinSize = 60\n 625→\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n 626→\t\ttargetBinCount = 72\n 627→\t\ttargetBinSize = 600\n 628→\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n 629→\t\ttargetBinCount = 48\n 630→\t\ttargetBinSize = 3600\n 631→\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n 632→\t\ttargetBinCount = 12\n 633→\t\ttargetBinSize = 21600\n 634→\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n 635→\t\ttargetBinCount = 14\n 636→\t\ttargetBinSize = 43200\n 637→\tdefault: // 24h\n 638→\t\ttargetBinCount = 24\n 639→\t\ttargetBinSize = 3600\n 640→\t}\n 641→\n 642→\tvar err error\n 643→\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n 644→\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n 645→\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n 646→\tif err != nil {\n 647→\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n 648→\t\treturn nil, err\n 649→\t}\n 650→\n 651→\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n 652→\tif err != nil {\n 653→\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n 654→\t\treturn nil, err\n 655→\t}\n 656→\n 657→\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n 658→\tif err != nil {\n 659→\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n 660→\t\treturn nil, err\n 661→\t}\n 662→\n 663→\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n 664→\tif err != nil {\n 665→\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n 666→\t\treturn nil, err\n 667→\t}\n 668→\n 669→\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n 670→\treturn stat, nil\n 671→}\n 672→\n 673→// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n 674→//\n 675→// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n 676→// and completed jobs differently: running jobs load data from metric backend, completed jobs\n 677→// use footprint data from database.\n 678→//\n 679→// Parameters:\n 680→// - ctx: Context for security checks\n 681→// - filter: Filters to apply (MUST contain State filter for running jobs)\n 682→// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n 683→// - stat: Statistics struct to augment (modified in-place)\n 684→// - targetBinCount: Number of histogram bins (default: 10)\n 685→//\n 686→// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n 687→//\n 688→// Binning algorithm:\n 689→// - Values normalized to metric's peak value from cluster configuration\n 690→// - Bins evenly distributed from 0 to peak\n 691→// - Pre-initialized with zeros for consistent visualization\n 692→//\n 693→// Limitations:\n 694→// - Running jobs: Limited to 5000 jobs for performance\n 695→// - Requires valid cluster configuration with metric peak values\n 696→// - Uses footprint statistic (avg/max/min) configured per metric\n 697→func (r *JobRepository) AddMetricHistograms(\n 698→\tctx context.Context,\n 699→\tfilter []*model.JobFilter,\n 700→\tmetrics []string,\n 701→\tstat *model.JobsStatistics,\n 702→\ttargetBinCount *int,\n 703→) (*model.JobsStatistics, error) {\n 704→\tstart := time.Now()\n 705→\n 706→\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n 707→\tfor _, f := range filter {\n 708→\t\tif f.State != nil {\n 709→\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n 710→\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n 711→\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n 712→\t\t\t\treturn stat, nil\n 713→\t\t\t}\n 714→\t\t}\n 715→\t}\n 716→\n 717→\t// All other cases: Query and make bins in sqlite directly\n 718→\tfor _, m := range metrics {\n 719→\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n 720→\t\tif err != nil {\n 721→\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n 722→\t\t\tcontinue\n 723→\t\t}\n 724→\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n 725→\t}\n 726→\n 727→\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n 728→\treturn stat, nil\n 729→}\n 730→\n 731→// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n 732→//\n 733→// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n 734→// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n 735→//\n 736→// Parameters:\n 737→// - value: SQL expression that produces the histogram value, aliased as \"value\"\n 738→// - filters: Job filters to apply\n 739→//\n 740→// Returns histogram points with Value (from column) and Count (number of jobs).\n 741→func (r *JobRepository) jobsStatisticsHistogram(\n 742→\tctx context.Context,\n 743→\tvalue string,\n 744→\tfilters []*model.JobFilter,\n 745→) ([]*model.HistoPoint, error) {\n 746→\tstart := time.Now()\n 747→\tquery, qerr := SecurityCheck(ctx,\n 748→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n 749→\n 750→\tif qerr != nil {\n 751→\t\treturn nil, qerr\n 752→\t}\n 753→\n 754→\tfor _, f := range filters {\n 755→\t\tquery = BuildWhereClause(f, query)\n 756→\t}\n 757→\n 758→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n 759→\tif err != nil {\n 760→\t\tcclog.Error(\"Error while running query\")\n 761→\t\treturn nil, err\n 762→\t}\n 763→\tdefer rows.Close()\n 764→\n 765→\tpoints := make([]*model.HistoPoint, 0)\n 766→\t// is it possible to introduce zero values here? requires info about bincount\n 767→\tfor rows.Next() {\n 768→\t\tpoint := model.HistoPoint{}\n 769→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n 770→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 771→\t\t\treturn nil, err\n 772→\t\t}\n 773→\n 774→\t\tpoints = append(points, &point)\n 775→\t}\n 776→\n 777→\tif err := rows.Err(); err != nil {\n 778→\t\treturn nil, err\n 779→\t}\n 780→\n 781→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 782→\treturn points, nil\n 783→}\n 784→\n 785→// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n 786→//\n 787→// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n 788→// histograms which only return bins with data. The value parameter should compute\n 789→// the bin number from job duration.\n 790→//\n 791→// Parameters:\n 792→// - value: SQL expression computing bin number from duration, aliased as \"value\"\n 793→// - filters: Job filters to apply\n 794→// - binSizeSeconds: Width of each bin in seconds\n 795→// - targetBinCount: Number of bins to pre-initialize\n 796→//\n 797→// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n 798→// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n 799→//\n 800→// Algorithm:\n 801→// 1. Pre-initialize targetBinCount bins with zero counts\n 802→// 2. Query database for actual counts per bin\n 803→// 3. Match query results to pre-initialized bins by value\n 804→// 4. Bins without matches remain at zero\n 805→func (r *JobRepository) jobsDurationStatisticsHistogram(\n 806→\tctx context.Context,\n 807→\tvalue string,\n 808→\tfilters []*model.JobFilter,\n 809→\tbinSizeSeconds int,\n 810→\ttargetBinCount *int,\n 811→) ([]*model.HistoPoint, error) {\n 812→\tstart := time.Now()\n 813→\tquery, qerr := SecurityCheck(ctx,\n 814→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n 815→\n 816→\tif qerr != nil {\n 817→\t\treturn nil, qerr\n 818→\t}\n 819→\n 820→\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n 821→\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n 822→\tpoints := make([]*model.HistoPoint, 0)\n 823→\tfor i := 1; i <= *targetBinCount; i++ {\n 824→\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n 825→\t\tpoints = append(points, &point)\n 826→\t}\n 827→\n 828→\tfor _, f := range filters {\n 829→\t\tquery = BuildWhereClause(f, query)\n 830→\t}\n 831→\n 832→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n 833→\tif err != nil {\n 834→\t\tcclog.Error(\"Error while running query\")\n 835→\t\treturn nil, err\n 836→\t}\n 837→\tdefer rows.Close()\n 838→\n 839→\t// Match query results to pre-initialized bins.\n 840→\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n 841→\tfor rows.Next() {\n 842→\t\tpoint := model.HistoPoint{}\n 843→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n 844→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 845→\t\t\treturn nil, err\n 846→\t\t}\n 847→\n 848→\t\tfor _, e := range points {\n 849→\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n 850→\t\t\t\te.Count = point.Count\n 851→\t\t\t\tbreak\n 852→\t\t\t}\n 853→\t\t}\n 854→\t}\n 855→\n 856→\tif err := rows.Err(); err != nil {\n 857→\t\treturn nil, err\n 858→\t}\n 859→\n 860→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 861→\treturn points, nil\n 862→}\n 863→\n 864→// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n 865→//\n 866→// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n 867→// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n 868→// and computing bin assignments in SQL.\n 869→//\n 870→// Parameters:\n 871→// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n 872→// - filters: Job filters to apply\n 873→// - bins: Number of bins to generate\n 874→//\n 875→// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n 876→//\n 877→// Algorithm:\n 878→// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n 879→// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n 880→// 3. Pre-initialize bins with min/max ranges based on peak value\n 881→// 4. Query database for counts per bin\n 882→// 5. Match results to pre-initialized bins\n 883→//\n 884→// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n 885→// peak by 0.999999999 to avoid creating an extra bin.\n 886→func (r *JobRepository) jobsMetricStatisticsHistogram(\n 887→\tctx context.Context,\n 888→\tmetric string,\n 889→\tfilters []*model.JobFilter,\n 890→\tbins *int,\n 891→) (*model.MetricHistoPoints, error) {\n 892→\t// Peak value defines the upper bound for binning: values are distributed across\n 893→\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n 894→\t// scan all clusters to find the maximum peak value.\n 895→\tvar metricConfig *schema.MetricConfig\n 896→\tvar peak float64\n 897→\tvar unit string\n 898→\tvar footprintStat string\n 899→\n 900→\t// Try to get metric config from filtered cluster\n 901→\tfor _, f := range filters {\n 902→\t\tif f.Cluster != nil {\n 903→\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n 904→\t\t\tpeak = metricConfig.Peak\n 905→\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n 906→\t\t\tfootprintStat = metricConfig.Footprint\n 907→\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n 908→\t\t}\n 909→\t}\n 910→\n 911→\t// If no cluster filter or peak not found, find largest peak across all clusters\n 912→\t// This ensures histogram can accommodate all possible values\n 913→\tif peak == 0.0 {\n 914→\t\tfor _, c := range archive.Clusters {\n 915→\t\t\tfor _, m := range c.MetricConfig {\n 916→\t\t\t\tif m.Name == metric {\n 917→\t\t\t\t\tif m.Peak > peak {\n 918→\t\t\t\t\t\tpeak = m.Peak\n 919→\t\t\t\t\t}\n 920→\t\t\t\t\tif unit == \"\" {\n 921→\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n 922→\t\t\t\t\t}\n 923→\t\t\t\t\tif footprintStat == \"\" {\n 924→\t\t\t\t\t\tfootprintStat = m.Footprint\n 925→\t\t\t\t\t}\n 926→\t\t\t\t}\n 927→\t\t\t}\n 928→\t\t}\n 929→\t}\n 930→\n 931→\t// Construct SQL histogram bins using normalized values.\n 932→\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n 933→\tstart := time.Now()\n 934→\n 935→\t// Bin calculation formula:\n 936→\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n 937→\t// Special case: value == peak would create bin N+1, so we test for equality\n 938→\t// and multiply peak by 0.999999999 to force it into bin N.\n 939→\tbinQuery := fmt.Sprintf(`CAST(\n 940→\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n 941→\t\t* %v as INTEGER )`,\n 942→\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n 943→\n 944→\tmainQuery := sq.Select(\n 945→\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n 946→\t\t`count(*) as count`,\n 947→\t).From(\"job\").Where(\n 948→\t\t\"JSON_VALID(footprint)\",\n 949→\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n 950→\n 951→\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n 952→\tif qerr != nil {\n 953→\t\treturn nil, qerr\n 954→\t}\n 955→\n 956→\tfor _, f := range filters {\n 957→\t\tmainQuery = BuildWhereClause(f, mainQuery)\n 958→\t}\n 959→\n 960→\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n 961→\n 962→\trows, err := mainQuery.RunWith(r.DB).Query()\n 963→\tif err != nil {\n 964→\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n 965→\t\treturn nil, err\n 966→\t}\n 967→\tdefer rows.Close()\n 968→\n 969→\t// Pre-initialize bins with calculated min/max ranges.\n 970→\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n 971→\tpoints := make([]*model.MetricHistoPoint, 0)\n 972→\tbinStep := int(peak) / *bins\n 973→\tfor i := 1; i <= *bins; i++ {\n 974→\t\tbinMin := (binStep * (i - 1))\n 975→\t\tbinMax := (binStep * i)\n 976→\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n 977→\t\tpoints = append(points, &epoint)\n 978→\t}\n 979→\n 980→\t// Match query results to pre-initialized bins.\n 981→\tfor rows.Next() {\n 982→\t\trpoint := model.MetricHistoPoint{}\n 983→\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n 984→\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n 985→\t\t\treturn nil, err\n 986→\t\t}\n 987→\n 988→\t\tfor _, e := range points {\n 989→\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n 990→\t\t\t\te.Count = rpoint.Count\n 991→\t\t\t\tbreak\n 992→\t\t\t}\n 993→\t\t}\n 994→\t}\n 995→\n 996→\tif err := rows.Err(); err != nil {\n 997→\t\treturn nil, err\n 998→\t}\n 999→\n 1000→\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n 1001→\n 1002→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 1003→\treturn &result, nil\n 1004→}\n 1005→\n 1006→// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n 1007→//\n 1008→// Unlike completed jobs which use footprint data from the database, running jobs require\n 1009→// fetching current metric averages from the metric backend (via metricdispatch).\n 1010→//\n 1011→// Parameters:\n 1012→// - metrics: List of metric names\n 1013→// - filters: Job filters (should filter to running jobs only)\n 1014→// - bins: Number of histogram bins\n 1015→//\n 1016→// Returns slice of MetricHistoPoints, one per metric.\n 1017→//\n 1018→// Limitations:\n 1019→// - Maximum 5000 jobs (returns nil if more jobs match)\n 1020→// - Requires metric backend availability\n 1021→// - Bins based on metric peak values from cluster configuration\n 1022→//\n 1023→// Algorithm:\n 1024→// 1. Query first 5001 jobs to check count limit\n 1025→// 2. Load metric averages for all jobs via metricdispatch\n 1026→// 3. For each metric, create bins based on peak value\n 1027→// 4. Iterate averages and count jobs per bin\n 1028→func (r *JobRepository) runningJobsMetricStatisticsHistogram(\n 1029→\tctx context.Context,\n 1030→\tmetrics []string,\n 1031→\tfilters []*model.JobFilter,\n 1032→\tbins *int,\n 1033→) []*model.MetricHistoPoints {\n 1034→\t// Get Jobs\n 1035→\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n 1036→\tif err != nil {\n 1037→\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n 1038→\t\treturn nil\n 1039→\t}\n 1040→\tif len(jobs) > 5000 {\n 1041→\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n 1042→\t\treturn nil\n 1043→\t}\n 1044→\n 1045→\t// Get AVGs from metric repo\n 1046→\tavgs := make([][]schema.Float, len(metrics))\n 1047→\tfor i := range avgs {\n 1048→\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n 1049→\t}\n 1050→\n 1051→\tfor _, job := range jobs {\n 1052→\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n 1053→\t\t\tcontinue\n 1054→\t\t}\n 1055→\n 1056→\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n 1057→\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n 1058→\t\t\treturn nil\n 1059→\t\t}\n 1060→\t}\n 1061→\n 1062→\t// Iterate metrics to fill endresult\n 1063→\tdata := make([]*model.MetricHistoPoints, 0)\n 1064→\tfor idx, metric := range metrics {\n 1065→\t\t// Get specific Peak or largest Peak\n 1066→\t\tvar metricConfig *schema.MetricConfig\n 1067→\t\tvar peak float64\n 1068→\t\tvar unit string\n 1069→\n 1070→\t\tfor _, f := range filters {\n 1071→\t\t\tif f.Cluster != nil {\n 1072→\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n 1073→\t\t\t\tpeak = metricConfig.Peak\n 1074→\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n 1075→\t\t\t}\n 1076→\t\t}\n 1077→\n 1078→\t\tif peak == 0.0 {\n 1079→\t\t\tfor _, c := range archive.Clusters {\n 1080→\t\t\t\tfor _, m := range c.MetricConfig {\n 1081→\t\t\t\t\tif m.Name == metric {\n 1082→\t\t\t\t\t\tif m.Peak > peak {\n 1083→\t\t\t\t\t\t\tpeak = m.Peak\n 1084→\t\t\t\t\t\t}\n 1085→\t\t\t\t\t\tif unit == \"\" {\n 1086→\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n 1087→\t\t\t\t\t\t}\n 1088→\t\t\t\t\t}\n 1089→\t\t\t\t}\n 1090→\t\t\t}\n 1091→\t\t}\n 1092→\n 1093→\t\t// Make and fill bins\n 1094→\t\tpeakBin := int(peak) / *bins\n 1095→\n 1096→\t\tpoints := make([]*model.MetricHistoPoint, 0)\n 1097→\t\tfor b := 0; b < *bins; b++ {\n 1098→\t\t\tcount := 0\n 1099→\t\t\tbindex := b + 1\n 1100→\t\t\tbmin := peakBin * b\n 1101→\t\t\tbmax := peakBin * (b + 1)\n 1102→\n 1103→\t\t\t// Iterate AVG values for indexed metric and count for bins\n 1104→\t\t\tfor _, val := range avgs[idx] {\n 1105→\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n 1106→\t\t\t\t\tcount += 1\n 1107→\t\t\t\t}\n 1108→\t\t\t}\n 1109→\n 1110→\t\t\t// Append Bin to Metric Result Array\n 1111→\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n 1112→\t\t\tpoints = append(points, &point)\n 1113→\t\t}\n 1114→\n 1115→\t\t// Append Metric Result Array to final results array\n 1116→\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n 1117→\t\tdata = append(data, &result)\n 1118→\t}\n 1119→\n 1120→\treturn data\n 1121→}\n 1122→"}]},"uuid":"0726bf91-dabe-439e-a6e6-9f72afb11ae2","timestamp":"2026-03-11T05:01:41.828Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRow()\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","numLines":1122,"startLine":1,"totalLines":1122}},"sourceToolAssistantUUID":"5b4bc6bd-552c-4979-bcde-bc6e1e726842"} +{"parentUuid":"0726bf91-dabe-439e-a6e6-9f72afb11ae2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01141KDjZ4GvFWJSbM7oMSN4","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/job.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8833},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"b9bcce1d-ae8c-4184-94be-9383ba3f0631","timestamp":"2026-03-11T05:01:42.042Z"} +{"parentUuid":"b9bcce1d-ae8c-4184-94be-9383ba3f0631","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PRqzmUw2xM1Ra5kk6M4d2E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018xr6d2BUf6fjuki1nZHX8q","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8833,"cache_read_input_tokens":8781,"output_tokens":390,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":8833,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhsLHSzP7FPK81HS9pG","type":"assistant","uuid":"a1e1f3b1-f2f5-4ad2-8d0f-47fc42ed89de","timestamp":"2026-03-11T05:01:42.047Z"} +{"parentUuid":"a1e1f3b1-f2f5-4ad2-8d0f-47fc42ed89de","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_018xr6d2BUf6fjuki1nZHX8q","toolUseID":"toolu_018xr6d2BUf6fjuki1nZHX8q","timestamp":"2026-03-11T05:01:42.051Z","uuid":"f2710aae-5bd4-46a0-8e8f-82025ff44bd1"} +{"parentUuid":"a1e1f3b1-f2f5-4ad2-8d0f-47fc42ed89de","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018xr6d2BUf6fjuki1nZHX8q","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"context\"\n 10→\t\"database/sql\"\n 11→\t\"fmt\"\n 12→\t\"time\"\n 13→\n 14→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n 15→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 16→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 17→\tsq \"github.com/Masterminds/squirrel\"\n 18→)\n 19→\n 20→// Find executes a SQL query to find a specific batch job.\n 21→// The job is queried using the batch job id, the cluster name,\n 22→// and the start time of the job in UNIX epoch time seconds.\n 23→// It returns a pointer to a schema.Job data structure and an error variable.\n 24→// To check if no job was found test err == sql.ErrNoRows\n 25→func (r *JobRepository) Find(\n 26→\tjobID *int64,\n 27→\tcluster *string,\n 28→\tstartTime *int64,\n 29→) (*schema.Job, error) {\n 30→\tif jobID == nil {\n 31→\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n 32→\t}\n 33→\n 34→\tstart := time.Now()\n 35→\tq := sq.Select(jobColumns...).From(\"job\").\n 36→\t\tWhere(\"job.job_id = ?\", *jobID)\n 37→\n 38→\tif cluster != nil {\n 39→\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n 40→\t}\n 41→\tif startTime != nil {\n 42→\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n 43→\t}\n 44→\n 45→\tq = q.OrderBy(\"job.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n 46→\n 47→\tcclog.Debugf(\"Timer Find %s\", time.Since(start))\n 48→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 49→}\n 50→\n 51→// FindCached executes a SQL query to find a specific batch job from the job_cache table.\n 52→// The job is queried using the batch job id, and optionally filtered by cluster name\n 53→// and start time (UNIX epoch time seconds). This method uses cached job data which\n 54→// may be stale but provides faster access than Find().\n 55→// It returns a pointer to a schema.Job data structure and an error variable.\n 56→// To check if no job was found test err == sql.ErrNoRows\n 57→func (r *JobRepository) FindCached(\n 58→\tjobID *int64,\n 59→\tcluster *string,\n 60→\tstartTime *int64,\n 61→) (*schema.Job, error) {\n 62→\tif jobID == nil {\n 63→\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n 64→\t}\n 65→\n 66→\tq := sq.Select(jobCacheColumns...).From(\"job_cache\").\n 67→\t\tWhere(\"job_cache.job_id = ?\", *jobID)\n 68→\n 69→\tif cluster != nil {\n 70→\t\tq = q.Where(\"job_cache.cluster = ?\", *cluster)\n 71→\t}\n 72→\tif startTime != nil {\n 73→\t\tq = q.Where(\"job_cache.start_time = ?\", *startTime)\n 74→\t}\n 75→\n 76→\tq = q.OrderBy(\"job_cache.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n 77→\n 78→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 79→}\n 80→\n 81→// FindAll executes a SQL query to find all batch jobs matching the given criteria.\n 82→// Jobs are queried using the batch job id, and optionally filtered by cluster name\n 83→// and start time (UNIX epoch time seconds).\n 84→// It returns a slice of pointers to schema.Job data structures and an error variable.\n 85→// An empty slice is returned if no matching jobs are found.\n 86→func (r *JobRepository) FindAll(\n 87→\tjobID *int64,\n 88→\tcluster *string,\n 89→\tstartTime *int64,\n 90→) ([]*schema.Job, error) {\n 91→\tif jobID == nil {\n 92→\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n 93→\t}\n 94→\n 95→\tstart := time.Now()\n 96→\tq := sq.Select(jobColumns...).From(\"job\").\n 97→\t\tWhere(\"job.job_id = ?\", *jobID)\n 98→\n 99→\tif cluster != nil {\n 100→\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n 101→\t}\n 102→\tif startTime != nil {\n 103→\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n 104→\t}\n 105→\n 106→\trows, err := q.RunWith(r.stmtCache).Query()\n 107→\tif err != nil {\n 108→\t\tcclog.Errorf(\"Error while running FindAll query for jobID=%d: %v\", *jobID, err)\n 109→\t\treturn nil, fmt.Errorf(\"failed to execute FindAll query: %w\", err)\n 110→\t}\n 111→\tdefer rows.Close()\n 112→\n 113→\tjobs := make([]*schema.Job, 0, 10)\n 114→\tfor rows.Next() {\n 115→\t\tjob, err := scanJob(rows)\n 116→\t\tif err != nil {\n 117→\t\t\tcclog.Warnf(\"Error while scanning rows in FindAll: %v\", err)\n 118→\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n 119→\t\t}\n 120→\t\tjobs = append(jobs, job)\n 121→\t}\n 122→\tcclog.Debugf(\"Timer FindAll %s\", time.Since(start))\n 123→\treturn jobs, nil\n 124→}\n 125→\n 126→// GetJobList returns job IDs for non-running jobs.\n 127→// This is useful to process large job counts and intended to be used\n 128→// together with FindById to process jobs one by one.\n 129→// Use limit and offset for pagination. Use limit=0 to get all results (not recommended for large datasets).\n 130→func (r *JobRepository) GetJobList(limit int, offset int) ([]int64, error) {\n 131→\tquery := sq.Select(\"id\").From(\"job\").\n 132→\t\tWhere(\"job.job_state != 'running'\")\n 133→\n 134→\t// Add pagination if limit is specified\n 135→\tif limit > 0 {\n 136→\t\tquery = query.Limit(uint64(limit)).Offset(uint64(offset))\n 137→\t}\n 138→\n 139→\trows, err := query.RunWith(r.stmtCache).Query()\n 140→\tif err != nil {\n 141→\t\tcclog.Errorf(\"Error while running GetJobList query (limit=%d, offset=%d): %v\", limit, offset, err)\n 142→\t\treturn nil, fmt.Errorf(\"failed to execute GetJobList query: %w\", err)\n 143→\t}\n 144→\tdefer rows.Close()\n 145→\n 146→\tjl := make([]int64, 0, 1000)\n 147→\tfor rows.Next() {\n 148→\t\tvar id int64\n 149→\t\terr := rows.Scan(&id)\n 150→\t\tif err != nil {\n 151→\t\t\tcclog.Warnf(\"Error while scanning rows in GetJobList: %v\", err)\n 152→\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID: %w\", err)\n 153→\t\t}\n 154→\t\tjl = append(jl, id)\n 155→\t}\n 156→\n 157→\tcclog.Debugf(\"JobRepository.GetJobList(): Return job count %d\", len(jl))\n 158→\treturn jl, nil\n 159→}\n 160→\n 161→// FindByID executes a SQL query to find a specific batch job.\n 162→// The job is queried using the database id.\n 163→// It returns a pointer to a schema.Job data structure and an error variable.\n 164→// To check if no job was found test err == sql.ErrNoRows\n 165→func (r *JobRepository) FindByID(ctx context.Context, jobID int64) (*schema.Job, error) {\n 166→\tq := sq.Select(jobColumns...).\n 167→\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n 168→\n 169→\tq, qerr := SecurityCheck(ctx, q)\n 170→\tif qerr != nil {\n 171→\t\treturn nil, qerr\n 172→\t}\n 173→\n 174→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 175→}\n 176→\n 177→// FindByIDWithUser executes a SQL query to find a specific batch job.\n 178→// The job is queried using the database id. The user is passed directly,\n 179→// instead as part of the context.\n 180→// It returns a pointer to a schema.Job data structure and an error variable.\n 181→// To check if no job was found test err == sql.ErrNoRows\n 182→func (r *JobRepository) FindByIDWithUser(user *schema.User, jobID int64) (*schema.Job, error) {\n 183→\tq := sq.Select(jobColumns...).\n 184→\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n 185→\n 186→\tq, qerr := SecurityCheckWithUser(user, q)\n 187→\tif qerr != nil {\n 188→\t\treturn nil, qerr\n 189→\t}\n 190→\n 191→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 192→}\n 193→\n 194→// FindByIDDirect executes a SQL query to find a specific batch job.\n 195→// The job is queried using the database id.\n 196→// It returns a pointer to a schema.Job data structure and an error variable.\n 197→// To check if no job was found test err == sql.ErrNoRows\n 198→func (r *JobRepository) FindByIDDirect(jobID int64) (*schema.Job, error) {\n 199→\tq := sq.Select(jobColumns...).\n 200→\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n 201→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 202→}\n 203→\n 204→// FindByJobID executes a SQL query to find a specific batch job.\n 205→// The job is queried using the slurm id and the clustername.\n 206→// It returns a pointer to a schema.Job data structure and an error variable.\n 207→// To check if no job was found test err == sql.ErrNoRows\n 208→func (r *JobRepository) FindByJobID(ctx context.Context, jobID int64, startTime int64, cluster string) (*schema.Job, error) {\n 209→\tq := sq.Select(jobColumns...).\n 210→\t\tFrom(\"job\").\n 211→\t\tWhere(\"job.job_id = ?\", jobID).\n 212→\t\tWhere(\"job.cluster = ?\", cluster).\n 213→\t\tWhere(\"job.start_time = ?\", startTime)\n 214→\n 215→\tq, qerr := SecurityCheck(ctx, q)\n 216→\tif qerr != nil {\n 217→\t\treturn nil, qerr\n 218→\t}\n 219→\n 220→\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n 221→}\n 222→\n 223→// IsJobOwner checks if the specified user owns the batch job identified by jobID,\n 224→// startTime, and cluster. Returns true if the user is the owner, false otherwise.\n 225→// This method does not return errors; it returns false for both non-existent jobs\n 226→// and jobs owned by other users.\n 227→func (r *JobRepository) IsJobOwner(jobID int64, startTime int64, user string, cluster string) bool {\n 228→\tq := sq.Select(\"id\").\n 229→\t\tFrom(\"job\").\n 230→\t\tWhere(\"job.job_id = ?\", jobID).\n 231→\t\tWhere(\"job.hpc_user = ?\", user).\n 232→\t\tWhere(\"job.cluster = ?\", cluster).\n 233→\t\tWhere(\"job.start_time = ?\", startTime)\n 234→\n 235→\t_, err := scanJob(q.RunWith(r.stmtCache).QueryRow())\n 236→\tif err != nil && err != sql.ErrNoRows {\n 237→\t\tcclog.Warnf(\"IsJobOwner: unexpected error for jobID=%d, user=%s, cluster=%s: %v\", jobID, user, cluster, err)\n 238→\t}\n 239→\treturn err != sql.ErrNoRows\n 240→}\n 241→\n 242→func (r *JobRepository) FindConcurrentJobs(\n 243→\tctx context.Context,\n 244→\tjob *schema.Job,\n 245→) (*model.JobLinkResultList, error) {\n 246→\tif job == nil {\n 247→\t\treturn nil, nil\n 248→\t}\n 249→\n 250→\tquery, qerr := SecurityCheck(ctx, sq.Select(\"job.id\", \"job.job_id\", \"job.start_time\").From(\"job\"))\n 251→\tif qerr != nil {\n 252→\t\treturn nil, qerr\n 253→\t}\n 254→\n 255→\tquery = query.Where(\"cluster = ?\", job.Cluster)\n 256→\n 257→\tif len(job.Resources) == 0 {\n 258→\t\treturn nil, fmt.Errorf(\"job has no resources defined\")\n 259→\t}\n 260→\n 261→\tvar startTime int64\n 262→\tvar stopTime int64\n 263→\n 264→\tstartTime = job.StartTime\n 265→\thostname := job.Resources[0].Hostname\n 266→\n 267→\tif job.State == schema.JobStateRunning {\n 268→\t\tstopTime = time.Now().Unix()\n 269→\t} else {\n 270→\t\tstopTime = startTime + int64(job.Duration)\n 271→\t}\n 272→\n 273→\t// Time buffer constant for finding overlapping jobs\n 274→\t// overlapBufferEnd: 200s buffer at job end to account for scheduling/cleanup overlap\n 275→\tconst overlapBufferEnd = 200\n 276→\n 277→\tstopTimeTail := stopTime - overlapBufferEnd\n 278→\tstartTimeFront := startTime + overlapBufferEnd\n 279→\n 280→\tqueryRunning := query.Where(\"job.job_state = ?\", \"running\").\n 281→\t\tWhere(\"job.start_time <= ?\", stopTimeTail)\n 282→\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n 283→\tqueryRunning = queryRunning.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n 284→\n 285→\tquery = query.Where(\"job.job_state != ?\", \"running\").\n 286→\t\tWhere(\"job.start_time < ?\", stopTimeTail).\n 287→\t\tWhere(\"(job.start_time + job.duration) > ?\", startTimeFront)\n 288→\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n 289→\tquery = query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n 290→\n 291→\trows, err := query.RunWith(r.stmtCache).Query()\n 292→\tif err != nil {\n 293→\t\tcclog.Errorf(\"Error while running concurrent jobs query: %v\", err)\n 294→\t\treturn nil, fmt.Errorf(\"failed to execute concurrent jobs query: %w\", err)\n 295→\t}\n 296→\tdefer rows.Close()\n 297→\n 298→\titems := make([]*model.JobLink, 0, 10)\n 299→\tqueryString := fmt.Sprintf(\"cluster=%s\", job.Cluster)\n 300→\n 301→\tfor rows.Next() {\n 302→\t\tvar id, jobID, startTime sql.NullInt64\n 303→\n 304→\t\tif err = rows.Scan(&id, &jobID, &startTime); err != nil {\n 305→\t\t\tcclog.Warnf(\"Error while scanning concurrent job rows: %v\", err)\n 306→\t\t\treturn nil, fmt.Errorf(\"failed to scan concurrent job row: %w\", err)\n 307→\t\t}\n 308→\n 309→\t\tif id.Valid {\n 310→\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n 311→\t\t\titems = append(items,\n 312→\t\t\t\t&model.JobLink{\n 313→\t\t\t\t\tID: fmt.Sprint(id.Int64),\n 314→\t\t\t\t\tJobID: int(jobID.Int64),\n 315→\t\t\t\t})\n 316→\t\t}\n 317→\t}\n 318→\n 319→\trows, err = queryRunning.RunWith(r.stmtCache).Query()\n 320→\tif err != nil {\n 321→\t\tcclog.Errorf(\"Error while running concurrent running jobs query: %v\", err)\n 322→\t\treturn nil, fmt.Errorf(\"failed to execute concurrent running jobs query: %w\", err)\n 323→\t}\n 324→\tdefer rows.Close()\n 325→\n 326→\tfor rows.Next() {\n 327→\t\tvar id, jobID, startTime sql.NullInt64\n 328→\n 329→\t\tif err := rows.Scan(&id, &jobID, &startTime); err != nil {\n 330→\t\t\tcclog.Warnf(\"Error while scanning running concurrent job rows: %v\", err)\n 331→\t\t\treturn nil, fmt.Errorf(\"failed to scan running concurrent job row: %w\", err)\n 332→\t\t}\n 333→\n 334→\t\tif id.Valid {\n 335→\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n 336→\t\t\titems = append(items,\n 337→\t\t\t\t&model.JobLink{\n 338→\t\t\t\t\tID: fmt.Sprint(id.Int64),\n 339→\t\t\t\t\tJobID: int(jobID.Int64),\n 340→\t\t\t\t})\n 341→\t\t}\n 342→\t}\n 343→\n 344→\tcnt := len(items)\n 345→\n 346→\treturn &model.JobLinkResultList{\n 347→\t\tListQuery: &queryString,\n 348→\t\tItems: items,\n 349→\t\tCount: &cnt,\n 350→\t}, nil\n 351→}\n 352→"}]},"uuid":"d6b66f05-698c-40ed-9294-018e5593cc88","timestamp":"2026-03-11T05:01:42.050Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// Find executes a SQL query to find a specific batch job.\n// The job is queried using the batch job id, the cluster name,\n// and the start time of the job in UNIX epoch time seconds.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) Find(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\tcclog.Debugf(\"Timer Find %s\", time.Since(start))\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindCached executes a SQL query to find a specific batch job from the job_cache table.\n// The job is queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds). This method uses cached job data which\n// may be stale but provides faster access than Find().\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindCached(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tq := sq.Select(jobCacheColumns...).From(\"job_cache\").\n\t\tWhere(\"job_cache.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job_cache.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job_cache.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job_cache.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindAll executes a SQL query to find all batch jobs matching the given criteria.\n// Jobs are queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds).\n// It returns a slice of pointers to schema.Job data structures and an error variable.\n// An empty slice is returned if no matching jobs are found.\nfunc (r *JobRepository) FindAll(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) ([]*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\trows, err := q.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindAll query for jobID=%d: %v\", *jobID, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute FindAll query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 10)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindAll: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\tcclog.Debugf(\"Timer FindAll %s\", time.Since(start))\n\treturn jobs, nil\n}\n\n// GetJobList returns job IDs for non-running jobs.\n// This is useful to process large job counts and intended to be used\n// together with FindById to process jobs one by one.\n// Use limit and offset for pagination. Use limit=0 to get all results (not recommended for large datasets).\nfunc (r *JobRepository) GetJobList(limit int, offset int) ([]int64, error) {\n\tquery := sq.Select(\"id\").From(\"job\").\n\t\tWhere(\"job.job_state != 'running'\")\n\n\t// Add pagination if limit is specified\n\tif limit > 0 {\n\t\tquery = query.Limit(uint64(limit)).Offset(uint64(offset))\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running GetJobList query (limit=%d, offset=%d): %v\", limit, offset, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute GetJobList query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjl := make([]int64, 0, 1000)\n\tfor rows.Next() {\n\t\tvar id int64\n\t\terr := rows.Scan(&id)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in GetJobList: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID: %w\", err)\n\t\t}\n\t\tjl = append(jl, id)\n\t}\n\n\tcclog.Debugf(\"JobRepository.GetJobList(): Return job count %d\", len(jl))\n\treturn jl, nil\n}\n\n// FindByID executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByID(ctx context.Context, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDWithUser executes a SQL query to find a specific batch job.\n// The job is queried using the database id. The user is passed directly,\n// instead as part of the context.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDWithUser(user *schema.User, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheckWithUser(user, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDDirect executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDDirect(jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByJobID executes a SQL query to find a specific batch job.\n// The job is queried using the slurm id and the clustername.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByJobID(ctx context.Context, jobID int64, startTime int64, cluster string) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,\n// startTime, and cluster. Returns true if the user is the owner, false otherwise.\n// This method does not return errors; it returns false for both non-existent jobs\n// and jobs owned by other users.\nfunc (r *JobRepository) IsJobOwner(jobID int64, startTime int64, user string, cluster string) bool {\n\tq := sq.Select(\"id\").\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.hpc_user = ?\", user).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\t_, err := scanJob(q.RunWith(r.stmtCache).QueryRow())\n\tif err != nil && err != sql.ErrNoRows {\n\t\tcclog.Warnf(\"IsJobOwner: unexpected error for jobID=%d, user=%s, cluster=%s: %v\", jobID, user, cluster, err)\n\t}\n\treturn err != sql.ErrNoRows\n}\n\nfunc (r *JobRepository) FindConcurrentJobs(\n\tctx context.Context,\n\tjob *schema.Job,\n) (*model.JobLinkResultList, error) {\n\tif job == nil {\n\t\treturn nil, nil\n\t}\n\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"job.id\", \"job.job_id\", \"job.start_time\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tquery = query.Where(\"cluster = ?\", job.Cluster)\n\n\tif len(job.Resources) == 0 {\n\t\treturn nil, fmt.Errorf(\"job has no resources defined\")\n\t}\n\n\tvar startTime int64\n\tvar stopTime int64\n\n\tstartTime = job.StartTime\n\thostname := job.Resources[0].Hostname\n\n\tif job.State == schema.JobStateRunning {\n\t\tstopTime = time.Now().Unix()\n\t} else {\n\t\tstopTime = startTime + int64(job.Duration)\n\t}\n\n\t// Time buffer constant for finding overlapping jobs\n\t// overlapBufferEnd: 200s buffer at job end to account for scheduling/cleanup overlap\n\tconst overlapBufferEnd = 200\n\n\tstopTimeTail := stopTime - overlapBufferEnd\n\tstartTimeFront := startTime + overlapBufferEnd\n\n\tqueryRunning := query.Where(\"job.job_state = ?\", \"running\").\n\t\tWhere(\"job.start_time <= ?\", stopTimeTail)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tqueryRunning = queryRunning.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\tquery = query.Where(\"job.job_state != ?\", \"running\").\n\t\tWhere(\"job.start_time < ?\", stopTimeTail).\n\t\tWhere(\"(job.start_time + job.duration) > ?\", startTimeFront)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tquery = query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\titems := make([]*model.JobLink, 0, 10)\n\tqueryString := fmt.Sprintf(\"cluster=%s\", job.Cluster)\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err = rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\trows, err = queryRunning.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent running jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent running jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err := rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning running concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan running concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tcnt := len(items)\n\n\treturn &model.JobLinkResultList{\n\t\tListQuery: &queryString,\n\t\tItems: items,\n\t\tCount: &cnt,\n\t}, nil\n}\n","numLines":352,"startLine":1,"totalLines":352}},"sourceToolAssistantUUID":"a1e1f3b1-f2f5-4ad2-8d0f-47fc42ed89de"} +{"parentUuid":"d6b66f05-698c-40ed-9294-018e5593cc88","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01141KDjZ4GvFWJSbM7oMSN4","toolUseID":"toolu_01141KDjZ4GvFWJSbM7oMSN4","timestamp":"2026-03-11T05:01:42.267Z","uuid":"323fbfcd-4303-42b5-b993-623157d2a06b"} +{"parentUuid":"b9bcce1d-ae8c-4184-94be-9383ba3f0631","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01141KDjZ4GvFWJSbM7oMSN4","type":"tool_result","content":"\nOutput too large (49.5KB). Full output saved to: /Users/jan/.claude/projects/-Users-jan-prg-CC-cc-backend/520afa6a-6a70-437b-96c1-35c40ed3ec48/tool-results/toolu_01141KDjZ4GvFWJSbM7oMSN4.txt\n\nPreview (first 2KB):\n 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package repository provides the data access layer for cc-backend using the repository pattern.\n 7→//\n 8→// The repository pattern abstracts database operations and provides a clean interface for\n 9→// data access. Each major entity (Job, User, Node, Tag) has its own repository with CRUD\n 10→// operations and specialized queries.\n 11→//\n 12→// # Database Connection\n 13→//\n 14→// Initialize the database connection before using any repository:\n 15→//\n 16→//\trepository.Connect(\"sqlite3\", \"./var/job.db\")\n 17→//\n 18→// # Configuration\n 19→//\n 20→// Optional: Configure repository settings before initialization:\n 21→//\n 22→//\trepository.SetConfig(&repository.RepositoryConfig{\n 23→//\t CacheSize: 2 * 1024 * 1024, // 2MB cache\n 24→//\t MaxOpenConnections: 8, // Connection pool size\n 25→//\t MinRunningJobDuration: 300, // Filter threshold\n 26→//\t})\n 27→//\n 28→// If not configured, sensible defaults are used automatically.\n 29→//\n 30→// # Repositories\n 31→//\n 32→// - JobRepository: Job lifecycle management and querying\n 33→// - UserRepository: User management and authentication\n 34→// - NodeRepository: Cluster node state tracking\n 35→// - Tags: Job tagging and categorization\n 36→//\n 37→// # Caching\n 38→//\n 39→// Repositories use LRU caching to improve performance. Cache keys are constructed\n 40→// as \"type:id\" (e.g., \"metadata:123\"). Cache is automatically invalidated on\n 41→// mutations to maintain consistency.\n 42→//\n 43→// # Transaction Support\n 44→//\n 45→// For batch operations, use transactions:\n 46→//\n 47→//\tt, err := jobRepo.TransactionInit()\n...\n"}]},"uuid":"0e7816b4-2772-4008-9f63-c6abf9617921","timestamp":"2026-03-11T05:01:42.266Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/job.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package repository provides the data access layer for cc-backend using the repository pattern.\n//\n// The repository pattern abstracts database operations and provides a clean interface for\n// data access. Each major entity (Job, User, Node, Tag) has its own repository with CRUD\n// operations and specialized queries.\n//\n// # Database Connection\n//\n// Initialize the database connection before using any repository:\n//\n//\trepository.Connect(\"sqlite3\", \"./var/job.db\")\n//\n// # Configuration\n//\n// Optional: Configure repository settings before initialization:\n//\n//\trepository.SetConfig(&repository.RepositoryConfig{\n//\t CacheSize: 2 * 1024 * 1024, // 2MB cache\n//\t MaxOpenConnections: 8, // Connection pool size\n//\t MinRunningJobDuration: 300, // Filter threshold\n//\t})\n//\n// If not configured, sensible defaults are used automatically.\n//\n// # Repositories\n//\n// - JobRepository: Job lifecycle management and querying\n// - UserRepository: User management and authentication\n// - NodeRepository: Cluster node state tracking\n// - Tags: Job tagging and categorization\n//\n// # Caching\n//\n// Repositories use LRU caching to improve performance. Cache keys are constructed\n// as \"type:id\" (e.g., \"metadata:123\"). Cache is automatically invalidated on\n// mutations to maintain consistency.\n//\n// # Transaction Support\n//\n// For batch operations, use transactions:\n//\n//\tt, err := jobRepo.TransactionInit()\n//\tif err != nil {\n//\t return err\n//\t}\n//\tdefer t.Rollback() // Rollback if not committed\n//\n//\t// Perform operations...\n//\tjobRepo.TransactionAdd(t, query, args...)\n//\n//\t// Commit when done\n//\tif err := t.Commit(); err != nil {\n//\t return err\n//\t}\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"maps\"\n\t\"math\"\n\t\"sort\"\n\t\"strconv\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n\t\"github.com/jmoiron/sqlx\"\n)\n\nvar (\n\t// jobRepoOnce ensures singleton initialization of the JobRepository\n\tjobRepoOnce sync.Once\n\t// jobRepoInstance holds the single instance of JobRepository\n\tjobRepoInstance *JobRepository\n)\n\n// JobRepository provides database access for job-related operations.\n// It implements the repository pattern to abstract database interactions\n// and provides caching for improved performance.\n//\n// The repository is a singleton initialized via GetJobRepository().\n// All database queries use prepared statements via stmtCache for efficiency.\n// Frequently accessed data (metadata, energy footprints) is cached in an LRU cache.\ntype JobRepository struct {\n\tDB *sqlx.DB // Database connection pool\n\tstmtCache *sq.StmtCache // Prepared statement cache for query optimization\n\tcache *lrucache.Cache // LRU cache for metadata and footprint data\n\tdriver string // Database driver name (e.g., \"sqlite3\")\n\tMutex sync.Mutex // Mutex for thread-safe operations\n}\n\n// GetJobRepository returns the singleton instance of JobRepository.\n// The repository is initialized lazily on first access with database connection,\n// prepared statement cache, and LRU cache configured from repoConfig.\n//\n// This function is thread-safe and ensures only one instance is created.\n// It must be called after Connect() has established a database connection.\nfunc GetJobRepository() *JobRepository {\n\tjobRepoOnce.Do(func() {\n\t\tdb := GetConnection()\n\n\t\tjobRepoInstance = &JobRepository{\n\t\t\tDB: db.DB,\n\t\t\tdriver: db.Driver,\n\n\t\t\tstmtCache: sq.NewStmtCache(db.DB),\n\t\t\tcache: lrucache.New(repoConfig.CacheSize),\n\t\t}\n\t})\n\treturn jobRepoInstance\n}\n\n// jobColumns defines the standard set of columns selected from the job table.\n// Used consistently across all job queries to ensure uniform data retrieval.\nvar jobColumns []string = []string{\n\t\"job.id\", \"job.job_id\", \"job.hpc_user\", \"job.project\", \"job.cluster\", \"job.subcluster\",\n\t\"job.start_time\", \"job.cluster_partition\", \"job.array_job_id\", \"job.num_nodes\",\n\t\"job.num_hwthreads\", \"job.num_acc\", \"job.shared\", \"job.monitoring_status\",\n\t\"job.smt\", \"job.job_state\", \"job.duration\", \"job.walltime\", \"job.resources\",\n\t\"job.footprint\", \"job.energy\",\n}\n\n// jobCacheColumns defines columns from the job_cache table, mirroring jobColumns.\n// Used for queries against cached job data for performance optimization.\nvar jobCacheColumns []string = []string{\n\t\"job_cache.id\", \"job_cache.job_id\", \"job_cache.hpc_user\", \"job_cache.project\", \"job_cache.cluster\",\n\t\"job_cache.subcluster\", \"job_cache.start_time\", \"job_cache.cluster_partition\",\n\t\"job_cache.array_job_id\", \"job_cache.num_nodes\", \"job_cache.num_hwthreads\",\n\t\"job_cache.num_acc\", \"job_cache.shared\", \"job_cache.monitoring_status\", \"job_cache.smt\",\n\t\"job_cache.job_state\", \"job_cache.duration\", \"job_cache.walltime\", \"job_cache.resources\",\n\t\"job_cache.footprint\", \"job_cache.energy\",\n}\n\n// scanJob converts a database row into a schema.Job struct.\n// It handles JSON unmarshaling of resources and footprint fields,\n// and calculates accurate duration for running jobs.\n//\n// Parameters:\n// - row: Database row implementing Scan() interface (sql.Row or sql.Rows)\n//\n// Returns the populated Job struct or an error if scanning or unmarshaling fails.\nfunc scanJob(row interface{ Scan(...any) error }) (*schema.Job, error) {\n\tjob := &schema.Job{}\n\n\tif err := row.Scan(\n\t\t&job.ID, &job.JobID, &job.User, &job.Project, &job.Cluster, &job.SubCluster,\n\t\t&job.StartTime, &job.Partition, &job.ArrayJobID, &job.NumNodes, &job.NumHWThreads,\n\t\t&job.NumAcc, &job.Shared, &job.MonitoringStatus, &job.SMT, &job.State,\n\t\t&job.Duration, &job.Walltime, &job.RawResources, &job.RawFootprint, &job.Energy); err != nil {\n\t\tcclog.Warnf(\"Error while scanning rows (Job): %v\", err)\n\t\treturn nil, err\n\t}\n\n\tif err := json.Unmarshal(job.RawResources, &job.Resources); err != nil {\n\t\tcclog.Warn(\"Error while unmarshaling raw resources json\")\n\t\treturn nil, err\n\t}\n\tjob.RawResources = nil\n\n\tif err := json.Unmarshal(job.RawFootprint, &job.Footprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw footprint json: %v\", err)\n\t\treturn nil, err\n\t}\n\tjob.RawFootprint = nil\n\n\t// Always ensure accurate duration for running jobs\n\tif job.State == schema.JobStateRunning {\n\t\tjob.Duration = int32(time.Now().Unix() - job.StartTime)\n\t}\n\n\treturn job, nil\n}\n\n// Optimize performs database optimization by running VACUUM command.\n// This reclaims unused space and defragments the database file.\n// Should be run periodically during maintenance windows.\nfunc (r *JobRepository) Optimize() error {\n\tif _, err := r.DB.Exec(`VACUUM`); err != nil {\n\t\tcclog.Errorf(\"Error while executing VACUUM: %v\", err)\n\t\treturn fmt.Errorf(\"failed to optimize database: %w\", err)\n\t}\n\treturn nil\n}\n\n// Flush removes all data from job-related tables (jobtag, tag, job).\n// WARNING: This is a destructive operation that deletes all job data.\n// Use with extreme caution, typically only for testing or complete resets.\nfunc (r *JobRepository) Flush() error {\n\tif _, err := r.DB.Exec(`DELETE FROM jobtag`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from jobtag table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush jobtag table: %w\", err)\n\t}\n\tif _, err := r.DB.Exec(`DELETE FROM tag`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from tag table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush tag table: %w\", err)\n\t}\n\tif _, err := r.DB.Exec(`DELETE FROM job`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from job table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush job table: %w\", err)\n\t}\n\treturn nil\n}\n\n// FetchMetadata retrieves and unmarshals the metadata JSON for a job.\n// Metadata is cached with a 24-hour TTL to improve performance.\n//\n// The metadata field stores arbitrary key-value pairs associated with a job,\n// such as tags, labels, or custom attributes added by external systems.\n//\n// Parameters:\n// - job: Job struct with valid ID field, metadata will be populated in job.MetaData\n//\n// Returns the metadata map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchMetadata(job *schema.Job) (map[string]string, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tcachekey := fmt.Sprintf(\"metadata:%d\", *job.ID)\n\tif cached := r.cache.Get(cachekey, nil); cached != nil {\n\t\tjob.MetaData = cached.(map[string]string)\n\t\treturn job.MetaData, nil\n\t}\n\n\tif err := sq.Select(\"job.meta_data\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawMetaData); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job metadata (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawMetaData) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawMetaData, &job.MetaData); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw metadata json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.MetaData, len(job.RawMetaData), 24*time.Hour)\n\tcclog.Debugf(\"Timer FetchMetadata %s\", time.Since(start))\n\treturn job.MetaData, nil\n}\n\n// UpdateMetadata adds or updates a single metadata key-value pair for a job.\n// The entire metadata map is re-marshaled and stored, and the cache is invalidated.\n// Also triggers archive metadata update via archive.UpdateMetadata.\n//\n// Parameters:\n// - job: Job struct with valid ID, existing metadata will be fetched if not present\n// - key: Metadata key to set\n// - val: Metadata value to set\n//\n// Returns an error if the job is nil, metadata fetch fails, or database update fails.\nfunc (r *JobRepository) UpdateMetadata(job *schema.Job, key, val string) (err error) {\n\tif job == nil {\n\t\treturn fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tcachekey := fmt.Sprintf(\"metadata:%d\", *job.ID)\n\tr.cache.Del(cachekey)\n\tif job.MetaData == nil {\n\t\tif _, err = r.FetchMetadata(job); err != nil {\n\t\t\tcclog.Warnf(\"Error while fetching metadata for job, DB ID '%v'\", *job.ID)\n\t\t\treturn fmt.Errorf(\"failed to fetch metadata for job %d: %w\", *job.ID, err)\n\t\t}\n\t}\n\n\tif job.MetaData != nil {\n\t\tcpy := make(map[string]string, len(job.MetaData)+1)\n\t\tmaps.Copy(cpy, job.MetaData)\n\t\tcpy[key] = val\n\t\tjob.MetaData = cpy\n\t} else {\n\t\tjob.MetaData = map[string]string{key: val}\n\t}\n\n\tif job.RawMetaData, err = json.Marshal(job.MetaData); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling metadata for job, DB ID '%v'\", *job.ID)\n\t\treturn fmt.Errorf(\"failed to marshal metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tif _, err = sq.Update(\"job\").\n\t\tSet(\"meta_data\", job.RawMetaData).\n\t\tWhere(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Warnf(\"Error while updating metadata for job, DB ID '%v'\", *job.ID)\n\t\treturn fmt.Errorf(\"failed to update metadata in database for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.MetaData, len(job.RawMetaData), 24*time.Hour)\n\treturn archive.UpdateMetadata(job, job.MetaData)\n}\n\n// FetchFootprint retrieves and unmarshals the performance footprint JSON for a job.\n// Unlike FetchMetadata, footprints are NOT cached as they can be large and change frequently.\n//\n// The footprint contains summary statistics (avg/min/max) for monitored metrics,\n// stored as JSON with keys like \"cpu_load_avg\", \"mem_used_max\", etc.\n//\n// Parameters:\n// - job: Job struct with valid ID, footprint will be populated in job.Footprint\n//\n// Returns the footprint map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchFootprint(job *schema.Job) (map[string]float64, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\n\tif err := sq.Select(\"job.footprint\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job footprint (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawFootprint) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawFootprint, &job.Footprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw footprint json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tcclog.Debugf(\"Timer FetchFootprint %s\", time.Since(start))\n\treturn job.Footprint, nil\n}\n\n// FetchEnergyFootprint retrieves and unmarshals the energy footprint JSON for a job.\n// Energy footprints are cached with a 24-hour TTL as they are frequently accessed but rarely change.\n//\n// The energy footprint contains calculated energy consumption (in kWh) per metric,\n// stored as JSON with keys like \"power_avg\", \"acc_power_avg\", etc.\n//\n// Parameters:\n// - job: Job struct with valid ID, energy footprint will be populated in job.EnergyFootprint\n//\n// Returns the energy footprint map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchEnergyFootprint(job *schema.Job) (map[string]float64, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tcachekey := fmt.Sprintf(\"energyFootprint:%d\", *job.ID)\n\tif cached := r.cache.Get(cachekey, nil); cached != nil {\n\t\tjob.EnergyFootprint = cached.(map[string]float64)\n\t\treturn job.EnergyFootprint, nil\n\t}\n\n\tif err := sq.Select(\"job.energy_footprint\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawEnergyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job energy_footprint (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch energy footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawEnergyFootprint) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawEnergyFootprint, &job.EnergyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw energy footprint json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal energy footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.EnergyFootprint, len(job.EnergyFootprint), 24*time.Hour)\n\tcclog.Debugf(\"Timer FetchEnergyFootprint %s\", time.Since(start))\n\treturn job.EnergyFootprint, nil\n}\n\n// DeleteJobsBefore removes jobs older than the specified start time.\n// Optionally preserves tagged jobs to protect important data from deletion.\n// Cache entries for deleted jobs are automatically invalidated.\n//\n// This is typically used for data retention policies and cleanup operations.\n// WARNING: This is a destructive operation that permanently deletes job records.\n//\n// Parameters:\n// - startTime: Unix timestamp, jobs with start_time < this value will be deleted\n// - omitTagged: \"none\" = delete all jobs, \"all\" = skip any tagged jobs,\n// \"user\" = skip jobs with user-created tags (not auto-tagger types \"app\"/\"jobClass\")\n//\n// Returns the count of deleted jobs or an error if the operation fails.\nfunc (r *JobRepository) DeleteJobsBefore(startTime int64, omitTagged string) (int, error) {\n\tvar cnt int\n\tq := sq.Select(\"count(*)\").From(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tq = q.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tq = q.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\n\tif err := q.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Errorf(\"Error counting jobs before %d: %v\", startTime, err)\n\t\treturn 0, err\n\t}\n\n\t// Invalidate cache for jobs being deleted (get job IDs first)\n\tif cnt > 0 {\n\t\tvar jobIds []int64\n\t\tselectQuery := sq.Select(\"id\").From(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\t\tswitch omitTagged {\n\t\tcase \"all\":\n\t\t\tselectQuery = selectQuery.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\t\tcase \"user\":\n\t\t\tselectQuery = selectQuery.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t\t}\n\n\t\trows, err := selectQuery.RunWith(r.DB).Query()\n\t\tif err == nil {\n\t\t\tdefer rows.Close()\n\t\t\tfor rows.Next() {\n\t\t\t\tvar id int64\n\t\t\t\tif err := rows.Scan(&id); err == nil {\n\t\t\t\t\tjobIds = append(jobIds, id)\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Invalidate cache entries\n\t\t\tfor _, id := range jobIds {\n\t\t\t\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", id))\n\t\t\t\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", id))\n\t\t\t}\n\t\t}\n\t}\n\n\tqd := sq.Delete(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tqd = qd.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tqd = qd.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\t_, err := qd.RunWith(r.DB).Exec()\n\n\tif err != nil {\n\t\ts, _, _ := qd.ToSql()\n\t\tcclog.Errorf(\" DeleteJobsBefore(%d) with %s: error %#v\", startTime, s, err)\n\t} else {\n\t\tcclog.Debugf(\"DeleteJobsBefore(%d): Deleted %d jobs\", startTime, cnt)\n\t}\n\treturn cnt, err\n}\n\n// DeleteJobByID permanently removes a single job by its database ID.\n// Cache entries for the deleted job are automatically invalidated.\n//\n// Parameters:\n// - id: Database ID (primary key) of the job to delete\n//\n// Returns an error if the deletion fails.\nfunc (r *JobRepository) DeleteJobByID(id int64) error {\n\t// Invalidate cache entries before deletion\n\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", id))\n\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", id))\n\n\tqd := sq.Delete(\"job\").Where(\"job.id = ?\", id)\n\t_, err := qd.RunWith(r.DB).Exec()\n\n\tif err != nil {\n\t\ts, _, _ := qd.ToSql()\n\t\tcclog.Errorf(\"DeleteJobById(%d) with %s : error %#v\", id, s, err)\n\t} else {\n\t\tcclog.Debugf(\"DeleteJobById(%d): Success\", id)\n\t}\n\treturn err\n}\n\n// FindUserOrProjectOrJobname attempts to interpret a search term as a job ID,\n// username, project ID, or job name by querying the database.\n//\n// Search logic (in priority order):\n// 1. If searchterm is numeric, treat as job ID (returned immediately)\n// 2. Try exact match in job.hpc_user column (username)\n// 3. Try LIKE match in hpc_user.name column (real name)\n// 4. Try exact match in job.project column (project ID)\n// 5. If no matches, return searchterm as jobname for GraphQL query\n//\n// This powers the searchbar functionality for flexible job searching.\n// Requires authenticated user for database lookups (returns empty if user is nil).\n//\n// Parameters:\n// - user: Authenticated user context, required for database access\n// - searchterm: Search string to interpret\n//\n// Returns up to one non-empty value among (jobid, username, project, jobname).\nfunc (r *JobRepository) FindUserOrProjectOrJobname(user *schema.User, searchterm string) (jobid string, username string, project string, jobname string) {\n\tif searchterm == \"\" {\n\t\treturn \"\", \"\", \"\", \"\"\n\t}\n\n\tif _, err := strconv.Atoi(searchterm); err == nil { // Return empty on successful conversion: parent method will redirect for integer jobId\n\t\treturn searchterm, \"\", \"\", \"\"\n\t} else { // Has to have letters and logged-in user for other guesses\n\t\tif user != nil {\n\t\t\t// Find username by username in job table (match)\n\t\t\turesult, _ := r.FindColumnValue(user, searchterm, \"job\", \"hpc_user\", \"hpc_user\", false)\n\t\t\tif uresult != \"\" {\n\t\t\t\treturn \"\", uresult, \"\", \"\"\n\t\t\t}\n\t\t\t// Find username by real name in hpc_user table (like)\n\t\t\tnresult, _ := r.FindColumnValue(user, searchterm, \"hpc_user\", \"username\", \"name\", true)\n\t\t\tif nresult != \"\" {\n\t\t\t\treturn \"\", nresult, \"\", \"\"\n\t\t\t}\n\t\t\t// Find projectId by projectId in job table (match)\n\t\t\tpresult, _ := r.FindColumnValue(user, searchterm, \"job\", \"project\", \"project\", false)\n\t\t\tif presult != \"\" {\n\t\t\t\treturn \"\", \"\", presult, \"\"\n\t\t\t}\n\t\t}\n\t\t// Return searchterm if no match before: Forward as jobname query to GQL in handleSearchbar function\n\t\treturn \"\", \"\", \"\", searchterm\n\t}\n}\n\nvar (\n\tErrNotFound = errors.New(\"no such jobname, project or user\")\n\tErrForbidden = errors.New(\"not authorized\")\n)\n\n// FindColumnValue performs a generic column lookup in a database table with role-based access control.\n// Only users with admin, support, or manager roles can execute this query.\n//\n// Parameters:\n// - user: User context for authorization check\n// - searchterm: Value to search for (exact match or LIKE pattern)\n// - table: Database table name to query\n// - selectColumn: Column name to return in results\n// - whereColumn: Column name to filter on\n// - isLike: If true, use LIKE with wildcards; if false, use exact equality\n//\n// Returns the first matching value, ErrForbidden if user lacks permission,\n// or ErrNotFound if no matches are found.\nfunc (r *JobRepository) FindColumnValue(user *schema.User, searchterm string, table string, selectColumn string, whereColumn string, isLike bool) (result string, err error) {\n\tif user == nil {\n\t\treturn \"\", fmt.Errorf(\"user cannot be nil\")\n\t}\n\n\tcompareStr := \" = ?\"\n\tquery := searchterm\n\tif isLike {\n\t\tcompareStr = \" LIKE ?\"\n\t\tquery = \"%\" + searchterm + \"%\"\n\t}\n\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\ttheQuery := sq.Select(table+\".\"+selectColumn).Distinct().From(table).\n\t\t\tWhere(table+\".\"+whereColumn+compareStr, query)\n\n\t\terr := theQuery.RunWith(r.stmtCache).QueryRow().Scan(&result)\n\n\t\tif err != nil && err != sql.ErrNoRows {\n\t\t\tcclog.Warnf(\"Error while querying FindColumnValue (table=%s, column=%s): %v\", table, selectColumn, err)\n\t\t\treturn \"\", fmt.Errorf(\"failed to find column value: %w\", err)\n\t\t} else if err == nil {\n\t\t\treturn result, nil\n\t\t}\n\t\treturn \"\", ErrNotFound\n\t} else {\n\t\tcclog.Infof(\"Non-Admin User %s : Requested Query '%s' on table '%s' : Forbidden\", user.Name, query, table)\n\t\treturn \"\", ErrForbidden\n\t}\n}\n\n// FindColumnValues performs a generic column lookup returning multiple matches with role-based access control.\n// Similar to FindColumnValue but returns all matching values instead of just the first.\n// Only users with admin, support, or manager roles can execute this query.\n//\n// Parameters:\n// - user: User context for authorization check\n// - query: Search pattern (always uses LIKE with wildcards)\n// - table: Database table name to query\n// - selectColumn: Column name to return in results\n// - whereColumn: Column name to filter on\n//\n// Returns a slice of matching values, ErrForbidden if user lacks permission,\n// or ErrNotFound if no matches are found.\nfunc (r *JobRepository) FindColumnValues(user *schema.User, query string, table string, selectColumn string, whereColumn string) (results []string, err error) {\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"user cannot be nil\")\n\t}\n\n\temptyResult := make([]string, 0)\n\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\trows, err := sq.Select(table+\".\"+selectColumn).Distinct().From(table).\n\t\t\tWhere(table+\".\"+whereColumn+\" LIKE ?\", fmt.Sprint(\"%\", query, \"%\")).\n\t\t\tRunWith(r.stmtCache).Query()\n\t\tif err != nil && err != sql.ErrNoRows {\n\t\t\tcclog.Errorf(\"Error while querying FindColumnValues (table=%s, column=%s): %v\", table, selectColumn, err)\n\t\t\treturn emptyResult, fmt.Errorf(\"failed to find column values: %w\", err)\n\t\t} else if err == nil {\n\t\t\tdefer rows.Close()\n\t\t\tfor rows.Next() {\n\t\t\t\tvar result string\n\t\t\t\terr := rows.Scan(&result)\n\t\t\t\tif err != nil {\n\t\t\t\t\tcclog.Warnf(\"Error while scanning rows in FindColumnValues: %v\", err)\n\t\t\t\t\treturn emptyResult, fmt.Errorf(\"failed to scan column value: %w\", err)\n\t\t\t\t}\n\t\t\t\tresults = append(results, result)\n\t\t\t}\n\t\t\treturn results, nil\n\t\t}\n\t\treturn emptyResult, ErrNotFound\n\n\t} else {\n\t\tcclog.Infof(\"Non-Admin User %s : Requested Query '%s' on table '%s' : Forbidden\", user.Name, query, table)\n\t\treturn emptyResult, ErrForbidden\n\t}\n}\n\n// Partitions returns a list of distinct cluster partitions for a given cluster.\n// Results are cached with a 1-hour TTL to improve performance.\n//\n// Parameters:\n// - cluster: Cluster name to query partitions for\n//\n// Returns a slice of partition names or an error if the database query fails.\nfunc (r *JobRepository) Partitions(cluster string) ([]string, error) {\n\tvar err error\n\tstart := time.Now()\n\tpartitions := r.cache.Get(\"partitions:\"+cluster, func() (any, time.Duration, int) {\n\t\tparts := []string{}\n\t\tif err = r.DB.Select(&parts, `SELECT DISTINCT job.cluster_partition FROM job WHERE job.cluster = ?;`, cluster); err != nil {\n\t\t\treturn nil, 0, 1000\n\t\t}\n\n\t\treturn parts, 1 * time.Hour, 1\n\t})\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tcclog.Debugf(\"Timer Partitions %s\", time.Since(start))\n\treturn partitions.([]string), nil\n}\n\n// AllocatedNodes returns a map of all subclusters to a map of hostnames to the amount of jobs running on that host.\n// Hosts with zero jobs running on them will not show up!\nfunc (r *JobRepository) AllocatedNodes(cluster string) (map[string]map[string]int, error) {\n\tstart := time.Now()\n\tsubclusters := make(map[string]map[string]int)\n\trows, err := sq.Select(\"resources\", \"subcluster\").From(\"job\").\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tRunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running AllocatedNodes query for cluster=%s: %v\", cluster, err)\n\t\treturn nil, fmt.Errorf(\"failed to query allocated nodes for cluster %s: %w\", cluster, err)\n\t}\n\n\tvar raw []byte\n\tdefer rows.Close()\n\tfor rows.Next() {\n\t\traw = raw[0:0]\n\t\tvar resources []*schema.Resource\n\t\tvar subcluster string\n\t\tif err := rows.Scan(&raw, &subcluster); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in AllocatedNodes: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan allocated nodes row: %w\", err)\n\t\t}\n\t\tif err := json.Unmarshal(raw, &resources); err != nil {\n\t\t\tcclog.Warnf(\"Error while unmarshaling raw resources json in AllocatedNodes: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to unmarshal resources in AllocatedNodes: %w\", err)\n\t\t}\n\n\t\thosts, ok := subclusters[subcluster]\n\t\tif !ok {\n\t\t\thosts = make(map[string]int)\n\t\t\tsubclusters[subcluster] = hosts\n\t\t}\n\n\t\tfor _, resource := range resources {\n\t\t\thosts[resource.Hostname] += 1\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AllocatedNodes %s\", time.Since(start))\n\treturn subclusters, nil\n}\n\n// StopJobsExceedingWalltimeBy marks running jobs as failed if they exceed their walltime limit.\n// This is typically called periodically to clean up stuck or orphaned jobs.\n//\n// Jobs are marked with:\n// - monitoring_status: MonitoringStatusArchivingFailed\n// - duration: 0\n// - job_state: JobStateFailed\n//\n// Parameters:\n// - seconds: Grace period beyond walltime before marking as failed\n//\n// Returns an error if the database update fails.\n// Logs the number of jobs marked as failed if any were affected.\nfunc (r *JobRepository) StopJobsExceedingWalltimeBy(seconds int) error {\n\tstart := time.Now()\n\tcurrentTime := time.Now().Unix()\n\tres, err := sq.Update(\"job\").\n\t\tSet(\"monitoring_status\", schema.MonitoringStatusArchivingFailed).\n\t\tSet(\"duration\", 0).\n\t\tSet(\"job_state\", schema.JobStateFailed).\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.walltime > 0\").\n\t\tWhere(\"job.start_time < (? - job.walltime)\", currentTime-int64(seconds)).\n\t\tRunWith(r.DB).Exec()\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while stopping jobs exceeding walltime: %v\", err)\n\t\treturn fmt.Errorf(\"failed to stop jobs exceeding walltime: %w\", err)\n\t}\n\n\trowsAffected, err := res.RowsAffected()\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while fetching affected rows after stopping due to exceeded walltime: %v\", err)\n\t\treturn fmt.Errorf(\"failed to get rows affected count: %w\", err)\n\t}\n\n\tif rowsAffected > 0 {\n\t\tcclog.Infof(\"%d jobs have been marked as failed due to running too long\", rowsAffected)\n\t}\n\tcclog.Debugf(\"Timer StopJobsExceedingWalltimeBy %s\", time.Since(start))\n\treturn nil\n}\n\n// FindJobIdsByTag returns all job database IDs associated with a specific tag.\n//\n// Parameters:\n// - tagID: Database ID of the tag to search for\n//\n// Returns a slice of job IDs or an error if the query fails.\nfunc (r *JobRepository) FindJobIdsByTag(tagID int64) ([]int64, error) {\n\tquery := sq.Select(\"job.id\").From(\"job\").\n\t\tJoin(\"jobtag ON jobtag.job_id = job.id\").\n\t\tWhere(sq.Eq{\"jobtag.tag_id\": tagID}).Distinct()\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindJobIdsByTag query for tagID=%d: %v\", tagID, err)\n\t\treturn nil, fmt.Errorf(\"failed to find job IDs by tag %d: %w\", tagID, err)\n\t}\n\tdefer rows.Close()\n\n\tjobIds := make([]int64, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar jobID int64\n\n\t\tif err := rows.Scan(&jobID); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindJobIdsByTag: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID in FindJobIdsByTag: %w\", err)\n\t\t}\n\n\t\tjobIds = append(jobIds, jobID)\n\t}\n\n\treturn jobIds, nil\n}\n\n// FindRunningJobs returns all currently running jobs for a specific cluster.\n// Filters out short-running jobs based on repoConfig.MinRunningJobDuration threshold.\n//\n// Parameters:\n// - cluster: Cluster name to filter jobs\n//\n// Returns a slice of running job objects or an error if the query fails.\nfunc (r *JobRepository) FindRunningJobs(cluster string) ([]*schema.Job, error) {\n\tquery := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.duration > ?\", repoConfig.MinRunningJobDuration)\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindRunningJobs query for cluster=%s: %v\", cluster, err)\n\t\treturn nil, fmt.Errorf(\"failed to find running jobs for cluster %s: %w\", cluster, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 50)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindRunningJobs: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job in FindRunningJobs: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tcclog.Debugf(\"JobRepository.FindRunningJobs(): Return job count %d (cluster: %s)\", len(jobs), cluster)\n\treturn jobs, nil\n}\n\n// UpdateDuration recalculates and updates the duration field for all running jobs.\n// Called periodically to keep job durations current without querying individual jobs.\n//\n// Duration is calculated as: current_time - job.start_time\n//\n// Returns an error if the database update fails.\nfunc (r *JobRepository) UpdateDuration() error {\n\tstmnt := sq.Update(\"job\").\n\t\tSet(\"duration\", sq.Expr(\"? - job.start_time\", time.Now().Unix())).\n\t\tWhere(\"job_state = 'running'\")\n\n\t_, err := stmnt.RunWith(r.stmtCache).Exec()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while updating duration for running jobs: %v\", err)\n\t\treturn fmt.Errorf(\"failed to update duration for running jobs: %w\", err)\n\t}\n\n\treturn nil\n}\n\n// FindJobsBetween returns jobs within a specified time range.\n// If startTimeBegin is 0, returns all jobs before startTimeEnd.\n// Optionally excludes tagged jobs from results.\n//\n// Parameters:\n// - startTimeBegin: Unix timestamp for range start (use 0 for unbounded start)\n// - startTimeEnd: Unix timestamp for range end\n// - omitTagged: \"none\" = include all jobs, \"all\" = exclude any tagged jobs,\n// \"user\" = exclude jobs with user-created tags (not auto-tagger types \"app\"/\"jobClass\")\n//\n// Returns a slice of jobs or an error if the time range is invalid or query fails.\nfunc (r *JobRepository) FindJobsBetween(startTimeBegin int64, startTimeEnd int64, omitTagged string) ([]*schema.Job, error) {\n\tvar query sq.SelectBuilder\n\n\tif startTimeBegin == startTimeEnd || startTimeBegin > startTimeEnd {\n\t\treturn nil, errors.New(\"startTimeBegin is equal or larger startTimeEnd\")\n\t}\n\n\tif startTimeBegin == 0 {\n\t\tcclog.Infof(\"Find jobs before %d\", startTimeEnd)\n\t\tquery = sq.Select(jobColumns...).From(\"job\").Where(\"job.start_time < ?\", startTimeEnd)\n\t} else {\n\t\tcclog.Infof(\"Find jobs between %d and %d\", startTimeBegin, startTimeEnd)\n\t\tquery = sq.Select(jobColumns...).From(\"job\").Where(\"job.start_time BETWEEN ? AND ?\", startTimeBegin, startTimeEnd)\n\t}\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tquery = query.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tquery = query.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\n\tquery = query.OrderBy(\"job.cluster ASC\", \"job.subcluster ASC\", \"job.project ASC\", \"job.start_time ASC\")\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindJobsBetween query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to find jobs between %d and %d: %w\", startTimeBegin, startTimeEnd, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 50)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindJobsBetween: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job in FindJobsBetween: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tcclog.Debugf(\"JobRepository.FindJobsBetween(): Return job count %d (omitTagged: %v)\", len(jobs), omitTagged)\n\treturn jobs, nil\n}\n\n// UpdateMonitoringStatus updates the monitoring status for a job and invalidates its cache entries.\n// Cache invalidation affects both metadata and energy footprint to ensure consistency.\n//\n// Parameters:\n// - job: Database ID of the job to update\n// - monitoringStatus: New monitoring status value (see schema.MonitoringStatus constants)\n//\n// Returns an error if the database update fails.\nfunc (r *JobRepository) UpdateMonitoringStatus(job int64, monitoringStatus int32) (err error) {\n\t// Invalidate cache entries as monitoring status affects job state\n\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", job))\n\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", job))\n\n\tstmt := sq.Update(\"job\").\n\t\tSet(\"monitoring_status\", monitoringStatus).\n\t\tWhere(\"job.id = ?\", job)\n\n\tif _, err = stmt.RunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Errorf(\"Error while updating monitoring status for job %d: %v\", job, err)\n\t\treturn fmt.Errorf(\"failed to update monitoring status for job %d: %w\", job, err)\n\t}\n\treturn nil\n}\n\n// Execute runs a Squirrel UpdateBuilder statement against the database.\n// This is a generic helper for executing pre-built update queries.\n//\n// Parameters:\n// - stmt: Squirrel UpdateBuilder with prepared update query\n//\n// Returns an error if the execution fails.\nfunc (r *JobRepository) Execute(stmt sq.UpdateBuilder) error {\n\tif _, err := stmt.RunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Errorf(\"Error while executing statement: %v\", err)\n\t\treturn fmt.Errorf(\"failed to execute update statement: %w\", err)\n\t}\n\n\treturn nil\n}\n\n// MarkArchived adds monitoring status update to an existing UpdateBuilder statement.\n// This is a builder helper used when constructing multi-field update queries.\n//\n// Parameters:\n// - stmt: Existing UpdateBuilder to modify\n// - monitoringStatus: Monitoring status value to set\n//\n// Returns the modified UpdateBuilder for method chaining.\nfunc (r *JobRepository) MarkArchived(\n\tstmt sq.UpdateBuilder,\n\tmonitoringStatus int32,\n) sq.UpdateBuilder {\n\treturn stmt.Set(\"monitoring_status\", monitoringStatus)\n}\n\n// UpdateEnergy calculates and updates the energy consumption for a job.\n// This is called for running jobs during intermediate updates or when archiving.\n//\n// Energy calculation formula:\n// - For \"power\" metrics: Energy (kWh) = (Power_avg * NumNodes * Duration_hours) / 1000\n// - For \"energy\" metrics: Currently not implemented (would need sum statistics)\n//\n// The calculation accounts for:\n// - Multi-node jobs: Multiplies by NumNodes to get total cluster energy\n// - Shared jobs: Node average is already based on partial resources, so NumNodes=1\n// - Unit conversion: Watts * hours / 1000 = kilowatt-hours (kWh)\n// - Rounding: Results rounded to 2 decimal places\nfunc (r *JobRepository) UpdateEnergy(\n\tstmt sq.UpdateBuilder,\n\tjobMeta *schema.Job,\n) (sq.UpdateBuilder, error) {\n\tsc, err := archive.GetSubCluster(jobMeta.Cluster, jobMeta.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"cannot get subcluster: %s\", err.Error())\n\t\treturn stmt, err\n\t}\n\tenergyFootprint := make(map[string]float64)\n\n\t// Accumulate total energy across all energy-related metrics\n\ttotalEnergy := 0.0\n\tfor _, fp := range sc.EnergyFootprint {\n\t\t// Calculate energy for this specific metric\n\t\tmetricEnergy := 0.0\n\t\tif i, err := archive.MetricIndex(sc.MetricConfig, fp); err == nil {\n\t\t\tswitch sc.MetricConfig[i].Energy {\n\t\t\tcase \"energy\": // Metric already in energy units (Joules or Wh)\n\t\t\t\tcclog.Warnf(\"Update EnergyFootprint for Job %d and Metric %s on cluster %s: Set to 'energy' in cluster.json: Not implemented, will return 0.0\", jobMeta.JobID, jobMeta.Cluster, fp)\n\t\t\t\t// FIXME: Needs sum as stats type to accumulate energy values over time\n\t\t\tcase \"power\": // Metric in power units (Watts)\n\t\t\t\t// Energy (kWh) = Power (W) × Time (h) / 1000\n\t\t\t\t// Formula: (avg_power_per_node * num_nodes) * (duration_sec / 3600) / 1000\n\t\t\t\t//\n\t\t\t\t// Breakdown:\n\t\t\t\t// LoadJobStat(jobMeta, fp, \"avg\") = average power per node (W)\n\t\t\t\t// jobMeta.NumNodes = number of nodes (1 for shared jobs)\n\t\t\t\t// jobMeta.Duration / 3600.0 = duration in hours\n\t\t\t\t// / 1000.0 = convert Wh to kWh\n\t\t\t\trawEnergy := ((LoadJobStat(jobMeta, fp, \"avg\") * float64(jobMeta.NumNodes)) * (float64(jobMeta.Duration) / 3600.0)) / 1000.0\n\t\t\t\tmetricEnergy = math.Round(rawEnergy*100.0) / 100.0 // Round to 2 decimal places\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Error while collecting energy metric %s for job, DB ID '%v', return '0.0'\", fp, jobMeta.ID)\n\t\t}\n\n\t\tenergyFootprint[fp] = metricEnergy\n\t\ttotalEnergy += metricEnergy\n\t}\n\n\tvar rawFootprint []byte\n\tif rawFootprint, err = json.Marshal(energyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling energy footprint for job INTO BYTES, DB ID '%v'\", jobMeta.ID)\n\t\treturn stmt, err\n\t}\n\n\treturn stmt.Set(\"energy_footprint\", string(rawFootprint)).Set(\"energy\", (math.Round(totalEnergy*100.0) / 100.0)), nil\n}\n\n// UpdateFootprint calculates and updates the performance footprint for a job.\n// This is called for running jobs during intermediate updates or when archiving.\n//\n// A footprint is a summary statistic (avg/min/max) for each monitored metric.\n// The specific statistic type is defined in the cluster config's Footprint field.\n// Results are stored as JSON with keys like \"metric_avg\", \"metric_max\", etc.\n//\n// Example: For a \"cpu_load\" metric with Footprint=\"avg\", this stores\n// the average CPU load across all nodes as \"cpu_load_avg\": 85.3\nfunc (r *JobRepository) UpdateFootprint(\n\tstmt sq.UpdateBuilder,\n\tjobMeta *schema.Job,\n) (sq.UpdateBuilder, error) {\n\tsc, err := archive.GetSubCluster(jobMeta.Cluster, jobMeta.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"cannot get subcluster: %s\", err.Error())\n\t\treturn stmt, err\n\t}\n\tfootprint := make(map[string]float64)\n\n\t// Build footprint map with metric_stattype as keys\n\tfor _, fp := range sc.Footprint {\n\t\t// Determine which statistic to use: avg, min, or max\n\t\t// First check global metric config, then cluster-specific config\n\t\tvar statType string\n\t\tfor _, gm := range archive.GlobalMetricList {\n\t\t\tif gm.Name == fp {\n\t\t\t\tstatType = gm.Footprint\n\t\t\t}\n\t\t}\n\n\t\t// Validate statistic type\n\t\tif statType != \"avg\" && statType != \"min\" && statType != \"max\" {\n\t\t\tcclog.Warnf(\"unknown statType for footprint update: %s\", statType)\n\t\t\treturn stmt, fmt.Errorf(\"unknown statType for footprint update: %s\", statType)\n\t\t}\n\n\t\t// Override with cluster-specific config if available\n\t\tif i, err := archive.MetricIndex(sc.MetricConfig, fp); err != nil {\n\t\t\tstatType = sc.MetricConfig[i].Footprint\n\t\t}\n\n\t\t// Store as \"metric_stattype\": value (e.g., \"cpu_load_avg\": 85.3)\n\t\tname := fmt.Sprintf(\"%s_%s\", fp, statType)\n\t\tfootprint[name] = LoadJobStat(jobMeta, fp, statType)\n\t}\n\n\tvar rawFootprint []byte\n\tif rawFootprint, err = json.Marshal(footprint); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling footprint for job INTO BYTES, DB ID '%v'\", jobMeta.ID)\n\t\treturn stmt, err\n\t}\n\n\treturn stmt.Set(\"footprint\", string(rawFootprint)), nil\n}\n\n// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n// that are currently in use by jobs that started before the given timestamp and\n// are still in running state.\n//\n// The timestamp parameter (ts) is compared against job.start_time to find\n// relevant jobs. Returns an error if the database query fails or row iteration\n// encounters errors. Individual row parsing errors are logged but don't fail\n// the entire operation.\nfunc (r *JobRepository) GetUsedNodes(ts int64) (map[string][]string, error) {\n\t// Note: Query expects index on (job_state, start_time) for optimal performance\n\tq := sq.Select(\"job.cluster\", \"job.resources\").From(\"job\").\n\t\tWhere(\"job.start_time < ?\", ts).\n\t\tWhere(sq.Eq{\"job.job_state\": \"running\"})\n\n\trows, err := q.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tqueryString, queryVars, _ := q.ToSql()\n\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n\t}\n\tdefer rows.Close()\n\n\t// Use a map of sets for efficient deduplication\n\tnodeSet := make(map[string]map[string]struct{})\n\n\tvar (\n\t\tcluster string\n\t\trawResources []byte\n\t\tresources []*schema.Resource\n\t\tskippedRows int\n\t)\n\n\tfor rows.Next() {\n\t\tif err := rows.Scan(&cluster, &rawResources); err != nil {\n\t\t\tcclog.Warnf(\"Error scanning job row in GetUsedNodes: %v\", err)\n\t\t\tskippedRows++\n\t\t\tcontinue\n\t\t}\n\n\t\tresources = resources[:0] // Clear slice, keep capacity\n\t\tif err := json.Unmarshal(rawResources, &resources); err != nil {\n\t\t\tcclog.Warnf(\"Error unmarshaling resources for cluster %s: %v\", cluster, err)\n\t\t\tskippedRows++\n\t\t\tcontinue\n\t\t}\n\n\t\tif len(resources) == 0 {\n\t\t\tcclog.Debugf(\"Job in cluster %s has no resources\", cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := nodeSet[cluster]; !ok {\n\t\t\tnodeSet[cluster] = make(map[string]struct{})\n\t\t}\n\n\t\tfor _, res := range resources {\n\t\t\tnodeSet[cluster][res.Hostname] = struct{}{}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, fmt.Errorf(\"error iterating rows: %w\", err)\n\t}\n\n\tif skippedRows > 0 {\n\t\tcclog.Warnf(\"GetUsedNodes: Skipped %d rows due to parsing errors\", skippedRows)\n\t}\n\n\t// Convert sets to sorted slices\n\tnodeList := make(map[string][]string, len(nodeSet))\n\tfor cluster, nodes := range nodeSet {\n\t\tlist := make([]string, 0, len(nodes))\n\t\tfor node := range nodes {\n\t\t\tlist = append(list, node)\n\t\t}\n\t\tsort.Strings(list)\n\t\tnodeList[cluster] = list\n\t}\n\n\treturn nodeList, nil\n}\n","numLines":1137,"startLine":1,"totalLines":1137}},"sourceToolAssistantUUID":"b9bcce1d-ae8c-4184-94be-9383ba3f0631"} 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Now let me check how `scanJob` works with `QueryRow` — I need to verify whether squirrel's `RunWith(stmtCache).QueryRow()` returns something that supports `QueryRowContext`."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":123,"cache_read_input_tokens":49995,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":123},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhtQo5zKfPL2RPyfYuo","type":"assistant","uuid":"476b28dc-a13a-41f7-9a6b-3e4f862bccdc","timestamp":"2026-03-11T05:01:55.717Z"} 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scanJob","path":"/Users/jan/prg/CC/cc-backend/internal/repository","output_mode":"content","-A":5},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":123,"cache_read_input_tokens":49995,"output_tokens":190,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":123,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhtQo5zKfPL2RPyfYuo","type":"assistant","uuid":"6887fc03-283c-41eb-83bc-9401d3f44875","timestamp":"2026-03-11T05:01:56.410Z"} 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+{"parentUuid":"3facb963-a32c-4202-aa40-aa03ef335c73","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01QvwPeeLse4EqQe1o5eGa4m","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have all the context. Let me implement all four fixes.\n\n**Fix 1: Cache size** and **Fix 2: soft_heap_limit** in `dbConnection.go`:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":528,"cache_read_input_tokens":50118,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":528},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhtmuqkHGsq7Bsc5XV3","type":"assistant","uuid":"ccb8800c-2d14-4b0a-ac10-be6ab66aa1f1","timestamp":"2026-03-11T05:02:04.524Z"} +{"parentUuid":"ccb8800c-2d14-4b0a-ac10-be6ab66aa1f1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01QvwPeeLse4EqQe1o5eGa4m","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GPnNiw42gR7R8EiMkgz24b","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go","old_string":"\tconnectionURLParams.Add(\"_cache_size\", \"1000000000\")","new_string":"\tconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection (negative = KiB)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":528,"cache_read_input_tokens":50118,"output_tokens":420,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":528,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhtmuqkHGsq7Bsc5XV3","type":"assistant","uuid":"ce7ed030-5dc8-4970-9af0-e5600daba5a8","timestamp":"2026-03-11T05:02:06.422Z"} +{"type":"file-history-snapshot","messageId":"ce7ed030-5dc8-4970-9af0-e5600daba5a8","snapshot":{"messageId":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","trackedFileBackups":{"internal/repository/dbConnection.go":{"backupFileName":"f4586ab7038e6775@v1","version":1,"backupTime":"2026-03-11T05:02:06.448Z"}},"timestamp":"2026-03-11T05:01:36.839Z"},"isSnapshotUpdate":true} +{"parentUuid":"ce7ed030-5dc8-4970-9af0-e5600daba5a8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01GPnNiw42gR7R8EiMkgz24b","toolUseID":"toolu_01GPnNiw42gR7R8EiMkgz24b","timestamp":"2026-03-11T05:02:06.450Z","uuid":"54b63dde-2f2f-4e34-8db2-ee55c948c4c3"} +{"parentUuid":"ce7ed030-5dc8-4970-9af0-e5600daba5a8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GPnNiw42gR7R8EiMkgz24b","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go has been updated successfully."}]},"uuid":"790e08ae-8653-4dd7-8f1f-78dde3d4e75f","timestamp":"2026-03-11T05:02:06.450Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go","oldString":"\tconnectionURLParams.Add(\"_cache_size\", \"1000000000\")","newString":"\tconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection (negative = KiB)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"fmt\"\n\t\"net/url\"\n\t\"sync\"\n\t\"time\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/jmoiron/sqlx\"\n\t\"github.com/mattn/go-sqlite3\"\n\t\"github.com/qustavo/sqlhooks/v2\"\n)\n\nvar (\n\tdbConnOnce sync.Once\n\tdbConnInstance *DBConnection\n)\n\ntype DBConnection struct {\n\tDB *sqlx.DB\n\tDriver string\n}\n\ntype DatabaseOptions struct {\n\tURL string\n\tMaxOpenConnections int\n\tMaxIdleConnections int\n\tConnectionMaxLifetime time.Duration\n\tConnectionMaxIdleTime time.Duration\n}\n\nfunc setupSqlite(db *sql.DB) error {\n\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t}\n\n\tfor _, pragma := range pragmas {\n\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\t}\n\n\t// Update query planner statistics so SQLite picks optimal indexes.\n\t// Without this, SQLite guesses row distributions and often chooses wrong\n\t// indexes for queries with IN clauses + ORDER BY, causing full table sorts\n\t// in temp B-trees instead of using covering indexes.\n\tif _, err := db.Exec(\"ANALYZE\"); err != nil {\n\t\tcclog.Warnf(\"Failed to run ANALYZE: %v\", err)\n\t}\n\n\treturn nil\n}\n\nfunc Connect(db string) {\n\tvar err error\n\tvar dbHandle *sqlx.DB\n\n\tdbConnOnce.Do(func() {\n\t\topts := DatabaseOptions{\n\t\t\tURL: db,\n\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n\t\t}\n\n\t\t// TODO: Have separate DB handles for Writes and Reads\n\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n\t\tconnectionURLParams := make(url.Values)\n\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n\t\tconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n\n\t\tif cclog.Loglevel() == \"debug\" {\n\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n\t\t} else {\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n\t\t}\n\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\terr = setupSqlite(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n\n\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n\t\terr = checkDBVersion(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n\t\t}\n\t})\n}\n\nfunc GetConnection() *DBConnection {\n\tif dbConnInstance == nil {\n\t\tcclog.Fatalf(\"Database connection not initialized!\")\n\t}\n\n\treturn dbConnInstance\n}\n\n// ResetConnection closes the current database connection and resets the connection state.\n// This function is intended for testing purposes only to allow test isolation.\nfunc ResetConnection() error {\n\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n\t\tif err := dbConnInstance.DB.Close(); err != nil {\n\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n\t\t}\n\t}\n\n\tdbConnInstance = nil\n\tdbConnOnce = sync.Once{}\n\tjobRepoInstance = nil\n\tjobRepoOnce = sync.Once{}\n\tnodeRepoInstance = nil\n\tnodeRepoOnce = sync.Once{}\n\tuserRepoInstance = nil\n\tuserRepoOnce = sync.Once{}\n\tuserCfgRepoInstance = nil\n\tuserCfgRepoOnce = sync.Once{}\n\n\treturn nil\n}\n","structuredPatch":[{"oldStart":79,"oldLines":7,"newStart":79,"newLines":7,"lines":[" connectionURLParams.Add(\"_journal_mode\", \"WAL\")"," connectionURLParams.Add(\"_busy_timeout\", \"5000\")"," connectionURLParams.Add(\"_synchronous\", \"NORMAL\")","- connectionURLParams.Add(\"_cache_size\", \"1000000000\")","+ connectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection (negative = KiB)"," connectionURLParams.Add(\"_foreign_keys\", \"true\")"," opts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"ce7ed030-5dc8-4970-9af0-e5600daba5a8"} +{"parentUuid":"790e08ae-8653-4dd7-8f1f-78dde3d4e75f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01HsWnagnU8Z7Ak7FasxWTBE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018LeEXjKdL8ttZSpYHkcvMb","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go","old_string":"\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t}","new_string":"\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t\t\"soft_heap_limit = 1073741824\", // 1GB process-wide soft limit; SQLite evicts cache pages to stay under\n\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":477,"cache_read_input_tokens":50646,"output_tokens":180,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":477,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhuWf6HPMt7C2fdGmGT","type":"assistant","uuid":"0a0c34a7-a9a5-41b1-8248-d20ddb32173d","timestamp":"2026-03-11T05:02:10.061Z"} +{"parentUuid":"0a0c34a7-a9a5-41b1-8248-d20ddb32173d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_018LeEXjKdL8ttZSpYHkcvMb","toolUseID":"toolu_018LeEXjKdL8ttZSpYHkcvMb","timestamp":"2026-03-11T05:02:10.083Z","uuid":"fcdf91a8-b6db-4281-acad-e439391f21fc"} +{"parentUuid":"0a0c34a7-a9a5-41b1-8248-d20ddb32173d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018LeEXjKdL8ttZSpYHkcvMb","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go has been updated successfully."}]},"uuid":"1e75e308-a752-4207-ac2e-545c48726ea0","timestamp":"2026-03-11T05:02:10.083Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go","oldString":"\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t}","newString":"\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t\t\"soft_heap_limit = 1073741824\", // 1GB process-wide soft limit; SQLite evicts cache pages to stay under\n\t}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"fmt\"\n\t\"net/url\"\n\t\"sync\"\n\t\"time\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/jmoiron/sqlx\"\n\t\"github.com/mattn/go-sqlite3\"\n\t\"github.com/qustavo/sqlhooks/v2\"\n)\n\nvar (\n\tdbConnOnce sync.Once\n\tdbConnInstance *DBConnection\n)\n\ntype DBConnection struct {\n\tDB *sqlx.DB\n\tDriver string\n}\n\ntype DatabaseOptions struct {\n\tURL string\n\tMaxOpenConnections int\n\tMaxIdleConnections int\n\tConnectionMaxLifetime time.Duration\n\tConnectionMaxIdleTime time.Duration\n}\n\nfunc setupSqlite(db *sql.DB) error {\n\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t}\n\n\tfor _, pragma := range pragmas {\n\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\t}\n\n\t// Update query planner statistics so SQLite picks optimal indexes.\n\t// Without this, SQLite guesses row distributions and often chooses wrong\n\t// indexes for queries with IN clauses + ORDER BY, causing full table sorts\n\t// in temp B-trees instead of using covering indexes.\n\tif _, err := db.Exec(\"ANALYZE\"); err != nil {\n\t\tcclog.Warnf(\"Failed to run ANALYZE: %v\", err)\n\t}\n\n\treturn nil\n}\n\nfunc Connect(db string) {\n\tvar err error\n\tvar dbHandle *sqlx.DB\n\n\tdbConnOnce.Do(func() {\n\t\topts := DatabaseOptions{\n\t\t\tURL: db,\n\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n\t\t}\n\n\t\t// TODO: Have separate DB handles for Writes and Reads\n\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n\t\tconnectionURLParams := make(url.Values)\n\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n\t\tconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection (negative = KiB)\n\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n\n\t\tif cclog.Loglevel() == \"debug\" {\n\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n\t\t} else {\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n\t\t}\n\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\terr = setupSqlite(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n\n\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n\t\terr = checkDBVersion(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n\t\t}\n\t})\n}\n\nfunc GetConnection() *DBConnection {\n\tif dbConnInstance == nil {\n\t\tcclog.Fatalf(\"Database connection not initialized!\")\n\t}\n\n\treturn dbConnInstance\n}\n\n// ResetConnection closes the current database connection and resets the connection state.\n// This function is intended for testing purposes only to allow test isolation.\nfunc ResetConnection() error {\n\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n\t\tif err := dbConnInstance.DB.Close(); err != nil {\n\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n\t\t}\n\t}\n\n\tdbConnInstance = nil\n\tdbConnOnce = sync.Once{}\n\tjobRepoInstance = nil\n\tjobRepoOnce = sync.Once{}\n\tnodeRepoInstance = nil\n\tnodeRepoOnce = sync.Once{}\n\tuserRepoInstance = nil\n\tuserRepoOnce = sync.Once{}\n\tuserCfgRepoInstance = nil\n\tuserCfgRepoOnce = sync.Once{}\n\n\treturn nil\n}\n","structuredPatch":[{"oldStart":39,"oldLines":6,"newStart":39,"newLines":7,"lines":[" func setupSqlite(db *sql.DB) error {"," pragmas := []string{"," \"temp_store = memory\",","+ \"soft_heap_limit = 1073741824\", // 1GB process-wide soft limit; SQLite evicts cache pages to stay under"," }"," "," for _, pragma := range pragmas {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"0a0c34a7-a9a5-41b1-8248-d20ddb32173d"} +{"parentUuid":"1e75e308-a752-4207-ac2e-545c48726ea0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01YUK2GE3Po95V1H1mvee2Hb","type":"message","role":"assistant","content":[{"type":"text","text":"**Fix 3: Pass context to database queries.**\n\n`jobQuery.go` — `QueryJobs` and `CountJobs`:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":239,"cache_read_input_tokens":51123,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":239},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhunFD729tWaBXAUEb5","type":"assistant","uuid":"bc995217-7ec7-411e-a39b-01fc30683f60","timestamp":"2026-03-11T05:02:12.909Z"} 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/Users/jan/prg/CC/cc-backend/internal/repository/jobQuery.go has been updated successfully."}]},"uuid":"dc981823-5b8a-4651-9b15-d7ef5fbffdb3","timestamp":"2026-03-11T05:02:13.868Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/jobQuery.go","oldString":"\trows, err := query.RunWith(r.stmtCache).Query()","newString":"\trows, err := query.RunWith(r.stmtCache).QueryContext(ctx)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package repository provides job query functionality with filtering, pagination,\n// and security controls. This file contains the main query builders and security\n// checks for job retrieval operations.\npackage repository\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"regexp\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\nconst (\n\t// Default initial capacity for job result slices\n\tdefaultJobsCapacity = 50\n)\n\n// QueryJobs retrieves jobs from the database with optional filtering, pagination,\n// and sorting. Security controls are automatically applied based on the user context.\n//\n// Parameters:\n// - ctx: Context containing user authentication information\n// - filters: Optional job filters (cluster, state, user, time ranges, etc.)\n// - page: Optional pagination parameters (page number and items per page)\n// - order: Optional sorting specification (column or footprint field)\n//\n// Returns a slice of jobs matching the criteria, or an error if the query fails.\n// The function enforces role-based access control through SecurityCheck.\nfunc (r *JobRepository) QueryJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n\tpage *model.PageRequest,\n\torder *model.OrderByInput,\n) ([]*schema.Job, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(jobColumns...).From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tif order != nil {\n\t\tfield := toSnakeCase(order.Field)\n\t\tif order.Type == \"col\" {\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for column\")\n\t\t\t}\n\t\t} else {\n\t\t\t// Order by footprint JSON field values\n\t\t\tquery = query.Where(\"JSON_VALID(footprint)\")\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for footprint\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tqueryString, queryVars, _ := query.ToSql()\n\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, defaultJobsCapacity)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error scanning job row: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, fmt.Errorf(\"error iterating job rows: %w\", err)\n\t}\n\n\treturn jobs, nil\n}\n\n// CountJobs returns the total number of jobs matching the given filters.\n// Security controls are automatically applied based on the user context.\n// Uses DISTINCT count to handle tag filters correctly (jobs may appear multiple\n// times when joined with the tag table).\nfunc (r *JobRepository) CountJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n) (int, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"count(DISTINCT job.id)\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn 0, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\tvar count int\n\tif err := query.RunWith(r.DB).Scan(&count); err != nil {\n\t\treturn 0, fmt.Errorf(\"failed to count jobs: %w\", err)\n\t}\n\n\treturn count, nil\n}\n\n// SecurityCheckWithUser applies role-based access control filters to a job query\n// based on the provided user's roles and permissions.\n//\n// Access rules by role:\n// - API role (exclusive): Full access to all jobs\n// - Admin/Support roles: Full access to all jobs\n// - Manager role: Access to jobs in managed projects plus own jobs\n// - User role: Access only to own jobs\n//\n// Returns an error if the user is nil or has no recognized roles.\nfunc SecurityCheckWithUser(user *schema.User, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tif user == nil {\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user context is nil\")\n\t}\n\n\tswitch {\n\tcase len(user.Roles) == 1 && user.HasRole(schema.RoleAPI):\n\t\treturn query, nil\n\tcase user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}):\n\t\treturn query, nil\n\tcase user.HasRole(schema.RoleManager):\n\t\tif len(user.Projects) != 0 {\n\t\t\treturn query.Where(sq.Or{sq.Eq{\"job.project\": user.Projects}, sq.Eq{\"job.hpc_user\": user.Username}}), nil\n\t\t}\n\t\tcclog.Debugf(\"Manager '%s' has no assigned projects, restricting to personal jobs\", user.Username)\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tcase user.HasRole(schema.RoleUser):\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tdefault:\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user has no or unknown roles\")\n\t}\n}\n\n// SecurityCheck extracts the user from the context and applies role-based access\n// control filters to the query. This is a convenience wrapper around SecurityCheckWithUser.\nfunc SecurityCheck(ctx context.Context, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tuser := GetUserFromContext(ctx)\n\treturn SecurityCheckWithUser(user, query)\n}\n\n// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n// them to the query. Supports filtering by job properties (cluster, state, user),\n// time ranges, resource usage, tags, and JSON field searches in meta_data,\n// footprint, and resources columns.\nfunc BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n\t// Primary Key\n\tif filter.DbID != nil {\n\t\tdbIDs := make([]string, len(filter.DbID))\n\t\tcopy(dbIDs, filter.DbID)\n\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n\t}\n\t// Explicit indices\n\tif filter.Cluster != nil {\n\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n\t}\n\tif filter.SubCluster != nil {\n\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n\t}\n\tif filter.Partition != nil {\n\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n\t}\n\tif filter.State != nil {\n\t\tstates := make([]string, len(filter.State))\n\t\tfor i, val := range filter.State {\n\t\t\tstates[i] = string(val)\n\t\t}\n\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n\t}\n\tif filter.Shared != nil {\n\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n\t}\n\tif filter.Project != nil {\n\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n\t}\n\tif filter.User != nil {\n\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n\t}\n\tif filter.NumNodes != nil {\n\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n\t}\n\tif filter.NumAccelerators != nil {\n\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n\t}\n\tif filter.NumHWThreads != nil {\n\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n\t}\n\tif filter.ArrayJobID != nil {\n\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n\t}\n\tif filter.StartTime != nil {\n\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n\t}\n\tif filter.Duration != nil {\n\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n\t}\n\tif filter.Energy != nil {\n\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n\t}\n\t// Indices on Tag Table\n\tif filter.Tags != nil {\n\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n\t}\n\t// No explicit Indices\n\tif filter.JobID != nil {\n\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n\t}\n\t// Queries Within JSONs\n\tif filter.MetricStats != nil {\n\t\tfor _, ms := range filter.MetricStats {\n\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n\t\t}\n\t}\n\tif filter.Node != nil {\n\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n\t}\n\tif filter.JobName != nil {\n\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n\t}\n\tif filter.Schedule != nil {\n\t\tinteractiveJobname := \"interactive\"\n\t\tswitch *filter.Schedule {\n\t\tcase \"interactive\":\n\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n\t\tcase \"batch\":\n\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n\t\t}\n\t}\n\n\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n\tif filter.MinRunningFor != nil {\n\t\tnow := time.Now().Unix()\n\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n\t}\n\treturn query\n}\n\n// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\nfunc buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1 && cond.To == 0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\nfunc buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildTimeCondition creates time range filters supporting absolute timestamps,\n// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\nfunc buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != nil && cond.To != nil {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n\t} else if cond.From != nil {\n\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n\t} else if cond.To != nil {\n\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n\t} else if cond.Range != \"\" {\n\t\tnow := time.Now().Unix()\n\t\tvar then int64\n\t\tswitch cond.Range {\n\t\tcase \"last6h\":\n\t\t\tthen = now - (60 * 60 * 6)\n\t\tcase \"last24h\":\n\t\t\tthen = now - (60 * 60 * 24)\n\t\tcase \"last7d\":\n\t\t\tthen = now - (60 * 60 * 24 * 7)\n\t\tcase \"last30d\":\n\t\t\tthen = now - (60 * 60 * 24 * 30)\n\t\tdefault:\n\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n\t\t\treturn query\n\t\t}\n\t\treturn query.Where(field+\" >= ?\", then)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\nfunc buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(footprint)\")\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildStringCondition creates filters for string fields supporting equality,\n// inequality, prefix, suffix, substring, and IN list matching.\nfunc buildStringCondition(field string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.Eq != nil {\n\t\treturn query.Where(field+\" = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(field+\" != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\tif cond.In != nil {\n\t\tqueryElements := make([]string, len(cond.In))\n\t\tcopy(queryElements, cond.In)\n\t\treturn query.Where(sq.Or{sq.Eq{field: queryElements}})\n\t}\n\treturn query\n}\n\n// buildMetaJSONCondition creates filters on fields within the meta_data JSON column.\nfunc buildMetaJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(meta_data)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\n// buildResourceJSONCondition creates filters on fields within the resources JSON array column.\n// Uses json_each to search within array elements.\nfunc buildResourceJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(resources)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") = ?)\", *cond.Eq)\n\t}\n\tif cond.Neq != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") != ?)\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\")) LIKE ?)\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\nvar (\n\tmatchFirstCap = regexp.MustCompile(\"(.)([A-Z][a-z]+)\")\n\tmatchAllCap = regexp.MustCompile(\"([a-z0-9])([A-Z])\")\n)\n\n// toSnakeCase converts camelCase strings to snake_case for SQL column names.\n// Includes security checks to prevent SQL injection attempts.\n// Panics if potentially dangerous characters are detected.\nfunc toSnakeCase(str string) string {\n\tfor _, c := range str {\n\t\tif c == '\\'' || c == '\\\\' || c == '\"' || c == ';' || c == '-' || c == ' ' {\n\t\t\tcclog.Panicf(\"toSnakeCase: potentially dangerous character detected in input: %q\", str)\n\t\t}\n\t}\n\n\tsnake := matchFirstCap.ReplaceAllString(str, \"${1}_${2}\")\n\tsnake = matchAllCap.ReplaceAllString(snake, \"${1}_${2}\")\n\treturn strings.ToLower(snake)\n}\n","structuredPatch":[{"oldStart":84,"oldLines":7,"newStart":84,"newLines":7,"lines":[" query = BuildWhereClause(f, query)"," }"," ","- rows, err := query.RunWith(r.stmtCache).Query()","+ rows, err := query.RunWith(r.stmtCache).QueryContext(ctx)"," if err != nil {"," queryString, queryVars, _ := query.ToSql()"," return nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"76803912-fa39-4ae2-9213-5de9f10dd6e7"} 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This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package repository provides job query functionality with filtering, pagination,\n// and security controls. This file contains the main query builders and security\n// checks for job retrieval operations.\npackage repository\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"regexp\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\nconst (\n\t// Default initial capacity for job result slices\n\tdefaultJobsCapacity = 50\n)\n\n// QueryJobs retrieves jobs from the database with optional filtering, pagination,\n// and sorting. Security controls are automatically applied based on the user context.\n//\n// Parameters:\n// - ctx: Context containing user authentication information\n// - filters: Optional job filters (cluster, state, user, time ranges, etc.)\n// - page: Optional pagination parameters (page number and items per page)\n// - order: Optional sorting specification (column or footprint field)\n//\n// Returns a slice of jobs matching the criteria, or an error if the query fails.\n// The function enforces role-based access control through SecurityCheck.\nfunc (r *JobRepository) QueryJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n\tpage *model.PageRequest,\n\torder *model.OrderByInput,\n) ([]*schema.Job, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(jobColumns...).From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tif order != nil {\n\t\tfield := toSnakeCase(order.Field)\n\t\tif order.Type == \"col\" {\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"job.%s DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for column\")\n\t\t\t}\n\t\t} else {\n\t\t\t// Order by footprint JSON field values\n\t\t\tquery = query.Where(\"JSON_VALID(footprint)\")\n\t\t\tswitch order.Order {\n\t\t\tcase model.SortDirectionEnumAsc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") ASC\", field))\n\t\t\tcase model.SortDirectionEnumDesc:\n\t\t\t\tquery = query.OrderBy(fmt.Sprintf(\"JSON_EXTRACT(footprint, \\\"$.%s\\\") DESC\", field))\n\t\t\tdefault:\n\t\t\t\treturn nil, errors.New(\"invalid sorting order for footprint\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).QueryContext(ctx)\n\tif err != nil {\n\t\tqueryString, queryVars, _ := query.ToSql()\n\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, defaultJobsCapacity)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error scanning job row: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, fmt.Errorf(\"error iterating job rows: %w\", err)\n\t}\n\n\treturn jobs, nil\n}\n\n// CountJobs returns the total number of jobs matching the given filters.\n// Security controls are automatically applied based on the user context.\n// Uses DISTINCT count to handle tag filters correctly (jobs may appear multiple\n// times when joined with the tag table).\nfunc (r *JobRepository) CountJobs(\n\tctx context.Context,\n\tfilters []*model.JobFilter,\n) (int, error) {\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"count(DISTINCT job.id)\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn 0, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\tvar count int\n\tif err := query.RunWith(r.DB).Scan(&count); err != nil {\n\t\treturn 0, fmt.Errorf(\"failed to count jobs: %w\", err)\n\t}\n\n\treturn count, nil\n}\n\n// SecurityCheckWithUser applies role-based access control filters to a job query\n// based on the provided user's roles and permissions.\n//\n// Access rules by role:\n// - API role (exclusive): Full access to all jobs\n// - Admin/Support roles: Full access to all jobs\n// - Manager role: Access to jobs in managed projects plus own jobs\n// - User role: Access only to own jobs\n//\n// Returns an error if the user is nil or has no recognized roles.\nfunc SecurityCheckWithUser(user *schema.User, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tif user == nil {\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user context is nil\")\n\t}\n\n\tswitch {\n\tcase len(user.Roles) == 1 && user.HasRole(schema.RoleAPI):\n\t\treturn query, nil\n\tcase user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}):\n\t\treturn query, nil\n\tcase user.HasRole(schema.RoleManager):\n\t\tif len(user.Projects) != 0 {\n\t\t\treturn query.Where(sq.Or{sq.Eq{\"job.project\": user.Projects}, sq.Eq{\"job.hpc_user\": user.Username}}), nil\n\t\t}\n\t\tcclog.Debugf(\"Manager '%s' has no assigned projects, restricting to personal jobs\", user.Username)\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tcase user.HasRole(schema.RoleUser):\n\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n\tdefault:\n\t\tvar qnil sq.SelectBuilder\n\t\treturn qnil, fmt.Errorf(\"user has no or unknown roles\")\n\t}\n}\n\n// SecurityCheck extracts the user from the context and applies role-based access\n// control filters to the query. This is a convenience wrapper around SecurityCheckWithUser.\nfunc SecurityCheck(ctx context.Context, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n\tuser := GetUserFromContext(ctx)\n\treturn SecurityCheckWithUser(user, query)\n}\n\n// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n// them to the query. Supports filtering by job properties (cluster, state, user),\n// time ranges, resource usage, tags, and JSON field searches in meta_data,\n// footprint, and resources columns.\nfunc BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n\t// Primary Key\n\tif filter.DbID != nil {\n\t\tdbIDs := make([]string, len(filter.DbID))\n\t\tcopy(dbIDs, filter.DbID)\n\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n\t}\n\t// Explicit indices\n\tif filter.Cluster != nil {\n\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n\t}\n\tif filter.SubCluster != nil {\n\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n\t}\n\tif filter.Partition != nil {\n\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n\t}\n\tif filter.State != nil {\n\t\tstates := make([]string, len(filter.State))\n\t\tfor i, val := range filter.State {\n\t\t\tstates[i] = string(val)\n\t\t}\n\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n\t}\n\tif filter.Shared != nil {\n\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n\t}\n\tif filter.Project != nil {\n\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n\t}\n\tif filter.User != nil {\n\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n\t}\n\tif filter.NumNodes != nil {\n\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n\t}\n\tif filter.NumAccelerators != nil {\n\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n\t}\n\tif filter.NumHWThreads != nil {\n\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n\t}\n\tif filter.ArrayJobID != nil {\n\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n\t}\n\tif filter.StartTime != nil {\n\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n\t}\n\tif filter.Duration != nil {\n\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n\t}\n\tif filter.Energy != nil {\n\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n\t}\n\t// Indices on Tag Table\n\tif filter.Tags != nil {\n\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n\t}\n\t// No explicit Indices\n\tif filter.JobID != nil {\n\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n\t}\n\t// Queries Within JSONs\n\tif filter.MetricStats != nil {\n\t\tfor _, ms := range filter.MetricStats {\n\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n\t\t}\n\t}\n\tif filter.Node != nil {\n\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n\t}\n\tif filter.JobName != nil {\n\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n\t}\n\tif filter.Schedule != nil {\n\t\tinteractiveJobname := \"interactive\"\n\t\tswitch *filter.Schedule {\n\t\tcase \"interactive\":\n\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n\t\tcase \"batch\":\n\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n\t\t}\n\t}\n\n\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n\tif filter.MinRunningFor != nil {\n\t\tnow := time.Now().Unix()\n\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n\t}\n\treturn query\n}\n\n// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\nfunc buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1 && cond.To == 0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\nfunc buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildTimeCondition creates time range filters supporting absolute timestamps,\n// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\nfunc buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != nil && cond.To != nil {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n\t} else if cond.From != nil {\n\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n\t} else if cond.To != nil {\n\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n\t} else if cond.Range != \"\" {\n\t\tnow := time.Now().Unix()\n\t\tvar then int64\n\t\tswitch cond.Range {\n\t\tcase \"last6h\":\n\t\t\tthen = now - (60 * 60 * 6)\n\t\tcase \"last24h\":\n\t\t\tthen = now - (60 * 60 * 24)\n\t\tcase \"last7d\":\n\t\t\tthen = now - (60 * 60 * 24 * 7)\n\t\tcase \"last30d\":\n\t\t\tthen = now - (60 * 60 * 24 * 30)\n\t\tdefault:\n\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n\t\t\treturn query\n\t\t}\n\t\treturn query.Where(field+\" >= ?\", then)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\nfunc buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(footprint)\")\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildStringCondition creates filters for string fields supporting equality,\n// inequality, prefix, suffix, substring, and IN list matching.\nfunc buildStringCondition(field string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.Eq != nil {\n\t\treturn query.Where(field+\" = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(field+\" != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(field+\" LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\tif cond.In != nil {\n\t\tqueryElements := make([]string, len(cond.In))\n\t\tcopy(queryElements, cond.In)\n\t\treturn query.Where(sq.Or{sq.Eq{field: queryElements}})\n\t}\n\treturn query\n}\n\n// buildMetaJSONCondition creates filters on fields within the meta_data JSON column.\nfunc buildMetaJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(meta_data)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") = ?\", *cond.Eq)\n\t}\n\tif cond.Neq != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") != ?\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"JSON_EXTRACT(meta_data, \\\"$.\"+jsonField+\"\\\") LIKE ?\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\n// buildResourceJSONCondition creates filters on fields within the resources JSON array column.\n// Uses json_each to search within array elements.\nfunc buildResourceJSONCondition(jsonField string, cond *model.StringInput, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(resources)\")\n\tif cond.Eq != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") = ?)\", *cond.Eq)\n\t}\n\tif cond.Neq != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") != ?)\", *cond.Neq)\n\t}\n\tif cond.StartsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\")) LIKE ?)\", fmt.Sprint(*cond.StartsWith, \"%\"))\n\t}\n\tif cond.EndsWith != nil { // Currently Unused\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.EndsWith))\n\t}\n\tif cond.Contains != nil {\n\t\treturn query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, \\\"$.\"+jsonField+\"\\\") LIKE ?)\", fmt.Sprint(\"%\", *cond.Contains, \"%\"))\n\t}\n\treturn query\n}\n\nvar (\n\tmatchFirstCap = regexp.MustCompile(\"(.)([A-Z][a-z]+)\")\n\tmatchAllCap = regexp.MustCompile(\"([a-z0-9])([A-Z])\")\n)\n\n// toSnakeCase converts camelCase strings to snake_case for SQL column names.\n// Includes security checks to prevent SQL injection attempts.\n// Panics if potentially dangerous characters are detected.\nfunc toSnakeCase(str string) string {\n\tfor _, c := range str {\n\t\tif c == '\\'' || c == '\\\\' || c == '\"' || c == ';' || c == '-' || c == ' ' {\n\t\t\tcclog.Panicf(\"toSnakeCase: potentially dangerous character detected in input: %q\", str)\n\t\t}\n\t}\n\n\tsnake := matchFirstCap.ReplaceAllString(str, \"${1}_${2}\")\n\tsnake = matchAllCap.ReplaceAllString(snake, \"${1}_${2}\")\n\treturn strings.ToLower(snake)\n}\n","structuredPatch":[{"oldStart":126,"oldLines":7,"newStart":126,"newLines":7,"lines":[" }"," "," var count int","- if err := query.RunWith(r.DB).Scan(&count); err != nil {","+ if err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&count); err != nil {"," return 0, fmt.Errorf(\"failed to count jobs: %w\", err)"," }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"54dd23b8-31f4-476d-877d-2ba3bd5320c5"} +{"parentUuid":"84b899d2-e96d-4505-96da-6fa0bdf94e4b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01G4EHT94fj3XCLPN91W78ss","type":"message","role":"assistant","content":[{"type":"text","text":"`stats.go` — all methods with ctx:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":219,"cache_read_input_tokens":51602,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":219},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhvHa4kjoJARuvJ4iqV","type":"assistant","uuid":"612784de-c5e3-45d0-96ea-b1855473450c","timestamp":"2026-03-11T05:02:18.768Z"} +{"parentUuid":"612784de-c5e3-45d0-96ea-b1855473450c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01G4EHT94fj3XCLPN91W78ss","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012DqfnspKBr56XebhrFGdKx","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {","new_string":"\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":219,"cache_read_input_tokens":51602,"output_tokens":1219,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":219,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhvHa4kjoJARuvJ4iqV","type":"assistant","uuid":"db3bbffa-b89e-4988-b123-62030343789b","timestamp":"2026-03-11T05:02:29.747Z"} +{"type":"file-history-snapshot","messageId":"db3bbffa-b89e-4988-b123-62030343789b","snapshot":{"messageId":"4a724aea-ee79-41a7-929d-fa3f906ac8b9","trackedFileBackups":{"internal/repository/dbConnection.go":{"backupFileName":"f4586ab7038e6775@v1","version":1,"backupTime":"2026-03-11T05:02:06.448Z"},"internal/repository/jobQuery.go":{"backupFileName":"33536efde97b8b50@v1","version":1,"backupTime":"2026-03-11T05:02:13.866Z"},"internal/repository/stats.go":{"backupFileName":"4426319480f78977@v1","version":1,"backupTime":"2026-03-11T05:02:29.753Z"}},"timestamp":"2026-03-11T05:01:36.839Z"},"isSnapshotUpdate":true} +{"parentUuid":"db3bbffa-b89e-4988-b123-62030343789b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_012DqfnspKBr56XebhrFGdKx","toolUseID":"toolu_012DqfnspKBr56XebhrFGdKx","timestamp":"2026-03-11T05:02:29.755Z","uuid":"bc863780-e1ab-4e2d-8f2e-11d71ecde134"} +{"parentUuid":"db3bbffa-b89e-4988-b123-62030343789b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012DqfnspKBr56XebhrFGdKx","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"6116b843-a948-49c7-8dd6-d416c66dc919","timestamp":"2026-03-11T05:02:29.755Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {","newString":"\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRow()\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":230,"oldLines":7,"newStart":230,"newLines":7,"lines":[" query = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)"," }"," ","- rows, err := query.RunWith(r.DB).Query()","+ rows, err := query.RunWith(r.DB).QueryContext(ctx)"," if err != nil {"," cclog.Warn(\"Error while querying DB for job statistics\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"db3bbffa-b89e-4988-b123-62030343789b"} +{"parentUuid":"6116b843-a948-49c7-8dd6-d416c66dc919","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_017S9jYkHrQgMYKSNfputwhx","type":"message","role":"assistant","content":[{"type":"text","text":"Now `JobsStats` — `QueryRow` → `QueryRowContext`:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1258,"cache_read_input_tokens":51821,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1258},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhwEKPNeNU6jdSd7Tzy","type":"assistant","uuid":"d19fc70e-fb1d-4b85-9500-6a597b866bb4","timestamp":"2026-03-11T05:02:32.284Z"} +{"parentUuid":"d19fc70e-fb1d-4b85-9500-6a597b866bb4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_017S9jYkHrQgMYKSNfputwhx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014Ucs3tD12kxRoCZF7g5dJb","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trow := query.RunWith(r.DB).QueryRow()","new_string":"\trow := query.RunWith(r.DB).QueryRowContext(ctx)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1258,"cache_read_input_tokens":51821,"output_tokens":178,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1258,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhwEKPNeNU6jdSd7Tzy","type":"assistant","uuid":"941e39ea-fa06-4bef-8f51-a91865d3dd4a","timestamp":"2026-03-11T05:02:33.690Z"} +{"parentUuid":"941e39ea-fa06-4bef-8f51-a91865d3dd4a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_014Ucs3tD12kxRoCZF7g5dJb","toolUseID":"toolu_014Ucs3tD12kxRoCZF7g5dJb","timestamp":"2026-03-11T05:02:33.705Z","uuid":"b26d66e0-041c-4606-9aa1-9f78a8fd3a93"} +{"parentUuid":"941e39ea-fa06-4bef-8f51-a91865d3dd4a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014Ucs3tD12kxRoCZF7g5dJb","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"e7affcfb-dba6-41d8-9c0c-3c7b126f9024","timestamp":"2026-03-11T05:02:33.705Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trow := query.RunWith(r.DB).QueryRow()","newString":"\trow := query.RunWith(r.DB).QueryRowContext(ctx)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRow()\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":355,"oldLines":7,"newStart":355,"newLines":7,"lines":[" return nil, err"," }"," ","- row := query.RunWith(r.DB).QueryRow()","+ row := query.RunWith(r.DB).QueryRowContext(ctx)"," stats := make([]*model.JobsStatistics, 0, 1)"," "," var jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"941e39ea-fa06-4bef-8f51-a91865d3dd4a"} +{"parentUuid":"e7affcfb-dba6-41d8-9c0c-3c7b126f9024","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01NM9ChNDts55N1SKrFTk6GA","type":"message","role":"assistant","content":[{"type":"text","text":"Now `JobCountGrouped` — line 443:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":328,"cache_read_input_tokens":53079,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":328},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhwXXyvfJ4oqTSDz2Wk","type":"assistant","uuid":"718078b4-c816-4076-8e4f-873e9ee15b62","timestamp":"2026-03-11T05:02:36.780Z"} +{"parentUuid":"718078b4-c816-4076-8e4f-873e9ee15b62","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01NM9ChNDts55N1SKrFTk6GA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017znNyj6VWBKWPfToftkS44","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}","new_string":"\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":328,"cache_read_input_tokens":53079,"output_tokens":652,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":328,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhwXXyvfJ4oqTSDz2Wk","type":"assistant","uuid":"f72e74e9-f29f-428c-8683-b3dc2dd99937","timestamp":"2026-03-11T05:02:41.903Z"} +{"parentUuid":"f72e74e9-f29f-428c-8683-b3dc2dd99937","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_017znNyj6VWBKWPfToftkS44","toolUseID":"toolu_017znNyj6VWBKWPfToftkS44","timestamp":"2026-03-11T05:02:41.926Z","uuid":"b35e6586-d971-4e45-b764-41dd99fe285d"} +{"parentUuid":"f72e74e9-f29f-428c-8683-b3dc2dd99937","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017znNyj6VWBKWPfToftkS44","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"2f233a37-acdc-483e-a0f0-8766030dc22d","timestamp":"2026-03-11T05:02:41.926Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}","newString":"\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":440,"oldLines":7,"newStart":440,"newLines":7,"lines":[" if err != nil {"," return nil, err"," }","- rows, err := query.RunWith(r.DB).Query()","+ rows, err := 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/Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"1b930fc0-683a-4125-b1fc-0496bc08c743","timestamp":"2026-03-11T05:02:45.502Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)","newString":"\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":501,"oldLines":7,"newStart":501,"newLines":7,"lines":[" if err != nil {"," return nil, err"," }","- rows, err := query.RunWith(r.DB).Query()","+ rows, err := query.RunWith(r.DB).QueryContext(ctx)"," if err != nil {"," cclog.Warn(\"Error while querying DB for job statistics\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"ddad595c-3b6c-4d00-9451-d4112eef1b95"} +{"parentUuid":"1b930fc0-683a-4125-b1fc-0496bc08c743","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01JvrPJ6q5SmRU2Utng3onAu","type":"message","role":"assistant","content":[{"type":"text","text":"Now `AddJobCount` — line 569:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":318,"cache_read_input_tokens":54098,"cache_creation":{"ephemeral_5m_input_tokens":318,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhxPdWAXY9MdPBsbrxT","type":"assistant","uuid":"adafe1b8-f930-45ef-86f1-bffb6b0f5bec","timestamp":"2026-03-11T05:02:47.359Z"} +{"parentUuid":"adafe1b8-f930-45ef-86f1-bffb6b0f5bec","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01JvrPJ6q5SmRU2Utng3onAu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012NHyo1jLUGzZBfAgdmcVVm","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); 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err != nil {","newString":"\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":566,"oldLines":7,"newStart":566,"newLines":7,"lines":[" return nil, err"," }"," var cnt sql.NullInt64","- if err := query.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {","+ if err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {"," cclog.Warn(\"Error while querying DB for job count\")"," return nil, err"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"3d965fc9-ba99-42ca-ad18-7f61af319d03"} +{"parentUuid":"20a7ba70-dfa3-44d2-8151-9d834029f457","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01A4aa5G2tdzwAAN17mhBhX3","type":"message","role":"assistant","content":[{"type":"text","text":"Now the histogram methods — `jobsStatisticsHistogram` line 758:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":232,"cache_read_input_tokens":54416,"cache_creation":{"ephemeral_5m_input_tokens":232,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhxc9aRGYZCjAET8cWb","type":"assistant","uuid":"2ffdee9a-b02e-463e-92cc-2527ca5b3d3b","timestamp":"2026-03-11T05:03:03.075Z"} +{"parentUuid":"2ffdee9a-b02e-463e-92cc-2527ca5b3d3b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01A4aa5G2tdzwAAN17mhBhX3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Qfj27yXKSJhFkVUjtLQeGy","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount","new_string":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":232,"cache_read_input_tokens":54416,"output_tokens":330,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":232},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhxc9aRGYZCjAET8cWb","type":"assistant","uuid":"8a3a93b5-8760-4781-bba0-608fe0e892bb","timestamp":"2026-03-11T05:03:06.144Z"} +{"parentUuid":"8a3a93b5-8760-4781-bba0-608fe0e892bb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01Qfj27yXKSJhFkVUjtLQeGy","toolUseID":"toolu_01Qfj27yXKSJhFkVUjtLQeGy","timestamp":"2026-03-11T05:03:06.162Z","uuid":"1fa18c45-2cc3-47d0-8a2d-18649a4125a1"} +{"parentUuid":"8a3a93b5-8760-4781-bba0-608fe0e892bb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qfj27yXKSJhFkVUjtLQeGy","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"aaa0bd58-90ea-4a48-a34e-bac0aad5654c","timestamp":"2026-03-11T05:03:06.162Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount","newString":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":755,"oldLines":7,"newStart":755,"newLines":7,"lines":[" query = BuildWhereClause(f, query)"," }"," ","- rows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()","+ rows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)"," if err != nil {"," cclog.Error(\"Error while running query\")"," return nil, err"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"8a3a93b5-8760-4781-bba0-608fe0e892bb"} +{"parentUuid":"aaa0bd58-90ea-4a48-a34e-bac0aad5654c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Lzus9xT8gaxFaA7QjxR7pa","type":"message","role":"assistant","content":[{"type":"text","text":"`jobsDurationStatisticsHistogram` line 832:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":369,"cache_read_input_tokens":54648,"cache_creation":{"ephemeral_5m_input_tokens":369,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhyuxAnkXbdYUMydtfA","type":"assistant","uuid":"627a2059-3b0a-4fdc-8fdb-decddd94e042","timestamp":"2026-03-11T05:03:08.127Z"} +{"parentUuid":"627a2059-3b0a-4fdc-8fdb-decddd94e042","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Lzus9xT8gaxFaA7QjxR7pa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01He14ju9D6EvjafWJ6msi6R","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.","new_string":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":369,"cache_read_input_tokens":54648,"output_tokens":280,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":369},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhyuxAnkXbdYUMydtfA","type":"assistant","uuid":"e55e5d58-e418-4b15-942f-a7542712316d","timestamp":"2026-03-11T05:03:10.121Z"} +{"parentUuid":"e55e5d58-e418-4b15-942f-a7542712316d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01He14ju9D6EvjafWJ6msi6R","toolUseID":"toolu_01He14ju9D6EvjafWJ6msi6R","timestamp":"2026-03-11T05:03:10.141Z","uuid":"2e873687-f0e1-453f-be5e-d418931855e4"} +{"parentUuid":"e55e5d58-e418-4b15-942f-a7542712316d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01He14ju9D6EvjafWJ6msi6R","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"0b9b183c-eda6-4120-b859-b2d9a3a18193","timestamp":"2026-03-11T05:03:10.141Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.","newString":"\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":829,"oldLines":7,"newStart":829,"newLines":7,"lines":[" query = BuildWhereClause(f, query)"," }"," ","- rows, err := 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962:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":430,"cache_read_input_tokens":55017,"cache_creation":{"ephemeral_5m_input_tokens":430,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvhzCz6mL6tY25LLVRhE","type":"assistant","uuid":"1e1f242f-30d4-4c78-9d34-83393dc8bf35","timestamp":"2026-03-11T05:03:13.746Z"} +{"parentUuid":"1e1f242f-30d4-4c78-9d34-83393dc8bf35","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_015uSjLVZ16aAsse6pP9QWrR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014aFqFQJkSNmKCan3qgZnD9","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","old_string":"\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.","new_string":"\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":430,"cache_read_input_tokens":55017,"output_tokens":293,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":430},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhzCz6mL6tY25LLVRhE","type":"assistant","uuid":"8604094b-4bbe-41af-9bc2-e7c9a5595267","timestamp":"2026-03-11T05:03:15.898Z"} 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/Users/jan/prg/CC/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"845149de-ff29-4bf8-8333-589dc0c5b14f","timestamp":"2026-03-11T05:03:15.923Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/stats.go","oldString":"\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.","newString":"\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n// - Overall (JobsStats): Single aggregate across all matching jobs\n// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n// - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n// - Duration, nodes, cores, accelerators (AddHistograms)\n// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n// - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n// - Resources: Natural value-based bins\n// - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n// - Duration calculated as (now - start_time) for running jobs\n// - Metric histograms for running jobs load data from metric backend instead of footprint\n// - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n// - totalJobs: Count of jobs\n// - totalUsers: Count of distinct users (always 0 when grouping by user)\n// - totalWalltime: Sum of job durations in hours\n// - totalNodes: Sum of nodes used across all jobs\n// - totalNodeHours: Sum of (duration × num_nodes) in hours\n// - totalCores: Sum of hardware threads used across all jobs\n// - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n// - totalAccs: Sum of accelerators used across all jobs\n// - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n// - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n// - Grouped queries: Also select grouping column and user's display name from hpc_user table\n// - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n// - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n// - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n// - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n// - ID: The group identifier (username, project name, cluster name, etc.)\n// - Name: Display name (for users, from hpc_user.name; empty for other groups)\n// - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tName: personName,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID: id.String,\n\t\t\t\t\t\tTotalJobs: totalJobs,\n\t\t\t\t\t\tTotalUsers: totalUsers,\n\t\t\t\t\t\tTotalWalltime: totalWalltime,\n\t\t\t\t\t\tTotalNodes: totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores: totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs: totalAccs,\n\t\t\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n// - ctx: Context for security checks and cancellation\n// - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n// - totalJobs, totalUsers, totalWalltime\n// - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs: int(jobs.Int64),\n\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours: totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n// - job: Job struct with populated Statistics field\n// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID: id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n// - stats: Existing statistics to augment (modified in-place by ID matching)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply\n// - stats: Existing statistics to augment (modified in-place)\n// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply to jobs included in histograms\n// - stat: Statistics struct to augment (modified in-place)\n// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n// - HistDuration: Job duration distribution (zero-padded bins)\n// - HistNumNodes: Node count distribution\n// - HistNumCores: Core (hwthread) count distribution\n// - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":959,"oldLines":7,"newStart":959,"newLines":7,"lines":[" "," mainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")"," ","- rows, err := 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/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go has been updated successfully."}]},"uuid":"b0686ab6-4400-4aee-bbdc-842712fa553c","timestamp":"2026-03-11T05:03:20.837Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go","oldString":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDWithUser executes a SQL query to find a specific batch job.","newString":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))\n}\n\n// FindByIDWithUser executes a SQL query to find a specific batch job.","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// Find executes a SQL query to find a specific batch job.\n// The job is queried using the batch job id, the cluster name,\n// and the start time of the job in UNIX epoch time seconds.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) Find(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\tcclog.Debugf(\"Timer Find %s\", time.Since(start))\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindCached executes a SQL query to find a specific batch job from the job_cache table.\n// The job is queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds). This method uses cached job data which\n// may be stale but provides faster access than Find().\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindCached(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tq := sq.Select(jobCacheColumns...).From(\"job_cache\").\n\t\tWhere(\"job_cache.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job_cache.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job_cache.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job_cache.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindAll executes a SQL query to find all batch jobs matching the given criteria.\n// Jobs are queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds).\n// It returns a slice of pointers to schema.Job data structures and an error variable.\n// An empty slice is returned if no matching jobs are found.\nfunc (r *JobRepository) FindAll(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) ([]*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\trows, err := q.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindAll query for jobID=%d: %v\", *jobID, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute FindAll query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 10)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindAll: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\tcclog.Debugf(\"Timer FindAll %s\", time.Since(start))\n\treturn jobs, nil\n}\n\n// GetJobList returns job IDs for non-running jobs.\n// This is useful to process large job counts and intended to be used\n// together with FindById to process jobs one by one.\n// Use limit and offset for pagination. Use limit=0 to get all results (not recommended for large datasets).\nfunc (r *JobRepository) GetJobList(limit int, offset int) ([]int64, error) {\n\tquery := sq.Select(\"id\").From(\"job\").\n\t\tWhere(\"job.job_state != 'running'\")\n\n\t// Add pagination if limit is specified\n\tif limit > 0 {\n\t\tquery = query.Limit(uint64(limit)).Offset(uint64(offset))\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running GetJobList query (limit=%d, offset=%d): %v\", limit, offset, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute GetJobList query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjl := make([]int64, 0, 1000)\n\tfor rows.Next() {\n\t\tvar id int64\n\t\terr := rows.Scan(&id)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in GetJobList: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID: %w\", err)\n\t\t}\n\t\tjl = append(jl, id)\n\t}\n\n\tcclog.Debugf(\"JobRepository.GetJobList(): Return job count %d\", len(jl))\n\treturn jl, nil\n}\n\n// FindByID executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByID(ctx context.Context, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDWithUser executes a SQL query to find a specific batch job.\n// The job is queried using the database id. The user is passed directly,\n// instead as part of the context.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDWithUser(user *schema.User, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheckWithUser(user, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDDirect executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDDirect(jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByJobID executes a SQL query to find a specific batch job.\n// The job is queried using the slurm id and the clustername.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByJobID(ctx context.Context, jobID int64, startTime int64, cluster string) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,\n// startTime, and cluster. Returns true if the user is the owner, false otherwise.\n// This method does not return errors; it returns false for both non-existent jobs\n// and jobs owned by other users.\nfunc (r *JobRepository) IsJobOwner(jobID int64, startTime int64, user string, cluster string) bool {\n\tq := sq.Select(\"id\").\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.hpc_user = ?\", user).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\t_, err := scanJob(q.RunWith(r.stmtCache).QueryRow())\n\tif err != nil && err != sql.ErrNoRows {\n\t\tcclog.Warnf(\"IsJobOwner: unexpected error for jobID=%d, user=%s, cluster=%s: %v\", jobID, user, cluster, err)\n\t}\n\treturn err != sql.ErrNoRows\n}\n\nfunc (r *JobRepository) FindConcurrentJobs(\n\tctx context.Context,\n\tjob *schema.Job,\n) (*model.JobLinkResultList, error) {\n\tif job == nil {\n\t\treturn nil, nil\n\t}\n\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"job.id\", \"job.job_id\", \"job.start_time\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tquery = query.Where(\"cluster = ?\", job.Cluster)\n\n\tif len(job.Resources) == 0 {\n\t\treturn nil, fmt.Errorf(\"job has no resources defined\")\n\t}\n\n\tvar startTime int64\n\tvar stopTime int64\n\n\tstartTime = job.StartTime\n\thostname := job.Resources[0].Hostname\n\n\tif job.State == schema.JobStateRunning {\n\t\tstopTime = time.Now().Unix()\n\t} else {\n\t\tstopTime = startTime + int64(job.Duration)\n\t}\n\n\t// Time buffer constant for finding overlapping jobs\n\t// overlapBufferEnd: 200s buffer at job end to account for scheduling/cleanup overlap\n\tconst overlapBufferEnd = 200\n\n\tstopTimeTail := stopTime - overlapBufferEnd\n\tstartTimeFront := startTime + overlapBufferEnd\n\n\tqueryRunning := query.Where(\"job.job_state = ?\", \"running\").\n\t\tWhere(\"job.start_time <= ?\", stopTimeTail)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tqueryRunning = queryRunning.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\tquery = query.Where(\"job.job_state != ?\", \"running\").\n\t\tWhere(\"job.start_time < ?\", stopTimeTail).\n\t\tWhere(\"(job.start_time + job.duration) > ?\", startTimeFront)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tquery = query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\titems := make([]*model.JobLink, 0, 10)\n\tqueryString := fmt.Sprintf(\"cluster=%s\", job.Cluster)\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err = rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\trows, err = queryRunning.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent running jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent running jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err := rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning running concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan running concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tcnt := len(items)\n\n\treturn &model.JobLinkResultList{\n\t\tListQuery: &queryString,\n\t\tItems: items,\n\t\tCount: &cnt,\n\t}, nil\n}\n","structuredPatch":[{"oldStart":171,"oldLines":7,"newStart":171,"newLines":7,"lines":[" return nil, qerr"," }"," ","- return scanJob(q.RunWith(r.stmtCache).QueryRow())","+ return scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))"," }"," "," // FindByIDWithUser executes a SQL query to find a specific batch job."]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"09ff3f79-a48d-4512-ae6d-120837325da8"} +{"parentUuid":"b0686ab6-4400-4aee-bbdc-842712fa553c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01DuGMuHM6KjKdodXrWGC3rU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WtoBgc5oiTsradaZ39oCGw","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go","old_string":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,","new_string":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":273,"cache_read_input_tokens":55779,"output_tokens":216,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":273},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvhzzhhXxgfRxuW9XpST","type":"assistant","uuid":"a94f958b-d84c-459b-888e-5a743bf9b6b1","timestamp":"2026-03-11T05:03:24.317Z"} +{"parentUuid":"a94f958b-d84c-459b-888e-5a743bf9b6b1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01WtoBgc5oiTsradaZ39oCGw","toolUseID":"toolu_01WtoBgc5oiTsradaZ39oCGw","timestamp":"2026-03-11T05:03:24.330Z","uuid":"ac766221-882c-413d-af7f-b7e713875a87"} +{"parentUuid":"a94f958b-d84c-459b-888e-5a743bf9b6b1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WtoBgc5oiTsradaZ39oCGw","type":"tool_result","content":"The file /Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go has been updated successfully."}]},"uuid":"e48cb08e-06e6-4458-b20a-bb2dc63534a4","timestamp":"2026-03-11T05:03:24.330Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/jobFind.go","oldString":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,","newString":"\treturn scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// Find executes a SQL query to find a specific batch job.\n// The job is queried using the batch job id, the cluster name,\n// and the start time of the job in UNIX epoch time seconds.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) Find(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\tcclog.Debugf(\"Timer Find %s\", time.Since(start))\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindCached executes a SQL query to find a specific batch job from the job_cache table.\n// The job is queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds). This method uses cached job data which\n// may be stale but provides faster access than Find().\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindCached(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) (*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tq := sq.Select(jobCacheColumns...).From(\"job_cache\").\n\t\tWhere(\"job_cache.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job_cache.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job_cache.start_time = ?\", *startTime)\n\t}\n\n\tq = q.OrderBy(\"job_cache.id DESC\").Limit(1) // always use newest matching job by db id if more than one match\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindAll executes a SQL query to find all batch jobs matching the given criteria.\n// Jobs are queried using the batch job id, and optionally filtered by cluster name\n// and start time (UNIX epoch time seconds).\n// It returns a slice of pointers to schema.Job data structures and an error variable.\n// An empty slice is returned if no matching jobs are found.\nfunc (r *JobRepository) FindAll(\n\tjobID *int64,\n\tcluster *string,\n\tstartTime *int64,\n) ([]*schema.Job, error) {\n\tif jobID == nil {\n\t\treturn nil, fmt.Errorf(\"jobID cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tq := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.job_id = ?\", *jobID)\n\n\tif cluster != nil {\n\t\tq = q.Where(\"job.cluster = ?\", *cluster)\n\t}\n\tif startTime != nil {\n\t\tq = q.Where(\"job.start_time = ?\", *startTime)\n\t}\n\n\trows, err := q.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindAll query for jobID=%d: %v\", *jobID, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute FindAll query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 10)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindAll: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job row: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\tcclog.Debugf(\"Timer FindAll %s\", time.Since(start))\n\treturn jobs, nil\n}\n\n// GetJobList returns job IDs for non-running jobs.\n// This is useful to process large job counts and intended to be used\n// together with FindById to process jobs one by one.\n// Use limit and offset for pagination. Use limit=0 to get all results (not recommended for large datasets).\nfunc (r *JobRepository) GetJobList(limit int, offset int) ([]int64, error) {\n\tquery := sq.Select(\"id\").From(\"job\").\n\t\tWhere(\"job.job_state != 'running'\")\n\n\t// Add pagination if limit is specified\n\tif limit > 0 {\n\t\tquery = query.Limit(uint64(limit)).Offset(uint64(offset))\n\t}\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running GetJobList query (limit=%d, offset=%d): %v\", limit, offset, err)\n\t\treturn nil, fmt.Errorf(\"failed to execute GetJobList query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tjl := make([]int64, 0, 1000)\n\tfor rows.Next() {\n\t\tvar id int64\n\t\terr := rows.Scan(&id)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in GetJobList: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID: %w\", err)\n\t\t}\n\t\tjl = append(jl, id)\n\t}\n\n\tcclog.Debugf(\"JobRepository.GetJobList(): Return job count %d\", len(jl))\n\treturn jl, nil\n}\n\n// FindByID executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByID(ctx context.Context, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))\n}\n\n// FindByIDWithUser executes a SQL query to find a specific batch job.\n// The job is queried using the database id. The user is passed directly,\n// instead as part of the context.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDWithUser(user *schema.User, jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\n\tq, qerr := SecurityCheckWithUser(user, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByIDDirect executes a SQL query to find a specific batch job.\n// The job is queried using the database id.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByIDDirect(jobID int64) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").Where(\"job.id = ?\", jobID)\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// FindByJobID executes a SQL query to find a specific batch job.\n// The job is queried using the slurm id and the clustername.\n// It returns a pointer to a schema.Job data structure and an error variable.\n// To check if no job was found test err == sql.ErrNoRows\nfunc (r *JobRepository) FindByJobID(ctx context.Context, jobID int64, startTime int64, cluster string) (*schema.Job, error) {\n\tq := sq.Select(jobColumns...).\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\tq, qerr := SecurityCheck(ctx, q)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\treturn scanJob(q.RunWith(r.stmtCache).QueryRow())\n}\n\n// IsJobOwner checks if the specified user owns the batch job identified by jobID,\n// startTime, and cluster. Returns true if the user is the owner, false otherwise.\n// This method does not return errors; it returns false for both non-existent jobs\n// and jobs owned by other users.\nfunc (r *JobRepository) IsJobOwner(jobID int64, startTime int64, user string, cluster string) bool {\n\tq := sq.Select(\"id\").\n\t\tFrom(\"job\").\n\t\tWhere(\"job.job_id = ?\", jobID).\n\t\tWhere(\"job.hpc_user = ?\", user).\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.start_time = ?\", startTime)\n\n\t_, err := scanJob(q.RunWith(r.stmtCache).QueryRow())\n\tif err != nil && err != sql.ErrNoRows {\n\t\tcclog.Warnf(\"IsJobOwner: unexpected error for jobID=%d, user=%s, cluster=%s: %v\", jobID, user, cluster, err)\n\t}\n\treturn err != sql.ErrNoRows\n}\n\nfunc (r *JobRepository) FindConcurrentJobs(\n\tctx context.Context,\n\tjob *schema.Job,\n) (*model.JobLinkResultList, error) {\n\tif job == nil {\n\t\treturn nil, nil\n\t}\n\n\tquery, qerr := SecurityCheck(ctx, sq.Select(\"job.id\", \"job.job_id\", \"job.start_time\").From(\"job\"))\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tquery = query.Where(\"cluster = ?\", job.Cluster)\n\n\tif len(job.Resources) == 0 {\n\t\treturn nil, fmt.Errorf(\"job has no resources defined\")\n\t}\n\n\tvar startTime int64\n\tvar stopTime int64\n\n\tstartTime = job.StartTime\n\thostname := job.Resources[0].Hostname\n\n\tif job.State == schema.JobStateRunning {\n\t\tstopTime = time.Now().Unix()\n\t} else {\n\t\tstopTime = startTime + int64(job.Duration)\n\t}\n\n\t// Time buffer constant for finding overlapping jobs\n\t// overlapBufferEnd: 200s buffer at job end to account for scheduling/cleanup overlap\n\tconst overlapBufferEnd = 200\n\n\tstopTimeTail := stopTime - overlapBufferEnd\n\tstartTimeFront := startTime + overlapBufferEnd\n\n\tqueryRunning := query.Where(\"job.job_state = ?\", \"running\").\n\t\tWhere(\"job.start_time <= ?\", stopTimeTail)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tqueryRunning = queryRunning.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\tquery = query.Where(\"job.job_state != ?\", \"running\").\n\t\tWhere(\"job.start_time < ?\", stopTimeTail).\n\t\tWhere(\"(job.start_time + job.duration) > ?\", startTimeFront)\n\t// Get At Least One Exact Hostname Match from JSON Resources Array in Database\n\tquery = query.Where(\"EXISTS (SELECT 1 FROM json_each(job.resources) WHERE json_extract(value, '$.hostname') = ?)\", hostname)\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\titems := make([]*model.JobLink, 0, 10)\n\tqueryString := fmt.Sprintf(\"cluster=%s\", job.Cluster)\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err = rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\trows, err = queryRunning.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running concurrent running jobs query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to execute concurrent running jobs query: %w\", err)\n\t}\n\tdefer rows.Close()\n\n\tfor rows.Next() {\n\t\tvar id, jobID, startTime sql.NullInt64\n\n\t\tif err := rows.Scan(&id, &jobID, &startTime); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning running concurrent job rows: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan running concurrent job row: %w\", err)\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tqueryString += fmt.Sprintf(\"&jobId=%d\", int(jobID.Int64))\n\t\t\titems = append(items,\n\t\t\t\t&model.JobLink{\n\t\t\t\t\tID: fmt.Sprint(id.Int64),\n\t\t\t\t\tJobID: int(jobID.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tcnt := len(items)\n\n\treturn &model.JobLinkResultList{\n\t\tListQuery: &queryString,\n\t\tItems: items,\n\t\tCount: &cnt,\n\t}, nil\n}\n","structuredPatch":[{"oldStart":217,"oldLines":7,"newStart":217,"newLines":7,"lines":[" return nil, qerr"," }"," ","- return scanJob(q.RunWith(r.stmtCache).QueryRow())","+ return scanJob(q.RunWith(r.stmtCache).QueryRowContext(ctx))"," }"," "," // IsJobOwner checks if the specified user owns the batch job identified by jobID,"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"a94f958b-d84c-459b-888e-5a743bf9b6b1"} 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This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport \"time\"\n\n// RepositoryConfig holds configuration for repository operations.\n// All fields have sensible defaults, so this configuration is optional.\ntype RepositoryConfig struct {\n\t// CacheSize is the LRU cache size in bytes for job metadata and energy footprints.\n\t// Default: 1MB (1024 * 1024 bytes)\n\tCacheSize int\n\n\t// MaxOpenConnections is the maximum number of open database connections.\n\t// Default: 4\n\tMaxOpenConnections int\n\n\t// MaxIdleConnections is the maximum number of idle database connections.\n\t// Default: 4\n\tMaxIdleConnections int\n\n\t// ConnectionMaxLifetime is the maximum amount of time a connection may be reused.\n\t// Default: 1 hour\n\tConnectionMaxLifetime time.Duration\n\n\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n\t// Default: 1 hour\n\tConnectionMaxIdleTime time.Duration\n\n\t// MinRunningJobDuration is the minimum duration in seconds for a job to be\n\t// considered in \"running jobs\" queries. This filters out very short jobs.\n\t// Default: 600 seconds (10 minutes)\n\tMinRunningJobDuration int\n}\n\n// DefaultConfig returns the default repository configuration.\n// These values are optimized for typical deployments.\nfunc DefaultConfig() *RepositoryConfig {\n\treturn &RepositoryConfig{\n\t\tCacheSize: 1 * 1024 * 1024, // 1MB\n\t\tMaxOpenConnections: 4,\n\t\tMaxIdleConnections: 4,\n\t\tConnectionMaxLifetime: time.Hour,\n\t\tConnectionMaxIdleTime: time.Hour,\n\t\tMinRunningJobDuration: 600, // 10 minutes\n\t}\n}\n\n// repoConfig is the package-level configuration instance.\n// It is initialized with defaults and can be overridden via SetConfig.\nvar repoConfig *RepositoryConfig = DefaultConfig()\n\n// SetConfig sets the repository configuration.\n// This must be called before any repository initialization (Connect, GetJobRepository, etc.).\n// If not called, default values from DefaultConfig() are used.\nfunc SetConfig(cfg *RepositoryConfig) {\n\tif cfg != nil {\n\t\trepoConfig = cfg\n\t}\n}\n\n// GetConfig returns the current repository configuration.\nfunc GetConfig() *RepositoryConfig {\n\treturn repoConfig\n}\n","structuredPatch":[{"oldStart":44,"oldLines":7,"newStart":44,"newLines":7,"lines":[" MaxOpenConnections: 4,"," MaxIdleConnections: 4,"," ConnectionMaxLifetime: time.Hour,","- ConnectionMaxIdleTime: time.Hour,","+ ConnectionMaxIdleTime: 10 * time.Minute,"," MinRunningJobDuration: 600, // 10 minutes"," }"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"993da51d-f346-422c-b817-94af870a35de"} 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/Users/jan/prg/CC/cc-backend/internal/repository/config.go has been updated successfully."}]},"uuid":"955e1fd9-7234-467c-a917-8dc8f6d564b2","timestamp":"2026-03-11T05:03:30.921Z","toolUseResult":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/repository/config.go","oldString":"\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n\t// Default: 1 hour","newString":"\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n\t// Default: 10 minutes","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport \"time\"\n\n// RepositoryConfig holds configuration for repository operations.\n// All fields have sensible defaults, so this configuration is optional.\ntype RepositoryConfig struct {\n\t// CacheSize is the LRU cache size in bytes for job metadata and energy footprints.\n\t// Default: 1MB (1024 * 1024 bytes)\n\tCacheSize int\n\n\t// MaxOpenConnections is the maximum number of open database connections.\n\t// Default: 4\n\tMaxOpenConnections int\n\n\t// MaxIdleConnections is the maximum number of idle database connections.\n\t// Default: 4\n\tMaxIdleConnections int\n\n\t// ConnectionMaxLifetime is the maximum amount of time a connection may be reused.\n\t// Default: 1 hour\n\tConnectionMaxLifetime time.Duration\n\n\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n\t// Default: 1 hour\n\tConnectionMaxIdleTime time.Duration\n\n\t// MinRunningJobDuration is the minimum duration in seconds for a job to be\n\t// considered in \"running jobs\" queries. This filters out very short jobs.\n\t// Default: 600 seconds (10 minutes)\n\tMinRunningJobDuration int\n}\n\n// DefaultConfig returns the default repository configuration.\n// These values are optimized for typical deployments.\nfunc DefaultConfig() *RepositoryConfig {\n\treturn &RepositoryConfig{\n\t\tCacheSize: 1 * 1024 * 1024, // 1MB\n\t\tMaxOpenConnections: 4,\n\t\tMaxIdleConnections: 4,\n\t\tConnectionMaxLifetime: time.Hour,\n\t\tConnectionMaxIdleTime: 10 * time.Minute,\n\t\tMinRunningJobDuration: 600, // 10 minutes\n\t}\n}\n\n// repoConfig is the package-level configuration instance.\n// It is initialized with defaults and can be overridden via SetConfig.\nvar repoConfig *RepositoryConfig = DefaultConfig()\n\n// SetConfig sets the repository configuration.\n// This must be called before any repository initialization (Connect, GetJobRepository, etc.).\n// If not called, default values from DefaultConfig() are used.\nfunc SetConfig(cfg *RepositoryConfig) {\n\tif cfg != nil {\n\t\trepoConfig = cfg\n\t}\n}\n\n// GetConfig returns the current repository configuration.\nfunc GetConfig() *RepositoryConfig {\n\treturn repoConfig\n}\n","structuredPatch":[{"oldStart":27,"oldLines":7,"newStart":27,"newLines":7,"lines":[" ConnectionMaxLifetime time.Duration"," "," // ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.","- // Default: 1 hour","+ // Default: 10 minutes"," ConnectionMaxIdleTime time.Duration"," "," // MinRunningJobDuration is the minimum duration in seconds for a job to be"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"d2265c53-bc30-43a1-aadb-244cbf827e8f"} +{"parentUuid":"955e1fd9-7234-467c-a917-8dc8f6d564b2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01CE1Fh1bgBfH6hJRF9qX1M5","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me verify it builds and run the tests:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":230,"cache_read_input_tokens":56529,"cache_creation":{"ephemeral_5m_input_tokens":230,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvi1jqGKCGzRFgpR41sD","type":"assistant","uuid":"aca87186-9500-4432-9fa5-65a4bea41101","timestamp":"2026-03-11T05:03:33.144Z"} +{"parentUuid":"aca87186-9500-4432-9fa5-65a4bea41101","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01CE1Fh1bgBfH6hJRF9qX1M5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ud67nBtB2kdpqBSE7fL6Xd","name":"Bash","input":{"command":"go build ./...","description":"Build all packages","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":230,"cache_read_input_tokens":56529,"output_tokens":118,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":230},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvi1jqGKCGzRFgpR41sD","type":"assistant","uuid":"a272ea7f-f4b7-46bc-8257-81a75645942d","timestamp":"2026-03-11T05:03:33.686Z"} +{"parentUuid":"a272ea7f-f4b7-46bc-8257-81a75645942d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ud67nBtB2kdpqBSE7fL6Xd","type":"tool_result","content":"","is_error":false}]},"uuid":"380ad9c3-7adb-4fe1-a71c-2b3e09c87d13","timestamp":"2026-03-11T05:03:40.266Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"a272ea7f-f4b7-46bc-8257-81a75645942d"} +{"parentUuid":"380ad9c3-7adb-4fe1-a71c-2b3e09c87d13","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01LheoKGEF7W7xWqnBsdnZGA","type":"message","role":"assistant","content":[{"type":"text","text":"Build succeeds. Now run the repository tests:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":223,"cache_read_input_tokens":56759,"cache_creation":{"ephemeral_5m_input_tokens":223,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYvi2Rm752YbcPfuPxzHi","type":"assistant","uuid":"4d7d5fd2-d540-450d-bde3-d5577448e546","timestamp":"2026-03-11T05:03:42.637Z"} +{"parentUuid":"4d7d5fd2-d540-450d-bde3-d5577448e546","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01LheoKGEF7W7xWqnBsdnZGA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VniDtmyLP1K88iYhrGDSZM","name":"Bash","input":{"command":"go test ./internal/repository/... -v -count=1 2>&1 | tail -50","description":"Run repository tests","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":223,"cache_read_input_tokens":56759,"output_tokens":137,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":223},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvi2Rm752YbcPfuPxzHi","type":"assistant","uuid":"ed433bd7-0726-492c-b95c-6e324a3e0781","timestamp":"2026-03-11T05:03:43.188Z"} +{"parentUuid":"ed433bd7-0726-492c-b95c-6e324a3e0781","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VniDtmyLP1K88iYhrGDSZM","type":"tool_result","content":" --- PASS: TestDelUser/delete_non-existent_user (0.00s)\n=== RUN TestListUsers\n=== RUN TestListUsers/list_all_users\n=== RUN TestListUsers/list_special_users_only\n--- PASS: TestListUsers (0.01s)\n --- PASS: TestListUsers/list_all_users (0.00s)\n --- PASS: TestListUsers/list_special_users_only (0.00s)\n=== RUN TestGetLdapUsernames\n--- PASS: TestGetLdapUsernames (0.01s)\n=== RUN TestAddRole\n=== RUN TestAddRole/add_valid_role\n=== RUN TestAddRole/add_duplicate_role\n=== RUN TestAddRole/add_invalid_role\n--- PASS: TestAddRole (0.01s)\n --- PASS: TestAddRole/add_valid_role (0.00s)\n --- PASS: TestAddRole/add_duplicate_role (0.00s)\n --- PASS: TestAddRole/add_invalid_role (0.00s)\n=== RUN TestRemoveRole\n=== RUN TestRemoveRole/remove_existing_role\n=== RUN TestRemoveRole/remove_non-existent_role\n=== RUN TestRemoveRole/remove_manager_role_with_projects\n--- PASS: TestRemoveRole (0.01s)\n --- PASS: TestRemoveRole/remove_existing_role (0.00s)\n --- PASS: TestRemoveRole/remove_non-existent_role (0.00s)\n --- PASS: TestRemoveRole/remove_manager_role_with_projects (0.00s)\n=== RUN TestAddProject\n=== RUN TestAddProject/add_project_to_manager\n=== RUN TestAddProject/add_project_to_non-manager\n=== RUN TestAddProject/add_duplicate_project\n--- PASS: TestAddProject (0.01s)\n --- PASS: TestAddProject/add_project_to_manager (0.00s)\n --- PASS: TestAddProject/add_project_to_non-manager (0.00s)\n --- PASS: TestAddProject/add_duplicate_project (0.00s)\n=== RUN TestRemoveProject\n=== RUN TestRemoveProject/remove_existing_project\n=== RUN TestRemoveProject/remove_non-existent_project\n=== RUN TestRemoveProject/remove_project_from_non-manager\n--- PASS: TestRemoveProject (0.01s)\n --- PASS: TestRemoveProject/remove_existing_project (0.00s)\n --- PASS: TestRemoveProject/remove_non-existent_project (0.00s)\n --- PASS: TestRemoveProject/remove_project_from_non-manager (0.00s)\n=== RUN TestGetUserFromContext\n=== RUN TestGetUserFromContext/get_user_from_context\n=== RUN TestGetUserFromContext/get_user_from_empty_context\n<4>[WARNING] 2026/03/11 06:03:45 user.go:470: no user retrieved from context\n--- PASS: TestGetUserFromContext (0.00s)\n --- PASS: TestGetUserFromContext/get_user_from_context (0.00s)\n --- PASS: TestGetUserFromContext/get_user_from_empty_context (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t1.097s","is_error":false}]},"uuid":"398822fd-3e09-41f9-8ac2-e3ee2f285317","timestamp":"2026-03-11T05:03:45.407Z","toolUseResult":{"stdout":" --- PASS: TestDelUser/delete_non-existent_user (0.00s)\n=== RUN TestListUsers\n=== RUN TestListUsers/list_all_users\n=== RUN TestListUsers/list_special_users_only\n--- PASS: TestListUsers (0.01s)\n --- PASS: TestListUsers/list_all_users (0.00s)\n --- PASS: TestListUsers/list_special_users_only (0.00s)\n=== RUN TestGetLdapUsernames\n--- PASS: TestGetLdapUsernames (0.01s)\n=== RUN TestAddRole\n=== RUN TestAddRole/add_valid_role\n=== RUN TestAddRole/add_duplicate_role\n=== RUN TestAddRole/add_invalid_role\n--- PASS: TestAddRole (0.01s)\n --- PASS: TestAddRole/add_valid_role (0.00s)\n --- PASS: TestAddRole/add_duplicate_role (0.00s)\n --- PASS: TestAddRole/add_invalid_role (0.00s)\n=== RUN TestRemoveRole\n=== RUN TestRemoveRole/remove_existing_role\n=== RUN TestRemoveRole/remove_non-existent_role\n=== RUN TestRemoveRole/remove_manager_role_with_projects\n--- PASS: TestRemoveRole (0.01s)\n --- PASS: TestRemoveRole/remove_existing_role (0.00s)\n --- PASS: TestRemoveRole/remove_non-existent_role (0.00s)\n --- PASS: TestRemoveRole/remove_manager_role_with_projects (0.00s)\n=== RUN TestAddProject\n=== RUN TestAddProject/add_project_to_manager\n=== RUN TestAddProject/add_project_to_non-manager\n=== RUN TestAddProject/add_duplicate_project\n--- PASS: TestAddProject (0.01s)\n --- PASS: TestAddProject/add_project_to_manager (0.00s)\n --- PASS: TestAddProject/add_project_to_non-manager (0.00s)\n --- PASS: TestAddProject/add_duplicate_project (0.00s)\n=== RUN TestRemoveProject\n=== RUN TestRemoveProject/remove_existing_project\n=== RUN TestRemoveProject/remove_non-existent_project\n=== RUN TestRemoveProject/remove_project_from_non-manager\n--- PASS: TestRemoveProject (0.01s)\n --- PASS: TestRemoveProject/remove_existing_project (0.00s)\n --- PASS: TestRemoveProject/remove_non-existent_project (0.00s)\n --- PASS: TestRemoveProject/remove_project_from_non-manager (0.00s)\n=== RUN TestGetUserFromContext\n=== RUN TestGetUserFromContext/get_user_from_context\n=== RUN TestGetUserFromContext/get_user_from_empty_context\n<4>[WARNING] 2026/03/11 06:03:45 user.go:470: no user retrieved from context\n--- PASS: TestGetUserFromContext (0.00s)\n --- PASS: TestGetUserFromContext/get_user_from_context (0.00s)\n --- PASS: TestGetUserFromContext/get_user_from_empty_context (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t1.097s","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"ed433bd7-0726-492c-b95c-6e324a3e0781"} +{"parentUuid":"398822fd-3e09-41f9-8ac2-e3ee2f285317","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_013DxkXdUAFkkKQBy1DjJtrL","type":"message","role":"assistant","content":[{"type":"text","text":"All tests pass. Here's a summary of all changes:\n\n### Fix 1: Cache size (`dbConnection.go:82`)\nChanged `_cache_size` from `1000000000` (~4TB) to `-200000` (200MB per connection in KiB notation). With 4 max connections, total page cache is capped at ~800MB.\n\n### Fix 2: Soft heap limit (`dbConnection.go:41`)\nAdded `soft_heap_limit = 1073741824` (1GB) PRAGMA. This is process-wide — SQLite will evict cache pages to stay under limit.\n\n### Fix 3: Context propagation (12 call sites)\nChanged `.Query()` → `.QueryContext(ctx)` and `.QueryRow()` → `.QueryRowContext(ctx)` in:\n- `jobQuery.go`: `QueryJobs`, `CountJobs`\n- `stats.go`: `JobsStatsGrouped`, `JobsStats`, `JobCountGrouped`, `AddJobCountGrouped`, `AddJobCount`, `jobsStatisticsHistogram`, `jobsDurationStatisticsHistogram`, `jobsMetricStatisticsHistogram`\n- `jobFind.go`: `FindByID`, `FindByJobID`\n\nThis ensures HTTP request cancellation/timeout stops the underlying SQLite query.\n\n### Fix 4: Idle connection timeout (`config.go`)\nReduced `ConnectionMaxIdleTime` from 1 hour to 10 minutes, so connections with bloated caches are released sooner."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1046,"cache_read_input_tokens":56982,"output_tokens":357,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1046},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYvi2ouNUohhSFMzTUKNx","type":"assistant","uuid":"66046969-3889-4640-8506-816cba7e19b7","timestamp":"2026-03-11T05:03:55.699Z"} +{"parentUuid":"66046969-3889-4640-8506-816cba7e19b7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"Stop","hookName":"Stop","command":"entire hooks claude-code stop"},"parentToolUseID":"4d48ccbc-00d9-46ee-8f48-46f6fb114760","toolUseID":"4d48ccbc-00d9-46ee-8f48-46f6fb114760","timestamp":"2026-03-11T05:03:55.726Z","uuid":"968e1bbd-bd05-4144-8bf9-119d042f9e16"} +{"parentUuid":"968e1bbd-bd05-4144-8bf9-119d042f9e16","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"system","subtype":"stop_hook_summary","hookCount":1,"hookInfos":[{"command":"entire hooks claude-code stop","durationMs":641}],"hookErrors":[],"preventedContinuation":false,"stopReason":"","hasOutput":false,"level":"suggestion","timestamp":"2026-03-11T05:03:56.367Z","uuid":"764233de-3b74-4434-b0c9-3cbce88c1a1e","toolUseID":"4d48ccbc-00d9-46ee-8f48-46f6fb114760"} +{"parentUuid":"764233de-3b74-4434-b0c9-3cbce88c1a1e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"system","subtype":"turn_duration","durationMs":139522,"timestamp":"2026-03-11T05:03:56.368Z","uuid":"a884881a-ce84-43e7-8a35-0052b1913e5e","isMeta":false} +{"type":"file-history-snapshot","messageId":"8f04deee-26cc-4f0e-b463-f24f7b407eec","snapshot":{"messageId":"8f04deee-26cc-4f0e-b463-f24f7b407eec","trackedFileBackups":{"internal/repository/dbConnection.go":{"backupFileName":"f4586ab7038e6775@v2","version":2,"backupTime":"2026-03-11T05:05:35.870Z"},"internal/repository/jobQuery.go":{"backupFileName":"33536efde97b8b50@v2","version":2,"backupTime":"2026-03-11T05:05:35.871Z"},"internal/repository/stats.go":{"backupFileName":"4426319480f78977@v2","version":2,"backupTime":"2026-03-11T05:05:35.871Z"},"internal/repository/jobFind.go":{"backupFileName":"19ca218ff524297c@v2","version":2,"backupTime":"2026-03-11T05:05:35.871Z"},"internal/repository/config.go":{"backupFileName":"8d09a7d81a4813eb@v2","version":2,"backupTime":"2026-03-11T05:05:35.871Z"}},"timestamp":"2026-03-11T05:05:35.870Z"},"isSnapshotUpdate":false} +{"parentUuid":"a884881a-ce84-43e7-8a35-0052b1913e5e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":"Our server has 512GB main memory. Does it make sense to make cache_size and soft_heap_limit configurable to make use of the main memory capacity?"},"uuid":"8f04deee-26cc-4f0e-b463-f24f7b407eec","timestamp":"2026-03-11T05:05:35.391Z","permissionMode":"plan"} +{"parentUuid":"8f04deee-26cc-4f0e-b463-f24f7b407eec","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01KiMis4ZpKWUCGdrdNaDs6S","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U9hcpo8iMtigQJEf6JZc3H","name":"Read","input":{"file_path":"/Users/jan/.claude/plans/refactored-wishing-journal.md"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1710,"cache_read_input_tokens":58028,"output_tokens":72,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1710},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviAx4iwLa5UPXjWuYhM","type":"assistant","uuid":"0207d447-0f95-4741-9e3f-357c5927b88d","timestamp":"2026-03-11T05:05:39.226Z"} +{"parentUuid":"0207d447-0f95-4741-9e3f-357c5927b88d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01U9hcpo8iMtigQJEf6JZc3H","toolUseID":"toolu_01U9hcpo8iMtigQJEf6JZc3H","timestamp":"2026-03-11T05:05:39.248Z","uuid":"d2a9c2df-c7ab-42cf-9177-b401caa1a604"} +{"parentUuid":"0207d447-0f95-4741-9e3f-357c5927b88d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U9hcpo8iMtigQJEf6JZc3H","type":"tool_result","content":" 1→# Fix SQLite Memory Not Released After Query Timeout\n 2→\n 3→## Context\n 4→\n 5→On the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n 6→\n 7→## Root Cause Analysis\n 8→\n 9→### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n 10→\n 11→**File:** `internal/repository/dbConnection.go:82`\n 12→\n 13→```go\n 14→connectionURLParams.Add(\"_cache_size\", \"1000000000\")\n 15→```\n 16→\n 17→SQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n 18→\n 19→For comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n 20→\n 21→### 2. No query context/timeout — queries run indefinitely\n 22→\n 23→**File:** `internal/repository/jobQuery.go:87`\n 24→\n 25→```go\n 26→rows, err := query.RunWith(r.stmtCache).Query() // No context!\n 27→```\n 28→\n 29→The `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n 30→\n 31→### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n 32→\n 33→SQLite has built-in memory management PRAGMAs that are not configured:\n 34→- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n 35→- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n 36→- **`shrink_memory`** — immediately releases all unused memory back to the OS\n 37→\n 38→None of these are set, so SQLite allocates freely and never releases.\n 39→\n 40→### 4. `temp_store = memory` amplifies the problem\n 41→\n 42→**File:** `internal/repository/dbConnection.go:41`\n 43→\n 44→Temporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n 45→\n 46→### 5. Connections live for 1 hour after use\n 47→\n 48→`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n 49→\n 50→## Proposed Changes\n 51→\n 52→### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n 53→\n 54→**File:** `internal/repository/dbConnection.go:82`\n 55→\n 56→Change from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n 57→\n 58→```go\n 59→connectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n 60→```\n 61→\n 62→With 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n 63→\n 64→### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n 65→\n 66→**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n 67→\n 68→```go\n 69→\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n 70→```\n 71→\n 72→This is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n 73→\n 74→### Fix 3: Pass context to database queries (medium impact, medium effort)\n 75→\n 76→Change `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n 77→\n 78→**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n 79→\n 80→**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n 81→\n 82→| File | Method | Line | Change |\n 83→|------|--------|------|--------|\n 84→| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n 85→| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n 86→| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n 87→| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n 88→| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n 89→| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n 90→| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n 91→| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n 92→| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n 93→| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n 94→| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n 95→| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n 96→| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n 97→| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n 98→\n 99→Methods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n 100→\n 101→### Fix 4: Reduce `ConnMaxIdleTime`\n 102→\n 103→**File:** `internal/repository/config.go`\n 104→\n 105→Reduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n 106→\n 107→## Scope — All Four Fixes\n 108→\n 109→All fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n 110→\n 111→## Files to Modify\n 112→\n 113→- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n 114→- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n 115→- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n 116→- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n 117→- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n 118→\n 119→## Verification\n 120→\n 121→1. `go build ./...` — compiles\n 122→2. `go test ./internal/repository/...` — tests pass\n 123→3. Monitor RSS memory before/after on production:\n 124→ - Trigger a heavy query, observe memory spike\n 125→ - Verify memory drops back down within seconds (not hours)\n 126→4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n 127→5. `PRAGMA soft_heap_limit;` — should show `1073741824`\n 128→"}]},"uuid":"0c26389c-193b-4713-8ceb-5410d277773c","timestamp":"2026-03-11T05:05:39.248Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/.claude/plans/refactored-wishing-journal.md","content":"# Fix SQLite Memory Not Released After Query Timeout\n\n## Context\n\nOn the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`\n","numLines":128,"startLine":1,"totalLines":128}},"sourceToolAssistantUUID":"0207d447-0f95-4741-9e3f-357c5927b88d"} +{"parentUuid":"0c26389c-193b-4713-8ceb-5410d277773c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Ty3nZThiDynrRqBk1xwS31","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2547,"cache_read_input_tokens":59738,"cache_creation":{"ephemeral_5m_input_tokens":2547,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviBCVPgMZuoywhNg8Vz","type":"assistant","uuid":"9a9b97a6-3d5d-416d-8e46-c998c035966c","timestamp":"2026-03-11T05:05:43.076Z"} +{"parentUuid":"9a9b97a6-3d5d-416d-8e46-c998c035966c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Ty3nZThiDynrRqBk1xwS31","type":"message","role":"assistant","content":[{"type":"text","text":"Good question. Let me look at how the config flows from the JSON config file into the repository to understand the best way to make these configurable."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2547,"cache_read_input_tokens":59738,"cache_creation":{"ephemeral_5m_input_tokens":2547,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviBCVPgMZuoywhNg8Vz","type":"assistant","uuid":"613fb8ca-356a-443d-87f3-455d87fdecf4","timestamp":"2026-03-11T05:05:43.972Z"} +{"parentUuid":"613fb8ca-356a-443d-87f3-455d87fdecf4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Ty3nZThiDynrRqBk1xwS31","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","name":"Agent","input":{"description":"Explore config flow for repository","subagent_type":"Explore","prompt":"I need to understand how RepositoryConfig is populated from the JSON config file and used in dbConnection.go. Specifically:\n\n1. How does the config.json \"repository\" section get parsed and passed to `repository.SetConfig()`? Find the call site.\n2. What does the JSON config struct look like for the repository section? (Check `internal/config/` for the struct definition)\n3. How is `repoConfig` used in `dbConnection.go:Connect()` — trace the full flow from config.json → struct → SetConfig → Connect.\n4. Are there any existing patterns for configuring memory/size values in the config (e.g., how are other byte-size configs handled)?\n\nSearch thoroughly in: internal/config/, cmd/cc-backend/, internal/repository/config.go, internal/repository/dbConnection.go"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2547,"cache_read_input_tokens":59738,"output_tokens":400,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2547},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviBCVPgMZuoywhNg8Vz","type":"assistant","uuid":"7a1b6782-b8b5-43bb-8821-ed42dff56c9c","timestamp":"2026-03-11T05:05:49.711Z"} +{"parentUuid":"7a1b6782-b8b5-43bb-8821-ed42dff56c9c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PreToolUse","hookName":"PreToolUse:Agent","command":"entire hooks claude-code pre-task"},"toolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"73527d5d-99cf-4bc4-9c56-b53b191ddc0b","timestamp":"2026-03-11T05:05:49.714Z"} +{"parentUuid":"73527d5d-99cf-4bc4-9c56-b53b191ddc0b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"I need to understand how RepositoryConfig is populated from the JSON config file and used in dbConnection.go. Specifically:\n\n1. How does the config.json \"repository\" section get parsed and passed to `repository.SetConfig()`? Find the call site.\n2. What does the JSON config struct look like for the repository section? (Check `internal/config/` for the struct definition)\n3. How is `repoConfig` used in `dbConnection.go:Connect()` — trace the full flow from config.json → struct → SetConfig → Connect.\n4. Are there any existing patterns for configuring memory/size values in the config (e.g., how are other byte-size configs handled)?\n\nSearch thoroughly in: internal/config/, cmd/cc-backend/, internal/repository/config.go, internal/repository/dbConnection.go"}]},"uuid":"6a1da6f8-1ee6-4bc3-9735-a299644b3251","timestamp":"2026-03-11T05:05:50.003Z"},"type":"agent_progress","prompt":"I need to understand how RepositoryConfig is populated from the JSON config file and used in dbConnection.go. Specifically:\n\n1. 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+{"parentUuid":"6bc0c549-62f1-496e-880c-9641dbdacc0b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EM5WUTHXkUxcpDJWiRBYJn","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import \"time\"\n 9→\n 10→// RepositoryConfig holds configuration for repository operations.\n 11→// All fields have sensible defaults, so this configuration is optional.\n 12→type RepositoryConfig struct {\n 13→\t// CacheSize is the LRU cache size in bytes for job metadata and energy footprints.\n 14→\t// Default: 1MB (1024 * 1024 bytes)\n 15→\tCacheSize int\n 16→\n 17→\t// MaxOpenConnections is the maximum number of open database connections.\n 18→\t// Default: 4\n 19→\tMaxOpenConnections int\n 20→\n 21→\t// MaxIdleConnections is the maximum number of idle database connections.\n 22→\t// Default: 4\n 23→\tMaxIdleConnections int\n 24→\n 25→\t// ConnectionMaxLifetime is the maximum amount of time a connection may be reused.\n 26→\t// Default: 1 hour\n 27→\tConnectionMaxLifetime time.Duration\n 28→\n 29→\t// ConnectionMaxIdleTime is the maximum amount of time a connection may be idle.\n 30→\t// Default: 10 minutes\n 31→\tConnectionMaxIdleTime time.Duration\n 32→\n 33→\t// MinRunningJobDuration is the minimum duration in seconds for a job to be\n 34→\t// considered in \"running jobs\" queries. This filters out very short jobs.\n 35→\t// Default: 600 seconds (10 minutes)\n 36→\tMinRunningJobDuration int\n 37→}\n 38→\n 39→// DefaultConfig returns the default repository configuration.\n 40→// These values are optimized for typical deployments.\n 41→func DefaultConfig() *RepositoryConfig {\n 42→\treturn &RepositoryConfig{\n 43→\t\tCacheSize: 1 * 1024 * 1024, // 1MB\n 44→\t\tMaxOpenConnections: 4,\n 45→\t\tMaxIdleConnections: 4,\n 46→\t\tConnectionMaxLifetime: time.Hour,\n 47→\t\tConnectionMaxIdleTime: 10 * time.Minute,\n 48→\t\tMinRunningJobDuration: 600, // 10 minutes\n 49→\t}\n 50→}\n 51→\n 52→// repoConfig is the package-level configuration instance.\n 53→// It is initialized with defaults and can be overridden via SetConfig.\n 54→var repoConfig *RepositoryConfig = DefaultConfig()\n 55→\n 56→// SetConfig sets the repository configuration.\n 57→// This must be called before any repository initialization (Connect, GetJobRepository, etc.).\n 58→// If not called, default values from DefaultConfig() are used.\n 59→func SetConfig(cfg *RepositoryConfig) {\n 60→\tif cfg != nil {\n 61→\t\trepoConfig = cfg\n 62→\t}\n 63→}\n 64→\n 65→// GetConfig returns the current repository configuration.\n 66→func GetConfig() *RepositoryConfig {\n 67→\treturn repoConfig\n 68→}\n 69→"}]},"uuid":"1f616132-7852-46e7-a955-ffbbbbfd79c3","timestamp":"2026-03-11T05:05:53.389Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"b1f698d6-f389-42ee-82bd-a2b6963bdc52","timestamp":"2026-03-11T05:05:53.594Z"} +{"parentUuid":"b1f698d6-f389-42ee-82bd-a2b6963bdc52","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-11T05:05:53.596Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_013yz4PWVwN3JeuTC2oxez2i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DK1si2Sx8Vv5iDaQh6os6X","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1292,"cache_read_input_tokens":16659,"cache_creation":{"ephemeral_5m_input_tokens":1292,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYviCAMS22jTHvdLPwUe4","uuid":"5c5f28f7-4d80-48ea-b14f-ce936ad86a8e"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"62c10c04-de49-4a7e-a4f6-a9f9b17b9586","timestamp":"2026-03-11T05:05:53.596Z"} +{"parentUuid":"62c10c04-de49-4a7e-a4f6-a9f9b17b9586","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DK1si2Sx8Vv5iDaQh6os6X","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"database/sql\"\n 10→\t\"fmt\"\n 11→\t\"net/url\"\n 12→\t\"sync\"\n 13→\t\"time\"\n 14→\n 15→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 16→\t\"github.com/jmoiron/sqlx\"\n 17→\t\"github.com/mattn/go-sqlite3\"\n 18→\t\"github.com/qustavo/sqlhooks/v2\"\n 19→)\n 20→\n 21→var (\n 22→\tdbConnOnce sync.Once\n 23→\tdbConnInstance *DBConnection\n 24→)\n 25→\n 26→type DBConnection struct {\n 27→\tDB *sqlx.DB\n 28→\tDriver string\n 29→}\n 30→\n 31→type DatabaseOptions struct {\n 32→\tURL string\n 33→\tMaxOpenConnections int\n 34→\tMaxIdleConnections int\n 35→\tConnectionMaxLifetime time.Duration\n 36→\tConnectionMaxIdleTime time.Duration\n 37→}\n 38→\n 39→func setupSqlite(db *sql.DB) error {\n 40→\tpragmas := []string{\n 41→\t\t\"temp_store = memory\",\n 42→\t\t\"soft_heap_limit = 1073741824\", // 1GB process-wide soft limit; SQLite evicts cache pages to stay under\n 43→\t}\n 44→\n 45→\tfor _, pragma := range pragmas {\n 46→\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n 47→\t\tif err != nil {\n 48→\t\t\treturn err\n 49→\t\t}\n 50→\t}\n 51→\n 52→\t// Update query planner statistics so SQLite picks optimal indexes.\n 53→\t// Without this, SQLite guesses row distributions and often chooses wrong\n 54→\t// indexes for queries with IN clauses + ORDER BY, causing full table sorts\n 55→\t// in temp B-trees instead of using covering indexes.\n 56→\tif _, err := db.Exec(\"ANALYZE\"); err != nil {\n 57→\t\tcclog.Warnf(\"Failed to run ANALYZE: %v\", err)\n 58→\t}\n 59→\n 60→\treturn nil\n 61→}\n 62→\n 63→func Connect(db string) {\n 64→\tvar err error\n 65→\tvar dbHandle *sqlx.DB\n 66→\n 67→\tdbConnOnce.Do(func() {\n 68→\t\topts := DatabaseOptions{\n 69→\t\t\tURL: db,\n 70→\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n 71→\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n 72→\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n 73→\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n 74→\t\t}\n 75→\n 76→\t\t// TODO: Have separate DB handles for Writes and Reads\n 77→\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n 78→\t\tconnectionURLParams := make(url.Values)\n 79→\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n 80→\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n 81→\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n 82→\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n 83→\t\tconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection (negative = KiB)\n 84→\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n 85→\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n 86→\n 87→\t\tif cclog.Loglevel() == \"debug\" {\n 88→\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n 89→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n 90→\t\t} else {\n 91→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n 92→\t\t}\n 93→\n 94→\t\tif err != nil {\n 95→\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n 96→\t\t}\n 97→\n 98→\t\terr = setupSqlite(dbHandle.DB)\n 99→\t\tif err != nil {\n 100→\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n 101→\t\t}\n 102→\n 103→\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n 104→\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n 105→\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n 106→\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n 107→\n 108→\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n 109→\t\terr = checkDBVersion(dbHandle.DB)\n 110→\t\tif err != nil {\n 111→\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n 112→\t\t}\n 113→\t})\n 114→}\n 115→\n 116→func GetConnection() *DBConnection {\n 117→\tif dbConnInstance == nil {\n 118→\t\tcclog.Fatalf(\"Database connection not initialized!\")\n 119→\t}\n 120→\n 121→\treturn dbConnInstance\n 122→}\n 123→\n 124→// ResetConnection closes the current database connection and resets the connection state.\n 125→// This function is intended for testing purposes only to allow test isolation.\n 126→func ResetConnection() error {\n 127→\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n 128→\t\tif err := dbConnInstance.DB.Close(); err != nil {\n 129→\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n 130→\t\t}\n 131→\t}\n 132→\n 133→\tdbConnInstance = nil\n 134→\tdbConnOnce = sync.Once{}\n 135→\tjobRepoInstance = nil\n 136→\tjobRepoOnce = sync.Once{}\n 137→\tnodeRepoInstance = nil\n 138→\tnodeRepoOnce = sync.Once{}\n 139→\tuserRepoInstance = nil\n 140→\tuserRepoOnce = sync.Once{}\n 141→\tuserCfgRepoInstance = nil\n 142→\tuserCfgRepoOnce = sync.Once{}\n 143→\n 144→\treturn nil\n 145→}\n 146→"}]},"uuid":"62863212-babf-4fd6-963a-9231a1c4a627","timestamp":"2026-03-11T05:05:53.614Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"026512c2-4357-40af-9ca3-ef7ae355e3f8","timestamp":"2026-03-11T05:05:53.800Z"} +{"parentUuid":"026512c2-4357-40af-9ca3-ef7ae355e3f8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-11T05:05:53.900Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_013yz4PWVwN3JeuTC2oxez2i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0157XyPQEi7QycYqH8vM3cR3","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/config/config.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1292,"cache_read_input_tokens":16659,"cache_creation":{"ephemeral_5m_input_tokens":1292,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYviCAMS22jTHvdLPwUe4","uuid":"27d3eca4-8af5-4555-99bd-e75a72076e0c"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"5dc8641d-2713-463d-b965-b437464f0fe1","timestamp":"2026-03-11T05:05:53.901Z"} +{"parentUuid":"5dc8641d-2713-463d-b965-b437464f0fe1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0157XyPQEi7QycYqH8vM3cR3","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package config implements the program configuration data structures, validation and parsing\n 7→package config\n 8→\n 9→import (\n 10→\t\"bytes\"\n 11→\t\"encoding/json\"\n 12→\t\"time\"\n 13→\n 14→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 15→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n 16→)\n 17→\n 18→type ProgramConfig struct {\n 19→\t// Address where the http (or https) server will listen on (for example: 'localhost:80').\n 20→\tAddr string `json:\"addr\"`\n 21→\n 22→\t// Addresses from which secured admin API endpoints can be reached, can be wildcard \"*\"\n 23→\tAPIAllowedIPs []string `json:\"api-allowed-ips\"`\n 24→\n 25→\tAPISubjects *NATSConfig `json:\"api-subjects\"`\n 26→\n 27→\t// Drop root permissions once .env was read and the port was taken.\n 28→\tUser string `json:\"user\"`\n 29→\tGroup string `json:\"group\"`\n 30→\n 31→\t// Disable authentication (for everything: API, Web-UI, ...)\n 32→\tDisableAuthentication bool `json:\"disable-authentication\"`\n 33→\n 34→\t// If `embed-static-files` is true (default), the frontend files are directly\n 35→\t// embeded into the go binary and expected to be in web/frontend. Only if\n 36→\t// it is false the files in `static-files` are served instead.\n 37→\tEmbedStaticFiles bool `json:\"embed-static-files\"`\n 38→\tStaticFiles string `json:\"static-files\"`\n 39→\n 40→\t// Path to SQLite database file\n 41→\tDB string `json:\"db\"`\n 42→\n 43→\tEnableJobTaggers bool `json:\"enable-job-taggers\"`\n 44→\n 45→\t// Validate json input against schema\n 46→\tValidate bool `json:\"validate\"`\n 47→\n 48→\t// If 0 or empty, the session does not expire!\n 49→\tSessionMaxAge string `json:\"session-max-age\"`\n 50→\n 51→\t// If both those options are not empty, use HTTPS using those certificates.\n 52→\tHTTPSCertFile string `json:\"https-cert-file\"`\n 53→\tHTTPSKeyFile string `json:\"https-key-file\"`\n 54→\n 55→\t// If not the empty string and `addr` does not end in \":80\",\n 56→\t// redirect every request incoming at port 80 to that url.\n 57→\tRedirectHTTPTo string `json:\"redirect-http-to\"`\n 58→\n 59→\t// Where to store MachineState files\n 60→\tMachineStateDir string `json:\"machine-state-dir\"`\n 61→\n 62→\t// If not zero, automatically mark jobs as stopped running X seconds longer than their walltime.\n 63→\tStopJobsExceedingWalltime int `json:\"stop-jobs-exceeding-walltime\"`\n 64→\n 65→\t// Defines time X in seconds in which jobs are considered to be \"short\" and will be filtered in specific views.\n 66→\tShortRunningJobsDuration int `json:\"short-running-jobs-duration\"`\n 67→\n 68→\t// Energy Mix CO2 Emission Constant [g/kWh]\n 69→\t// If entered, displays estimated CO2 emission for job based on jobs totalEnergy\n 70→\tEmissionConstant int `json:\"emission-constant\"`\n 71→\n 72→\t// If exists, will enable dynamic zoom in frontend metric plots using the configured values\n 73→\tEnableResampling *ResampleConfig `json:\"resampling\"`\n 74→\n 75→\t// Systemd unit name for log viewer (default: \"clustercockpit\")\n 76→\tSystemdUnit string `json:\"systemd-unit\"`\n 77→\n 78→\t// Node state retention configuration\n 79→\tNodeStateRetention *NodeStateRetention `json:\"nodestate-retention\"`\n 80→}\n 81→\n 82→type NodeStateRetention struct {\n 83→\tPolicy string `json:\"policy\"` // \"delete\" or \"move\"\n 84→\tAge int `json:\"age\"` // hours, default 24\n 85→\tTargetKind string `json:\"target-kind\"` // \"file\" or \"s3\"\n 86→\tTargetPath string `json:\"target-path\"`\n 87→\tTargetEndpoint string `json:\"target-endpoint\"`\n 88→\tTargetBucket string `json:\"target-bucket\"`\n 89→\tTargetAccessKey string `json:\"target-access-key\"`\n 90→\tTargetSecretKey string `json:\"target-secret-key\"`\n 91→\tTargetRegion string `json:\"target-region\"`\n 92→\tTargetUsePathStyle bool `json:\"target-use-path-style\"`\n 93→\tMaxFileSizeMB int `json:\"max-file-size-mb\"`\n 94→}\n 95→\n 96→type ResampleConfig struct {\n 97→\t// Minimum number of points to trigger resampling of data\n 98→\tMinimumPoints int `json:\"minimum-points\"`\n 99→\t// Array of resampling target resolutions, in seconds; Example: [600,300,60]\n 100→\tResolutions []int `json:\"resolutions\"`\n 101→\t// Trigger next zoom level at less than this many visible datapoints\n 102→\tTrigger int `json:\"trigger\"`\n 103→}\n 104→\n 105→type NATSConfig struct {\n 106→\tSubjectJobEvent string `json:\"subject-job-event\"`\n 107→\tSubjectNodeState string `json:\"subject-node-state\"`\n 108→}\n 109→\n 110→type IntRange struct {\n 111→\tFrom int `json:\"from\"`\n 112→\tTo int `json:\"to\"`\n 113→}\n 114→\n 115→type TimeRange struct {\n 116→\tFrom *time.Time `json:\"from\"`\n 117→\tTo *time.Time `json:\"to\"`\n 118→\tRange string `json:\"range,omitempty\"`\n 119→}\n 120→\n 121→type FilterRanges struct {\n 122→\tDuration *IntRange `json:\"duration\"`\n 123→\tNumNodes *IntRange `json:\"num-nodes\"`\n 124→\tStartTime *TimeRange `json:\"start-time\"`\n 125→}\n 126→\n 127→var Keys ProgramConfig = ProgramConfig{\n 128→\tAddr: \"localhost:8080\",\n 129→\tEmbedStaticFiles: true,\n 130→\tDB: \"./var/job.db\",\n 131→\tSessionMaxAge: \"168h\",\n 132→\tStopJobsExceedingWalltime: 0,\n 133→\tShortRunningJobsDuration: 5 * 60,\n 134→}\n 135→\n 136→func Init(mainConfig json.RawMessage) {\n 137→\tValidate(configSchema, mainConfig)\n 138→\tdec := json.NewDecoder(bytes.NewReader(mainConfig))\n 139→\tdec.DisallowUnknownFields()\n 140→\tif err := dec.Decode(&Keys); err != nil {\n 141→\t\tcclog.Abortf(\"Config Init: Could not decode config file '%s'.\\nError: %s\\n\", mainConfig, err.Error())\n 142→\t}\n 143→\n 144→\tif Keys.EnableResampling != nil && Keys.EnableResampling.MinimumPoints > 0 {\n 145→\t\tresampler.SetMinimumRequiredPoints(Keys.EnableResampling.MinimumPoints)\n 146→\t}\n 147→}\n 148→"}]},"uuid":"3bbac35a-b8a8-485b-b189-5ccc0ea18333","timestamp":"2026-03-11T05:05:53.915Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"dab9fd9d-1e04-4e42-80a2-1f59074335a0","timestamp":"2026-03-11T05:05:53.917Z"} 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This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package repository provides the data access layer for cc-backend using the repository pattern.\n 7→//\n 8→// The repository pattern abstracts database operations and provides a clean interface for\n 9→// data access. Each major entity (Job, User, Node, Tag) has its own repository with CRUD\n 10→// operations and specialized queries.\n 11→//\n 12→// # Database Connection\n 13→//\n 14→// Initialize the database connection before using any repository:\n 15→//\n 16→//\trepository.Connect(\"sqlite3\", \"./var/job.db\")\n 17→//\n 18→// # Configuration\n 19→//\n 20→// Optional: Configure repository settings before initialization:\n 21→//\n 22→//\trepository.SetConfig(&repository.RepositoryConfig{\n 23→//\t CacheSize: 2 * 1024 * 1024, // 2MB cache\n 24→//\t MaxOpenConnections: 8, // Connection pool size\n 25→//\t MinRunningJobDuration: 300, // Filter threshold\n 26→//\t})\n 27→//\n 28→// If not configured, sensible defaults are used automatically.\n 29→//\n 30→// # Repositories\n 31→//\n 32→// - JobRepository: Job lifecycle management and querying\n 33→// - UserRepository: User management and authentication\n 34→// - NodeRepository: Cluster node state tracking\n 35→// - Tags: Job tagging and categorization\n 36→//\n 37→// # Caching\n 38→//\n 39→// Repositories use LRU caching to improve performance. Cache keys are constructed\n 40→// as \"type:id\" (e.g., \"metadata:123\"). Cache is automatically invalidated on\n 41→// mutations to maintain consistency.\n 42→//\n 43→// # Transaction Support\n 44→//\n 45→// For batch operations, use transactions:\n 46→//\n 47→//\tt, err := jobRepo.TransactionInit()\n 48→//\tif err != nil {\n 49→//\t return err\n 50→//\t}"}]},"uuid":"c62b8243-ff1d-4401-af6b-522795086fc1","timestamp":"2026-03-11T05:05:59.368Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"88386c95-7495-42f1-adc4-56d1141e8d3a","timestamp":"2026-03-11T05:05:59.558Z"} +{"parentUuid":"88386c95-7495-42f1-adc4-56d1141e8d3a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-11T05:05:59.574Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_015NvNSpB7YpiM1hEZMktYDm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BWDQcahgua3ixZzH4EPFLm","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":391,"cache_read_input_tokens":23765,"cache_creation":{"ephemeral_5m_input_tokens":391,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYviCaPYo6w7VPqUdpJH2","uuid":"92d7bc6c-0469-45e1-826c-d82dd2c4e903"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"eff0d214-a326-4149-b882-fb86043e07e8","timestamp":"2026-03-11T05:05:59.574Z"} +{"parentUuid":"eff0d214-a326-4149-b882-fb86043e07e8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BWDQcahgua3ixZzH4EPFLm","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package main provides the entry point for the ClusterCockpit backend server.\n 7→// It orchestrates initialization of all subsystems including configuration,\n 8→// database, authentication, and the HTTP server.\n 9→package main\n 10→\n 11→import (\n 12→\t\"context\"\n 13→\t\"encoding/json\"\n 14→\t\"fmt\"\n 15→\t\"os\"\n 16→\t\"os/signal\"\n 17→\t\"runtime/debug\"\n 18→\t\"strings\"\n 19→\t\"sync\"\n 20→\t\"syscall\"\n 21→\t\"time\"\n 22→\n 23→\t\"github.com/ClusterCockpit/cc-backend/internal/archiver\"\n 24→\t\"github.com/ClusterCockpit/cc-backend/internal/auth\"\n 25→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n 26→\t\"github.com/ClusterCockpit/cc-backend/internal/importer\"\n 27→\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n 28→\t\"github.com/ClusterCockpit/cc-backend/internal/repository\"\n 29→\t\"github.com/ClusterCockpit/cc-backend/internal/tagger\"\n 30→\t\"github.com/ClusterCockpit/cc-backend/internal/taskmanager\"\n 31→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n 32→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n 33→\t\"github.com/ClusterCockpit/cc-backend/web\"\n 34→\tccconf \"github.com/ClusterCockpit/cc-lib/v2/ccConfig\"\n 35→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 36→\t\"github.com/ClusterCockpit/cc-lib/v2/nats\"\n 37→\t\"github.com/ClusterCockpit/cc-lib/v2/runtime\"\n 38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 39→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n 40→\t\"github.com/google/gops/agent\"\n 41→\t\"github.com/joho/godotenv\"\n 42→\n 43→\t_ \"github.com/mattn/go-sqlite3\"\n 44→)\n 45→\n 46→const logoString = `\n 47→ _____ _ _ ____ _ _ _\n 48→/ ___| |_ _ ___| |_ ___ _ __ / ___|___ ___| | ___ __ (_) |_\n 49→| | | | | | / __| __/ _ \\ '__| | / _ \\ / __| |/ / '_ \\| | __|\n 50→| |___| | |_| \\__ \\ || __/ | | |__| (_) | (__| <| |_) | | |_\n 51→\\_____|_|\\__,_|___/\\__\\___|_| \\____\\___/ \\___|_|\\_\\ .__/|_|\\__|\n 52→ |_|\n 53→`\n 54→\n 55→// Environment variable names\n 56→const (\n 57→\tenvGOGC = \"GOGC\"\n 58→)\n 59→\n 60→// Default configurations\n 61→const (\n 62→\tdefaultArchiveConfig = `{\"kind\":\"file\",\"path\":\"./var/job-archive\"}`\n 63→)\n 64→\n 65→var (\n 66→\tdate string\n 67→\tcommit string\n 68→\tversion string\n 69→)\n 70→\n 71→func printVersion() {\n 72→\tfmt.Print(logoString)\n 73→\tfmt.Printf(\"Version:\\t%s\\n\", version)\n 74→\tfmt.Printf(\"Git hash:\\t%s\\n\", commit)\n 75→\tfmt.Printf(\"Build time:\\t%s\\n\", date)\n 76→\tfmt.Printf(\"SQL db version:\\t%d\\n\", repository.Version)\n 77→\tfmt.Printf(\"Job archive version:\\t%d\\n\", archive.Version)\n 78→}\n 79→\n 80→func initGops() error {\n 81→\tif !flagGops {\n 82→\t\treturn nil\n 83→\t}\n 84→\n 85→\tif err := agent.Listen(agent.Options{}); err != nil {\n 86→\t\treturn fmt.Errorf(\"starting gops agent: %w\", err)\n 87→\t}\n 88→\treturn nil\n 89→}\n 90→\n 91→func loadEnvironment() error {\n 92→\tif err := godotenv.Load(); err != nil {\n 93→\t\treturn fmt.Errorf(\"loading .env file: %w\", err)\n 94→\t}\n 95→\treturn nil\n 96→}\n 97→\n 98→func initConfiguration() error {\n 99→\tccconf.Init(flagConfigFile)\n 100→\n 101→\tcfg := ccconf.GetPackageConfig(\"main\")\n 102→\tif cfg == nil {\n 103→\t\treturn fmt.Errorf(\"main configuration must be present\")\n 104→\t}\n 105→\n 106→\tconfig.Init(cfg)\n 107→\treturn nil\n 108→}\n 109→\n 110→func initDatabase() error {\n 111→\trepository.Connect(config.Keys.DB)\n 112→\treturn nil\n 113→}\n 114→\n 115→func handleDatabaseCommands() error {\n 116→\tif flagMigrateDB {\n 117→\t\terr := repository.MigrateDB(config.Keys.DB)\n 118→\t\tif err != nil {\n 119→\t\t\treturn fmt.Errorf(\"migrating database to version %d: %w\", repository.Version, err)\n 120→\t\t}\n 121→\t\tcclog.Exitf(\"MigrateDB Success: Migrated SQLite database at '%s' to version %d.\\n\",\n 122→\t\t\tconfig.Keys.DB, repository.Version)\n 123→\t}\n 124→\n 125→\tif flagRevertDB {\n 126→\t\terr := repository.RevertDB(config.Keys.DB)\n 127→\t\tif err != nil {\n 128→\t\t\treturn fmt.Errorf(\"reverting database to version %d: %w\", repository.Version-1, err)\n 129→\t\t}\n 130→\t\tcclog.Exitf(\"RevertDB Success: Reverted SQLite database at '%s' to version %d.\\n\",\n 131→\t\t\tconfig.Keys.DB, repository.Version-1)\n 132→\t}\n 133→\n 134→\tif flagForceDB {\n 135→\t\terr := repository.ForceDB(config.Keys.DB)\n 136→\t\tif err != nil {\n 137→\t\t\treturn fmt.Errorf(\"forcing database to version %d: %w\", repository.Version, err)\n 138→\t\t}\n 139→\t\tcclog.Exitf(\"ForceDB Success: Forced SQLite database at '%s' to version %d.\\n\",\n 140→\t\t\tconfig.Keys.DB, repository.Version)\n 141→\t}\n 142→\n 143→\treturn nil\n 144→}\n 145→\n 146→func handleUserCommands() error {\n 147→\tif config.Keys.DisableAuthentication && (flagNewUser != \"\" || flagDelUser != \"\") {\n 148→\t\treturn fmt.Errorf(\"--add-user and --del-user can only be used if authentication is enabled\")\n 149→\t}\n 150→\n 151→\tif !config.Keys.DisableAuthentication {\n 152→\t\tif cfg := ccconf.GetPackageConfig(\"auth\"); cfg != nil {\n 153→\t\t\tauth.Init(&cfg)\n 154→\t\t} else {\n 155→\t\t\tcclog.Warn(\"Authentication disabled due to missing configuration\")\n 156→\t\t\tauth.Init(nil)\n 157→\t\t}\n 158→\n 159→\t\t// Check for default security keys\n 160→\t\tcheckDefaultSecurityKeys()\n 161→\n 162→\t\tif flagNewUser != \"\" {\n 163→\t\t\tif err := addUser(flagNewUser); err != nil {\n 164→\t\t\t\treturn err\n 165→\t\t\t}\n 166→\t\t}\n 167→\n 168→\t\tif flagDelUser != \"\" {\n 169→\t\t\tif err := delUser(flagDelUser); err != nil {\n 170→\t\t\t\treturn err\n 171→\t\t\t}\n 172→\t\t}\n 173→\n 174→\t\tauthHandle := auth.GetAuthInstance()\n 175→\n 176→\t\tif flagSyncLDAP {\n 177→\t\t\tif err := syncLDAP(authHandle); err != nil {\n 178→\t\t\t\treturn err\n 179→\t\t\t}\n 180→\t\t}\n 181→\n 182→\t\tif flagGenJWT != \"\" {\n 183→\t\t\tif err := generateJWT(authHandle, flagGenJWT); err != nil {\n 184→\t\t\t\treturn err\n 185→\t\t\t}\n 186→\t\t}\n 187→\t}\n 188→\n 189→\treturn nil\n 190→}\n 191→\n 192→// checkDefaultSecurityKeys warns if default JWT keys are detected\n 193→func checkDefaultSecurityKeys() {\n 194→\t// Default JWT public key from init.go\n 195→\tdefaultJWTPublic := \"REDACTED\"\n 196→\n 197→\tif os.Getenv(\"JWT_PUBLIC_KEY\") == defaultJWTPublic {\n 198→\t\tcclog.Warn(\"Using default JWT keys - not recommended for production environments\")\n 199→\t}\n 200→}\n 201→\n 202→func addUser(userSpec string) error {\n 203→\tparts := strings.SplitN(userSpec, \":\", 3)\n 204→\tif len(parts) != 3 || len(parts[0]) == 0 {\n 205→\t\treturn fmt.Errorf(\"invalid user format, want: :[admin,support,manager,api,user]:, have: %s\", userSpec)\n 206→\t}\n 207→\n 208→\tur := repository.GetUserRepository()\n 209→\tif err := ur.AddUser(&schema.User{\n 210→\t\tUsername: parts[0],\n 211→\t\tProjects: make([]string, 0),\n 212→\t\tPassword: parts[2],\n 213→\t\tRoles: strings.Split(parts[1], \",\"),\n 214→\t}); err != nil {\n 215→\t\treturn fmt.Errorf(\"adding user '%s' with roles '%s': %w\", parts[0], parts[1], err)\n 216→\t}\n 217→\n 218→\tcclog.Infof(\"Add User: Added new user '%s' with roles '%s'\", parts[0], parts[1])\n 219→\treturn nil\n 220→}\n 221→\n 222→func delUser(username string) error {\n 223→\tur := repository.GetUserRepository()\n 224→\tif err := ur.DelUser(username); err != nil {\n 225→\t\treturn fmt.Errorf(\"deleting user '%s': %w\", username, err)\n 226→\t}\n 227→\tcclog.Infof(\"Delete User: Deleted user '%s' from DB\", username)\n 228→\treturn nil\n 229→}\n 230→\n 231→func syncLDAP(authHandle *auth.Authentication) error {\n 232→\tif authHandle.LdapAuth == nil {\n 233→\t\treturn fmt.Errorf(\"LDAP authentication is not configured\")\n 234→\t}\n 235→\n 236→\tif err := authHandle.LdapAuth.Sync(); err != nil {\n 237→\t\treturn fmt.Errorf(\"synchronizing LDAP: %w\", err)\n 238→\t}\n 239→\n 240→\tcclog.Print(\"Sync LDAP: LDAP synchronization successfull.\")\n 241→\treturn nil\n 242→}\n 243→\n 244→func generateJWT(authHandle *auth.Authentication, username string) error {\n 245→\tur := repository.GetUserRepository()\n 246→\tuser, err := ur.GetUser(username)\n 247→\tif err != nil {\n 248→\t\treturn fmt.Errorf(\"getting user '%s': %w\", username, err)\n 249→\t}\n 250→\n 251→\tif !user.HasRole(schema.RoleAPI) {\n 252→\t\tcclog.Warnf(\"JWT: User '%s' does not have the role 'api'. REST API endpoints will return error!\\n\", user.Username)\n 253→\t}\n 254→\n 255→\tjwt, err := authHandle.JwtAuth.ProvideJWT(user)\n 256→\tif err != nil {\n 257→\t\treturn fmt.Errorf(\"generating JWT for user '%s': %w\", user.Username, err)\n 258→\t}\n 259→\n 260→\tcclog.Printf(\"JWT: Successfully generated JWT for user '%s': %s\\n\", user.Username, jwt)\n 261→\treturn nil\n 262→}\n 263→\n 264→func initSubsystems() error {\n 265→\t// Initialize nats client\n 266→\tnatsConfig := ccconf.GetPackageConfig(\"nats\")\n 267→\tif err := nats.Init(natsConfig); err != nil {\n 268→\t\tcclog.Warnf(\"initializing (optional) nats client: %s\", err.Error())\n 269→\t}\n 270→\tnats.Connect()\n 271→\n 272→\t// Initialize job archive\n 273→\tarchiveCfg := ccconf.GetPackageConfig(\"archive\")\n 274→\tif archiveCfg == nil {\n 275→\t\tcclog.Debug(\"Archive configuration not found, using default archive configuration\")\n 276→\t\tarchiveCfg = json.RawMessage(defaultArchiveConfig)\n 277→\t}\n 278→\tif err := archive.Init(archiveCfg); err != nil {\n 279→\t\treturn fmt.Errorf(\"initializing archive: %w\", err)\n 280→\t}\n 281→\n 282→\t// Handle database re-initialization\n 283→\tif flagReinitDB {\n 284→\t\tif err := importer.InitDB(); err != nil {\n 285→\t\t\treturn fmt.Errorf(\"re-initializing repository DB: %w\", err)\n 286→\t\t}\n 287→\t\tcclog.Print(\"Init DB: Successfully re-initialized repository DB.\")\n 288→\t}\n 289→\n 290→\t// Handle job import\n 291→\tif flagImportJob != \"\" {\n 292→\t\tif err := importer.HandleImportFlag(flagImportJob); err != nil {\n 293→\t\t\treturn fmt.Errorf(\"importing job: %w\", err)\n 294→\t\t}\n 295→\t\tcclog.Infof(\"Import Job: Imported Job '%s' into DB\", flagImportJob)\n 296→\t}\n 297→\n 298→\t// Initialize taggers\n 299→\tif config.Keys.EnableJobTaggers {\n 300→\t\ttagger.Init()\n 301→\t}\n 302→\n 303→\t// Apply tags if requested\n 304→\tif flagApplyTags {\n 305→\t\ttagger.Init()\n 306→\n 307→\t\tif err := tagger.RunTaggers(); err != nil {\n 308→\t\t\treturn fmt.Errorf(\"running job taggers: %w\", err)\n 309→\t\t}\n 310→\t}\n 311→\n 312→\treturn nil\n 313→}\n 314→\n 315→func runServer(ctx context.Context) error {\n 316→\tvar wg sync.WaitGroup\n 317→\n 318→\t// Initialize metric store if configuration is provided\n 319→\thaveMetricstore := false\n 320→\tmscfg := ccconf.GetPackageConfig(\"metric-store\")\n 321→\tif mscfg != nil {\n 322→\t\tmetrics := metricstore.BuildMetricList()\n 323→\t\tmetricstore.Init(mscfg, metrics, &wg)\n 324→\n 325→\t\t// Inject repository as NodeProvider to break import cycle\n 326→\t\tms := metricstore.GetMemoryStore()\n 327→\t\tjobRepo := repository.GetJobRepository()\n 328→\t\tms.SetNodeProvider(jobRepo)\n 329→\t\tmetricstore.MetricStoreHandle = &metricstore.InternalMetricStore{}\n 330→\t\thaveMetricstore = true\n 331→\t} else {\n 332→\t\tmetricstore.MetricStoreHandle = nil\n 333→\t\tcclog.Debug(\"missing internal metricstore configuration\")\n 334→\t}\n 335→\n 336→\t// Initialize external metric stores if configuration is provided\n 337→\tmscfg = ccconf.GetPackageConfig(\"metric-store-external\")\n 338→\tif mscfg != nil {\n 339→\t\terr := metricdispatch.Init(mscfg)\n 340→\n 341→\t\tif err != nil {\n 342→\t\t\tcclog.Debugf(\"error while initializing external metricdispatch: %v\", err)\n 343→\t\t} else {\n 344→\t\t\thaveMetricstore = true\n 345→\t\t}\n 346→\t}\n 347→\n 348→\tif !haveMetricstore {\n 349→\t\treturn fmt.Errorf(\"missing metricstore configuration\")\n 350→\t}\n 351→\n 352→\t// Start archiver and task manager\n 353→\tarchiver.Start(repository.GetJobRepository(), ctx)\n 354→\ttaskmanager.Start(ccconf.GetPackageConfig(\"cron\"), ccconf.GetPackageConfig(\"archive\"))\n 355→\n 356→\t// Initialize web UI\n 357→\tcfg := ccconf.GetPackageConfig(\"ui\")\n 358→\tif err := web.Init(cfg); err != nil {\n 359→\t\treturn fmt.Errorf(\"initializing web UI: %w\", err)\n 360→\t}\n 361→\n 362→\t// Initialize HTTP server\n 363→\tsrv, err := NewServer(version, commit, date)\n 364→\tif err != nil {\n 365→\t\treturn fmt.Errorf(\"creating server: %w\", err)\n 366→\t}\n 367→\n 368→\t// Channel to collect errors from server\n 369→\terrChan := make(chan error, 1)\n 370→\n 371→\t// Start HTTP server\n 372→\twg.Go(func() {\n 373→\t\tif err := srv.Start(ctx); err != nil {\n 374→\t\t\terrChan <- err\n 375→\t\t}\n 376→\t})\n 377→\n 378→\t// Handle shutdown signals\n 379→\twg.Add(1)\n 380→\tsigs := make(chan os.Signal, 1)\n 381→\tsignal.Notify(sigs, syscall.SIGINT, syscall.SIGTERM)\n 382→\tgo func() {\n 383→\t\tdefer wg.Done()\n 384→\t\tselect {\n 385→\t\tcase <-sigs:\n 386→\t\t\tcclog.Info(\"Shutdown signal received\")\n 387→\t\tcase <-ctx.Done():\n 388→\t\t}\n 389→\n 390→\t\truntime.SystemdNotify(false, \"Shutting down ...\")\n 391→\t\tsrv.Shutdown(ctx)\n 392→\t\tutil.FsWatcherShutdown()\n 393→\t\ttaskmanager.Shutdown()\n 394→\t}()\n 395→\n 396→\t// Set GC percent if not configured\n 397→\tif os.Getenv(envGOGC) == \"\" {\n 398→\t\t// trigger GC when heap grows 15% above the previous live set\n 399→\t\tdebug.SetGCPercent(15)\n 400→\t}\n 401→\truntime.SystemdNotify(true, \"running\")\n 402→\n 403→\twaitDone := make(chan struct{})\n 404→\tgo func() {\n 405→\t\twg.Wait()\n 406→\t\tclose(waitDone)\n 407→\t}()\n 408→\n 409→\tgo func() {\n 410→\t\t<-waitDone\n 411→\t\tclose(errChan)\n 412→\t}()\n 413→\n 414→\t// Wait for either:\n 415→\t// 1. An error from server startup\n 416→\t// 2. Completion of all goroutines (normal shutdown or crash)\n 417→\tselect {\n 418→\tcase err := <-errChan:\n 419→\t\t// errChan will be closed when waitDone is closed, which happens\n 420→\t\t// when all goroutines complete (either from normal shutdown or error)\n 421→\t\tif err != nil {\n 422→\t\t\treturn err\n 423→\t\t}\n 424→\tcase <-time.After(100 * time.Millisecond):\n 425→\t\t// Give the server 100ms to start and report any immediate startup errors\n 426→\t\t// After that, just wait for normal shutdown completion\n 427→\t\tselect {\n 428→\t\tcase err := <-errChan:\n 429→\t\t\tif err != nil {\n 430→\t\t\t\treturn err\n 431→\t\t\t}\n 432→\t\tcase <-waitDone:\n 433→\t\t\t// Normal shutdown completed\n 434→\t\t}\n 435→\t}\n 436→\n 437→\tcclog.Print(\"Graceful shutdown completed!\")\n 438→\treturn nil\n 439→}\n 440→\n 441→func run() error {\n 442→\tcliInit()\n 443→\n 444→\tif flagVersion {\n 445→\t\tprintVersion()\n 446→\t\treturn nil\n 447→\t}\n 448→\n 449→\t// Initialize logger\n 450→\tcclog.Init(flagLogLevel, flagLogDateTime)\n 451→\n 452→\t// Handle init flag\n 453→\tif flagInit {\n 454→\t\tinitEnv()\n 455→\t\tcclog.Exit(\"Successfully setup environment!\\n\" +\n 456→\t\t\t\"Please review config.json and .env and adjust it to your needs.\\n\" +\n 457→\t\t\t\"Add your job-archive at ./var/job-archive.\")\n 458→\t}\n 459→\n 460→\t// Initialize gops agent\n 461→\tif err := initGops(); err != nil {\n 462→\t\treturn err\n 463→\t}\n 464→\n 465→\t// Initialize subsystems in dependency order:\n 466→\t// 1. Load environment variables from .env file (contains sensitive configuration)\n 467→\t// 2. Load configuration from config.json (may reference environment variables)\n 468→\t// 3. Handle database migration commands if requested\n 469→\t// 4. Initialize database connection (requires config for connection string)\n 470→\t// 5. Handle user commands if requested (requires database and authentication config)\n 471→\t// 6. Initialize subsystems like archive and metrics (require config and database)\n 472→\n 473→\t// Load environment and configuration\n 474→\tif err := loadEnvironment(); err != nil {\n 475→\t\treturn err\n 476→\t}\n 477→\n 478→\tif err := initConfiguration(); err != nil {\n 479→\t\treturn err\n 480→\t}\n 481→\n 482→\t// Handle database migration (migrate, revert, force)\n 483→\tif err := handleDatabaseCommands(); err != nil {\n 484→\t\treturn err\n 485→\t}\n 486→\n 487→\t// Initialize database\n 488→\tif err := initDatabase(); err != nil {\n 489→\t\treturn err\n 490→\t}\n 491→\n 492→\t// Handle user commands (add, delete, sync, JWT)\n 493→\tif err := handleUserCommands(); err != nil {\n 494→\t\treturn err\n 495→\t}\n 496→\n 497→\t// Initialize subsystems (archive, metrics, taggers)\n 498→\tif err := initSubsystems(); err != nil {\n 499→\t\treturn err\n 500→\t}\n 501→\n 502→\t// Exit if start server is not requested\n 503→\tif !flagServer {\n 504→\t\tcclog.Exit(\"No errors, server flag not set. Exiting cc-backend.\")\n 505→\t}\n 506→\n 507→\t// Run server with context\n 508→\tctx, cancel := context.WithCancel(context.Background())\n 509→\tdefer cancel()\n 510→\n 511→\treturn runServer(ctx)\n 512→}\n 513→\n 514→func main() {\n 515→\tif err := run(); err != nil {\n 516→\t\tcclog.Error(err.Error())\n 517→\t\tos.Exit(1)\n 518→\t}\n 519→}\n 520→"}]},"uuid":"69fdc1ea-84c1-4d9a-b608-5dfa6ed5dc50","timestamp":"2026-03-11T05:05:59.590Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"4fc4daf9-3556-4c8f-9625-a3bf8516638e","timestamp":"2026-03-11T05:05:59.669Z"} +{"parentUuid":"4fc4daf9-3556-4c8f-9625-a3bf8516638e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-11T05:06:01.790Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01MhqV6GoU95jiSridAGLmYy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FN1rAybMW8mpQ7eEtddqZH","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/config/schema.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":7532,"cache_read_input_tokens":24156,"cache_creation":{"ephemeral_5m_input_tokens":7532,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYviChmJnZrf2sAsANkBH","uuid":"034440f0-ff26-4c35-99cd-84853723de69"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"1f87353a-1ed6-4368-bf16-cea9e8013590","timestamp":"2026-03-11T05:06:01.791Z"} +{"parentUuid":"1f87353a-1ed6-4368-bf16-cea9e8013590","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FN1rAybMW8mpQ7eEtddqZH","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package config\n 7→\n 8→var configSchema = `\n 9→{\n 10→ \"type\": \"object\",\n 11→ \"properties\": {\n 12→ \"addr\": {\n 13→ \"description\": \"Address where the http (or https) server will listen on (for example: 'localhost:80').\",\n 14→ \"type\": \"string\"\n 15→ },\n 16→ \"api-allowed-ips\": {\n 17→ \"description\": \"Addresses from which secured API endpoints can be reached\",\n 18→ \"type\": \"array\",\n 19→ \"items\": {\n 20→ \"type\": \"string\"\n 21→ }\n 22→ },\n 23→ \"user\": {\n 24→ \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n 25→ \"type\": \"string\"\n 26→ },\n 27→ \"group\": {\n 28→ \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n 29→ \"type\": \"string\"\n 30→ },\n 31→ \"disable-authentication\": {\n 32→ \"description\": \"Disable authentication (for everything: API, Web-UI, ...).\",\n 33→ \"type\": \"boolean\"\n 34→ },\n 35→ \"embed-static-files\": {\n 36→ \"description\": \"If all files in web/frontend/public should be served from within the binary itself (they are embedded) or not.\",\n 37→ \"type\": \"boolean\"\n 38→ },\n 39→ \"static-files\": {\n 40→ \"description\": \"Folder where static assets can be found, if embed-static-files is false.\",\n 41→ \"type\": \"string\"\n 42→ },\n 43→ \"db\": {\n 44→ \"description\": \"Path to SQLite database file (e.g., './var/job.db')\",\n 45→ \"type\": \"string\"\n 46→ },\n 47→ \"enable-job-taggers\": {\n 48→ \"description\": \"Turn on automatic application and jobclass taggers\",\n 49→ \"type\": \"boolean\"\n 50→ },\n 51→ \"validate\": {\n 52→ \"description\": \"Validate all input json documents against json schema.\",\n 53→ \"type\": \"boolean\"\n 54→ },\n 55→ \"session-max-age\": {\n 56→ \"description\": \"Specifies for how long a session shall be valid as a string parsable by time.ParseDuration(). If 0 or empty, the session/token does not expire!\",\n 57→ \"type\": \"string\"\n 58→ },\n 59→ \"https-cert-file\": {\n 60→ \"description\": \"Filepath to SSL certificate. If also https-key-file is set use HTTPS using those certificates.\",\n 61→ \"type\": \"string\"\n 62→ },\n 63→ \"https-key-file\": {\n 64→ \"description\": \"Filepath to SSL key file. If also https-cert-file is set use HTTPS using those certificates.\",\n 65→ \"type\": \"string\"\n 66→ },\n 67→ \"redirect-http-to\": {\n 68→ \"description\": \"If not the empty string and addr does not end in :80, redirect every request incoming at port 80 to that url.\",\n 69→ \"type\": \"string\"\n 70→ },\n 71→ \"stop-jobs-exceeding-walltime\": {\n 72→ \"description\": \"If not zero, automatically mark jobs as stopped running X seconds longer than their walltime. Only applies if walltime is set for job.\",\n 73→ \"type\": \"integer\"\n 74→ },\n 75→ \"short-running-jobs-duration\": {\n 76→ \"description\": \"Do not show running jobs shorter than X seconds.\",\n 77→ \"type\": \"integer\"\n 78→ },\n 79→ \"emission-constant\": {\n 80→ \"description\": \"Energy mix CO2 emission constant [g/kWh]. If set, displays estimated CO2 emission for jobs.\",\n 81→ \"type\": \"integer\"\n 82→ },\n 83→ \"machine-state-dir\": {\n 84→ \"description\": \"Where to store MachineState files.\",\n 85→ \"type\": \"string\"\n 86→ },\n 87→ \"systemd-unit\": {\n 88→ \"description\": \"Systemd unit name for log viewer (default: 'clustercockpit').\",\n 89→ \"type\": \"string\"\n 90→ },\n 91→ \"resampling\": {\n 92→ \"description\": \"Enable dynamic zoom in frontend metric plots.\",\n 93→ \"type\": \"object\",\n 94→ \"properties\": {\n 95→ \"minimum-points\": {\n 96→ \"description\": \"Minimum points to trigger resampling of time-series data.\",\n 97→ \"type\": \"integer\"\n 98→ },\n 99→ \"trigger\": {\n 100→ \"description\": \"Trigger next zoom level at less than this many visible datapoints.\",\n 101→ \"type\": \"integer\"\n 102→ },\n 103→ \"resolutions\": {\n 104→ \"description\": \"Array of resampling target resolutions, in seconds.\",\n 105→ \"type\": \"array\",\n 106→ \"items\": {\n 107→ \"type\": \"integer\"\n 108→ }\n 109→ }\n 110→ },\n 111→ \"required\": [\"trigger\", \"resolutions\"]\n 112→ },\n 113→ \"api-subjects\": {\n 114→ \"description\": \"NATS subjects configuration for subscribing to job and node events.\",\n 115→ \"type\": \"object\",\n 116→ \"properties\": {\n 117→ \"subject-job-event\": {\n 118→ \"description\": \"NATS subject for job events (start_job, stop_job)\",\n 119→ \"type\": \"string\"\n 120→ },\n 121→ \"subject-node-state\": {\n 122→ \"description\": \"NATS subject for node state updates\",\n 123→ \"type\": \"string\"\n 124→ }\n 125→ },\n 126→ \"required\": [\"subject-job-event\", \"subject-node-state\"]\n 127→ },\n 128→ \"nodestate-retention\": {\n 129→ \"description\": \"Node state retention configuration for cleaning up old node_state rows.\",\n 130→ \"type\": \"object\",\n 131→ \"properties\": {\n 132→ \"policy\": {\n 133→ \"description\": \"Retention policy: 'delete' to remove old rows, 'move' to archive to Parquet then delete.\",\n 134→ \"type\": \"string\",\n 135→ \"enum\": [\"delete\", \"move\"]\n 136→ },\n 137→ \"age\": {\n 138→ \"description\": \"Retention age in hours (default: 24).\",\n 139→ \"type\": \"integer\"\n 140→ },\n 141→ \"target-kind\": {\n 142→ \"description\": \"Target kind for parquet archiving: 'file' or 's3'.\",\n 143→ \"type\": \"string\",\n 144→ \"enum\": [\"file\", \"s3\"]\n 145→ },\n 146→ \"target-path\": {\n 147→ \"description\": \"Filesystem path for parquet file target.\",\n 148→ \"type\": \"string\"\n 149→ },\n 150→ \"target-endpoint\": {\n 151→ \"description\": \"S3 endpoint URL.\",\n 152→ \"type\": \"string\"\n 153→ },\n 154→ \"target-bucket\": {\n 155→ \"description\": \"S3 bucket name.\",\n 156→ \"type\": \"string\"\n 157→ },\n 158→ \"target-access-key\": {\n 159→ \"description\": \"S3 access key.\",\n 160→ \"type\": \"string\"\n 161→ },\n 162→ \"target-secret-key\": {\n 163→ \"description\": \"S3 secret key.\",\n 164→ \"type\": \"string\"\n 165→ },\n 166→ \"target-region\": {\n 167→ \"description\": \"S3 region.\",\n 168→ \"type\": \"string\"\n 169→ },\n 170→ \"target-use-path-style\": {\n 171→ \"description\": \"Use path-style S3 addressing.\",\n 172→ \"type\": \"boolean\"\n 173→ },\n 174→ \"max-file-size-mb\": {\n 175→ \"description\": \"Maximum parquet file size in MB (default: 128).\",\n 176→ \"type\": \"integer\"\n 177→ }\n 178→ },\n 179→ \"required\": [\"policy\"]\n 180→ }\n 181→ }\n 182→}`\n 183→"}]},"uuid":"6a56719a-2ef6-4fa4-ad93-76288a3c6aea","timestamp":"2026-03-11T05:06:01.801Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"a36d3a0c-6465-4131-84fe-b0b501626aa3","timestamp":"2026-03-11T05:06:01.975Z"} 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17→ \"minimum-points\": 600,\n 18→ \"trigger\": 180,\n 19→ \"resolutions\": [240, 60]\n 20→ },\n 21→ \"api-subjects\": {\n 22→ \"subject-job-event\": \"cc.job.event\",\n 23→ \"subject-node-state\": \"cc.node.state\"\n 24→ }\n 25→ },\n 26→ \"nats\": {\n 27→ \"address\": \"nats://0.0.0.0:4222\",\n 28→ \"username\": \"root\",\n 29→ \"password\": \"root\"\n 30→ },\n 31→ \"auth\": {\n 32→ \"jwts\": {\n 33→ \"max-age\": \"2000h\"\n 34→ }\n 35→ },\n 36→ \"cron\": {\n 37→ \"commit-job-worker\": \"1m\",\n 38→ \"duration-worker\": \"5m\",\n 39→ \"footprint-worker\": \"10m\"\n 40→ },\n 41→ \"archive\": {\n 42→ \"kind\": \"s3\",\n 43→ \"endpoint\": \"http://x.x.x.x\",\n 44→ \"bucket\": \"jobarchive\",\n 45→ \"access-key\": \"xx\",\n 46→ \"secret-key\": \"xx\",\n 47→ \"retention\": {\n 48→ \"policy\": \"move\",\n 49→ \"age\": 365,\n 50→ \"location\": \"./var/archive\"\n 51→ }\n 52→ },\n 53→ \"metric-store-external\": [\n 54→ {\n 55→ \"scope\": \"*\",\n 56→ \"url\": \"http://x.x.x.x:8082\",\n 57→ \"token\": \"MySecret\"\n 58→ },\n 59→ {\n 60→ \"scope\": \"fritz\",\n 61→ \"url\": \"http://x.x.x.x:8084\",\n 62→ \"token\": \"MySecret\"\n 63→ },\n 64→ {\n 65→ \"scope\": \"fritz-spr1tb\",\n 66→ \"url\": \"http://x.x.x.x:8083\",\n 67→ \"token\": \"MySecret\"\n 68→ },\n 69→ {\n 70→ \"scope\": \"alex\",\n 71→ \"url\": \"http://x.x.x.x:8084\",\n 72→ \"token\": \"MySecret\"\n 73→ }\n 74→ ],\n 75→ \"metric-store\": {\n 76→ \"checkpoints\": {\n 77→ \"directory\": \"./var/checkpoints\"\n 78→ },\n 79→ \"memory-cap\": 100,\n 80→ \"retention-in-memory\": \"24h\",\n 81→ \"cleanup\": {\n 82→ \"mode\": \"archive\",\n 83→ \"directory\": \"./var/archive\"\n 84→ },\n 85→ \"nats-subscriptions\": [\n 86→ {\n 87→ \"subscribe-to\": \"hpc-nats\",\n 88→ \"cluster-tag\": \"fritz\"\n 89→ },\n 90→ {\n 91→ \"subscribe-to\": \"hpc-nats\",\n 92→ \"cluster-tag\": \"alex\"\n 93→ }\n 94→ ]\n 95→ },\n 96→ \"ui-file\": \"ui-config.json\"\n 97→}\n 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+{"parentUuid":"f9d156ab-a94c-4f01-a36e-7bc124eba3b3","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MiQb28PSfM6nXpsjaihwSd","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package config\n 7→\n 8→import (\n 9→\t\"encoding/json\"\n 10→\n 11→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 12→\t\"github.com/santhosh-tekuri/jsonschema/v5\"\n 13→)\n 14→\n 15→func Validate(schema string, instance json.RawMessage) {\n 16→\tsch, err := jsonschema.CompileString(\"schema.json\", schema)\n 17→\tif err != nil {\n 18→\t\tcclog.Fatalf(\"%#v\", err)\n 19→\t}\n 20→\n 21→\tvar v any\n 22→\tif err := json.Unmarshal([]byte(instance), &v); err != nil {\n 23→\t\tcclog.Fatal(err)\n 24→\t}\n 25→\n 26→\tif err = sch.Validate(v); err != nil {\n 27→\t\tcclog.Fatalf(\"%#v\", err)\n 28→\t}\n 29→}\n 30→"}]},"uuid":"f806be33-2ef4-4d09-9237-33f722c0a77d","timestamp":"2026-03-11T05:06:08.882Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"b52c58e3-eea9-4085-961d-c8a1407009d3","timestamp":"2026-03-11T05:06:08.895Z"} +{"parentUuid":"b52c58e3-eea9-4085-961d-c8a1407009d3","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-11T05:06:12.340Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01CB9xA2TT2mu7yk1XXj2J4k","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PLnAaUzpLgYq1mwbEtj4cp","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/repository/repository_test.go","limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":963,"cache_read_input_tokens":36856,"cache_creation":{"ephemeral_5m_input_tokens":963,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYviDPJKpk7jGWamBVxkh","uuid":"0951736f-9277-4272-9507-40bd5550d09c"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"34400d32-6443-47f3-adad-8756d3c2cc1c","timestamp":"2026-03-11T05:06:12.341Z"} +{"parentUuid":"34400d32-6443-47f3-adad-8756d3c2cc1c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PLnAaUzpLgYq1mwbEtj4cp","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→package repository\n 6→\n 7→import (\n 8→\t\"context\"\n 9→\t\"os\"\n 10→\t\"path/filepath\"\n 11→\t\"testing\"\n 12→\n 13→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n 14→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 15→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n 16→\t_ \"github.com/mattn/go-sqlite3\"\n 17→)\n 18→\n 19→func TestPragma(t *testing.T) {\n 20→\tt.Run(\"sets up a new DB\", func(t *testing.T) {\n 21→\t\tdb := setup(t)\n 22→\n 23→\t\tfor _, pragma := range []string{\"synchronous\", \"journal_mode\", \"busy_timeout\", \"auto_vacuum\", \"foreign_keys\"} {\n 24→\t\t\tt.Log(\"PRAGMA\", pragma, getPragma(db, pragma))\n 25→\t\t}\n 26→\t})\n 27→}\n 28→\n 29→func getPragma(db *JobRepository, name string) string {\n 30→\tvar s string\n 31→\tif err := db.DB.QueryRow(`PRAGMA ` + name).Scan(&s); err != nil {\n 32→\t\tpanic(err)\n 33→\t}\n 34→\treturn s\n 35→}\n 36→\n 37→func BenchmarkSelect1(b *testing.B) {\n 38→\tdb := setup(b)\n 39→\n 40→\tb.ResetTimer()\n 41→\n 42→\tb.RunParallel(func(pb *testing.PB) {\n 43→\t\tfor pb.Next() {\n 44→\t\t\t_, err := db.DB.Exec(`select 1`)\n 45→\t\t\tnoErr(b, err)\n 46→\t\t}\n 47→\t})\n 48→}\n 49→\n 50→func BenchmarkDB_FindJobById(b *testing.B) {\n 51→\tvar jobID int64 = 1677322\n 52→\n 53→\tb.Run(\"FindJobById\", func(b *testing.B) {\n 54→\t\tdb := setup(b)\n 55→\n 56→\t\tb.ResetTimer()\n 57→\n 58→\t\tb.RunParallel(func(pb *testing.PB) {\n 59→\t\t\tfor pb.Next() {\n 60→\t\t\t\t_, err := db.FindByID(getContext(b), jobID)\n 61→\t\t\t\tnoErr(b, err)\n 62→\t\t\t}\n 63→\t\t})\n 64→\t})\n 65→}\n 66→\n 67→func BenchmarkDB_FindJob(b *testing.B) {\n 68→\tvar jobID int64 = 107266\n 69→\tvar startTime int64 = 1657557241\n 70→\tcluster := \"fritz\"\n 71→\n 72→\tb.Run(\"FindJob\", func(b *testing.B) {\n 73→\t\tdb := setup(b)\n 74→\n 75→\t\tb.ResetTimer()\n 76→\n 77→\t\tb.RunParallel(func(pb *testing.PB) {\n 78→\t\t\tfor pb.Next() {\n 79→\t\t\t\t_, err := db.Find(&jobID, &cluster, &startTime)\n 80→\t\t\t\tnoErr(b, err)\n 81→\t\t\t}\n 82→\t\t})\n 83→\t})\n 84→}\n 85→\n 86→func BenchmarkDB_CountJobs(b *testing.B) {\n 87→\tfilter := &model.JobFilter{}\n 88→\tfilter.State = append(filter.State, \"running\")\n 89→\tcluster := \"fritz\"\n 90→\tfilter.Cluster = &model.StringInput{Eq: &cluster}\n 91→\tuser := \"mppi133h\"\n 92→\tfilter.User = &model.StringInput{Eq: &user}\n 93→\n 94→\tb.Run(\"CountJobs\", func(b *testing.B) {\n 95→\t\tdb := setup(b)\n 96→\n 97→\t\tb.ResetTimer()\n 98→\n 99→\t\tb.RunParallel(func(pb *testing.PB) {\n 100→\t\t\tfor pb.Next() 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*RepositoryConfig = DefaultConfig()\ninternal/repository/config.go:56:// SetConfig sets the repository configuration.\ninternal/repository/config.go:58:// If not called, default values from DefaultConfig() are used.\ninternal/repository/config.go:59:func SetConfig(cfg *RepositoryConfig) {\ninternal/repository/config.go:66:func GetConfig() *RepositoryConfig {\ninternal/repository/job.go:22://\trepository.SetConfig(&repository.RepositoryConfig{"}]},"uuid":"6f52bc2e-42c4-4cba-a596-e7eb4a908aed","timestamp":"2026-03-11T05:06:13.394Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"b3cce2fa-e863-4113-9be6-0eba8e21b653","timestamp":"2026-03-11T05:06:13.438Z"} 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ccconf.GetPackageConfig(\"nats\")\n/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go:\tarchiveCfg := ccconf.GetPackageConfig(\"archive\")\n/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go:\tmscfg := ccconf.GetPackageConfig(\"metric-store\")\n/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go:\tmscfg = ccconf.GetPackageConfig(\"metric-store-external\")\n/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go:\ttaskmanager.Start(ccconf.GetPackageConfig(\"cron\"), ccconf.GetPackageConfig(\"archive\"))\n/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go:\tcfg := ccconf.GetPackageConfig(\"ui\")","is_error":false}]},"uuid":"25bd92a6-ea35-4405-bddb-ba824074058e","timestamp":"2026-03-11T05:06:15.523Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"c26de651-2a14-4c25-a656-7e36709b6efd","timestamp":"2026-03-11T05:06:15.535Z"} 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footprints.\n/Users/jan/prg/CC/cc-backend/internal/taskmanager/nodestateRetentionService.go:\tmaxFileSizeMB := cfg.MaxFileSizeMB\n/Users/jan/prg/CC/cc-backend/internal/taskmanager/taskManager.go:\tMaxFileSizeMB int `json:\"max-file-size-mb\"`\n/Users/jan/prg/CC/cc-backend/internal/taskmanager/retentionService.go:\tmaxFileSizeMB := cfg.MaxFileSizeMB\n/Users/jan/prg/CC/cc-backend/internal/taskmanager/retentionService.go:\tmaxFileSizeMB := cfg.MaxFileSizeMB\n/Users/jan/prg/CC/cc-backend/internal/config/config.go:\tMaxFileSizeMB int `json:\"max-file-size-mb\"`\n/Users/jan/prg/CC/cc-backend/internal/config/schema.go: \"max-file-size-mb\": {\n/Users/jan/prg/CC/cc-backend/internal/metricdispatch/dataLoader.go:// - LRU cache for performance optimization (128 MB default cache size)\n/Users/jan/prg/CC/cc-backend/internal/metricdispatch/dataLoader.go:\tdata := cache.Get(cacheKey(job, metrics, scopes, resolution), func() (_ any, ttl time.Duration, size int) {\n/Users/jan/prg/CC/cc-backend/pkg/archive/sqliteBackend.go:\t\t\"PRAGMA cache_size=-64000\", // 64MB cache\n/Users/jan/prg/CC/cc-backend/pkg/archive/parquet/writer.go:// maxSizeMB sets the approximate maximum size per parquet file in megabytes.\n/Users/jan/prg/CC/cc-backend/pkg/archive/ConfigSchema.go: \"max-file-size-mb\": {\n/Users/jan/prg/CC/cc-backend/pkg/archive/json.go:\tdata := cache.Get(k, func() (value any, ttl time.Duration, size int) {","is_error":false}]},"uuid":"507bf017-0439-4299-82d0-9df2b7a3e8f5","timestamp":"2026-03-11T05:06:17.208Z"},"type":"agent_progress","prompt":"","agentId":"afa5b3492136fa9f7"},"toolUseID":"agent_msg_01Ty3nZThiDynrRqBk1xwS31","parentToolUseID":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","uuid":"871683da-54ce-40de-aa21-303043f0804b","timestamp":"2026-03-11T05:06:17.209Z"} 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+{"parentUuid":"7a1b6782-b8b5-43bb-8821-ed42dff56c9c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Fg8BspZD8Gf4W2rKpqcLxE","type":"tool_result","content":[{"type":"text","text":"Perfect! Now I have a good understanding. Let me create a comprehensive summary document:\n\nBased on my thorough analysis of the codebase, here's what I found:\n\n## Summary: RepositoryConfig Flow and Integration\n\n### 1. **Current State: SetConfig is NOT Called Anywhere**\n\nThe function `repository.SetConfig()` exists in `/Users/jan/prg/CC/cc-backend/internal/repository/config.go` but is **never called** from the main initialization flow. Currently, the system uses only default values from `DefaultConfig()`.\n\n### 2. **RepositoryConfig Structure** \n(Location: `/Users/jan/prg/CC/cc-backend/internal/repository/config.go`, lines 12-37)\n\n```go\ntype RepositoryConfig struct {\n CacheSize int // Bytes, default: 1MB (1024 * 1024)\n MaxOpenConnections int // Default: 4\n MaxIdleConnections int // Default: 4\n ConnectionMaxLifetime time.Duration // Default: 1 hour\n ConnectionMaxIdleTime time.Duration // Default: 10 minutes\n MinRunningJobDuration int // Seconds, default: 600 (10 minutes)\n}\n```\n\n**Default values** (lines 43-49):\n- CacheSize: 1MB\n- MaxOpenConnections: 4\n- MaxIdleConnections: 4\n- ConnectionMaxLifetime: time.Hour\n- ConnectionMaxIdleTime: 10 * time.Minute\n- MinRunningJobDuration: 600 seconds\n\n### 3. **How SetConfig and GetConfig Work**\n\n**SetConfig** (lines 59-63): Sets package-level `repoConfig` variable (initialized at line 54)\n**GetConfig** (lines 66-68): Returns the current configuration\n**DefaultConfig** (lines 41-50): Factory function for defaults\n\n### 4. **How Connect() Uses repoConfig**\n(Location: `/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go`, lines 63-114)\n\nThe `Connect()` function reads from package-level `repoConfig`:\n- Line 70: `MaxOpenConnections: repoConfig.MaxOpenConnections`\n- Line 71: `MaxIdleConnections: repoConfig.MaxIdleConnections`\n- Line 72: `ConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime`\n- Line 73: `ConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime`\n\nThese are passed to `DatabaseOptions` struct (lines 31-37), then applied to sqlx.DB (lines 103-106):\n```go\ndbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\ndbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\ndbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\ndbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n```\n\n### 5. **Missing Integration Point: No JSON Config Section**\n\nThe main config structure (`ProgramConfig` in `/Users/jan/prg/CC/cc-backend/internal/config/config.go`) does **NOT** have a repository field. The JSON schema (`internal/config/schema.go`) also has no repository section.\n\n### 6. **How Configuration Is Currently Loaded**\n(Location: `/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go`)\n\n- Line 99: `ccconf.Init(flagConfigFile)` - Loads config via cc-lib\n- Line 101: `cfg := ccconf.GetPackageConfig(\"main\")` - Gets \"main\" section\n- Line 106: `config.Init(cfg)` - Parses into `ProgramConfig` struct\n- Line 111: `repository.Connect(config.Keys.DB)` - **Calls Connect() WITHOUT calling SetConfig()**\n\n### 7. **Byte-Size Config Pattern in Codebase**\n\nThe codebase uses **integer fields in MB** for byte-size configs:\n- `MaxFileSizeMB` in `/Users/jan/prg/CC/cc-backend/internal/config/config.go` (line 93)\n- Same in `/Users/jan/prg/CC/cc-backend/internal/taskmanager/taskManager.go` (line 18)\n- JSON schema documents as `\"type\": \"integer\"` with description mentioning \"MB\"\n\nExample from config schema (schema.go, lines 174-176):\n```json\n\"max-file-size-mb\": {\n \"description\": \"Maximum parquet file size in MB (default: 128).\",\n \"type\": \"integer\"\n}\n```\n\n### 8. **Duration Parsing Pattern**\n\nDuration values use **ISO 8601 string format** parseable by `time.ParseDuration()`:\n- `session-max-age` in schema (line 56): `\"string parsable by time.ParseDuration()\"`\n- Example from config: `\"max-age\": \"2000h\"` in auth section\n\n### 9. **Current Flow: config.json → main → initDatabase()**\n\n```\nconfig.json (main section)\n ↓\nccconf.Init() [cc-lib]\n ↓\nccconf.GetPackageConfig(\"main\")\n ↓\nconfig.Init() → ProgramConfig struct\n ↓\ninitDatabase() calls repository.Connect(config.Keys.DB)\n ↓ [NO SetConfig call]\n ↓\nConnect() uses default repoConfig\n```\n\n### Key Files\n- **Config definition**: `/Users/jan/prg/CC/cc-backend/internal/repository/config.go` (69 lines)\n- **DB connection usage**: `/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go` (146 lines)\n- **Main initialization**: `/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go` (520 lines)\n- **Config struct**: `/Users/jan/prg/CC/cc-backend/internal/config/config.go` (148 lines)\n- **JSON schema**: `/Users/jan/prg/CC/cc-backend/internal/config/schema.go` (182 lines)\n\n### Summary\nCurrently, `SetConfig()` is intended to be called **before `Connect()`** but is never used. The integration would need to:\n1. Add repository config fields to `ProgramConfig` struct\n2. Add schema definition in JSON schema\n3. Add a call to `repository.SetConfig()` in main's `initConfiguration()` or `initDatabase()` function before `Connect()` is called"},{"type":"text","text":"agentId: afa5b3492136fa9f7 (for resuming to continue this agent's work if needed)\ntotal_tokens: 43146\ntool_uses: 25\nduration_ms: 41126"}]}]},"uuid":"8735cbe6-818d-4561-b3a3-75adc246800a","timestamp":"2026-03-11T05:06:31.129Z","toolUseResult":{"status":"completed","prompt":"I need to understand how RepositoryConfig is populated from the JSON config file and used in dbConnection.go. Specifically:\n\n1. How does the config.json \"repository\" section get parsed and passed to `repository.SetConfig()`? Find the call site.\n2. What does the JSON config struct look like for the repository section? (Check `internal/config/` for the struct definition)\n3. How is `repoConfig` used in `dbConnection.go:Connect()` — trace the full flow from config.json → struct → SetConfig → Connect.\n4. Are there any existing patterns for configuring memory/size values in the config (e.g., how are other byte-size configs handled)?\n\nSearch thoroughly in: internal/config/, cmd/cc-backend/, internal/repository/config.go, internal/repository/dbConnection.go","agentId":"afa5b3492136fa9f7","content":[{"type":"text","text":"Perfect! Now I have a good understanding. Let me create a comprehensive summary document:\n\nBased on my thorough analysis of the codebase, here's what I found:\n\n## Summary: RepositoryConfig Flow and Integration\n\n### 1. **Current State: SetConfig is NOT Called Anywhere**\n\nThe function `repository.SetConfig()` exists in `/Users/jan/prg/CC/cc-backend/internal/repository/config.go` but is **never called** from the main initialization flow. Currently, the system uses only default values from `DefaultConfig()`.\n\n### 2. **RepositoryConfig Structure** \n(Location: `/Users/jan/prg/CC/cc-backend/internal/repository/config.go`, lines 12-37)\n\n```go\ntype RepositoryConfig struct {\n CacheSize int // Bytes, default: 1MB (1024 * 1024)\n MaxOpenConnections int // Default: 4\n MaxIdleConnections int // Default: 4\n ConnectionMaxLifetime time.Duration // Default: 1 hour\n ConnectionMaxIdleTime time.Duration // Default: 10 minutes\n MinRunningJobDuration int // Seconds, default: 600 (10 minutes)\n}\n```\n\n**Default values** (lines 43-49):\n- CacheSize: 1MB\n- MaxOpenConnections: 4\n- MaxIdleConnections: 4\n- ConnectionMaxLifetime: time.Hour\n- ConnectionMaxIdleTime: 10 * time.Minute\n- MinRunningJobDuration: 600 seconds\n\n### 3. **How SetConfig and GetConfig Work**\n\n**SetConfig** (lines 59-63): Sets package-level `repoConfig` variable (initialized at line 54)\n**GetConfig** (lines 66-68): Returns the current configuration\n**DefaultConfig** (lines 41-50): Factory function for defaults\n\n### 4. **How Connect() Uses repoConfig**\n(Location: `/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go`, lines 63-114)\n\nThe `Connect()` function reads from package-level `repoConfig`:\n- Line 70: `MaxOpenConnections: repoConfig.MaxOpenConnections`\n- Line 71: `MaxIdleConnections: repoConfig.MaxIdleConnections`\n- Line 72: `ConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime`\n- Line 73: `ConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime`\n\nThese are passed to `DatabaseOptions` struct (lines 31-37), then applied to sqlx.DB (lines 103-106):\n```go\ndbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\ndbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\ndbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\ndbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n```\n\n### 5. **Missing Integration Point: No JSON Config Section**\n\nThe main config structure (`ProgramConfig` in `/Users/jan/prg/CC/cc-backend/internal/config/config.go`) does **NOT** have a repository field. The JSON schema (`internal/config/schema.go`) also has no repository section.\n\n### 6. **How Configuration Is Currently Loaded**\n(Location: `/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go`)\n\n- Line 99: `ccconf.Init(flagConfigFile)` - Loads config via cc-lib\n- Line 101: `cfg := ccconf.GetPackageConfig(\"main\")` - Gets \"main\" section\n- Line 106: `config.Init(cfg)` - Parses into `ProgramConfig` struct\n- Line 111: `repository.Connect(config.Keys.DB)` - **Calls Connect() WITHOUT calling SetConfig()**\n\n### 7. **Byte-Size Config Pattern in Codebase**\n\nThe codebase uses **integer fields in MB** for byte-size configs:\n- `MaxFileSizeMB` in `/Users/jan/prg/CC/cc-backend/internal/config/config.go` (line 93)\n- Same in `/Users/jan/prg/CC/cc-backend/internal/taskmanager/taskManager.go` (line 18)\n- JSON schema documents as `\"type\": \"integer\"` with description mentioning \"MB\"\n\nExample from config schema (schema.go, lines 174-176):\n```json\n\"max-file-size-mb\": {\n \"description\": \"Maximum parquet file size in MB (default: 128).\",\n \"type\": \"integer\"\n}\n```\n\n### 8. **Duration Parsing Pattern**\n\nDuration values use **ISO 8601 string format** parseable by `time.ParseDuration()`:\n- `session-max-age` in schema (line 56): `\"string parsable by time.ParseDuration()\"`\n- Example from config: `\"max-age\": \"2000h\"` in auth section\n\n### 9. **Current Flow: config.json → main → initDatabase()**\n\n```\nconfig.json (main section)\n ↓\nccconf.Init() [cc-lib]\n ↓\nccconf.GetPackageConfig(\"main\")\n ↓\nconfig.Init() → ProgramConfig struct\n ↓\ninitDatabase() calls repository.Connect(config.Keys.DB)\n ↓ [NO SetConfig call]\n ↓\nConnect() uses default repoConfig\n```\n\n### Key Files\n- **Config definition**: `/Users/jan/prg/CC/cc-backend/internal/repository/config.go` (69 lines)\n- **DB connection usage**: `/Users/jan/prg/CC/cc-backend/internal/repository/dbConnection.go` (146 lines)\n- **Main initialization**: `/Users/jan/prg/CC/cc-backend/cmd/cc-backend/main.go` (520 lines)\n- **Config struct**: `/Users/jan/prg/CC/cc-backend/internal/config/config.go` (148 lines)\n- **JSON schema**: `/Users/jan/prg/CC/cc-backend/internal/config/schema.go` (182 lines)\n\n### Summary\nCurrently, `SetConfig()` is intended to be called **before `Connect()`** but is never used. The integration would need to:\n1. Add repository config fields to `ProgramConfig` struct\n2. Add schema definition in JSON schema\n3. Add a call to `repository.SetConfig()` in main's `initConfiguration()` or `initDatabase()` function before `Connect()` is called"}],"totalDurationMs":41126,"totalTokens":43146,"totalToolUseCount":25,"usage":{"input_tokens":5,"cache_creation_input_tokens":873,"cache_read_input_tokens":40665,"output_tokens":1603,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":873},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"7a1b6782-b8b5-43bb-8821-ed42dff56c9c"} +{"parentUuid":"8735cbe6-818d-4561-b3a3-75adc246800a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01H51jYoTZEDXFxwyoPgnd7g","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2070,"cache_read_input_tokens":62285,"cache_creation":{"ephemeral_5m_input_tokens":2070,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviF3aDzkBNoWie4w55B","type":"assistant","uuid":"a927f8a4-7624-4e8b-bd8a-fbb3e928841f","timestamp":"2026-03-11T05:06:44.590Z"} +{"parentUuid":"a927f8a4-7624-4e8b-bd8a-fbb3e928841f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01H51jYoTZEDXFxwyoPgnd7g","type":"message","role":"assistant","content":[{"type":"text","text":"Good question. Let me read the config structures to understand the full picture."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2070,"cache_read_input_tokens":62285,"cache_creation":{"ephemeral_5m_input_tokens":2070,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviF3aDzkBNoWie4w55B","type":"assistant","uuid":"8afe2211-c80e-43bd-974d-79ba25214a85","timestamp":"2026-03-11T05:06:45.480Z"} +{"parentUuid":"8afe2211-c80e-43bd-974d-79ba25214a85","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01H51jYoTZEDXFxwyoPgnd7g","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ni8ex16v7CrYvXJVL625qg","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/config/config.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2070,"cache_read_input_tokens":62285,"cache_creation":{"ephemeral_5m_input_tokens":2070,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviF3aDzkBNoWie4w55B","type":"assistant","uuid":"a97aac77-5732-42aa-9773-ad0d59047c72","timestamp":"2026-03-11T05:06:45.939Z"} +{"parentUuid":"a97aac77-5732-42aa-9773-ad0d59047c72","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01H51jYoTZEDXFxwyoPgnd7g","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xc34zdv2JP91KmnzYBQzif","name":"Read","input":{"file_path":"/Users/jan/prg/CC/cc-backend/internal/config/schema.go"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2070,"cache_read_input_tokens":62285,"output_tokens":566,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2070},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviF3aDzkBNoWie4w55B","type":"assistant","uuid":"b2dc1e62-6a8d-43ec-b268-7a3bcbcadcf0","timestamp":"2026-03-11T05:06:45.946Z"} +{"parentUuid":"b2dc1e62-6a8d-43ec-b268-7a3bcbcadcf0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01Ni8ex16v7CrYvXJVL625qg","toolUseID":"toolu_01Ni8ex16v7CrYvXJVL625qg","timestamp":"2026-03-11T05:06:45.965Z","uuid":"06d67101-9046-408c-9540-38d42f583df9"} +{"parentUuid":"a97aac77-5732-42aa-9773-ad0d59047c72","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ni8ex16v7CrYvXJVL625qg","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→// Package config implements the program configuration data structures, validation and parsing\n 7→package config\n 8→\n 9→import (\n 10→\t\"bytes\"\n 11→\t\"encoding/json\"\n 12→\t\"time\"\n 13→\n 14→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 15→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n 16→)\n 17→\n 18→type ProgramConfig struct {\n 19→\t// Address where the http (or https) server will listen on (for example: 'localhost:80').\n 20→\tAddr string `json:\"addr\"`\n 21→\n 22→\t// Addresses from which secured admin API endpoints can be reached, can be wildcard \"*\"\n 23→\tAPIAllowedIPs []string `json:\"api-allowed-ips\"`\n 24→\n 25→\tAPISubjects *NATSConfig `json:\"api-subjects\"`\n 26→\n 27→\t// Drop root permissions once .env was read and the port was taken.\n 28→\tUser string `json:\"user\"`\n 29→\tGroup string `json:\"group\"`\n 30→\n 31→\t// Disable authentication (for everything: API, Web-UI, ...)\n 32→\tDisableAuthentication bool `json:\"disable-authentication\"`\n 33→\n 34→\t// If `embed-static-files` is true (default), the frontend files are directly\n 35→\t// embeded into the go binary and expected to be in web/frontend. Only if\n 36→\t// it is false the files in `static-files` are served instead.\n 37→\tEmbedStaticFiles bool `json:\"embed-static-files\"`\n 38→\tStaticFiles string `json:\"static-files\"`\n 39→\n 40→\t// Path to SQLite database file\n 41→\tDB string `json:\"db\"`\n 42→\n 43→\tEnableJobTaggers bool `json:\"enable-job-taggers\"`\n 44→\n 45→\t// Validate json input against schema\n 46→\tValidate bool `json:\"validate\"`\n 47→\n 48→\t// If 0 or empty, the session does not expire!\n 49→\tSessionMaxAge string `json:\"session-max-age\"`\n 50→\n 51→\t// If both those options are not empty, use HTTPS using those certificates.\n 52→\tHTTPSCertFile string `json:\"https-cert-file\"`\n 53→\tHTTPSKeyFile string `json:\"https-key-file\"`\n 54→\n 55→\t// If not the empty string and `addr` does not end in \":80\",\n 56→\t// redirect every request incoming at port 80 to that url.\n 57→\tRedirectHTTPTo string `json:\"redirect-http-to\"`\n 58→\n 59→\t// Where to store MachineState files\n 60→\tMachineStateDir string `json:\"machine-state-dir\"`\n 61→\n 62→\t// If not zero, automatically mark jobs as stopped running X seconds longer than their walltime.\n 63→\tStopJobsExceedingWalltime int `json:\"stop-jobs-exceeding-walltime\"`\n 64→\n 65→\t// Defines time X in seconds in which jobs are considered to be \"short\" and will be filtered in specific views.\n 66→\tShortRunningJobsDuration int `json:\"short-running-jobs-duration\"`\n 67→\n 68→\t// Energy Mix CO2 Emission Constant [g/kWh]\n 69→\t// If entered, displays estimated CO2 emission for job based on jobs totalEnergy\n 70→\tEmissionConstant int `json:\"emission-constant\"`\n 71→\n 72→\t// If exists, will enable dynamic zoom in frontend metric plots using the configured values\n 73→\tEnableResampling *ResampleConfig `json:\"resampling\"`\n 74→\n 75→\t// Systemd unit name for log viewer (default: \"clustercockpit\")\n 76→\tSystemdUnit string `json:\"systemd-unit\"`\n 77→\n 78→\t// Node state retention configuration\n 79→\tNodeStateRetention *NodeStateRetention `json:\"nodestate-retention\"`\n 80→}\n 81→\n 82→type NodeStateRetention struct {\n 83→\tPolicy string `json:\"policy\"` // \"delete\" or \"move\"\n 84→\tAge int `json:\"age\"` // hours, default 24\n 85→\tTargetKind string `json:\"target-kind\"` // \"file\" or \"s3\"\n 86→\tTargetPath string `json:\"target-path\"`\n 87→\tTargetEndpoint string `json:\"target-endpoint\"`\n 88→\tTargetBucket string `json:\"target-bucket\"`\n 89→\tTargetAccessKey string `json:\"target-access-key\"`\n 90→\tTargetSecretKey string `json:\"target-secret-key\"`\n 91→\tTargetRegion string `json:\"target-region\"`\n 92→\tTargetUsePathStyle bool `json:\"target-use-path-style\"`\n 93→\tMaxFileSizeMB int `json:\"max-file-size-mb\"`\n 94→}\n 95→\n 96→type ResampleConfig struct {\n 97→\t// Minimum number of points to trigger resampling of data\n 98→\tMinimumPoints int `json:\"minimum-points\"`\n 99→\t// Array of resampling target resolutions, in seconds; Example: [600,300,60]\n 100→\tResolutions []int `json:\"resolutions\"`\n 101→\t// Trigger next zoom level at less than this many visible datapoints\n 102→\tTrigger int `json:\"trigger\"`\n 103→}\n 104→\n 105→type NATSConfig struct {\n 106→\tSubjectJobEvent string `json:\"subject-job-event\"`\n 107→\tSubjectNodeState string `json:\"subject-node-state\"`\n 108→}\n 109→\n 110→type IntRange struct {\n 111→\tFrom int `json:\"from\"`\n 112→\tTo int `json:\"to\"`\n 113→}\n 114→\n 115→type TimeRange struct {\n 116→\tFrom *time.Time `json:\"from\"`\n 117→\tTo *time.Time `json:\"to\"`\n 118→\tRange string `json:\"range,omitempty\"`\n 119→}\n 120→\n 121→type FilterRanges struct {\n 122→\tDuration *IntRange `json:\"duration\"`\n 123→\tNumNodes *IntRange `json:\"num-nodes\"`\n 124→\tStartTime *TimeRange `json:\"start-time\"`\n 125→}\n 126→\n 127→var Keys ProgramConfig = ProgramConfig{\n 128→\tAddr: \"localhost:8080\",\n 129→\tEmbedStaticFiles: true,\n 130→\tDB: \"./var/job.db\",\n 131→\tSessionMaxAge: \"168h\",\n 132→\tStopJobsExceedingWalltime: 0,\n 133→\tShortRunningJobsDuration: 5 * 60,\n 134→}\n 135→\n 136→func Init(mainConfig json.RawMessage) {\n 137→\tValidate(configSchema, mainConfig)\n 138→\tdec := json.NewDecoder(bytes.NewReader(mainConfig))\n 139→\tdec.DisallowUnknownFields()\n 140→\tif err := dec.Decode(&Keys); err != nil {\n 141→\t\tcclog.Abortf(\"Config Init: Could not decode config file '%s'.\\nError: %s\\n\", mainConfig, err.Error())\n 142→\t}\n 143→\n 144→\tif Keys.EnableResampling != nil && Keys.EnableResampling.MinimumPoints > 0 {\n 145→\t\tresampler.SetMinimumRequiredPoints(Keys.EnableResampling.MinimumPoints)\n 146→\t}\n 147→}\n 148→"}]},"uuid":"e6358d7f-bcda-40eb-92a8-0532a738776b","timestamp":"2026-03-11T05:06:45.965Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/config/config.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package config implements the program configuration data structures, validation and parsing\npackage config\n\nimport (\n\t\"bytes\"\n\t\"encoding/json\"\n\t\"time\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n)\n\ntype ProgramConfig struct {\n\t// Address where the http (or https) server will listen on (for example: 'localhost:80').\n\tAddr string `json:\"addr\"`\n\n\t// Addresses from which secured admin API endpoints can be reached, can be wildcard \"*\"\n\tAPIAllowedIPs []string `json:\"api-allowed-ips\"`\n\n\tAPISubjects *NATSConfig `json:\"api-subjects\"`\n\n\t// Drop root permissions once .env was read and the port was taken.\n\tUser string `json:\"user\"`\n\tGroup string `json:\"group\"`\n\n\t// Disable authentication (for everything: API, Web-UI, ...)\n\tDisableAuthentication bool `json:\"disable-authentication\"`\n\n\t// If `embed-static-files` is true (default), the frontend files are directly\n\t// embeded into the go binary and expected to be in web/frontend. Only if\n\t// it is false the files in `static-files` are served instead.\n\tEmbedStaticFiles bool `json:\"embed-static-files\"`\n\tStaticFiles string `json:\"static-files\"`\n\n\t// Path to SQLite database file\n\tDB string `json:\"db\"`\n\n\tEnableJobTaggers bool `json:\"enable-job-taggers\"`\n\n\t// Validate json input against schema\n\tValidate bool `json:\"validate\"`\n\n\t// If 0 or empty, the session does not expire!\n\tSessionMaxAge string `json:\"session-max-age\"`\n\n\t// If both those options are not empty, use HTTPS using those certificates.\n\tHTTPSCertFile string `json:\"https-cert-file\"`\n\tHTTPSKeyFile string `json:\"https-key-file\"`\n\n\t// If not the empty string and `addr` does not end in \":80\",\n\t// redirect every request incoming at port 80 to that url.\n\tRedirectHTTPTo string `json:\"redirect-http-to\"`\n\n\t// Where to store MachineState files\n\tMachineStateDir string `json:\"machine-state-dir\"`\n\n\t// If not zero, automatically mark jobs as stopped running X seconds longer than their walltime.\n\tStopJobsExceedingWalltime int `json:\"stop-jobs-exceeding-walltime\"`\n\n\t// Defines time X in seconds in which jobs are considered to be \"short\" and will be filtered in specific views.\n\tShortRunningJobsDuration int `json:\"short-running-jobs-duration\"`\n\n\t// Energy Mix CO2 Emission Constant [g/kWh]\n\t// If entered, displays estimated CO2 emission for job based on jobs totalEnergy\n\tEmissionConstant int `json:\"emission-constant\"`\n\n\t// If exists, will enable dynamic zoom in frontend metric plots using the configured values\n\tEnableResampling *ResampleConfig `json:\"resampling\"`\n\n\t// Systemd unit name for log viewer (default: \"clustercockpit\")\n\tSystemdUnit string `json:\"systemd-unit\"`\n\n\t// Node state retention configuration\n\tNodeStateRetention *NodeStateRetention `json:\"nodestate-retention\"`\n}\n\ntype NodeStateRetention struct {\n\tPolicy string `json:\"policy\"` // \"delete\" or \"move\"\n\tAge int `json:\"age\"` // hours, default 24\n\tTargetKind string `json:\"target-kind\"` // \"file\" or \"s3\"\n\tTargetPath string `json:\"target-path\"`\n\tTargetEndpoint string `json:\"target-endpoint\"`\n\tTargetBucket string `json:\"target-bucket\"`\n\tTargetAccessKey string `json:\"target-access-key\"`\n\tTargetSecretKey string `json:\"target-secret-key\"`\n\tTargetRegion string `json:\"target-region\"`\n\tTargetUsePathStyle bool `json:\"target-use-path-style\"`\n\tMaxFileSizeMB int `json:\"max-file-size-mb\"`\n}\n\ntype ResampleConfig struct {\n\t// Minimum number of points to trigger resampling of data\n\tMinimumPoints int `json:\"minimum-points\"`\n\t// Array of resampling target resolutions, in seconds; Example: [600,300,60]\n\tResolutions []int `json:\"resolutions\"`\n\t// Trigger next zoom level at less than this many visible datapoints\n\tTrigger int `json:\"trigger\"`\n}\n\ntype NATSConfig struct {\n\tSubjectJobEvent string `json:\"subject-job-event\"`\n\tSubjectNodeState string `json:\"subject-node-state\"`\n}\n\ntype IntRange struct {\n\tFrom int `json:\"from\"`\n\tTo int `json:\"to\"`\n}\n\ntype TimeRange struct {\n\tFrom *time.Time `json:\"from\"`\n\tTo *time.Time `json:\"to\"`\n\tRange string `json:\"range,omitempty\"`\n}\n\ntype FilterRanges struct {\n\tDuration *IntRange `json:\"duration\"`\n\tNumNodes *IntRange `json:\"num-nodes\"`\n\tStartTime *TimeRange `json:\"start-time\"`\n}\n\nvar Keys ProgramConfig = ProgramConfig{\n\tAddr: \"localhost:8080\",\n\tEmbedStaticFiles: true,\n\tDB: \"./var/job.db\",\n\tSessionMaxAge: \"168h\",\n\tStopJobsExceedingWalltime: 0,\n\tShortRunningJobsDuration: 5 * 60,\n}\n\nfunc Init(mainConfig json.RawMessage) {\n\tValidate(configSchema, mainConfig)\n\tdec := json.NewDecoder(bytes.NewReader(mainConfig))\n\tdec.DisallowUnknownFields()\n\tif err := dec.Decode(&Keys); err != nil {\n\t\tcclog.Abortf(\"Config Init: Could not decode config file '%s'.\\nError: %s\\n\", mainConfig, err.Error())\n\t}\n\n\tif Keys.EnableResampling != nil && Keys.EnableResampling.MinimumPoints > 0 {\n\t\tresampler.SetMinimumRequiredPoints(Keys.EnableResampling.MinimumPoints)\n\t}\n}\n","numLines":148,"startLine":1,"totalLines":148}},"sourceToolAssistantUUID":"a97aac77-5732-42aa-9773-ad0d59047c72"} +{"parentUuid":"e6358d7f-bcda-40eb-92a8-0532a738776b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01Xc34zdv2JP91KmnzYBQzif","toolUseID":"toolu_01Xc34zdv2JP91KmnzYBQzif","timestamp":"2026-03-11T05:06:45.966Z","uuid":"90a7241d-a445-4f84-82b1-beb4bf40de4c"} +{"parentUuid":"b2dc1e62-6a8d-43ec-b268-7a3bcbcadcf0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xc34zdv2JP91KmnzYBQzif","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package config\n 7→\n 8→var configSchema = `\n 9→{\n 10→ \"type\": \"object\",\n 11→ \"properties\": {\n 12→ \"addr\": {\n 13→ \"description\": \"Address where the http (or https) server will listen on (for example: 'localhost:80').\",\n 14→ \"type\": \"string\"\n 15→ },\n 16→ \"api-allowed-ips\": {\n 17→ \"description\": \"Addresses from which secured API endpoints can be reached\",\n 18→ \"type\": \"array\",\n 19→ \"items\": {\n 20→ \"type\": \"string\"\n 21→ }\n 22→ },\n 23→ \"user\": {\n 24→ \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n 25→ \"type\": \"string\"\n 26→ },\n 27→ \"group\": {\n 28→ \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n 29→ \"type\": \"string\"\n 30→ },\n 31→ \"disable-authentication\": {\n 32→ \"description\": \"Disable authentication (for everything: API, Web-UI, ...).\",\n 33→ \"type\": \"boolean\"\n 34→ },\n 35→ \"embed-static-files\": {\n 36→ \"description\": \"If all files in web/frontend/public should be served from within the binary itself (they are embedded) or not.\",\n 37→ \"type\": \"boolean\"\n 38→ },\n 39→ \"static-files\": {\n 40→ \"description\": \"Folder where static assets can be found, if embed-static-files is false.\",\n 41→ \"type\": \"string\"\n 42→ },\n 43→ \"db\": {\n 44→ \"description\": \"Path to SQLite database file (e.g., './var/job.db')\",\n 45→ \"type\": \"string\"\n 46→ },\n 47→ \"enable-job-taggers\": {\n 48→ \"description\": \"Turn on automatic application and jobclass taggers\",\n 49→ \"type\": \"boolean\"\n 50→ },\n 51→ \"validate\": {\n 52→ \"description\": \"Validate all input json documents against json schema.\",\n 53→ \"type\": \"boolean\"\n 54→ },\n 55→ \"session-max-age\": {\n 56→ \"description\": \"Specifies for how long a session shall be valid as a string parsable by time.ParseDuration(). If 0 or empty, the session/token does not expire!\",\n 57→ \"type\": \"string\"\n 58→ },\n 59→ \"https-cert-file\": {\n 60→ \"description\": \"Filepath to SSL certificate. If also https-key-file is set use HTTPS using those certificates.\",\n 61→ \"type\": \"string\"\n 62→ },\n 63→ \"https-key-file\": {\n 64→ \"description\": \"Filepath to SSL key file. If also https-cert-file is set use HTTPS using those certificates.\",\n 65→ \"type\": \"string\"\n 66→ },\n 67→ \"redirect-http-to\": {\n 68→ \"description\": \"If not the empty string and addr does not end in :80, redirect every request incoming at port 80 to that url.\",\n 69→ \"type\": \"string\"\n 70→ },\n 71→ \"stop-jobs-exceeding-walltime\": {\n 72→ \"description\": \"If not zero, automatically mark jobs as stopped running X seconds longer than their walltime. Only applies if walltime is set for job.\",\n 73→ \"type\": \"integer\"\n 74→ },\n 75→ \"short-running-jobs-duration\": {\n 76→ \"description\": \"Do not show running jobs shorter than X seconds.\",\n 77→ \"type\": \"integer\"\n 78→ },\n 79→ \"emission-constant\": {\n 80→ \"description\": \"Energy mix CO2 emission constant [g/kWh]. If set, displays estimated CO2 emission for jobs.\",\n 81→ \"type\": \"integer\"\n 82→ },\n 83→ \"machine-state-dir\": {\n 84→ \"description\": \"Where to store MachineState files.\",\n 85→ \"type\": \"string\"\n 86→ },\n 87→ \"systemd-unit\": {\n 88→ \"description\": \"Systemd unit name for log viewer (default: 'clustercockpit').\",\n 89→ \"type\": \"string\"\n 90→ },\n 91→ \"resampling\": {\n 92→ \"description\": \"Enable dynamic zoom in frontend metric plots.\",\n 93→ \"type\": \"object\",\n 94→ \"properties\": {\n 95→ \"minimum-points\": {\n 96→ \"description\": \"Minimum points to trigger resampling of time-series data.\",\n 97→ \"type\": \"integer\"\n 98→ },\n 99→ \"trigger\": {\n 100→ \"description\": \"Trigger next zoom level at less than this many visible datapoints.\",\n 101→ \"type\": \"integer\"\n 102→ },\n 103→ \"resolutions\": {\n 104→ \"description\": \"Array of resampling target resolutions, in seconds.\",\n 105→ \"type\": \"array\",\n 106→ \"items\": {\n 107→ \"type\": \"integer\"\n 108→ }\n 109→ }\n 110→ },\n 111→ \"required\": [\"trigger\", \"resolutions\"]\n 112→ },\n 113→ \"api-subjects\": {\n 114→ \"description\": \"NATS subjects configuration for subscribing to job and node events.\",\n 115→ \"type\": \"object\",\n 116→ \"properties\": {\n 117→ \"subject-job-event\": {\n 118→ \"description\": \"NATS subject for job events (start_job, stop_job)\",\n 119→ \"type\": \"string\"\n 120→ },\n 121→ \"subject-node-state\": {\n 122→ \"description\": \"NATS subject for node state updates\",\n 123→ \"type\": \"string\"\n 124→ }\n 125→ },\n 126→ \"required\": [\"subject-job-event\", \"subject-node-state\"]\n 127→ },\n 128→ \"nodestate-retention\": {\n 129→ \"description\": \"Node state retention configuration for cleaning up old node_state rows.\",\n 130→ \"type\": \"object\",\n 131→ \"properties\": {\n 132→ \"policy\": {\n 133→ \"description\": \"Retention policy: 'delete' to remove old rows, 'move' to archive to Parquet then delete.\",\n 134→ \"type\": \"string\",\n 135→ \"enum\": [\"delete\", \"move\"]\n 136→ },\n 137→ \"age\": {\n 138→ \"description\": \"Retention age in hours (default: 24).\",\n 139→ \"type\": \"integer\"\n 140→ },\n 141→ \"target-kind\": {\n 142→ \"description\": \"Target kind for parquet archiving: 'file' or 's3'.\",\n 143→ \"type\": \"string\",\n 144→ \"enum\": [\"file\", \"s3\"]\n 145→ },\n 146→ \"target-path\": {\n 147→ \"description\": \"Filesystem path for parquet file target.\",\n 148→ \"type\": \"string\"\n 149→ },\n 150→ \"target-endpoint\": {\n 151→ \"description\": \"S3 endpoint URL.\",\n 152→ \"type\": \"string\"\n 153→ },\n 154→ \"target-bucket\": {\n 155→ \"description\": \"S3 bucket name.\",\n 156→ \"type\": \"string\"\n 157→ },\n 158→ \"target-access-key\": {\n 159→ \"description\": \"S3 access key.\",\n 160→ \"type\": \"string\"\n 161→ },\n 162→ \"target-secret-key\": {\n 163→ \"description\": \"S3 secret key.\",\n 164→ \"type\": \"string\"\n 165→ },\n 166→ \"target-region\": {\n 167→ \"description\": \"S3 region.\",\n 168→ \"type\": \"string\"\n 169→ },\n 170→ \"target-use-path-style\": {\n 171→ \"description\": \"Use path-style S3 addressing.\",\n 172→ \"type\": \"boolean\"\n 173→ },\n 174→ \"max-file-size-mb\": {\n 175→ \"description\": \"Maximum parquet file size in MB (default: 128).\",\n 176→ \"type\": \"integer\"\n 177→ }\n 178→ },\n 179→ \"required\": [\"policy\"]\n 180→ }\n 181→ }\n 182→}`\n 183→"}]},"uuid":"c9c163fe-8747-4410-b8ad-2bc70df78e1f","timestamp":"2026-03-11T05:06:45.966Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/CC/cc-backend/internal/config/schema.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage config\n\nvar configSchema = `\n{\n \"type\": \"object\",\n \"properties\": {\n \"addr\": {\n \"description\": \"Address where the http (or https) server will listen on (for example: 'localhost:80').\",\n \"type\": \"string\"\n },\n \"api-allowed-ips\": {\n \"description\": \"Addresses from which secured API endpoints can be reached\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"user\": {\n \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n \"type\": \"string\"\n },\n \"group\": {\n \"description\": \"Drop root permissions once .env was read and the port was taken. Only applicable if using privileged port.\",\n \"type\": \"string\"\n },\n \"disable-authentication\": {\n \"description\": \"Disable authentication (for everything: API, Web-UI, ...).\",\n \"type\": \"boolean\"\n },\n \"embed-static-files\": {\n \"description\": \"If all files in web/frontend/public should be served from within the binary itself (they are embedded) or not.\",\n \"type\": \"boolean\"\n },\n \"static-files\": {\n \"description\": \"Folder where static assets can be found, if embed-static-files is false.\",\n \"type\": \"string\"\n },\n \"db\": {\n \"description\": \"Path to SQLite database file (e.g., './var/job.db')\",\n \"type\": \"string\"\n },\n \"enable-job-taggers\": {\n \"description\": \"Turn on automatic application and jobclass taggers\",\n \"type\": \"boolean\"\n },\n \"validate\": {\n \"description\": \"Validate all input json documents against json schema.\",\n \"type\": \"boolean\"\n },\n \"session-max-age\": {\n \"description\": \"Specifies for how long a session shall be valid as a string parsable by time.ParseDuration(). If 0 or empty, the session/token does not expire!\",\n \"type\": \"string\"\n },\n \"https-cert-file\": {\n \"description\": \"Filepath to SSL certificate. If also https-key-file is set use HTTPS using those certificates.\",\n \"type\": \"string\"\n },\n \"https-key-file\": {\n \"description\": \"Filepath to SSL key file. If also https-cert-file is set use HTTPS using those certificates.\",\n \"type\": \"string\"\n },\n \"redirect-http-to\": {\n \"description\": \"If not the empty string and addr does not end in :80, redirect every request incoming at port 80 to that url.\",\n \"type\": \"string\"\n },\n \"stop-jobs-exceeding-walltime\": {\n \"description\": \"If not zero, automatically mark jobs as stopped running X seconds longer than their walltime. Only applies if walltime is set for job.\",\n \"type\": \"integer\"\n },\n \"short-running-jobs-duration\": {\n \"description\": \"Do not show running jobs shorter than X seconds.\",\n \"type\": \"integer\"\n },\n \"emission-constant\": {\n \"description\": \"Energy mix CO2 emission constant [g/kWh]. If set, displays estimated CO2 emission for jobs.\",\n \"type\": \"integer\"\n },\n \"machine-state-dir\": {\n \"description\": \"Where to store MachineState files.\",\n \"type\": \"string\"\n },\n \"systemd-unit\": {\n \"description\": \"Systemd unit name for log viewer (default: 'clustercockpit').\",\n \"type\": \"string\"\n },\n \"resampling\": {\n \"description\": \"Enable dynamic zoom in frontend metric plots.\",\n \"type\": \"object\",\n \"properties\": {\n \"minimum-points\": {\n \"description\": \"Minimum points to trigger resampling of time-series data.\",\n \"type\": \"integer\"\n },\n \"trigger\": {\n \"description\": \"Trigger next zoom level at less than this many visible datapoints.\",\n \"type\": \"integer\"\n },\n \"resolutions\": {\n \"description\": \"Array of resampling target resolutions, in seconds.\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"integer\"\n }\n }\n },\n \"required\": [\"trigger\", \"resolutions\"]\n },\n \"api-subjects\": {\n \"description\": \"NATS subjects configuration for subscribing to job and node events.\",\n \"type\": \"object\",\n \"properties\": {\n \"subject-job-event\": {\n \"description\": \"NATS subject for job events (start_job, stop_job)\",\n \"type\": \"string\"\n },\n \"subject-node-state\": {\n \"description\": \"NATS subject for node state updates\",\n \"type\": \"string\"\n }\n },\n \"required\": [\"subject-job-event\", \"subject-node-state\"]\n },\n \"nodestate-retention\": {\n \"description\": \"Node state retention configuration for cleaning up old node_state rows.\",\n \"type\": \"object\",\n \"properties\": {\n \"policy\": {\n \"description\": \"Retention policy: 'delete' to remove old rows, 'move' to archive to Parquet then delete.\",\n \"type\": \"string\",\n \"enum\": [\"delete\", \"move\"]\n },\n \"age\": {\n \"description\": \"Retention age in hours (default: 24).\",\n \"type\": \"integer\"\n },\n \"target-kind\": {\n \"description\": \"Target kind for parquet archiving: 'file' or 's3'.\",\n \"type\": \"string\",\n \"enum\": [\"file\", \"s3\"]\n },\n \"target-path\": {\n \"description\": \"Filesystem path for parquet file target.\",\n \"type\": \"string\"\n },\n \"target-endpoint\": {\n \"description\": \"S3 endpoint URL.\",\n \"type\": \"string\"\n },\n \"target-bucket\": {\n \"description\": \"S3 bucket name.\",\n \"type\": \"string\"\n },\n \"target-access-key\": {\n \"description\": \"S3 access key.\",\n \"type\": \"string\"\n },\n \"target-secret-key\": {\n \"description\": \"S3 secret key.\",\n \"type\": \"string\"\n },\n \"target-region\": {\n \"description\": \"S3 region.\",\n \"type\": \"string\"\n },\n \"target-use-path-style\": {\n \"description\": \"Use path-style S3 addressing.\",\n \"type\": \"boolean\"\n },\n \"max-file-size-mb\": {\n \"description\": \"Maximum parquet file size in MB (default: 128).\",\n \"type\": \"integer\"\n }\n },\n \"required\": [\"policy\"]\n }\n }\n}`\n","numLines":183,"startLine":1,"totalLines":183}},"sourceToolAssistantUUID":"b2dc1e62-6a8d-43ec-b268-7a3bcbcadcf0"} 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+{"parentUuid":"6e68d15c-f572-494e-bee5-07d66ce78555","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01PxDgtD8AXogveVvBbaut5v","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check how `initDatabase` works in main.go."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":5155,"cache_read_input_tokens":64355,"cache_creation":{"ephemeral_5m_input_tokens":5155,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviG7j31EEmbWpDgagib","type":"assistant","uuid":"6d6ba849-b57c-4df3-a2d9-ec5062bf05f6","timestamp":"2026-03-11T05:06:53.907Z"} 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+{"parentUuid":"b3b5ee7f-7ce6-4d75-9802-19b73b898ff5","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Grep","command":"callback"},"parentToolUseID":"toolu_014mBadNPV4FgBaRG36z8NE8","toolUseID":"toolu_014mBadNPV4FgBaRG36z8NE8","timestamp":"2026-03-11T05:06:55.574Z","uuid":"cb22177d-2b91-4303-b88c-5cea7157ceaa"} +{"parentUuid":"b3b5ee7f-7ce6-4d75-9802-19b73b898ff5","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014mBadNPV4FgBaRG36z8NE8","type":"tool_result","content":"108-}\n109-\n110:func initDatabase() error {\n111:\trepository.Connect(config.Keys.DB)\n112-\treturn nil\n113-}\n114-\n115-func handleDatabaseCommands() error {\n116-\tif flagMigrateDB {\n--\n486-\n487-\t// Initialize database\n488:\tif err := initDatabase(); err != nil {\n489-\t\treturn err\n490-\t}\n491-\n492-\t// Handle user commands (add, delete, sync, JWT)\n493-\tif err := handleUserCommands(); err != nil {"}]},"uuid":"66ba65fe-f2ac-4c6f-a050-ffa0fa0cbe21","timestamp":"2026-03-11T05:06:55.573Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"108-}\n109-\n110:func initDatabase() error {\n111:\trepository.Connect(config.Keys.DB)\n112-\treturn nil\n113-}\n114-\n115-func handleDatabaseCommands() error {\n116-\tif flagMigrateDB {\n--\n486-\n487-\t// Initialize database\n488:\tif err := initDatabase(); err != nil {\n489-\t\treturn err\n490-\t}\n491-\n492-\t// Handle user commands (add, delete, sync, JWT)\n493-\tif err := handleUserCommands(); err != nil {","numLines":18},"sourceToolAssistantUUID":"b3b5ee7f-7ce6-4d75-9802-19b73b898ff5"} +{"parentUuid":"66ba65fe-f2ac-4c6f-a050-ffa0fa0cbe21","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01MXTji8okeCEHHUGCGdgysa","type":"message","role":"assistant","content":[{"type":"text","text":"I have all the information I need. Here's my assessment:\n\n**Yes, it makes sense** to make these configurable, especially since:\n1. `RepositoryConfig` + `SetConfig()` already exist but are **never wired up** — there's no way to override defaults from config.json today\n2. A 512GB server could comfortably use 2-4GB per connection cache (8-16GB total) and a 16GB soft heap limit\n3. The pattern already exists in the codebase (`MaxOpenConnections`, `MaxIdleConnections` are in `RepositoryConfig` but also can't be tuned from config.json)\n\nThe scope would be:\n1. Add `DbCacheSizeMB` and `DbSoftHeapLimitMB` fields to `RepositoryConfig`\n2. Add a `\"db-config\"` section to `ProgramConfig` and the JSON schema\n3. Wire `SetConfig()` into `initDatabase()` in main.go\n4. Use the config values in `Connect()` and `setupSqlite()`\n\nLet me update the plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":474,"cache_read_input_tokens":69510,"cache_creation":{"ephemeral_5m_input_tokens":474,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviGpoLQyUixeE1NdMVu","type":"assistant","uuid":"f079606e-7e06-421b-af72-0892227940b9","timestamp":"2026-03-11T05:07:04.456Z"} +{"type":"queue-operation","operation":"enqueue","timestamp":"2026-03-11T05:07:23.941Z","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","content":"The production db is 80GB. So the suggested values for cache and heap limit potentially limit the performance"} +{"parentUuid":"f079606e-7e06-421b-af72-0892227940b9","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01MXTji8okeCEHHUGCGdgysa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0156Ar3pVufKheshkitKUACQ","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/.claude/plans/refactored-wishing-journal.md","old_string":"# Fix SQLite Memory Not Released After Query Timeout\n\n## Context\n\nOn the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`","new_string":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 200, // 200MB per connection\nDbSoftHeapLimitMB: 1024, // 1GB process-wide\n```\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. 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The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`","newString":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 200, // 200MB per connection\nDbSoftHeapLimitMB: 1024, // 1GB process-wide\n```\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. Check startup log shows effective values","originalFile":"# Fix SQLite Memory Not Released After Query Timeout\n\n## Context\n\nOn the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.\n\n## Root Cause Analysis\n\n### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)\n\n**File:** `internal/repository/dbConnection.go:82`\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"1000000000\")\n```\n\nSQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.\n\nFor comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).\n\n### 2. No query context/timeout — queries run indefinitely\n\n**File:** `internal/repository/jobQuery.go:87`\n\n```go\nrows, err := query.RunWith(r.stmtCache).Query() // No context!\n```\n\nThe `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.\n\n### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`\n\nSQLite has built-in memory management PRAGMAs that are not configured:\n- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)\n- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded\n- **`shrink_memory`** — immediately releases all unused memory back to the OS\n\nNone of these are set, so SQLite allocates freely and never releases.\n\n### 4. `temp_store = memory` amplifies the problem\n\n**File:** `internal/repository/dbConnection.go:41`\n\nTemporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.\n\n### 5. Connections live for 1 hour after use\n\n`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.\n\n## Proposed Changes\n\n### Fix 1: Set reasonable `cache_size` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go:82`\n\nChange from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):\n\n```go\nconnectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection\n```\n\nWith 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.\n\n### Fix 2: Add `soft_heap_limit` (high impact, low risk)\n\n**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:\n\n```go\n\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections\n```\n\nThis is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.\n\n### Fix 3: Pass context to database queries (medium impact, medium effort)\n\nChange `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.\n\n**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.\n\n**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):\n\n| File | Method | Line | Change |\n|------|--------|------|--------|\n| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |\n| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |\n| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |\n| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |\n| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |\n| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |\n| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |\n\nMethods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.\n\n### Fix 4: Reduce `ConnMaxIdleTime`\n\n**File:** `internal/repository/config.go`\n\nReduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.\n\n## Scope — All Four Fixes\n\nAll fixes are complementary, independent, and low-risk. No disadvantages to applying all together.\n\n## Files to Modify\n\n- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)\n- `internal/repository/jobQuery.go` — QueryContext (Fix 3)\n- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)\n- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)\n- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/...` — tests pass\n3. Monitor RSS memory before/after on production:\n - Trigger a heavy query, observe memory spike\n - Verify memory drops back down within seconds (not hours)\n4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`\n5. `PRAGMA soft_heap_limit;` — should show `1073741824`\n","structuredPatch":[{"oldStart":1,"oldLines":127,"newStart":1,"newLines":177,"lines":["-# Fix SQLite Memory Not Released After Query Timeout","+# Make SQLite Memory Limits Configurable via config.json"," "," ## Context"," ","-On the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration.","+Fixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json."," ","-## Root Cause Analysis","+## Current State (already implemented)"," ","-### 1. `_cache_size=1000000000` is effectively unlimited (~4TB)","+- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)","+- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)","+- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)","+- `MaxOpenConnections = 4` (hardcoded default in `config.go`)","+- Context propagation to all query call sites (already done)"," ","-**File:** `internal/repository/dbConnection.go:82`","+## Problem"," ","-```go","-connectionURLParams.Add(\"_cache_size\", \"1000000000\")","-```","+`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema."," ","-SQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means \"never evict cached pages.\" After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes.","+## Proposed Changes"," ","-For comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB).","+### 1. Add SQLite memory fields to `RepositoryConfig`"," ","-### 2. No query context/timeout — queries run indefinitely","+**File:** `internal/repository/config.go`"," ","-**File:** `internal/repository/jobQuery.go:87`","+Add two new fields with sensible defaults:"," "," ```go","-rows, err := query.RunWith(r.stmtCache).Query() // No context!","-```","+type RepositoryConfig struct {","+ // ... existing fields ..."," ","-The `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache.","+ // DbCacheSizeMB is the SQLite page cache size per connection in MB.","+ // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4","+ // and DbCacheSizeMB=200, total page cache is up to 800MB.","+ // Default: 200 (MB)","+ DbCacheSizeMB int"," ","-### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory`","+ // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.","+ // SQLite will try to release cache pages to stay under this limit.","+ // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.","+ // Default: 1024 (1GB)","+ DbSoftHeapLimitMB int","+}","+```"," ","-SQLite has built-in memory management PRAGMAs that are not configured:","-- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit)","-- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded","-- **`shrink_memory`** — immediately releases all unused memory back to the OS","+Update `DefaultConfig()`:","+```go","+DbCacheSizeMB: 200, // 200MB per connection","+DbSoftHeapLimitMB: 1024, // 1GB process-wide","+```"," ","-None of these are set, so SQLite allocates freely and never releases.","+### 2. Use config values in `Connect()` and `setupSqlite()`"," ","-### 4. `temp_store = memory` amplifies the problem","+**File:** `internal/repository/dbConnection.go`"," ","-**File:** `internal/repository/dbConnection.go:41`","+In `Connect()`, replace the hardcoded cache_size:","+```go","+cacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB","+connectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))","+```"," ","-Temporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache.","+Change `setupSqlite()` to accept the config and use it for soft_heap_limit:","+```go","+func setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {","+ pragmas := []string{","+ \"temp_store = memory\",","+ fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),","+ }","+ // ...","+}","+```"," ","-### 5. Connections live for 1 hour after use","-","-`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages.","-","-## Proposed Changes","-","-### Fix 1: Set reasonable `cache_size` (high impact, low risk)","-","-**File:** `internal/repository/dbConnection.go:82`","-","-Change from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection):","-","+Update the call site in `Connect()`:"," ```go","-connectionURLParams.Add(\"_cache_size\", \"-200000\") // 200MB per connection","+err = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)"," ```"," ","-With 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans.","+### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema"," ","-### Fix 2: Add `soft_heap_limit` (high impact, low risk)","+**File:** `internal/config/config.go`"," ","-**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`:","+Add a new struct and field to `ProgramConfig`:"," "," ```go","-\"soft_heap_limit = 1073741824\", // 1GB soft limit across all connections","+type DbConfig struct {","+ CacheSizeMB int `json:\"cache-size-mb\"`","+ SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`","+ MaxOpenConnections int `json:\"max-open-connections\"`","+ MaxIdleConnections int `json:\"max-idle-connections\"`","+ ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`","+}","+","+type ProgramConfig struct {","+ // ... existing fields ...","+ DbConfig *DbConfig `json:\"db-config\"`","+}"," ```"," ","-This is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively.","+**File:** `internal/config/schema.go`"," ","-### Fix 3: Pass context to database queries (medium impact, medium effort)","+Add the schema section for validation."," ","-Change `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query.","+### 4. Wire `SetConfig()` in `initDatabase()`"," ","-**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed.","+**File:** `cmd/cc-backend/main.go`"," ","-**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`):","+```go","+func initDatabase() error {","+ if config.Keys.DbConfig != nil {","+ cfg := repository.DefaultConfig()","+ dc := config.Keys.DbConfig","+ if dc.CacheSizeMB > 0 {","+ cfg.DbCacheSizeMB = dc.CacheSizeMB","+ }","+ if dc.SoftHeapLimitMB > 0 {","+ cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB","+ }","+ if dc.MaxOpenConnections > 0 {","+ cfg.MaxOpenConnections = dc.MaxOpenConnections","+ }","+ if dc.MaxIdleConnections > 0 {","+ cfg.MaxIdleConnections = dc.MaxIdleConnections","+ }","+ if dc.ConnectionMaxIdleTimeMins > 0 {","+ cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute","+ }","+ repository.SetConfig(cfg)","+ }","+ repository.Connect(config.Keys.DB)","+ return nil","+}","+```"," ","-| File | Method | Line | Change |","-|------|--------|------|--------|","-| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` |","-| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` |","-| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` |","-| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` |","-| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` |","-| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` |","-| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` |","-| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` |","-| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` |","-| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` |","-| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` |","-| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` |","-| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) |","-| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) |","+### 5. Log effective values on startup"," ","-Methods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`.","+**File:** `internal/repository/dbConnection.go`"," ","-### Fix 4: Reduce `ConnMaxIdleTime`","+After setting PRAGMAs, log the effective values so operators can verify:","+```go","+cclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",","+ repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)","+```"," ","-**File:** `internal/repository/config.go`","+## Example config.json (for 512GB server)"," ","-Reduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner.","+```json","+{","+ \"main\": {","+ \"db\": \"./var/job.db\",","+ \"db-config\": {","+ \"cache-size-mb\": 2048,","+ \"soft-heap-limit-mb\": 16384,","+ \"max-open-connections\": 8,","+ \"max-idle-time-minutes\": 30","+ }","+ }","+}","+```"," ","-## Scope — All Four Fixes","+This would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom."," ","-All fixes are complementary, independent, and low-risk. No disadvantages to applying all together.","-"," ## Files to Modify"," ","-- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2)","-- `internal/repository/jobQuery.go` — QueryContext (Fix 3)","-- `internal/repository/job.go` — QueryContext for other query methods (Fix 3)","-- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3)","-- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional)","+| File | Changes |","+|------|---------|","+| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |","+| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |","+| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |","+| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |","+| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |"," "," ## Verification"," "," 1. `go build ./...` — compiles","-2. `go test ./internal/repository/...` — tests pass","-3. Monitor RSS memory before/after on production:","- - Trigger a heavy query, observe memory spike","- - Verify memory drops back down within seconds (not hours)","-4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000`","-5. `PRAGMA soft_heap_limit;` — should show `1073741824`","+2. `go test ./internal/repository/... ./internal/config/...` — tests pass","+3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible","+4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI","+5. Check startup log shows effective values"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"7faccf2d-1f17-4294-a901-5a4b1b3de26a"} +{"type":"queue-operation","operation":"remove","timestamp":"2026-03-11T05:08:15.005Z","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48"} +{"parentUuid":"61c87ffd-5bfc-42ee-8630-b26707a84e57","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01A3ekBhzXtpjyAoVo65Gqm8","type":"message","role":"assistant","content":[{"type":"text","text":"Good point — with an 80GB database on a 512GB server, you'd want the cache to hold a significant portion of the DB. Let me adjust the defaults and example values."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4493,"cache_read_input_tokens":69984,"cache_creation":{"ephemeral_5m_input_tokens":4493,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviNgQE59TEmykkZWeWR","type":"assistant","uuid":"6ca2dff1-9508-4357-b7ba-fd646ca60ca7","timestamp":"2026-03-11T05:08:18.254Z"} +{"parentUuid":"6ca2dff1-9508-4357-b7ba-fd646ca60ca7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01A3ekBhzXtpjyAoVo65Gqm8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TqTNB47PkGxnz4wtXTvodB","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/.claude/plans/refactored-wishing-journal.md","old_string":"Update `DefaultConfig()`:\n```go\nDbCacheSizeMB: 200, // 200MB per connection\nDbSoftHeapLimitMB: 1024, // 1GB process-wide\n```","new_string":"Update `DefaultConfig()`:\n```go\nDbCacheSizeMB: 2048, // 2GB per connection\nDbSoftHeapLimitMB: 16384, // 16GB process-wide\n```\n\n**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4493,"cache_read_input_tokens":69984,"output_tokens":384,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4493},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviNgQE59TEmykkZWeWR","type":"assistant","uuid":"b5416b24-6e78-4fa0-a8f6-305051b010d8","timestamp":"2026-03-11T05:08:23.716Z"} +{"parentUuid":"b5416b24-6e78-4fa0-a8f6-305051b010d8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01TqTNB47PkGxnz4wtXTvodB","toolUseID":"toolu_01TqTNB47PkGxnz4wtXTvodB","timestamp":"2026-03-11T05:08:23.737Z","uuid":"e91c02d9-3963-4c8f-a4f6-5ca6909d4833"} +{"parentUuid":"b5416b24-6e78-4fa0-a8f6-305051b010d8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TqTNB47PkGxnz4wtXTvodB","type":"tool_result","content":"The file /Users/jan/.claude/plans/refactored-wishing-journal.md has been updated successfully."}]},"uuid":"831fde90-b9d9-44cb-88cc-c4d4d7dd0b83","timestamp":"2026-03-11T05:08:23.737Z","toolUseResult":{"filePath":"/Users/jan/.claude/plans/refactored-wishing-journal.md","oldString":"Update `DefaultConfig()`:\n```go\nDbCacheSizeMB: 200, // 200MB per connection\nDbSoftHeapLimitMB: 1024, // 1GB process-wide\n```","newString":"Update `DefaultConfig()`:\n```go\nDbCacheSizeMB: 2048, // 2GB per connection\nDbSoftHeapLimitMB: 16384, // 16GB process-wide\n```\n\n**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production.","originalFile":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 200, // 200MB per connection\nDbSoftHeapLimitMB: 1024, // 1GB process-wide\n```\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. Check startup log shows effective values\n","structuredPatch":[{"oldStart":44,"oldLines":10,"newStart":44,"newLines":12,"lines":[" "," Update `DefaultConfig()`:"," ```go","-DbCacheSizeMB: 200, // 200MB per connection","-DbSoftHeapLimitMB: 1024, // 1GB process-wide","+DbCacheSizeMB: 2048, // 2GB per connection","+DbSoftHeapLimitMB: 16384, // 16GB process-wide"," ```"," ","+**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production.","+"," ### 2. Use config values in `Connect()` and `setupSqlite()`"," "," **File:** `internal/repository/dbConnection.go`"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"b5416b24-6e78-4fa0-a8f6-305051b010d8"} +{"parentUuid":"831fde90-b9d9-44cb-88cc-c4d4d7dd0b83","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01J7V1jT3A86bqSxZauvqydn","type":"message","role":"assistant","content":[{"type":"text","text":"Also update the example config and the already-hardcoded values discussion:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":516,"cache_read_input_tokens":74477,"cache_creation":{"ephemeral_5m_input_tokens":516,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviPL7NLRceMEzP1ogKW","type":"assistant","uuid":"64618314-535b-49ef-adb2-1f3cf31468a7","timestamp":"2026-03-11T05:08:26.478Z"} +{"parentUuid":"64618314-535b-49ef-adb2-1f3cf31468a7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01J7V1jT3A86bqSxZauvqydn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GHJF9LEqF3wDy58mUERbGL","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/.claude/plans/refactored-wishing-journal.md","old_string":"## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.","new_string":"## Example config.json (for 512GB server with 80GB database)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 16384,\n \"soft-heap-limit-mb\": 131072,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 16GB cache = 128GB max page cache, with a 128GB soft heap limit. The entire 80GB database can be cached in memory. On a 512GB server that's ~25% of RAM.\n\n**Sizing guidance (for documentation):**\n- `cache-size-mb`: Set to `DB_size / max-open-connections` to allow the entire DB to be cached. E.g., 80GB DB with 8 connections → 10GB per connection minimum.\n- `soft-heap-limit-mb`: Set to total desired SQLite memory budget. Should be ≥ `cache-size-mb × max-open-connections` to avoid cache thrashing."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":516,"cache_read_input_tokens":74477,"output_tokens":571,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":516},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviPL7NLRceMEzP1ogKW","type":"assistant","uuid":"1e236768-8934-40ac-aae9-f59af7fa0b05","timestamp":"2026-03-11T05:08:33.880Z"} +{"parentUuid":"1e236768-8934-40ac-aae9-f59af7fa0b05","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01GHJF9LEqF3wDy58mUERbGL","toolUseID":"toolu_01GHJF9LEqF3wDy58mUERbGL","timestamp":"2026-03-11T05:08:33.894Z","uuid":"298cdc3f-2f96-4c36-a624-ac360c7be873"} +{"parentUuid":"1e236768-8934-40ac-aae9-f59af7fa0b05","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GHJF9LEqF3wDy58mUERbGL","type":"tool_result","content":"The file /Users/jan/.claude/plans/refactored-wishing-journal.md has been updated successfully."}]},"uuid":"fa8a7945-9ab7-4343-a332-69828167503d","timestamp":"2026-03-11T05:08:33.894Z","toolUseResult":{"filePath":"/Users/jan/.claude/plans/refactored-wishing-journal.md","oldString":"## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.","newString":"## Example config.json (for 512GB server with 80GB database)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 16384,\n \"soft-heap-limit-mb\": 131072,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 16GB cache = 128GB max page cache, with a 128GB soft heap limit. The entire 80GB database can be cached in memory. On a 512GB server that's ~25% of RAM.\n\n**Sizing guidance (for documentation):**\n- `cache-size-mb`: Set to `DB_size / max-open-connections` to allow the entire DB to be cached. E.g., 80GB DB with 8 connections → 10GB per connection minimum.\n- `soft-heap-limit-mb`: Set to total desired SQLite memory budget. Should be ≥ `cache-size-mb × max-open-connections` to avoid cache thrashing.","originalFile":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 2048, // 2GB per connection\nDbSoftHeapLimitMB: 16384, // 16GB process-wide\n```\n\n**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production.\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 2048,\n \"soft-heap-limit-mb\": 16384,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. Check startup log shows effective values\n","structuredPatch":[{"oldStart":142,"oldLines":15,"newStart":142,"newLines":15,"lines":[" repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)"," ```"," ","-## Example config.json (for 512GB server)","+## Example config.json (for 512GB server with 80GB database)"," "," ```json"," {"," \"main\": {"," \"db\": \"./var/job.db\","," \"db-config\": {","- \"cache-size-mb\": 2048,","- \"soft-heap-limit-mb\": 16384,","+ \"cache-size-mb\": 16384,","+ \"soft-heap-limit-mb\": 131072,"," \"max-open-connections\": 8,"," \"max-idle-time-minutes\": 30"," }"]},{"oldStart":158,"oldLines":8,"newStart":158,"newLines":12,"lines":[" }"," ```"," ","-This would give: 8 connections × 2GB cache = 16GB max page cache, with a 16GB soft heap limit. On a 512GB server that's ~3% of RAM — plenty of headroom.","+This would give: 8 connections × 16GB cache = 128GB max page cache, with a 128GB soft heap limit. The entire 80GB database can be cached in memory. On a 512GB server that's ~25% of RAM."," ","+**Sizing guidance (for documentation):**","+- `cache-size-mb`: Set to `DB_size / max-open-connections` to allow the entire DB to be cached. E.g., 80GB DB with 8 connections → 10GB per connection minimum.","+- `soft-heap-limit-mb`: Set to total desired SQLite memory budget. Should be ≥ `cache-size-mb × max-open-connections` to avoid cache thrashing.","+"," ## Files to Modify"," "," | File | Changes |"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"1e236768-8934-40ac-aae9-f59af7fa0b05"} +{"parentUuid":"fa8a7945-9ab7-4343-a332-69828167503d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01VQ5VY6M3nzB2eQXEzb3SJ3","type":"message","role":"assistant","content":[{"type":"text","text":"Now update the \"Current State\" section to note these hardcoded values need to be replaced:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":608,"cache_read_input_tokens":74993,"cache_creation":{"ephemeral_5m_input_tokens":608,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYviQ5GRTiKQy6nuJ9XyS","type":"assistant","uuid":"b924cf44-e2f1-4b48-b3be-956987675a17","timestamp":"2026-03-11T05:08:36.514Z"} +{"parentUuid":"b924cf44-e2f1-4b48-b3be-956987675a17","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01VQ5VY6M3nzB2eQXEzb3SJ3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YUuhJ8WNsPPHN7v6vnTmVk","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/.claude/plans/refactored-wishing-journal.md","old_string":"## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)","new_string":"## Current State (already implemented on this branch)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded) — **too low for 80GB DB, will be made configurable**\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded) — **too low, will be made configurable**\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default)\n- `MaxOpenConnections = 4` (hardcoded default)\n- Context propagation to all query call sites (already done)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":608,"cache_read_input_tokens":74993,"output_tokens":420,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":608},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviQ5GRTiKQy6nuJ9XyS","type":"assistant","uuid":"a1668cda-71a1-44cd-9715-92597549df49","timestamp":"2026-03-11T05:08:41.022Z"} 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/Users/jan/.claude/plans/refactored-wishing-journal.md has been updated successfully."}]},"uuid":"83f7398f-ec8b-47fd-90d4-cbb823a4270d","timestamp":"2026-03-11T05:08:41.047Z","toolUseResult":{"filePath":"/Users/jan/.claude/plans/refactored-wishing-journal.md","oldString":"## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)","newString":"## Current State (already implemented on this branch)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded) — **too low for 80GB DB, will be made configurable**\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded) — **too low, will be made configurable**\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default)\n- `MaxOpenConnections = 4` (hardcoded default)\n- Context propagation to all query call sites (already done)","originalFile":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)\n- `MaxOpenConnections = 4` (hardcoded default in `config.go`)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 2048, // 2GB per connection\nDbSoftHeapLimitMB: 16384, // 16GB process-wide\n```\n\n**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production.\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server with 80GB database)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 16384,\n \"soft-heap-limit-mb\": 131072,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 16GB cache = 128GB max page cache, with a 128GB soft heap limit. The entire 80GB database can be cached in memory. On a 512GB server that's ~25% of RAM.\n\n**Sizing guidance (for documentation):**\n- `cache-size-mb`: Set to `DB_size / max-open-connections` to allow the entire DB to be cached. E.g., 80GB DB with 8 connections → 10GB per connection minimum.\n- `soft-heap-limit-mb`: Set to total desired SQLite memory budget. Should be ≥ `cache-size-mb × max-open-connections` to avoid cache thrashing.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. Check startup log shows effective values\n","structuredPatch":[{"oldStart":4,"oldLines":12,"newStart":4,"newLines":12,"lines":[" "," Fixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json."," ","-## Current State (already implemented)","+## Current State (already implemented on this branch)"," ","-- `_cache_size = -200000` (200MB per connection, hardcoded in `dbConnection.go:82`)","-- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded in `setupSqlite()`)","-- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default in `config.go`)","-- `MaxOpenConnections = 4` (hardcoded default in `config.go`)","+- `_cache_size = -200000` (200MB per connection, hardcoded) — **too low for 80GB DB, will be made configurable**","+- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded) — **too low, will be made configurable**","+- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default)","+- `MaxOpenConnections = 4` (hardcoded default)"," - Context propagation to all query call sites (already done)"," "," ## Problem"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"a1668cda-71a1-44cd-9715-92597549df49"} +{"parentUuid":"83f7398f-ec8b-47fd-90d4-cbb823a4270d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01Wj8tFinSLJZ7pmPC427A8n","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HX8TkSNWLKGdX8zjtE4LyH","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":457,"cache_read_input_tokens":75601,"output_tokens":78,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":457},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviQc65am3fQJFNK3Rtq","type":"assistant","uuid":"37563d84-c83f-48a8-930c-a0ac84966b90","timestamp":"2026-03-11T05:08:43.784Z"} +{"parentUuid":"37563d84-c83f-48a8-930c-a0ac84966b90","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01HX8TkSNWLKGdX8zjtE4LyH","content":[{"type":"tool_reference","tool_name":"ExitPlanMode"}]}]},"uuid":"ce3a62f9-e93f-4971-a6fd-b61f48f0a7c0","timestamp":"2026-03-11T05:08:43.787Z","toolUseResult":{"matches":["ExitPlanMode"],"query":"select:ExitPlanMode","total_deferred_tools":21},"sourceToolAssistantUUID":"37563d84-c83f-48a8-930c-a0ac84966b90"} +{"parentUuid":"ce3a62f9-e93f-4971-a6fd-b61f48f0a7c0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01XpeDMGgP7dAAU3v5waH6fC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T6eZ9nZpwWy887kh8erVav","name":"ExitPlanMode","input":{"plan":"# Make SQLite Memory Limits Configurable via config.json\n\n## Context\n\nFixes 1-4 for the SQLite memory leak are already implemented on this branch. The hardcoded defaults (200MB cache per connection, 1GB soft heap limit) are conservative. On the production server with 512GB RAM, these could be tuned higher for better query performance. Additionally, `RepositoryConfig` and `SetConfig()` exist but are **never wired up** — there's currently no way to override any repository defaults from config.json.\n\n## Current State (already implemented on this branch)\n\n- `_cache_size = -200000` (200MB per connection, hardcoded) — **too low for 80GB DB, will be made configurable**\n- `soft_heap_limit = 1073741824` (1GB process-wide, hardcoded) — **too low, will be made configurable**\n- `ConnectionMaxIdleTime = 10 * time.Minute` (hardcoded default)\n- `MaxOpenConnections = 4` (hardcoded default)\n- Context propagation to all query call sites (already done)\n\n## Problem\n\n`repository.SetConfig()` exists but is never called from `main.go`. The `initDatabase()` function (line 110) just calls `repository.Connect(config.Keys.DB)` directly. There's no `\"db-config\"` section in `ProgramConfig` or the JSON schema.\n\n## Proposed Changes\n\n### 1. Add SQLite memory fields to `RepositoryConfig`\n\n**File:** `internal/repository/config.go`\n\nAdd two new fields with sensible defaults:\n\n```go\ntype RepositoryConfig struct {\n // ... existing fields ...\n\n // DbCacheSizeMB is the SQLite page cache size per connection in MB.\n // Uses negative PRAGMA cache_size notation (KiB). With MaxOpenConnections=4\n // and DbCacheSizeMB=200, total page cache is up to 800MB.\n // Default: 200 (MB)\n DbCacheSizeMB int\n\n // DbSoftHeapLimitMB is the process-wide SQLite soft heap limit in MB.\n // SQLite will try to release cache pages to stay under this limit.\n // It's a soft limit — queries won't fail, but cache eviction becomes more aggressive.\n // Default: 1024 (1GB)\n DbSoftHeapLimitMB int\n}\n```\n\nUpdate `DefaultConfig()`:\n```go\nDbCacheSizeMB: 2048, // 2GB per connection\nDbSoftHeapLimitMB: 16384, // 16GB process-wide\n```\n\n**Rationale for defaults:** With an 80GB production database on a 512GB server, we want the cache to hold a significant portion of the DB. At 4 connections × 2GB = 8GB default page cache, plus 16GB soft heap limit. The previous 200MB/1GB hardcoded values were too conservative and would hurt query performance by forcing excessive cache eviction. These defaults use ~5% of a 512GB server — still safe for smaller machines, while enabling good performance on production.\n\n### 2. Use config values in `Connect()` and `setupSqlite()`\n\n**File:** `internal/repository/dbConnection.go`\n\nIn `Connect()`, replace the hardcoded cache_size:\n```go\ncacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\nconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n```\n\nChange `setupSqlite()` to accept the config and use it for soft_heap_limit:\n```go\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n pragmas := []string{\n \"temp_store = memory\",\n fmt.Sprintf(\"soft_heap_limit = %d\", cfg.DbSoftHeapLimitMB*1024*1024),\n }\n // ...\n}\n```\n\nUpdate the call site in `Connect()`:\n```go\nerr = setupSqlite(dbHandle.DB, &opts) // was: setupSqlite(dbHandle.DB)\n```\n\n### 3. Add `\"db-config\"` section to `ProgramConfig` and JSON schema\n\n**File:** `internal/config/config.go`\n\nAdd a new struct and field to `ProgramConfig`:\n\n```go\ntype DbConfig struct {\n CacheSizeMB int `json:\"cache-size-mb\"`\n SoftHeapLimitMB int `json:\"soft-heap-limit-mb\"`\n MaxOpenConnections int `json:\"max-open-connections\"`\n MaxIdleConnections int `json:\"max-idle-connections\"`\n ConnectionMaxIdleTimeMins int `json:\"max-idle-time-minutes\"`\n}\n\ntype ProgramConfig struct {\n // ... existing fields ...\n DbConfig *DbConfig `json:\"db-config\"`\n}\n```\n\n**File:** `internal/config/schema.go`\n\nAdd the schema section for validation.\n\n### 4. Wire `SetConfig()` in `initDatabase()`\n\n**File:** `cmd/cc-backend/main.go`\n\n```go\nfunc initDatabase() error {\n if config.Keys.DbConfig != nil {\n cfg := repository.DefaultConfig()\n dc := config.Keys.DbConfig\n if dc.CacheSizeMB > 0 {\n cfg.DbCacheSizeMB = dc.CacheSizeMB\n }\n if dc.SoftHeapLimitMB > 0 {\n cfg.DbSoftHeapLimitMB = dc.SoftHeapLimitMB\n }\n if dc.MaxOpenConnections > 0 {\n cfg.MaxOpenConnections = dc.MaxOpenConnections\n }\n if dc.MaxIdleConnections > 0 {\n cfg.MaxIdleConnections = dc.MaxIdleConnections\n }\n if dc.ConnectionMaxIdleTimeMins > 0 {\n cfg.ConnectionMaxIdleTime = time.Duration(dc.ConnectionMaxIdleTimeMins) * time.Minute\n }\n repository.SetConfig(cfg)\n }\n repository.Connect(config.Keys.DB)\n return nil\n}\n```\n\n### 5. Log effective values on startup\n\n**File:** `internal/repository/dbConnection.go`\n\nAfter setting PRAGMAs, log the effective values so operators can verify:\n```go\ncclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n repoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n```\n\n## Example config.json (for 512GB server with 80GB database)\n\n```json\n{\n \"main\": {\n \"db\": \"./var/job.db\",\n \"db-config\": {\n \"cache-size-mb\": 16384,\n \"soft-heap-limit-mb\": 131072,\n \"max-open-connections\": 8,\n \"max-idle-time-minutes\": 30\n }\n }\n}\n```\n\nThis would give: 8 connections × 16GB cache = 128GB max page cache, with a 128GB soft heap limit. The entire 80GB database can be cached in memory. On a 512GB server that's ~25% of RAM.\n\n**Sizing guidance (for documentation):**\n- `cache-size-mb`: Set to `DB_size / max-open-connections` to allow the entire DB to be cached. E.g., 80GB DB with 8 connections → 10GB per connection minimum.\n- `soft-heap-limit-mb`: Set to total desired SQLite memory budget. Should be ≥ `cache-size-mb × max-open-connections` to avoid cache thrashing.\n\n## Files to Modify\n\n| File | Changes |\n|------|---------|\n| `internal/repository/config.go` | Add `DbCacheSizeMB`, `DbSoftHeapLimitMB` fields + defaults |\n| `internal/repository/dbConnection.go` | Use config values instead of hardcoded; pass config to `setupSqlite`; add startup log |\n| `internal/config/config.go` | Add `DbConfig` struct and field to `ProgramConfig` |\n| `internal/config/schema.go` | Add `\"db-config\"` JSON schema section |\n| `cmd/cc-backend/main.go` | Wire `SetConfig()` in `initDatabase()` |\n\n## Verification\n\n1. `go build ./...` — compiles\n2. `go test ./internal/repository/... ./internal/config/...` — tests pass\n3. Without `db-config` in config.json: defaults apply (200MB cache, 1GB heap) — backwards compatible\n4. With `db-config`: verify with `PRAGMA cache_size;` and `PRAGMA soft_heap_limit;` in sqlite3 CLI\n5. Check startup log shows effective values\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":831,"cache_read_input_tokens":76058,"output_tokens":39,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":831},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviQoUvhoXvjVDZ2FyXX","type":"assistant","uuid":"a75bcb1d-fa24-448d-ab80-1449274f64e8","timestamp":"2026-03-11T05:08:45.604Z"} +{"parentUuid":"a75bcb1d-fa24-448d-ab80-1449274f64e8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.","is_error":true,"tool_use_id":"toolu_01T6eZ9nZpwWy887kh8erVav"}]},"uuid":"dc021bc4-2a78-4451-bf33-b74a665948a2","timestamp":"2026-03-11T05:09:30.792Z","toolUseResult":"Error: The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.","sourceToolAssistantUUID":"a75bcb1d-fa24-448d-ab80-1449274f64e8"} +{"parentUuid":"dc021bc4-2a78-4451-bf33-b74a665948a2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","message":{"model":"claude-opus-4-6","id":"msg_01SZg5V6vhiGwLroskH8kCr6","type":"message","role":"assistant","content":[{"type":"text","text":"Understood — waiting for your input on how to proceed."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":227,"cache_read_input_tokens":76889,"output_tokens":15,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":227},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYviUGagTo8tFRQwD5GBm","type":"assistant","uuid":"a9c7b015-b216-4bce-bd92-d584e89e6260","timestamp":"2026-03-11T05:09:33.117Z"} +{"parentUuid":"a9c7b015-b216-4bce-bd92-d584e89e6260","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"progress","data":{"type":"hook_progress","hookEvent":"Stop","hookName":"Stop","command":"entire hooks claude-code stop"},"parentToolUseID":"075a5215-590c-4da1-887c-540b891d20bb","toolUseID":"075a5215-590c-4da1-887c-540b891d20bb","timestamp":"2026-03-11T05:09:33.133Z","uuid":"1251e0dc-96d2-484c-9086-863d1a7bae94"} +{"parentUuid":"1251e0dc-96d2-484c-9086-863d1a7bae94","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"system","subtype":"stop_hook_summary","hookCount":1,"hookInfos":[{"command":"entire hooks claude-code stop","durationMs":677}],"hookErrors":[],"preventedContinuation":false,"stopReason":"","hasOutput":false,"level":"suggestion","timestamp":"2026-03-11T05:09:33.810Z","uuid":"eb77b0d3-55a8-4cd2-8fc4-8fc8b2f89ea7","toolUseID":"075a5215-590c-4da1-887c-540b891d20bb"} +{"parentUuid":"eb77b0d3-55a8-4cd2-8fc4-8fc8b2f89ea7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/CC/cc-backend","sessionId":"520afa6a-6a70-437b-96c1-35c40ed3ec48","version":"2.1.72","gitBranch":"optimize-db-indices","slug":"refactored-wishing-journal","type":"system","subtype":"turn_duration","durationMs":192763,"timestamp":"2026-03-11T05:09:33.811Z","uuid":"828e1b6d-90a8-4444-a5b3-af3ce28339ba","isMeta":false} diff --git a/e3/68e6d8abf3/1/metadata.json b/e3/68e6d8abf3/1/metadata.json new file mode 100644 index 00000000..2fa408b7 --- /dev/null +++ b/e3/68e6d8abf3/1/metadata.json @@ -0,0 +1,34 @@ +{ + "cli_version": "0.4.8", + "checkpoint_id": "e368e6d8abf3", + "session_id": "520afa6a-6a70-437b-96c1-35c40ed3ec48", + "strategy": "manual-commit", + "created_at": "2026-03-11T05:14:07.476561Z", + "branch": "optimize-db-indices", + "checkpoints_count": 2, + "files_touched": [ + "internal/repository/config.go", + "internal/repository/dbConnection.go", + "internal/repository/jobFind.go", + "internal/repository/jobQuery.go", + "internal/repository/stats.go" + ], + "agent": "Claude Code", + "turn_id": "443ef781634b", + "token_usage": { + "input_tokens": 5108, + "cache_creation_tokens": 68335, + "cache_read_tokens": 1908216, + "output_tokens": 13846, + "api_call_count": 34 + }, + "initial_attribution": { + "calculated_at": "2026-03-11T05:14:07.210716Z", + "agent_lines": 37, + "human_added": 98, + "human_modified": 0, + "human_removed": 0, + "total_committed": 135, + "agent_percentage": 27.40740740740741 + } +} diff --git a/e3/68e6d8abf3/1/prompt.txt b/e3/68e6d8abf3/1/prompt.txt new file mode 100644 index 00000000..ae9373bb --- /dev/null +++ b/e3/68e6d8abf3/1/prompt.txt @@ -0,0 +1,136 @@ +Implement the following plan: + +# Fix SQLite Memory Not Released After Query Timeout + +## Context + +On the production 20M-row database, when a query runs into a timeout (due to full-table scan with wrong index), the memory allocated by SQLite is **not released afterwards**. The process stays bloated until restarted. This is caused by three compounding issues in the current SQLite configuration. + +## Root Cause Analysis + +### 1. `_cache_size=1000000000` is effectively unlimited (~4TB) + +**File:** `internal/repository/dbConnection.go:82` + +```go +connectionURLParams.Add("_cache_size", "1000000000") +``` + +SQLite's `cache_size` PRAGMA interprets **positive values as page count** (default page size = 4KB). So 1,000,000,000 pages × 4KB = ~4TB. In practice, this means "never evict cached pages." After a full-table scan of 20M rows, every page touched stays in SQLite's page cache. With 4 connections (`MaxOpenConns=4`), each can independently cache gigabytes. + +For comparison, the SQLite archive backend in `pkg/archive/sqliteBackend.go` uses `PRAGMA cache_size=-64000` (64MB — negative = KiB). + +### 2. No query context/timeout — queries run indefinitely + +**File:** `internal/repository/jobQuery.go:87` + +```go +rows, err := query.RunWith(r.stmtCache).Query() // No context! +``` + +The `ctx` parameter is available but never passed to the database layer. Squirrel supports `.QueryContext(ctx)` but it's not used. If the HTTP request times out or the client disconnects, the query keeps running and scanning pages into cache. + +### 3. No SQLite memory limit — no `soft_heap_limit` or `shrink_memory` + +SQLite has built-in memory management PRAGMAs that are not configured: +- **`soft_heap_limit`** — asks SQLite to keep heap usage below N bytes (best-effort, releases cache pages to stay under limit) +- **`hard_heap_limit`** — hard cap, queries fail with SQLITE_NOMEM if exceeded +- **`shrink_memory`** — immediately releases all unused memory back to the OS + +None of these are set, so SQLite allocates freely and never releases. + +### 4. `temp_store = memory` amplifies the problem + +**File:** `internal/repository/dbConnection.go:41` + +Temporary B-tree sorts (exactly what happens during ORDER BY on a full-table scan) are stored in RAM. With 20M rows and no sort optimization, this can be gigabytes of temporary memory on top of the page cache. + +### 5. Connections live for 1 hour after use + +`ConnMaxIdleTime = 1 hour` means a connection that just did a massive full-table scan sits idle in the pool for up to an hour, holding all its cached pages. + +## Proposed Changes + +### Fix 1: Set reasonable `cache_size` (high impact, low risk) + +**File:** `internal/repository/dbConnection.go:82` + +Change from `1000000000` (1B pages ≈ 4TB) to `-200000` (200MB in KiB notation, per connection): + +```go +connectionURLParams.Add("_cache_size", "-200000") // 200MB per connection +``` + +With 4 max connections: 4 × 200MB = 800MB max page cache. This is generous enough for normal queries but prevents runaway memory after full-table scans. + +### Fix 2: Add `soft_heap_limit` (high impact, low risk) + +**File:** `internal/repository/dbConnection.go`, in `setupSqlite()`: + +```go +"soft_heap_limit = 1073741824", // 1GB soft limit across all connections +``` + +This is a **process-wide** limit (not per-connection). SQLite will try to release cache pages to stay under 1GB total. It's a soft limit — it won't abort queries, just evicts cache more aggressively. + +### Fix 3: Pass context to database queries (medium impact, medium effort) + +Change `.Query()` to `.QueryContext(ctx)`, `.QueryRow()` to `.QueryRowContext(ctx)`, and `.Scan()` to `.ScanContext(ctx)` for all query methods that already receive a `ctx` parameter. This allows HTTP request cancellation to stop the SQLite query. + +**Note:** The `stmtCache` from squirrel supports `QueryContext`/`QueryRowContext`. Only methods that already have `ctx` are changed — no signature changes needed. + +**Call sites to update** (methods that have `ctx` and call `.Query()`/`.QueryRow()`/`.Scan()`): + +| File | Method | Line | Change | +|------|--------|------|--------| +| `jobQuery.go` | `QueryJobs` | 87 | `.Query()` → `.QueryContext(ctx)` | +| `jobQuery.go` | `CountJobs` | 129 | `.Scan()` → `.ScanContext(ctx)` | +| `stats.go` | `JobsStatsGrouped` | 233 | `.Query()` → `.QueryContext(ctx)` | +| `stats.go` | `JobsStats` | 358 | `.QueryRow()` → `.QueryRowContext(ctx)` | +| `stats.go` | `JobCountGrouped` | 443 | `.Query()` → `.QueryContext(ctx)` | +| `stats.go` | `AddJobCountGrouped` | 504 | `.Query()` → `.QueryContext(ctx)` | +| `stats.go` | `AddJobCount` | 569 | `.Scan()` → `.ScanContext(ctx)` | +| `stats.go` | `jobsStatisticsHistogram` | 758 | `.Query()` → `.QueryContext(ctx)` | +| `stats.go` | `jobsDurationStatisticsHistogram` | 832 | `.Query()` → `.QueryContext(ctx)` | +| `stats.go` | `jobsMetricStatisticsHistogram` | 962 | `.Query()` → `.QueryContext(ctx)` | +| `jobFind.go` | `FindByID` | 174 | `.QueryRow()` → `.QueryRowContext(ctx)` | +| `jobFind.go` | `FindByJobID` | 220 | `.QueryRow()` → `.QueryRowContext(ctx)` | +| `job.go` | `CountGroupedJobs` | 410 | `.Scan()` → `.ScanContext(ctx)` (needs ctx added to signature) | +| `job.go` | `GetJobList` | 751 | `.Query()` → `.QueryContext(ctx)` (needs ctx added to signature) | + +Methods without `ctx` in their signature (e.g., `GetJobList`, `CountGroupedJobs`) can either have `ctx` added or be left for a follow-up. The priority is the methods already accepting `ctx`. + +### Fix 4: Reduce `ConnMaxIdleTime` + +**File:** `internal/repository/config.go` + +Reduce `ConnectionMaxIdleTime` from 1 hour to 10 minutes. Idle connections with cached pages are released sooner. + +## Scope — All Four Fixes + +All fixes are complementary, independent, and low-risk. No disadvantages to applying all together. + +## Files to Modify + +- `internal/repository/dbConnection.go` — cache_size, soft_heap_limit (Fixes 1 & 2) +- `internal/repository/jobQuery.go` — QueryContext (Fix 3) +- `internal/repository/job.go` — QueryContext for other query methods (Fix 3) +- `internal/repository/stats.go` — QueryContext for stats queries (Fix 3) +- `internal/repository/config.go` — ConnMaxIdleTime (Fix 4, optional) + +## Verification + +1. `go build ./...` — compiles +2. `go test ./internal/repository/...` — tests pass +3. Monitor RSS memory before/after on production: + - Trigger a heavy query, observe memory spike + - Verify memory drops back down within seconds (not hours) +4. `PRAGMA cache_size;` in sqlite3 CLI on running DB — should show `-200000` +5. `PRAGMA soft_heap_limit;` — should show `1073741824` + + +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/c31c699a-f492-48f7-bcf0-35d3ceeac243.jsonl + +--- + +Our server has 512GB main memory. Does it make sense to make cache_size and soft_heap_limit configurable to make use of the main memory capacity? \ No newline at end of file diff --git a/e3/68e6d8abf3/metadata.json b/e3/68e6d8abf3/metadata.json index 8971e564..da58a538 100644 --- a/e3/68e6d8abf3/metadata.json +++ b/e3/68e6d8abf3/metadata.json @@ -3,7 +3,7 @@ "checkpoint_id": "e368e6d8abf3", "strategy": "manual-commit", "branch": "optimize-db-indices", - "checkpoints_count": 1, + "checkpoints_count": 3, "files_touched": [ "cmd/cc-backend/main.go", "internal/config/config.go", @@ -21,13 +21,20 @@ "context": "/e3/68e6d8abf3/0/context.md", "content_hash": "/e3/68e6d8abf3/0/content_hash.txt", "prompt": "/e3/68e6d8abf3/0/prompt.txt" + }, + { + "metadata": "/e3/68e6d8abf3/1/metadata.json", + "transcript": "/e3/68e6d8abf3/1/full.jsonl", + "context": "/e3/68e6d8abf3/1/context.md", + "content_hash": "/e3/68e6d8abf3/1/content_hash.txt", + "prompt": "/e3/68e6d8abf3/1/prompt.txt" } ], "token_usage": { - "input_tokens": 15, - "cache_creation_tokens": 27408, - "cache_read_tokens": 402383, - "output_tokens": 3673, - "api_call_count": 13 + "input_tokens": 5123, + "cache_creation_tokens": 95743, + "cache_read_tokens": 2310599, + "output_tokens": 17519, + "api_call_count": 47 } }