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cc-backend/17/cdf997acff/0/full.jsonl
Jan Eitzinger 05396fe3b1 Checkpoint: 17cdf997acff 
Entire-Session: 50633cd8-2ab8-4107-9a64-7f258725f2c3
Entire-Strategy: manual-commit
Entire-Agent: Claude Code
Ephemeral-branch: entire/c449996-e3b0c4
2026-03-18 10:05:09 +01:00

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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":360,"oldLines":7,"newStart":360,"newLines":7,"lines":[" "," var jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64"," if err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {","- cclog.Warn(\"Error while scanning rows\")","+ cclog.Warnf(\"Error scanning job statistics row: %v\", err)"," return nil, err"," }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"81df65e2-1ff7-4289-ae50-143a949138bb","userType":"external","entrypoint":"cli","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"50633cd8-2ab8-4107-9a64-7f258725f2c3","version":"2.1.78","gitBranch":"hotfix","slug":"eventual-meandering-frog"}
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{"parentUuid":"4d28811a-2e65-446a-ac4e-f453f4cf4e4a","isSidechain":false,"promptId":"0be7b781-e914-4948-9577-6a6a0c7e6920","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ei8Q26EiLnKqGNgtJy1bNH","type":"tool_result","content":" 1→# CLAUDE.md\n 2→\n 3→This file provides guidance to Claude Code (claude.ai/code) when working with\n 4→code in this repository.\n 5→\n 6→## Project Overview\n 7→\n 8→ClusterCockpit is a job-specific performance monitoring framework for HPC\n 9→clusters. This is a Golang backend that provides REST and GraphQL APIs, serves a\n 10→Svelte-based frontend, and manages job archives and metric data from various\n 11→time-series databases.\n 12→\n 13→## Build and Development Commands\n 14→\n 15→### Building\n 16→\n 17→```bash\n 18→# Build everything (frontend + backend)\n 19→make\n 20→"}]},"uuid":"e8fa4dc6-c386-4b5f-8755-940566ea6598","timestamp":"2026-03-18T09:03:44.456Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/CLAUDE.md","content":"# CLAUDE.md\n\nThis file provides guidance to Claude Code (claude.ai/code) when working with\ncode in this repository.\n\n## Project Overview\n\nClusterCockpit is a job-specific performance monitoring framework for HPC\nclusters. This is a Golang backend that provides REST and GraphQL APIs, serves a\nSvelte-based frontend, and manages job archives and metric data from various\ntime-series databases.\n\n## Build and Development Commands\n\n### Building\n\n```bash\n# Build everything (frontend + backend)\nmake\n","numLines":20,"startLine":1,"totalLines":316}},"sourceToolAssistantUUID":"4d28811a-2e65-446a-ac4e-f453f4cf4e4a","userType":"external","entrypoint":"cli","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"50633cd8-2ab8-4107-9a64-7f258725f2c3","version":"2.1.78","gitBranch":"hotfix","slug":"eventual-meandering-frog"}
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{"parentUuid":"f5557cf4-4494-4c7e-bdeb-80b2ab37c0e2","isSidechain":false,"promptId":"0be7b781-e914-4948-9577-6a6a0c7e6920","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011XWu5emf1AjAKUKyk4QguE","type":"tool_result","content":" 280→- `event`: JSON payload containing job data (see REST API documentation for schema)\n 281→\n 282→#### Node State Updates\n 283→\n 284→```json\n 285→{\n 286→ \"cluster\": \"testcluster\",\n 287→ \"nodes\": [\n 288→ {\n 289→ \"hostname\": \"node001\",\n 290→ \"states\": [\"allocated\"],\n 291→ \"cpusAllocated\": 8,\n 292→ \"memoryAllocated\": 16384,\n 293→ \"gpusAllocated\": 0,\n 294→ \"jobsRunning\": 1\n 295→ }\n 296→ ]\n 297→}\n 298→```\n 299→\n 300→### Implementation Notes\n 301→\n 302→- NATS API mirrors REST API functionality but uses messaging\n 303→- Job start/stop events are processed asynchronously via configurable worker pools\n 304→- Duplicate job detection is handled (same as REST API)\n 305→- All validation rules from REST API apply\n 306→- Node state updates include health checks against the metric store (identical to REST handler): nodes are grouped by subcluster, metric configurations are fetched, and `HealthCheck()` is called per subcluster. Nodes default to `MonitoringStateFailed` if no health data is available.\n 307→- Messages are logged; no responses are sent back to publishers\n 308→- If NATS client is unavailable, API subscriptions are skipped (logged as warning)\n 309→\n 310→## Dependencies\n 311→\n 312→- Go 1.24.0+ (check go.mod for exact version)\n 313→- Node.js (for frontend builds)\n 314→- SQLite 3 (only supported database)\n 315→- Optional: NATS server for NATS API integration\n 316→"}]},"uuid":"9c423e6c-c794-4ad0-a770-7a080c16aa8e","timestamp":"2026-03-18T09:03:46.781Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/CLAUDE.md","content":"- `event`: JSON payload containing job data (see REST API documentation for schema)\n\n#### Node State Updates\n\n```json\n{\n \"cluster\": \"testcluster\",\n \"nodes\": [\n {\n \"hostname\": \"node001\",\n \"states\": [\"allocated\"],\n \"cpusAllocated\": 8,\n \"memoryAllocated\": 16384,\n \"gpusAllocated\": 0,\n \"jobsRunning\": 1\n }\n ]\n}\n```\n\n### Implementation Notes\n\n- NATS API mirrors REST API functionality but uses messaging\n- Job start/stop events are processed asynchronously via configurable worker pools\n- Duplicate job detection is handled (same as REST API)\n- All validation rules from REST API apply\n- Node state updates include health checks against the metric store (identical to REST handler): nodes are grouped by subcluster, metric configurations are fetched, and `HealthCheck()` is called per subcluster. Nodes default to `MonitoringStateFailed` if no health data is available.\n- Messages are logged; no responses are sent back to publishers\n- If NATS client is unavailable, API subscriptions are skipped (logged as warning)\n\n## Dependencies\n\n- Go 1.24.0+ (check go.mod for exact version)\n- Node.js (for frontend builds)\n- SQLite 3 (only supported database)\n- Optional: NATS server for NATS API integration\n","numLines":37,"startLine":280,"totalLines":316}},"sourceToolAssistantUUID":"f5557cf4-4494-4c7e-bdeb-80b2ab37c0e2","userType":"external","entrypoint":"cli","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"50633cd8-2ab8-4107-9a64-7f258725f2c3","version":"2.1.78","gitBranch":"hotfix","slug":"eventual-meandering-frog"}
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{"parentUuid":"1b5fbfcd-99da-4e32-b02a-e264431c4f7f","isSidechain":false,"promptId":"0be7b781-e914-4948-9577-6a6a0c7e6920","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CAd8mvgV32i8JMFe2ZF1TK","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/CLAUDE.md has been updated successfully."}]},"uuid":"57374c9b-10eb-4fd1-b749-0bbfc6718294","timestamp":"2026-03-18T09:03:56.383Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/CLAUDE.md","oldString":"## Dependencies","newString":"## Development Guidelines\n\n### Performance\n\nThis application processes large volumes of HPC monitoring data (metrics, job\nrecords, archives) at scale. All code changes must prioritize maximum throughput\nand minimal latency. Avoid unnecessary allocations, prefer streaming over\nbuffering, and be mindful of lock contention. When in doubt, benchmark.\n\n### Change Impact Analysis\n\nFor any significant change, you MUST:\n\n1. **Check all call paths**: Trace every caller of modified functions to ensure\n correctness is preserved throughout the call chain.\n2. **Evaluate side effects**: Identify and verify all side effects — database\n writes, cache invalidations, channel sends, goroutine lifecycle changes, file\n I/O, and external API calls.\n3. **Consider concurrency implications**: This codebase uses goroutines and\n channels extensively. Verify that changes do not introduce races, deadlocks,\n or contention bottlenecks.\n\n## Dependencies","originalFile":"# CLAUDE.md\n\nThis file provides guidance to Claude Code (claude.ai/code) when working with\ncode in this repository.\n\n## Project Overview\n\nClusterCockpit is a job-specific performance monitoring framework for HPC\nclusters. This is a Golang backend that provides REST and GraphQL APIs, serves a\nSvelte-based frontend, and manages job archives and metric data from various\ntime-series databases.\n\n## Build and Development Commands\n\n### Building\n\n```bash\n# Build everything (frontend + backend)\nmake\n\n# Build only the frontend\nmake frontend\n\n# Build only the backend (requires frontend to be built first)\ngo build -ldflags='-s -X main.date=$(date +\"%Y-%m-%d:T%H:%M:%S\") -X main.version=1.5.0 -X main.commit=$(git rev-parse --short HEAD)' ./cmd/cc-backend\n```\n\n### Testing\n\n```bash\n# Run all tests\nmake test\n\n# Run tests with verbose output\ngo test -v ./...\n\n# Run tests for a specific package\ngo test ./internal/repository\n```\n\n### Code Generation\n\n```bash\n# Regenerate GraphQL schema and resolvers (after modifying api/schema.graphqls)\nmake graphql\n\n# Regenerate Swagger/OpenAPI docs (after modifying API comments)\nmake swagger\n```\n\n### Frontend Development\n\n```bash\ncd web/frontend\n\n# Install dependencies\nnpm install\n\n# Build for production\nnpm run build\n\n# Development mode with watch\nnpm run dev\n```\n\n### Running\n\n```bash\n# Initialize database and create admin user\n./cc-backend -init-db -add-user demo:admin:demo\n\n# Start server in development mode (enables GraphQL Playground and Swagger UI)\n./cc-backend -server -dev -loglevel info\n\n# Start demo with sample data\n./startDemo.sh\n```\n\n## Architecture\n\n### Backend Structure\n\nThe backend follows a layered architecture with clear separation of concerns:\n\n- **cmd/cc-backend**: Entry point, orchestrates initialization of all subsystems\n- **internal/repository**: Data access layer using repository pattern\n - Abstracts database operations (SQLite3 only)\n - Implements LRU caching for performance\n - Provides repositories for Job, User, Node, and Tag entities\n - Transaction support for batch operations\n- **internal/api**: REST API endpoints (Swagger/OpenAPI documented)\n- **internal/graph**: GraphQL API (uses gqlgen)\n - Schema in `api/schema.graphqls`\n - Generated code in `internal/graph/generated/`\n - Resolvers in `internal/graph/schema.resolvers.go`\n- **internal/auth**: Authentication layer\n - Supports local accounts, LDAP, OIDC, and JWT tokens\n - Implements rate limiting for login attempts\n- **pkg/metricstore**: Metric store with data loading API\n - In-memory metric storage with checkpointing\n - Query API for loading job metric data\n- **internal/archiver**: Job archiving to file-based archive\n- **internal/api/nats.go**: NATS-based API for job and node operations\n - Subscribes to NATS subjects for job events (start/stop)\n - Handles node state updates via NATS\n - Uses InfluxDB line protocol message format\n- **pkg/archive**: Job archive backend implementations\n - File system backend (default)\n - S3 backend\n - SQLite backend (experimental)\n - **parquet** sub-package: Parquet format support (schema, reader, writer, conversion)\n- **internal/metricstoreclient**: Client for cc-metric-store queries\n\n### Frontend Structure\n\n- **web/frontend**: Svelte 5 application\n - Uses Rollup for building\n - Components organized by feature (analysis, job, user, etc.)\n - GraphQL client using @urql/svelte\n - Bootstrap 5 + SvelteStrap for UI\n - uPlot for time-series visualization\n- **web/templates**: Server-side Go templates\n\n### Key Concepts\n\n**Job Archive**: Completed jobs are stored in a file-based archive following the\n[ClusterCockpit job-archive\nspecification](https://github.com/ClusterCockpit/cc-specifications/tree/master/job-archive).\nEach job has a `meta.json` file with metadata and metric data files.\n\n**Metric Data Repositories**: Time-series metric data is stored separately from\njob metadata. The system supports multiple backends (cc-metric-store is\nrecommended). Configuration is per-cluster in `config.json`.\n\n**Authentication Flow**:\n\n1. Multiple authenticators can be configured (local, LDAP, OIDC, JWT)\n2. Each authenticator's `CanLogin` method is called to determine if it should handle the request\n3. The first authenticator that returns true performs the actual `Login`\n4. JWT tokens are used for API authentication\n\n**Database Migrations**: SQL migrations in `internal/repository/migrations/sqlite3/` are\napplied automatically on startup. Version tracking in `version` table.\n\n**Scopes**: Metrics can be collected at different scopes:\n\n- Node scope (always available)\n- Core scope (for jobs with ≤8 nodes)\n- Accelerator scope (for GPU/accelerator metrics)\n\n## Configuration\n\n- **config.json**: Main configuration (clusters, metric repositories, archive settings)\n - `main.api-subjects`: NATS subject configuration (optional)\n - `subject-job-event`: Subject for job start/stop events (e.g., \"cc.job.event\")\n - `subject-node-state`: Subject for node state updates (e.g., \"cc.node.state\")\n - `job-concurrency`: Worker goroutines for job events (default: 8)\n - `node-concurrency`: Worker goroutines for node state events (default: 2)\n - `nats`: NATS client connection configuration (optional)\n - `address`: NATS server address (e.g., \"nats://localhost:4222\")\n - `username`: Authentication username (optional)\n - `password`: Authentication password (optional)\n - `creds-file-path`: Path to NATS credentials file (optional)\n- **.env**: Environment variables (secrets like JWT keys)\n - Copy from `configs/env-template.txt`\n - NEVER commit this file\n- **cluster.json**: Cluster topology and metric definitions (loaded from archive or config)\n\n## Database\n\n- Default: SQLite 3 (`./var/job.db`)\n- Connection managed by `internal/repository`\n- Schema version in `internal/repository/migration.go`\n\n## Code Generation\n\n**GraphQL** (gqlgen):\n\n- Schema: `api/schema.graphqls`\n- Config: `gqlgen.yml`\n- Generated code: `internal/graph/generated/`\n- Custom resolvers: `internal/graph/schema.resolvers.go`\n- Run `make graphql` after schema changes\n\n**Swagger/OpenAPI**:\n\n- Annotations in `internal/api/*.go`\n- Generated docs: `internal/api/docs.go`, `api/swagger.yaml`\n- Run `make swagger` after API changes\n\n## Testing Conventions\n\n- Test files use `_test.go` suffix\n- Test data in `testdata/` subdirectories\n- Repository tests use in-memory SQLite\n- API tests use httptest\n\n## Common Workflows\n\n### Adding a new GraphQL field\n\n1. Edit schema in `api/schema.graphqls`\n2. Run `make graphql`\n3. Implement resolver in `internal/graph/schema.resolvers.go`\n\n### Adding a new REST endpoint\n\n1. Add handler in `internal/api/*.go`\n2. Add route in `internal/api/rest.go`\n3. Add Swagger annotations\n4. Run `make swagger`\n\n### Adding a new metric data backend\n\n1. Implement metric loading functions in `pkg/metricstore/query.go`\n2. Add cluster configuration to metric store initialization\n3. Update config.json schema documentation\n\n### Modifying database schema\n\n1. Create new migration in `internal/repository/migrations/sqlite3/`\n2. Increment `repository.Version`\n3. Test with fresh database and existing database\n\n## NATS API\n\nThe backend supports a NATS-based API as an alternative to the REST API for job and node operations.\n\n### Setup\n\n1. Configure NATS client connection in `config.json`:\n ```json\n {\n \"nats\": {\n \"address\": \"nats://localhost:4222\",\n \"username\": \"user\",\n \"password\": \"pass\"\n }\n }\n ```\n\n2. Configure API subjects in `config.json` under `main`:\n ```json\n {\n \"main\": {\n \"api-subjects\": {\n \"subject-job-event\": \"cc.job.event\",\n \"subject-node-state\": \"cc.node.state\",\n \"job-concurrency\": 8,\n \"node-concurrency\": 2\n }\n }\n }\n ```\n - `subject-job-event` (required): NATS subject for job start/stop events\n - `subject-node-state` (required): NATS subject for node state updates\n - `job-concurrency` (optional, default: 8): Number of concurrent worker goroutines for job events\n - `node-concurrency` (optional, default: 2): Number of concurrent worker goroutines for node state events\n\n### Message Format\n\nMessages use **InfluxDB line protocol** format with the following structure:\n\n#### Job Events\n\n**Start Job:**\n```\njob,function=start_job event=\"{\\\"jobId\\\":123,\\\"user\\\":\\\"alice\\\",\\\"cluster\\\":\\\"test\\\", ...}\" 1234567890000000000\n```\n\n**Stop Job:**\n```\njob,function=stop_job event=\"{\\\"jobId\\\":123,\\\"cluster\\\":\\\"test\\\",\\\"startTime\\\":1234567890,\\\"stopTime\\\":1234571490,\\\"jobState\\\":\\\"completed\\\"}\" 1234571490000000000\n```\n\n**Tags:**\n- `function`: Either `start_job` or `stop_job`\n\n**Fields:**\n- `event`: JSON payload containing job data (see REST API documentation for schema)\n\n#### Node State Updates\n\n```json\n{\n \"cluster\": \"testcluster\",\n \"nodes\": [\n {\n \"hostname\": \"node001\",\n \"states\": [\"allocated\"],\n \"cpusAllocated\": 8,\n \"memoryAllocated\": 16384,\n \"gpusAllocated\": 0,\n \"jobsRunning\": 1\n }\n ]\n}\n```\n\n### Implementation Notes\n\n- NATS API mirrors REST API functionality but uses messaging\n- Job start/stop events are processed asynchronously via configurable worker pools\n- Duplicate job detection is handled (same as REST API)\n- All validation rules from REST API apply\n- Node state updates include health checks against the metric store (identical to REST handler): nodes are grouped by subcluster, metric configurations are fetched, and `HealthCheck()` is called per subcluster. Nodes default to `MonitoringStateFailed` if no health data is available.\n- Messages are logged; no responses are sent back to publishers\n- If NATS client is unavailable, API subscriptions are skipped (logged as warning)\n\n## Dependencies\n\n- Go 1.24.0+ (check go.mod for exact version)\n- Node.js (for frontend builds)\n- SQLite 3 (only supported database)\n- Optional: NATS server for NATS API integration\n","structuredPatch":[{"oldStart":307,"oldLines":6,"newStart":307,"newLines":28,"lines":[" - Messages are logged; no responses are sent back to publishers"," - If NATS client is unavailable, API subscriptions are skipped (logged as warning)"," ","+## Development Guidelines","+","+### Performance","+","+This application processes large volumes of HPC monitoring data (metrics, job","+records, archives) at scale. All code changes must prioritize maximum throughput","+and minimal latency. 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