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cc-backend/d4/9917ff4b10/2/full.jsonl
Jan Eitzinger 800cc0539e Checkpoint: d49917ff4b10 
Entire-Session: 6dbc68b3-c35d-4697-b545-0298c2b303cb
Entire-Strategy: manual-commit
Entire-Agent: Claude Code
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2026-03-12 20:12:50 +01:00

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{"parentUuid":"70036f3e-22b3-46d5-8db9-3308ceed7b2d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Fix: buildStatsQuery hangs on `/monitoring/users/?cluster=woody&startTime=last30d`\n\n## Context\n\nThe users list page (`/monitoring/users/?cluster=woody&startTime=last30d`) executes a `GROUP BY job.hpc_user` stats query with `WHERE job.cluster = ? AND job.start_time >= ?`. On production databases with hundreds of thousands of jobs in the last 30 days, this query never returns.\n\n**Root cause**: The covering index from migration 12 has column order `(cluster, start_time, hpc_user, ...)`. Since `start_time` is a **range** condition before `hpc_user`, SQLite cannot use index ordering for `GROUP BY hpc_user`. It must collect all matching rows into a **temp B-Tree** and sort them — extremely expensive for large result sets.\n\n## Changes\n\n### 1. Reorder covering index columns (critical fix)\n\n**File**: `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql`\n\nChange from: `(cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)`\nChange to: `(cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc)`\n\nThis lets SQLite:\n- Match `cluster = ?` (equality prefix)\n- Walk `hpc_user` in index order (GROUP BY without temp B-Tree)\n- Within each `(cluster, hpc_user)` group, binary-search `start_time >= ?`\n- Read all aggregation columns from the index (covering)\n\nAdd `DROP INDEX IF EXISTS jobs_cluster_starttime_user_stats` to handle databases that already applied the old migration 12.\n\n**File**: `internal/repository/migrations/sqlite3/12_stats-covering-index.down.sql`\n\nUpdate to drop the renamed index `jobs_cluster_user_starttime_stats`.\n\n### 2. Use `COUNT(*)` instead of `COUNT(job.id)`\n\n**File**: `internal/repository/stats.go`\n\nLines to change: 108, 110, 156, 751, 817\n\n`COUNT(*)` is idiomatic for counting rows and avoids any ambiguity about whether the rowid is available in a covering index scan.\n\n### 3. Fix double-quoted string literals in CASE expressions\n\n**File**: `internal/repository/stats.go`\n\nLines 147, 647: Change `job.job_state = \"running\"` to `job.job_state = 'running'`\n\nDouble quotes are SQL identifiers, not string literals. SQLite accepts them as a compatibility quirk but proper single quotes are safer for the query planner.\n\n## Not changing\n\n- **`jobs_cluster_starttime` index**: Still needed for job list queries (`ORDER BY start_time`).\n- **`jobs_cluster_user` index**: Technically redundant with new covering index prefix, but harmless. Can clean up later.\n- **Migration version**: Stays at 12 (same migration number, updated content).\n\n## Verification\n\n```bash\n# Run existing tests\ngo test -v ./internal/repository -run TestJobStats\ngo test -v ./internal/repository -run TestBuildJobStatsQuery\n\n# On production DB copy, verify query plan no longer shows temp B-Tree:\n# EXPLAIN QUERY PLAN SELECT job.hpc_user, COUNT(*) ... FROM job\n# WHERE job.cluster = 'woody' AND job.start_time >= <30d_ago> GROUP BY job.hpc_user;\n# Before: \"USE TEMP B-TREE FOR GROUP BY\"\n# After: direct index scan, no temp B-Tree\n```\n\n\nIf you need specific details from before exiting plan mode (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/jan/.claude/projects/-Users-jan-prg-ClusterCockpit-cc-backend/3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb.jsonl"},"uuid":"c6ac1da4-901c-4306-a990-4051377fb534","timestamp":"2026-03-12T11:51:34.234Z","planContent":"# Fix: buildStatsQuery hangs on `/monitoring/users/?cluster=woody&startTime=last30d`\n\n## Context\n\nThe users list page (`/monitoring/users/?cluster=woody&startTime=last30d`) executes a `GROUP BY job.hpc_user` stats query with `WHERE job.cluster = ? AND job.start_time >= ?`. On production databases with hundreds of thousands of jobs in the last 30 days, this query never returns.\n\n**Root cause**: The covering index from migration 12 has column order `(cluster, start_time, hpc_user, ...)`. Since `start_time` is a **range** condition before `hpc_user`, SQLite cannot use index ordering for `GROUP BY hpc_user`. It must collect all matching rows into a **temp B-Tree** and sort them — extremely expensive for large result sets.\n\n## Changes\n\n### 1. Reorder covering index columns (critical fix)\n\n**File**: `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql`\n\nChange from: `(cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)`\nChange to: `(cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc)`\n\nThis lets SQLite:\n- Match `cluster = ?` (equality prefix)\n- Walk `hpc_user` in index order (GROUP BY without temp B-Tree)\n- Within each `(cluster, hpc_user)` group, binary-search `start_time >= ?`\n- Read all aggregation columns from the index (covering)\n\nAdd `DROP INDEX IF EXISTS jobs_cluster_starttime_user_stats` to handle databases that already applied the old migration 12.\n\n**File**: `internal/repository/migrations/sqlite3/12_stats-covering-index.down.sql`\n\nUpdate to drop the renamed index `jobs_cluster_user_starttime_stats`.\n\n### 2. Use `COUNT(*)` instead of `COUNT(job.id)`\n\n**File**: `internal/repository/stats.go`\n\nLines to change: 108, 110, 156, 751, 817\n\n`COUNT(*)` is idiomatic for counting rows and avoids any ambiguity about whether the rowid is available in a covering index scan.\n\n### 3. Fix double-quoted string literals in CASE expressions\n\n**File**: `internal/repository/stats.go`\n\nLines 147, 647: Change `job.job_state = \"running\"` to `job.job_state = 'running'`\n\nDouble quotes are SQL identifiers, not string literals. SQLite accepts them as a compatibility quirk but proper single quotes are safer for the query planner.\n\n## Not changing\n\n- **`jobs_cluster_starttime` index**: Still needed for job list queries (`ORDER BY start_time`).\n- **`jobs_cluster_user` index**: Technically redundant with new covering index prefix, but harmless. Can clean up later.\n- **Migration version**: Stays at 12 (same migration number, updated content).\n\n## Verification\n\n```bash\n# Run existing tests\ngo test -v ./internal/repository -run TestJobStats\ngo test -v ./internal/repository -run TestBuildJobStatsQuery\n\n# On production DB copy, verify query plan no longer shows temp B-Tree:\n# EXPLAIN QUERY PLAN SELECT job.hpc_user, COUNT(*) ... FROM job\n# WHERE job.cluster = 'woody' AND job.start_time >= <30d_ago> GROUP BY job.hpc_user;\n# Before: \"USE TEMP B-TREE FOR GROUP BY\"\n# After: direct index scan, no temp B-Tree\n```\n"}
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{"parentUuid":"2581a3c1-1682-4491-ada4-69d62043e2cc","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011PZGPdWTsV9Bqen7LHrLzN","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 job statistics with optional grouping.\n 128→// Only requested columns are computed; unrequested columns select 0 as placeholder.\n 129→//\n 130→// Parameters:\n 131→// - filter: Job filters to apply (cluster, user, time range, etc.)\n 132→// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n 133→// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n 134→// - reqFields: Set of requested field names; nil means compute all fields\n 135→func (r *JobRepository) buildStatsQuery(\n 136→\tfilter []*model.JobFilter,\n 137→\tcol string,\n 138→\tshortThreshold int,\n 139→\treqFields map[string]bool,\n 140→) sq.SelectBuilder {\n 141→\tnow := time.Now().Unix()\n 142→\n 143→\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n 144→\tneed := func(field string) bool {\n 145→\t\treturn reqFields == nil || reqFields[field]\n 146→\t}\n 147→\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)`, now)\n 148→\n 149→\t// Build column list\n 150→\tcolumns := make([]string, 0, 14)\n 151→\n 152→\tif col != \"\" {\n 153→\t\tcolumns = append(columns, col)\n 154→\t}\n 155→\n 156→\tcolumns = append(columns, \"COUNT(job.id) as totalJobs\")\n 157→\n 158→\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n 159→\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n 160→\t} else {\n 161→\t\tcolumns = append(columns, \"0 AS totalUsers\")\n 162→\t}\n 163→\n 164→\tif need(\"totalWalltime\") {\n 165→\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n 166→\t} else {\n 167→\t\tcolumns = append(columns, \"0 as totalWalltime\")\n 168→\t}\n 169→\n 170→\tif need(\"totalNodes\") {\n 171→\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n 172→\t} else {\n 173→\t\tcolumns = append(columns, \"0 as totalNodes\")\n 174→\t}\n 175→\n 176→\tif need(\"totalNodeHours\") {\n 177→\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n 178→\t} else {\n 179→\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n 180→\t}\n 181→\n 182→\tif need(\"totalCores\") {\n 183→\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n 184→\t} else {\n 185→\t\tcolumns = append(columns, \"0 as totalCores\")\n 186→\t}\n 187→\n 188→\tif need(\"totalCoreHours\") {\n 189→\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n 190→\t} else {\n 191→\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n 192→\t}\n 193→\n 194→\tif need(\"totalAccs\") {\n 195→\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n 196→\t} else {\n 197→\t\tcolumns = append(columns, \"0 as totalAccs\")\n 198→\t}\n 199→\n 200→\tif need(\"totalAccHours\") {\n 201→\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n 202→\t} else {\n 203→\t\tcolumns = append(columns, \"0 as totalAccHours\")\n 204→\t}\n 205→\n 206→\tif need(\"runningJobs\") {\n 207→\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n 208→\t} else {\n 209→\t\tcolumns = append(columns, \"0 as runningJobs\")\n 210→\t}\n 211→\n 212→\tif need(\"shortJobs\") {\n 213→\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n 214→\t} else {\n 215→\t\tcolumns = append(columns, \"0 as shortJobs\")\n 216→\t}\n 217→\n 218→\tquery := sq.Select(columns...).From(\"job\")\n 219→\tif col != \"\" {\n 220→\t\tquery = query.GroupBy(col)\n 221→\t}\n 222→\n 223→\tfor _, f := range filter {\n 224→\t\tquery = BuildWhereClause(f, query)\n 225→\t}\n 226→\n 227→\treturn query\n 228→}\n 229→\n 230→// JobsStatsGrouped computes job statistics grouped by a dimension (user, project, cluster, or subcluster).\n 231→// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n 232→// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\n 233→func (r *JobRepository) JobsStatsGrouped(\n 234→\tctx context.Context,\n 235→\tfilter []*model.JobFilter,\n 236→\tpage *model.PageRequest,\n 237→\tsortBy *model.SortByAggregate,\n 238→\tgroupBy *model.Aggregate,\n 239→\treqFields map[string]bool,\n 240→) ([]*model.JobsStatistics, error) {\n 241→\tstart := time.Now()\n 242→\tcol := groupBy2column[*groupBy]\n 243→\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\n 244→\n 245→\tquery, err := SecurityCheck(ctx, query)\n 246→\tif err != nil {\n 247→\t\treturn nil, err\n 248→\t}\n 249→\n 250→\tif sortBy != nil {\n 251→\t\tsortBy := sortBy2column[*sortBy]\n 252→\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n 253→\t}\n 254→\tif page != nil && page.ItemsPerPage != -1 {\n 255→\t\tlimit := uint64(page.ItemsPerPage)\n 256→\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n 257→\t}\n 258→\n 259→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n 260→\tif err != nil {\n 261→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 262→\t\treturn nil, err\n 263→\t}\n 264→\tdefer rows.Close()\n 265→\n 266→\tstats := make([]*model.JobsStatistics, 0, 100)\n 267→\n 268→\tfor rows.Next() {\n 269→\t\tvar id sql.NullString\n 270→\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n 271→\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {\n 272→\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n 273→\t\t\treturn nil, err\n 274→\t\t}\n 275→\n 276→\t\tif id.Valid {\n 277→\t\t\tstats = append(stats,\n 278→\t\t\t\t&model.JobsStatistics{\n 279→\t\t\t\t\tID: id.String,\n 280→\t\t\t\t\tTotalJobs: int(jobs.Int64),\n 281→\t\t\t\t\tTotalUsers: int(users.Int64),\n 282→\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n 283→\t\t\t\t\tTotalNodes: int(nodes.Int64),\n 284→\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n 285→\t\t\t\t\tTotalCores: int(cores.Int64),\n 286→\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n 287→\t\t\t\t\tTotalAccs: int(accs.Int64),\n 288→\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n 289→\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n 290→\t\t\t\t\tShortJobs: int(shortJobs.Int64),\n 291→\t\t\t\t})\n 292→\t\t}\n 293→\t}\n 294→\n 295→\tif err := rows.Err(); err != nil {\n 296→\t\treturn nil, err\n 297→\t}\n 298→\n 299→\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n 300→\tif col == \"job.hpc_user\" && len(stats) > 0 {\n 301→\t\tusernames := make([]any, len(stats))\n 302→\t\tfor i, s := range stats {\n 303→\t\t\tusernames[i] = s.ID\n 304→\t\t}\n 305→\n 306→\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n 307→\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n 308→\t\tif err != nil {\n 309→\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n 310→\t\t\t// Non-fatal: stats are still valid without display names\n 311→\t\t} else {\n 312→\t\t\tdefer nameRows.Close()\n 313→\t\t\tnameMap := make(map[string]string, len(stats))\n 314→\t\t\tfor nameRows.Next() {\n 315→\t\t\t\tvar username, name string\n 316→\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n 317→\t\t\t\t\tnameMap[username] = name\n 318→\t\t\t\t}\n 319→\t\t\t}\n 320→\t\t\tfor _, s := range stats {\n 321→\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n 322→\t\t\t\t\ts.Name = name\n 323→\t\t\t\t}\n 324→\t\t\t}\n 325→\t\t}\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→\treqFields map[string]bool,\n 351→) ([]*model.JobsStatistics, error) {\n 352→\tstart := time.Now()\n 353→\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\n 354→\tquery, err := SecurityCheck(ctx, query)\n 355→\tif err != nil {\n 356→\t\treturn nil, err\n 357→\t}\n 358→\n 359→\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n 360→\tstats := make([]*model.JobsStatistics, 0, 1)\n 361→\n 362→\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n 363→\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {\n 364→\t\tcclog.Warn(\"Error while scanning rows\")\n 365→\t\treturn nil, err\n 366→\t}\n 367→\n 368→\tif jobs.Valid {\n 369→\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n 370→\n 371→\t\tif nodeHours.Valid {\n 372→\t\t\ttotalNodeHours = int(nodeHours.Int64)\n 373→\t\t}\n 374→\t\tif coreHours.Valid {\n 375→\t\t\ttotalCoreHours = int(coreHours.Int64)\n 376→\t\t}\n 377→\t\tif accHours.Valid {\n 378→\t\t\ttotalAccHours = int(accHours.Int64)\n 379→\t\t}\n 380→\t\tstats = append(stats,\n 381→\t\t\t&model.JobsStatistics{\n 382→\t\t\t\tTotalJobs: int(jobs.Int64),\n 383→\t\t\t\tTotalUsers: int(users.Int64),\n 384→\t\t\t\tTotalWalltime: int(walltime.Int64),\n 385→\t\t\t\tTotalNodeHours: totalNodeHours,\n 386→\t\t\t\tTotalCoreHours: totalCoreHours,\n 387→\t\t\t\tTotalAccHours: totalAccHours,\n 388→\t\t\t\tRunningJobs: int(runningJobs.Int64),\n 389→\t\t\t\tShortJobs: int(shortJobs.Int64),\n 390→\t\t\t})\n 391→\t}\n 392→\n 393→\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n 394→\treturn stats, nil\n 395→}\n 396→\n 397→// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n 398→// Returns 0.0 if the metric is not found or statType is invalid.\n 399→//\n 400→// Parameters:\n 401→// - job: Job struct with populated Statistics field\n 402→// - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n 403→// - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n 404→//\n 405→// Returns the requested statistic value or 0.0 if not found.\n 406→func LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n 407→\tif stats, ok := job.Statistics[metric]; ok {\n 408→\t\tswitch statType {\n 409→\t\tcase \"avg\":\n 410→\t\t\treturn stats.Avg\n 411→\t\tcase \"max\":\n 412→\t\t\treturn stats.Max\n 413→\t\tcase \"min\":\n 414→\t\t\treturn stats.Min\n 415→\t\tdefault:\n 416→\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n 417→\t\t}\n 418→\t}\n 419→\n 420→\treturn 0.0\n 421→}\n 422→\n 423→// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n 424→//\n 425→// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n 426→// avoiding the overhead of calculating walltime and resource usage metrics.\n 427→//\n 428→// Parameters:\n 429→// - ctx: Context for security checks\n 430→// - filter: Filters to apply\n 431→// - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n 432→//\n 433→// Returns JobsStatistics with only ID and TotalJobs populated for each group.\n 434→func (r *JobRepository) JobCountGrouped(\n 435→\tctx context.Context,\n 436→\tfilter []*model.JobFilter,\n 437→\tgroupBy *model.Aggregate,\n 438→) ([]*model.JobsStatistics, error) {\n 439→\tstart := time.Now()\n 440→\tcol := groupBy2column[*groupBy]\n 441→\tquery := r.buildCountQuery(filter, \"\", col)\n 442→\tquery, err := SecurityCheck(ctx, query)\n 443→\tif err != nil {\n 444→\t\treturn nil, err\n 445→\t}\n 446→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n 447→\tif err != nil {\n 448→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 449→\t\treturn nil, err\n 450→\t}\n 451→\tdefer rows.Close()\n 452→\n 453→\tstats := make([]*model.JobsStatistics, 0, 100)\n 454→\n 455→\tfor rows.Next() {\n 456→\t\tvar id sql.NullString\n 457→\t\tvar cnt sql.NullInt64\n 458→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n 459→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 460→\t\t\treturn nil, err\n 461→\t\t}\n 462→\t\tif id.Valid {\n 463→\t\t\tstats = append(stats,\n 464→\t\t\t\t&model.JobsStatistics{\n 465→\t\t\t\t\tID: id.String,\n 466→\t\t\t\t\tTotalJobs: int(cnt.Int64),\n 467→\t\t\t\t})\n 468→\t\t}\n 469→\t}\n 470→\n 471→\tif err := rows.Err(); err != nil {\n 472→\t\treturn nil, err\n 473→\t}\n 474→\n 475→\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n 476→\treturn stats, nil\n 477→}\n 478→\n 479→// AddJobCountGrouped augments existing statistics with additional job counts by category.\n 480→//\n 481→// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n 482→// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n 483→//\n 484→// Parameters:\n 485→// - ctx: Context for security checks\n 486→// - filter: Filters to apply\n 487→// - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n 488→// - stats: Existing statistics to augment (modified in-place by ID matching)\n 489→// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n 490→//\n 491→// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n 492→// Groups without matching jobs will have 0 for the added field.\n 493→func (r *JobRepository) AddJobCountGrouped(\n 494→\tctx context.Context,\n 495→\tfilter []*model.JobFilter,\n 496→\tgroupBy *model.Aggregate,\n 497→\tstats []*model.JobsStatistics,\n 498→\tkind string,\n 499→) ([]*model.JobsStatistics, error) {\n 500→\tstart := time.Now()\n 501→\tcol := groupBy2column[*groupBy]\n 502→\tquery := r.buildCountQuery(filter, kind, col)\n 503→\tquery, err := SecurityCheck(ctx, query)\n 504→\tif err != nil {\n 505→\t\treturn nil, err\n 506→\t}\n 507→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n 508→\tif err != nil {\n 509→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n 510→\t\treturn nil, err\n 511→\t}\n 512→\tdefer rows.Close()\n 513→\n 514→\tcounts := make(map[string]int)\n 515→\n 516→\tfor rows.Next() {\n 517→\t\tvar id sql.NullString\n 518→\t\tvar cnt sql.NullInt64\n 519→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n 520→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 521→\t\t\treturn nil, err\n 522→\t\t}\n 523→\t\tif id.Valid {\n 524→\t\t\tcounts[id.String] = int(cnt.Int64)\n 525→\t\t}\n 526→\t}\n 527→\n 528→\tif err := rows.Err(); err != nil {\n 529→\t\treturn nil, err\n 530→\t}\n 531→\n 532→\tswitch kind {\n 533→\tcase \"running\":\n 534→\t\tfor _, s := range stats {\n 535→\t\t\ts.RunningJobs = counts[s.ID]\n 536→\t\t}\n 537→\tcase \"short\":\n 538→\t\tfor _, s := range stats {\n 539→\t\t\ts.ShortJobs = counts[s.ID]\n 540→\t\t}\n 541→\t}\n 542→\n 543→\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n 544→\treturn stats, nil\n 545→}\n 546→\n 547→// AddJobCount augments existing overall statistics with additional job counts by category.\n 548→//\n 549→// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n 550→// to all statistics entries (typically just one).\n 551→//\n 552→// Parameters:\n 553→// - ctx: Context for security checks\n 554→// - filter: Filters to apply\n 555→// - stats: Existing statistics to augment (modified in-place)\n 556→// - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n 557→//\n 558→// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\n 559→func (r *JobRepository) AddJobCount(\n 560→\tctx context.Context,\n 561→\tfilter []*model.JobFilter,\n 562→\tstats []*model.JobsStatistics,\n 563→\tkind string,\n 564→) ([]*model.JobsStatistics, error) {\n 565→\tstart := time.Now()\n 566→\tquery := r.buildCountQuery(filter, kind, \"\")\n 567→\tquery, err := SecurityCheck(ctx, query)\n 568→\tif err != nil {\n 569→\t\treturn nil, err\n 570→\t}\n 571→\tvar cnt sql.NullInt64\n 572→\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n 573→\t\tcclog.Warn(\"Error while querying DB for job count\")\n 574→\t\treturn nil, err\n 575→\t}\n 576→\n 577→\tcount := int(cnt.Int64)\n 578→\n 579→\tswitch kind {\n 580→\tcase \"running\":\n 581→\t\tfor _, s := range stats {\n 582→\t\t\ts.RunningJobs = count\n 583→\t\t}\n 584→\tcase \"short\":\n 585→\t\tfor _, s := range stats {\n 586→\t\t\ts.ShortJobs = count\n 587→\t\t}\n 588→\t}\n 589→\n 590→\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n 591→\treturn stats, nil\n 592→}\n 593→\n 594→// AddHistograms augments statistics with distribution histograms for job properties.\n 595→//\n 596→// Generates histogram data for visualization of job duration, node count, core count,\n 597→// and accelerator count distributions. Duration histogram uses intelligent binning based\n 598→// on the requested resolution.\n 599→//\n 600→// Parameters:\n 601→// - ctx: Context for security checks\n 602→// - filter: Filters to apply to jobs included in histograms\n 603→// - stat: Statistics struct to augment (modified in-place)\n 604→// - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n 605→//\n 606→// Populates these fields in stat:\n 607→// - HistDuration: Job duration distribution (zero-padded bins)\n 608→// - HistNumNodes: Node count distribution\n 609→// - HistNumCores: Core (hwthread) count distribution\n 610→// - HistNumAccs: Accelerator count distribution\n 611→//\n 612→// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n 613→// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\n 614→func (r *JobRepository) AddHistograms(\n 615→\tctx context.Context,\n 616→\tfilter []*model.JobFilter,\n 617→\tstat *model.JobsStatistics,\n 618→\tdurationBins *string,\n 619→) (*model.JobsStatistics, error) {\n 620→\tstart := time.Now()\n 621→\n 622→\tvar targetBinCount int\n 623→\tvar targetBinSize int\n 624→\tswitch *durationBins {\n 625→\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n 626→\t\ttargetBinCount = 60\n 627→\t\ttargetBinSize = 60\n 628→\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n 629→\t\ttargetBinCount = 72\n 630→\t\ttargetBinSize = 600\n 631→\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n 632→\t\ttargetBinCount = 48\n 633→\t\ttargetBinSize = 3600\n 634→\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n 635→\t\ttargetBinCount = 12\n 636→\t\ttargetBinSize = 21600\n 637→\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n 638→\t\ttargetBinCount = 14\n 639→\t\ttargetBinSize = 43200\n 640→\tdefault: // 24h\n 641→\t\ttargetBinCount = 24\n 642→\t\ttargetBinSize = 3600\n 643→\t}\n 644→\n 645→\tvar err error\n 646→\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n 647→\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 648→\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n 649→\tif err != nil {\n 650→\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n 651→\t\treturn nil, err\n 652→\t}\n 653→\n 654→\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n 655→\tif err != nil {\n 656→\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n 657→\t\treturn nil, err\n 658→\t}\n 659→\n 660→\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n 661→\tif err != nil {\n 662→\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n 663→\t\treturn nil, err\n 664→\t}\n 665→\n 666→\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n 667→\tif err != nil {\n 668→\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n 669→\t\treturn nil, err\n 670→\t}\n 671→\n 672→\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n 673→\treturn stat, nil\n 674→}\n 675→\n 676→// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n 677→//\n 678→// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n 679→// and completed jobs differently: running jobs load data from metric backend, completed jobs\n 680→// use footprint data from database.\n 681→//\n 682→// Parameters:\n 683→// - ctx: Context for security checks\n 684→// - filter: Filters to apply (MUST contain State filter for running jobs)\n 685→// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n 686→// - stat: Statistics struct to augment (modified in-place)\n 687→// - targetBinCount: Number of histogram bins (default: 10)\n 688→//\n 689→// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n 690→//\n 691→// Binning algorithm:\n 692→// - Values normalized to metric's peak value from cluster configuration\n 693→// - Bins evenly distributed from 0 to peak\n 694→// - Pre-initialized with zeros for consistent visualization\n 695→//\n 696→// Limitations:\n 697→// - Running jobs: Limited to 5000 jobs for performance\n 698→// - Requires valid cluster configuration with metric peak values\n 699→// - Uses footprint statistic (avg/max/min) configured per metric\n 700→func (r *JobRepository) AddMetricHistograms(\n 701→\tctx context.Context,\n 702→\tfilter []*model.JobFilter,\n 703→\tmetrics []string,\n 704→\tstat *model.JobsStatistics,\n 705→\ttargetBinCount *int,\n 706→) (*model.JobsStatistics, error) {\n 707→\tstart := time.Now()\n 708→\n 709→\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n 710→\tfor _, f := range filter {\n 711→\t\tif f.State != nil {\n 712→\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n 713→\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n 714→\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n 715→\t\t\t\treturn stat, nil\n 716→\t\t\t}\n 717→\t\t}\n 718→\t}\n 719→\n 720→\t// All other cases: Query and make bins in sqlite directly\n 721→\tfor _, m := range metrics {\n 722→\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n 723→\t\tif err != nil {\n 724→\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n 725→\t\t\tcontinue\n 726→\t\t}\n 727→\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n 728→\t}\n 729→\n 730→\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n 731→\treturn stat, nil\n 732→}\n 733→\n 734→// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n 735→//\n 736→// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n 737→// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n 738→//\n 739→// Parameters:\n 740→// - value: SQL expression that produces the histogram value, aliased as \"value\"\n 741→// - filters: Job filters to apply\n 742→//\n 743→// Returns histogram points with Value (from column) and Count (number of jobs).\n 744→func (r *JobRepository) jobsStatisticsHistogram(\n 745→\tctx context.Context,\n 746→\tvalue string,\n 747→\tfilters []*model.JobFilter,\n 748→) ([]*model.HistoPoint, error) {\n 749→\tstart := time.Now()\n 750→\tquery, qerr := SecurityCheck(ctx,\n 751→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n 752→\n 753→\tif qerr != nil {\n 754→\t\treturn nil, qerr\n 755→\t}\n 756→\n 757→\tfor _, f := range filters {\n 758→\t\tquery = BuildWhereClause(f, query)\n 759→\t}\n 760→\n 761→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n 762→\tif err != nil {\n 763→\t\tcclog.Error(\"Error while running query\")\n 764→\t\treturn nil, err\n 765→\t}\n 766→\tdefer rows.Close()\n 767→\n 768→\tpoints := make([]*model.HistoPoint, 0)\n 769→\t// is it possible to introduce zero values here? requires info about bincount\n 770→\tfor rows.Next() {\n 771→\t\tpoint := model.HistoPoint{}\n 772→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n 773→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 774→\t\t\treturn nil, err\n 775→\t\t}\n 776→\n 777→\t\tpoints = append(points, &point)\n 778→\t}\n 779→\n 780→\tif err := rows.Err(); err != nil {\n 781→\t\treturn nil, err\n 782→\t}\n 783→\n 784→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 785→\treturn points, nil\n 786→}\n 787→\n 788→// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n 789→//\n 790→// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n 791→// histograms which only return bins with data. The value parameter should compute\n 792→// the bin number from job duration.\n 793→//\n 794→// Parameters:\n 795→// - value: SQL expression computing bin number from duration, aliased as \"value\"\n 796→// - filters: Job filters to apply\n 797→// - binSizeSeconds: Width of each bin in seconds\n 798→// - targetBinCount: Number of bins to pre-initialize\n 799→//\n 800→// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n 801→// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n 802→//\n 803→// Algorithm:\n 804→// 1. Pre-initialize targetBinCount bins with zero counts\n 805→// 2. Query database for actual counts per bin\n 806→// 3. Match query results to pre-initialized bins by value\n 807→// 4. Bins without matches remain at zero\n 808→func (r *JobRepository) jobsDurationStatisticsHistogram(\n 809→\tctx context.Context,\n 810→\tvalue string,\n 811→\tfilters []*model.JobFilter,\n 812→\tbinSizeSeconds int,\n 813→\ttargetBinCount *int,\n 814→) ([]*model.HistoPoint, error) {\n 815→\tstart := time.Now()\n 816→\tquery, qerr := SecurityCheck(ctx,\n 817→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n 818→\n 819→\tif qerr != nil {\n 820→\t\treturn nil, qerr\n 821→\t}\n 822→\n 823→\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n 824→\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n 825→\tpoints := make([]*model.HistoPoint, 0, *targetBinCount)\n 826→\tfor i := 1; i <= *targetBinCount; i++ {\n 827→\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n 828→\t\tpoints = append(points, &point)\n 829→\t}\n 830→\n 831→\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n 832→\tbinMap := make(map[int]int, len(points))\n 833→\tfor i, p := range points {\n 834→\t\tbinMap[p.Value] = i\n 835→\t}\n 836→\n 837→\tfor _, f := range filters {\n 838→\t\tquery = BuildWhereClause(f, query)\n 839→\t}\n 840→\n 841→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n 842→\tif err != nil {\n 843→\t\tcclog.Error(\"Error while running query\")\n 844→\t\treturn nil, err\n 845→\t}\n 846→\tdefer rows.Close()\n 847→\n 848→\t// Match query results to pre-initialized bins using map lookup.\n 849→\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\n 850→\tfor rows.Next() {\n 851→\t\tpoint := model.HistoPoint{}\n 852→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n 853→\t\t\tcclog.Warn(\"Error while scanning rows\")\n 854→\t\t\treturn nil, err\n 855→\t\t}\n 856→\n 857→\t\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n 858→\t\t\tpoints[idx].Count = point.Count\n 859→\t\t}\n 860→\t}\n 861→\n 862→\tif err := rows.Err(); err != nil {\n 863→\t\treturn nil, err\n 864→\t}\n 865→\n 866→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 867→\treturn points, nil\n 868→}\n 869→\n 870→// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n 871→//\n 872→// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n 873→// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n 874→// and computing bin assignments in SQL.\n 875→//\n 876→// Parameters:\n 877→// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n 878→// - filters: Job filters to apply\n 879→// - bins: Number of bins to generate\n 880→//\n 881→// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n 882→//\n 883→// Algorithm:\n 884→// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n 885→// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n 886→// 3. Pre-initialize bins with min/max ranges based on peak value\n 887→// 4. Query database for counts per bin\n 888→// 5. Match results to pre-initialized bins\n 889→//\n 890→// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n 891→// peak by 0.999999999 to avoid creating an extra bin.\n 892→func (r *JobRepository) jobsMetricStatisticsHistogram(\n 893→\tctx context.Context,\n 894→\tmetric string,\n 895→\tfilters []*model.JobFilter,\n 896→\tbins *int,\n 897→) (*model.MetricHistoPoints, error) {\n 898→\t// Peak value defines the upper bound for binning: values are distributed across\n 899→\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n 900→\t// scan all clusters to find the maximum peak value.\n 901→\tvar metricConfig *schema.MetricConfig\n 902→\tvar peak float64\n 903→\tvar unit string\n 904→\tvar footprintStat string\n 905→\n 906→\t// Try to get metric config from filtered cluster\n 907→\tfor _, f := range filters {\n 908→\t\tif f.Cluster != nil {\n 909→\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n 910→\t\t\tpeak = metricConfig.Peak\n 911→\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n 912→\t\t\tfootprintStat = metricConfig.Footprint\n 913→\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n 914→\t\t}\n 915→\t}\n 916→\n 917→\t// If no cluster filter or peak not found, find largest peak across all clusters\n 918→\t// This ensures histogram can accommodate all possible values\n 919→\tif peak == 0.0 {\n 920→\t\tfor _, c := range archive.Clusters {\n 921→\t\t\tfor _, m := range c.MetricConfig {\n 922→\t\t\t\tif m.Name == metric {\n 923→\t\t\t\t\tif m.Peak > peak {\n 924→\t\t\t\t\t\tpeak = m.Peak\n 925→\t\t\t\t\t}\n 926→\t\t\t\t\tif unit == \"\" {\n 927→\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n 928→\t\t\t\t\t}\n 929→\t\t\t\t\tif footprintStat == \"\" {\n 930→\t\t\t\t\t\tfootprintStat = m.Footprint\n 931→\t\t\t\t\t}\n 932→\t\t\t\t}\n 933→\t\t\t}\n 934→\t\t}\n 935→\t}\n 936→\n 937→\t// Construct SQL histogram bins using normalized values.\n 938→\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n 939→\tstart := time.Now()\n 940→\n 941→\t// Bin calculation formula:\n 942→\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n 943→\t// Special case: value == peak would create bin N+1, so we test for equality\n 944→\t// and multiply peak by 0.999999999 to force it into bin N.\n 945→\tbinQuery := fmt.Sprintf(`CAST(\n 946→\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n 947→\t\t* %v as INTEGER )`,\n 948→\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n 949→\n 950→\tmainQuery := sq.Select(\n 951→\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n 952→\t\t`count(*) as count`,\n 953→\t).From(\"job\").Where(\n 954→\t\t\"JSON_VALID(footprint)\",\n 955→\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n 956→\n 957→\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n 958→\tif qerr != nil {\n 959→\t\treturn nil, qerr\n 960→\t}\n 961→\n 962→\tfor _, f := range filters {\n 963→\t\tmainQuery = BuildWhereClause(f, mainQuery)\n 964→\t}\n 965→\n 966→\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n 967→\n 968→\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n 969→\tif err != nil {\n 970→\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n 971→\t\treturn nil, err\n 972→\t}\n 973→\tdefer rows.Close()\n 974→\n 975→\t// Pre-initialize bins with calculated min/max ranges.\n 976→\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n 977→\tpoints := make([]*model.MetricHistoPoint, 0, *bins)\n 978→\tbinStep := int(peak) / *bins\n 979→\tfor i := 1; i <= *bins; i++ {\n 980→\t\tbinMin := (binStep * (i - 1))\n 981→\t\tbinMax := (binStep * i)\n 982→\t\tidx := i\n 983→\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n 984→\t\tpoints = append(points, &epoint)\n 985→\t}\n 986→\n 987→\t// Build a map from bin number to slice index for O(1) lookup.\n 988→\tbinMap := make(map[int]int, len(points))\n 989→\tfor i, p := range points {\n 990→\t\tif p.Bin != nil {\n 991→\t\t\tbinMap[*p.Bin] = i\n 992→\t\t}\n 993→\t}\n 994→\n 995→\t// Match query results to pre-initialized bins using map lookup.\n 996→\tfor rows.Next() {\n 997→\t\trpoint := model.MetricHistoPoint{}\n 998→\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n 999→\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n 1000→\t\t\treturn nil, err\n 1001→\t\t}\n 1002→\n 1003→\t\tif rpoint.Bin != nil {\n 1004→\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n 1005→\t\t\t\tpoints[idx].Count = rpoint.Count\n 1006→\t\t\t}\n 1007→\t\t}\n 1008→\t}\n 1009→\n 1010→\tif err := rows.Err(); err != nil {\n 1011→\t\treturn nil, err\n 1012→\t}\n 1013→\n 1014→\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n 1015→\n 1016→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n 1017→\treturn &result, nil\n 1018→}\n 1019→\n 1020→// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n 1021→//\n 1022→// Unlike completed jobs which use footprint data from the database, running jobs require\n 1023→// fetching current metric averages from the metric backend (via metricdispatch).\n 1024→//\n 1025→// Parameters:\n 1026→// - metrics: List of metric names\n 1027→// - filters: Job filters (should filter to running jobs only)\n 1028→// - bins: Number of histogram bins\n 1029→//\n 1030→// Returns slice of MetricHistoPoints, one per metric.\n 1031→//\n 1032→// Limitations:\n 1033→// - Maximum 5000 jobs (returns nil if more jobs match)\n 1034→// - Requires metric backend availability\n 1035→// - Bins based on metric peak values from cluster configuration\n 1036→//\n 1037→// Algorithm:\n 1038→// 1. Query first 5001 jobs to check count limit\n 1039→// 2. Load metric averages for all jobs via metricdispatch\n 1040→// 3. For each metric, create bins based on peak value\n 1041→// 4. Iterate averages and count jobs per bin\n 1042→func (r *JobRepository) runningJobsMetricStatisticsHistogram(\n 1043→\tctx context.Context,\n 1044→\tmetrics []string,\n 1045→\tfilters []*model.JobFilter,\n 1046→\tbins *int,\n 1047→) []*model.MetricHistoPoints {\n 1048→\t// Get Jobs\n 1049→\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n 1050→\tif err != nil {\n 1051→\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n 1052→\t\treturn nil\n 1053→\t}\n 1054→\tif len(jobs) > 5000 {\n 1055→\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n 1056→\t\treturn nil\n 1057→\t}\n 1058→\n 1059→\t// Get AVGs from metric repo\n 1060→\tavgs := make([][]schema.Float, len(metrics))\n 1061→\tfor i := range avgs {\n 1062→\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n 1063→\t}\n 1064→\n 1065→\tfor _, job := range jobs {\n 1066→\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n 1067→\t\t\tcontinue\n 1068→\t\t}\n 1069→\n 1070→\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n 1071→\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n 1072→\t\t\treturn nil\n 1073→\t\t}\n 1074→\t}\n 1075→\n 1076→\t// Iterate metrics to fill endresult\n 1077→\tdata := make([]*model.MetricHistoPoints, 0)\n 1078→\tfor idx, metric := range metrics {\n 1079→\t\t// Get specific Peak or largest Peak\n 1080→\t\tvar metricConfig *schema.MetricConfig\n 1081→\t\tvar peak float64\n 1082→\t\tvar unit string\n 1083→\n 1084→\t\tfor _, f := range filters {\n 1085→\t\t\tif f.Cluster != nil {\n 1086→\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n 1087→\t\t\t\tpeak = metricConfig.Peak\n 1088→\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n 1089→\t\t\t}\n 1090→\t\t}\n 1091→\n 1092→\t\tif peak == 0.0 {\n 1093→\t\t\tfor _, c := range archive.Clusters {\n 1094→\t\t\t\tfor _, m := range c.MetricConfig {\n 1095→\t\t\t\t\tif m.Name == metric {\n 1096→\t\t\t\t\t\tif m.Peak > peak {\n 1097→\t\t\t\t\t\t\tpeak = m.Peak\n 1098→\t\t\t\t\t\t}\n 1099→\t\t\t\t\t\tif unit == \"\" {\n 1100→\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n 1101→\t\t\t\t\t\t}\n 1102→\t\t\t\t\t}\n 1103→\t\t\t\t}\n 1104→\t\t\t}\n 1105→\t\t}\n 1106→\n 1107→\t\t// Make and fill bins\n 1108→\t\tpeakBin := int(peak) / *bins\n 1109→\n 1110→\t\tpoints := make([]*model.MetricHistoPoint, 0)\n 1111→\t\tfor b := 0; b < *bins; b++ {\n 1112→\t\t\tcount := 0\n 1113→\t\t\tbindex := b + 1\n 1114→\t\t\tbmin := peakBin * b\n 1115→\t\t\tbmax := peakBin * (b + 1)\n 1116→\n 1117→\t\t\t// Iterate AVG values for indexed metric and count for bins\n 1118→\t\t\tfor _, val := range avgs[idx] {\n 1119→\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n 1120→\t\t\t\t\tcount += 1\n 1121→\t\t\t\t}\n 1122→\t\t\t}\n 1123→\n 1124→\t\t\t// Append Bin to Metric Result Array\n 1125→\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n 1126→\t\t\tpoints = append(points, &point)\n 1127→\t\t}\n 1128→\n 1129→\t\t// Append Metric Result Array to final results array\n 1130→\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n 1131→\t\tdata = append(data, &result)\n 1132→\t}\n 1133→\n 1134→\treturn data\n 1135→}\n 1136→"}]},"uuid":"3341abd2-9369-45e8-8757-f22c0b1e25d9","timestamp":"2026-03-12T11:51:42.239Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(job.id) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":1136,"startLine":1,"totalLines":1136}},"sourceToolAssistantUUID":"2581a3c1-1682-4491-ada4-69d62043e2cc"}
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{"parentUuid":"ff2e0045-1fea-437c-b5a9-0a42d57c0c57","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013SFbYSsQ3xsE8GrVZkj543","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"c062e2f5-4c40-4ad4-9ac9-891d664df532","timestamp":"2026-03-12T11:51:53.686Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)`, now)","newString":"\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)","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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(job.id) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":144,"oldLines":7,"newStart":144,"newLines":7,"lines":[" need := func(field string) bool {"," return reqFields == nil || reqFields[field]"," }","- durationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)`, now)","+ durationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)"," "," // Build column list"," columns := make([]string, 0, 14)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"ff2e0045-1fea-437c-b5a9-0a42d57c0c57"}
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{"parentUuid":"4c9a4485-7516-462b-ac76-b10c5e980388","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0117BRkuuXmTEgfhVUfHPwx7","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"10247372-bdb7-48fa-88ab-8306c36aca8d","timestamp":"2026-03-12T11:51:55.016Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\tcolumns = append(columns, \"COUNT(job.id) as totalJobs\")","newString":"\tcolumns = append(columns, \"COUNT(*) as totalJobs\")","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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(job.id) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":153,"oldLines":7,"newStart":153,"newLines":7,"lines":[" columns = append(columns, col)"," }"," ","- columns = append(columns, \"COUNT(job.id) as totalJobs\")","+ columns = append(columns, \"COUNT(*) as totalJobs\")"," "," if need(\"totalUsers\") && col != \"job.hpc_user\" {"," columns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"4c9a4485-7516-462b-ac76-b10c5e980388"}
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{"parentUuid":"6e668094-c4ba-4d15-82f7-f23d86c45fdc","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014xEC9iKizt9rmfBCj9q7x2","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"72f51efc-c1f8-4e38-9f32-a82e8bb7fe62","timestamp":"2026-03-12T11:51:57.408Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\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)","newString":"\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)","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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(*) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":644,"oldLines":7,"newStart":644,"newLines":7,"lines":[" "," var err error"," // Return X-Values always as seconds, will be formatted into minutes and hours in frontend","- value := 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)","+ value := 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)"," stat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)"," if err != nil {"," cclog.Warn(\"Error while loading job statistics histogram: job duration\")"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"6e668094-c4ba-4d15-82f7-f23d86c45fdc"}
{"parentUuid":"72f51efc-c1f8-4e38-9f32-a82e8bb7fe62","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01Hoe3dJareKuAnQqAhMnAAw","type":"message","role":"assistant","content":[{"type":"text","text":"Now update `COUNT(job.id)` in `buildCountQuery` and the histogram functions."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1376,"cache_read_input_tokens":35891,"cache_creation":{"ephemeral_5m_input_tokens":1376,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8y8mjv1gQZP72in9xS","type":"assistant","uuid":"e238703d-f09b-4e8f-92b2-1f55dd46f8a8","timestamp":"2026-03-12T11:52:00.132Z"}
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{"parentUuid":"f138c41e-de7b-4c46-bb6f-ea4337067194","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U6NsnNYMDfnapTVLbcFBHz","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"50426ce4-2551-462a-b0ac-b5bbd85dfe56","timestamp":"2026-03-12T11:52:01.949Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\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\")","newString":"\t\tquery = sq.Select(col, \"COUNT(*)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(*)\").From(\"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\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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(*) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":105,"oldLines":9,"newStart":105,"newLines":9,"lines":[" var query sq.SelectBuilder"," "," if col != \"\" {","- query = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)","+ query = sq.Select(col, \"COUNT(*)\").From(\"job\").GroupBy(col)"," } else {","- query = sq.Select(\"COUNT(job.id)\").From(\"job\")","+ query = sq.Select(\"COUNT(*)\").From(\"job\")"," }"," "," switch kind {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"f138c41e-de7b-4c46-bb6f-ea4337067194"}
{"parentUuid":"50426ce4-2551-462a-b0ac-b5bbd85dfe56","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01Hoe3dJareKuAnQqAhMnAAw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PEebSWrjrt3xvn8oUVkdeU","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"\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() {","new_string":"\t\tsq.Select(value, \"COUNT(*) 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() {"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1376,"cache_read_input_tokens":35891,"cache_creation":{"ephemeral_5m_input_tokens":1376,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8y8mjv1gQZP72in9xS","type":"assistant","uuid":"b24779cf-a314-4aa8-acb4-ded088ca9b64","timestamp":"2026-03-12T11:52:07.334Z"}
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{"parentUuid":"b24779cf-a314-4aa8-acb4-ded088ca9b64","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PEebSWrjrt3xvn8oUVkdeU","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"b0ebd3b7-c9f7-48df-a03b-52d88d30fb30","timestamp":"2026-03-12T11:52:07.348Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\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() {","newString":"\t\tsq.Select(value, \"COUNT(*) 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() {","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(*)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(*)\").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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(*) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":748,"oldLines":7,"newStart":748,"newLines":7,"lines":[" ) ([]*model.HistoPoint, error) {"," start := time.Now()"," query, qerr := SecurityCheck(ctx,","- sq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))","+ sq.Select(value, \"COUNT(*) AS count\").From(\"job\"))"," "," if qerr != nil {"," return nil, qerr"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"b24779cf-a314-4aa8-acb4-ded088ca9b64"}
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{"parentUuid":"6013ce24-cb9b-4372-9b40-6d0d7ee8a4a2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013oMMs7tLdHxnnTTDTSDJn4","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"08f1105c-f8ff-4c40-8895-e82d610ba3ed","timestamp":"2026-03-12T11:52:08.560Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\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","newString":"\t\tsq.Select(value, \"COUNT(*) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents","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(*)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(*)\").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 job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\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// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(*) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\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 job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration, reqFields)\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 jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\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(jobs.Int64),\n\t\t\t\t\tTotalUsers: int(users.Int64),\n\t\t\t\t\tTotalWalltime: int(walltime.Int64),\n\t\t\t\t\tTotalNodes: int(nodes.Int64),\n\t\t\t\t\tTotalNodeHours: int(nodeHours.Int64),\n\t\t\t\t\tTotalCores: int(cores.Int64),\n\t\t\t\t\tTotalCoreHours: int(coreHours.Int64),\n\t\t\t\t\tTotalAccs: int(accs.Int64),\n\t\t\t\t\tTotalAccHours: int(accHours.Int64),\n\t\t\t\t\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\t\tShortJobs: int(shortJobs.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\t// Post-query name lookup for user grouping (avoids LEFT JOIN on aggregation query)\n\tif col == \"job.hpc_user\" && len(stats) > 0 {\n\t\tusernames := make([]any, len(stats))\n\t\tfor i, s := range stats {\n\t\t\tusernames[i] = s.ID\n\t\t}\n\n\t\tnameQuery := sq.Select(\"username\", \"name\").From(\"hpc_user\").Where(sq.Eq{\"username\": usernames})\n\t\tnameRows, err := nameQuery.RunWith(r.DB).QueryContext(ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error looking up user names: %s\", err.Error())\n\t\t\t// Non-fatal: stats are still valid without display names\n\t\t} else {\n\t\t\tdefer nameRows.Close()\n\t\t\tnameMap := make(map[string]string, len(stats))\n\t\t\tfor nameRows.Next() {\n\t\t\t\tvar username, name string\n\t\t\t\tif err := nameRows.Scan(&username, &name); err == nil {\n\t\t\t\t\tnameMap[username] = name\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor _, s := range stats {\n\t\t\t\tif name, ok := nameMap[s.ID]; ok {\n\t\t\t\t\ts.Name = name\n\t\t\t\t}\n\t\t\t}\n\t\t}\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\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration, reqFields)\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, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); 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\tRunningJobs: int(runningJobs.Int64),\n\t\t\t\tShortJobs: int(shortJobs.Int64),\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(*) 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, *targetBinCount)\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\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\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 using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\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\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\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).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.\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, *bins)\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\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\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\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\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":814,"oldLines":7,"newStart":814,"newLines":7,"lines":[" ) ([]*model.HistoPoint, error) {"," start := time.Now()"," query, qerr := SecurityCheck(ctx,","- sq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))","+ sq.Select(value, \"COUNT(*) AS count\").From(\"job\"))"," "," if qerr != nil {"," return nil, qerr"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"6013ce24-cb9b-4372-9b40-6d0d7ee8a4a2"}
{"parentUuid":"08f1105c-f8ff-4c40-8895-e82d610ba3ed","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01WfCFRLLoV61C2Nmos8a3tG","type":"message","role":"assistant","content":[{"type":"text","text":"Now let's run the tests."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1079,"cache_read_input_tokens":37267,"cache_creation":{"ephemeral_5m_input_tokens":1079,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8yxQvf2G571zo25j7E","type":"assistant","uuid":"bb2bef03-6e21-4bf6-8d17-0006049b86af","timestamp":"2026-03-12T11:52:14.303Z"}
{"parentUuid":"bb2bef03-6e21-4bf6-8d17-0006049b86af","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01WfCFRLLoV61C2Nmos8a3tG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BVeHFYjm6jTGgsNS2msN7s","name":"Bash","input":{"command":"go test -v ./internal/repository/ -run 'TestJobStats|TestBuildJobStatsQuery' 2>&1 | tail -30","description":"Run stats-related tests","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1079,"cache_read_input_tokens":37267,"output_tokens":151,"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":1079},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8yxQvf2G571zo25j7E","type":"assistant","uuid":"d438bde0-39e7-41c6-a749-b2c6b426ca9d","timestamp":"2026-03-12T11:52:16.786Z"}
{"parentUuid":"d438bde0-39e7-41c6-a749-b2c6b426ca9d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BVeHFYjm6jTGgsNS2msN7s","type":"tool_result","content":"=== RUN TestBuildJobStatsQuery\nSQL: SELECT USER, COUNT(*) as totalJobs, COUNT(DISTINCT job.hpc_user) AS totalUsers, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime, CAST(SUM(job.num_nodes) as int) as totalNodes, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours, CAST(SUM(job.num_hwthreads) as int) as totalCores, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours, CAST(SUM(job.num_acc) as int) as totalAccs, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours, COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs, COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs FROM job GROUP BY USER\n--- PASS: TestBuildJobStatsQuery (0.01s)\n=== RUN TestJobStats\n--- PASS: TestJobStats (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t0.603s","is_error":false}]},"uuid":"d868ae5c-cc94-4473-84d8-20e13b4a2376","timestamp":"2026-03-12T11:52:19.878Z","toolUseResult":{"stdout":"=== RUN TestBuildJobStatsQuery\nSQL: SELECT USER, COUNT(*) as totalJobs, COUNT(DISTINCT job.hpc_user) AS totalUsers, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime, CAST(SUM(job.num_nodes) as int) as totalNodes, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours, CAST(SUM(job.num_hwthreads) as int) as totalCores, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours, CAST(SUM(job.num_acc) as int) as totalAccs, CAST(ROUND(SUM((CASE WHEN job.job_state = 'running' THEN 1773316339 - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours, COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs, COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs FROM job GROUP BY USER\n--- PASS: TestBuildJobStatsQuery (0.01s)\n=== RUN TestJobStats\n--- PASS: TestJobStats (0.00s)\nPASS\nok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t0.603s","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"d438bde0-39e7-41c6-a749-b2c6b426ca9d"}
{"parentUuid":"d868ae5c-cc94-4473-84d8-20e13b4a2376","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01JMzXMiDdwze1692aJ9mtMQ","type":"message","role":"assistant","content":[{"type":"text","text":"Both tests pass. Let me also run the full repository test suite to make sure nothing else broke."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":631,"cache_read_input_tokens":38346,"cache_creation":{"ephemeral_5m_input_tokens":631,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8znozgAzDQFKYndLdx","type":"assistant","uuid":"a71aea15-ea1e-40d1-89a5-a12bb36a5488","timestamp":"2026-03-12T11:52:22.614Z"}
{"parentUuid":"a71aea15-ea1e-40d1-89a5-a12bb36a5488","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01JMzXMiDdwze1692aJ9mtMQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Rt1xERjum1kGzaWBEj1eJq","name":"Bash","input":{"command":"go test ./internal/repository/ 2>&1 | tail -5","description":"Run all repository tests","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":631,"cache_read_input_tokens":38346,"output_tokens":126,"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":631},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8znozgAzDQFKYndLdx","type":"assistant","uuid":"1a3e27eb-f064-4f26-a20d-8783722d69d6","timestamp":"2026-03-12T11:52:23.228Z"}
{"parentUuid":"1a3e27eb-f064-4f26-a20d-8783722d69d6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Rt1xERjum1kGzaWBEj1eJq","type":"tool_result","content":"ok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t0.689s","is_error":false}]},"uuid":"092d62af-68b5-478b-9520-29259dbfe38b","timestamp":"2026-03-12T11:52:24.889Z","toolUseResult":{"stdout":"ok \tgithub.com/ClusterCockpit/cc-backend/internal/repository\t0.689s","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1a3e27eb-f064-4f26-a20d-8783722d69d6"}
{"parentUuid":"092d62af-68b5-478b-9520-29259dbfe38b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01H2Mhz59L3TupXaCJmRfzR3","type":"message","role":"assistant","content":[{"type":"text","text":"All tests pass. Here's a summary of the changes:\n\n### Migration fix (`12_stats-covering-index.up.sql`)\n- Reordered index columns from `(cluster, start_time, hpc_user, ...)` to `(cluster, hpc_user, start_time, ...)`\n- Added `DROP INDEX IF EXISTS jobs_cluster_starttime_user_stats` to handle databases with the old index\n- Renamed index to `jobs_cluster_user_starttime_stats`\n\nThis lets SQLite walk `hpc_user` in index order for `GROUP BY` instead of building a temp B-Tree.\n\n### SQL cleanup (`stats.go`)\n- `COUNT(job.id)` → `COUNT(*)` (5 locations) — idiomatic and avoids rowid lookup in covering index scans\n- `job.job_state = \"running\"` → `job.job_state = 'running'` (2 CASE expressions) — proper SQL string literals instead of double-quoted identifiers"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":163,"cache_read_input_tokens":38977,"output_tokens":236,"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":163},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy91AEMTRwf6rYJR4Z4T","type":"assistant","uuid":"795f4be7-0cb1-4194-99c8-9db21d05e13e","timestamp":"2026-03-12T11:52:31.574Z"}
{"parentUuid":"795f4be7-0cb1-4194-99c8-9db21d05e13e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"6dbc68b3-c35d-4697-b545-0298c2b303cb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"type":"hook_progress","hookEvent":"Stop","hookName":"Stop","command":"entire hooks claude-code stop"},"parentToolUseID":"dcdef9b1-e3ce-48be-9691-2d74a1cf15a7","toolUseID":"dcdef9b1-e3ce-48be-9691-2d74a1cf15a7","timestamp":"2026-03-12T11:52:31.592Z","uuid":"0cba08f3-3c3a-4f54-98be-93d62f295838"}
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