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entire/checkpoints/v1
cc-backend/35/f06df74b51/1/full.jsonl
Jan Eitzinger adb9549b8b Checkpoint: 35f06df74b51 
Entire-Session: d9b09c8f-9f72-4f35-986e-fb422e1b4064
Entire-Strategy: manual-commit
Entire-Agent: Claude Code
Ephemeral-branch: entire/df2a13d-e3b0c4
2026-03-13 15:16:08 +01:00

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{"parentUuid":"f1787422-78d8-483b-822e-54024888084c","isSidechain":false,"promptId":"cef5abc9-1504-479e-870d-845cb284bb17","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Inline literal job_state in remaining parameterized sites\n\n## Files to change\n\n- **`internal/repository/stats.go:116`** (`buildCountQuery`) — change `query.Where(\"job.job_state = ?\", \"running\")` → `query.Where(\"job.job_state = 'running'\")`. Highest impact: called by dashboard count queries.\n- **`internal/repository/job.go:1069`** (`GetUsedNodes`) — change `sq.Eq{\"job.job_state\": \"running\"}` → `query.Where(\"job.job_state = 'running'\")`. Benefits node allocation queries.\n\n## Sites intentionally NOT changed\n\n- **`jobFind.go:280,285`** (`FindConcurrentJobs`) — equality/inequality on job_state isn't the leading filter here (time overlap + resources dominate), so partial index unlikely to help. Also uses `!=` which doesn't match `WHERE job_state = 'running'` partial indexes.\n\n## Verification\n```bash\ngo build ./cmd/cc-backend && go test ./internal/repository/...\n```\n\n\nIf you need specific details from before exiting plan mode (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/jan/.claude/projects/-Users-jan-prg-ClusterCockpit-cc-backend/889ef150-c6ca-4f35-81e5-c19dc95fe399.jsonl"},"uuid":"541242d1-c34c-47b7-bfae-1b5638c57305","timestamp":"2026-03-13T14:14:11.616Z","planContent":"# Inline literal job_state in remaining parameterized sites\n\n## Files to change\n\n- **`internal/repository/stats.go:116`** (`buildCountQuery`) — change `query.Where(\"job.job_state = ?\", \"running\")` → `query.Where(\"job.job_state = 'running'\")`. Highest impact: called by dashboard count queries.\n- **`internal/repository/job.go:1069`** (`GetUsedNodes`) — change `sq.Eq{\"job.job_state\": \"running\"}` → `query.Where(\"job.job_state = 'running'\")`. Benefits node allocation queries.\n\n## Sites intentionally NOT changed\n\n- **`jobFind.go:280,285`** (`FindConcurrentJobs`) — equality/inequality on job_state isn't the leading filter here (time overlap + resources dominate), so partial index unlikely to help. Also uses `!=` which doesn't match `WHERE job_state = 'running'` partial indexes.\n\n## Verification\n```bash\ngo build ./cmd/cc-backend && go test ./internal/repository/...\n```\n","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"d9b09c8f-9f72-4f35-986e-fb422e1b4064","version":"2.1.74","gitBranch":"hotfix","slug":"recursive-giggling-volcano"}
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This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// 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\t\"golang.org/x/sync/errgroup\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser: \"job.hpc_user\",\n\tmodel.AggregateProject: \"job.project\",\n\tmodel.AggregateCluster: \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs: \"totalJobs\",\n\tmodel.SortByAggregateTotalusers: \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime: \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes: \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores: \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs: \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours: \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n// - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n// - Single count: COUNT(job.id) when col is empty\n// - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n// - \"running\": Only jobs with job_state = 'running'\n// - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n// - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(*)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(*)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute job statistics with optional grouping.\n// Only requested columns are computed; unrequested columns select 0 as placeholder.\n//\n// Parameters:\n// - filter: Job filters to apply (cluster, user, time range, etc.)\n// - col: Column name to GROUP BY; empty string for overall statistics without grouping\n// - shortThreshold: Duration threshold in seconds for counting short-running jobs\n// - reqFields: Set of requested field names; nil means compute all fields\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n\treqFields map[string]bool,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\n\t// Helper: return real expression if field is requested (or reqFields is nil), else \"0 as alias\"\n\tneed := func(field string) bool {\n\t\treturn reqFields == nil || reqFields[field]\n\t}\n\tdurationExpr := fmt.Sprintf(`(CASE WHEN job.job_state = 'running' THEN %d - job.start_time ELSE job.duration END)`, now)\n\n\t// Build column list\n\tcolumns := make([]string, 0, 14)\n\n\tif col != \"\" {\n\t\tcolumns = append(columns, col)\n\t}\n\n\tcolumns = append(columns, \"COUNT(*) as totalJobs\")\n\n\tif need(\"totalUsers\") && col != \"job.hpc_user\" {\n\t\tcolumns = append(columns, \"COUNT(DISTINCT job.hpc_user) AS totalUsers\")\n\t} else {\n\t\tcolumns = append(columns, \"0 AS totalUsers\")\n\t}\n\n\tif need(\"totalWalltime\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s) / 3600) as int) as totalWalltime`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalWalltime\")\n\t}\n\n\tif need(\"totalNodes\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_nodes) as int) as totalNodes`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodes\")\n\t}\n\n\tif need(\"totalNodeHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_nodes) / 3600) as int) as totalNodeHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalNodeHours\")\n\t}\n\n\tif need(\"totalCores\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_hwthreads) as int) as totalCores`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCores\")\n\t}\n\n\tif need(\"totalCoreHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_hwthreads) / 3600) as int) as totalCoreHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalCoreHours\")\n\t}\n\n\tif need(\"totalAccs\") {\n\t\tcolumns = append(columns, `CAST(SUM(job.num_acc) as int) as totalAccs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccs\")\n\t}\n\n\tif need(\"totalAccHours\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`CAST(ROUND(SUM(%s * job.num_acc) / 3600) as int) as totalAccHours`, durationExpr))\n\t} else {\n\t\tcolumns = append(columns, \"0 as totalAccHours\")\n\t}\n\n\tif need(\"runningJobs\") {\n\t\tcolumns = append(columns, `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`)\n\t} else {\n\t\tcolumns = append(columns, \"0 as runningJobs\")\n\t}\n\n\tif need(\"shortJobs\") {\n\t\tcolumns = append(columns, fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold))\n\t} else {\n\t\tcolumns = append(columns, \"0 as shortJobs\")\n\t}\n\n\tquery := sq.Select(columns...).From(\"job\")\n\tif col != \"\" {\n\t\tquery = query.GroupBy(col)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes job statistics grouped by a dimension (user, project, cluster, or subcluster).\n// Only columns listed in reqFields are computed; others return 0. User display names are looked up\n// in a separate lightweight query to avoid JOIN overhead on the main aggregation.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n\treqFields map[string]bool,\n) ([]*model.JobsStatistics, error) {\n\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\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\t// Run all 4 histogram queries in parallel — each writes a distinct struct field.\n\tg, gctx := errgroup.WithContext(ctx)\n\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\n\tg.Go(func() error {\n\t\tvar err error\n\t\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(gctx, value, filter, targetBinSize, &targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\t}\n\t\treturn err\n\t})\n\tg.Go(func() error {\n\t\tvar err error\n\t\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(gctx, \"job.num_nodes as value\", filter)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\t}\n\t\treturn err\n\t})\n\tg.Go(func() error {\n\t\tvar err error\n\t\tstat.HistNumCores, err = r.jobsStatisticsHistogram(gctx, \"job.num_hwthreads as value\", filter)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\t}\n\t\treturn err\n\t})\n\tg.Go(func() error {\n\t\tvar err error\n\t\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(gctx, \"job.num_acc as value\", filter)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\t}\n\t\treturn err\n\t})\n\n\tif err := g.Wait(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n// - ctx: Context for security checks\n// - filter: Filters to apply (MUST contain State filter for running jobs)\n// - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n// - stat: Statistics struct to augment (modified in-place)\n// - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n// - Values normalized to metric's peak value from cluster configuration\n// - Bins evenly distributed from 0 to peak\n// - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n// - Running jobs: Limited to 5000 jobs for performance\n// - Requires valid cluster configuration with metric peak values\n// - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n// - value: SQL expression that produces the histogram value, aliased as \"value\"\n// - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(*) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n// - value: SQL expression computing bin number from duration, aliased as \"value\"\n// - filters: Job filters to apply\n// - binSizeSeconds: Width of each bin in seconds\n// - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n// 1. Pre-initialize targetBinCount bins with zero counts\n// 2. Query database for actual counts per bin\n// 3. Match query results to pre-initialized bins by value\n// 4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(*) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0, *targetBinCount)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n// - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n// - filters: Job filters to apply\n// - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n// 1. Determine peak value from cluster configuration (filtered cluster or max across all)\n// 2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n// 3. Pre-initialize bins with min/max ranges based on peak value\n// 4. Query database for counts per bin\n// 5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t// bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0, *bins)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tidx := i\n\t\tepoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Build a map from bin number to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tif p.Bin != nil {\n\t\t\tbinMap[*p.Bin] = i\n\t\t}\n\t}\n\n\t// Match query results to pre-initialized bins using map lookup.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif rpoint.Bin != nil {\n\t\t\tif idx, ok := binMap[*rpoint.Bin]; ok {\n\t\t\t\tpoints[idx].Count = rpoint.Count\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n// - metrics: List of metric names\n// - filters: Job filters (should filter to running jobs only)\n// - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n// - Maximum 5000 jobs (returns nil if more jobs match)\n// - Requires metric backend availability\n// - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n// 1. Query first 5001 jobs to check count limit\n// 2. Load metric averages for all jobs via metricdispatch\n// 3. For each metric, create bins based on peak value\n// 4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":113,"oldLines":7,"newStart":113,"newLines":7,"lines":[" "," switch kind {"," case \"running\":","- query = query.Where(\"job.job_state = ?\", \"running\")","+ query = query.Where(\"job.job_state = 'running'\")"," case \"short\":"," query = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)"," }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"77428f4f-822d-4e79-b515-0258064f1ea3","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"d9b09c8f-9f72-4f35-986e-fb422e1b4064","version":"2.1.74","gitBranch":"hotfix","slug":"recursive-giggling-volcano"}
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{"parentUuid":"604bdedd-91db-4e50-a7eb-0b541443aeda","isSidechain":false,"promptId":"cef5abc9-1504-479e-870d-845cb284bb17","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PLKQdEs9GwRWEfnXYQuxeC","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/job.go has been updated successfully."}]},"uuid":"05c768ef-6588-4169-b423-de1c8f7cf9c1","timestamp":"2026-03-13T14:14:23.784Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/job.go","oldString":"\t\tWhere(sq.Eq{\"job.job_state\": \"running\"})","newString":"\t\tWhere(\"job.job_state = 'running'\")","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package repository provides the data access layer for cc-backend using the repository pattern.\n//\n// The repository pattern abstracts database operations and provides a clean interface for\n// data access. Each major entity (Job, User, Node, Tag) has its own repository with CRUD\n// operations and specialized queries.\n//\n// # Database Connection\n//\n// Initialize the database connection before using any repository:\n//\n//\trepository.Connect(\"sqlite3\", \"./var/job.db\")\n//\n// # Configuration\n//\n// Optional: Configure repository settings before initialization:\n//\n//\trepository.SetConfig(&repository.RepositoryConfig{\n//\t CacheSize: 2 * 1024 * 1024, // 2MB cache\n//\t MaxOpenConnections: 8, // Connection pool size\n//\t MinRunningJobDuration: 300, // Filter threshold\n//\t})\n//\n// If not configured, sensible defaults are used automatically.\n//\n// # Repositories\n//\n// - JobRepository: Job lifecycle management and querying\n// - UserRepository: User management and authentication\n// - NodeRepository: Cluster node state tracking\n// - Tags: Job tagging and categorization\n//\n// # Caching\n//\n// Repositories use LRU caching to improve performance. Cache keys are constructed\n// as \"type:id\" (e.g., \"metadata:123\"). Cache is automatically invalidated on\n// mutations to maintain consistency.\n//\n// # Transaction Support\n//\n// For batch operations, use transactions:\n//\n//\tt, err := jobRepo.TransactionInit()\n//\tif err != nil {\n//\t return err\n//\t}\n//\tdefer t.Rollback() // Rollback if not committed\n//\n//\t// Perform operations...\n//\tjobRepo.TransactionAdd(t, query, args...)\n//\n//\t// Commit when done\n//\tif err := t.Commit(); err != nil {\n//\t return err\n//\t}\npackage repository\n\nimport (\n\t\"database/sql\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"maps\"\n\t\"math\"\n\t\"sort\"\n\t\"strconv\"\n\t\"sync\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/lrucache\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n\t\"github.com/jmoiron/sqlx\"\n)\n\nvar (\n\t// jobRepoOnce ensures singleton initialization of the JobRepository\n\tjobRepoOnce sync.Once\n\t// jobRepoInstance holds the single instance of JobRepository\n\tjobRepoInstance *JobRepository\n)\n\n// JobRepository provides database access for job-related operations.\n// It implements the repository pattern to abstract database interactions\n// and provides caching for improved performance.\n//\n// The repository is a singleton initialized via GetJobRepository().\n// All database queries use prepared statements via stmtCache for efficiency.\n// Frequently accessed data (metadata, energy footprints) is cached in an LRU cache.\ntype JobRepository struct {\n\tDB *sqlx.DB // Database connection pool\n\tstmtCache *sq.StmtCache // Prepared statement cache for query optimization\n\tcache *lrucache.Cache // LRU cache for metadata and footprint data\n\tdriver string // Database driver name (e.g., \"sqlite3\")\n\tMutex sync.Mutex // Mutex for thread-safe operations\n}\n\n// GetJobRepository returns the singleton instance of JobRepository.\n// The repository is initialized lazily on first access with database connection,\n// prepared statement cache, and LRU cache configured from repoConfig.\n//\n// This function is thread-safe and ensures only one instance is created.\n// It must be called after Connect() has established a database connection.\nfunc GetJobRepository() *JobRepository {\n\tjobRepoOnce.Do(func() {\n\t\tdb := GetConnection()\n\n\t\tjobRepoInstance = &JobRepository{\n\t\t\tDB: db.DB,\n\t\t\tdriver: db.Driver,\n\n\t\t\tstmtCache: sq.NewStmtCache(db.DB),\n\t\t\tcache: lrucache.New(repoConfig.CacheSize),\n\t\t}\n\t})\n\treturn jobRepoInstance\n}\n\n// jobColumns defines the standard set of columns selected from the job table.\n// Used consistently across all job queries to ensure uniform data retrieval.\nvar jobColumns []string = []string{\n\t\"job.id\", \"job.job_id\", \"job.hpc_user\", \"job.project\", \"job.cluster\", \"job.subcluster\",\n\t\"job.start_time\", \"job.cluster_partition\", \"job.array_job_id\", \"job.num_nodes\",\n\t\"job.num_hwthreads\", \"job.num_acc\", \"job.shared\", \"job.monitoring_status\",\n\t\"job.smt\", \"job.job_state\", \"job.duration\", \"job.walltime\", \"job.resources\",\n\t\"job.footprint\", \"job.energy\",\n}\n\n// jobCacheColumns defines columns from the job_cache table, mirroring jobColumns.\n// Used for queries against cached job data for performance optimization.\nvar jobCacheColumns []string = []string{\n\t\"job_cache.id\", \"job_cache.job_id\", \"job_cache.hpc_user\", \"job_cache.project\", \"job_cache.cluster\",\n\t\"job_cache.subcluster\", \"job_cache.start_time\", \"job_cache.cluster_partition\",\n\t\"job_cache.array_job_id\", \"job_cache.num_nodes\", \"job_cache.num_hwthreads\",\n\t\"job_cache.num_acc\", \"job_cache.shared\", \"job_cache.monitoring_status\", \"job_cache.smt\",\n\t\"job_cache.job_state\", \"job_cache.duration\", \"job_cache.walltime\", \"job_cache.resources\",\n\t\"job_cache.footprint\", \"job_cache.energy\",\n}\n\n// scanJob converts a database row into a schema.Job struct.\n// It handles JSON unmarshaling of resources and footprint fields,\n// and calculates accurate duration for running jobs.\n//\n// Parameters:\n// - row: Database row implementing Scan() interface (sql.Row or sql.Rows)\n//\n// Returns the populated Job struct or an error if scanning or unmarshaling fails.\nfunc scanJob(row interface{ Scan(...any) error }) (*schema.Job, error) {\n\tjob := &schema.Job{}\n\n\tif err := row.Scan(\n\t\t&job.ID, &job.JobID, &job.User, &job.Project, &job.Cluster, &job.SubCluster,\n\t\t&job.StartTime, &job.Partition, &job.ArrayJobID, &job.NumNodes, &job.NumHWThreads,\n\t\t&job.NumAcc, &job.Shared, &job.MonitoringStatus, &job.SMT, &job.State,\n\t\t&job.Duration, &job.Walltime, &job.RawResources, &job.RawFootprint, &job.Energy); err != nil {\n\t\tcclog.Warnf(\"Error while scanning rows (Job): %v\", err)\n\t\treturn nil, err\n\t}\n\n\tif err := json.Unmarshal(job.RawResources, &job.Resources); err != nil {\n\t\tcclog.Warn(\"Error while unmarshaling raw resources json\")\n\t\treturn nil, err\n\t}\n\tjob.RawResources = nil\n\n\tif err := json.Unmarshal(job.RawFootprint, &job.Footprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw footprint json: %v\", err)\n\t\treturn nil, err\n\t}\n\tjob.RawFootprint = nil\n\n\t// Always ensure accurate duration for running jobs\n\tif job.State == schema.JobStateRunning {\n\t\tjob.Duration = int32(time.Now().Unix() - job.StartTime)\n\t}\n\n\treturn job, nil\n}\n\n// Optimize performs database optimization by running VACUUM command.\n// This reclaims unused space and defragments the database file.\n// Should be run periodically during maintenance windows.\nfunc (r *JobRepository) Optimize() error {\n\tif _, err := r.DB.Exec(`VACUUM`); err != nil {\n\t\tcclog.Errorf(\"Error while executing VACUUM: %v\", err)\n\t\treturn fmt.Errorf(\"failed to optimize database: %w\", err)\n\t}\n\treturn nil\n}\n\n// Flush removes all data from job-related tables (jobtag, tag, job).\n// WARNING: This is a destructive operation that deletes all job data.\n// Use with extreme caution, typically only for testing or complete resets.\nfunc (r *JobRepository) Flush() error {\n\tif _, err := r.DB.Exec(`DELETE FROM jobtag`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from jobtag table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush jobtag table: %w\", err)\n\t}\n\tif _, err := r.DB.Exec(`DELETE FROM tag`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from tag table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush tag table: %w\", err)\n\t}\n\tif _, err := r.DB.Exec(`DELETE FROM job`); err != nil {\n\t\tcclog.Errorf(\"Error while deleting from job table: %v\", err)\n\t\treturn fmt.Errorf(\"failed to flush job table: %w\", err)\n\t}\n\treturn nil\n}\n\n// FetchMetadata retrieves and unmarshals the metadata JSON for a job.\n// Metadata is cached with a 24-hour TTL to improve performance.\n//\n// The metadata field stores arbitrary key-value pairs associated with a job,\n// such as tags, labels, or custom attributes added by external systems.\n//\n// Parameters:\n// - job: Job struct with valid ID field, metadata will be populated in job.MetaData\n//\n// Returns the metadata map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchMetadata(job *schema.Job) (map[string]string, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tcachekey := fmt.Sprintf(\"metadata:%d\", *job.ID)\n\tif cached := r.cache.Get(cachekey, nil); cached != nil {\n\t\tjob.MetaData = cached.(map[string]string)\n\t\treturn job.MetaData, nil\n\t}\n\n\tif err := sq.Select(\"job.meta_data\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawMetaData); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job metadata (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawMetaData) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawMetaData, &job.MetaData); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw metadata json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.MetaData, len(job.RawMetaData), 24*time.Hour)\n\tcclog.Debugf(\"Timer FetchMetadata %s\", time.Since(start))\n\treturn job.MetaData, nil\n}\n\n// UpdateMetadata adds or updates a single metadata key-value pair for a job.\n// The entire metadata map is re-marshaled and stored, and the cache is invalidated.\n// Also triggers archive metadata update via archive.UpdateMetadata.\n//\n// Parameters:\n// - job: Job struct with valid ID, existing metadata will be fetched if not present\n// - key: Metadata key to set\n// - val: Metadata value to set\n//\n// Returns an error if the job is nil, metadata fetch fails, or database update fails.\nfunc (r *JobRepository) UpdateMetadata(job *schema.Job, key, val string) (err error) {\n\tif job == nil {\n\t\treturn fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tcachekey := fmt.Sprintf(\"metadata:%d\", *job.ID)\n\tr.cache.Del(cachekey)\n\tif job.MetaData == nil {\n\t\tif _, err = r.FetchMetadata(job); err != nil {\n\t\t\tcclog.Warnf(\"Error while fetching metadata for job, DB ID '%v'\", *job.ID)\n\t\t\treturn fmt.Errorf(\"failed to fetch metadata for job %d: %w\", *job.ID, err)\n\t\t}\n\t}\n\n\tif job.MetaData != nil {\n\t\tcpy := make(map[string]string, len(job.MetaData)+1)\n\t\tmaps.Copy(cpy, job.MetaData)\n\t\tcpy[key] = val\n\t\tjob.MetaData = cpy\n\t} else {\n\t\tjob.MetaData = map[string]string{key: val}\n\t}\n\n\tif job.RawMetaData, err = json.Marshal(job.MetaData); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling metadata for job, DB ID '%v'\", *job.ID)\n\t\treturn fmt.Errorf(\"failed to marshal metadata for job %d: %w\", *job.ID, err)\n\t}\n\n\tif _, err = sq.Update(\"job\").\n\t\tSet(\"meta_data\", job.RawMetaData).\n\t\tWhere(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Warnf(\"Error while updating metadata for job, DB ID '%v'\", *job.ID)\n\t\treturn fmt.Errorf(\"failed to update metadata in database for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.MetaData, len(job.RawMetaData), 24*time.Hour)\n\treturn archive.UpdateMetadata(job, job.MetaData)\n}\n\n// FetchFootprint retrieves and unmarshals the performance footprint JSON for a job.\n// Unlike FetchMetadata, footprints are NOT cached as they can be large and change frequently.\n//\n// The footprint contains summary statistics (avg/min/max) for monitored metrics,\n// stored as JSON with keys like \"cpu_load_avg\", \"mem_used_max\", etc.\n//\n// Parameters:\n// - job: Job struct with valid ID, footprint will be populated in job.Footprint\n//\n// Returns the footprint map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchFootprint(job *schema.Job) (map[string]float64, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\n\tif err := sq.Select(\"job.footprint\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job footprint (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawFootprint) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawFootprint, &job.Footprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw footprint json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tcclog.Debugf(\"Timer FetchFootprint %s\", time.Since(start))\n\treturn job.Footprint, nil\n}\n\n// FetchEnergyFootprint retrieves and unmarshals the energy footprint JSON for a job.\n// Energy footprints are cached with a 24-hour TTL as they are frequently accessed but rarely change.\n//\n// The energy footprint contains calculated energy consumption (in kWh) per metric,\n// stored as JSON with keys like \"power_avg\", \"acc_power_avg\", etc.\n//\n// Parameters:\n// - job: Job struct with valid ID, energy footprint will be populated in job.EnergyFootprint\n//\n// Returns the energy footprint map or an error if the job is nil or database query fails.\nfunc (r *JobRepository) FetchEnergyFootprint(job *schema.Job) (map[string]float64, error) {\n\tif job == nil {\n\t\treturn nil, fmt.Errorf(\"job cannot be nil\")\n\t}\n\n\tstart := time.Now()\n\tcachekey := fmt.Sprintf(\"energyFootprint:%d\", *job.ID)\n\tif cached := r.cache.Get(cachekey, nil); cached != nil {\n\t\tjob.EnergyFootprint = cached.(map[string]float64)\n\t\treturn job.EnergyFootprint, nil\n\t}\n\n\tif err := sq.Select(\"job.energy_footprint\").From(\"job\").Where(\"job.id = ?\", job.ID).\n\t\tRunWith(r.stmtCache).QueryRow().Scan(&job.RawEnergyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while scanning for job energy_footprint (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to fetch energy footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tif len(job.RawEnergyFootprint) == 0 {\n\t\treturn nil, nil\n\t}\n\n\tif err := json.Unmarshal(job.RawEnergyFootprint, &job.EnergyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while unmarshaling raw energy footprint json (ID=%d): %v\", *job.ID, err)\n\t\treturn nil, fmt.Errorf(\"failed to unmarshal energy footprint for job %d: %w\", *job.ID, err)\n\t}\n\n\tr.cache.Put(cachekey, job.EnergyFootprint, len(job.EnergyFootprint), 24*time.Hour)\n\tcclog.Debugf(\"Timer FetchEnergyFootprint %s\", time.Since(start))\n\treturn job.EnergyFootprint, nil\n}\n\n// DeleteJobsBefore removes jobs older than the specified start time.\n// Optionally preserves tagged jobs to protect important data from deletion.\n// Cache entries for deleted jobs are automatically invalidated.\n//\n// This is typically used for data retention policies and cleanup operations.\n// WARNING: This is a destructive operation that permanently deletes job records.\n//\n// Parameters:\n// - startTime: Unix timestamp, jobs with start_time < this value will be deleted\n// - omitTagged: \"none\" = delete all jobs, \"all\" = skip any tagged jobs,\n// \"user\" = skip jobs with user-created tags (not auto-tagger types \"app\"/\"jobClass\")\n//\n// Returns the count of deleted jobs or an error if the operation fails.\nfunc (r *JobRepository) DeleteJobsBefore(startTime int64, omitTagged string) (int, error) {\n\tvar cnt int\n\tq := sq.Select(\"count(*)\").From(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tq = q.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tq = q.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\n\tif err := q.RunWith(r.DB).QueryRow().Scan(&cnt); err != nil {\n\t\tcclog.Errorf(\"Error counting jobs before %d: %v\", startTime, err)\n\t\treturn 0, err\n\t}\n\n\t// Invalidate cache for jobs being deleted (get job IDs first)\n\tif cnt > 0 {\n\t\tvar jobIds []int64\n\t\tselectQuery := sq.Select(\"id\").From(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\t\tswitch omitTagged {\n\t\tcase \"all\":\n\t\t\tselectQuery = selectQuery.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\t\tcase \"user\":\n\t\t\tselectQuery = selectQuery.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t\t}\n\n\t\trows, err := selectQuery.RunWith(r.DB).Query()\n\t\tif err == nil {\n\t\t\tdefer rows.Close()\n\t\t\tfor rows.Next() {\n\t\t\t\tvar id int64\n\t\t\t\tif err := rows.Scan(&id); err == nil {\n\t\t\t\t\tjobIds = append(jobIds, id)\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Invalidate cache entries\n\t\t\tfor _, id := range jobIds {\n\t\t\t\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", id))\n\t\t\t\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", id))\n\t\t\t}\n\t\t}\n\t}\n\n\tqd := sq.Delete(\"job\").Where(\"job.start_time < ?\", startTime)\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tqd = qd.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tqd = qd.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\t_, err := qd.RunWith(r.DB).Exec()\n\n\tif err != nil {\n\t\ts, _, _ := qd.ToSql()\n\t\tcclog.Errorf(\" DeleteJobsBefore(%d) with %s: error %#v\", startTime, s, err)\n\t} else {\n\t\tcclog.Debugf(\"DeleteJobsBefore(%d): Deleted %d jobs\", startTime, cnt)\n\t}\n\treturn cnt, err\n}\n\n// DeleteJobByID permanently removes a single job by its database ID.\n// Cache entries for the deleted job are automatically invalidated.\n//\n// Parameters:\n// - id: Database ID (primary key) of the job to delete\n//\n// Returns an error if the deletion fails.\nfunc (r *JobRepository) DeleteJobByID(id int64) error {\n\t// Invalidate cache entries before deletion\n\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", id))\n\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", id))\n\n\tqd := sq.Delete(\"job\").Where(\"job.id = ?\", id)\n\t_, err := qd.RunWith(r.DB).Exec()\n\n\tif err != nil {\n\t\ts, _, _ := qd.ToSql()\n\t\tcclog.Errorf(\"DeleteJobById(%d) with %s : error %#v\", id, s, err)\n\t} else {\n\t\tcclog.Debugf(\"DeleteJobById(%d): Success\", id)\n\t}\n\treturn err\n}\n\n// FindUserOrProjectOrJobname attempts to interpret a search term as a job ID,\n// username, project ID, or job name by querying the database.\n//\n// Search logic (in priority order):\n// 1. If searchterm is numeric, treat as job ID (returned immediately)\n// 2. Try exact match in job.hpc_user column (username)\n// 3. Try LIKE match in hpc_user.name column (real name)\n// 4. Try exact match in job.project column (project ID)\n// 5. If no matches, return searchterm as jobname for GraphQL query\n//\n// This powers the searchbar functionality for flexible job searching.\n// Requires authenticated user for database lookups (returns empty if user is nil).\n//\n// Parameters:\n// - user: Authenticated user context, required for database access\n// - searchterm: Search string to interpret\n//\n// Returns up to one non-empty value among (jobid, username, project, jobname).\nfunc (r *JobRepository) FindUserOrProjectOrJobname(user *schema.User, searchterm string) (jobid string, username string, project string, jobname string) {\n\tif searchterm == \"\" {\n\t\treturn \"\", \"\", \"\", \"\"\n\t}\n\n\tif _, err := strconv.Atoi(searchterm); err == nil { // Return empty on successful conversion: parent method will redirect for integer jobId\n\t\treturn searchterm, \"\", \"\", \"\"\n\t} else { // Has to have letters and logged-in user for other guesses\n\t\tif user != nil {\n\t\t\t// Find username by username in job table (match)\n\t\t\turesult, _ := r.FindColumnValue(user, searchterm, \"job\", \"hpc_user\", \"hpc_user\", false)\n\t\t\tif uresult != \"\" {\n\t\t\t\treturn \"\", uresult, \"\", \"\"\n\t\t\t}\n\t\t\t// Find username by real name in hpc_user table (like)\n\t\t\tnresult, _ := r.FindColumnValue(user, searchterm, \"hpc_user\", \"username\", \"name\", true)\n\t\t\tif nresult != \"\" {\n\t\t\t\treturn \"\", nresult, \"\", \"\"\n\t\t\t}\n\t\t\t// Find projectId by projectId in job table (match)\n\t\t\tpresult, _ := r.FindColumnValue(user, searchterm, \"job\", \"project\", \"project\", false)\n\t\t\tif presult != \"\" {\n\t\t\t\treturn \"\", \"\", presult, \"\"\n\t\t\t}\n\t\t}\n\t\t// Return searchterm if no match before: Forward as jobname query to GQL in handleSearchbar function\n\t\treturn \"\", \"\", \"\", searchterm\n\t}\n}\n\nvar (\n\tErrNotFound = errors.New(\"no such jobname, project or user\")\n\tErrForbidden = errors.New(\"not authorized\")\n)\n\n// FindColumnValue performs a generic column lookup in a database table with role-based access control.\n// Only users with admin, support, or manager roles can execute this query.\n//\n// Parameters:\n// - user: User context for authorization check\n// - searchterm: Value to search for (exact match or LIKE pattern)\n// - table: Database table name to query\n// - selectColumn: Column name to return in results\n// - whereColumn: Column name to filter on\n// - isLike: If true, use LIKE with wildcards; if false, use exact equality\n//\n// Returns the first matching value, ErrForbidden if user lacks permission,\n// or ErrNotFound if no matches are found.\nfunc (r *JobRepository) FindColumnValue(user *schema.User, searchterm string, table string, selectColumn string, whereColumn string, isLike bool) (result string, err error) {\n\tif user == nil {\n\t\treturn \"\", fmt.Errorf(\"user cannot be nil\")\n\t}\n\n\tcompareStr := \" = ?\"\n\tquery := searchterm\n\tif isLike {\n\t\tcompareStr = \" LIKE ?\"\n\t\tquery = \"%\" + searchterm + \"%\"\n\t}\n\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\ttheQuery := sq.Select(table+\".\"+selectColumn).Distinct().From(table).\n\t\t\tWhere(table+\".\"+whereColumn+compareStr, query)\n\n\t\terr := theQuery.RunWith(r.stmtCache).QueryRow().Scan(&result)\n\n\t\tif err != nil && err != sql.ErrNoRows {\n\t\t\tcclog.Warnf(\"Error while querying FindColumnValue (table=%s, column=%s): %v\", table, selectColumn, err)\n\t\t\treturn \"\", fmt.Errorf(\"failed to find column value: %w\", err)\n\t\t} else if err == nil {\n\t\t\treturn result, nil\n\t\t}\n\t\treturn \"\", ErrNotFound\n\t} else {\n\t\tcclog.Infof(\"Non-Admin User %s : Requested Query '%s' on table '%s' : Forbidden\", user.Name, query, table)\n\t\treturn \"\", ErrForbidden\n\t}\n}\n\n// FindColumnValues performs a generic column lookup returning multiple matches with role-based access control.\n// Similar to FindColumnValue but returns all matching values instead of just the first.\n// Only users with admin, support, or manager roles can execute this query.\n//\n// Parameters:\n// - user: User context for authorization check\n// - query: Search pattern (always uses LIKE with wildcards)\n// - table: Database table name to query\n// - selectColumn: Column name to return in results\n// - whereColumn: Column name to filter on\n//\n// Returns a slice of matching values, ErrForbidden if user lacks permission,\n// or ErrNotFound if no matches are found.\nfunc (r *JobRepository) FindColumnValues(user *schema.User, query string, table string, selectColumn string, whereColumn string) (results []string, err error) {\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"user cannot be nil\")\n\t}\n\n\temptyResult := make([]string, 0)\n\tif user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\trows, err := sq.Select(table+\".\"+selectColumn).Distinct().From(table).\n\t\t\tWhere(table+\".\"+whereColumn+\" LIKE ?\", fmt.Sprint(\"%\", query, \"%\")).\n\t\t\tRunWith(r.stmtCache).Query()\n\t\tif err != nil && err != sql.ErrNoRows {\n\t\t\tcclog.Errorf(\"Error while querying FindColumnValues (table=%s, column=%s): %v\", table, selectColumn, err)\n\t\t\treturn emptyResult, fmt.Errorf(\"failed to find column values: %w\", err)\n\t\t} else if err == nil {\n\t\t\tdefer rows.Close()\n\t\t\tfor rows.Next() {\n\t\t\t\tvar result string\n\t\t\t\terr := rows.Scan(&result)\n\t\t\t\tif err != nil {\n\t\t\t\t\tcclog.Warnf(\"Error while scanning rows in FindColumnValues: %v\", err)\n\t\t\t\t\treturn emptyResult, fmt.Errorf(\"failed to scan column value: %w\", err)\n\t\t\t\t}\n\t\t\t\tresults = append(results, result)\n\t\t\t}\n\t\t\treturn results, nil\n\t\t}\n\t\treturn emptyResult, ErrNotFound\n\n\t} else {\n\t\tcclog.Infof(\"Non-Admin User %s : Requested Query '%s' on table '%s' : Forbidden\", user.Name, query, table)\n\t\treturn emptyResult, ErrForbidden\n\t}\n}\n\n// Partitions returns a list of distinct cluster partitions for a given cluster.\n// Results are cached with a 1-hour TTL to improve performance.\n//\n// Parameters:\n// - cluster: Cluster name to query partitions for\n//\n// Returns a slice of partition names or an error if the database query fails.\nfunc (r *JobRepository) Partitions(cluster string) ([]string, error) {\n\tvar err error\n\tstart := time.Now()\n\tpartitions := r.cache.Get(\"partitions:\"+cluster, func() (any, time.Duration, int) {\n\t\tparts := []string{}\n\t\tif err = r.DB.Select(&parts, `SELECT DISTINCT job.cluster_partition FROM job WHERE job.cluster = ?;`, cluster); err != nil {\n\t\t\treturn nil, 0, 1000\n\t\t}\n\n\t\treturn parts, 1 * time.Hour, 1\n\t})\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tcclog.Debugf(\"Timer Partitions %s\", time.Since(start))\n\treturn partitions.([]string), nil\n}\n\n// AllocatedNodes returns a map of all subclusters to a map of hostnames to the amount of jobs running on that host.\n// Hosts with zero jobs running on them will not show up!\nfunc (r *JobRepository) AllocatedNodes(cluster string) (map[string]map[string]int, error) {\n\tstart := time.Now()\n\tsubclusters := make(map[string]map[string]int)\n\trows, err := sq.Select(\"resources\", \"subcluster\").From(\"job\").\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tRunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running AllocatedNodes query for cluster=%s: %v\", cluster, err)\n\t\treturn nil, fmt.Errorf(\"failed to query allocated nodes for cluster %s: %w\", cluster, err)\n\t}\n\n\tvar raw []byte\n\tdefer rows.Close()\n\tfor rows.Next() {\n\t\traw = raw[0:0]\n\t\tvar resources []*schema.Resource\n\t\tvar subcluster string\n\t\tif err := rows.Scan(&raw, &subcluster); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in AllocatedNodes: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan allocated nodes row: %w\", err)\n\t\t}\n\t\tif err := json.Unmarshal(raw, &resources); err != nil {\n\t\t\tcclog.Warnf(\"Error while unmarshaling raw resources json in AllocatedNodes: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to unmarshal resources in AllocatedNodes: %w\", err)\n\t\t}\n\n\t\thosts, ok := subclusters[subcluster]\n\t\tif !ok {\n\t\t\thosts = make(map[string]int)\n\t\t\tsubclusters[subcluster] = hosts\n\t\t}\n\n\t\tfor _, resource := range resources {\n\t\t\thosts[resource.Hostname] += 1\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AllocatedNodes %s\", time.Since(start))\n\treturn subclusters, nil\n}\n\n// StopJobsExceedingWalltimeBy marks running jobs as failed if they exceed their walltime limit.\n// This is typically called periodically to clean up stuck or orphaned jobs.\n//\n// Jobs are marked with:\n// - monitoring_status: MonitoringStatusArchivingFailed\n// - duration: 0\n// - job_state: JobStateFailed\n//\n// Parameters:\n// - seconds: Grace period beyond walltime before marking as failed\n//\n// Returns an error if the database update fails.\n// Logs the number of jobs marked as failed if any were affected.\nfunc (r *JobRepository) StopJobsExceedingWalltimeBy(seconds int) error {\n\tstart := time.Now()\n\tcurrentTime := time.Now().Unix()\n\tres, err := sq.Update(\"job\").\n\t\tSet(\"monitoring_status\", schema.MonitoringStatusArchivingFailed).\n\t\tSet(\"duration\", 0).\n\t\tSet(\"job_state\", schema.JobStateFailed).\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.walltime > 0\").\n\t\tWhere(\"job.start_time < (? - job.walltime)\", currentTime-int64(seconds)).\n\t\tRunWith(r.DB).Exec()\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while stopping jobs exceeding walltime: %v\", err)\n\t\treturn fmt.Errorf(\"failed to stop jobs exceeding walltime: %w\", err)\n\t}\n\n\trowsAffected, err := res.RowsAffected()\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while fetching affected rows after stopping due to exceeded walltime: %v\", err)\n\t\treturn fmt.Errorf(\"failed to get rows affected count: %w\", err)\n\t}\n\n\tif rowsAffected > 0 {\n\t\tcclog.Infof(\"%d jobs have been marked as failed due to running too long\", rowsAffected)\n\t}\n\tcclog.Debugf(\"Timer StopJobsExceedingWalltimeBy %s\", time.Since(start))\n\treturn nil\n}\n\n// FindJobIdsByTag returns all job database IDs associated with a specific tag.\n//\n// Parameters:\n// - tagID: Database ID of the tag to search for\n//\n// Returns a slice of job IDs or an error if the query fails.\nfunc (r *JobRepository) FindJobIdsByTag(tagID int64) ([]int64, error) {\n\tquery := sq.Select(\"job.id\").From(\"job\").\n\t\tJoin(\"jobtag ON jobtag.job_id = job.id\").\n\t\tWhere(sq.Eq{\"jobtag.tag_id\": tagID}).Distinct()\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindJobIdsByTag query for tagID=%d: %v\", tagID, err)\n\t\treturn nil, fmt.Errorf(\"failed to find job IDs by tag %d: %w\", tagID, err)\n\t}\n\tdefer rows.Close()\n\n\tjobIds := make([]int64, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar jobID int64\n\n\t\tif err := rows.Scan(&jobID); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindJobIdsByTag: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job ID in FindJobIdsByTag: %w\", err)\n\t\t}\n\n\t\tjobIds = append(jobIds, jobID)\n\t}\n\n\treturn jobIds, nil\n}\n\n// FindRunningJobs returns all currently running jobs for a specific cluster.\n// Filters out short-running jobs based on repoConfig.MinRunningJobDuration threshold.\n//\n// Parameters:\n// - cluster: Cluster name to filter jobs\n//\n// Returns a slice of running job objects or an error if the query fails.\nfunc (r *JobRepository) FindRunningJobs(cluster string) ([]*schema.Job, error) {\n\tquery := sq.Select(jobColumns...).From(\"job\").\n\t\tWhere(\"job.cluster = ?\", cluster).\n\t\tWhere(\"job.job_state = 'running'\").\n\t\tWhere(\"job.duration > ?\", repoConfig.MinRunningJobDuration)\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindRunningJobs query for cluster=%s: %v\", cluster, err)\n\t\treturn nil, fmt.Errorf(\"failed to find running jobs for cluster %s: %w\", cluster, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 50)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindRunningJobs: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job in FindRunningJobs: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tcclog.Debugf(\"JobRepository.FindRunningJobs(): Return job count %d (cluster: %s)\", len(jobs), cluster)\n\treturn jobs, nil\n}\n\n// UpdateDuration recalculates and updates the duration field for all running jobs.\n// Called periodically to keep job durations current without querying individual jobs.\n//\n// Duration is calculated as: current_time - job.start_time\n//\n// Returns an error if the database update fails.\nfunc (r *JobRepository) UpdateDuration() error {\n\tstmnt := sq.Update(\"job\").\n\t\tSet(\"duration\", sq.Expr(\"? - job.start_time\", time.Now().Unix())).\n\t\tWhere(\"job_state = 'running'\")\n\n\t_, err := stmnt.RunWith(r.stmtCache).Exec()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while updating duration for running jobs: %v\", err)\n\t\treturn fmt.Errorf(\"failed to update duration for running jobs: %w\", err)\n\t}\n\n\treturn nil\n}\n\n// FindJobsBetween returns jobs within a specified time range.\n// If startTimeBegin is 0, returns all jobs before startTimeEnd.\n// Optionally excludes tagged jobs from results.\n//\n// Parameters:\n// - startTimeBegin: Unix timestamp for range start (use 0 for unbounded start)\n// - startTimeEnd: Unix timestamp for range end\n// - omitTagged: \"none\" = include all jobs, \"all\" = exclude any tagged jobs,\n// \"user\" = exclude jobs with user-created tags (not auto-tagger types \"app\"/\"jobClass\")\n//\n// Returns a slice of jobs or an error if the time range is invalid or query fails.\nfunc (r *JobRepository) FindJobsBetween(startTimeBegin int64, startTimeEnd int64, omitTagged string) ([]*schema.Job, error) {\n\tvar query sq.SelectBuilder\n\n\tif startTimeBegin == startTimeEnd || startTimeBegin > startTimeEnd {\n\t\treturn nil, errors.New(\"startTimeBegin is equal or larger startTimeEnd\")\n\t}\n\n\tif startTimeBegin == 0 {\n\t\tcclog.Infof(\"Find jobs before %d\", startTimeEnd)\n\t\tquery = sq.Select(jobColumns...).From(\"job\").Where(\"job.start_time < ?\", startTimeEnd)\n\t} else {\n\t\tcclog.Infof(\"Find jobs between %d and %d\", startTimeBegin, startTimeEnd)\n\t\tquery = sq.Select(jobColumns...).From(\"job\").Where(\"job.start_time BETWEEN ? AND ?\", startTimeBegin, startTimeEnd)\n\t}\n\n\tswitch omitTagged {\n\tcase \"all\":\n\t\tquery = query.Where(\"NOT EXISTS (SELECT 1 FROM jobtag WHERE jobtag.job_id = job.id)\")\n\tcase \"user\":\n\t\tquery = query.Where(\"NOT EXISTS (SELECT 1 FROM jobtag JOIN tag ON tag.id = jobtag.tag_id WHERE jobtag.job_id = job.id AND tag.tag_type NOT IN ('app', 'jobClass'))\")\n\t}\n\n\tquery = query.OrderBy(\"job.cluster ASC\", \"job.subcluster ASC\", \"job.project ASC\", \"job.start_time ASC\")\n\n\trows, err := query.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running FindJobsBetween query: %v\", err)\n\t\treturn nil, fmt.Errorf(\"failed to find jobs between %d and %d: %w\", startTimeBegin, startTimeEnd, err)\n\t}\n\tdefer rows.Close()\n\n\tjobs := make([]*schema.Job, 0, 50)\n\tfor rows.Next() {\n\t\tjob, err := scanJob(rows)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows in FindJobsBetween: %v\", err)\n\t\t\treturn nil, fmt.Errorf(\"failed to scan job in FindJobsBetween: %w\", err)\n\t\t}\n\t\tjobs = append(jobs, job)\n\t}\n\n\tcclog.Debugf(\"JobRepository.FindJobsBetween(): Return job count %d (omitTagged: %v)\", len(jobs), omitTagged)\n\treturn jobs, nil\n}\n\n// UpdateMonitoringStatus updates the monitoring status for a job and invalidates its cache entries.\n// Cache invalidation affects both metadata and energy footprint to ensure consistency.\n//\n// Parameters:\n// - job: Database ID of the job to update\n// - monitoringStatus: New monitoring status value (see schema.MonitoringStatus constants)\n//\n// Returns an error if the database update fails.\nfunc (r *JobRepository) UpdateMonitoringStatus(job int64, monitoringStatus int32) (err error) {\n\t// Invalidate cache entries as monitoring status affects job state\n\tr.cache.Del(fmt.Sprintf(\"metadata:%d\", job))\n\tr.cache.Del(fmt.Sprintf(\"energyFootprint:%d\", job))\n\n\tstmt := sq.Update(\"job\").\n\t\tSet(\"monitoring_status\", monitoringStatus).\n\t\tWhere(\"job.id = ?\", job)\n\n\tif _, err = stmt.RunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Errorf(\"Error while updating monitoring status for job %d: %v\", job, err)\n\t\treturn fmt.Errorf(\"failed to update monitoring status for job %d: %w\", job, err)\n\t}\n\treturn nil\n}\n\n// Execute runs a Squirrel UpdateBuilder statement against the database.\n// This is a generic helper for executing pre-built update queries.\n//\n// Parameters:\n// - stmt: Squirrel UpdateBuilder with prepared update query\n//\n// Returns an error if the execution fails.\nfunc (r *JobRepository) Execute(stmt sq.UpdateBuilder) error {\n\tif _, err := stmt.RunWith(r.stmtCache).Exec(); err != nil {\n\t\tcclog.Errorf(\"Error while executing statement: %v\", err)\n\t\treturn fmt.Errorf(\"failed to execute update statement: %w\", err)\n\t}\n\n\treturn nil\n}\n\n// MarkArchived adds monitoring status update to an existing UpdateBuilder statement.\n// This is a builder helper used when constructing multi-field update queries.\n//\n// Parameters:\n// - stmt: Existing UpdateBuilder to modify\n// - monitoringStatus: Monitoring status value to set\n//\n// Returns the modified UpdateBuilder for method chaining.\nfunc (r *JobRepository) MarkArchived(\n\tstmt sq.UpdateBuilder,\n\tmonitoringStatus int32,\n) sq.UpdateBuilder {\n\treturn stmt.Set(\"monitoring_status\", monitoringStatus)\n}\n\n// UpdateEnergy calculates and updates the energy consumption for a job.\n// This is called for running jobs during intermediate updates or when archiving.\n//\n// Energy calculation formula:\n// - For \"power\" metrics: Energy (kWh) = (Power_avg * NumNodes * Duration_hours) / 1000\n// - For \"energy\" metrics: Currently not implemented (would need sum statistics)\n//\n// The calculation accounts for:\n// - Multi-node jobs: Multiplies by NumNodes to get total cluster energy\n// - Shared jobs: Node average is already based on partial resources, so NumNodes=1\n// - Unit conversion: Watts * hours / 1000 = kilowatt-hours (kWh)\n// - Rounding: Results rounded to 2 decimal places\nfunc (r *JobRepository) UpdateEnergy(\n\tstmt sq.UpdateBuilder,\n\tjobMeta *schema.Job,\n) (sq.UpdateBuilder, error) {\n\tsc, err := archive.GetSubCluster(jobMeta.Cluster, jobMeta.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"cannot get subcluster: %s\", err.Error())\n\t\treturn stmt, err\n\t}\n\tenergyFootprint := make(map[string]float64)\n\n\t// Accumulate total energy across all energy-related metrics\n\ttotalEnergy := 0.0\n\tfor _, fp := range sc.EnergyFootprint {\n\t\t// Calculate energy for this specific metric\n\t\tmetricEnergy := 0.0\n\t\tif i, err := archive.MetricIndex(sc.MetricConfig, fp); err == nil {\n\t\t\tswitch sc.MetricConfig[i].Energy {\n\t\t\tcase \"energy\": // Metric already in energy units (Joules or Wh)\n\t\t\t\tcclog.Warnf(\"Update EnergyFootprint for Job %d and Metric %s on cluster %s: Set to 'energy' in cluster.json: Not implemented, will return 0.0\", jobMeta.JobID, jobMeta.Cluster, fp)\n\t\t\t\t// FIXME: Needs sum as stats type to accumulate energy values over time\n\t\t\tcase \"power\": // Metric in power units (Watts)\n\t\t\t\t// Energy (kWh) = Power (W) × Time (h) / 1000\n\t\t\t\t// Formula: (avg_power_per_node * num_nodes) * (duration_sec / 3600) / 1000\n\t\t\t\t//\n\t\t\t\t// Breakdown:\n\t\t\t\t// LoadJobStat(jobMeta, fp, \"avg\") = average power per node (W)\n\t\t\t\t// jobMeta.NumNodes = number of nodes (1 for shared jobs)\n\t\t\t\t// jobMeta.Duration / 3600.0 = duration in hours\n\t\t\t\t// / 1000.0 = convert Wh to kWh\n\t\t\t\trawEnergy := ((LoadJobStat(jobMeta, fp, \"avg\") * float64(jobMeta.NumNodes)) * (float64(jobMeta.Duration) / 3600.0)) / 1000.0\n\t\t\t\tmetricEnergy = math.Round(rawEnergy*100.0) / 100.0 // Round to 2 decimal places\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Error while collecting energy metric %s for job, DB ID '%v', return '0.0'\", fp, jobMeta.ID)\n\t\t}\n\n\t\tenergyFootprint[fp] = metricEnergy\n\t\ttotalEnergy += metricEnergy\n\t}\n\n\tvar rawFootprint []byte\n\tif rawFootprint, err = json.Marshal(energyFootprint); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling energy footprint for job INTO BYTES, DB ID '%v'\", jobMeta.ID)\n\t\treturn stmt, err\n\t}\n\n\treturn stmt.Set(\"energy_footprint\", string(rawFootprint)).Set(\"energy\", (math.Round(totalEnergy*100.0) / 100.0)), nil\n}\n\n// UpdateFootprint calculates and updates the performance footprint for a job.\n// This is called for running jobs during intermediate updates or when archiving.\n//\n// A footprint is a summary statistic (avg/min/max) for each monitored metric.\n// The specific statistic type is defined in the cluster config's Footprint field.\n// Results are stored as JSON with keys like \"metric_avg\", \"metric_max\", etc.\n//\n// Example: For a \"cpu_load\" metric with Footprint=\"avg\", this stores\n// the average CPU load across all nodes as \"cpu_load_avg\": 85.3\nfunc (r *JobRepository) UpdateFootprint(\n\tstmt sq.UpdateBuilder,\n\tjobMeta *schema.Job,\n) (sq.UpdateBuilder, error) {\n\tsc, err := archive.GetSubCluster(jobMeta.Cluster, jobMeta.SubCluster)\n\tif err != nil {\n\t\tcclog.Errorf(\"cannot get subcluster: %s\", err.Error())\n\t\treturn stmt, err\n\t}\n\tfootprint := make(map[string]float64)\n\n\t// Build footprint map with metric_stattype as keys\n\tfor _, fp := range sc.Footprint {\n\t\t// Determine which statistic to use: avg, min, or max\n\t\t// First check global metric config, then cluster-specific config\n\t\tvar statType string\n\t\tfor _, gm := range archive.GlobalMetricList {\n\t\t\tif gm.Name == fp {\n\t\t\t\tstatType = gm.Footprint\n\t\t\t}\n\t\t}\n\n\t\t// Validate statistic type\n\t\tif statType != \"avg\" && statType != \"min\" && statType != \"max\" {\n\t\t\tcclog.Warnf(\"unknown statType for footprint update: %s\", statType)\n\t\t\treturn stmt, fmt.Errorf(\"unknown statType for footprint update: %s\", statType)\n\t\t}\n\n\t\t// Override with cluster-specific config if available\n\t\tif i, err := archive.MetricIndex(sc.MetricConfig, fp); err != nil {\n\t\t\tstatType = sc.MetricConfig[i].Footprint\n\t\t}\n\n\t\t// Store as \"metric_stattype\": value (e.g., \"cpu_load_avg\": 85.3)\n\t\tname := fmt.Sprintf(\"%s_%s\", fp, statType)\n\t\tfootprint[name] = LoadJobStat(jobMeta, fp, statType)\n\t}\n\n\tvar rawFootprint []byte\n\tif rawFootprint, err = json.Marshal(footprint); err != nil {\n\t\tcclog.Warnf(\"Error while marshaling footprint for job INTO BYTES, DB ID '%v'\", jobMeta.ID)\n\t\treturn stmt, err\n\t}\n\n\treturn stmt.Set(\"footprint\", string(rawFootprint)), nil\n}\n\n// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n// that are currently in use by jobs that started before the given timestamp and\n// are still in running state.\n//\n// The timestamp parameter (ts) is compared against job.start_time to find\n// relevant jobs. Returns an error if the database query fails or row iteration\n// encounters errors. Individual row parsing errors are logged but don't fail\n// the entire operation.\nfunc (r *JobRepository) GetUsedNodes(ts int64) (map[string][]string, error) {\n\t// Note: Query expects index on (job_state, start_time) for optimal performance\n\tq := sq.Select(\"job.cluster\", \"job.resources\").From(\"job\").\n\t\tWhere(\"job.start_time < ?\", ts).\n\t\tWhere(sq.Eq{\"job.job_state\": \"running\"})\n\n\trows, err := q.RunWith(r.stmtCache).Query()\n\tif err != nil {\n\t\tqueryString, queryVars, _ := q.ToSql()\n\t\treturn nil, fmt.Errorf(\"query failed [%s] %v: %w\", queryString, queryVars, err)\n\t}\n\tdefer rows.Close()\n\n\t// Use a map of sets for efficient deduplication\n\tnodeSet := make(map[string]map[string]struct{})\n\n\tvar (\n\t\tcluster string\n\t\trawResources []byte\n\t\tresources []*schema.Resource\n\t\tskippedRows int\n\t)\n\n\tfor rows.Next() {\n\t\tif err := rows.Scan(&cluster, &rawResources); err != nil {\n\t\t\tcclog.Warnf(\"Error scanning job row in GetUsedNodes: %v\", err)\n\t\t\tskippedRows++\n\t\t\tcontinue\n\t\t}\n\n\t\tresources = resources[:0] // Clear slice, keep capacity\n\t\tif err := json.Unmarshal(rawResources, &resources); err != nil {\n\t\t\tcclog.Warnf(\"Error unmarshaling resources for cluster %s: %v\", cluster, err)\n\t\t\tskippedRows++\n\t\t\tcontinue\n\t\t}\n\n\t\tif len(resources) == 0 {\n\t\t\tcclog.Debugf(\"Job in cluster %s has no resources\", cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := nodeSet[cluster]; !ok {\n\t\t\tnodeSet[cluster] = make(map[string]struct{})\n\t\t}\n\n\t\tfor _, res := range resources {\n\t\t\tnodeSet[cluster][res.Hostname] = struct{}{}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, fmt.Errorf(\"error iterating rows: %w\", err)\n\t}\n\n\tif skippedRows > 0 {\n\t\tcclog.Warnf(\"GetUsedNodes: Skipped %d rows due to parsing errors\", skippedRows)\n\t}\n\n\t// Convert sets to sorted slices\n\tnodeList := make(map[string][]string, len(nodeSet))\n\tfor cluster, nodes := range nodeSet {\n\t\tlist := make([]string, 0, len(nodes))\n\t\tfor node := range nodes {\n\t\t\tlist = append(list, node)\n\t\t}\n\t\tsort.Strings(list)\n\t\tnodeList[cluster] = list\n\t}\n\n\treturn nodeList, nil\n}\n","structuredPatch":[{"oldStart":1066,"oldLines":7,"newStart":1066,"newLines":7,"lines":[" // Note: Query expects index on (job_state, start_time) for optimal performance"," q := sq.Select(\"job.cluster\", \"job.resources\").From(\"job\")."," Where(\"job.start_time < ?\", ts).","- Where(sq.Eq{\"job.job_state\": \"running\"})","+ Where(\"job.job_state = 'running'\")"," "," rows, err := q.RunWith(r.stmtCache).Query()"," if err != nil {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"604bdedd-91db-4e50-a7eb-0b541443aeda","userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"d9b09c8f-9f72-4f35-986e-fb422e1b4064","version":"2.1.74","gitBranch":"hotfix","slug":"recursive-giggling-volcano"}
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