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cc-backend/d4/9917ff4b10/1/full.jsonl
Jan Eitzinger 8afd8a8221 Checkpoint: d49917ff4b10 
Entire-Session: 3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb
Entire-Strategy: manual-commit
Entire-Agent: Claude Code
Ephemeral-branch: entire/7f069f1-e3b0c4
2026-03-12 20:12:49 +01:00

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{"parentUuid":"dc7c0192-ae15-4ccf-b3c2-e9caad328838","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":"The buildStatsQuery is still very slow and does not return. This is the endpoint http://localhost:8080/monitoring/users/?cluster=woody&startTime=last30d . Check again the query and find improvements."},"uuid":"4a0082bc-6d63-45f6-ae39-7f7345e0c7d1","timestamp":"2026-03-12T11:44:53.811Z","permissionMode":"plan"}
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{"parentUuid":"8c698a29-2e88-4863-92ad-6e9203a0e5f1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_013WfRAEbaNeUquoVGrRgUjj","type":"message","role":"assistant","content":[{"type":"text","text":"Let me explore the stats query code and the relevant endpoint to understand the performance issue."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":5466,"cache_read_input_tokens":14245,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":5466},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8RyHNWHu3gqEWb1Y2g","type":"assistant","uuid":"18621cda-7cb8-45d4-b9bb-e86cd0824334","timestamp":"2026-03-12T11:44:57.216Z"}
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{"parentUuid":"52cf1c20-0092-4eb8-8122-36566a1a74c8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VJYhDpez1AkDGAkYWJjvBp","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":"3a60dd95-a8c5-4db3-8231-416cd742a565","timestamp":"2026-03-12T11:44:58.436Z","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":"52cf1c20-0092-4eb8-8122-36566a1a74c8"}
{"parentUuid":"3a60dd95-a8c5-4db3-8231-416cd742a565","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_017mCGn1fu9DfpDrXAMH7sSB","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me look at the existing migration file for the covering index and the GraphQL resolver to understand the full query path."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":491,"cache_creation_input_tokens":16199,"cache_read_input_tokens":19711,"cache_creation":{"ephemeral_5m_input_tokens":16199,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8SFaQ4SKr1uracTvJL","type":"assistant","uuid":"bf584abe-8ba3-4a08-8395-70e8e9158b64","timestamp":"2026-03-12T11:45:00.438Z"}
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{"parentUuid":"061f8bdd-869a-4a95-8b22-2a19031c48c7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B2XJZujScvYv2UaP3rcvaa","type":"tool_result","content":" 1→-- Migration 12: Add covering index for grouped stats queries\n 2→-- Covers the common pattern: WHERE cluster=X AND start_time >= Y GROUP BY hpc_user\n 3→-- Includes aggregated columns to avoid main table lookups entirely.\n 4→\n 5→CREATE INDEX IF NOT EXISTS jobs_cluster_starttime_user_stats\n 6→ ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n 7→\n 8→PRAGMA optimize;\n 9→"}]},"uuid":"b163bfcc-1de1-46ce-b4f6-d93a533ce743","timestamp":"2026-03-12T11:45:01.863Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql","content":"-- Migration 12: Add covering index for grouped stats queries\n-- Covers the common pattern: WHERE cluster=X AND start_time >= Y GROUP BY hpc_user\n-- Includes aggregated columns to avoid main table lookups entirely.\n\nCREATE INDEX IF NOT EXISTS jobs_cluster_starttime_user_stats\n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n\nPRAGMA optimize;\n","numLines":9,"startLine":1,"totalLines":9}},"sourceToolAssistantUUID":"061f8bdd-869a-4a95-8b22-2a19031c48c7"}
{"parentUuid":"b163bfcc-1de1-46ce-b4f6-d93a533ce743","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_017mCGn1fu9DfpDrXAMH7sSB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L2wX5pHxehktnRkRAHuwgZ","name":"Agent","input":{"subagent_type":"Explore","description":"Find stats query callers","prompt":"In /Users/jan/prg/ClusterCockpit/cc-backend, find all code that calls JobsStatsGrouped or leads to the `/monitoring/users/` endpoint. I need to understand:\n1. What GraphQL query is made when visiting `/monitoring/users/?cluster=woody&startTime=last30d`\n2. What filters are passed to buildStatsQuery\n3. What the BuildWhereClause function does (in internal/repository/)\n4. What indexes exist on the job table (check migration files in internal/repository/migrations/sqlite3/)\n5. The schema of the job table\n\nBe thorough - check the frontend Svelte files (web/frontend/src/List.root.svelte), GraphQL schema (api/schema.graphqls), resolvers (internal/graph/schema.resolvers.go), and the filter/where clause builder."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":491,"cache_creation_input_tokens":16199,"cache_read_input_tokens":19711,"output_tokens":388,"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":16199},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8SFaQ4SKr1uracTvJL","type":"assistant","uuid":"ddc8fdb6-a88e-4698-8001-c60c67c34a95","timestamp":"2026-03-12T11:45:07.271Z"}
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{"parentUuid":"705c7115-5c12-4236-b1dd-2fbba84d2a1c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Crgf5gteA9TZQU7jW9XvxX","type":"tool_result","content":" 1→<!--\n 2→ @component Main component for listing users or projects\n 3→\n 4→ Properties:\n 5→ - `type String?`: The type of list ['USER' || 'PROJECT']\n 6→ - `filterPresets Object?`: Optional predefined filter values [Default: {}]\n 7→-->\n 8→\n 9→<script>\n 10→ import { getContext, onMount } from \"svelte\";\n 11→ import {\n 12→ Row,\n 13→ Col,\n 14→ Button,\n 15→ Icon,\n 16→ Table,\n 17→ Card,\n 18→ Spinner,\n 19→ InputGroup,\n 20→ Input,\n 21→ Tooltip\n 22→ } from \"@sveltestrap/sveltestrap\";\n 23→ import {\n 24→ queryStore,\n 25→ gql,\n 26→ getContextClient,\n 27→ } from \"@urql/svelte\";\n 28→ import {\n 29→ init,\n 30→ scramble,\n 31→ scrambleNames,\n 32→ } from \"./generic/utils.js\";\n 33→ import {\n 34→ formatDurationTime\n 35→ } from \"./generic/units.js\";\n 36→ import Filters from \"./generic/Filters.svelte\";\n 37→ import Pagination from \"./generic/joblist/Pagination.svelte\";\n 38→\n 39→ /* Svelte 5 Props */\n 40→ let {\n 41→ type,\n 42→ filterPresets\n 43→ } = $props();\n 44→\n 45→ /* Const Init */\n 46→ const {} = init();\n 47→ const client = getContextClient();\n 48→ const shortDuration = getContext(\"cc-config\").jobList_hideShortRunningJobs; // Always configured\n 49→\n 50→ /* State Init*/\n 51→ let filterComponent = $state(); // see why here: https://stackoverflow.com/questions/58287729/how-can-i-export-a-function-from-a-svelte-component-that-changes-a-value-in-the\n 52→ let jobFilters = $state([]);\n 53→ let nameFilter = $state(\"\");\n 54→ let sorting = $state({ field: \"totalJobs\", direction: \"desc\" });\n 55→ let page = $state(1);\n 56→ let itemsPerPage = $state(25);\n 57→\n 58→ /* Derived Vars */\n 59→ const fetchRunning = $derived(jobFilters.some(jf => jf?.state?.length == 1 && jf?.state?.includes(\"running\")));\n 60→ const numCols = $derived.by(() => {\n 61→ let colbase = 6\n 62→ if (fetchRunning) {\n 63→ colbase += 2\n 64→ }\n 65→ return colbase\n 66→ })\n 67→ const sortedRows = $derived(\n 68→ $stats.data ? sort($stats.data.rows, sorting, nameFilter) : []\n 69→ );\n 70→ const paginatedRows = $derived(\n 71→ sortedRows.slice((page - 1) * itemsPerPage, page * itemsPerPage)\n 72→ );\n 73→\n 74→ /* Reset page when sorting or filter changes */\n 75→ $effect(() => { sorting; nameFilter; page = 1; });\n 76→\n 77→ let stats = $derived(\n 78→ queryStore({\n 79→ client: client,\n 80→ query: gql`\n 81→ query($jobFilters: [JobFilter!]!, $fetchRunning: Boolean!) {\n 82→ rows: jobsStatistics(filter: $jobFilters, groupBy: ${type}) {\n 83→ id\n 84→ name\n 85→ totalJobs\n 86→ shortJobs\n 87→ totalCores @include(if: $fetchRunning)\n 88→ totalAccs @include(if: $fetchRunning)\n 89→ totalWalltime\n 90→ totalCoreHours\n 91→ totalAccHours\n 92→ }\n 93→ }`,\n 94→ variables: {\n 95→ jobFilters,\n 96→ fetchRunning\n 97→ },\n 98→ })\n 99→ );\n 100→\n 101→ /* Functions */\n 102→ function exportCsv() {\n 103→ const isUser = type === \"USER\";\n 104→ const header = [\n 105→ isUser ? \"Username\" : \"Project\",\n 106→ ...(isUser ? [\"Name\"] : []),\n 107→ \"Total Jobs\",\n 108→ \"Short Jobs\",\n 109→ ...(fetchRunning ? [\"Total Cores\", \"Total Accelerators\"] : []),\n 110→ \"Total Walltime\",\n 111→ \"Total Core Hours\",\n 112→ \"Total Accelerator Hours\",\n 113→ ];\n 114→ const rows = sortedRows.map((row) => [\n 115→ row.id,\n 116→ ...(isUser ? [row?.name ?? \"\"] : []),\n 117→ row.totalJobs,\n 118→ row.shortJobs,\n 119→ ...(fetchRunning ? [row.totalCores, row.totalAccs] : []),\n 120→ row.totalWalltime,\n 121→ row.totalCoreHours,\n 122→ row.totalAccHours,\n 123→ ]);\n 124→ const csv = [header, ...rows]\n 125→ .map((row) => row.map((v) => `\"${String(v ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\"))\n 126→ .join(\"\\n\");\n 127→ const blob = new Blob([csv], { type: \"text/csv\" });\n 128→ const url = URL.createObjectURL(blob);\n 129→ const a = document.createElement(\"a\");\n 130→ a.href = url;\n 131→ a.download = `${type.toLowerCase()}s.csv`;\n 132→ a.click();\n 133→ URL.revokeObjectURL(url);\n 134→ }\n 135→\n 136→ function changeSorting(newField) {\n 137→ if (sorting.field == newField) {\n 138→ // Same Field, Change Direction\n 139→ sorting = { field: newField, direction: sorting.direction == \"desc\" ? \"asc\" : \"desc\" };\n 140→ } else {\n 141→ // Change Field, Apply Default Direction\n 142→ sorting = { field: newField, direction: \"desc\" };\n 143→ }\n 144→ }\n 145→\n 146→ function sort(stats, sorting, nameFilter) {\n 147→ const idCmp = sorting.direction == \"asc\"\n 148→ ? (a, b) => b.id.localeCompare(a.id)\n 149→ : (a, b) => a.id.localeCompare(b.id)\n 150→\n 151→ // Force empty or undefined strings to the end of the list\n 152→ const nameCmp = sorting.direction == \"asc\"\n 153→ ? (a, b) => !a?.name ? 1 : (!b?.name ? -1 : (b.name.localeCompare(a.name)))\n 154→ : (a, b) => !a?.name ? 1 : (!b?.name ? -1 : (a.name.localeCompare(b.name)))\n 155→\n 156→ const intCmp = sorting.direction == \"asc\"\n 157→ ? (a, b) => a[sorting.field] - b[sorting.field]\n 158→ : (a, b) => b[sorting.field] - a[sorting.field];\n 159→\n 160→ if (sorting.field == \"id\") {\n 161→ return stats.filter((u) => u.id.includes(nameFilter)).sort(idCmp)\n 162→ } else if (sorting.field == \"name\") {\n 163→ return stats.filter((u) => u.id.includes(nameFilter)).sort(nameCmp)\n 164→ } else {\n 165→ return stats.filter((u) => u.id.includes(nameFilter)).sort(intCmp)\n 166→ }\n 167→ }\n 168→\n 169→ /* On Mount */\n 170→ onMount(() => filterComponent.updateFilters());\n 171→</script>\n 172→\n 173→<Row cols={{ xs: 1, md: 2}}>\n 174→ <Col xs=\"12\" md=\"5\" lg=\"4\" xl=\"3\" class=\"mb-2 mb-md-0\">\n 175→ <InputGroup>\n 176→ <Button disabled outline>\n 177→ Search {type.toLowerCase()}s\n 178→ </Button>\n 179→ <Input\n 180→ bind:value={nameFilter}\n 181→ placeholder=\"Filter by {{\n 182→ USER: 'username',\n 183→ PROJECT: 'project',\n 184→ }[type]}\"\n 185→ />\n 186→ <Button\n 187→ color=\"success\"\n 188→ title=\"Export current view as CSV\"\n 189→ disabled={!$stats.data}\n 190→ onclick={() => exportCsv()}\n 191→ >\n 192→ <Icon name=\"download\" /> CSV\n 193→ </Button>\n 194→ </InputGroup>\n 195→ </Col>\n 196→ <Col xs=\"12\" md=\"7\" lg=\"8\" xl=\"9\">\n 197→ <Filters\n 198→ startTimeQuickSelect\n 199→ bind:this={filterComponent}\n 200→ {filterPresets}\n 201→ menuText=\"Only {type.toLowerCase()}s with matching jobs will be displayed.\"\n 202→ applyFilters={(detail) => {\n 203→ jobFilters = detail.filters;\n 204→ }}\n 205→ />\n 206→ </Col>\n 207→</Row>\n 208→<Table>\n 209→ <thead>\n 210→ <tr>\n 211→ <th scope=\"col\">\n 212→ {#if type === 'USER'}\n 213→ Username\n 214→ {:else if type === 'PROJECT'}\n 215→ Project Name\n 216→ {/if}\n 217→ <Button\n 218→ color={sorting.field == \"id\" ? \"primary\" : \"light\"}\n 219→ size=\"sm\"\n 220→ onclick={() => changeSorting(\"id\")}\n 221→ >\n 222→ {#if sorting?.field == \"id\"}\n 223→ <!-- Note on Icon-Name: Arrow-indicator always down, only alpha-indicator switches -->\n 224→ <Icon name={`sort-alpha-${sorting?.direction == 'desc' ? 'down' : 'down-alt'}`} />\n 225→ {:else}\n 226→ <Icon name=\"three-dots-vertical\" />\n 227→ {/if}\n 228→ </Button>\n 229→ </th>\n 230→ {#if type == \"USER\"}\n 231→ <th scope=\"col\">\n 232→ Name\n 233→ <Button\n 234→ color={sorting.field == \"name\" ? \"primary\" : \"light\"}\n 235→ size=\"sm\"\n 236→ onclick={() => changeSorting(\"name\")}\n 237→ >\n 238→ {#if sorting?.field == \"name\"}\n 239→ <Icon name={`sort-alpha-${sorting?.direction == 'desc' ? 'down' : 'down-alt'}`} />\n 240→ {:else}\n 241→ <Icon name=\"three-dots-vertical\" />\n 242→ {/if}\n 243→ </Button>\n 244→ </th>\n 245→ {/if}\n 246→ <th scope=\"col\">\n 247→ Total Jobs\n 248→ <Button\n 249→ color={sorting.field == \"totalJobs\" ? \"primary\" : \"light\"}\n 250→ size=\"sm\"\n 251→ onclick={() => changeSorting(\"totalJobs\")}\n 252→ >\n 253→ {#if sorting?.field == \"totalJobs\"}\n 254→ <!-- Note on Icon-Name: Arrow-indicator always down, only numeric-indicator switches -->\n 255→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 256→ {:else}\n 257→ <Icon name=\"three-dots-vertical\" />\n 258→ {/if}\n 259→ </Button>\n 260→ </th>\n 261→ <th scope=\"col\">\n 262→ <span class=\"mr-1\">\n 263→ Short Jobs\n 264→ <Icon id=\"shortjobs-info\" style=\"cursor:help;\" size=\"sm\" name=\"info-circle\"/>\n 265→ </span>\n 266→ <Tooltip target={`shortjobs-info`} placement=\"top\">\n 267→ Job duration less than {formatDurationTime(shortDuration)}\n 268→ </Tooltip>\n 269→ &#8239; <!-- Narrow Non-Breaking Space -->\n 270→ <Button\n 271→ color={sorting.field == \"shortJobs\" ? \"primary\" : \"light\"}\n 272→ size=\"sm\"\n 273→ onclick={() => changeSorting(\"shortJobs\")}\n 274→ >\n 275→ {#if sorting?.field == \"shortJobs\"}\n 276→ <!-- Note on Icon-Name: Arrow-indicator always down, only numeric-indicator switches -->\n 277→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 278→ {:else}\n 279→ <Icon name=\"three-dots-vertical\" />\n 280→ {/if}\n 281→ </Button>\n 282→ </th>\n 283→ {#if fetchRunning}\n 284→ <th scope=\"col\">\n 285→ Total Cores\n 286→ <Button\n 287→ color={sorting.field == \"totalCores\" ? \"primary\" : \"light\"}\n 288→ size=\"sm\"\n 289→ onclick={() => changeSorting(\"totalCores\")}\n 290→ >\n 291→ {#if sorting?.field == \"totalJCores\"}\n 292→ <!-- Note on Icon-Name: Arrow-indicator always down, only numeric-indicator switches -->\n 293→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 294→ {:else}\n 295→ <Icon name=\"three-dots-vertical\" />\n 296→ {/if}\n 297→ </Button>\n 298→ </th>\n 299→ <th scope=\"col\">\n 300→ Total Accelerators\n 301→ <Button\n 302→ color={sorting.field == \"totalAccs\" ? \"primary\" : \"light\"}\n 303→ size=\"sm\"\n 304→ onclick={() => changeSorting(\"totalAccs\")}\n 305→ >\n 306→ {#if sorting?.field == \"totalAccs\"}\n 307→ <!-- Note on Icon-Name: Arrow-indicator always down, only numeric-indicator switches -->\n 308→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 309→ {:else}\n 310→ <Icon name=\"three-dots-vertical\" />\n 311→ {/if}\n 312→ </Button>\n 313→ </th>\n 314→ {/if}\n 315→ <th scope=\"col\">\n 316→ Total Walltime\n 317→ <Button\n 318→ color={sorting.field == \"totalWalltime\" ? \"primary\" : \"light\"}\n 319→ size=\"sm\"\n 320→ onclick={() => changeSorting(\"totalWalltime\")}\n 321→ >\n 322→ {#if sorting?.field == \"totalWalltime\"}\n 323→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 324→ {:else}\n 325→ <Icon name=\"three-dots-vertical\" />\n 326→ {/if}\n 327→ </Button>\n 328→ </th>\n 329→ <th scope=\"col\">\n 330→ Total Core Hours\n 331→ <Button\n 332→ color={sorting.field == \"totalCoreHours\" ? \"primary\" : \"light\"}\n 333→ size=\"sm\"\n 334→ onclick={() => changeSorting(\"totalCoreHours\")}\n 335→ >\n 336→ {#if sorting?.field == \"totalCoreHours\"}\n 337→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 338→ {:else}\n 339→ <Icon name=\"three-dots-vertical\" />\n 340→ {/if}\n 341→ </Button>\n 342→ </th>\n 343→ <th scope=\"col\">\n 344→ Total Accelerator Hours\n 345→ <Button\n 346→ color={sorting.field == \"totalAccHours\" ? \"primary\" : \"light\"}\n 347→ size=\"sm\"\n 348→ onclick={() => changeSorting(\"totalAccHours\")}\n 349→ >\n 350→ {#if sorting?.field == \"totalAccHours\"}\n 351→ <Icon name={`sort-numeric-${sorting?.direction == 'desc' ? 'down-alt' : 'down'}`} />\n 352→ {:else}\n 353→ <Icon name=\"three-dots-vertical\" />\n 354→ {/if}\n 355→ </Button>\n 356→ </th>\n 357→ </tr>\n 358→ </thead>\n 359→ <tbody>\n 360→ {#if $stats.fetching}\n 361→ <tr>\n 362→ <td colspan={numCols} style=\"text-align: center;\"><Spinner secondary /></td>\n 363→ </tr>\n 364→ {:else if $stats.error}\n 365→ <tr>\n 366→ <td colspan={numCols}\n 367→ ><Card body color=\"danger\" class=\"mb-3\">{$stats.error.message}</Card\n 368→ ></td\n 369→ >\n 370→ </tr>\n 371→ {:else if $stats.data}\n 372→ {#each paginatedRows as row (row.id)}\n 373→ <tr>\n 374→ <td>\n 375→ {#if type == \"USER\"}\n 376→ <a href=\"/monitoring/user/{row.id}{fetchRunning ? '?state=running' : ''}\"\n 377→ >{scrambleNames ? scramble(row.id) : row.id}</a\n 378→ >\n 379→ {:else if type == \"PROJECT\"}\n 380→ <a href=\"/monitoring/jobs/?project={row.id}{fetchRunning ? '&state=running' : ''}\"\n 381→ >{scrambleNames ? scramble(row.id) : row.id}</a\n 382→ >\n 383→ {:else}\n 384→ {row.id}\n 385→ {/if}\n 386→ </td>\n 387→ {#if type == \"USER\"}\n 388→ <td\n 389→ >{scrambleNames\n 390→ ? scramble(row?.name ? row.name : \"-\")\n 391→ : row?.name\n 392→ ? row.name\n 393→ : \"-\"}</td\n 394→ >\n 395→ {/if}\n 396→ <td>{row.totalJobs}</td>\n 397→ <td>{row.shortJobs}</td>\n 398→ {#if fetchRunning}\n 399→ <td>{row.totalCores}</td>\n 400→ <td>{row.totalAccs}</td>\n 401→ {/if}\n 402→ <td>{row.totalWalltime}</td>\n 403→ <td>{row.totalCoreHours}</td>\n 404→ <td>{row.totalAccHours}</td>\n 405→ </tr>\n 406→ {:else}\n 407→ <tr>\n 408→ <td colspan={numCols}><i>No {type.toLowerCase()}s/jobs found</i></td>\n 409→ </tr>\n 410→ {/each}\n 411→ {/if}\n 412→ </tbody>\n 413→</Table>\n 414→{#if sortedRows.length > 0}\n 415→ <Pagination\n 416→ {page}\n 417→ {itemsPerPage}\n 418→ totalItems={sortedRows.length}\n 419→ itemText={type === 'USER' ? 'Users' : 'Projects'}\n 420→ pageSizes={[25, 50, 100]}\n 421→ updatePaging={(detail) => {\n 422→ itemsPerPage = detail.itemsPerPage;\n 423→ page = detail.page;\n 424→ }}\n 425→ />\n 426→{/if}\n 427→"}]},"uuid":"1edf3962-6d6c-4f7c-8be3-a8573a649c14","timestamp":"2026-03-12T11:45:11.430Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"ce853454-313c-4896-8f02-dbeec41cf507","timestamp":"2026-03-12T11:45:11.577Z"}
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{"parentUuid":"76b0e7b2-80fb-4f60-ae26-5695cb87a4cb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SZs8jnxZZK7jGqd8J8Tg2B","type":"tool_result","content":" 1→scalar Time\n 2→scalar Any\n 3→\n 4→scalar NullableFloat\n 5→scalar MetricScope\n 6→scalar JobState\n 7→scalar SchedulerState\n 8→scalar MonitoringState\n 9→\n 10→type Node {\n 11→ id: ID!\n 12→ hostname: String!\n 13→ cluster: String!\n 14→ subCluster: String!\n 15→ jobsRunning: Int!\n 16→ cpusAllocated: Int\n 17→ memoryAllocated: Int\n 18→ gpusAllocated: Int\n 19→ schedulerState: SchedulerState!\n 20→ healthState: MonitoringState!\n 21→ metaData: Any\n 22→ healthData: Any\n 23→}\n 24→\n 25→type NodeStates {\n 26→ state: String!\n 27→ count: Int!\n 28→}\n 29→\n 30→type NodeStatesTimed {\n 31→ state: String!\n 32→ counts: [Int!]!\n 33→ times: [Int!]!\n 34→}\n 35→\n 36→type Job {\n 37→ id: ID!\n 38→ jobId: Int!\n 39→ user: String!\n 40→ project: String!\n 41→ cluster: String!\n 42→ subCluster: String!\n 43→ startTime: Time!\n 44→ duration: Int!\n 45→ walltime: Int!\n 46→ numNodes: Int!\n 47→ numHWThreads: Int!\n 48→ numAcc: Int!\n 49→ energy: Float!\n 50→ SMT: Int!\n 51→ shared: String!\n 52→ partition: String!\n 53→ arrayJobId: Int!\n 54→ monitoringStatus: Int!\n 55→ state: JobState!\n 56→ tags: [Tag!]!\n 57→ resources: [Resource!]!\n 58→ concurrentJobs: JobLinkResultList\n 59→ footprint: [FootprintValue]\n 60→ energyFootprint: [EnergyFootprintValue]\n 61→ metaData: Any\n 62→ userData: User\n 63→}\n 64→\n 65→type JobLink {\n 66→ id: ID!\n 67→ jobId: Int!\n 68→}\n 69→\n 70→type Cluster {\n 71→ name: String!\n 72→ partitions: [String!]! # Slurm partitions\n 73→ subClusters: [SubCluster!]! # Hardware partitions/subclusters\n 74→}\n 75→\n 76→type SubCluster {\n 77→ name: String!\n 78→ nodes: String!\n 79→ numberOfNodes: Int!\n 80→ processorType: String!\n 81→ socketsPerNode: Int!\n 82→ coresPerSocket: Int!\n 83→ threadsPerCore: Int!\n 84→ flopRateScalar: MetricValue!\n 85→ flopRateSimd: MetricValue!\n 86→ memoryBandwidth: MetricValue!\n 87→ topology: Topology!\n 88→ metricConfig: [MetricConfig!]!\n 89→ footprint: [String!]!\n 90→}\n 91→\n 92→type FootprintValue {\n 93→ name: String!\n 94→ stat: String!\n 95→ value: Float!\n 96→}\n 97→\n 98→type EnergyFootprintValue {\n 99→ hardware: String!\n 100→ metric: String!\n 101→ value: Float!\n 102→}\n 103→\n 104→type MetricValue {\n 105→ name: String\n 106→ unit: Unit!\n 107→ value: Float!\n 108→}\n 109→\n 110→type Topology {\n 111→ node: [Int!]\n 112→ socket: [[Int!]!]\n 113→ memoryDomain: [[Int!]!]\n 114→ die: [[Int!]!]\n 115→ core: [[Int!]!]\n 116→ accelerators: [Accelerator!]\n 117→}\n 118→\n 119→type Accelerator {\n 120→ id: String!\n 121→ type: String!\n 122→ model: String!\n 123→}\n 124→\n 125→type SubClusterConfig {\n 126→ name: String!\n 127→ peak: Float\n 128→ normal: Float\n 129→ caution: Float\n 130→ alert: Float\n 131→ remove: Boolean\n 132→}\n 133→\n 134→type MetricConfig {\n 135→ name: String!\n 136→ unit: Unit!\n 137→ scope: MetricScope!\n 138→ aggregation: String!\n 139→ timestep: Int!\n 140→ peak: Float!\n 141→ normal: Float\n 142→ caution: Float!\n 143→ alert: Float!\n 144→ lowerIsBetter: Boolean\n 145→ subClusters: [SubClusterConfig!]!\n 146→}\n 147→\n 148→type Tag {\n 149→ id: ID!\n 150→ type: String!\n 151→ name: String!\n 152→ scope: String!\n 153→}\n 154→\n 155→type Resource {\n 156→ hostname: String!\n 157→ hwthreads: [Int!]\n 158→ accelerators: [String!]\n 159→ configuration: String\n 160→}\n 161→\n 162→type JobMetricWithName {\n 163→ name: String!\n 164→ scope: MetricScope!\n 165→ metric: JobMetric!\n 166→}\n 167→\n 168→type ClusterMetricWithName {\n 169→ name: String!\n 170→ unit: Unit\n 171→ timestep: Int!\n 172→ data: [NullableFloat!]!\n 173→}\n 174→\n 175→type JobMetric {\n 176→ unit: Unit\n 177→ timestep: Int!\n 178→ series: [Series!]\n 179→ statisticsSeries: StatsSeries\n 180→}\n 181→\n 182→type Series {\n 183→ hostname: String!\n 184→ id: String\n 185→ statistics: MetricStatistics\n 186→ data: [NullableFloat!]!\n 187→}\n 188→\n 189→type StatsSeries {\n 190→ mean: [NullableFloat!]!\n 191→ median: [NullableFloat!]!\n 192→ min: [NullableFloat!]!\n 193→ max: [NullableFloat!]!\n 194→}\n 195→\n 196→type NamedStatsWithScope {\n 197→ name: String!\n 198→ scope: MetricScope!\n 199→ stats: [ScopedStats!]!\n 200→}\n 201→\n 202→type ScopedStats {\n 203→ hostname: String!\n 204→ id: String\n 205→ data: MetricStatistics!\n 206→}\n 207→\n 208→type JobStats {\n 209→ id: Int!\n 210→ jobId: String!\n 211→ startTime: Int!\n 212→ duration: Int!\n 213→ cluster: String!\n 214→ subCluster: String!\n 215→ numNodes: Int!\n 216→ numHWThreads: Int\n 217→ numAccelerators: Int\n 218→ stats: [NamedStats!]!\n 219→}\n 220→\n 221→type NamedStats {\n 222→ name: String!\n 223→ data: MetricStatistics!\n 224→}\n 225→\n 226→type Unit {\n 227→ base: String!\n 228→ prefix: String\n 229→}\n 230→\n 231→type MetricStatistics {\n 232→ avg: Float!\n 233→ min: Float!\n 234→ max: Float!\n 235→}\n 236→\n 237→type MetricFootprints {\n 238→ metric: String!\n 239→ data: [NullableFloat!]!\n 240→}\n 241→\n 242→type Footprints {\n 243→ timeWeights: TimeWeights!\n 244→ metrics: [MetricFootprints!]!\n 245→}\n 246→\n 247→type TimeWeights {\n 248→ nodeHours: [NullableFloat!]!\n 249→ accHours: [NullableFloat!]!\n 250→ coreHours: [NullableFloat!]!\n 251→}\n 252→\n 253→enum Aggregate {\n 254→ USER\n 255→ PROJECT\n 256→ CLUSTER\n 257→ SUBCLUSTER\n 258→}\n 259→enum SortByAggregate {\n 260→ TOTALWALLTIME\n 261→ TOTALJOBS\n 262→ TOTALUSERS\n 263→ TOTALNODES\n 264→ TOTALNODEHOURS\n 265→ TOTALCORES\n 266→ TOTALCOREHOURS\n 267→ TOTALACCS\n 268→ TOTALACCHOURS\n 269→}\n 270→\n 271→type NodeMetrics {\n 272→ host: String!\n 273→ state: String!\n 274→ subCluster: String!\n 275→ metrics: [JobMetricWithName!]!\n 276→}\n 277→\n 278→type ClusterMetrics {\n 279→ nodeCount: Int!\n 280→ metrics: [ClusterMetricWithName!]!\n 281→}\n 282→\n 283→type NodesResultList {\n 284→ items: [NodeMetrics!]!\n 285→ offset: Int\n 286→ limit: Int\n 287→ count: Int\n 288→ totalNodes: Int\n 289→ hasNextPage: Boolean\n 290→}\n 291→\n 292→type ClusterSupport {\n 293→ cluster: String!\n 294→ subClusters: [String!]!\n 295→}\n 296→\n 297→type GlobalMetricListItem {\n 298→ name: String!\n 299→ unit: Unit!\n 300→ scope: MetricScope!\n 301→ footprint: String\n 302→ availability: [ClusterSupport!]!\n 303→}\n 304→\n 305→type Count {\n 306→ name: String!\n 307→ count: Int!\n 308→}\n 309→\n 310→type User {\n 311→ username: String!\n 312→ name: String!\n 313→ email: String!\n 314→}\n 315→\n 316→input MetricStatItem {\n 317→ metricName: String!\n 318→ range: FloatRange!\n 319→}\n 320→\n 321→type Query {\n 322→ clusters: [Cluster!]! # List of all clusters\n 323→ tags: [Tag!]! # List of all tags\n 324→ globalMetrics: [GlobalMetricListItem!]!\n 325→\n 326→ user(username: String!): User\n 327→ allocatedNodes(cluster: String!): [Count!]!\n 328→\n 329→ ## Node Queries New\n 330→ node(id: ID!): Node\n 331→ nodes(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n 332→ nodesWithMeta(filter: [NodeFilter!], order: OrderByInput): NodeStateResultList!\n 333→ nodeStates(filter: [NodeFilter!]): [NodeStates!]!\n 334→ nodeStatesTimed(filter: [NodeFilter!], type: String!): [NodeStatesTimed!]!\n 335→\n 336→ job(id: ID!): Job\n 337→ jobMetrics(\n 338→ id: ID!\n 339→ metrics: [String!]\n 340→ scopes: [MetricScope!]\n 341→ resolution: Int\n 342→ ): [JobMetricWithName!]!\n 343→\n 344→ jobStats(id: ID!, metrics: [String!]): [NamedStats!]!\n 345→\n 346→ scopedJobStats(\n 347→ id: ID!\n 348→ metrics: [String!]\n 349→ scopes: [MetricScope!]\n 350→ ): [NamedStatsWithScope!]!\n 351→\n 352→ jobs(\n 353→ filter: [JobFilter!]\n 354→ page: PageRequest\n 355→ order: OrderByInput\n 356→ ): JobResultList!\n 357→\n 358→ jobsStatistics(\n 359→ filter: [JobFilter!]\n 360→ metrics: [String!]\n 361→ page: PageRequest\n 362→ sortBy: SortByAggregate\n 363→ groupBy: Aggregate\n 364→ numDurationBins: String\n 365→ numMetricBins: Int\n 366→ ): [JobsStatistics!]!\n 367→\n 368→ jobsMetricStats(filter: [JobFilter!], metrics: [String!]): [JobStats!]!\n 369→ jobsFootprints(filter: [JobFilter!], metrics: [String!]!): Footprints\n 370→\n 371→ rooflineHeatmap(\n 372→ filter: [JobFilter!]!\n 373→ rows: Int!\n 374→ cols: Int!\n 375→ minX: Float!\n 376→ minY: Float!\n 377→ maxX: Float!\n 378→ maxY: Float!\n 379→ ): [[Float!]!]!\n 380→\n 381→ nodeMetrics(\n 382→ cluster: String!\n 383→ nodes: [String!]\n 384→ scopes: [MetricScope!]\n 385→ metrics: [String!]\n 386→ from: Time!\n 387→ to: Time!\n 388→ ): [NodeMetrics!]!\n 389→\n 390→ nodeMetricsList(\n 391→ cluster: String!\n 392→ subCluster: String!\n 393→ stateFilter: String!\n 394→ nodeFilter: String!\n 395→ scopes: [MetricScope!]\n 396→ metrics: [String!]\n 397→ from: Time!\n 398→ to: Time!\n 399→ page: PageRequest\n 400→ resolution: Int\n 401→ ): NodesResultList!\n 402→\n 403→ clusterMetrics(\n 404→ cluster: String!\n 405→ metrics: [String!]\n 406→ from: Time!\n 407→ to: Time!\n 408→ ): ClusterMetrics!\n 409→}\n 410→\n 411→type Mutation {\n 412→ createTag(type: String!, name: String!, scope: String!): Tag!\n 413→ deleteTag(id: ID!): ID!\n 414→ addTagsToJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n 415→ removeTagsFromJob(job: ID!, tagIds: [ID!]!): [Tag!]!\n 416→ removeTagFromList(tagIds: [ID!]!): [Int!]!\n 417→\n 418→ updateConfiguration(name: String!, value: String!): String\n 419→}\n 420→\n 421→type IntRangeOutput {\n 422→ from: Int!\n 423→ to: Int!\n 424→}\n 425→type TimeRangeOutput {\n 426→ range: String\n 427→ from: Time!\n 428→ to: Time!\n 429→}\n 430→\n 431→input NodeFilter {\n 432→ hostname: StringInput\n 433→ cluster: StringInput\n 434→ subCluster: StringInput\n 435→ schedulerState: SchedulerState\n 436→ healthState: MonitoringState\n 437→ timeStart: Int\n 438→}\n 439→\n 440→input JobFilter {\n 441→ tags: [ID!]\n 442→ dbId: [ID!]\n 443→ jobId: StringInput\n 444→ arrayJobId: Int\n 445→ user: StringInput\n 446→ project: StringInput\n 447→ jobName: StringInput\n 448→ cluster: StringInput\n 449→ subCluster: StringInput\n 450→ partition: StringInput\n 451→ duration: IntRange\n 452→ energy: FloatRange\n 453→\n 454→ minRunningFor: Int\n 455→\n 456→ numNodes: IntRange\n 457→ numAccelerators: IntRange\n 458→ numHWThreads: IntRange\n 459→\n 460→ startTime: TimeRange\n 461→ state: [JobState!]\n 462→ metricStats: [MetricStatItem!]\n 463→ shared: String\n 464→ schedule: String\n 465→ node: StringInput\n 466→}\n 467→\n 468→input OrderByInput {\n 469→ field: String!\n 470→ type: String!\n 471→ order: SortDirectionEnum! = ASC\n 472→}\n 473→\n 474→enum SortDirectionEnum {\n 475→ DESC\n 476→ ASC\n 477→}\n 478→\n 479→input StringInput {\n 480→ eq: String\n 481→ neq: String\n 482→ contains: String\n 483→ startsWith: String\n 484→ endsWith: String\n 485→ in: [String!]\n 486→}\n 487→\n 488→input IntRange {\n 489→ from: Int!\n 490→ to: Int!\n 491→}\n 492→input TimeRange {\n 493→ range: String\n 494→ from: Time\n 495→ to: Time\n 496→}\n 497→\n 498→input FloatRange {\n 499→ from: Float!\n 500→ to: Float!\n 501→}\n 502→\n 503→type NodeStateResultList {\n 504→ items: [Node!]!\n 505→ count: Int\n 506→}\n 507→\n 508→type JobResultList {\n 509→ items: [Job!]!\n 510→ offset: Int\n 511→ limit: Int\n 512→ count: Int\n 513→ hasNextPage: Boolean\n 514→}\n 515→\n 516→type JobLinkResultList {\n 517→ listQuery: String\n 518→ items: [JobLink!]!\n 519→ count: Int\n 520→}\n 521→\n 522→type HistoPoint {\n 523→ count: Int!\n 524→ value: Int!\n 525→}\n 526→\n 527→type MetricHistoPoints {\n 528→ metric: String!\n 529→ unit: String!\n 530→ stat: String\n 531→ data: [MetricHistoPoint!]\n 532→}\n 533→\n 534→type MetricHistoPoint {\n 535→ bin: Int\n 536→ count: Int!\n 537→ min: Int\n 538→ max: Int\n 539→}\n 540→\n 541→type JobsStatistics {\n 542→ id: ID! # If `groupBy` was used, ID of the user/project/cluster/subcluster\n 543→ name: String! # if User-Statistics: Given Name of Account (ID) Owner\n 544→ totalUsers: Int! # if *not* User-Statistics: Number of active users (based on running jobs)\n 545→ totalJobs: Int! # Number of jobs\n 546→ runningJobs: Int! # Number of running jobs\n 547→ shortJobs: Int! # Number of jobs with a duration of less than config'd ShortRunningJobsDuration\n 548→ totalWalltime: Int! # Sum of the duration of all matched jobs in hours\n 549→ totalNodes: Int! # Sum of the nodes of all matched jobs\n 550→ totalNodeHours: Int! # Sum of the node hours of all matched jobs\n 551→ totalCores: Int! # Sum of the cores of all matched jobs\n 552→ totalCoreHours: Int! # Sum of the core hours of all matched jobs\n 553→ totalAccs: Int! # Sum of the accs of all matched jobs\n 554→ totalAccHours: Int! # Sum of the gpu hours of all matched jobs\n 555→ histDuration: [HistoPoint!]! # value: hour, count: number of jobs with a rounded duration of value\n 556→ histNumNodes: [HistoPoint!]! # value: number of nodes, count: number of jobs with that number of nodes\n 557→ histNumCores: [HistoPoint!]! # value: number of cores, count: number of jobs with that number of cores\n 558→ histNumAccs: [HistoPoint!]! # value: number of accs, count: number of jobs with that number of accs\n 559→ histMetrics: [MetricHistoPoints!]! # metric: metricname, data array of histopoints: value: metric average bin, count: number of jobs with that metric average\n 560→}\n 561→\n 562→input PageRequest {\n 563→ itemsPerPage: Int!\n 564→ page: Int!\n 565→}\n 566→"}]},"uuid":"3c5015b7-1d35-4a55-b5c3-ba16b0e57a45","timestamp":"2026-03-12T11:45:16.017Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"c1b33d3e-8a0e-48b8-958b-f0dca7290b06","timestamp":"2026-03-12T11:45:16.019Z"}
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{"parentUuid":"fde380bb-2589-48ed-8e83-1fee0a8473cd","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U6TJ6oa9echF7j5ULHBD3q","type":"tool_result","content":"641-\t}\n642-\n643-\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n644-}\n645-\n646:// JobsStatistics is the resolver for the jobsStatistics field.\n647:func (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n648-\tstartOverall := time.Now()\n649-\tvar err error\n650:\tvar stats []*model.JobsStatistics\n651-\n652-\t// Top Level Defaults\n653-\tdefaultDurationBins := \"1h\"\n654-\tdefaultMetricBins := 10\n655-\n--\n674-\t\t\tstartGrouped := time.Now()\n675-\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n676-\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n677-\t\t}\n678-\t} else {\n679:\t\tstats = make([]*model.JobsStatistics, 0, 1)\n680:\t\tstats = append(stats, &model.JobsStatistics{})\n681-\t}\n682-\n683-\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n684-\t// Only run separate count queries if main stats were not fetched.\n685-\tif !fetchedMainStats {\n--\n734-\t\t} else {\n735-\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n736-\t\t}\n737-\t}\n738-\n739:\tcclog.Infof(\"Timer JobsStatistics overall: %s\", time.Since(startOverall))\n740-\treturn stats, nil\n741-}\n742-\n743-// JobsMetricStats is the resolver for the jobsMetricStats field.\n744-func (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n\n[Showing results with pagination = limit: 50, offset: 0]"}]},"uuid":"58fada81-9554-42ac-98b4-f2800480ef6f","timestamp":"2026-03-12T11:45:18.409Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"5b9d902c-1d33-4b8a-9751-c6b4558bbdbd","timestamp":"2026-03-12T11:45:18.412Z"}
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{"parentUuid":"7601a494-c78e-4bc7-97ed-296942727105","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VMLg9Chu7DYBoqTJxHbDHA","type":"tool_result","content":" 646→// JobsStatistics is the resolver for the jobsStatistics field.\n 647→func (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n 648→\tstartOverall := time.Now()\n 649→\tvar err error\n 650→\tvar stats []*model.JobsStatistics\n 651→\n 652→\t// Top Level Defaults\n 653→\tdefaultDurationBins := \"1h\"\n 654→\tdefaultMetricBins := 10\n 655→\n 656→\t// Build requested fields map for selective column computation\n 657→\tstatsFields := []string{\"totalJobs\", \"totalUsers\", \"totalWalltime\", \"totalNodes\", \"totalCores\",\n 658→\t\t\"totalAccs\", \"totalNodeHours\", \"totalCoreHours\", \"totalAccHours\", \"runningJobs\", \"shortJobs\"}\n 659→\treqFields := make(map[string]bool, len(statsFields))\n 660→\tfetchedMainStats := false\n 661→\tfor _, f := range statsFields {\n 662→\t\tif requireField(ctx, f) {\n 663→\t\t\treqFields[f] = true\n 664→\t\t\tif f != \"runningJobs\" && f != \"shortJobs\" {\n 665→\t\t\t\tfetchedMainStats = true\n 666→\t\t\t}\n 667→\t\t}\n 668→\t}\n 669→\n 670→\tif fetchedMainStats {\n 671→\t\tif groupBy == nil {\n 672→\t\t\tstats, err = r.Repo.JobsStats(ctx, filter, reqFields)\n 673→\t\t} else {\n 674→\t\t\tstartGrouped := time.Now()\n 675→\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n 676→\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n 677→\t\t}\n 678→\t} else {\n 679→\t\tstats = make([]*model.JobsStatistics, 0, 1)\n 680→\t\tstats = append(stats, &model.JobsStatistics{})\n 681→\t}\n 682→\n 683→\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n 684→\t// Only run separate count queries if main stats were not fetched.\n 685→\tif !fetchedMainStats {\n 686→\t\tif groupBy != nil {\n 687→\t\t\tif requireField(ctx, \"shortJobs\") {\n 688→\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n 689→\t\t\t}\n 690→\t\t\tif requireField(ctx, \"runningJobs\") {\n 691→\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n 692→\t\t\t}\n 693→\t\t} else {\n 694→\t\t\tif requireField(ctx, \"shortJobs\") {\n 695→\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n 696→\t\t\t}\n 697→\t\t\tif requireField(ctx, \"runningJobs\") {\n 698→\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n 699→\t\t\t}\n 700→\t\t}\n 701→\t}\n 702→\n 703→\tif err != nil {\n 704→\t\treturn nil, err\n 705→\t}\n 706→\n 707→\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n 708→\n 709→\t\tif numDurationBins == nil {\n 710→\t\t\tnumDurationBins = &defaultDurationBins\n 711→\t\t}\n 712→\n 713→\t\tif groupBy == nil {\n 714→\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n 715→\t\t\tif err != nil {\n 716→\t\t\t\treturn nil, err\n 717→\t\t\t}\n 718→\t\t} else {\n 719→\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n 720→\t\t}\n 721→\t}\n 722→\n 723→\tif requireField(ctx, \"histMetrics\") {\n 724→\n 725→\t\tif numMetricBins == nil {\n 726→\t\t\tnumMetricBins = &defaultMetricBins\n 727→\t\t}\n 728→\n 729→\t\tif groupBy == nil {\n 730→\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n 731→\t\t\tif err != nil {\n 732→\t\t\t\treturn nil, err\n 733→\t\t\t}\n 734→\t\t} else {\n 735→\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n 736→\t\t}\n 737→\t}\n 738→\n 739→\tcclog.Infof(\"Timer JobsStatistics overall: %s\", time.Since(startOverall))\n 740→\treturn stats, nil\n 741→}\n 742→\n 743→// JobsMetricStats is the resolver for the jobsMetricStats field.\n 744→func (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n 745→\t// No Paging, Fixed Order by StartTime ASC\n 746→\torder := &model.OrderByInput{\n 747→\t\tField: \"startTime\",\n 748→\t\tType: \"col\",\n 749→\t\tOrder: \"ASC\",\n 750→\t}\n 751→\n 752→\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n 753→\tif err != nil {\n 754→\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n 755→\t\treturn nil, err\n 756→\t}\n 757→\n 758→\tres := []*model.JobStats{}\n 759→\tfor _, job := range jobs {\n 760→\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n 761→\t\tif err != nil {\n 762→\t\t\tcclog.Warnf(\"Error while loading comparison jobStats data for job id %d\", job.JobID)\n 763→\t\t\tcontinue\n 764→\t\t\t// return nil, err\n 765→\t\t}\n 766→\n 767→\t\tsres := []*model.NamedStats{}\n 768→\t\tfor name, md := range data {\n 769→\t\t\tsres = append(sres, &model.NamedStats{\n 770→\t\t\t\tName: name,\n 771→\t\t\t\tData: &md,\n 772→\t\t\t})\n 773→\t\t}\n 774→\n 775→\t\tnumThreadsInt := int(job.NumHWThreads)\n 776→\t\tnumAccsInt := int(job.NumAcc)\n 777→\t\tres = append(res, &model.JobStats{\n 778→\t\t\tID: int(*job.ID),\n 779→\t\t\tJobID: strconv.Itoa(int(job.JobID)),\n 780→\t\t\tStartTime: int(job.StartTime),\n 781→\t\t\tDuration: int(job.Duration),\n 782→\t\t\tCluster: job.Cluster,\n 783→\t\t\tSubCluster: job.SubCluster,\n 784→\t\t\tNumNodes: int(job.NumNodes),\n 785→\t\t\tNumHWThreads: &numThreadsInt,\n 786→\t\t\tNumAccelerators: &numAccsInt,\n 787→\t\t\tStats: sres,\n 788→\t\t})\n 789→\t}\n 790→\treturn res, err\n 791→}\n 792→\n 793→// JobsFootprints is the resolver for the jobsFootprints field.\n 794→func (r *queryResolver) JobsFootprints(ctx context.Context, filter []*model.JobFilter, metrics []string) (*model.Footprints, error) {\n 795→\t// NOTE: Legacy Naming! This resolver is for normalized histograms in analysis view only - *Not* related to DB \"footprint\" column!"}]},"uuid":"14ac14c8-e2e1-4c61-904a-aa687f11ad67","timestamp":"2026-03-12T11:45:19.604Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"2ff61d98-d607-45b9-908a-a52e22e94d02","timestamp":"2026-03-12T11:45:19.616Z"}
{"parentUuid":"2ff61d98-d607-45b9-908a-a52e22e94d02","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:25.660Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_013B5WyceVnrmAnmjDz69sBE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013yoYzR49cdhwYJZnWhj8NH","name":"Grep","input":{"pattern":"JobsStatsGrouped|buildStatsQuery","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","output_mode":"content","context":10,"head_limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":2575,"cache_read_input_tokens":36752,"cache_creation":{"ephemeral_5m_input_tokens":2575,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8TozKiN1rwvgNgFnGT","uuid":"52293bc4-c507-46c3-80bf-2d57bbc002ee"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"1b6d2fd8-072c-414d-9543-6f92aa090a2b","timestamp":"2026-03-12T11:45:25.660Z"}
{"parentUuid":"1b6d2fd8-072c-414d-9543-6f92aa090a2b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013yoYzR49cdhwYJZnWhj8NH","type":"tool_result","content":"3-// Use of this source code is governed by a MIT-style\n4-// license that can be found in the LICENSE file.\n5-\n6-// This file contains job statistics and histogram generation functionality for the JobRepository.\n7-//\n8-// # Job Statistics\n9-//\n10-// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n11-/ walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n12-/ - Overall (JobsStats): Single aggregate across all matching jobs\n13:// - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n14-/ - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n15-//\n16-// All statistics methods support filtering via JobFilter and respect security contexts.\n17-//\n18-// # Histograms\n19-//\n20-/ Histogram methods generate distribution data for visualization:\n21-// - Duration, nodes, cores, accelerators (AddHistograms)\n22-// - Job metrics like CPU load, memory usage (AddMetricHistograms)\n23-//\n--\n51-\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n52-\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n53-\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n54-\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n55-\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n56-\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n57-\tsq \"github.com/Masterminds/squirrel\"\n58-)\n59-\n60-// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n61:// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n62-// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\n63-var groupBy2column = map[model.Aggregate]string{\n64-\tmodel.AggregateUser: \"job.hpc_user\",\n65-\tmodel.AggregateProject: \"job.project\",\n66-\tmodel.AggregateCluster: \"job.cluster\",\n67-\tmodel.AggregateSubcluster: \"job.subcluster\",\n68-}\n69-\n70-// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n71:// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n72:// Column names match the AS aliases used in buildStatsQuery.\n73-var sortBy2column = map[model.SortByAggregate]string{\n74-\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n75-\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n76-\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n77-\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n78-\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n79-\tmodel.SortByAggregateTotalcores: \"totalCores\",\n80-\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n81-\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n82-\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n--\n117-\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n118-\t}\n119-\n120-\tfor _, f := range filter {\n121-\t\tquery = BuildWhereClause(f, query)\n122-\t}\n123-\n124-\treturn query\n125-}\n126-\n127:// buildStatsQuery constructs a SQL query to compute job statistics with optional grouping.\n128-// Only requested columns are computed; unrequested columns select 0 as placeholder.\n129-//\n130-/ Parameters:\n131-/ - filter: Job filters to apply (cluster, user, time range, etc.)\n132-/ - col: Column name to GROUP BY; empty string for overall statistics without grouping\n133-/ - shortThreshold: Duration threshold in seconds for counting short-running jobs\n134-/ - reqFields: Set of requested field names; nil means compute all fields\n135:func (r *JobRepository) buildStatsQuery(\n136-\tfilter []*model.JobFilter,\n137-\tcol string,\n138-\tshortThreshold int,\n139-\treqFields map[string]bool,\n140-) sq.SelectBuilder {\n141-\tnow := time.Now().Unix()\n142-\n143-\t/ Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n144-\tneed := func(field string) bool {\n145-\t\treturn reqFields == nil || reqFields[field]\n--\n220-\t\tquery = query.GroupBy(col)\n221-\t}\n222-\n223-\tfor _, f := range filter {\n224-\t\tquery = BuildWhereClause(f, query)\n225-\t}\n226-\n227-\treturn query\n228-}\n229-\n230:// JobsStatsGrouped computes job statistics grouped by a dimension (user, project, cluster, or subcluster).\n231-// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n232-// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\n233:func (r *JobRepository) JobsStatsGrouped(\n234-\tctx context.Context,\n\n[Showing results with pagination = limit: 100, offset: 0]"}]},"uuid":"211748f5-b2e5-47b4-b446-898153fe6229","timestamp":"2026-03-12T11:45:25.692Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"4738e703-158e-48e4-8443-ae1b54dca9de","timestamp":"2026-03-12T11:45:25.693Z"}
{"parentUuid":"4738e703-158e-48e4-8443-ae1b54dca9de","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:27.069Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_015s8nTCgioyMSzUYyAkU1bG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017QcpFJyqpkQMd8jAhx3365","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","offset":230,"limit":250},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1806,"cache_read_input_tokens":39327,"cache_creation":{"ephemeral_5m_input_tokens":1806,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8UFzFxvNZGv8uPBhY5","uuid":"1958924e-7320-47d3-b446-e7b6941ac144"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"9e241730-5c43-4ed7-8b3e-104ea5a60530","timestamp":"2026-03-12T11:45:27.070Z"}
{"parentUuid":"9e241730-5c43-4ed7-8b3e-104ea5a60530","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017QcpFJyqpkQMd8jAhx3365","type":"tool_result","content":" 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."}]},"uuid":"578fac42-1480-49c1-b037-f40b02b0ead0","timestamp":"2026-03-12T11:45:27.082Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"6b453d10-a1ff-4bbf-9f67-4f0772bf5733","timestamp":"2026-03-12T11:45:27.084Z"}
{"parentUuid":"6b453d10-a1ff-4bbf-9f67-4f0772bf5733","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:29.933Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01EnhSzFsdszvWMh9frM8tnB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NFck9m5JM4XWuB2mm8Z1ZN","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","offset":127,"limit":102},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3623,"cache_read_input_tokens":41133,"cache_creation":{"ephemeral_5m_input_tokens":3623,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8UMzvC1ZmnFE9KRCx3","uuid":"e22f7ee2-117a-4fb2-ac74-b20b40ff2979"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"e18e24fe-c885-4680-bf59-b260c01ccb3e","timestamp":"2026-03-12T11:45:29.934Z"}
{"parentUuid":"e18e24fe-c885-4680-bf59-b260c01ccb3e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NFck9m5JM4XWuB2mm8Z1ZN","type":"tool_result","content":" 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→}"}]},"uuid":"bec19ab1-1059-4697-ab9c-9d49d3d969fe","timestamp":"2026-03-12T11:45:29.954Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"6c7b44c8-5ea8-4199-b1bc-b82034e9cd2f","timestamp":"2026-03-12T11:45:30.050Z"}
{"parentUuid":"6c7b44c8-5ea8-4199-b1bc-b82034e9cd2f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:31.648Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01VRARxnJNsJaJpo8irWMhwN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Unj1PpKLiktDuVyofXtvDR","name":"Grep","input":{"pattern":"func BuildWhereClause","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository","output_mode":"files_with_matches"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1900,"cache_read_input_tokens":44756,"cache_creation":{"ephemeral_5m_input_tokens":1900,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8UagQa8KR97RKEfpSG","uuid":"249ceddd-a7ba-417a-b06b-c10944de92d8"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"f3fa7c7d-a1e8-474f-8f6f-66d8c8c861e8","timestamp":"2026-03-12T11:45:31.648Z"}
{"parentUuid":"f3fa7c7d-a1e8-474f-8f6f-66d8c8c861e8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Unj1PpKLiktDuVyofXtvDR","type":"tool_result","content":"Found 1 file\ninternal/repository/jobQuery.go"}]},"uuid":"6273b275-d1c6-472c-a87f-82f918b6ed83","timestamp":"2026-03-12T11:45:31.678Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"e74a703f-c95d-4e94-816d-da799873194e","timestamp":"2026-03-12T11:45:31.679Z"}
{"parentUuid":"e74a703f-c95d-4e94-816d-da799873194e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:32.888Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_012nnruVggWZXxURrdYB7h6J","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Nm6sCyu2HqzHosmJ1viUqU","name":"Grep","input":{"pattern":"func BuildWhereClause","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/jobQuery.go","output_mode":"content","context":50},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":191,"cache_read_input_tokens":46656,"cache_creation":{"ephemeral_5m_input_tokens":191,"ephemeral_1h_input_tokens":0},"output_tokens":24,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8UhZ9i7EZ22zQUshEE","uuid":"a756ff83-fa0f-4e72-b952-ad92d6630c86"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"827237db-37fd-4d34-8402-378d570f3aac","timestamp":"2026-03-12T11:45:32.889Z"}
{"parentUuid":"827237db-37fd-4d34-8402-378d570f3aac","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Nm6sCyu2HqzHosmJ1viUqU","type":"tool_result","content":"132-\n133-\treturn count, nil\n134-}\n135-\n136-// SecurityCheckWithUser applies role-based access control filters to a job query\n137-// based on the provided user's roles and permissions.\n138-//\n139-/ Access rules by role:\n140-/ - API role (exclusive): Full access to all jobs\n141-/ - Admin/Support roles: Full access to all jobs\n142-/ - Manager role: Access to jobs in managed projects plus own jobs\n143-/ - User role: Access only to own jobs\n144-//\n145-// Returns an error if the user is nil or has no recognized roles.\n146-func SecurityCheckWithUser(user *schema.User, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n147-\tif user == nil {\n148-\t\tvar qnil sq.SelectBuilder\n149-\t\treturn qnil, fmt.Errorf(\"user context is nil\")\n150-\t}\n151-\n152-\tswitch {\n153-\tcase len(user.Roles) == 1 && user.HasRole(schema.RoleAPI):\n154-\t\treturn query, nil\n155-\tcase user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}):\n156-\t\treturn query, nil\n157-\tcase user.HasRole(schema.RoleManager):\n158-\t\tif len(user.Projects) != 0 {\n159-\t\t\treturn query.Where(sq.Or{sq.Eq{\"job.project\": user.Projects}, sq.Eq{\"job.hpc_user\": user.Username}}), nil\n160-\t\t}\n161-\t\tcclog.Debugf(\"Manager '%s' has no assigned projects, restricting to personal jobs\", user.Username)\n162-\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n163-\tcase user.HasRole(schema.RoleUser):\n164-\t\treturn query.Where(\"job.hpc_user = ?\", user.Username), nil\n165-\tdefault:\n166-\t\tvar qnil sq.SelectBuilder\n167-\t\treturn qnil, fmt.Errorf(\"user has no or unknown roles\")\n168-\t}\n169-}\n170-\n171-// SecurityCheck extracts the user from the context and applies role-based access\n172-// control filters to the query. This is a convenience wrapper around SecurityCheckWithUser.\n173-func SecurityCheck(ctx context.Context, query sq.SelectBuilder) (sq.SelectBuilder, error) {\n174-\tuser := GetUserFromContext(ctx)\n175-\treturn SecurityCheckWithUser(user, query)\n176-}\n177-\n178-// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n179-// them to the query. Supports filtering by job properties (cluster, state, user),\n180-// time ranges, resource usage, tags, and JSON field searches in meta_data,\n181-// footprint, and resources columns.\n182:func BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n183-\t// Primary Key\n184-\tif filter.DbID != nil {\n185-\t\tdbIDs := make([]string, len(filter.DbID))\n186-\t\tcopy(dbIDs, filter.DbID)\n187-\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n188-\t}\n189-\t// Explicit indices\n190-\tif filter.Cluster != nil {\n191-\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n192-\t}\n193-\tif filter.SubCluster != nil {\n194-\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n195-\t}\n196-\tif filter.Partition != nil {\n197-\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n198-\t}\n199-\tif filter.State != nil {\n200-\t\tstates := make([]string, len(filter.State))\n201-\t\tfor i, val := range filter.State {\n202-\t\t\tstates[i] = string(val)\n203-\t\t}\n204-\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n205-\t}\n206-\tif filter.Shared != nil {\n207-\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n208-\t}\n209-\tif filter.Project != nil {\n210-\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n211-\t}\n212-\tif filter.User != nil {\n213-\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n214-\t}\n215-\tif filter.NumNodes != nil {\n216-\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n217-\t}\n218-\tif filter.NumAccelerators != nil {\n219-\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n220-\t}\n221-\tif filter.NumHWThreads != nil {\n222-\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n223-\t}\n224-\tif filter.ArrayJobID != nil {\n225-\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n226-\t}\n227-\tif filter.StartTime != nil {\n228-\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n229-\t}\n230-\tif filter.Duration != nil {\n231-\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n232-\t}"}]},"uuid":"27be81ff-d3db-455c-8606-bfcdd159eff3","timestamp":"2026-03-12T11:45:32.917Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"43ad5e4e-af5d-4e63-8596-be020ca70e74","timestamp":"2026-03-12T11:45:32.918Z"}
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{"parentUuid":"61096e11-2932-411d-ac2c-d43b73532791","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EjgCDPWyV5tZPTwLnPetc4","type":"tool_result","content":" 178→// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n 179→// them to the query. Supports filtering by job properties (cluster, state, user),\n 180→// time ranges, resource usage, tags, and JSON field searches in meta_data,\n 181→// footprint, and resources columns.\n 182→func BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n 183→\t// Primary Key\n 184→\tif filter.DbID != nil {\n 185→\t\tdbIDs := make([]string, len(filter.DbID))\n 186→\t\tcopy(dbIDs, filter.DbID)\n 187→\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n 188→\t}\n 189→\t// Explicit indices\n 190→\tif filter.Cluster != nil {\n 191→\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n 192→\t}\n 193→\tif filter.SubCluster != nil {\n 194→\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n 195→\t}\n 196→\tif filter.Partition != nil {\n 197→\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n 198→\t}\n 199→\tif filter.State != nil {\n 200→\t\tstates := make([]string, len(filter.State))\n 201→\t\tfor i, val := range filter.State {\n 202→\t\t\tstates[i] = string(val)\n 203→\t\t}\n 204→\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n 205→\t}\n 206→\tif filter.Shared != nil {\n 207→\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n 208→\t}\n 209→\tif filter.Project != nil {\n 210→\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n 211→\t}\n 212→\tif filter.User != nil {\n 213→\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n 214→\t}\n 215→\tif filter.NumNodes != nil {\n 216→\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n 217→\t}\n 218→\tif filter.NumAccelerators != nil {\n 219→\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n 220→\t}\n 221→\tif filter.NumHWThreads != nil {\n 222→\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n 223→\t}\n 224→\tif filter.ArrayJobID != nil {\n 225→\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n 226→\t}\n 227→\tif filter.StartTime != nil {\n 228→\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n 229→\t}\n 230→\tif filter.Duration != nil {\n 231→\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n 232→\t}\n 233→\tif filter.Energy != nil {\n 234→\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n 235→\t}\n 236→\t// Indices on Tag Table\n 237→\tif filter.Tags != nil {\n 238→\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n 239→\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n 240→\t}\n 241→\t// No explicit Indices\n 242→\tif filter.JobID != nil {\n 243→\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n 244→\t}\n 245→\t// Queries Within JSONs\n 246→\tif filter.MetricStats != nil {\n 247→\t\tfor _, ms := range filter.MetricStats {\n 248→\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n 249→\t\t}\n 250→\t}\n 251→\tif filter.Node != nil {\n 252→\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n 253→\t}\n 254→\tif filter.JobName != nil {\n 255→\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n 256→\t}\n 257→\tif filter.Schedule != nil {\n 258→\t\tinteractiveJobname := \"interactive\"\n 259→\t\tswitch *filter.Schedule {\n 260→\t\tcase \"interactive\":\n 261→\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n 262→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n 263→\t\tcase \"batch\":\n 264→\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n 265→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n 266→\t\t}\n 267→\t}\n 268→\n 269→\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n 270→\tif filter.MinRunningFor != nil {\n 271→\t\tnow := time.Now().Unix()\n 272→\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n 273→\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n 274→\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n 275→\t}\n 276→\treturn query\n 277→}\n 278→\n 279→// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\n 280→func buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n 281→\tif cond.From != 1 && cond.To != 0 {\n 282→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 283→\t} else if cond.From != 1 && cond.To == 0 {\n 284→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 285→\t} else if cond.From == 1 && cond.To != 0 {\n 286→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 287→\t} else {\n 288→\t\treturn query\n 289→\t}\n 290→}\n 291→\n 292→// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\n 293→func buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n 294→\tif cond.From != 1.0 && cond.To != 0.0 {\n 295→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 296→\t} else if cond.From != 1.0 && cond.To == 0.0 {\n 297→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 298→\t} else if cond.From == 1.0 && cond.To != 0.0 {\n 299→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 300→\t} else {\n 301→\t\treturn query\n 302→\t}\n 303→}\n 304→\n 305→// buildTimeCondition creates time range filters supporting absolute timestamps,\n 306→// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n 307→// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\n 308→func buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n 309→\tif cond.From != nil && cond.To != nil {\n 310→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n 311→\t} else if cond.From != nil {\n 312→\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n 313→\t} else if cond.To != nil {\n 314→\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n 315→\t} else if cond.Range != \"\" {\n 316→\t\tnow := time.Now().Unix()\n 317→\t\tvar then int64\n 318→\t\tswitch cond.Range {\n 319→\t\tcase \"last6h\":\n 320→\t\t\tthen = now - (60 * 60 * 6)\n 321→\t\tcase \"last24h\":\n 322→\t\t\tthen = now - (60 * 60 * 24)\n 323→\t\tcase \"last7d\":\n 324→\t\t\tthen = now - (60 * 60 * 24 * 7)\n 325→\t\tcase \"last30d\":\n 326→\t\t\tthen = now - (60 * 60 * 24 * 30)\n 327→\t\tdefault:"}]},"uuid":"9e865ff5-0dfc-459c-9823-055bf7a91f7e","timestamp":"2026-03-12T11:45:34.544Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"de69279a-c0a6-4710-92da-4be03bd9d325","timestamp":"2026-03-12T11:45:34.567Z"}
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{"parentUuid":"1bb54a7c-9f96-4db0-ac75-1578cce4bfb2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GPwkPVR766xpyKFKnRvZYH","type":"tool_result","content":" 1→CREATE TABLE IF NOT EXISTS job (\n 2→id INTEGER PRIMARY KEY,\n 3→job_id BIGINT NOT NULL,\n 4→cluster VARCHAR(255) NOT NULL,\n 5→subcluster VARCHAR(255) NOT NULL,\n 6→start_time BIGINT NOT NULL, -- Unix timestamp\n 7→user VARCHAR(255) NOT NULL,\n 8→project VARCHAR(255) NOT NULL,\n 9→partition VARCHAR(255),\n 10→array_job_id BIGINT,\n 11→duration INT NOT NULL,\n 12→walltime INT NOT NULL,\n 13→job_state VARCHAR(255) NOT NULL\n 14→CHECK(job_state IN ('running', 'completed', 'failed', 'canceled', 'stopped', 'timeout')),\n 15→meta_data TEXT, -- JSON\n 16→resources TEXT NOT NULL, -- JSON\n 17→num_nodes INT NOT NULL,\n 18→num_hwthreads INT,\n 19→num_acc INT,\n 20→smt TINYINT NOT NULL DEFAULT 1 CHECK(smt IN (0, 1 )),\n 21→exclusive TINYINT NOT NULL DEFAULT 1 CHECK(exclusive IN (0, 1, 2)),\n 22→monitoring_status TINYINT NOT NULL DEFAULT 1 CHECK(monitoring_status IN (0, 1, 2, 3)),\n 23→mem_used_max REAL NOT NULL DEFAULT 0.0,\n 24→flops_any_avg REAL NOT NULL DEFAULT 0.0,\n 25→mem_bw_avg REAL NOT NULL DEFAULT 0.0,\n 26→load_avg REAL NOT NULL DEFAULT 0.0,\n 27→net_bw_avg REAL NOT NULL DEFAULT 0.0,\n 28→net_data_vol_total REAL NOT NULL DEFAULT 0.0,\n 29→file_bw_avg REAL NOT NULL DEFAULT 0.0,\n 30→file_data_vol_total REAL NOT NULL DEFAULT 0.0);\n 31→\n 32→CREATE TABLE IF NOT EXISTS tag (\n 33→id INTEGER PRIMARY KEY,\n 34→tag_type VARCHAR(255) NOT NULL,\n 35→tag_name VARCHAR(255) NOT NULL,\n 36→UNIQUE (tag_type, tag_name));\n 37→\n 38→CREATE TABLE IF NOT EXISTS jobtag (\n 39→job_id INTEGER,\n 40→tag_id INTEGER,\n 41→PRIMARY KEY (job_id, tag_id),\n 42→FOREIGN KEY (job_id) REFERENCES job (id) ON DELETE CASCADE,\n 43→FOREIGN KEY (tag_id) REFERENCES tag (id) ON DELETE CASCADE);\n 44→\n 45→CREATE TABLE IF NOT EXISTS user (\n 46→username varchar(255) PRIMARY KEY NOT NULL,\n 47→password varchar(255) DEFAULT NULL,\n 48→ldap tinyint NOT NULL DEFAULT 0, /* col called \"ldap\" for historic reasons, fills the \"AuthSource\" */\n 49→name varchar(255) DEFAULT NULL,\n 50→roles varchar(255) NOT NULL DEFAULT \"[]\",\n 51→email varchar(255) DEFAULT NULL);\n 52→\n 53→CREATE TABLE IF NOT EXISTS configuration (\n 54→username varchar(255),\n 55→confkey varchar(255),\n 56→value varchar(255),\n 57→PRIMARY KEY (username, confkey),\n 58→FOREIGN KEY (username) REFERENCES user (username) ON DELETE CASCADE ON UPDATE NO ACTION);\n 59→\n 60→\n 61→"}]},"uuid":"ed2835f2-9787-4525-a910-da4b8a3b9199","timestamp":"2026-03-12T11:45:36.615Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"aa0e0bfe-22ae-476e-b627-64b3359581cf","timestamp":"2026-03-12T11:45:36.625Z"}
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{"parentUuid":"94c232ef-14f1-430e-8c1d-7455047606e7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ELsFSMZnyumxkhxLZiJwSS","type":"tool_result","content":" 1→DROP INDEX IF EXISTS job_stats;\n 2→DROP INDEX IF EXISTS job_by_user;\n 3→DROP INDEX IF EXISTS job_by_starttime;\n 4→DROP INDEX IF EXISTS job_by_job_id;\n 5→DROP INDEX IF EXISTS job_list;\n 6→DROP INDEX IF EXISTS job_list_user;\n 7→DROP INDEX IF EXISTS job_list_users;\n 8→DROP INDEX IF EXISTS job_list_users_start;\n 9→\n 10→ALTER TABLE job ADD COLUMN energy REAL NOT NULL DEFAULT 0.0;\n 11→ALTER TABLE job ADD COLUMN energy_footprint TEXT DEFAULT NULL;\n 12→\n 13→ALTER TABLE job ADD COLUMN footprint TEXT DEFAULT NULL;\n 14→ALTER TABLE tag ADD COLUMN tag_scope TEXT NOT NULL DEFAULT 'global';\n 15→\n 16→-- Do not use reserved keywords anymore\n 17→ALTER TABLE \"user\" RENAME TO hpc_user;\n 18→ALTER TABLE job RENAME COLUMN \"user\" TO hpc_user;\n 19→ALTER TABLE job RENAME COLUMN \"partition\" TO cluster_partition;\n 20→\n 21→UPDATE job SET footprint = '{\"flops_any_avg\": 0.0}';\n 22→UPDATE job SET footprint = json_replace(footprint, '$.flops_any_avg', job.flops_any_avg);\n 23→UPDATE job SET footprint = json_insert(footprint, '$.mem_bw_avg', job.mem_bw_avg);\n 24→UPDATE job SET footprint = json_insert(footprint, '$.mem_used_max', job.mem_used_max);\n 25→UPDATE job SET footprint = json_insert(footprint, '$.cpu_load_avg', job.load_avg);\n 26→UPDATE job SET footprint = json_insert(footprint, '$.net_bw_avg', job.net_bw_avg) WHERE job.net_bw_avg != 0;\n 27→UPDATE job SET footprint = json_insert(footprint, '$.net_data_vol_total', job.net_data_vol_total) WHERE job.net_data_vol_total != 0;\n 28→UPDATE job SET footprint = json_insert(footprint, '$.file_bw_avg', job.file_bw_avg) WHERE job.file_bw_avg != 0;\n 29→UPDATE job SET footprint = json_insert(footprint, '$.file_data_vol_total', job.file_data_vol_total) WHERE job.file_data_vol_total != 0;\n 30→\n 31→ALTER TABLE job DROP flops_any_avg;\n 32→ALTER TABLE job DROP mem_bw_avg;\n 33→ALTER TABLE job DROP mem_used_max;\n 34→ALTER TABLE job DROP load_avg;\n 35→ALTER TABLE job DROP net_bw_avg;\n 36→ALTER TABLE job DROP net_data_vol_total;\n 37→ALTER TABLE job DROP file_bw_avg;\n 38→ALTER TABLE job DROP file_data_vol_total;\n 39→\n 40→-- Indices for: Single filters, combined filters, sorting, sorting with filters\n 41→-- Cluster Filter\n 42→CREATE INDEX IF NOT EXISTS jobs_cluster ON job (cluster);\n 43→CREATE INDEX IF NOT EXISTS jobs_cluster_user ON job (cluster, hpc_user);\n 44→CREATE INDEX IF NOT EXISTS jobs_cluster_project ON job (cluster, project);\n 45→CREATE INDEX IF NOT EXISTS jobs_cluster_subcluster ON job (cluster, subcluster);\n 46→-- Cluster Filter Sorting\n 47→CREATE INDEX IF NOT EXISTS jobs_cluster_starttime ON job (cluster, start_time);\n 48→CREATE INDEX IF NOT EXISTS jobs_cluster_duration ON job (cluster, duration);\n 49→CREATE INDEX IF NOT EXISTS jobs_cluster_numnodes ON job (cluster, num_nodes);\n 50→CREATE INDEX IF NOT EXISTS jobs_cluster_numhwthreads ON job (cluster, num_hwthreads);\n 51→CREATE INDEX IF NOT EXISTS jobs_cluster_numacc ON job (cluster, num_acc);\n 52→CREATE INDEX IF NOT EXISTS jobs_cluster_energy ON job (cluster, energy);\n 53→\n 54→-- Cluster+Partition Filter\n 55→CREATE INDEX IF NOT EXISTS jobs_cluster_partition ON job (cluster, cluster_partition);\n 56→-- Cluster+Partition Filter Sorting\n 57→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_starttime ON job (cluster, cluster_partition, start_time);\n 58→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_duration ON job (cluster, cluster_partition, duration);\n 59→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numnodes ON job (cluster, cluster_partition, num_nodes);\n 60→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numhwthreads ON job (cluster, cluster_partition, num_hwthreads);\n 61→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numacc ON job (cluster, cluster_partition, num_acc);\n 62→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_energy ON job (cluster, cluster_partition, energy);\n 63→\n 64→-- Cluster+Partition+Jobstate Filter\n 65→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate ON job (cluster, cluster_partition, job_state);\n 66→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_user ON job (cluster, cluster_partition, job_state, hpc_user);\n 67→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_project ON job (cluster, cluster_partition, job_state, project);\n 68→-- Cluster+Partition+Jobstate Filter Sorting\n 69→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_starttime ON job (cluster, cluster_partition, job_state, start_time);\n 70→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_duration ON job (cluster, cluster_partition, job_state, duration);\n 71→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numnodes ON job (cluster, cluster_partition, job_state, num_nodes);\n 72→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numhwthreads ON job (cluster, cluster_partition, job_state, num_hwthreads);\n 73→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numacc ON job (cluster, cluster_partition, job_state, num_acc);\n 74→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_energy ON job (cluster, cluster_partition, job_state, energy);\n 75→\n 76→-- Cluster+JobState Filter\n 77→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate ON job (cluster, job_state);\n 78→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_user ON job (cluster, job_state, hpc_user);\n 79→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_project ON job (cluster, job_state, project);\n 80→-- Cluster+JobState Filter Sorting\n 81→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_starttime ON job (cluster, job_state, start_time);\n 82→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_duration ON job (cluster, job_state, duration);\n 83→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numnodes ON job (cluster, job_state, num_nodes);\n 84→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numhwthreads ON job (cluster, job_state, num_hwthreads);\n 85→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numacc ON job (cluster, job_state, num_acc);\n 86→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_energy ON job (cluster, job_state, energy);\n 87→\n 88→-- User Filter\n 89→CREATE INDEX IF NOT EXISTS jobs_user ON job (hpc_user);\n 90→-- User Filter Sorting\n 91→CREATE INDEX IF NOT EXISTS jobs_user_starttime ON job (hpc_user, start_time);\n 92→CREATE INDEX IF NOT EXISTS jobs_user_duration ON job (hpc_user, duration);\n 93→CREATE INDEX IF NOT EXISTS jobs_user_numnodes ON job (hpc_user, num_nodes);\n 94→CREATE INDEX IF NOT EXISTS jobs_user_numhwthreads ON job (hpc_user, num_hwthreads);\n 95→CREATE INDEX IF NOT EXISTS jobs_user_numacc ON job (hpc_user, num_acc);\n 96→CREATE INDEX IF NOT EXISTS jobs_user_energy ON job (hpc_user, energy);\n 97→\n 98→-- Project Filter\n 99→CREATE INDEX IF NOT EXISTS jobs_project ON job (project);\n 100→CREATE INDEX IF NOT EXISTS jobs_project_user ON job (project, hpc_user);\n 101→-- Project Filter Sorting\n 102→CREATE INDEX IF NOT EXISTS jobs_project_starttime ON job (project, start_time);\n 103→CREATE INDEX IF NOT EXISTS jobs_project_duration ON job (project, duration);\n 104→CREATE INDEX IF NOT EXISTS jobs_project_numnodes ON job (project, num_nodes);\n 105→CREATE INDEX IF NOT EXISTS jobs_project_numhwthreads ON job (project, num_hwthreads);\n 106→CREATE INDEX IF NOT EXISTS jobs_project_numacc ON job (project, num_acc);\n 107→CREATE INDEX IF NOT EXISTS jobs_project_energy ON job (project, energy);\n 108→\n 109→-- JobState Filter\n 110→CREATE INDEX IF NOT EXISTS jobs_jobstate ON job (job_state);\n 111→CREATE INDEX IF NOT EXISTS jobs_jobstate_user ON job (job_state, hpc_user);\n 112→CREATE INDEX IF NOT EXISTS jobs_jobstate_project ON job (job_state, project);\n 113→CREATE INDEX IF NOT EXISTS jobs_jobstate_cluster ON job (job_state, cluster);\n 114→-- JobState Filter Sorting\n 115→CREATE INDEX IF NOT EXISTS jobs_jobstate_starttime ON job (job_state, start_time);\n 116→CREATE INDEX IF NOT EXISTS jobs_jobstate_duration ON job (job_state, duration);\n 117→CREATE INDEX IF NOT EXISTS jobs_jobstate_numnodes ON job (job_state, num_nodes);\n 118→CREATE INDEX IF NOT EXISTS jobs_jobstate_numhwthreads ON job (job_state, num_hwthreads);\n 119→CREATE INDEX IF NOT EXISTS jobs_jobstate_numacc ON job (job_state, num_acc);\n 120→CREATE INDEX IF NOT EXISTS jobs_jobstate_energy ON job (job_state, energy);\n 121→\n 122→-- ArrayJob Filter\n 123→CREATE INDEX IF NOT EXISTS jobs_arrayjobid_starttime ON job (array_job_id, start_time);\n 124→CREATE INDEX IF NOT EXISTS jobs_cluster_arrayjobid_starttime ON job (cluster, array_job_id, start_time);\n 125→\n 126→-- Sorting without active filters\n 127→CREATE INDEX IF NOT EXISTS jobs_starttime ON job (start_time);\n 128→CREATE INDEX IF NOT EXISTS jobs_duration ON job (duration);\n 129→CREATE INDEX IF NOT EXISTS jobs_numnodes ON job (num_nodes);\n 130→CREATE INDEX IF NOT EXISTS jobs_numhwthreads ON job (num_hwthreads);\n 131→CREATE INDEX IF NOT EXISTS jobs_numacc ON job (num_acc);\n 132→CREATE INDEX IF NOT EXISTS jobs_energy ON job (energy);\n 133→\n 134→-- Single filters with default starttime sorting\n 135→CREATE INDEX IF NOT EXISTS jobs_duration_starttime ON job (duration, start_time);\n 136→CREATE INDEX IF NOT EXISTS jobs_numnodes_starttime ON job (num_nodes, start_time);\n 137→CREATE INDEX IF NOT EXISTS jobs_numhwthreads_starttime ON job (num_hwthreads, start_time);\n 138→CREATE INDEX IF NOT EXISTS jobs_numacc_starttime ON job (num_acc, start_time);\n 139→CREATE INDEX IF NOT EXISTS jobs_energy_starttime ON job (energy, start_time);\n 140→\n 141→-- Optimize DB index usage\n 142→PRAGMA optimize;\n 143→"}]},"uuid":"2207b0c1-61a8-4558-a958-604915153cb9","timestamp":"2026-03-12T11:45:44.600Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"966a447e-a444-4315-8391-3baefad6453e","timestamp":"2026-03-12T11:45:44.608Z"}
{"parentUuid":"966a447e-a444-4315-8391-3baefad6453e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:47.017Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01U89gzAkqVj5k3D54f11pj3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HuVDTSezKjY982Wyhv9RZQ","name":"Bash","input":{"command":"grep -n \"cluster_partition\\|hpc_user\" /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/08_add-footprint.up.sql | head -20"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":3396,"cache_read_input_tokens":55378,"cache_creation":{"ephemeral_5m_input_tokens":3396,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8Vf37vgdDJcBaSmzPk","uuid":"0c444cfe-f4bd-4837-aaa5-32bf1e3caa89"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"d55c1393-c83e-4c54-87e6-24cfba316f0c","timestamp":"2026-03-12T11:45:47.018Z"}
{"parentUuid":"d55c1393-c83e-4c54-87e6-24cfba316f0c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HuVDTSezKjY982Wyhv9RZQ","type":"tool_result","content":"17:ALTER TABLE \"user\" RENAME TO hpc_user;\n18:ALTER TABLE job RENAME COLUMN \"user\" TO hpc_user;\n19:ALTER TABLE job RENAME COLUMN \"partition\" TO cluster_partition;\n43:CREATE INDEX IF NOT EXISTS jobs_cluster_user ON job (cluster, hpc_user);\n55:CREATE INDEX IF NOT EXISTS jobs_cluster_partition ON job (cluster, cluster_partition);\n57:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_starttime ON job (cluster, cluster_partition, start_time);\n58:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_duration ON job (cluster, cluster_partition, duration);\n59:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numnodes ON job (cluster, cluster_partition, num_nodes);\n60:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numhwthreads ON job (cluster, cluster_partition, num_hwthreads);\n61:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numacc ON job (cluster, cluster_partition, num_acc);\n62:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_energy ON job (cluster, cluster_partition, energy);\n65:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate ON job (cluster, cluster_partition, job_state);\n66:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_user ON job (cluster, cluster_partition, job_state, hpc_user);\n67:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_project ON job (cluster, cluster_partition, job_state, project);\n69:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_starttime ON job (cluster, cluster_partition, job_state, start_time);\n70:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_duration ON job (cluster, cluster_partition, job_state, duration);\n71:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numnodes ON job (cluster, cluster_partition, job_state, num_nodes);\n72:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numhwthreads ON job (cluster, cluster_partition, job_state, num_hwthreads);\n73:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numacc ON job (cluster, cluster_partition, job_state, num_acc);\n74:CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_energy ON job (cluster, cluster_partition, job_state, energy);","is_error":false}]},"uuid":"0b54865e-1b5e-418c-b4ef-c67a976f8b1b","timestamp":"2026-03-12T11:45:47.049Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"6d21d09b-51c1-4f76-a1c8-f648d15cd41c","timestamp":"2026-03-12T11:45:47.049Z"}
{"parentUuid":"6d21d09b-51c1-4f76-a1c8-f648d15cd41c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:48.685Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01SSgc1qDYgTu6epQp4MdU6m","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VHKkqMRXWctHAsUi7Yk61B","name":"Grep","input":{"pattern":"monitoring/users|monitoring/user","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","output_mode":"content","context":5},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":874,"cache_read_input_tokens":58774,"cache_creation":{"ephemeral_5m_input_tokens":874,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8VqHmBSySwTfLCEeuk","uuid":"f18d4ea9-d1ed-4b5c-b678-623a8025b251"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"4a035b40-7005-47d8-a77a-ce4f78825820","timestamp":"2026-03-12T11:45:48.685Z"}
{"parentUuid":"4a035b40-7005-47d8-a77a-ce4f78825820","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VHKkqMRXWctHAsUi7Yk61B","type":"tool_result","content":"36-var routes []Route = []Route{\n37-\t{\"/\", \"home.tmpl\", \"ClusterCockpit\", false, setupHomeRoute},\n38-\t{\"/config\", \"config.tmpl\", \"Settings\", false, setupConfigRoute},\n39-\t{\"/monitoring/jobs/\", \"monitoring/jobs.tmpl\", \"Jobs - ClusterCockpit\", true, func(i InfoType, r *http.Request) InfoType { return i }},\n40-\t{\"/monitoring/job/{id:[0-9]+}\", \"monitoring/job.tmpl\", \"Job <ID> - ClusterCockpit\", false, setupJobRoute},\n41:\t{\"/monitoring/users/\", \"monitoring/list.tmpl\", \"Users - ClusterCockpit\", true, func(i InfoType, r *http.Request) InfoType { i[\"listType\"] = \"USER\"; return i }},\n42-\t{\"/monitoring/projects/\", \"monitoring/list.tmpl\", \"Projects - ClusterCockpit\", true, func(i InfoType, r *http.Request) InfoType { i[\"listType\"] = \"PROJECT\"; return i }},\n43-\t{\"/monitoring/tags/\", \"monitoring/taglist.tmpl\", \"Tags - ClusterCockpit\", false, setupTaglistRoute},\n44:\t{\"/monitoring/user/{id}\", \"monitoring/user.tmpl\", \"User <ID> - ClusterCockpit\", true, setupUserRoute},\n45-\t{\"/monitoring/systems/{cluster}\", \"monitoring/systems.tmpl\", \"Cluster <ID> Node Overview - ClusterCockpit\", false, setupClusterOverviewRoute},\n46-\t{\"/monitoring/systems/list/{cluster}\", \"monitoring/systems.tmpl\", \"Cluster <ID> Node List - ClusterCockpit\", false, setupClusterListRoute},\n47-\t{\"/monitoring/systems/list/{cluster}/{subcluster}\", \"monitoring/systems.tmpl\", \"Cluster <ID> <SID> Node List - ClusterCockpit\", false, setupClusterListRoute},\n48-\t{\"/monitoring/node/{cluster}/{hostname}\", \"monitoring/node.tmpl\", \"Node <ID> - ClusterCockpit\", false, setupNodeRoute},\n49-\t{\"/monitoring/analysis/{cluster}\", \"monitoring/analysis.tmpl\", \"Analysis - ClusterCockpit\", true, setupAnalysisRoute},\n--\n538-\t\t\t\tfromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)\n539-\n540-\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?startTime=\"+fromTime+\"-\"+untilTime+\"&arrayJobId=\"+url.QueryEscape(strings.Trim(splitSearch[1], \" \")), http.StatusFound) / All Users: Redirect to Tablequery\n541-\t\t\tcase \"username\":\n542-\t\t\t\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n543:\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=\"+url.QueryEscape(strings.Trim(splitSearch[1], \" \"))+\"&startTime=last30d\", http.StatusFound)\n544-\t\t\t\t} else {\n545-\t\t\t\t\tweb.RenderTemplate(rw, \"message.tmpl\", &web.Page{Title: \"Error\", MsgType: \"alert-danger\", Message: \"Missing Access Rights\", User: *user, Roles: availableRoles, Build: buildInfo})\n546-\t\t\t\t}\n547-\t\t\tcase \"name\":\n548-\t\t\t\tusernames, _ := repo.FindColumnValues(user, strings.Trim(splitSearch[1], \" \"), \"user\", \"username\", \"name\")\n549-\t\t\t\tif len(usernames) != 0 {\n550-\t\t\t\t\tjoinedNames := strings.Join(usernames, \"&user=\")\n551:\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=\"+joinedNames+\"&startTime=last30d\", http.StatusFound)\n552-\t\t\t\t} else {\n553-\t\t\t\t\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n554:\t\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=NoUserNameFound&startTime=last30d\", http.StatusPermanentRedirect)\n555-\t\t\t\t\t} else {\n556-\t\t\t\t\t\tweb.RenderTemplate(rw, \"message.tmpl\", &web.Page{Title: \"Error\", MsgType: \"alert-danger\", Message: \"Missing Access Rights\", User: *user, Roles: availableRoles, Build: buildInfo})\n557-\t\t\t\t\t}\n558-\t\t\t\t}\n559-\t\t\tdefault:\n--\n564-\t\t\tjobid, username, project, jobname := repo.FindUserOrProjectOrJobname(user, strings.Trim(search, \" \"))\n565-\n566-\t\t\tif jobid != \"\" {\n567-\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?jobId=\"+url.QueryEscape(jobid), http.StatusFound) // JobId (Match)\n568-\t\t\t} else if username != \"\" {\n569:\t\t\t\thttp.Redirect(rw, r, \"/monitoring/user/\"+username, http.StatusFound) // User: Redirect to user page of first match\n570-\t\t\t} else if project != \"\" {\n571-\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?projectMatch=eq&project=\"+url.QueryEscape(project), http.StatusFound) // projectId (equal)\n572-\t\t\t} else if jobname != \"\" {\n573-\t\t\t\t// Add Last 30 Days to migitate timeouts\n574-\t\t\t\tuntilTime := strconv.FormatInt(time.Now().Unix(), 10)"}]},"uuid":"221cd2eb-c418-41d4-831f-487d91ca4185","timestamp":"2026-03-12T11:45:48.708Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"a1c5d749-fc2b-4fed-94cb-cf75e9b26c42","timestamp":"2026-03-12T11:45:48.709Z"}
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{"parentUuid":"eeb4faa5-9447-4209-bbe2-e80cf49c13df","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:54.354Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_016aDYoT1dcTyQGL43Mt8eke","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MtwwNg1X3FvrhUV1EYxLDY","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/web/templates/monitoring/list.tmpl"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":0,"cache_read_input_tokens":61678,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":25,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8W63XTz3vVyghkVbow","uuid":"daab230d-6172-4898-abcd-a7fc92c7fb24"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"372d9475-40fb-4aef-8f6a-da0d9a79cbc6","timestamp":"2026-03-12T11:45:54.355Z"}
{"parentUuid":"372d9475-40fb-4aef-8f6a-da0d9a79cbc6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MtwwNg1X3FvrhUV1EYxLDY","type":"tool_result","content":" 1→{{define \"content\"}}\n 2→ <div id=\"svelte-app\"></div>\n 3→{{end}}\n 4→\n 5→{{define \"stylesheets\"}}\n 6→ <link rel='stylesheet' href='/build/list.css'>\n 7→{{end}}\n 8→{{define \"javascript\"}}\n 9→ <script>\n 10→ const listType = {{ .Infos.listType }};\n 11→ const filterPresets = {{ .FilterPresets }};\n 12→ const clusterCockpitConfig = {{ .Config }};\n 13→ </script>\n 14→ <script src='/build/list.js'></script>\n 15→{{end}}\n 16→"}]},"uuid":"0d261087-3e68-448f-adac-05160a3b2d36","timestamp":"2026-03-12T11:45:54.379Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"6fe1854a-31f9-4820-8708-284c0d688c75","timestamp":"2026-03-12T11:45:54.390Z"}
{"parentUuid":"6fe1854a-31f9-4820-8708-284c0d688c75","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:55.994Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01Gu8tps1eWYVGr81u9rwmiz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012eG73AVRX3rLMGVVqfRda1","name":"Bash","input":{"command":"grep -n \"FilterPresets\\|setupListRoute\\|monitoring/list\" /Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go | head -20"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":311,"cache_read_input_tokens":61678,"cache_creation":{"ephemeral_5m_input_tokens":311,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8WNg9aotmJoSJebqcF","uuid":"2fc93a04-ce1e-4e33-845b-a047802455ef"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"827bcd41-4ad6-4e3c-a314-e1adcdbb673a","timestamp":"2026-03-12T11:45:55.995Z"}
{"parentUuid":"827bcd41-4ad6-4e3c-a314-e1adcdbb673a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012eG73AVRX3rLMGVVqfRda1","type":"tool_result","content":"41:\t{\"/monitoring/users/\", \"monitoring/list.tmpl\", \"Users - ClusterCockpit\", true, func(i InfoType, r *http.Request) InfoType { i[\"listType\"] = \"USER\"; return i }},\n42:\t{\"/monitoring/projects/\", \"monitoring/list.tmpl\", \"Projects - ClusterCockpit\", true, func(i InfoType, r *http.Request) InfoType { i[\"listType\"] = \"PROJECT\"; return i }},\n235:func buildFilterPresets(query url.Values) map[string]any {\n507:\t\t\t\tpage.FilterPresets = buildFilterPresets(r.URL.Query())","is_error":false}]},"uuid":"d342b42a-a48d-4e01-95d5-75013cc5ea95","timestamp":"2026-03-12T11:45:56.030Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"51f892f1-9c2b-4e6c-8dec-a7a047790c47","timestamp":"2026-03-12T11:45:56.031Z"}
{"parentUuid":"51f892f1-9c2b-4e6c-8dec-a7a047790c47","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:45:57.860Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011v8yvhWFGfS3vUmuShKw9r","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018chEozHHx37kWsMnzhYz5o","name":"Grep","input":{"pattern":"func buildFilterPresets","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","output_mode":"content","context":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":334,"cache_read_input_tokens":61989,"cache_creation":{"ephemeral_5m_input_tokens":334,"ephemeral_1h_input_tokens":0},"output_tokens":26,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8WVfqb2cTKMQVeK4WG","uuid":"685a02e4-9be3-4315-aa7f-fe445c759c54"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"f30f6d21-927b-4020-90b6-84a8ee42557e","timestamp":"2026-03-12T11:45:57.861Z"}
{"parentUuid":"f30f6d21-927b-4020-90b6-84a8ee42557e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018chEozHHx37kWsMnzhYz5o","type":"tool_result","content":"205-\tif userAuthlevel >= 4 { / Support+ : Show tags for all scopes, regardless of count\n206-\t\tfor _, tag := range tags {\n207-\t\t\ttagItem := map[string]any{\n208-\t\t\t\t\"id\": tag.ID,\n209-\t\t\t\t\"name\": tag.Name,\n210-\t\t\t\t\"scope\": tag.Scope,\n211-\t\t\t\t\"count\": counts[fmt.Sprint(tag.Type, tag.Name, tag.ID)],\n212-\t\t\t}\n213-\t\t\ttagMap[tag.Type] = append(tagMap[tag.Type], tagItem)\n214-\t\t}\n215-\t} else if userAuthlevel < 4 && userAuthlevel >= 2 { / User+ : Show global and admin scope only if at least 1 tag used, private scope regardless of count\n216-\t\tfor _, tag := range tags {\n217-\t\t\ttagCount := counts[fmt.Sprint(tag.Type, tag.Name, tag.ID)]\n218-\t\t\tif ((tag.Scope == \"global\" || tag.Scope == \"admin\") && tagCount >= 1) || (tag.Scope != \"global\" && tag.Scope != \"admin\") {\n219-\t\t\t\ttagItem := map[string]any{\n220-\t\t\t\t\t\"id\": tag.ID,\n221-\t\t\t\t\t\"name\": tag.Name,\n222-\t\t\t\t\t\"scope\": tag.Scope,\n223-\t\t\t\t\t\"count\": tagCount,\n224-\t\t\t\t}\n225-\t\t\t\ttagMap[tag.Type] = append(tagMap[tag.Type], tagItem)\n226-\t\t\t}\n227-\t\t}\n228-\t} // auth < 2 return nothing for this route\n229-\n230-\ti[\"tagmap\"] = tagMap\n231-\treturn i\n232-}\n233-\n234-/ FIXME: Lots of redundant code. Needs refactoring\n235:func buildFilterPresets(query url.Values) map[string]any {\n236-\tfilterPresets := map[string]any{}\n237-\n238-\tif query.Get(\"cluster\") != \"\" {\n239-\t\tfilterPresets[\"cluster\"] = query.Get(\"cluster\")\n240-\t}\n241-\tif query.Get(\"partition\") != \"\" {\n242-\t\tfilterPresets[\"partition\"] = query.Get(\"partition\")\n243-\t}\n244-\tif query.Get(\"project\") != \"\" {\n245-\t\tfilterPresets[\"project\"] = query.Get(\"project\")\n246-\t\tfilterPresets[\"projectMatch\"] = \"eq\"\n247-\t}\n248-\tif query.Get(\"jobName\") != \"\" {\n249-\t\tfilterPresets[\"jobName\"] = query.Get(\"jobName\")\n250-\t}\n251-\tif len(query[\"user\"]) != 0 {\n252-\t\tif len(query[\"user\"]) == 1 {\n253-\t\t\tfilterPresets[\"user\"] = query.Get(\"user\")\n254-\t\t\tfilterPresets[\"userMatch\"] = \"contains\"\n255-\t\t} else {\n256-\t\t\tfilterPresets[\"user\"] = query[\"user\"]\n257-\t\t\tfilterPresets[\"userMatch\"] = \"in\"\n258-\t\t}\n259-\t}\n260-\tif len(query[\"state\"]) != 0 {\n261-\t\tfilterPresets[\"state\"] = query[\"state\"]\n262-\t}\n263-\tif query.Get(\"shared\") != \"\" {\n264-\t\tfilterPresets[\"shared\"] = query.Get(\"shared\")\n265-\t}"}]},"uuid":"afe1f0de-00af-4dd1-bd75-2eacb5d5cad5","timestamp":"2026-03-12T11:45:57.882Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"080b77bb-1cb1-498f-8901-5d7827c8538f","timestamp":"2026-03-12T11:45:57.883Z"}
{"parentUuid":"080b77bb-1cb1-498f-8901-5d7827c8538f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:46:00.706Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01TPCiRRrnz1PveRwEF8DnK5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CMeiHLLitHFb1N4FqiE1FD","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","offset":235,"limit":80},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1012,"cache_read_input_tokens":62323,"cache_creation":{"ephemeral_5m_input_tokens":1012,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8Wdca7UXZUX47tUg6f","uuid":"a43d55ec-12e3-42b0-beea-50d4294a744d"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"c41f9a9b-f8df-40cf-be33-30ecf0b10516","timestamp":"2026-03-12T11:46:00.707Z"}
{"parentUuid":"c41f9a9b-f8df-40cf-be33-30ecf0b10516","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CMeiHLLitHFb1N4FqiE1FD","type":"tool_result","content":" 235→func buildFilterPresets(query url.Values) map[string]any {\n 236→\tfilterPresets := map[string]any{}\n 237→\n 238→\tif query.Get(\"cluster\") != \"\" {\n 239→\t\tfilterPresets[\"cluster\"] = query.Get(\"cluster\")\n 240→\t}\n 241→\tif query.Get(\"partition\") != \"\" {\n 242→\t\tfilterPresets[\"partition\"] = query.Get(\"partition\")\n 243→\t}\n 244→\tif query.Get(\"project\") != \"\" {\n 245→\t\tfilterPresets[\"project\"] = query.Get(\"project\")\n 246→\t\tfilterPresets[\"projectMatch\"] = \"eq\"\n 247→\t}\n 248→\tif query.Get(\"jobName\") != \"\" {\n 249→\t\tfilterPresets[\"jobName\"] = query.Get(\"jobName\")\n 250→\t}\n 251→\tif len(query[\"user\"]) != 0 {\n 252→\t\tif len(query[\"user\"]) == 1 {\n 253→\t\t\tfilterPresets[\"user\"] = query.Get(\"user\")\n 254→\t\t\tfilterPresets[\"userMatch\"] = \"contains\"\n 255→\t\t} else {\n 256→\t\t\tfilterPresets[\"user\"] = query[\"user\"]\n 257→\t\t\tfilterPresets[\"userMatch\"] = \"in\"\n 258→\t\t}\n 259→\t}\n 260→\tif len(query[\"state\"]) != 0 {\n 261→\t\tfilterPresets[\"state\"] = query[\"state\"]\n 262→\t}\n 263→\tif query.Get(\"shared\") != \"\" {\n 264→\t\tfilterPresets[\"shared\"] = query.Get(\"shared\")\n 265→\t}\n 266→\tif query.Get(\"schedule\") != \"\" {\n 267→\t\tfilterPresets[\"schedule\"] = query.Get(\"schedule\")\n 268→\t}\n 269→\tif rawtags, ok := query[\"tag\"]; ok {\n 270→\t\ttags := make([]int, len(rawtags))\n 271→\t\tfor i, tid := range rawtags {\n 272→\t\t\tvar err error\n 273→\t\t\ttags[i], err = strconv.Atoi(tid)\n 274→\t\t\tif err != nil {\n 275→\t\t\t\ttags[i] = -1\n 276→\t\t\t}\n 277→\t\t}\n 278→\t\tfilterPresets[\"tags\"] = tags\n 279→\t}\n 280→\tif query.Get(\"duration\") != \"\" {\n 281→\t\tparts := strings.Split(query.Get(\"duration\"), \"-\")\n 282→\t\tif len(parts) == 2 {\n 283→\t\t\tif parts[0] == \"lessthan\" {\n 284→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 285→\t\t\t\tif lte == nil {\n 286→\t\t\t\t\tfilterPresets[\"duration\"] = map[string]int{\"lessThan\": lt, \"from\": 0, \"to\": 0}\n 287→\t\t\t\t}\n 288→\t\t\t} else if parts[0] == \"morethan\" {\n 289→\t\t\t\tmt, mte := strconv.Atoi(parts[1])\n 290→\t\t\t\tif mte == nil {\n 291→\t\t\t\t\tfilterPresets[\"duration\"] = map[string]int{\"moreThan\": mt, \"from\": 0, \"to\": 0}\n 292→\t\t\t\t}\n 293→\t\t\t} else {\n 294→\t\t\t\ta, e1 := strconv.Atoi(parts[0])\n 295→\t\t\t\tb, e2 := strconv.Atoi(parts[1])\n 296→\t\t\t\tif e1 == nil && e2 == nil {\n 297→\t\t\t\t\tfilterPresets[\"duration\"] = map[string]int{\"from\": a, \"to\": b}\n 298→\t\t\t\t}\n 299→\t\t\t}\n 300→\t\t}\n 301→\t}\n 302→\tif query.Get(\"node\") != \"\" {\n 303→\t\tfilterPresets[\"node\"] = query.Get(\"node\")\n 304→\t}\n 305→\tif query.Get(\"numNodes\") != \"\" {\n 306→\t\tparts := strings.Split(query.Get(\"numNodes\"), \"-\")\n 307→\t\tif len(parts) == 2 {\n 308→\t\t\tif parts[0] == \"lessthan\" {\n 309→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 310→\t\t\t\tif lte == nil {\n 311→\t\t\t\t\tfilterPresets[\"numNodes\"] = map[string]int{\"from\": 1, \"to\": lt}\n 312→\t\t\t\t}\n 313→\t\t\t} else if parts[0] == \"morethan\" {\n 314→\t\t\t\tmt, mte := strconv.Atoi(parts[1])"}]},"uuid":"fbf82b86-6ed3-4c09-a57e-4eae9447423a","timestamp":"2026-03-12T11:46:00.732Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"af72b628-7428-408e-b97e-df68bf99f366","timestamp":"2026-03-12T11:46:00.800Z"}
{"parentUuid":"af72b628-7428-408e-b97e-df68bf99f366","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:46:02.649Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01SMgnVD1KoTS9gXCkqVYahH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XoFfBePZmigwwTjbh8EXJ6","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","offset":300,"limit":80},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1687,"cache_read_input_tokens":63335,"cache_creation":{"ephemeral_5m_input_tokens":1687,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8WrE6dehKNV87XCuDm","uuid":"fd26497b-c349-4de5-baff-755090af241a"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"83fb17db-ea36-4484-91fc-100539bd1dc2","timestamp":"2026-03-12T11:46:02.649Z"}
{"parentUuid":"83fb17db-ea36-4484-91fc-100539bd1dc2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XoFfBePZmigwwTjbh8EXJ6","type":"tool_result","content":" 300→\t\t}\n 301→\t}\n 302→\tif query.Get(\"node\") != \"\" {\n 303→\t\tfilterPresets[\"node\"] = query.Get(\"node\")\n 304→\t}\n 305→\tif query.Get(\"numNodes\") != \"\" {\n 306→\t\tparts := strings.Split(query.Get(\"numNodes\"), \"-\")\n 307→\t\tif len(parts) == 2 {\n 308→\t\t\tif parts[0] == \"lessthan\" {\n 309→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 310→\t\t\t\tif lte == nil {\n 311→\t\t\t\t\tfilterPresets[\"numNodes\"] = map[string]int{\"from\": 1, \"to\": lt}\n 312→\t\t\t\t}\n 313→\t\t\t} else if parts[0] == \"morethan\" {\n 314→\t\t\t\tmt, mte := strconv.Atoi(parts[1])\n 315→\t\t\t\tif mte == nil {\n 316→\t\t\t\t\tfilterPresets[\"numNodes\"] = map[string]int{\"from\": mt, \"to\": 0}\n 317→\t\t\t\t}\n 318→\t\t\t} else {\n 319→\t\t\t\ta, e1 := strconv.Atoi(parts[0])\n 320→\t\t\t\tb, e2 := strconv.Atoi(parts[1])\n 321→\t\t\t\tif e1 == nil && e2 == nil {\n 322→\t\t\t\t\tfilterPresets[\"numNodes\"] = map[string]int{\"from\": a, \"to\": b}\n 323→\t\t\t\t}\n 324→\t\t\t}\n 325→\t\t}\n 326→\t}\n 327→\tif query.Get(\"numHWThreads\") != \"\" {\n 328→\t\tparts := strings.Split(query.Get(\"numHWThreads\"), \"-\")\n 329→\t\tif len(parts) == 2 {\n 330→\t\t\tif parts[0] == \"lessthan\" {\n 331→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 332→\t\t\t\tif lte == nil {\n 333→\t\t\t\t\tfilterPresets[\"numHWThreads\"] = map[string]int{\"from\": 1, \"to\": lt}\n 334→\t\t\t\t}\n 335→\t\t\t} else if parts[0] == \"morethan\" {\n 336→\t\t\t\tmt, mte := strconv.Atoi(parts[1])\n 337→\t\t\t\tif mte == nil {\n 338→\t\t\t\t\tfilterPresets[\"numHWThreads\"] = map[string]int{\"from\": mt, \"to\": 0}\n 339→\t\t\t\t}\n 340→\t\t\t} else {\n 341→\t\t\t\ta, e1 := strconv.Atoi(parts[0])\n 342→\t\t\t\tb, e2 := strconv.Atoi(parts[1])\n 343→\t\t\t\tif e1 == nil && e2 == nil {\n 344→\t\t\t\t\tfilterPresets[\"numHWThreads\"] = map[string]int{\"from\": a, \"to\": b}\n 345→\t\t\t\t}\n 346→\t\t\t}\n 347→\t\t}\n 348→\t}\n 349→\tif query.Get(\"numAccelerators\") != \"\" {\n 350→\t\tparts := strings.Split(query.Get(\"numAccelerators\"), \"-\")\n 351→\t\tif len(parts) == 2 {\n 352→\t\t\tif parts[0] == \"lessthan\" {\n 353→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 354→\t\t\t\tif lte == nil {\n 355→\t\t\t\t\tfilterPresets[\"numAccelerators\"] = map[string]int{\"from\": 1, \"to\": lt}\n 356→\t\t\t\t}\n 357→\t\t\t} else if parts[0] == \"morethan\" {\n 358→\t\t\t\tmt, mte := strconv.Atoi(parts[1])\n 359→\t\t\t\tif mte == nil {\n 360→\t\t\t\t\tfilterPresets[\"numAccelerators\"] = map[string]int{\"from\": mt, \"to\": 0}\n 361→\t\t\t\t}\n 362→\t\t\t} else {\n 363→\t\t\t\ta, e1 := strconv.Atoi(parts[0])\n 364→\t\t\t\tb, e2 := strconv.Atoi(parts[1])\n 365→\t\t\t\tif e1 == nil && e2 == nil {\n 366→\t\t\t\t\tfilterPresets[\"numAccelerators\"] = map[string]int{\"from\": a, \"to\": b}\n 367→\t\t\t\t}\n 368→\t\t\t}\n 369→\t\t}\n 370→\t}\n 371→\tif len(query[\"dbId\"]) != 0 {\n 372→\t\tfilterPresets[\"dbId\"] = query[\"dbId\"]\n 373→\t}\n 374→\tif query.Get(\"jobId\") != \"\" {\n 375→\t\tif len(query[\"jobId\"]) == 1 {\n 376→\t\t\tfilterPresets[\"jobId\"] = query.Get(\"jobId\")\n 377→\t\t\tfilterPresets[\"jobIdMatch\"] = \"eq\"\n 378→\t\t} else {\n 379→\t\t\tfilterPresets[\"jobId\"] = query[\"jobId\"]"}]},"uuid":"ac4ecd24-b46e-4fce-a7c4-58cd90f0d12c","timestamp":"2026-03-12T11:46:02.660Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"b920217f-ac38-4b56-82df-8906a995517c","timestamp":"2026-03-12T11:46:02.675Z"}
{"parentUuid":"b920217f-ac38-4b56-82df-8906a995517c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:46:05.056Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01DSPjTc7aHL2NnPZQyB465u","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SGcXfwvAY1stpcwKW85ysi","name":"Grep","input":{"pattern":"startTime|query.Get","path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","output_mode":"content","context":2,"head_limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1423,"cache_read_input_tokens":65022,"cache_creation":{"ephemeral_5m_input_tokens":1423,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8Wz7s9ZZnEi7g78nQc","uuid":"eaf3d5d7-12a3-4748-9b25-b0274fbc8dc4"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"f6692360-7af0-415a-9c7d-70cc844ebfb4","timestamp":"2026-03-12T11:46:05.057Z"}
{"parentUuid":"f6692360-7af0-415a-9c7d-70cc844ebfb4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SGcXfwvAY1stpcwKW85ysi","type":"tool_result","content":"236-\tfilterPresets := map[string]any{}\n237-\n238:\tif query.Get(\"cluster\") != \"\" {\n239:\t\tfilterPresets[\"cluster\"] = query.Get(\"cluster\")\n240-\t}\n241:\tif query.Get(\"partition\") != \"\" {\n242:\t\tfilterPresets[\"partition\"] = query.Get(\"partition\")\n243-\t}\n244:\tif query.Get(\"project\") != \"\" {\n245:\t\tfilterPresets[\"project\"] = query.Get(\"project\")\n246-\t\tfilterPresets[\"projectMatch\"] = \"eq\"\n247-\t}\n248:\tif query.Get(\"jobName\") != \"\" {\n249:\t\tfilterPresets[\"jobName\"] = query.Get(\"jobName\")\n250-\t}\n251-\tif len(query[\"user\"]) != 0 {\n252-\t\tif len(query[\"user\"]) == 1 {\n253:\t\t\tfilterPresets[\"user\"] = query.Get(\"user\")\n254-\t\t\tfilterPresets[\"userMatch\"] = \"contains\"\n255-\t\t} else {\n--\n261-\t\tfilterPresets[\"state\"] = query[\"state\"]\n262-\t}\n263:\tif query.Get(\"shared\") != \"\" {\n264:\t\tfilterPresets[\"shared\"] = query.Get(\"shared\")\n265-\t}\n266:\tif query.Get(\"schedule\") != \"\" {\n267:\t\tfilterPresets[\"schedule\"] = query.Get(\"schedule\")\n268-\t}\n269-\tif rawtags, ok := query[\"tag\"]; ok {\n\n[Showing results with pagination = limit: 30, offset: 0]"}]},"uuid":"8bf01b63-c072-46c4-8c49-544611dd8871","timestamp":"2026-03-12T11:46:05.077Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"f6efedc0-d446-4f26-a229-1d4a11d65146","timestamp":"2026-03-12T11:46:05.090Z"}
{"parentUuid":"f6efedc0-d446-4f26-a229-1d4a11d65146","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:46:06.256Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01BUZdKB5K4YQJJ8Nc6ee58E","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KM4iyY8YzW8yiS63ERCGkB","name":"Bash","input":{"command":"grep -n \"startTime\\|fromTime\\|toTime\" /Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go | head -20"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":592,"cache_read_input_tokens":66445,"cache_creation":{"ephemeral_5m_input_tokens":592,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8XARjJnyfPcC7CBPrU","uuid":"7c9b9410-74fc-49b3-b494-076eb37e2b07"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"ff4f1b07-dd78-4fb5-9bfc-d60b31241d67","timestamp":"2026-03-12T11:46:06.257Z"}
{"parentUuid":"ff4f1b07-dd78-4fb5-9bfc-d60b31241d67","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KM4iyY8YzW8yiS63ERCGkB","type":"tool_result","content":"388:\tif query.Get(\"startTime\") != \"\" {\n389:\t\tparts := strings.Split(query.Get(\"startTime\"), \"-\")\n394:\t\t\t\tfilterPresets[\"startTime\"] = map[string]string{\n400:\t\t\tfilterPresets[\"startTime\"] = map[string]string{\n401:\t\t\t\t\"range\": query.Get(\"startTime\"),\n530:\t\t\t\tfromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)\n532:\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?startTime=\"+fromTime+\"-\"+untilTime+\"&jobName=\"+url.QueryEscape(strings.Trim(splitSearch[1], \" \")), http.StatusFound) // All Users: Redirect to Tablequery\n538:\t\t\t\tfromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)\n540:\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?startTime=\"+fromTime+\"-\"+untilTime+\"&arrayJobId=\"+url.QueryEscape(strings.Trim(splitSearch[1], \" \")), http.StatusFound) // All Users: Redirect to Tablequery\n543:\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=\"+url.QueryEscape(strings.Trim(splitSearch[1], \" \"))+\"&startTime=last30d\", http.StatusFound)\n551:\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=\"+joinedNames+\"&startTime=last30d\", http.StatusFound)\n554:\t\t\t\t\t\thttp.Redirect(rw, r, \"/monitoring/users/?user=NoUserNameFound&startTime=last30d\", http.StatusPermanentRedirect)\n575:\t\t\t\tfromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)\n577:\t\t\t\thttp.Redirect(rw, r, \"/monitoring/jobs/?startTime=\"+fromTime+\"-\"+untilTime+\"&jobName=\"+url.QueryEscape(jobname), http.StatusFound) // 30D Fitler + JobName (contains)","is_error":false}]},"uuid":"7205366d-9497-4ba0-bf4f-1a3269714e1d","timestamp":"2026-03-12T11:46:06.305Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"f4e1dd73-e638-4947-b05f-d5865be9805a","timestamp":"2026-03-12T11:46:06.306Z"}
{"parentUuid":"f4e1dd73-e638-4947-b05f-d5865be9805a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-12T11:46:09.826Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01KFZCgocbThnsNWNMTEAfxQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PfJSrsRMdD7Yx9wWDQvC6K","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go","offset":388,"limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":931,"cache_read_input_tokens":67037,"cache_creation":{"ephemeral_5m_input_tokens":931,"ephemeral_1h_input_tokens":0},"output_tokens":18,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYy8XFcXBXov9WQo4ntya","uuid":"76fa7a03-dafe-4d58-b423-2ec377cbe93d"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"86456818-e43a-4883-b58e-aa791a851234","timestamp":"2026-03-12T11:46:09.826Z"}
{"parentUuid":"86456818-e43a-4883-b58e-aa791a851234","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PfJSrsRMdD7Yx9wWDQvC6K","type":"tool_result","content":" 388→\tif query.Get(\"startTime\") != \"\" {\n 389→\t\tparts := strings.Split(query.Get(\"startTime\"), \"-\")\n 390→\t\tif len(parts) == 2 { // Time in seconds, from - to\n 391→\t\t\ta, e1 := strconv.ParseInt(parts[0], 10, 64)\n 392→\t\t\tb, e2 := strconv.ParseInt(parts[1], 10, 64)\n 393→\t\t\tif e1 == nil && e2 == nil {\n 394→\t\t\t\tfilterPresets[\"startTime\"] = map[string]string{\n 395→\t\t\t\t\t\"from\": time.Unix(a, 0).Format(time.RFC3339),\n 396→\t\t\t\t\t\"to\": time.Unix(b, 0).Format(time.RFC3339),\n 397→\t\t\t\t}\n 398→\t\t\t}\n 399→\t\t} else { // named range\n 400→\t\t\tfilterPresets[\"startTime\"] = map[string]string{\n 401→\t\t\t\t\"range\": query.Get(\"startTime\"),\n 402→\t\t\t}\n 403→\t\t}\n 404→\t}\n 405→\tif query.Get(\"energy\") != \"\" {\n 406→\t\tparts := strings.Split(query.Get(\"energy\"), \"-\")\n 407→\t\tif len(parts) == 2 {\n 408→\t\t\tif parts[0] == \"lessthan\" {\n 409→\t\t\t\tlt, lte := strconv.Atoi(parts[1])\n 410→\t\t\t\tif lte == nil {\n 411→\t\t\t\t\tfilterPresets[\"energy\"] = map[string]int{\"from\": 1, \"to\": lt}\n 412→\t\t\t\t}\n 413→\t\t\t} else if parts[0] == \"morethan\" {\n 414→\t\t\t\tmt, mte := strconv.Atoi(parts[1])\n 415→\t\t\t\tif mte == nil {\n 416→\t\t\t\t\tfilterPresets[\"energy\"] = map[string]int{\"from\": mt, \"to\": 0}\n 417→\t\t\t\t}"}]},"uuid":"cb0163d7-3092-40be-b148-4d4832855b21","timestamp":"2026-03-12T11:46:09.832Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"c367fbf0-76fc-422c-8c79-0e493b30dc30","timestamp":"2026-03-12T11:46:09.833Z"}
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{"parentUuid":"46f4f36c-6136-4cbf-85c5-0e6cc83019b3","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PGbb1iKmrwg1wncXfXnJJ8","type":"tool_result","content":"8- - `startTimeQuickSelect Bool?`: Render startTime quick selections [Default: false]\n9- - `shortJobQuickSelect Bool?`: Render short job quick selections [Default: false]\n10- - `shortJobCutoff Int?`: Time in seconds for jobs to be considered short [Default: null]\n11- - `matchedJobs Number?`: Number of jobs matching the filter [Default: -2]\n12- - `showFilter Func`: If the filter component should be rendered in addition to total count info [Default: true]\n13: - `applyFilters Func`: The callback function to apply current filter selection\n14- \n15- Functions:\n16: - `void updateFilters (additionalFilters: Object, force: Bool)`:\n17- Handles new filters from nested components, triggers upstream update event. \n18- 'additionalFilters' usually added to existing selection, but can be forced to overwrite instead.\n19--->\n20-\n21-<script>\n--\n49- startTimeQuickSelect = false,\n50- shortJobQuickSelect = false,\n51- shortJobCutoff = 0,\n52- matchedJobs = -2,\n53- showFilter = true,\n54: applyFilters\n55- } = $props();\n56-\n57- /* Const Init */\n58- const nodeMatchLabels = {\n59- eq: \"\",\n--\n142- let isStatsOpen = $state(false)\n143-\n144- /* Functions */\n145- // Can be called from the outside to trigger a 'update' event from this component.\n146- // 'force' option empties existing filters and then applies only 'additionalFilters'\n147: export function updateFilters(additionalFilters = null, force = false) {\n148- // Empty Current Filter For Force\n149- if (additionalFilters != null && force) {\n150- filters = {...filterReset}\n151- }\n152- // Add Additional Filters\n--\n215- if (filters.stats.length != 0)\n216- items.push({ metricStats: filters.stats.map((st) => { return { metricName: st.field, range: { from: st.from, to: st.to }} }) });\n217- if (filters.node) items.push({ node: { [filters.nodeMatch]: filters.node } });\n218- if (filters.jobName) items.push({ jobName: { contains: filters.jobName } });\n219- if (filters.schedule) items.push({ schedule: filters.schedule });\n220: applyFilters({ filters: items });\n221- changeURL();\n222- return items;\n223- }\n224-\n225- function changeURL() {\n\n[Showing results with pagination = limit: 50, offset: 0]"}]},"uuid":"48ab5ad8-2467-4b41-828b-5b53207206b1","timestamp":"2026-03-12T11:46:11.700Z"},"type":"agent_progress","prompt":"","agentId":"a76ed373ae73bdc18"},"toolUseID":"agent_msg_017mCGn1fu9DfpDrXAMH7sSB","parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","uuid":"a1be2c0e-bf5e-4616-a77a-d3abeef49338","timestamp":"2026-03-12T11:46:11.701Z"}
{"parentUuid":"a1be2c0e-bf5e-4616-a77a-d3abeef49338","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Agent","command":"entire hooks claude-code post-task"},"parentToolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","toolUseID":"toolu_01L2wX5pHxehktnRkRAHuwgZ","timestamp":"2026-03-12T11:46:30.756Z","uuid":"277382eb-dc2e-41c6-86c7-d2162c6d428b"}
{"parentUuid":"ddc8fdb6-a88e-4698-8001-c60c67c34a95","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L2wX5pHxehktnRkRAHuwgZ","type":"tool_result","content":[{"type":"text","text":"Perfect! Now I have comprehensive information. Let me create a summary report of my findings:\n\n## Summary of Code Path Analysis for `/monitoring/users/?cluster=woody&startTime=last30d`\n\nBased on my thorough investigation of the ClusterCockpit codebase, here's the complete flow:\n\n### 1. GraphQL Query Made When Visiting `/monitoring/users/?cluster=woody&startTime=last30d`\n\nThe **`List.root.svelte`** component (lines 77-99) executes this GraphQL query:\n\n```graphql\nquery($jobFilters: [JobFilter!]!, $fetchRunning: Boolean!) {\n rows: jobsStatistics(filter: $jobFilters, groupBy: USER) {\n id\n name\n totalJobs\n shortJobs\n totalCores @include(if: $fetchRunning)\n totalAccs @include(if: $fetchRunning)\n totalWalltime\n totalCoreHours\n totalAccHours\n }\n}\n```\n\n**Variables passed:**\n- `jobFilters`: Array of JobFilter objects (built from query parameters)\n- `fetchRunning`: Boolean (true if running jobs are being filtered)\n\n### 2. Filters Passed to `buildStatsQuery`\n\nFor the URL `/monitoring/users/?cluster=woody&startTime=last30d`, the following filters are extracted:\n\n**In `buildFilterPresets()` (routes.go, lines 235-404):**\n- `cluster: \"woody\"` \n- `startTime: { range: \"last30d\" }`\n\n**These become `JobFilter` objects:**\n```\n[\n {\n cluster: { eq: \"woody\" },\n startTime: { range: \"last30d\" }\n }\n]\n```\n\nThese filters are passed to **`JobRepository.JobsStatsGrouped()`** (stats.go, line 233) which calls **`buildStatsQuery()`** (stats.go, line 135).\n\n### 3. What `BuildWhereClause` Does\n\nLocated in `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/jobQuery.go` (lines 178-277), `BuildWhereClause()` translates GraphQL filter expressions into SQL WHERE conditions:\n\n**For cluster filter:**\n- Line 190-191: Translates `cluster` → `job.cluster` column via `buildStringCondition()`\n\n**For startTime filter:**\n- Line 227-228: Calls `buildTimeCondition()` which:\n - Recognizes the \"last30d\" range (line 325)\n - Converts to: `now - (60 * 60 * 24 * 30)` seconds\n - Generates WHERE clause: `job.start_time >= <timestamp>`\n\n**Other supported filters include:**\n- User, project, job state, partition\n- Duration, energy, number of nodes/accelerators\n- Tags (via JOIN on jobtag table)\n- JSON searches in meta_data, footprint, resources\n\n### 4. Indexes on the Job Table\n\n**Key migration files:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/02_add-index.up.sql` - Initial basic indexes\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/08_add-footprint.up.sql` - Comprehensive index coverage\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/11_optimize-indexes.up.sql` - Index optimization (removes 30 redundant indexes)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - **NEW covering index for stats queries**\n\n**Active indexes after all migrations:**\n\nBasic indexes:\n```sql\njob_stats ON (cluster, subcluster, user) -- Legacy name, actually hpc_user\njob_by_user ON (user) -- Legacy column name\njob_by_starttime ON (start_time)\njob_list ON (cluster, job_state)\n```\n\nCluster-specific indexes:\n```sql\njobs_cluster ON (cluster)\njobs_cluster_user ON (cluster, hpc_user)\njobs_cluster_project ON (cluster, project)\njobs_cluster_starttime ON (cluster, start_time)\n```\n\n**CRITICAL NEW COVERING INDEX (Migration 12):**\n```sql\njobs_cluster_starttime_user_stats \n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)\n```\n\nThis covering index is specifically designed for the grouped stats query pattern and includes all columns needed for the GROUP BY and aggregation calculations.\n\n### 5. Job Table Schema\n\n**Full schema from Migration 01 + subsequent modifications (Migration 08):**\n\n```\nCREATE TABLE job (\n id INTEGER PRIMARY KEY,\n job_id BIGINT NOT NULL,\n cluster VARCHAR(255) NOT NULL,\n subcluster VARCHAR(255) NOT NULL,\n start_time BIGINT NOT NULL, -- Unix timestamp (INDEXED)\n hpc_user VARCHAR(255) NOT NULL, -- Renamed from \"user\" in Migration 08 (INDEXED)\n project VARCHAR(255) NOT NULL,\n cluster_partition VARCHAR(255), -- Renamed from \"partition\" in Migration 08\n array_job_id BIGINT,\n duration INT NOT NULL,\n walltime INT NOT NULL,\n job_state VARCHAR(255) NOT NULL, -- CHECK: running|completed|failed|canceled|stopped|timeout\n meta_data TEXT, -- JSON\n resources TEXT NOT NULL, -- JSON\n num_nodes INT NOT NULL,\n num_hwthreads INT,\n num_acc INT,\n smt TINYINT DEFAULT 1,\n exclusive TINYINT DEFAULT 1,\n monitoring_status TINYINT DEFAULT 1,\n energy REAL DEFAULT 0.0, -- Added in Migration 08\n footprint TEXT DEFAULT NULL, -- JSON, added in Migration 08\n energy_footprint TEXT DEFAULT NULL -- JSON, added in Migration 08\n)\n```\n\n**Supporting tables:**\n- `hpc_user` (users)\n- `tag` (tags with scope)\n- `jobtag` (job-tag relationships with indexes on both job_id and tag_id)\n- `node` and `node_state` (node tracking, added in Migration 10)\n\n### Data Flow Summary\n\n```\n/monitoring/users/?cluster=woody&startTime=last30d\n ↓\nroutes.go: buildFilterPresets() extracts query params\n ↓\nList.root.svelte: Builds GraphQL query with jobFilters\n ↓\nGraphQL Resolver: JobsStatistics() [schema.resolvers.go:647]\n ↓\nJobRepository.JobsStatsGrouped() [stats.go:233]\n - Calls buildStatsQuery() with groupBy=\"job.hpc_user\"\n - Applies BuildWhereClause() for each JobFilter\n - Uses index: jobs_cluster_starttime_user_stats (covering index)\n ↓\nSQL Query executes GROUP BY hpc_user with aggregations:\n - COUNT(job.id) as totalJobs\n - CAST(ROUND(SUM(...) / 3600) as int) as totalWalltime\n - SUM(num_nodes), SUM(num_hwthreads), SUM(num_acc) aggregations\n ↓\nPost-query user name lookup (avoids JOIN overhead)\n ↓\nReturns [JobsStatistics] array grouped by user\n```\n\nThe system is heavily optimized for this exact query pattern, evidenced by the new covering index in Migration 12 that was specifically created for \"grouped stats queries\" with the exact columns needed for statistics aggregation."},{"type":"text","text":"agentId: a76ed373ae73bdc18 (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 71496\ntool_uses: 38\nduration_ms: 83172</usage>"}]}]},"uuid":"5b4a3113-e5ca-42eb-96f7-4cb6365d397d","timestamp":"2026-03-12T11:46:30.756Z","toolUseResult":{"status":"completed","prompt":"In /Users/jan/prg/ClusterCockpit/cc-backend, find all code that calls JobsStatsGrouped or leads to the `/monitoring/users/` endpoint. I need to understand:\n1. What GraphQL query is made when visiting `/monitoring/users/?cluster=woody&startTime=last30d`\n2. What filters are passed to buildStatsQuery\n3. What the BuildWhereClause function does (in internal/repository/)\n4. What indexes exist on the job table (check migration files in internal/repository/migrations/sqlite3/)\n5. The schema of the job table\n\nBe thorough - check the frontend Svelte files (web/frontend/src/List.root.svelte), GraphQL schema (api/schema.graphqls), resolvers (internal/graph/schema.resolvers.go), and the filter/where clause builder.","agentId":"a76ed373ae73bdc18","content":[{"type":"text","text":"Perfect! Now I have comprehensive information. Let me create a summary report of my findings:\n\n## Summary of Code Path Analysis for `/monitoring/users/?cluster=woody&startTime=last30d`\n\nBased on my thorough investigation of the ClusterCockpit codebase, here's the complete flow:\n\n### 1. GraphQL Query Made When Visiting `/monitoring/users/?cluster=woody&startTime=last30d`\n\nThe **`List.root.svelte`** component (lines 77-99) executes this GraphQL query:\n\n```graphql\nquery($jobFilters: [JobFilter!]!, $fetchRunning: Boolean!) {\n rows: jobsStatistics(filter: $jobFilters, groupBy: USER) {\n id\n name\n totalJobs\n shortJobs\n totalCores @include(if: $fetchRunning)\n totalAccs @include(if: $fetchRunning)\n totalWalltime\n totalCoreHours\n totalAccHours\n }\n}\n```\n\n**Variables passed:**\n- `jobFilters`: Array of JobFilter objects (built from query parameters)\n- `fetchRunning`: Boolean (true if running jobs are being filtered)\n\n### 2. Filters Passed to `buildStatsQuery`\n\nFor the URL `/monitoring/users/?cluster=woody&startTime=last30d`, the following filters are extracted:\n\n**In `buildFilterPresets()` (routes.go, lines 235-404):**\n- `cluster: \"woody\"` \n- `startTime: { range: \"last30d\" }`\n\n**These become `JobFilter` objects:**\n```\n[\n {\n cluster: { eq: \"woody\" },\n startTime: { range: \"last30d\" }\n }\n]\n```\n\nThese filters are passed to **`JobRepository.JobsStatsGrouped()`** (stats.go, line 233) which calls **`buildStatsQuery()`** (stats.go, line 135).\n\n### 3. What `BuildWhereClause` Does\n\nLocated in `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/jobQuery.go` (lines 178-277), `BuildWhereClause()` translates GraphQL filter expressions into SQL WHERE conditions:\n\n**For cluster filter:**\n- Line 190-191: Translates `cluster` → `job.cluster` column via `buildStringCondition()`\n\n**For startTime filter:**\n- Line 227-228: Calls `buildTimeCondition()` which:\n - Recognizes the \"last30d\" range (line 325)\n - Converts to: `now - (60 * 60 * 24 * 30)` seconds\n - Generates WHERE clause: `job.start_time >= <timestamp>`\n\n**Other supported filters include:**\n- User, project, job state, partition\n- Duration, energy, number of nodes/accelerators\n- Tags (via JOIN on jobtag table)\n- JSON searches in meta_data, footprint, resources\n\n### 4. Indexes on the Job Table\n\n**Key migration files:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/02_add-index.up.sql` - Initial basic indexes\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/08_add-footprint.up.sql` - Comprehensive index coverage\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/11_optimize-indexes.up.sql` - Index optimization (removes 30 redundant indexes)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - **NEW covering index for stats queries**\n\n**Active indexes after all migrations:**\n\nBasic indexes:\n```sql\njob_stats ON (cluster, subcluster, user) -- Legacy name, actually hpc_user\njob_by_user ON (user) -- Legacy column name\njob_by_starttime ON (start_time)\njob_list ON (cluster, job_state)\n```\n\nCluster-specific indexes:\n```sql\njobs_cluster ON (cluster)\njobs_cluster_user ON (cluster, hpc_user)\njobs_cluster_project ON (cluster, project)\njobs_cluster_starttime ON (cluster, start_time)\n```\n\n**CRITICAL NEW COVERING INDEX (Migration 12):**\n```sql\njobs_cluster_starttime_user_stats \n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)\n```\n\nThis covering index is specifically designed for the grouped stats query pattern and includes all columns needed for the GROUP BY and aggregation calculations.\n\n### 5. Job Table Schema\n\n**Full schema from Migration 01 + subsequent modifications (Migration 08):**\n\n```\nCREATE TABLE job (\n id INTEGER PRIMARY KEY,\n job_id BIGINT NOT NULL,\n cluster VARCHAR(255) NOT NULL,\n subcluster VARCHAR(255) NOT NULL,\n start_time BIGINT NOT NULL, -- Unix timestamp (INDEXED)\n hpc_user VARCHAR(255) NOT NULL, -- Renamed from \"user\" in Migration 08 (INDEXED)\n project VARCHAR(255) NOT NULL,\n cluster_partition VARCHAR(255), -- Renamed from \"partition\" in Migration 08\n array_job_id BIGINT,\n duration INT NOT NULL,\n walltime INT NOT NULL,\n job_state VARCHAR(255) NOT NULL, -- CHECK: running|completed|failed|canceled|stopped|timeout\n meta_data TEXT, -- JSON\n resources TEXT NOT NULL, -- JSON\n num_nodes INT NOT NULL,\n num_hwthreads INT,\n num_acc INT,\n smt TINYINT DEFAULT 1,\n exclusive TINYINT DEFAULT 1,\n monitoring_status TINYINT DEFAULT 1,\n energy REAL DEFAULT 0.0, -- Added in Migration 08\n footprint TEXT DEFAULT NULL, -- JSON, added in Migration 08\n energy_footprint TEXT DEFAULT NULL -- JSON, added in Migration 08\n)\n```\n\n**Supporting tables:**\n- `hpc_user` (users)\n- `tag` (tags with scope)\n- `jobtag` (job-tag relationships with indexes on both job_id and tag_id)\n- `node` and `node_state` (node tracking, added in Migration 10)\n\n### Data Flow Summary\n\n```\n/monitoring/users/?cluster=woody&startTime=last30d\n ↓\nroutes.go: buildFilterPresets() extracts query params\n ↓\nList.root.svelte: Builds GraphQL query with jobFilters\n ↓\nGraphQL Resolver: JobsStatistics() [schema.resolvers.go:647]\n ↓\nJobRepository.JobsStatsGrouped() [stats.go:233]\n - Calls buildStatsQuery() with groupBy=\"job.hpc_user\"\n - Applies BuildWhereClause() for each JobFilter\n - Uses index: jobs_cluster_starttime_user_stats (covering index)\n ↓\nSQL Query executes GROUP BY hpc_user with aggregations:\n - COUNT(job.id) as totalJobs\n - CAST(ROUND(SUM(...) / 3600) as int) as totalWalltime\n - SUM(num_nodes), SUM(num_hwthreads), SUM(num_acc) aggregations\n ↓\nPost-query user name lookup (avoids JOIN overhead)\n ↓\nReturns [JobsStatistics] array grouped by user\n```\n\nThe system is heavily optimized for this exact query pattern, evidenced by the new covering index in Migration 12 that was specifically created for \"grouped stats queries\" with the exact columns needed for statistics aggregation."}],"totalDurationMs":83172,"totalTokens":71496,"totalToolUseCount":38,"usage":{"input_tokens":0,"cache_creation_input_tokens":926,"cache_read_input_tokens":68619,"output_tokens":1951,"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":926},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"ddc8fdb6-a88e-4698-8001-c60c67c34a95"}
{"parentUuid":"5b4a3113-e5ca-42eb-96f7-4cb6365d397d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01A75wsG5xKtA4jhuKsEVGga","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EusCCkYICxgCKkAFq66kDirsdtSAvX+krhs/zkE+8aUfKFpOP4PVsdjw2V8J3LDb5n97yw7UkljtzgmNvzxxbR2PT66MPFyCyWHHEgzPYQXb+zD4PMy1d6caDDYcu5AXiABifJ340CIwNeBCly3Og/H7NWXDQCYbd8LwLdzevEOA2FtWfFq7sFpl6uLqp30tj/v9uKFHLXGNKtIBcyfcuAN02VwItC9/yz8jD2ocIti4/1ByUbZWxVkM5jDfW1Bx9gLI5Cz5J6F5B2BV/SKdFcRiQxley7VBcq89We4VSGq+4LVoI2lZHnZyk2t3CUl3NcCu+sG//TUTDxOrpLvgHJJc+qRGMcjzqyvjZmluRfAfnmSxgntJVipZ3Qph9Kj/15kVAsWAke1S0ffcHGulsxJhDRVCZEc5bVKqVqMUqqkBDJ7rNsG/2obgLQpbaMgmc3cmDQ7QZH30wZaV/d7vcRl457SqjeFemInV4LJBGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":3182,"cache_read_input_tokens":35910,"cache_creation":{"ephemeral_5m_input_tokens":3182,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8Z685AoZsKnt2FPj8C","type":"assistant","uuid":"1319983d-203e-4a5a-861e-e3380d92f1c5","timestamp":"2026-03-12T11:46:33.783Z"}
{"parentUuid":"1319983d-203e-4a5a-861e-e3380d92f1c5","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01A75wsG5xKtA4jhuKsEVGga","type":"message","role":"assistant","content":[{"type":"text","text":"Let me check the resolver to see exactly what queries are executed for this endpoint, and look at the `buildTimeCondition` to understand the filter."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":3182,"cache_read_input_tokens":35910,"cache_creation":{"ephemeral_5m_input_tokens":3182,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8Z685AoZsKnt2FPj8C","type":"assistant","uuid":"6368e17f-102e-4794-bbf5-23222a433a1a","timestamp":"2026-03-12T11:46:35.052Z"}
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{"parentUuid":"4e35acf7-c0f6-4038-a858-f90784617e35","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W3UxqFxG66MKhLhUcJJ7BU","type":"tool_result","content":" 640→\t\thasNextPage = len(nextJobs) == 1\n 641→\t}\n 642→\n 643→\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n 644→}\n 645→\n 646→// JobsStatistics is the resolver for the jobsStatistics field.\n 647→func (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n 648→\tstartOverall := time.Now()\n 649→\tvar err error\n 650→\tvar stats []*model.JobsStatistics\n 651→\n 652→\t// Top Level Defaults\n 653→\tdefaultDurationBins := \"1h\"\n 654→\tdefaultMetricBins := 10\n 655→\n 656→\t// Build requested fields map for selective column computation\n 657→\tstatsFields := []string{\"totalJobs\", \"totalUsers\", \"totalWalltime\", \"totalNodes\", \"totalCores\",\n 658→\t\t\"totalAccs\", \"totalNodeHours\", \"totalCoreHours\", \"totalAccHours\", \"runningJobs\", \"shortJobs\"}\n 659→\treqFields := make(map[string]bool, len(statsFields))\n 660→\tfetchedMainStats := false\n 661→\tfor _, f := range statsFields {\n 662→\t\tif requireField(ctx, f) {\n 663→\t\t\treqFields[f] = true\n 664→\t\t\tif f != \"runningJobs\" && f != \"shortJobs\" {\n 665→\t\t\t\tfetchedMainStats = true\n 666→\t\t\t}\n 667→\t\t}\n 668→\t}\n 669→\n 670→\tif fetchedMainStats {\n 671→\t\tif groupBy == nil {\n 672→\t\t\tstats, err = r.Repo.JobsStats(ctx, filter, reqFields)\n 673→\t\t} else {\n 674→\t\t\tstartGrouped := time.Now()\n 675→\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n 676→\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n 677→\t\t}\n 678→\t} else {\n 679→\t\tstats = make([]*model.JobsStatistics, 0, 1)\n 680→\t\tstats = append(stats, &model.JobsStatistics{})\n 681→\t}\n 682→\n 683→\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n 684→\t// Only run separate count queries if main stats were not fetched.\n 685→\tif !fetchedMainStats {\n 686→\t\tif groupBy != nil {\n 687→\t\t\tif requireField(ctx, \"shortJobs\") {\n 688→\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n 689→\t\t\t}\n 690→\t\t\tif requireField(ctx, \"runningJobs\") {\n 691→\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n 692→\t\t\t}\n 693→\t\t} else {\n 694→\t\t\tif requireField(ctx, \"shortJobs\") {\n 695→\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n 696→\t\t\t}\n 697→\t\t\tif requireField(ctx, \"runningJobs\") {\n 698→\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n 699→\t\t\t}\n 700→\t\t}\n 701→\t}\n 702→\n 703→\tif err != nil {\n 704→\t\treturn nil, err\n 705→\t}\n 706→\n 707→\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n 708→\n 709→\t\tif numDurationBins == nil {\n 710→\t\t\tnumDurationBins = &defaultDurationBins\n 711→\t\t}\n 712→\n 713→\t\tif groupBy == nil {\n 714→\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n 715→\t\t\tif err != nil {\n 716→\t\t\t\treturn nil, err\n 717→\t\t\t}\n 718→\t\t} else {\n 719→\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")"}]},"uuid":"228a4f3f-d520-4629-9567-78f8e76074ad","timestamp":"2026-03-12T11:46:36.406Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go","content":"\t\thasNextPage = len(nextJobs) == 1\n\t}\n\n\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n}\n\n// JobsStatistics is the resolver for the jobsStatistics field.\nfunc (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n\tstartOverall := time.Now()\n\tvar err error\n\tvar stats []*model.JobsStatistics\n\n\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\t// Build requested fields map for selective column computation\n\tstatsFields := []string{\"totalJobs\", \"totalUsers\", \"totalWalltime\", \"totalNodes\", \"totalCores\",\n\t\t\"totalAccs\", \"totalNodeHours\", \"totalCoreHours\", \"totalAccHours\", \"runningJobs\", \"shortJobs\"}\n\treqFields := make(map[string]bool, len(statsFields))\n\tfetchedMainStats := false\n\tfor _, f := range statsFields {\n\t\tif requireField(ctx, f) {\n\t\t\treqFields[f] = true\n\t\t\tif f != \"runningJobs\" && f != \"shortJobs\" {\n\t\t\t\tfetchedMainStats = true\n\t\t\t}\n\t\t}\n\t}\n\n\tif fetchedMainStats {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter, reqFields)\n\t\t} else {\n\t\t\tstartGrouped := time.Now()\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy, reqFields)\n\t\t\tcclog.Infof(\"Timer JobsStatsGrouped call: %s\", time.Since(startGrouped))\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n\t// Only run separate count queries if main stats were not fetched.\n\tif !fetchedMainStats {\n\t\tif groupBy != nil {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t\t}\n\t\t} else {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t\t}\n\t\t}\n\t}\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n\n\t\tif numDurationBins == nil {\n\t\t\tnumDurationBins = &defaultDurationBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")","numLines":80,"startLine":640,"totalLines":1062}},"sourceToolAssistantUUID":"4e35acf7-c0f6-4038-a858-f90784617e35"}
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{"parentUuid":"85bf7688-c4c9-4817-8c6b-7d15429d9dd1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01UoXpGhH3CbURn6djhFx8Zo","toolUseID":"toolu_01UoXpGhH3CbURn6djhFx8Zo","timestamp":"2026-03-12T11:46:36.938Z","uuid":"963c352b-e016-4f60-8a3a-a58efbece685"}
{"parentUuid":"85bf7688-c4c9-4817-8c6b-7d15429d9dd1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UoXpGhH3CbURn6djhFx8Zo","type":"tool_result","content":" 178→// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n 179→// them to the query. Supports filtering by job properties (cluster, state, user),\n 180→// time ranges, resource usage, tags, and JSON field searches in meta_data,\n 181→// footprint, and resources columns.\n 182→func BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n 183→\t// Primary Key\n 184→\tif filter.DbID != nil {\n 185→\t\tdbIDs := make([]string, len(filter.DbID))\n 186→\t\tcopy(dbIDs, filter.DbID)\n 187→\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n 188→\t}\n 189→\t// Explicit indices\n 190→\tif filter.Cluster != nil {\n 191→\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n 192→\t}\n 193→\tif filter.SubCluster != nil {\n 194→\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n 195→\t}\n 196→\tif filter.Partition != nil {\n 197→\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n 198→\t}\n 199→\tif filter.State != nil {\n 200→\t\tstates := make([]string, len(filter.State))\n 201→\t\tfor i, val := range filter.State {\n 202→\t\t\tstates[i] = string(val)\n 203→\t\t}\n 204→\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n 205→\t}\n 206→\tif filter.Shared != nil {\n 207→\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n 208→\t}\n 209→\tif filter.Project != nil {\n 210→\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n 211→\t}\n 212→\tif filter.User != nil {\n 213→\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n 214→\t}\n 215→\tif filter.NumNodes != nil {\n 216→\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n 217→\t}\n 218→\tif filter.NumAccelerators != nil {\n 219→\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n 220→\t}\n 221→\tif filter.NumHWThreads != nil {\n 222→\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n 223→\t}\n 224→\tif filter.ArrayJobID != nil {\n 225→\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n 226→\t}\n 227→\tif filter.StartTime != nil {\n 228→\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n 229→\t}\n 230→\tif filter.Duration != nil {\n 231→\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n 232→\t}\n 233→\tif filter.Energy != nil {\n 234→\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n 235→\t}\n 236→\t// Indices on Tag Table\n 237→\tif filter.Tags != nil {\n 238→\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n 239→\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n 240→\t}\n 241→\t// No explicit Indices\n 242→\tif filter.JobID != nil {\n 243→\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n 244→\t}\n 245→\t// Queries Within JSONs\n 246→\tif filter.MetricStats != nil {\n 247→\t\tfor _, ms := range filter.MetricStats {\n 248→\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n 249→\t\t}\n 250→\t}\n 251→\tif filter.Node != nil {\n 252→\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n 253→\t}\n 254→\tif filter.JobName != nil {\n 255→\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n 256→\t}\n 257→\tif filter.Schedule != nil {\n 258→\t\tinteractiveJobname := \"interactive\"\n 259→\t\tswitch *filter.Schedule {\n 260→\t\tcase \"interactive\":\n 261→\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n 262→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n 263→\t\tcase \"batch\":\n 264→\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n 265→\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n 266→\t\t}\n 267→\t}\n 268→\n 269→\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n 270→\tif filter.MinRunningFor != nil {\n 271→\t\tnow := time.Now().Unix()\n 272→\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n 273→\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n 274→\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n 275→\t}\n 276→\treturn query\n 277→}\n 278→\n 279→// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\n 280→func buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n 281→\tif cond.From != 1 && cond.To != 0 {\n 282→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 283→\t} else if cond.From != 1 && cond.To == 0 {\n 284→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 285→\t} else if cond.From == 1 && cond.To != 0 {\n 286→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 287→\t} else {\n 288→\t\treturn query\n 289→\t}\n 290→}\n 291→\n 292→// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\n 293→func buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n 294→\tif cond.From != 1.0 && cond.To != 0.0 {\n 295→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n 296→\t} else if cond.From != 1.0 && cond.To == 0.0 {\n 297→\t\treturn query.Where(field+\" >= ?\", cond.From)\n 298→\t} else if cond.From == 1.0 && cond.To != 0.0 {\n 299→\t\treturn query.Where(field+\" <= ?\", cond.To)\n 300→\t} else {\n 301→\t\treturn query\n 302→\t}\n 303→}\n 304→\n 305→// buildTimeCondition creates time range filters supporting absolute timestamps,\n 306→// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n 307→// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\n 308→func buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n 309→\tif cond.From != nil && cond.To != nil {\n 310→\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n 311→\t} else if cond.From != nil {\n 312→\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n 313→\t} else if cond.To != nil {\n 314→\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n 315→\t} else if cond.Range != \"\" {\n 316→\t\tnow := time.Now().Unix()\n 317→\t\tvar then int64\n 318→\t\tswitch cond.Range {\n 319→\t\tcase \"last6h\":\n 320→\t\t\tthen = now - (60 * 60 * 6)\n 321→\t\tcase \"last24h\":\n 322→\t\t\tthen = now - (60 * 60 * 24)\n 323→\t\tcase \"last7d\":\n 324→\t\t\tthen = now - (60 * 60 * 24 * 7)\n 325→\t\tcase \"last30d\":\n 326→\t\t\tthen = now - (60 * 60 * 24 * 30)\n 327→\t\tdefault:\n 328→\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n 329→\t\t\treturn query\n 330→\t\t}\n 331→\t\treturn query.Where(field+\" >= ?\", then)\n 332→\t} else {\n 333→\t\treturn query\n 334→\t}\n 335→}\n 336→\n 337→// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\n 338→func buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n 339→\tquery = query.Where(\"JSON_VALID(footprint)\")\n 340→\tif cond.From != 1.0 && cond.To != 0.0 {\n 341→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n 342→\t} else if cond.From != 1.0 && cond.To == 0.0 {\n 343→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n 344→\t} else if cond.From == 1.0 && cond.To != 0.0 {\n 345→\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n 346→\t} else {\n 347→\t\treturn query"}]},"uuid":"ba54da83-ff31-4966-9e81-669d1ac1fc93","timestamp":"2026-03-12T11:46:36.938Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/jobQuery.go","content":"// BuildWhereClause constructs SQL WHERE conditions from a JobFilter and applies\n// them to the query. Supports filtering by job properties (cluster, state, user),\n// time ranges, resource usage, tags, and JSON field searches in meta_data,\n// footprint, and resources columns.\nfunc BuildWhereClause(filter *model.JobFilter, query sq.SelectBuilder) sq.SelectBuilder {\n\t// Primary Key\n\tif filter.DbID != nil {\n\t\tdbIDs := make([]string, len(filter.DbID))\n\t\tcopy(dbIDs, filter.DbID)\n\t\tquery = query.Where(sq.Eq{\"job.id\": dbIDs})\n\t}\n\t// Explicit indices\n\tif filter.Cluster != nil {\n\t\tquery = buildStringCondition(\"job.cluster\", filter.Cluster, query)\n\t}\n\tif filter.SubCluster != nil {\n\t\tquery = buildStringCondition(\"job.subcluster\", filter.SubCluster, query)\n\t}\n\tif filter.Partition != nil {\n\t\tquery = buildStringCondition(\"job.cluster_partition\", filter.Partition, query)\n\t}\n\tif filter.State != nil {\n\t\tstates := make([]string, len(filter.State))\n\t\tfor i, val := range filter.State {\n\t\t\tstates[i] = string(val)\n\t\t}\n\t\tquery = query.Where(sq.Eq{\"job.job_state\": states})\n\t}\n\tif filter.Shared != nil {\n\t\tquery = query.Where(\"job.shared = ?\", *filter.Shared)\n\t}\n\tif filter.Project != nil {\n\t\tquery = buildStringCondition(\"job.project\", filter.Project, query)\n\t}\n\tif filter.User != nil {\n\t\tquery = buildStringCondition(\"job.hpc_user\", filter.User, query)\n\t}\n\tif filter.NumNodes != nil {\n\t\tquery = buildIntCondition(\"job.num_nodes\", filter.NumNodes, query)\n\t}\n\tif filter.NumAccelerators != nil {\n\t\tquery = buildIntCondition(\"job.num_acc\", filter.NumAccelerators, query)\n\t}\n\tif filter.NumHWThreads != nil {\n\t\tquery = buildIntCondition(\"job.num_hwthreads\", filter.NumHWThreads, query)\n\t}\n\tif filter.ArrayJobID != nil {\n\t\tquery = query.Where(\"job.array_job_id = ?\", *filter.ArrayJobID)\n\t}\n\tif filter.StartTime != nil {\n\t\tquery = buildTimeCondition(\"job.start_time\", filter.StartTime, query)\n\t}\n\tif filter.Duration != nil {\n\t\tquery = buildIntCondition(\"job.duration\", filter.Duration, query)\n\t}\n\tif filter.Energy != nil {\n\t\tquery = buildFloatCondition(\"job.energy\", filter.Energy, query)\n\t}\n\t// Indices on Tag Table\n\tif filter.Tags != nil {\n\t\t// This is an OR-Logic query: Returns all distinct jobs with at least one of the requested tags; TODO: AND-Logic query?\n\t\tquery = query.Join(\"jobtag ON jobtag.job_id = job.id\").Where(sq.Eq{\"jobtag.tag_id\": filter.Tags}).Distinct()\n\t}\n\t// No explicit Indices\n\tif filter.JobID != nil {\n\t\tquery = buildStringCondition(\"job.job_id\", filter.JobID, query)\n\t}\n\t// Queries Within JSONs\n\tif filter.MetricStats != nil {\n\t\tfor _, ms := range filter.MetricStats {\n\t\t\tquery = buildFloatJSONCondition(ms.MetricName, ms.Range, query)\n\t\t}\n\t}\n\tif filter.Node != nil {\n\t\tquery = buildResourceJSONCondition(\"hostname\", filter.Node, query)\n\t}\n\tif filter.JobName != nil {\n\t\tquery = buildMetaJSONCondition(\"jobName\", filter.JobName, query)\n\t}\n\tif filter.Schedule != nil {\n\t\tinteractiveJobname := \"interactive\"\n\t\tswitch *filter.Schedule {\n\t\tcase \"interactive\":\n\t\t\tiFilter := model.StringInput{Eq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &iFilter, query)\n\t\tcase \"batch\":\n\t\t\tsFilter := model.StringInput{Neq: &interactiveJobname}\n\t\t\tquery = buildMetaJSONCondition(\"jobName\", &sFilter, query)\n\t\t}\n\t}\n\n\t// Configurable Filter to exclude recently started jobs, see config.go: ShortRunningJobsDuration\n\tif filter.MinRunningFor != nil {\n\t\tnow := time.Now().Unix()\n\t\t// Only jobs whose start timestamp is more than MinRunningFor seconds in the past\n\t\t// If a job completed within the configured timeframe, it will still show up after the start_time matches the condition!\n\t\tquery = query.Where(sq.Lt{\"job.start_time\": (now - int64(*filter.MinRunningFor))})\n\t}\n\treturn query\n}\n\n// buildIntCondition creates clauses for integer range filters, using BETWEEN only if required.\nfunc buildIntCondition(field string, cond *config.IntRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1 && cond.To == 0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1 && cond.To != 0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatCondition creates a clauses for float range filters, using BETWEEN only if required.\nfunc buildFloatCondition(field string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(field+\" >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(field+\" <= ?\", cond.To)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildTimeCondition creates time range filters supporting absolute timestamps,\n// relative time ranges (last6h, last24h, last7d, last30d), or open-ended ranges.\n// Reminder: BETWEEN Queries are slower and dont use indices as frequently: Only use if both conditions required\nfunc buildTimeCondition(field string, cond *config.TimeRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tif cond.From != nil && cond.To != nil {\n\t\treturn query.Where(field+\" BETWEEN ? AND ?\", cond.From.Unix(), cond.To.Unix())\n\t} else if cond.From != nil {\n\t\treturn query.Where(field+\" >= ?\", cond.From.Unix())\n\t} else if cond.To != nil {\n\t\treturn query.Where(field+\" <= ?\", cond.To.Unix())\n\t} else if cond.Range != \"\" {\n\t\tnow := time.Now().Unix()\n\t\tvar then int64\n\t\tswitch cond.Range {\n\t\tcase \"last6h\":\n\t\t\tthen = now - (60 * 60 * 6)\n\t\tcase \"last24h\":\n\t\t\tthen = now - (60 * 60 * 24)\n\t\tcase \"last7d\":\n\t\t\tthen = now - (60 * 60 * 24 * 7)\n\t\tcase \"last30d\":\n\t\t\tthen = now - (60 * 60 * 24 * 30)\n\t\tdefault:\n\t\t\tcclog.Debugf(\"No known named timeRange: startTime.range = %s\", cond.Range)\n\t\t\treturn query\n\t\t}\n\t\treturn query.Where(field+\" >= ?\", then)\n\t} else {\n\t\treturn query\n\t}\n}\n\n// buildFloatJSONCondition creates a filter on a numeric field within the footprint JSON column, using BETWEEN only if required.\nfunc buildFloatJSONCondition(jsonField string, cond *model.FloatRange, query sq.SelectBuilder) sq.SelectBuilder {\n\tquery = query.Where(\"JSON_VALID(footprint)\")\n\tif cond.From != 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") BETWEEN ? AND ?\", cond.From, cond.To)\n\t} else if cond.From != 1.0 && cond.To == 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") >= ?\", cond.From)\n\t} else if cond.From == 1.0 && cond.To != 0.0 {\n\t\treturn query.Where(\"JSON_EXTRACT(footprint, \\\"$.\"+jsonField+\"\\\") <= ?\", cond.To)\n\t} else {\n\t\treturn query","numLines":170,"startLine":178,"totalLines":439}},"sourceToolAssistantUUID":"85bf7688-c4c9-4817-8c6b-7d15429d9dd1"}
{"parentUuid":"ba54da83-ff31-4966-9e81-669d1ac1fc93","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01PfqijMxak3UtDGqwRaCCZH","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check what the frontend actually requests to understand which fields are needed."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4390,"cache_read_input_tokens":39092,"cache_creation":{"ephemeral_5m_input_tokens":4390,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8ZWeTMCsUHaD1viuaY","type":"assistant","uuid":"7ff40ae1-fde0-480e-a66d-91a48b6108a7","timestamp":"2026-03-12T11:46:38.955Z"}
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{"parentUuid":"d4cace65-d6cb-4812-b3e7-6e30a5332817","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ApUsTZxvMRBb9Mrq9R8FZ1","type":"tool_result","content":" 1→-- Migration 11: Remove overly specific table indexes formerly used in sorting\n 2→-- When one or two indexed columns are used, sorting usually is fast\n 3→-- Reduces from ~78 indexes to 48 for better write performance,\n 4→-- reduced disk usage, and more reliable query planner decisions.\n 5→-- Requires ANALYZE to be run after migration (done automatically on startup).\n 6→\n 7→-- ============================================================\n 8→-- Drop SELECTED existing job indexes (from migrations 08/09)\n 9→-- sqlite_autoindex_job_1 (UNIQUE constraint) is kept automatically\n 10→-- ============================================================\n 11→\n 12→-- Cluster+Partition Filter Sorting\n 13→DROP INDEX IF EXISTS jobs_cluster_partition_numnodes;\n 14→DROP INDEX IF EXISTS jobs_cluster_partition_numhwthreads;\n 15→DROP INDEX IF EXISTS jobs_cluster_partition_numacc;\n 16→DROP INDEX IF EXISTS jobs_cluster_partition_energy;\n 17→\n 18→-- Cluster+JobState Filter Sorting\n 19→DROP INDEX IF EXISTS jobs_cluster_jobstate_numnodes;\n 20→DROP INDEX IF EXISTS jobs_cluster_jobstate_numhwthreads;\n 21→DROP INDEX IF EXISTS jobs_cluster_jobstate_numacc;\n 22→DROP INDEX IF EXISTS jobs_cluster_jobstate_energy;\n 23→\n 24→-- Cluster+Shared Filter Sorting\n 25→DROP INDEX IF EXISTS jobs_cluster_shared_numnodes;\n 26→DROP INDEX IF EXISTS jobs_cluster_shared_numhwthreads;\n 27→DROP INDEX IF EXISTS jobs_cluster_shared_numacc;\n 28→DROP INDEX IF EXISTS jobs_cluster_shared_energy;\n 29→\n 30→-- User Filter Sorting\n 31→DROP INDEX IF EXISTS jobs_user_numnodes;\n 32→DROP INDEX IF EXISTS jobs_user_numhwthreads;\n 33→DROP INDEX IF EXISTS jobs_user_numacc;\n 34→DROP INDEX IF EXISTS jobs_user_energy;\n 35→\n 36→-- Project Filter Sorting\n 37→DROP INDEX IF EXISTS jobs_project_numnodes;\n 38→DROP INDEX IF EXISTS jobs_project_numhwthreads;\n 39→DROP INDEX IF EXISTS jobs_project_numacc;\n 40→DROP INDEX IF EXISTS jobs_project_energy;\n 41→\n 42→-- JobState Filter Sorting\n 43→DROP INDEX IF EXISTS jobs_jobstate_numnodes;\n 44→DROP INDEX IF EXISTS jobs_jobstate_numhwthreads;\n 45→DROP INDEX IF EXISTS jobs_jobstate_numacc;\n 46→DROP INDEX IF EXISTS jobs_jobstate_energy;\n 47→\n 48→-- Shared Filter Sorting\n 49→DROP INDEX IF EXISTS jobs_shared_numnodes;\n 50→DROP INDEX IF EXISTS jobs_shared_numhwthreads;\n 51→DROP INDEX IF EXISTS jobs_shared_numacc;\n 52→DROP INDEX IF EXISTS jobs_shared_energy;\n 53→\n 54→-- ArrayJob Filter\n 55→DROP INDEX IF EXISTS jobs_cluster_arrayjobid_starttime;\n 56→\n 57→-- Backup Indices For High Variety Columns\n 58→DROP INDEX IF EXISTS jobs_duration;\n 59→\n 60→-- Optimize DB index usage\n 61→PRAGMA optimize;\n 62→"}]},"uuid":"f93b04e8-cdc6-4f59-bc07-dde56cdfd693","timestamp":"2026-03-12T11:46:41.189Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/11_optimize-indexes.up.sql","content":"-- Migration 11: Remove overly specific table indexes formerly used in sorting\n-- When one or two indexed columns are used, sorting usually is fast\n-- Reduces from ~78 indexes to 48 for better write performance,\n-- reduced disk usage, and more reliable query planner decisions.\n-- Requires ANALYZE to be run after migration (done automatically on startup).\n\n-- ============================================================\n-- Drop SELECTED existing job indexes (from migrations 08/09)\n-- sqlite_autoindex_job_1 (UNIQUE constraint) is kept automatically\n-- ============================================================\n\n-- Cluster+Partition Filter Sorting\nDROP INDEX 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{"parentUuid":"c5a400cb-0408-4684-9d73-7a613122be0d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019RTz8dsWa1Rbmr99g4eGwJ","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"database/sql\"\n 10→\t\"fmt\"\n 11→\t\"net/url\"\n 12→\t\"sync\"\n 13→\t\"time\"\n 14→\n 15→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 16→\t\"github.com/jmoiron/sqlx\"\n 17→\t\"github.com/mattn/go-sqlite3\"\n 18→\t\"github.com/qustavo/sqlhooks/v2\"\n 19→)\n 20→\n 21→var (\n 22→\tdbConnOnce sync.Once\n 23→\tdbConnInstance *DBConnection\n 24→)\n 25→\n 26→type DBConnection struct {\n 27→\tDB *sqlx.DB\n 28→\tDriver string\n 29→}\n 30→\n 31→type DatabaseOptions struct {\n 32→\tURL string\n 33→\tMaxOpenConnections int\n 34→\tMaxIdleConnections int\n 35→\tConnectionMaxLifetime time.Duration\n 36→\tConnectionMaxIdleTime time.Duration\n 37→}\n 38→\n 39→func setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n 40→\tpragmas := []string{\n 41→\t\t\"temp_store = memory\",\n 42→\t\tfmt.Sprintf(\"soft_heap_limit = %d\", int64(cfg.DbSoftHeapLimitMB)*1024*1024),\n 43→\t}\n 44→\n 45→\tfor _, pragma := range pragmas {\n 46→\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n 47→\t\tif err != nil {\n 48→\t\t\treturn err\n 49→\t\t}\n 50→\t}\n 51→\n 52→\treturn nil\n 53→}\n 54→\n 55→func Connect(db string) {\n 56→\tvar err error\n 57→\tvar dbHandle *sqlx.DB\n 58→\n 59→\tdbConnOnce.Do(func() {\n 60→\t\topts := DatabaseOptions{\n 61→\t\t\tURL: db,\n 62→\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n 63→\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n 64→\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n 65→\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n 66→\t\t}\n 67→\n 68→\t\t// TODO: Have separate DB handles for Writes and Reads\n 69→\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n 70→\t\tconnectionURLParams := make(url.Values)\n 71→\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n 72→\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n 73→\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n 74→\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n 75→\t\tcacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\n 76→\t\tconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n 77→\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n 78→\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n 79→\n 80→\t\tif cclog.Loglevel() == \"debug\" {\n 81→\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n 82→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n 83→\t\t} else {\n 84→\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n 85→\t\t}\n 86→\n 87→\t\tif err != nil {\n 88→\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n 89→\t\t}\n 90→\n 91→\t\terr = setupSqlite(dbHandle.DB, repoConfig)\n 92→\t\tif err != nil {\n 93→\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n 94→\t\t}\n 95→\n 96→\t\tcclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n 97→\t\t\trepoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n 98→\n 99→\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n 100→\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n 101→\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n 102→\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n 103→\n 104→\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n 105→\t\terr = checkDBVersion(dbHandle.DB)\n 106→\t\tif err != nil {\n 107→\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n 108→\t\t}\n 109→\t})\n 110→}\n 111→\n 112→func GetConnection() *DBConnection {\n 113→\tif dbConnInstance == nil {\n 114→\t\tcclog.Fatalf(\"Database connection not initialized!\")\n 115→\t}\n 116→\n 117→\treturn dbConnInstance\n 118→}\n 119→\n 120→// ResetConnection closes the current database connection and resets the connection state.\n 121→// This function is intended for testing purposes only to allow test isolation.\n 122→func ResetConnection() error {\n 123→\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n 124→\t\tif err := dbConnInstance.DB.Close(); err != nil {\n 125→\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n 126→\t\t}\n 127→\t}\n 128→\n 129→\tdbConnInstance = nil\n 130→\tdbConnOnce = sync.Once{}\n 131→\tjobRepoInstance = nil\n 132→\tjobRepoOnce = sync.Once{}\n 133→\tnodeRepoInstance = nil\n 134→\tnodeRepoOnce = sync.Once{}\n 135→\tuserRepoInstance = nil\n 136→\tuserRepoOnce = sync.Once{}\n 137→\tuserCfgRepoInstance = nil\n 138→\tuserCfgRepoOnce = sync.Once{}\n 139→\n 140→\treturn nil\n 141→}\n 142→"}]},"uuid":"79e22a45-c62d-47d8-b36e-9da7e5787c18","timestamp":"2026-03-12T11:47:23.877Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/dbConnection.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"fmt\"\n\t\"net/url\"\n\t\"sync\"\n\t\"time\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/jmoiron/sqlx\"\n\t\"github.com/mattn/go-sqlite3\"\n\t\"github.com/qustavo/sqlhooks/v2\"\n)\n\nvar (\n\tdbConnOnce sync.Once\n\tdbConnInstance *DBConnection\n)\n\ntype DBConnection struct {\n\tDB *sqlx.DB\n\tDriver string\n}\n\ntype DatabaseOptions struct {\n\tURL string\n\tMaxOpenConnections int\n\tMaxIdleConnections int\n\tConnectionMaxLifetime time.Duration\n\tConnectionMaxIdleTime time.Duration\n}\n\nfunc setupSqlite(db *sql.DB, cfg *RepositoryConfig) error {\n\tpragmas := []string{\n\t\t\"temp_store = memory\",\n\t\tfmt.Sprintf(\"soft_heap_limit = %d\", int64(cfg.DbSoftHeapLimitMB)*1024*1024),\n\t}\n\n\tfor _, pragma := range pragmas {\n\t\t_, err := db.Exec(\"PRAGMA \" + pragma)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\t}\n\n\treturn nil\n}\n\nfunc Connect(db string) {\n\tvar err error\n\tvar dbHandle *sqlx.DB\n\n\tdbConnOnce.Do(func() {\n\t\topts := DatabaseOptions{\n\t\t\tURL: db,\n\t\t\tMaxOpenConnections: repoConfig.MaxOpenConnections,\n\t\t\tMaxIdleConnections: repoConfig.MaxIdleConnections,\n\t\t\tConnectionMaxLifetime: repoConfig.ConnectionMaxLifetime,\n\t\t\tConnectionMaxIdleTime: repoConfig.ConnectionMaxIdleTime,\n\t\t}\n\n\t\t// TODO: Have separate DB handles for Writes and Reads\n\t\t// Optimize SQLite connection: https://kerkour.com/sqlite-for-servers\n\t\tconnectionURLParams := make(url.Values)\n\t\tconnectionURLParams.Add(\"_txlock\", \"immediate\")\n\t\tconnectionURLParams.Add(\"_journal_mode\", \"WAL\")\n\t\tconnectionURLParams.Add(\"_busy_timeout\", \"5000\")\n\t\tconnectionURLParams.Add(\"_synchronous\", \"NORMAL\")\n\t\tcacheSizeKiB := repoConfig.DbCacheSizeMB * 1024 // Convert MB to KiB\n\t\tconnectionURLParams.Add(\"_cache_size\", fmt.Sprintf(\"-%d\", cacheSizeKiB))\n\t\tconnectionURLParams.Add(\"_foreign_keys\", \"true\")\n\t\topts.URL = fmt.Sprintf(\"file:%s?%s\", opts.URL, connectionURLParams.Encode())\n\n\t\tif cclog.Loglevel() == \"debug\" {\n\t\t\tsql.Register(\"sqlite3WithHooks\", sqlhooks.Wrap(&sqlite3.SQLiteDriver{}, &Hooks{}))\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3WithHooks\", opts.URL)\n\t\t} else {\n\t\t\tdbHandle, err = sqlx.Open(\"sqlite3\", opts.URL)\n\t\t}\n\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Could not connect to SQLite database with sqlx.Open().\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\terr = setupSqlite(dbHandle.DB, repoConfig)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"Failed sqlite db setup.\\nError: %s\\n\", err.Error())\n\t\t}\n\n\t\tcclog.Infof(\"SQLite config: cache_size=%dMB/conn, soft_heap_limit=%dMB, max_conns=%d\",\n\t\t\trepoConfig.DbCacheSizeMB, repoConfig.DbSoftHeapLimitMB, repoConfig.MaxOpenConnections)\n\n\t\tdbHandle.SetMaxOpenConns(opts.MaxOpenConnections)\n\t\tdbHandle.SetMaxIdleConns(opts.MaxIdleConnections)\n\t\tdbHandle.SetConnMaxLifetime(opts.ConnectionMaxLifetime)\n\t\tdbHandle.SetConnMaxIdleTime(opts.ConnectionMaxIdleTime)\n\n\t\tdbConnInstance = &DBConnection{DB: dbHandle}\n\t\terr = checkDBVersion(dbHandle.DB)\n\t\tif err != nil {\n\t\t\tcclog.Abortf(\"DB Connection: Failed DB version check.\\nError: %s\\n\", err.Error())\n\t\t}\n\t})\n}\n\nfunc GetConnection() *DBConnection {\n\tif dbConnInstance == nil {\n\t\tcclog.Fatalf(\"Database connection not initialized!\")\n\t}\n\n\treturn dbConnInstance\n}\n\n// ResetConnection closes the current database connection and resets the connection state.\n// This function is intended for testing purposes only to allow test isolation.\nfunc ResetConnection() error {\n\tif dbConnInstance != nil && dbConnInstance.DB != nil {\n\t\tif err := dbConnInstance.DB.Close(); err != nil {\n\t\t\treturn fmt.Errorf(\"failed to close database connection: %w\", err)\n\t\t}\n\t}\n\n\tdbConnInstance = nil\n\tdbConnOnce = sync.Once{}\n\tjobRepoInstance = nil\n\tjobRepoOnce = sync.Once{}\n\tnodeRepoInstance = nil\n\tnodeRepoOnce = sync.Once{}\n\tuserRepoInstance = nil\n\tuserRepoOnce = sync.Once{}\n\tuserCfgRepoInstance = nil\n\tuserCfgRepoOnce = sync.Once{}\n\n\treturn nil\n}\n","numLines":142,"startLine":1,"totalLines":142}},"sourceToolAssistantUUID":"c5a400cb-0408-4684-9d73-7a613122be0d"}
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{"parentUuid":"d3fd8710-6371-4f41-b62f-3b90a179181f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0159TNBatePRMC5k5MF5coFt","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"database/sql\"\n 10→\t\"embed\"\n 11→\t\"fmt\"\n 12→\n 13→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 14→\t\"github.com/golang-migrate/migrate/v4\"\n 15→\t\"github.com/golang-migrate/migrate/v4/database/sqlite3\"\n 16→\t\"github.com/golang-migrate/migrate/v4/source/iofs\"\n 17→)\n 18→\n 19→// Version is the current database schema version required by this version of cc-backend.\n 20→// When the database schema changes, this version is incremented and a new migration file\n 21→// is added to internal/repository/migrations/sqlite3/.\n 22→//\n 23→// Version history:\n 24→// - Version 12: Add covering index for stats queries (cluster, start_time, hpc_user, ...)\n 25→// - Version 11: Optimize job table indexes (reduce from ~78 to 48)\n 26→// - Version 10: Node table\n 27→//\n 28→// Migration files are embedded at build time from the migrations directory.\n 29→const Version uint = 12\n 30→\n 31→//go:embed migrations/*\n 32→var migrationFiles embed.FS\n 33→\n 34→// checkDBVersion verifies that the database schema version matches the expected version.\n 35→// This is called automatically during Connect() to ensure schema compatibility.\n 36→//\n 37→// Returns an error if:\n 38→// - Database version is older than expected (needs migration)\n 39→// - Database version is newer than expected (needs app upgrade)\n 40→// - Database is in a dirty state (failed migration)\n 41→//\n 42→// A \"dirty\" database indicates a migration was started but not completed successfully.\n 43→// This requires manual intervention to fix the database and force the version.\n 44→func checkDBVersion(db *sql.DB) error {\n 45→\tdriver, err := sqlite3.WithInstance(db, &sqlite3.Config{})\n 46→\tif err != nil {\n 47→\t\treturn err\n 48→\t}\n 49→\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n 50→\tif err != nil {\n 51→\t\treturn err\n 52→\t}\n 53→\n 54→\tm, err := migrate.NewWithInstance(\"iofs\", d, \"sqlite3\", driver)\n 55→\tif err != nil {\n 56→\t\treturn err\n 57→\t}\n 58→\n 59→\tv, dirty, err := m.Version()\n 60→\tif err != nil {\n 61→\t\tif err == migrate.ErrNilVersion {\n 62→\t\t\tcclog.Warn(\"Legacy database without version or missing database file!\")\n 63→\t\t} else {\n 64→\t\t\treturn err\n 65→\t\t}\n 66→\t}\n 67→\n 68→\tif v < Version {\n 69→\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n 70→\t} else if v > Version {\n 71→\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease refer to documentation how to downgrade db with external migrate tool\", v, Version)\n 72→\t}\n 73→\n 74→\tif dirty {\n 75→\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n 76→\t}\n 77→\n 78→\treturn nil\n 79→}\n 80→\n 81→// getMigrateInstance creates a new migration instance for the given database file.\n 82→// This is used internally by MigrateDB, RevertDB, and ForceDB.\n 83→func getMigrateInstance(db string) (m *migrate.Migrate, err error) {\n 84→\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n 85→\tif err != nil {\n 86→\t\treturn nil, err\n 87→\t}\n 88→\n 89→\tm, err = migrate.NewWithSourceInstance(\"iofs\", d, fmt.Sprintf(\"sqlite3://%s?_foreign_keys=on\", db))\n 90→\tif err != nil {\n 91→\t\treturn nil, err\n 92→\t}\n 93→\n 94→\treturn m, nil\n 95→}\n 96→\n 97→// MigrateDB applies all pending database migrations to bring the schema up to date.\n 98→// This should be run with the -migrate-db flag before starting the application\n 99→// after upgrading to a new version that requires schema changes.\n 100→//\n 101→// Process:\n 102→// 1. Checks current database version\n 103→// 2. Applies all migrations from current version to target Version\n 104→// 3. Updates schema_migrations table to track applied migrations\n 105→//\n 106→// Important:\n 107→// - Always backup your database before running migrations\n 108→// - Migrations are irreversible without manual intervention\n 109→// - If a migration fails, the database is marked \"dirty\" and requires manual fix\n 110→//\n 111→// Usage:\n 112→//\n 113→//\tcc-backend -migrate-db\n 114→func MigrateDB(db string) error {\n 115→\tm, err := getMigrateInstance(db)\n 116→\tif err != nil {\n 117→\t\treturn err\n 118→\t}\n 119→\n 120→\tv, dirty, err := m.Version()\n 121→\tif err != nil {\n 122→\t\tif err == migrate.ErrNilVersion {\n 123→\t\t\tcclog.Info(\"Legacy database without version or missing database file!\")\n 124→\t\t} else {\n 125→\t\t\treturn err\n 126→\t\t}\n 127→\t}\n 128→\n 129→\tif v < Version {\n 130→\t\tcclog.Infof(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n 131→\t}\n 132→\n 133→\tif dirty {\n 134→\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n 135→\t}\n 136→\n 137→\tif err := m.Up(); err != nil {\n 138→\t\tif err == migrate.ErrNoChange {\n 139→\t\t\tcclog.Info(\"DB already up to date!\")\n 140→\t\t} else {\n 141→\t\t\treturn err\n 142→\t\t}\n 143→\t}\n 144→\n 145→\tm.Close()\n 146→\treturn nil\n 147→}\n 148→\n 149→// RevertDB rolls back the database schema to the previous version (Version - 1).\n 150→// This is primarily used for testing or emergency rollback scenarios.\n 151→//\n 152→// Warning:\n 153→// - This may cause data loss if newer schema added columns/tables\n 154→// - Always backup before reverting\n 155→// - Not all migrations are safely reversible\n 156→//\n 157→// Usage:\n 158→//\n 159→//\tcc-backend -revert-db\n 160→func RevertDB(db string) error {\n 161→\tm, err := getMigrateInstance(db)\n 162→\tif err != nil {\n 163→\t\treturn err\n 164→\t}\n 165→\n 166→\tif err := m.Migrate(Version - 1); err != nil {\n 167→\t\tif err == migrate.ErrNoChange {\n 168→\t\t\tcclog.Info(\"DB already up to date!\")\n 169→\t\t} else {\n 170→\t\t\treturn err\n 171→\t\t}\n 172→\t}\n 173→\n 174→\tm.Close()\n 175→\treturn nil\n 176→}\n 177→\n 178→// ForceDB forces the database schema version to the current Version without running migrations.\n 179→// This is only used to recover from failed migrations that left the database in a \"dirty\" state.\n 180→//\n 181→// When to use:\n 182→// - After manually fixing a failed migration\n 183→// - When you've manually applied schema changes and need to update the version marker\n 184→//\n 185→// Warning:\n 186→// - This does NOT apply any schema changes\n 187→// - Only use after manually verifying the schema is correct\n 188→// - Improper use can cause schema/version mismatch\n 189→//\n 190→// Usage:\n 191→//\n 192→//\tcc-backend -force-db\n 193→func ForceDB(db string) error {\n 194→\tm, err := getMigrateInstance(db)\n 195→\tif err != nil {\n 196→\t\treturn err\n 197→\t}\n 198→\n 199→\tif err := m.Force(int(Version)); err != nil {\n 200→\t\treturn err\n 201→\t}\n 202→\n 203→\tm.Close()\n 204→\treturn nil\n 205→}\n 206→"}]},"uuid":"87edbf6f-0a51-4bd7-8ba1-e7c9739ac434","timestamp":"2026-03-12T11:47:23.937Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migration.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"embed\"\n\t\"fmt\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/golang-migrate/migrate/v4\"\n\t\"github.com/golang-migrate/migrate/v4/database/sqlite3\"\n\t\"github.com/golang-migrate/migrate/v4/source/iofs\"\n)\n\n// Version is the current database schema version required by this version of cc-backend.\n// When the database schema changes, this version is incremented and a new migration file\n// is added to internal/repository/migrations/sqlite3/.\n//\n// Version history:\n// - Version 12: Add covering index for stats queries (cluster, start_time, hpc_user, ...)\n// - Version 11: Optimize job table indexes (reduce from ~78 to 48)\n// - Version 10: Node table\n//\n// Migration files are embedded at build time from the migrations directory.\nconst Version uint = 12\n\n//go:embed migrations/*\nvar migrationFiles embed.FS\n\n// checkDBVersion verifies that the database schema version matches the expected version.\n// This is called automatically during Connect() to ensure schema compatibility.\n//\n// Returns an error if:\n// - Database version is older than expected (needs migration)\n// - Database version is newer than expected (needs app upgrade)\n// - Database is in a dirty state (failed migration)\n//\n// A \"dirty\" database indicates a migration was started but not completed successfully.\n// This requires manual intervention to fix the database and force the version.\nfunc checkDBVersion(db *sql.DB) error {\n\tdriver, err := sqlite3.WithInstance(db, &sqlite3.Config{})\n\tif err != nil {\n\t\treturn err\n\t}\n\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tm, err := migrate.NewWithInstance(\"iofs\", d, \"sqlite3\", driver)\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tv, dirty, err := m.Version()\n\tif err != nil {\n\t\tif err == migrate.ErrNilVersion {\n\t\t\tcclog.Warn(\"Legacy database without version or missing database file!\")\n\t\t} else {\n\t\t\treturn err\n\t\t}\n\t}\n\n\tif v < Version {\n\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n\t} else if v > Version {\n\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease refer to documentation how to downgrade db with external migrate tool\", v, Version)\n\t}\n\n\tif dirty {\n\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n\t}\n\n\treturn nil\n}\n\n// getMigrateInstance creates a new migration instance for the given database file.\n// This is used internally by MigrateDB, RevertDB, and ForceDB.\nfunc getMigrateInstance(db string) (m *migrate.Migrate, err error) {\n\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tm, err = migrate.NewWithSourceInstance(\"iofs\", d, fmt.Sprintf(\"sqlite3://%s?_foreign_keys=on\", db))\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn m, nil\n}\n\n// MigrateDB applies all pending database migrations to bring the schema up to date.\n// This should be run with the -migrate-db flag before starting the application\n// after upgrading to a new version that requires schema changes.\n//\n// Process:\n// 1. Checks current database version\n// 2. Applies all migrations from current version to target Version\n// 3. Updates schema_migrations table to track applied migrations\n//\n// Important:\n// - Always backup your database before running migrations\n// - Migrations are irreversible without manual intervention\n// - If a migration fails, the database is marked \"dirty\" and requires manual fix\n//\n// Usage:\n//\n//\tcc-backend -migrate-db\nfunc MigrateDB(db string) error {\n\tm, err := getMigrateInstance(db)\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tv, dirty, err := m.Version()\n\tif err != nil {\n\t\tif err == migrate.ErrNilVersion {\n\t\t\tcclog.Info(\"Legacy database without version or missing database file!\")\n\t\t} else {\n\t\t\treturn err\n\t\t}\n\t}\n\n\tif v < Version {\n\t\tcclog.Infof(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n\t}\n\n\tif dirty {\n\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n\t}\n\n\tif err := m.Up(); err != nil {\n\t\tif err == migrate.ErrNoChange {\n\t\t\tcclog.Info(\"DB already up to date!\")\n\t\t} else {\n\t\t\treturn err\n\t\t}\n\t}\n\n\tm.Close()\n\treturn nil\n}\n\n// RevertDB rolls back the database schema to the previous version (Version - 1).\n// This is primarily used for testing or emergency rollback scenarios.\n//\n// Warning:\n// - This may cause data loss if newer schema added columns/tables\n// - Always backup before reverting\n// - Not all migrations are safely reversible\n//\n// Usage:\n//\n//\tcc-backend -revert-db\nfunc RevertDB(db string) error {\n\tm, err := getMigrateInstance(db)\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tif err := m.Migrate(Version - 1); err != nil {\n\t\tif err == migrate.ErrNoChange {\n\t\t\tcclog.Info(\"DB already up to date!\")\n\t\t} else {\n\t\t\treturn err\n\t\t}\n\t}\n\n\tm.Close()\n\treturn nil\n}\n\n// ForceDB forces the database schema version to the current Version without running migrations.\n// This is only used to recover from failed migrations that left the database in a \"dirty\" state.\n//\n// When to use:\n// - After manually fixing a failed migration\n// - When you've manually applied schema changes and need to update the version marker\n//\n// Warning:\n// - This does NOT apply any schema changes\n// - Only use after manually verifying the schema is correct\n// - Improper use can cause schema/version mismatch\n//\n// Usage:\n//\n//\tcc-backend -force-db\nfunc ForceDB(db string) error {\n\tm, err := getMigrateInstance(db)\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tif err := m.Force(int(Version)); err != nil {\n\t\treturn err\n\t}\n\n\tm.Close()\n\treturn nil\n}\n","numLines":206,"startLine":1,"totalLines":206}},"sourceToolAssistantUUID":"d3fd8710-6371-4f41-b62f-3b90a179181f"}
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"}],"st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{"parentUuid":"01db5ff5-5cf4-4b8d-b3fb-60437608e431","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01V5DxQShBpqsySWrY33PHd2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019e3taRrMwXjtXNn76VRa1M","name":"Agent","input":{"subagent_type":"Plan","description":"Design stats query optimization","prompt":"I need to optimize a slow SQLite stats query in a Go backend (ClusterCockpit cc-backend). Here's the situation:\n\n## Problem\nThe endpoint `/monitoring/users/?cluster=woody&startTime=last30d` calls `buildStatsQuery()` which generates a SQL query like:\n\n```sql\nSELECT job.hpc_user,\n COUNT(job.id) as totalJobs,\n 0 AS totalUsers,\n CAST(ROUND(SUM(CASE WHEN job.job_state = \"running\" THEN <now> - job.start_time ELSE job.duration END) / 3600) as int) as totalWalltime,\n ...similar aggregations for totalCoreHours, totalAccHours...\n COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs\nFROM job\nWHERE job.cluster = ? AND job.start_time >= ?\nGROUP BY job.hpc_user\n```\n\nThe query \"does not return\" - essentially hangs on a production database.\n\n## Current Index (Migration 12)\n```sql\nCREATE INDEX jobs_cluster_starttime_user_stats\n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n```\n\n## Other relevant indexes\n- `jobs_cluster_starttime ON (cluster, start_time)` - from migration 08\n- `jobs_cluster_user ON (cluster, hpc_user)` - from migration 08\n\n## Key Observations\n\n1. **Index column order problem**: The covering index has `(cluster, start_time, hpc_user, ...)`. The WHERE clause uses `cluster = ?` (equality) and `start_time >= ?` (range). The GROUP BY is on `hpc_user`. Since `start_time` is a range condition BEFORE `hpc_user`, the index can't provide ordered GROUP BY output. SQLite must sort all matching rows into a temp B-Tree before aggregating. For hundreds of thousands of jobs in 30 days, this sort is extremely expensive.\n\n2. **Better index order**: `(cluster, hpc_user, start_time, ...)` would allow:\n - Equality on cluster (prefix)\n - Index-ordered GROUP BY on hpc_user (next column)\n - Within each (cluster, hpc_user) group, start_time is sorted so the >= filter uses efficient skip-scan\n - All aggregation columns are in the index (covering)\n\n3. **COUNT(job.id)**: Uses the rowid which is implicitly in the index, but `COUNT(*)` is clearer and avoids any potential table lookups.\n\n4. **Double quotes in CASE**: `job.job_state = \"running\"` uses double quotes (SQL identifiers). Should be single quotes for string literals, though SQLite handles this gracefully.\n\n## Files to modify\n- `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - reorder index columns\n- `internal/repository/stats.go` - change COUNT(job.id) to COUNT(*), fix double quotes\n\nPlease provide a detailed plan for these optimizations, including:\n1. The exact index change (with reasoning)\n2. Code changes in stats.go\n3. Whether we should drop the redundant `jobs_cluster_starttime` index (since we have a covering index that starts with cluster)\n4. Testing/verification approach"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":6110,"cache_read_input_tokens":45369,"output_tokens":5034,"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":6110},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8cyz6SLGMZHSa4bH84","type":"assistant","uuid":"3e6e3f24-bb4b-44cf-b60c-dc212e75408d","timestamp":"2026-03-12T11:49:01.447Z"}
{"parentUuid":"3e6e3f24-bb4b-44cf-b60c-dc212e75408d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"type":"hook_progress","hookEvent":"PreToolUse","hookName":"PreToolUse:Agent","command":"entire hooks claude-code pre-task"},"toolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"b56355e2-cfdf-4114-a906-67dc9067f54b","timestamp":"2026-03-12T11:49:01.449Z"}
{"parentUuid":"b56355e2-cfdf-4114-a906-67dc9067f54b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"I need to optimize a slow SQLite stats query in a Go backend (ClusterCockpit cc-backend). Here's the situation:\n\n## Problem\nThe endpoint `/monitoring/users/?cluster=woody&startTime=last30d` calls `buildStatsQuery()` which generates a SQL query like:\n\n```sql\nSELECT job.hpc_user,\n COUNT(job.id) as totalJobs,\n 0 AS totalUsers,\n CAST(ROUND(SUM(CASE WHEN job.job_state = \"running\" THEN <now> - job.start_time ELSE job.duration END) / 3600) as int) as totalWalltime,\n ...similar aggregations for totalCoreHours, totalAccHours...\n COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs\nFROM job\nWHERE job.cluster = ? AND job.start_time >= ?\nGROUP BY job.hpc_user\n```\n\nThe query \"does not return\" - essentially hangs on a production database.\n\n## Current Index (Migration 12)\n```sql\nCREATE INDEX jobs_cluster_starttime_user_stats\n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n```\n\n## Other relevant indexes\n- `jobs_cluster_starttime ON (cluster, start_time)` - from migration 08\n- `jobs_cluster_user ON (cluster, hpc_user)` - from migration 08\n\n## Key Observations\n\n1. **Index column order problem**: The covering index has `(cluster, start_time, hpc_user, ...)`. The WHERE clause uses `cluster = ?` (equality) and `start_time >= ?` (range). The GROUP BY is on `hpc_user`. Since `start_time` is a range condition BEFORE `hpc_user`, the index can't provide ordered GROUP BY output. SQLite must sort all matching rows into a temp B-Tree before aggregating. For hundreds of thousands of jobs in 30 days, this sort is extremely expensive.\n\n2. **Better index order**: `(cluster, hpc_user, start_time, ...)` would allow:\n - Equality on cluster (prefix)\n - Index-ordered GROUP BY on hpc_user (next column)\n - Within each (cluster, hpc_user) group, start_time is sorted so the >= filter uses efficient skip-scan\n - All aggregation columns are in the index (covering)\n\n3. **COUNT(job.id)**: Uses the rowid which is implicitly in the index, but `COUNT(*)` is clearer and avoids any potential table lookups.\n\n4. **Double quotes in CASE**: `job.job_state = \"running\"` uses double quotes (SQL identifiers). Should be single quotes for string literals, though SQLite handles this gracefully.\n\n## Files to modify\n- `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - reorder index columns\n- `internal/repository/stats.go` - change COUNT(job.id) to COUNT(*), fix double quotes\n\nPlease provide a detailed plan for these optimizations, including:\n1. The exact index change (with reasoning)\n2. Code changes in stats.go\n3. Whether we should drop the redundant `jobs_cluster_starttime` index (since we have a covering index that starts with cluster)\n4. Testing/verification approach"}]},"uuid":"4bb333e5-9eed-41fb-af2c-6efe47c1201f","timestamp":"2026-03-12T11:49:01.945Z"},"type":"agent_progress","prompt":"I need to optimize a slow SQLite stats query in a Go backend (ClusterCockpit cc-backend). Here's the situation:\n\n## Problem\nThe endpoint `/monitoring/users/?cluster=woody&startTime=last30d` calls `buildStatsQuery()` which generates a SQL query like:\n\n```sql\nSELECT job.hpc_user,\n COUNT(job.id) as totalJobs,\n 0 AS totalUsers,\n CAST(ROUND(SUM(CASE WHEN job.job_state = \"running\" THEN <now> - job.start_time ELSE job.duration END) / 3600) as int) as totalWalltime,\n ...similar aggregations for totalCoreHours, totalAccHours...\n COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs\nFROM job\nWHERE job.cluster = ? AND job.start_time >= ?\nGROUP BY job.hpc_user\n```\n\nThe query \"does not return\" - essentially hangs on a production database.\n\n## Current Index (Migration 12)\n```sql\nCREATE INDEX jobs_cluster_starttime_user_stats\n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n```\n\n## Other relevant indexes\n- `jobs_cluster_starttime ON (cluster, start_time)` - from migration 08\n- `jobs_cluster_user ON (cluster, hpc_user)` - from migration 08\n\n## Key Observations\n\n1. **Index column order problem**: The covering index has `(cluster, start_time, hpc_user, ...)`. The WHERE clause uses `cluster = ?` (equality) and `start_time >= ?` (range). The GROUP BY is on `hpc_user`. Since `start_time` is a range condition BEFORE `hpc_user`, the index can't provide ordered GROUP BY output. SQLite must sort all matching rows into a temp B-Tree before aggregating. For hundreds of thousands of jobs in 30 days, this sort is extremely expensive.\n\n2. **Better index order**: `(cluster, hpc_user, start_time, ...)` would allow:\n - Equality on cluster (prefix)\n - Index-ordered GROUP BY on hpc_user (next column)\n - Within each (cluster, hpc_user) group, start_time is sorted so the >= filter uses efficient skip-scan\n - All aggregation columns are in the index (covering)\n\n3. **COUNT(job.id)**: Uses the rowid which is implicitly in the index, but `COUNT(*)` is clearer and avoids any potential table lookups.\n\n4. **Double quotes in CASE**: `job.job_state = \"running\"` uses double quotes (SQL identifiers). Should be single quotes for string literals, though SQLite handles this gracefully.\n\n## Files to modify\n- `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - reorder index columns\n- `internal/repository/stats.go` - change COUNT(job.id) to COUNT(*), fix double quotes\n\nPlease provide a detailed plan for these optimizations, including:\n1. The exact index change (with reasoning)\n2. Code changes in stats.go\n3. Whether we should drop the redundant `jobs_cluster_starttime` index (since we have a covering index that starts with cluster)\n4. Testing/verification approach","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"ee3835d6-c71a-4416-83cd-e859c0004ff3","timestamp":"2026-03-12T11:49:01.945Z"}
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{"parentUuid":"a8f6726c-4702-45ad-9b83-8b0b5be15748","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014AWKnZfL6zcG1jqVqmEywz","type":"tool_result","content":" 1→-- Migration 12: Add covering index for grouped stats queries\n 2→-- Covers the common pattern: WHERE cluster=X AND start_time >= Y GROUP BY hpc_user\n 3→-- Includes aggregated columns to avoid main table lookups entirely.\n 4→\n 5→CREATE INDEX IF NOT EXISTS jobs_cluster_starttime_user_stats\n 6→ ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n 7→\n 8→PRAGMA optimize;\n 9→"}]},"uuid":"22a6bec0-b1ee-42e5-a4dc-5698214db166","timestamp":"2026-03-12T11:49:04.391Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"e2017f48-c881-4205-90aa-16944d33aaab","timestamp":"2026-03-12T11:49:04.833Z"}
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{"parentUuid":"c403f08b-8f09-468f-b421-18e6d9d0de6c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RCp4KVXbsjL2Fbgnycvofo","type":"tool_result","content":" 1→DROP INDEX IF EXISTS jobs_cluster_starttime_user_stats;\n 2→"}]},"uuid":"27bb8f49-b91b-4990-ae4d-26b6a71cdec9","timestamp":"2026-03-12T11:49:04.850Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"9872ab54-3840-4c6f-a4a7-c380624d831f","timestamp":"2026-03-12T11:49:05.334Z"}
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{"parentUuid":"5aaab22e-3912-4b11-b294-ad7e035554fd","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KCh1GXiPBQSE4WqmqEQCU6","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":"a8a99b64-5c50-4d83-b520-ad6c985791a3","timestamp":"2026-03-12T11:49:05.611Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"cd2969a7-deba-41c5-b900-a8c1a8bb5ad8","timestamp":"2026-03-12T11:49:05.730Z"}
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{"parentUuid":"64ffa8af-7913-4b83-a80d-dc12a9d7886b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XfgCoGBidUn46yHDmcge6M","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"database/sql\"\n 10→\t\"embed\"\n 11→\t\"fmt\"\n 12→\n 13→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n 14→\t\"github.com/golang-migrate/migrate/v4\"\n 15→\t\"github.com/golang-migrate/migrate/v4/database/sqlite3\"\n 16→\t\"github.com/golang-migrate/migrate/v4/source/iofs\"\n 17→)\n 18→\n 19→// Version is the current database schema version required by this version of cc-backend.\n 20→// When the database schema changes, this version is incremented and a new migration file\n 21→// is added to internal/repository/migrations/sqlite3/.\n 22→//\n 23→// Version history:\n 24→// - Version 12: Add covering index for stats queries (cluster, start_time, hpc_user, ...)\n 25→// - Version 11: Optimize job table indexes (reduce from ~78 to 48)\n 26→// - Version 10: Node table\n 27→//\n 28→// Migration files are embedded at build time from the migrations directory.\n 29→const Version uint = 12\n 30→\n 31→//go:embed migrations/*\n 32→var migrationFiles embed.FS\n 33→\n 34→// checkDBVersion verifies that the database schema version matches the expected version.\n 35→// This is called automatically during Connect() to ensure schema compatibility.\n 36→//\n 37→// Returns an error if:\n 38→// - Database version is older than expected (needs migration)\n 39→// - Database version is newer than expected (needs app upgrade)\n 40→// - Database is in a dirty state (failed migration)\n 41→//\n 42→// A \"dirty\" database indicates a migration was started but not completed successfully.\n 43→// This requires manual intervention to fix the database and force the version.\n 44→func checkDBVersion(db *sql.DB) error {\n 45→\tdriver, err := sqlite3.WithInstance(db, &sqlite3.Config{})\n 46→\tif err != nil {\n 47→\t\treturn err\n 48→\t}\n 49→\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n 50→\tif err != nil {\n 51→\t\treturn err\n 52→\t}\n 53→\n 54→\tm, err := migrate.NewWithInstance(\"iofs\", d, \"sqlite3\", driver)\n 55→\tif err != nil {\n 56→\t\treturn err\n 57→\t}\n 58→\n 59→\tv, dirty, err := m.Version()\n 60→\tif err != nil {\n 61→\t\tif err == migrate.ErrNilVersion {\n 62→\t\t\tcclog.Warn(\"Legacy database without version or missing database file!\")\n 63→\t\t} else {\n 64→\t\t\treturn err\n 65→\t\t}\n 66→\t}\n 67→\n 68→\tif v < Version {\n 69→\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n 70→\t} else if v > Version {\n 71→\t\treturn fmt.Errorf(\"unsupported database version %d, need %d.\\nPlease refer to documentation how to downgrade db with external migrate tool\", v, Version)\n 72→\t}\n 73→\n 74→\tif dirty {\n 75→\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n 76→\t}\n 77→\n 78→\treturn nil\n 79→}\n 80→\n 81→// getMigrateInstance creates a new migration instance for the given database file.\n 82→// This is used internally by MigrateDB, RevertDB, and ForceDB.\n 83→func getMigrateInstance(db string) (m *migrate.Migrate, err error) {\n 84→\td, err := iofs.New(migrationFiles, \"migrations/sqlite3\")\n 85→\tif err != nil {\n 86→\t\treturn nil, err\n 87→\t}\n 88→\n 89→\tm, err = migrate.NewWithSourceInstance(\"iofs\", d, fmt.Sprintf(\"sqlite3://%s?_foreign_keys=on\", db))\n 90→\tif err != nil {\n 91→\t\treturn nil, err\n 92→\t}\n 93→\n 94→\treturn m, nil\n 95→}\n 96→\n 97→// MigrateDB applies all pending database migrations to bring the schema up to date.\n 98→// This should be run with the -migrate-db flag before starting the application\n 99→// after upgrading to a new version that requires schema changes.\n 100→//\n 101→// Process:\n 102→// 1. Checks current database version\n 103→// 2. Applies all migrations from current version to target Version\n 104→// 3. Updates schema_migrations table to track applied migrations\n 105→//\n 106→// Important:\n 107→// - Always backup your database before running migrations\n 108→// - Migrations are irreversible without manual intervention\n 109→// - If a migration fails, the database is marked \"dirty\" and requires manual fix\n 110→//\n 111→// Usage:\n 112→//\n 113→//\tcc-backend -migrate-db\n 114→func MigrateDB(db string) error {\n 115→\tm, err := getMigrateInstance(db)\n 116→\tif err != nil {\n 117→\t\treturn err\n 118→\t}\n 119→\n 120→\tv, dirty, err := m.Version()\n 121→\tif err != nil {\n 122→\t\tif err == migrate.ErrNilVersion {\n 123→\t\t\tcclog.Info(\"Legacy database without version or missing database file!\")\n 124→\t\t} else {\n 125→\t\t\treturn err\n 126→\t\t}\n 127→\t}\n 128→\n 129→\tif v < Version {\n 130→\t\tcclog.Infof(\"unsupported database version %d, need %d.\\nPlease backup your database file and run cc-backend -migrate-db\", v, Version)\n 131→\t}\n 132→\n 133→\tif dirty {\n 134→\t\treturn fmt.Errorf(\"last migration to version %d has failed, please fix the db manually and force version with -force-db flag\", Version)\n 135→\t}\n 136→\n 137→\tif err := m.Up(); err != nil {\n 138→\t\tif err == migrate.ErrNoChange {\n 139→\t\t\tcclog.Info(\"DB already up to date!\")\n 140→\t\t} else {\n 141→\t\t\treturn err\n 142→\t\t}\n 143→\t}\n 144→\n 145→\tm.Close()\n 146→\treturn nil\n 147→}\n 148→\n 149→// RevertDB rolls back the database schema to the previous version (Version - 1).\n 150→// This is primarily used for testing or emergency rollback scenarios.\n 151→//\n 152→// Warning:\n 153→// - This may cause data loss if newer schema added columns/tables\n 154→// - Always backup before reverting\n 155→// - Not all migrations are safely reversible\n 156→//\n 157→// Usage:\n 158→//\n 159→//\tcc-backend -revert-db\n 160→func RevertDB(db string) error {\n 161→\tm, err := getMigrateInstance(db)\n 162→\tif err != nil {\n 163→\t\treturn err\n 164→\t}\n 165→\n 166→\tif err := m.Migrate(Version - 1); err != nil {\n 167→\t\tif err == migrate.ErrNoChange {\n 168→\t\t\tcclog.Info(\"DB already up to date!\")\n 169→\t\t} else {\n 170→\t\t\treturn err\n 171→\t\t}\n 172→\t}\n 173→\n 174→\tm.Close()\n 175→\treturn nil\n 176→}\n 177→\n 178→// ForceDB forces the database schema version to the current Version without running migrations.\n 179→// This is only used to recover from failed migrations that left the database in a \"dirty\" state.\n 180→//\n 181→// When to use:\n 182→// - After manually fixing a failed migration\n 183→// - When you've manually applied schema changes and need to update the version marker\n 184→//\n 185→// Warning:\n 186→// - This does NOT apply any schema changes\n 187→// - Only use after manually verifying the schema is correct\n 188→// - Improper use can cause schema/version mismatch\n 189→//\n 190→// Usage:\n 191→//\n 192→//\tcc-backend -force-db\n 193→func ForceDB(db string) error {\n 194→\tm, err := getMigrateInstance(db)\n 195→\tif err != nil {\n 196→\t\treturn err\n 197→\t}\n 198→\n 199→\tif err := m.Force(int(Version)); err != nil {\n 200→\t\treturn err\n 201→\t}\n 202→\n 203→\tm.Close()\n 204→\treturn nil\n 205→}\n 206→"}]},"uuid":"1b7c350a-235e-465c-b514-64e534440e44","timestamp":"2026-03-12T11:49:05.753Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"9a822e68-9dee-4732-a7cc-3429d76e6ff5","timestamp":"2026-03-12T11:49:05.754Z"}
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{"parentUuid":"fc9a0462-44fa-40cc-b29a-afadd3f6b124","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01752VtZfhR43Q3wQvZSEZYF","type":"tool_result","content":" 1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n 2→// All rights reserved. This file is part of cc-backend.\n 3→// Use of this source code is governed by a MIT-style\n 4→// license that can be found in the LICENSE file.\n 5→\n 6→package repository\n 7→\n 8→import (\n 9→\t\"fmt\"\n 10→\t\"testing\"\n 11→\n 12→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n 13→)\n 14→\n 15→func TestBuildJobStatsQuery(t *testing.T) {\n 16→\tr := setup(t)\n 17→\tq := r.buildStatsQuery(nil, \"USER\", 300, nil)\n 18→\n 19→\tsql, _, err := q.ToSql()\n 20→\tnoErr(t, err)\n 21→\n 22→\tfmt.Printf(\"SQL: %s\\n\", sql)\n 23→}\n 24→\n 25→func TestJobStats(t *testing.T) {\n 26→\tr := setup(t)\n 27→\n 28→\tvar expectedCount int\n 29→\terr := r.DB.QueryRow(`SELECT COUNT(*) FROM job`).Scan(&expectedCount)\n 30→\tnoErr(t, err)\n 31→\n 32→\tstats, err := r.JobsStats(getContext(t), []*model.JobFilter{}, nil)\n 33→\tnoErr(t, err)\n 34→\n 35→\tif stats[0].TotalJobs != expectedCount {\n 36→\t\tt.Fatalf(\"Want %d, Got %d\", expectedCount, stats[0].TotalJobs)\n 37→\t}\n 38→}\n 39→"}]},"uuid":"e6f8af58-871f-4e14-ba85-a9b53f3c4f53","timestamp":"2026-03-12T11:49:10.262Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"ec20545b-fd11-4f96-b696-d29cbc799c8b","timestamp":"2026-03-12T11:49:10.266Z"}
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{"parentUuid":"1a394a3c-6f04-4457-b277-fc99fe384f77","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DDA56exfEkgS2j8pjDe8xM","type":"tool_result","content":" 1→DROP INDEX IF EXISTS job_stats;\n 2→DROP INDEX IF EXISTS job_by_user;\n 3→DROP INDEX IF EXISTS job_by_starttime;\n 4→DROP INDEX IF EXISTS job_by_job_id;\n 5→DROP INDEX IF EXISTS job_list;\n 6→DROP INDEX IF EXISTS job_list_user;\n 7→DROP INDEX IF EXISTS job_list_users;\n 8→DROP INDEX IF EXISTS job_list_users_start;\n 9→\n 10→ALTER TABLE job ADD COLUMN energy REAL NOT NULL DEFAULT 0.0;\n 11→ALTER TABLE job ADD COLUMN energy_footprint TEXT DEFAULT NULL;\n 12→\n 13→ALTER TABLE job ADD COLUMN footprint TEXT DEFAULT NULL;\n 14→ALTER TABLE tag ADD COLUMN tag_scope TEXT NOT NULL DEFAULT 'global';\n 15→\n 16→-- Do not use reserved keywords anymore\n 17→ALTER TABLE \"user\" RENAME TO hpc_user;\n 18→ALTER TABLE job RENAME COLUMN \"user\" TO hpc_user;\n 19→ALTER TABLE job RENAME COLUMN \"partition\" TO cluster_partition;\n 20→\n 21→UPDATE job SET footprint = '{\"flops_any_avg\": 0.0}';\n 22→UPDATE job SET footprint = json_replace(footprint, '$.flops_any_avg', job.flops_any_avg);\n 23→UPDATE job SET footprint = json_insert(footprint, '$.mem_bw_avg', job.mem_bw_avg);\n 24→UPDATE job SET footprint = json_insert(footprint, '$.mem_used_max', job.mem_used_max);\n 25→UPDATE job SET footprint = json_insert(footprint, '$.cpu_load_avg', job.load_avg);\n 26→UPDATE job SET footprint = json_insert(footprint, '$.net_bw_avg', job.net_bw_avg) WHERE job.net_bw_avg != 0;\n 27→UPDATE job SET footprint = json_insert(footprint, '$.net_data_vol_total', job.net_data_vol_total) WHERE job.net_data_vol_total != 0;\n 28→UPDATE job SET footprint = json_insert(footprint, '$.file_bw_avg', job.file_bw_avg) WHERE job.file_bw_avg != 0;\n 29→UPDATE job SET footprint = json_insert(footprint, '$.file_data_vol_total', job.file_data_vol_total) WHERE job.file_data_vol_total != 0;\n 30→\n 31→ALTER TABLE job DROP flops_any_avg;\n 32→ALTER TABLE job DROP mem_bw_avg;\n 33→ALTER TABLE job DROP mem_used_max;\n 34→ALTER TABLE job DROP load_avg;\n 35→ALTER TABLE job DROP net_bw_avg;\n 36→ALTER TABLE job DROP net_data_vol_total;\n 37→ALTER TABLE job DROP file_bw_avg;\n 38→ALTER TABLE job DROP file_data_vol_total;\n 39→\n 40→-- Indices for: Single filters, combined filters, sorting, sorting with filters\n 41→-- Cluster Filter\n 42→CREATE INDEX IF NOT EXISTS jobs_cluster ON job (cluster);\n 43→CREATE INDEX IF NOT EXISTS jobs_cluster_user ON job (cluster, hpc_user);\n 44→CREATE INDEX IF NOT EXISTS jobs_cluster_project ON job (cluster, project);\n 45→CREATE INDEX IF NOT EXISTS jobs_cluster_subcluster ON job (cluster, subcluster);\n 46→-- Cluster Filter Sorting\n 47→CREATE INDEX IF NOT EXISTS jobs_cluster_starttime ON job (cluster, start_time);\n 48→CREATE INDEX IF NOT EXISTS jobs_cluster_duration ON job (cluster, duration);\n 49→CREATE INDEX IF NOT EXISTS jobs_cluster_numnodes ON job (cluster, num_nodes);\n 50→CREATE INDEX IF NOT EXISTS jobs_cluster_numhwthreads ON job (cluster, num_hwthreads);\n 51→CREATE INDEX IF NOT EXISTS jobs_cluster_numacc ON job (cluster, num_acc);\n 52→CREATE INDEX IF NOT EXISTS jobs_cluster_energy ON job (cluster, energy);\n 53→\n 54→-- Cluster+Partition Filter\n 55→CREATE INDEX IF NOT EXISTS jobs_cluster_partition ON job (cluster, cluster_partition);\n 56→-- Cluster+Partition Filter Sorting\n 57→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_starttime ON job (cluster, cluster_partition, start_time);\n 58→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_duration ON job (cluster, cluster_partition, duration);\n 59→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numnodes ON job (cluster, cluster_partition, num_nodes);\n 60→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numhwthreads ON job (cluster, cluster_partition, num_hwthreads);\n 61→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_numacc ON job (cluster, cluster_partition, num_acc);\n 62→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_energy ON job (cluster, cluster_partition, energy);\n 63→\n 64→-- Cluster+Partition+Jobstate Filter\n 65→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate ON job (cluster, cluster_partition, job_state);\n 66→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_user ON job (cluster, cluster_partition, job_state, hpc_user);\n 67→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_project ON job (cluster, cluster_partition, job_state, project);\n 68→-- Cluster+Partition+Jobstate Filter Sorting\n 69→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_starttime ON job (cluster, cluster_partition, job_state, start_time);\n 70→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_duration ON job (cluster, cluster_partition, job_state, duration);\n 71→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numnodes ON job (cluster, cluster_partition, job_state, num_nodes);\n 72→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numhwthreads ON job (cluster, cluster_partition, job_state, num_hwthreads);\n 73→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_numacc ON job (cluster, cluster_partition, job_state, num_acc);\n 74→CREATE INDEX IF NOT EXISTS jobs_cluster_partition_jobstate_energy ON job (cluster, cluster_partition, job_state, energy);\n 75→\n 76→-- Cluster+JobState Filter\n 77→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate ON job (cluster, job_state);\n 78→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_user ON job (cluster, job_state, hpc_user);\n 79→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_project ON job (cluster, job_state, project);\n 80→-- Cluster+JobState Filter Sorting\n 81→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_starttime ON job (cluster, job_state, start_time);\n 82→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_duration ON job (cluster, job_state, duration);\n 83→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numnodes ON job (cluster, job_state, num_nodes);\n 84→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numhwthreads ON job (cluster, job_state, num_hwthreads);\n 85→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_numacc ON job (cluster, job_state, num_acc);\n 86→CREATE INDEX IF NOT EXISTS jobs_cluster_jobstate_energy ON job (cluster, job_state, energy);\n 87→\n 88→-- User Filter\n 89→CREATE INDEX IF NOT EXISTS jobs_user ON job (hpc_user);\n 90→-- User Filter Sorting\n 91→CREATE INDEX IF NOT EXISTS jobs_user_starttime ON job (hpc_user, start_time);\n 92→CREATE INDEX IF NOT EXISTS jobs_user_duration ON job (hpc_user, duration);\n 93→CREATE INDEX IF NOT EXISTS jobs_user_numnodes ON job (hpc_user, num_nodes);\n 94→CREATE INDEX IF NOT EXISTS jobs_user_numhwthreads ON job (hpc_user, num_hwthreads);\n 95→CREATE INDEX IF NOT EXISTS jobs_user_numacc ON job (hpc_user, num_acc);\n 96→CREATE INDEX IF NOT EXISTS jobs_user_energy ON job (hpc_user, energy);\n 97→\n 98→-- Project Filter\n 99→CREATE INDEX IF NOT EXISTS jobs_project ON job (project);\n 100→CREATE INDEX IF NOT EXISTS jobs_project_user ON job (project, hpc_user);\n 101→-- Project Filter Sorting\n 102→CREATE INDEX IF NOT EXISTS jobs_project_starttime ON job (project, start_time);\n 103→CREATE INDEX IF NOT EXISTS jobs_project_duration ON job (project, duration);\n 104→CREATE INDEX IF NOT EXISTS jobs_project_numnodes ON job (project, num_nodes);\n 105→CREATE INDEX IF NOT EXISTS jobs_project_numhwthreads ON job (project, num_hwthreads);\n 106→CREATE INDEX IF NOT EXISTS jobs_project_numacc ON job (project, num_acc);\n 107→CREATE INDEX IF NOT EXISTS jobs_project_energy ON job (project, energy);\n 108→\n 109→-- JobState Filter\n 110→CREATE INDEX IF NOT EXISTS jobs_jobstate ON job (job_state);\n 111→CREATE INDEX IF NOT EXISTS jobs_jobstate_user ON job (job_state, hpc_user);\n 112→CREATE INDEX IF NOT EXISTS jobs_jobstate_project ON job (job_state, project);\n 113→CREATE INDEX IF NOT EXISTS jobs_jobstate_cluster ON job (job_state, cluster);\n 114→-- JobState Filter Sorting\n 115→CREATE INDEX IF NOT EXISTS jobs_jobstate_starttime ON job (job_state, start_time);\n 116→CREATE INDEX IF NOT EXISTS jobs_jobstate_duration ON job (job_state, duration);\n 117→CREATE INDEX IF NOT EXISTS jobs_jobstate_numnodes ON job (job_state, num_nodes);\n 118→CREATE INDEX IF NOT EXISTS jobs_jobstate_numhwthreads ON job (job_state, num_hwthreads);\n 119→CREATE INDEX IF NOT EXISTS jobs_jobstate_numacc ON job (job_state, num_acc);\n 120→CREATE INDEX IF NOT EXISTS jobs_jobstate_energy ON job (job_state, energy);\n 121→\n 122→-- ArrayJob Filter\n 123→CREATE INDEX IF NOT EXISTS jobs_arrayjobid_starttime ON job (array_job_id, start_time);\n 124→CREATE INDEX IF NOT EXISTS jobs_cluster_arrayjobid_starttime ON job (cluster, array_job_id, start_time);\n 125→\n 126→-- Sorting without active filters\n 127→CREATE INDEX IF NOT EXISTS jobs_starttime ON job (start_time);\n 128→CREATE INDEX IF NOT EXISTS jobs_duration ON job (duration);\n 129→CREATE INDEX IF NOT EXISTS jobs_numnodes ON job (num_nodes);\n 130→CREATE INDEX IF NOT EXISTS jobs_numhwthreads ON job (num_hwthreads);\n 131→CREATE INDEX IF NOT EXISTS jobs_numacc ON job (num_acc);\n 132→CREATE INDEX IF NOT EXISTS jobs_energy ON job (energy);\n 133→\n 134→-- Single filters with default starttime sorting\n 135→CREATE INDEX IF NOT EXISTS jobs_duration_starttime ON job (duration, start_time);\n 136→CREATE INDEX IF NOT EXISTS jobs_numnodes_starttime ON job (num_nodes, start_time);\n 137→CREATE INDEX IF NOT EXISTS jobs_numhwthreads_starttime ON job (num_hwthreads, start_time);\n 138→CREATE INDEX IF NOT EXISTS jobs_numacc_starttime ON job (num_acc, start_time);\n 139→CREATE INDEX IF NOT EXISTS jobs_energy_starttime ON job (energy, start_time);\n 140→\n 141→-- Optimize DB index usage\n 142→PRAGMA optimize;\n 143→"}]},"uuid":"92b5c9e4-27a0-4e51-91a9-8bc939432c88","timestamp":"2026-03-12T11:49:13.416Z"},"type":"agent_progress","prompt":"","agentId":"a9b266f25baa3e441"},"toolUseID":"agent_msg_01V5DxQShBpqsySWrY33PHd2","parentToolUseID":"toolu_019e3taRrMwXjtXNn76VRa1M","uuid":"0b4f248c-53a1-4839-ad0c-9576a1e7ae04","timestamp":"2026-03-12T11:49:13.421Z"}
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{"parentUuid":"3e6e3f24-bb4b-44cf-b60c-dc212e75408d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019e3taRrMwXjtXNn76VRa1M","type":"tool_result","content":[{"type":"text","text":"Good. I have a complete picture now. Here is the detailed optimization plan.\n\n---\n\n## Detailed Optimization Plan\n\n### 1. Index Column Reorder (Migration 12)\n\n**The core problem**: The current index is `(cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)`. For the query pattern `WHERE cluster = ? AND start_time >= ? GROUP BY hpc_user`, SQLite processes it as:\n\n1. Use index prefix `cluster = ?` (equality) -- good\n2. Scan `start_time >= ?` (range) -- good, but this is where ordered index traversal ends\n3. For `GROUP BY hpc_user` -- SQLite cannot use index order because `hpc_user` comes AFTER the range column. It must collect all matching rows and sort them into a temp B-Tree.\n\n**The fix**: Reorder to `(cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc)`.\n\nWith this order:\n1. `cluster = ?` (equality on prefix) -- efficient\n2. `GROUP BY hpc_user` -- the next column after the equality prefix, so SQLite can walk the index in order, producing groups naturally without a temp B-Tree\n3. Within each `(cluster, hpc_user)` group, `start_time` is sorted, so `start_time >= ?` becomes a binary search within each group (skip to the first qualifying row and scan forward)\n4. Remaining columns `(duration, job_state, num_nodes, num_hwthreads, num_acc)` make it a covering index -- no table lookups needed\n\n**The query plan changes from**:\n- Scan index for `(cluster, start_time >= ?)` -> collect all rows -> sort by `hpc_user` (temp B-Tree) -> aggregate\n\n**To**:\n- Walk index in `(cluster, hpc_user)` order -> within each group, skip-scan to `start_time >= ?` -> aggregate directly from index\n\nFor 300k+ jobs over 30 days, eliminating the temp B-Tree sort is the critical win.\n\n**New migration SQL** for `12_stats-covering-index.up.sql`:\n\n```sql\n-- Migration 12: Add covering index for grouped stats queries\n-- Column order: cluster (equality), hpc_user (GROUP BY), start_time (range filter)\n-- followed by aggregation columns to make it a covering index.\n-- This order lets SQLite produce GROUP BY output directly from index order,\n-- avoiding an expensive temp B-Tree sort.\n\nDROP INDEX IF EXISTS jobs_cluster_starttime_user_stats;\n\nCREATE INDEX IF NOT EXISTS jobs_cluster_user_starttime_stats\n ON job (cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc);\n\nPRAGMA optimize;\n```\n\nNote: The `DROP INDEX` is needed because this migration may run on databases that already applied the old version 12. The index name changes to reflect the new column order.\n\n**Down migration** `12_stats-covering-index.down.sql`:\n\n```sql\nDROP INDEX IF EXISTS jobs_cluster_user_starttime_stats;\n```\n\n### 2. Code Changes in stats.go\n\n**Change A: `COUNT(job.id)` to `COUNT(*)`**\n\nLine 156: Change `\"COUNT(job.id) as totalJobs\"` to `\"COUNT(*) as totalJobs\"`\n\nLine 108 (in `buildCountQuery`): Change `\"COUNT(job.id)\"` to `\"COUNT(*)\"` in both branches.\n\nLines 751, 817: In `jobsStatisticsHistogram` and `jobsDurationStatisticsHistogram`, change `\"COUNT(job.id) AS count\"` to `\"COUNT(*) AS count\"`.\n\n**Reasoning**: `COUNT(*)` is semantically clearer for \"count all rows in each group.\" With `COUNT(job.id)`, SQLite must technically check that `job.id` is not NULL for each row (even though the rowid can never be NULL). While SQLite likely optimizes this away, `COUNT(*)` is the idiomatic form and removes any ambiguity. In a covering index scan where the rowid is implicitly present but not explicitly listed, `COUNT(*)` is the safer choice.\n\n**Change B: Fix double-quoted string literals**\n\nLine 147: Change `\"running\"` (double quotes) to `''running''` (single quotes inside the backtick raw string):\n\n```go\ndurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n```\n\nLine 647 (in `AddHistograms`): Same fix:\n\n```go\nvalue := 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```\n\n**Reasoning**: In SQL, double quotes denote identifiers (table/column names), not string literals. SQLite has a compatibility quirk that accepts double-quoted strings when no matching identifier exists, but this is non-standard. Using proper single quotes avoids any risk of the query planner misinterpreting the token as a column reference, which could affect index usage decisions.\n\n### 3. Whether to Drop `jobs_cluster_starttime`\n\n**Recommendation: Do NOT drop it in this migration.**\n\nThe `jobs_cluster_starttime ON (cluster, start_time)` index from migration 08 serves a different purpose -- it supports the very common job list query pattern `WHERE cluster = ? ORDER BY start_time DESC LIMIT N` (the default job list view). The new covering index `(cluster, hpc_user, start_time, ...)` has `hpc_user` between `cluster` and `start_time`, so it cannot efficiently serve `ORDER BY start_time` without a user filter.\n\nThe `jobs_cluster_user ON (cluster, hpc_user)` index from migration 08 IS made redundant by the new covering index (which has `(cluster, hpc_user, ...)` as its prefix). However, the overhead of one small 2-column index is negligible, and dropping it would require careful analysis of all query paths. This can be a follow-up cleanup if desired.\n\n**Summary**: Both existing indexes serve query patterns that the new covering index does not optimally cover. Leave them.\n\n### 4. Other GROUP BY Dimensions\n\nThe `groupBy2column` map shows four possible grouping columns: `hpc_user`, `project`, `cluster`, `subcluster`. The dominant use case driving this fix is the users list page (`GROUP BY hpc_user` with `cluster` + `start_time` filters). The other dimensions are less common and typically faster (fewer distinct values for cluster/subcluster, or project queries use different filter patterns). Creating additional covering indexes for every GROUP BY variant would be counterproductive -- more indexes slow writes and consume disk. The single reordered index addresses the primary bottleneck.\n\n### 5. Testing and Verification Approach\n\n**Unit tests** (no changes needed to test file, existing tests cover correctness):\n\n```bash\ngo test -v ./internal/repository -run TestJobStats\ngo test -v ./internal/repository -run TestBuildJobStatsQuery\n```\n\nThe `TestBuildJobStatsQuery` test prints the generated SQL -- verify it now uses `COUNT(*)` and single-quoted `'running'`.\n\n**Query plan verification on production database** (before/after):\n\n```sql\n-- Before: check current plan\nEXPLAIN QUERY PLAN\nSELECT job.hpc_user, COUNT(*) as totalJobs, ...\nFROM job\nWHERE job.cluster = 'woody' AND job.start_time >= 1709251200\nGROUP BY job.hpc_user;\n```\n\nBefore the fix, you will see `USE TEMP B-TREE FOR GROUP BY` in the output. After applying the new index, this line should disappear, replaced by direct index scan.\n\n**Performance measurement**:\n\n```sql\n-- Time the actual query (SQLite CLI)\n.timer on\nSELECT job.hpc_user, COUNT(*) as totalJobs, ...\nFROM job\nWHERE job.cluster = 'woody' AND job.start_time >= 1709251200\nGROUP BY job.hpc_user;\n```\n\nExpected improvement: from \"does not return\" (minutes+) to sub-second, because the temp B-Tree sort over hundreds of thousands of rows is eliminated.\n\n**Migration testing**:\n\n1. Test on a fresh database: `cc-backend -migrate-db` should apply all 12 migrations cleanly.\n2. Test on existing v11 database: migration 12 should create the index with new column order.\n3. Test on existing v12 database (old index): the `DROP INDEX IF EXISTS` + `CREATE INDEX` handles this case -- the old index is dropped and new one created.\n\n### 6. Implementation Sequence\n\n1. Modify `12_stats-covering-index.up.sql` -- reorder columns, add DROP for old index name, rename index\n2. Modify `12_stats-covering-index.down.sql` -- update to drop the new index name\n3. Modify `stats.go` -- `COUNT(job.id)` to `COUNT(*)`, fix double-quoted string literals\n4. Run tests: `go test ./internal/repository/...`\n5. Verify generated SQL with `TestBuildJobStatsQuery`\n6. Test migration on a copy of production database\n7. Verify query plan with `EXPLAIN QUERY PLAN`\n\n### Critical Files for Implementation\n\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - Reorder index columns from `(cluster, start_time, hpc_user, ...)` to `(cluster, hpc_user, start_time, ...)`\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.down.sql` - Update to drop the renamed index\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go` - Fix COUNT(job.id) to COUNT(*), fix double-quoted 'running' string literals (lines 108, 147, 156, 647, 751, 817)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats_test.go` - Existing tests to run for validation (no changes needed)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migration.go` - No changes needed (Version stays at 12), but important reference for migration framework"},{"type":"text","text":"agentId: a9b266f25baa3e441 (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 40684\ntool_uses: 13\nduration_ms: 86652</usage>"}]}]},"uuid":"574c5ca1-ffd4-49e4-b003-bc3c9096f085","timestamp":"2026-03-12T11:50:28.596Z","toolUseResult":{"status":"completed","prompt":"I need to optimize a slow SQLite stats query in a Go backend (ClusterCockpit cc-backend). Here's the situation:\n\n## Problem\nThe endpoint `/monitoring/users/?cluster=woody&startTime=last30d` calls `buildStatsQuery()` which generates a SQL query like:\n\n```sql\nSELECT job.hpc_user,\n COUNT(job.id) as totalJobs,\n 0 AS totalUsers,\n CAST(ROUND(SUM(CASE WHEN job.job_state = \"running\" THEN <now> - job.start_time ELSE job.duration END) / 3600) as int) as totalWalltime,\n ...similar aggregations for totalCoreHours, totalAccHours...\n COUNT(CASE WHEN job.duration < 300 THEN 1 END) as shortJobs\nFROM job\nWHERE job.cluster = ? AND job.start_time >= ?\nGROUP BY job.hpc_user\n```\n\nThe query \"does not return\" - essentially hangs on a production database.\n\n## Current Index (Migration 12)\n```sql\nCREATE INDEX jobs_cluster_starttime_user_stats\n ON job (cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc);\n```\n\n## Other relevant indexes\n- `jobs_cluster_starttime ON (cluster, start_time)` - from migration 08\n- `jobs_cluster_user ON (cluster, hpc_user)` - from migration 08\n\n## Key Observations\n\n1. **Index column order problem**: The covering index has `(cluster, start_time, hpc_user, ...)`. The WHERE clause uses `cluster = ?` (equality) and `start_time >= ?` (range). The GROUP BY is on `hpc_user`. Since `start_time` is a range condition BEFORE `hpc_user`, the index can't provide ordered GROUP BY output. SQLite must sort all matching rows into a temp B-Tree before aggregating. For hundreds of thousands of jobs in 30 days, this sort is extremely expensive.\n\n2. **Better index order**: `(cluster, hpc_user, start_time, ...)` would allow:\n - Equality on cluster (prefix)\n - Index-ordered GROUP BY on hpc_user (next column)\n - Within each (cluster, hpc_user) group, start_time is sorted so the >= filter uses efficient skip-scan\n - All aggregation columns are in the index (covering)\n\n3. **COUNT(job.id)**: Uses the rowid which is implicitly in the index, but `COUNT(*)` is clearer and avoids any potential table lookups.\n\n4. **Double quotes in CASE**: `job.job_state = \"running\"` uses double quotes (SQL identifiers). Should be single quotes for string literals, though SQLite handles this gracefully.\n\n## Files to modify\n- `internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - reorder index columns\n- `internal/repository/stats.go` - change COUNT(job.id) to COUNT(*), fix double quotes\n\nPlease provide a detailed plan for these optimizations, including:\n1. The exact index change (with reasoning)\n2. Code changes in stats.go\n3. Whether we should drop the redundant `jobs_cluster_starttime` index (since we have a covering index that starts with cluster)\n4. Testing/verification approach","agentId":"a9b266f25baa3e441","content":[{"type":"text","text":"Good. I have a complete picture now. Here is the detailed optimization plan.\n\n---\n\n## Detailed Optimization Plan\n\n### 1. Index Column Reorder (Migration 12)\n\n**The core problem**: The current index is `(cluster, start_time, hpc_user, duration, job_state, num_nodes, num_hwthreads, num_acc)`. For the query pattern `WHERE cluster = ? AND start_time >= ? GROUP BY hpc_user`, SQLite processes it as:\n\n1. Use index prefix `cluster = ?` (equality) -- good\n2. Scan `start_time >= ?` (range) -- good, but this is where ordered index traversal ends\n3. For `GROUP BY hpc_user` -- SQLite cannot use index order because `hpc_user` comes AFTER the range column. It must collect all matching rows and sort them into a temp B-Tree.\n\n**The fix**: Reorder to `(cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc)`.\n\nWith this order:\n1. `cluster = ?` (equality on prefix) -- efficient\n2. `GROUP BY hpc_user` -- the next column after the equality prefix, so SQLite can walk the index in order, producing groups naturally without a temp B-Tree\n3. Within each `(cluster, hpc_user)` group, `start_time` is sorted, so `start_time >= ?` becomes a binary search within each group (skip to the first qualifying row and scan forward)\n4. Remaining columns `(duration, job_state, num_nodes, num_hwthreads, num_acc)` make it a covering index -- no table lookups needed\n\n**The query plan changes from**:\n- Scan index for `(cluster, start_time >= ?)` -> collect all rows -> sort by `hpc_user` (temp B-Tree) -> aggregate\n\n**To**:\n- Walk index in `(cluster, hpc_user)` order -> within each group, skip-scan to `start_time >= ?` -> aggregate directly from index\n\nFor 300k+ jobs over 30 days, eliminating the temp B-Tree sort is the critical win.\n\n**New migration SQL** for `12_stats-covering-index.up.sql`:\n\n```sql\n-- Migration 12: Add covering index for grouped stats queries\n-- Column order: cluster (equality), hpc_user (GROUP BY), start_time (range filter)\n-- followed by aggregation columns to make it a covering index.\n-- This order lets SQLite produce GROUP BY output directly from index order,\n-- avoiding an expensive temp B-Tree sort.\n\nDROP INDEX IF EXISTS jobs_cluster_starttime_user_stats;\n\nCREATE INDEX IF NOT EXISTS jobs_cluster_user_starttime_stats\n ON job (cluster, hpc_user, start_time, duration, job_state, num_nodes, num_hwthreads, num_acc);\n\nPRAGMA optimize;\n```\n\nNote: The `DROP INDEX` is needed because this migration may run on databases that already applied the old version 12. The index name changes to reflect the new column order.\n\n**Down migration** `12_stats-covering-index.down.sql`:\n\n```sql\nDROP INDEX IF EXISTS jobs_cluster_user_starttime_stats;\n```\n\n### 2. Code Changes in stats.go\n\n**Change A: `COUNT(job.id)` to `COUNT(*)`**\n\nLine 156: Change `\"COUNT(job.id) as totalJobs\"` to `\"COUNT(*) as totalJobs\"`\n\nLine 108 (in `buildCountQuery`): Change `\"COUNT(job.id)\"` to `\"COUNT(*)\"` in both branches.\n\nLines 751, 817: In `jobsStatisticsHistogram` and `jobsDurationStatisticsHistogram`, change `\"COUNT(job.id) AS count\"` to `\"COUNT(*) AS count\"`.\n\n**Reasoning**: `COUNT(*)` is semantically clearer for \"count all rows in each group.\" With `COUNT(job.id)`, SQLite must technically check that `job.id` is not NULL for each row (even though the rowid can never be NULL). While SQLite likely optimizes this away, `COUNT(*)` is the idiomatic form and removes any ambiguity. In a covering index scan where the rowid is implicitly present but not explicitly listed, `COUNT(*)` is the safer choice.\n\n**Change B: Fix double-quoted string literals**\n\nLine 147: Change `\"running\"` (double quotes) to `''running''` (single quotes inside the backtick raw string):\n\n```go\ndurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n```\n\nLine 647 (in `AddHistograms`): Same fix:\n\n```go\nvalue := 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```\n\n**Reasoning**: In SQL, double quotes denote identifiers (table/column names), not string literals. SQLite has a compatibility quirk that accepts double-quoted strings when no matching identifier exists, but this is non-standard. Using proper single quotes avoids any risk of the query planner misinterpreting the token as a column reference, which could affect index usage decisions.\n\n### 3. Whether to Drop `jobs_cluster_starttime`\n\n**Recommendation: Do NOT drop it in this migration.**\n\nThe `jobs_cluster_starttime ON (cluster, start_time)` index from migration 08 serves a different purpose -- it supports the very common job list query pattern `WHERE cluster = ? ORDER BY start_time DESC LIMIT N` (the default job list view). The new covering index `(cluster, hpc_user, start_time, ...)` has `hpc_user` between `cluster` and `start_time`, so it cannot efficiently serve `ORDER BY start_time` without a user filter.\n\nThe `jobs_cluster_user ON (cluster, hpc_user)` index from migration 08 IS made redundant by the new covering index (which has `(cluster, hpc_user, ...)` as its prefix). However, the overhead of one small 2-column index is negligible, and dropping it would require careful analysis of all query paths. This can be a follow-up cleanup if desired.\n\n**Summary**: Both existing indexes serve query patterns that the new covering index does not optimally cover. Leave them.\n\n### 4. Other GROUP BY Dimensions\n\nThe `groupBy2column` map shows four possible grouping columns: `hpc_user`, `project`, `cluster`, `subcluster`. The dominant use case driving this fix is the users list page (`GROUP BY hpc_user` with `cluster` + `start_time` filters). The other dimensions are less common and typically faster (fewer distinct values for cluster/subcluster, or project queries use different filter patterns). Creating additional covering indexes for every GROUP BY variant would be counterproductive -- more indexes slow writes and consume disk. The single reordered index addresses the primary bottleneck.\n\n### 5. Testing and Verification Approach\n\n**Unit tests** (no changes needed to test file, existing tests cover correctness):\n\n```bash\ngo test -v ./internal/repository -run TestJobStats\ngo test -v ./internal/repository -run TestBuildJobStatsQuery\n```\n\nThe `TestBuildJobStatsQuery` test prints the generated SQL -- verify it now uses `COUNT(*)` and single-quoted `'running'`.\n\n**Query plan verification on production database** (before/after):\n\n```sql\n-- Before: check current plan\nEXPLAIN QUERY PLAN\nSELECT job.hpc_user, COUNT(*) as totalJobs, ...\nFROM job\nWHERE job.cluster = 'woody' AND job.start_time >= 1709251200\nGROUP BY job.hpc_user;\n```\n\nBefore the fix, you will see `USE TEMP B-TREE FOR GROUP BY` in the output. After applying the new index, this line should disappear, replaced by direct index scan.\n\n**Performance measurement**:\n\n```sql\n-- Time the actual query (SQLite CLI)\n.timer on\nSELECT job.hpc_user, COUNT(*) as totalJobs, ...\nFROM job\nWHERE job.cluster = 'woody' AND job.start_time >= 1709251200\nGROUP BY job.hpc_user;\n```\n\nExpected improvement: from \"does not return\" (minutes+) to sub-second, because the temp B-Tree sort over hundreds of thousands of rows is eliminated.\n\n**Migration testing**:\n\n1. Test on a fresh database: `cc-backend -migrate-db` should apply all 12 migrations cleanly.\n2. Test on existing v11 database: migration 12 should create the index with new column order.\n3. Test on existing v12 database (old index): the `DROP INDEX IF EXISTS` + `CREATE INDEX` handles this case -- the old index is dropped and new one created.\n\n### 6. Implementation Sequence\n\n1. Modify `12_stats-covering-index.up.sql` -- reorder columns, add DROP for old index name, rename index\n2. Modify `12_stats-covering-index.down.sql` -- update to drop the new index name\n3. Modify `stats.go` -- `COUNT(job.id)` to `COUNT(*)`, fix double-quoted string literals\n4. Run tests: `go test ./internal/repository/...`\n5. Verify generated SQL with `TestBuildJobStatsQuery`\n6. Test migration on a copy of production database\n7. Verify query plan with `EXPLAIN QUERY PLAN`\n\n### Critical Files for Implementation\n\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.up.sql` - Reorder index columns from `(cluster, start_time, hpc_user, ...)` to `(cluster, hpc_user, start_time, ...)`\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migrations/sqlite3/12_stats-covering-index.down.sql` - Update to drop the renamed index\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go` - Fix COUNT(job.id) to COUNT(*), fix double-quoted 'running' string literals (lines 108, 147, 156, 647, 751, 817)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats_test.go` - Existing tests to run for validation (no changes needed)\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/migration.go` - No changes needed (Version stays at 12), but important reference for migration framework"}],"totalDurationMs":86652,"totalTokens":40684,"totalToolUseCount":13,"usage":{"input_tokens":1,"cache_creation_input_tokens":291,"cache_read_input_tokens":37801,"output_tokens":2591,"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":291},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"3e6e3f24-bb4b-44cf-b60c-dc212e75408d"}
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{"parentUuid":"0b1619f6-ad71-4780-99ce-93fd2e80a0f8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01PcbsMEwTai5xqDzvCAGhg8","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have all the details. Let me write the final plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":251,"cache_read_input_tokens":59676,"cache_creation":{"ephemeral_5m_input_tokens":251,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8s9hdM88fKrkHaBgMF","type":"assistant","uuid":"84c6d410-c3eb-48a4-bb40-051ac4b301d7","timestamp":"2026-03-12T11:50:37.641Z"}
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{"parentUuid":"84c6d410-c3eb-48a4-bb40-051ac4b301d7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01PcbsMEwTai5xqDzvCAGhg8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WJz9AsKzR5WHQv4pGx3gf4","name":"Write","input":{"file_path":"/Users/jan/.claude/plans/calm-sniffing-frog.md","content":"# 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"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":251,"cache_read_input_tokens":59676,"output_tokens":982,"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":251},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8s9hdM88fKrkHaBgMF","type":"assistant","uuid":"bb7b6f1f-8b1d-48c3-b658-f38c5f1c8440","timestamp":"2026-03-12T11:50:57.763Z"}
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{"parentUuid":"bb7b6f1f-8b1d-48c3-b658-f38c5f1c8440","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WJz9AsKzR5WHQv4pGx3gf4","type":"tool_result","content":"File created successfully at: /Users/jan/.claude/plans/calm-sniffing-frog.md"}]},"uuid":"81e14894-e164-4935-860a-2f8f218da869","timestamp":"2026-03-12T11:50:57.771Z","toolUseResult":{"type":"create","filePath":"/Users/jan/.claude/plans/calm-sniffing-frog.md","content":"# 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","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"bb7b6f1f-8b1d-48c3-b658-f38c5f1c8440"}
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{"parentUuid":"8240399d-dcd4-4722-bbac-31e91e5c473c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01P3emny4xRHpo4fQmRm3b3d","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nThe plan is written. The key fix is reordering the covering index columns from `(cluster, start_time, hpc_user, ...)` to `(cluster, hpc_user, start_time, ...)` so SQLite can produce GROUP BY output directly from the index order, eliminating the expensive temp B-Tree sort that causes the query to hang."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":738,"cache_read_input_tokens":61389,"cache_creation":{"ephemeral_5m_input_tokens":738,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYy8ttZrWAA2XhUhJpKnk","type":"assistant","uuid":"e51ce716-5359-4e99-8b82-822e701c8a5f","timestamp":"2026-03-12T11:51:04.342Z"}
{"parentUuid":"e51ce716-5359-4e99-8b82-822e701c8a5f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01P3emny4xRHpo4fQmRm3b3d","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Mjj9wg5HgAJx9YJaqctoEi","name":"ExitPlanMode","input":{"plan":"# 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"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":738,"cache_read_input_tokens":61389,"output_tokens":120,"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":738},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8ttZrWAA2XhUhJpKnk","type":"assistant","uuid":"66220c37-c33e-46ca-a5cd-c3cca789eb71","timestamp":"2026-03-12T11:51:04.349Z"}
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{"parentUuid":"aa49fe61-d5f7-48f8-8fbb-ca702b0fefb8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"3f83174b-0ec7-4466-8bc9-b6b52eb8bbfb","version":"2.1.72","gitBranch":"hotfix","slug":"calm-sniffing-frog","message":{"model":"claude-opus-4-6","id":"msg_01Qu8YhMa33SkbuJhfskn5z3","type":"message","role":"assistant","content":[{"type":"text","text":"I'll wait for your feedback on the plan."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":198,"cache_read_input_tokens":62127,"output_tokens":13,"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":198},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYy8wRke5DZ3fdEU7aXyP","type":"assistant","uuid":"11566afc-2abc-4eed-b8af-f2011ca6c102","timestamp":"2026-03-12T11:51:36.205Z"}
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