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{"parentUuid":"115e31cc-517e-40f4-9bb8-b86659fe05d2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":"Implement the following plan:\n\n# Plan: Improve Group-By Stats Query Performance\n\n## Context\n\nThe stats endpoint (`JobsStatistics` GraphQL query) times out for large job counts. A typical stats page with grouping (by user/project/cluster) triggers **3 separate full-table-scan + GROUP BY queries** against the job table:\n1. `JobsStatsGrouped` — main aggregation (totalJobs, walltime, node/core/acc hours)\n2. `AddJobCountGrouped(\"short\")` — second full scan for short-running jobs count\n3. `AddJobCountGrouped(\"running\")` — third full scan for running jobs count\n\nEach query independently scans and aggregates every row matching the filter. With millions of jobs, this is prohibitively slow.\n\nAdditional minor inefficiencies:\n- Unnecessary `LEFT JOIN hpc_user` even when grouping by project/cluster/subcluster\n- O(n × bins) loop matching in duration histogram bin assignment (should be O(1) map lookup)\n- `time.Now().Unix()` called multiple times in a single query build\n\n## Critical Files\n\n- `internal/repository/stats.go` — all stats/histogram logic\n- `internal/graph/schema.resolvers.go` — GraphQL resolver that orchestrates query calls (lines 655–711)\n\n## Implementation\n\n### Fix 1: Merge 3 queries into 1 (biggest impact)\n\nModify `buildStatsQuery` to add `runningJobs` and `shortJobs` as inline conditional counts:\n\n```sql\nCOUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs,\nCOUNT(CASE WHEN job.duration < :shortThreshold THEN 1 END) as shortJobs\n```\n\n- `buildStatsQuery(filter, col)` gains a `shortThreshold int` parameter and always returns these two extra columns\n- `JobsStatsGrouped` and `JobsStats` scan the new columns and populate `RunningJobs`/`ShortJobs` on each result\n- In `schema.resolvers.go`, remove the separate `AddJobCountGrouped` / `AddJobCount` calls when the main stats were already fetched (i.e., when `requireField(ctx, \"totalJobs\")` was true)\n- The separate `buildCountQuery` / `JobCountGrouped` / `AddJobCountGrouped` / `AddJobCount` paths are kept for the edge case where ONLY `runningJobs`/`shortJobs` are requested without main stats fields\n\n### Fix 2: Remove unnecessary LEFT JOIN for non-user grouping\n\nIn `buildStatsQuery`, only add `.LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\")` and select `name` when `col == \"job.hpc_user\"`. For project/cluster/subcluster grouping, omit the join and select `'' as name` or remove that column entirely.\n\nThis eliminates a join that always returns NULL for 3 of the 4 grouping dimensions.\n\n### Fix 3: Fix O(n × bins) histogram bin matching\n\nIn `jobsDurationStatisticsHistogram` (stats.go:848–853), replace the inner loop over `points` with a map lookup:\n\n```go\nbinMap := make(map[int]int, len(points))\nfor i, p := range points {\n    binMap[p.Value] = i\n}\n// then in rows.Next():\nif idx, ok := binMap[point.Value * binSizeSeconds]; ok {\n    points[idx].Count = point.Count\n}\n```\n\nSame fix applies to `jobsMetricStatisticsHistogram` (stats.go:988–993).\n\n### Fix 4: Compute timestamp once per query build\n\nIn `buildStatsQuery`, call `time.Now().Unix()` once at the top of the function and reuse the variable in all 4–5 CASE WHEN expressions. Minor correctness and readability improvement.\n\n## Resolver Change Summary\n\n`schema.resolvers.go` around line 667–681:\n\n```go\n// Before: 3 separate queries\nstats, err = r.Repo.JobsStatsGrouped(...)   // query 1\nstats, err = r.Repo.AddJobCountGrouped(..., \"short\")   // query 2\nstats, err = r.Repo.AddJobCountGrouped(..., \"running\") // query 3\n\n// After: 1 query (runningJobs/shortJobs already in stats result)\nstats, err = r.Repo.JobsStatsGrouped(...)   // query 1 only\n// AddJobCountGrouped calls removed when main stats were fetched\n```\n\n## Verification\n\n1. Build: `go build ./...`\n2. Run existing tests: `go test ./internal/repository/...`\n3. Manual test: start server with `./cc-backend -server -dev`, open the analysis/stats page with a large cluster and verify it loads without timeout\n4. Check debug log output for `Timer JobsStatsGrouped` — should drop significantly (1 query vs 3)\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/be115332-505c-4b8f-acdf-f8de6ddbed14.jsonl"},"uuid":"6078cd0c-d1f9-49a0-a3c4-553c4bc74d13","timestamp":"2026-03-11T13:38:48.100Z","planContent":"# Plan: Improve Group-By Stats Query Performance\n\n## Context\n\nThe stats endpoint (`JobsStatistics` GraphQL query) times out for large job counts. A typical stats page with grouping (by user/project/cluster) triggers **3 separate full-table-scan + GROUP BY queries** against the job table:\n1. `JobsStatsGrouped` — main aggregation (totalJobs, walltime, node/core/acc hours)\n2. `AddJobCountGrouped(\"short\")` — second full scan for short-running jobs count\n3. `AddJobCountGrouped(\"running\")` — third full scan for running jobs count\n\nEach query independently scans and aggregates every row matching the filter. With millions of jobs, this is prohibitively slow.\n\nAdditional minor inefficiencies:\n- Unnecessary `LEFT JOIN hpc_user` even when grouping by project/cluster/subcluster\n- O(n × bins) loop matching in duration histogram bin assignment (should be O(1) map lookup)\n- `time.Now().Unix()` called multiple times in a single query build\n\n## Critical Files\n\n- `internal/repository/stats.go` — all stats/histogram logic\n- `internal/graph/schema.resolvers.go` — GraphQL resolver that orchestrates query calls (lines 655–711)\n\n## Implementation\n\n### Fix 1: Merge 3 queries into 1 (biggest impact)\n\nModify `buildStatsQuery` to add `runningJobs` and `shortJobs` as inline conditional counts:\n\n```sql\nCOUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs,\nCOUNT(CASE WHEN job.duration < :shortThreshold THEN 1 END) as shortJobs\n```\n\n- `buildStatsQuery(filter, col)` gains a `shortThreshold int` parameter and always returns these two extra columns\n- `JobsStatsGrouped` and `JobsStats` scan the new columns and populate `RunningJobs`/`ShortJobs` on each result\n- In `schema.resolvers.go`, remove the separate `AddJobCountGrouped` / `AddJobCount` calls when the main stats were already fetched (i.e., when `requireField(ctx, \"totalJobs\")` was true)\n- The separate `buildCountQuery` / `JobCountGrouped` / `AddJobCountGrouped` / `AddJobCount` paths are kept for the edge case where ONLY `runningJobs`/`shortJobs` are requested without main stats fields\n\n### Fix 2: Remove unnecessary LEFT JOIN for non-user grouping\n\nIn `buildStatsQuery`, only add `.LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\")` and select `name` when `col == \"job.hpc_user\"`. For project/cluster/subcluster grouping, omit the join and select `'' as name` or remove that column entirely.\n\nThis eliminates a join that always returns NULL for 3 of the 4 grouping dimensions.\n\n### Fix 3: Fix O(n × bins) histogram bin matching\n\nIn `jobsDurationStatisticsHistogram` (stats.go:848–853), replace the inner loop over `points` with a map lookup:\n\n```go\nbinMap := make(map[int]int, len(points))\nfor i, p := range points {\n    binMap[p.Value] = i\n}\n// then in rows.Next():\nif idx, ok := binMap[point.Value * binSizeSeconds]; ok {\n    points[idx].Count = point.Count\n}\n```\n\nSame fix applies to `jobsMetricStatisticsHistogram` (stats.go:988–993).\n\n### Fix 4: Compute timestamp once per query build\n\nIn `buildStatsQuery`, call `time.Now().Unix()` once at the top of the function and reuse the variable in all 4–5 CASE WHEN expressions. Minor correctness and readability improvement.\n\n## Resolver Change Summary\n\n`schema.resolvers.go` around line 667–681:\n\n```go\n// Before: 3 separate queries\nstats, err = r.Repo.JobsStatsGrouped(...)   // query 1\nstats, err = r.Repo.AddJobCountGrouped(..., \"short\")   // query 2\nstats, err = r.Repo.AddJobCountGrouped(..., \"running\") // query 3\n\n// After: 1 query (runningJobs/shortJobs already in stats result)\nstats, err = r.Repo.JobsStatsGrouped(...)   // query 1 only\n// AddJobCountGrouped calls removed when main stats were fetched\n```\n\n## Verification\n\n1. Build: `go build ./...`\n2. Run existing tests: `go test ./internal/repository/...`\n3. Manual test: start server with `./cc-backend -server -dev`, open the analysis/stats page with a large cluster and verify it loads without timeout\n4. Check debug log output for `Timer JobsStatsGrouped` — should drop significantly (1 query vs 3)\n"}
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{"parentUuid":"8a467d4d-173b-4734-9903-3b0f5eefcf7d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011BVJWhrmQaBZhHNtMRVLVB","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file contains job statistics and histogram generation functionality for the JobRepository.\n     7→//\n     8→// # Job Statistics\n     9→//\n    10→// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n    11→// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n    12→//   - Overall (JobsStats): Single aggregate across all matching jobs\n    13→//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n    14→//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n    15→//\n    16→// All statistics methods support filtering via JobFilter and respect security contexts.\n    17→//\n    18→// # Histograms\n    19→//\n    20→// Histogram methods generate distribution data for visualization:\n    21→//   - Duration, nodes, cores, accelerators (AddHistograms)\n    22→//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n    23→//\n    24→// Histograms use intelligent binning:\n    25→//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n    26→//   - Resources: Natural value-based bins\n    27→//   - Metrics: Normalized to peak values with configurable bin counts\n    28→//\n    29→// # Running vs. Completed Jobs\n    30→//\n    31→// Statistics handle running jobs specially:\n    32→//   - Duration calculated as (now - start_time) for running jobs\n    33→//   - Metric histograms for running jobs load data from metric backend instead of footprint\n    34→//   - Job state filtering distinguishes running/completed jobs\n    35→//\n    36→// # Performance Considerations\n    37→//\n    38→// - All queries use prepared statements via stmtCache\n    39→// - Complex aggregations use SQL for efficiency\n    40→// - Histogram pre-initialization ensures consistent bin ranges\n    41→// - Metric histogram queries limited to 5000 jobs for running job analysis\n    42→\n    43→package repository\n    44→\n    45→import (\n    46→\t\"context\"\n    47→\t\"database/sql\"\n    48→\t\"fmt\"\n    49→\t\"time\"\n    50→\n    51→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n    52→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n    53→\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n    54→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    55→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    56→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    57→\tsq \"github.com/Masterminds/squirrel\"\n    58→)\n    59→\n    60→// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n    61→// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n    62→// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\n    63→var groupBy2column = map[model.Aggregate]string{\n    64→\tmodel.AggregateUser:       \"job.hpc_user\",\n    65→\tmodel.AggregateProject:    \"job.project\",\n    66→\tmodel.AggregateCluster:    \"job.cluster\",\n    67→\tmodel.AggregateSubcluster: \"job.subcluster\",\n    68→}\n    69→\n    70→// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n    71→// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n    72→// Column names match the AS aliases used in buildStatsQuery.\n    73→var sortBy2column = map[model.SortByAggregate]string{\n    74→\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n    75→\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n    76→\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n    77→\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n    78→\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n    79→\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n    80→\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n    81→\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n    82→\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n    83→}\n    84→\n    85→// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n    86→//\n    87→// Parameters:\n    88→//   - filter: Job filters to apply (cluster, user, time range, etc.)\n    89→//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n    90→//   - col: Column name to GROUP BY; empty string for total count without grouping\n    91→//\n    92→// Returns a SelectBuilder that produces either:\n    93→//   - Single count: COUNT(job.id) when col is empty\n    94→//   - Grouped counts: col, COUNT(job.id) when col is specified\n    95→//\n    96→// The kind parameter enables counting specific job categories:\n    97→//   - \"running\": Only jobs with job_state = 'running'\n    98→//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n    99→//   - empty: All jobs matching filters\n   100→func (r *JobRepository) buildCountQuery(\n   101→\tfilter []*model.JobFilter,\n   102→\tkind string,\n   103→\tcol string,\n   104→) sq.SelectBuilder {\n   105→\tvar query sq.SelectBuilder\n   106→\n   107→\tif col != \"\" {\n   108→\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n   109→\t} else {\n   110→\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n   111→\t}\n   112→\n   113→\tswitch kind {\n   114→\tcase \"running\":\n   115→\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n   116→\tcase \"short\":\n   117→\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n   118→\t}\n   119→\n   120→\tfor _, f := range filter {\n   121→\t\tquery = BuildWhereClause(f, query)\n   122→\t}\n   123→\n   124→\treturn query\n   125→}\n   126→\n   127→// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n   128→//\n   129→// Parameters:\n   130→//   - filter: Job filters to apply (cluster, user, time range, etc.)\n   131→//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n   132→//\n   133→// Returns a SelectBuilder that produces comprehensive statistics:\n   134→//   - totalJobs: Count of jobs\n   135→//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n   136→//   - totalWalltime: Sum of job durations in hours\n   137→//   - totalNodes: Sum of nodes used across all jobs\n   138→//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n   139→//   - totalCores: Sum of hardware threads used across all jobs\n   140→//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n   141→//   - totalAccs: Sum of accelerators used across all jobs\n   142→//   - totalAccHours: Sum of (duration × num_acc) in hours\n   143→//\n   144→// Special handling:\n   145→//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n   146→//   - Grouped queries: Also select grouping column and user's display name from hpc_user table\n   147→//   - All time values converted from seconds to hours (÷ 3600) and rounded\n   148→func (r *JobRepository) buildStatsQuery(\n   149→\tfilter []*model.JobFilter,\n   150→\tcol string,\n   151→) sq.SelectBuilder {\n   152→\tvar query sq.SelectBuilder\n   153→\n   154→\tif col != \"\" {\n   155→\t\tquery = sq.Select(\n   156→\t\t\tcol,\n   157→\t\t\t\"name\",\n   158→\t\t\t\"COUNT(job.id) as totalJobs\",\n   159→\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n   160→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n   161→\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n   162→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n   163→\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n   164→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n   165→\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n   166→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n   167→\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n   168→\t} else {\n   169→\t\tquery = sq.Select(\n   170→\t\t\t\"COUNT(job.id) as totalJobs\",\n   171→\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n   172→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n   173→\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n   174→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n   175→\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n   176→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n   177→\t\t\t`CAST(SUM(job.num_acc) as int)`,\n   178→\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n   179→\t\t).From(\"job\")\n   180→\t}\n   181→\n   182→\tfor _, f := range filter {\n   183→\t\tquery = BuildWhereClause(f, query)\n   184→\t}\n   185→\n   186→\treturn query\n   187→}\n   188→\n   189→// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n   190→//\n   191→// This is the primary method for generating aggregated statistics views in the UI, providing\n   192→// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n   193→//\n   194→// Parameters:\n   195→//   - ctx: Context for security checks and cancellation\n   196→//   - filter: Filters to apply (time range, cluster, job state, etc.)\n   197→//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n   198→//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n   199→//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n   200→//\n   201→// Returns a slice of JobsStatistics, one per group, with:\n   202→//   - ID: The group identifier (username, project name, cluster name, etc.)\n   203→//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n   204→//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n   205→//\n   206→// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n   207→// Performance: Results are sorted in SQL and pagination applied before scanning rows.\n   208→func (r *JobRepository) JobsStatsGrouped(\n   209→\tctx context.Context,\n   210→\tfilter []*model.JobFilter,\n   211→\tpage *model.PageRequest,\n   212→\tsortBy *model.SortByAggregate,\n   213→\tgroupBy *model.Aggregate,\n   214→) ([]*model.JobsStatistics, error) {\n   215→\tstart := time.Now()\n   216→\tcol := groupBy2column[*groupBy]\n   217→\tquery := r.buildStatsQuery(filter, col)\n   218→\n   219→\tquery, err := SecurityCheck(ctx, query)\n   220→\tif err != nil {\n   221→\t\treturn nil, err\n   222→\t}\n   223→\n   224→\tif sortBy != nil {\n   225→\t\tsortBy := sortBy2column[*sortBy]\n   226→\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n   227→\t}\n   228→\tif page != nil && page.ItemsPerPage != -1 {\n   229→\t\tlimit := uint64(page.ItemsPerPage)\n   230→\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n   231→\t}\n   232→\n   233→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n   234→\tif err != nil {\n   235→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n   236→\t\treturn nil, err\n   237→\t}\n   238→\tdefer rows.Close()\n   239→\n   240→\tstats := make([]*model.JobsStatistics, 0, 100)\n   241→\n   242→\tfor rows.Next() {\n   243→\t\tvar id sql.NullString\n   244→\t\tvar name sql.NullString\n   245→\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n   246→\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n   247→\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n   248→\t\t\treturn nil, err\n   249→\t\t}\n   250→\n   251→\t\tif id.Valid {\n   252→\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n   253→\t\t\tvar personName string\n   254→\n   255→\t\t\tif name.Valid {\n   256→\t\t\t\tpersonName = name.String\n   257→\t\t\t}\n   258→\n   259→\t\t\tif jobs.Valid {\n   260→\t\t\t\ttotalJobs = int(jobs.Int64)\n   261→\t\t\t}\n   262→\n   263→\t\t\tif users.Valid {\n   264→\t\t\t\ttotalUsers = int(users.Int64)\n   265→\t\t\t}\n   266→\n   267→\t\t\tif walltime.Valid {\n   268→\t\t\t\ttotalWalltime = int(walltime.Int64)\n   269→\t\t\t}\n   270→\n   271→\t\t\tif nodes.Valid {\n   272→\t\t\t\ttotalNodes = int(nodes.Int64)\n   273→\t\t\t}\n   274→\t\t\tif cores.Valid {\n   275→\t\t\t\ttotalCores = int(cores.Int64)\n   276→\t\t\t}\n   277→\t\t\tif accs.Valid {\n   278→\t\t\t\ttotalAccs = int(accs.Int64)\n   279→\t\t\t}\n   280→\n   281→\t\t\tif nodeHours.Valid {\n   282→\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n   283→\t\t\t}\n   284→\t\t\tif coreHours.Valid {\n   285→\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n   286→\t\t\t}\n   287→\t\t\tif accHours.Valid {\n   288→\t\t\t\ttotalAccHours = int(accHours.Int64)\n   289→\t\t\t}\n   290→\n   291→\t\t\tif col == \"job.hpc_user\" {\n   292→\t\t\t\t// name := r.getUserName(ctx, id.String)\n   293→\t\t\t\tstats = append(stats,\n   294→\t\t\t\t\t&model.JobsStatistics{\n   295→\t\t\t\t\t\tID:             id.String,\n   296→\t\t\t\t\t\tName:           personName,\n   297→\t\t\t\t\t\tTotalJobs:      totalJobs,\n   298→\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n   299→\t\t\t\t\t\tTotalNodes:     totalNodes,\n   300→\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n   301→\t\t\t\t\t\tTotalCores:     totalCores,\n   302→\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n   303→\t\t\t\t\t\tTotalAccs:      totalAccs,\n   304→\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n   305→\t\t\t\t\t})\n   306→\t\t\t} else {\n   307→\t\t\t\tstats = append(stats,\n   308→\t\t\t\t\t&model.JobsStatistics{\n   309→\t\t\t\t\t\tID:             id.String,\n   310→\t\t\t\t\t\tTotalJobs:      totalJobs,\n   311→\t\t\t\t\t\tTotalUsers:     totalUsers,\n   312→\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n   313→\t\t\t\t\t\tTotalNodes:     totalNodes,\n   314→\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n   315→\t\t\t\t\t\tTotalCores:     totalCores,\n   316→\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n   317→\t\t\t\t\t\tTotalAccs:      totalAccs,\n   318→\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n   319→\t\t\t\t\t})\n   320→\t\t\t}\n   321→\t\t}\n   322→\t}\n   323→\n   324→\tif err := rows.Err(); err != nil {\n   325→\t\treturn nil, err\n   326→\t}\n   327→\n   328→\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n   329→\treturn stats, nil\n   330→}\n   331→\n   332→// JobsStats computes overall job statistics across all matching jobs without grouping.\n   333→//\n   334→// This method provides a single aggregate view of job metrics, useful for dashboard\n   335→// summaries and overall system utilization reports.\n   336→//\n   337→// Parameters:\n   338→//   - ctx: Context for security checks and cancellation\n   339→//   - filter: Filters to apply (time range, cluster, job state, etc.)\n   340→//\n   341→// Returns a single-element slice containing aggregate statistics:\n   342→//   - totalJobs, totalUsers, totalWalltime\n   343→//   - totalNodeHours, totalCoreHours, totalAccHours\n   344→//\n   345→// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n   346→// Security checks are applied via SecurityCheck to respect user access levels.\n   347→func (r *JobRepository) JobsStats(\n   348→\tctx context.Context,\n   349→\tfilter []*model.JobFilter,\n   350→) ([]*model.JobsStatistics, error) {\n   351→\tstart := time.Now()\n   352→\tquery := r.buildStatsQuery(filter, \"\")\n   353→\tquery, err := SecurityCheck(ctx, query)\n   354→\tif err != nil {\n   355→\t\treturn nil, err\n   356→\t}\n   357→\n   358→\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n   359→\tstats := make([]*model.JobsStatistics, 0, 1)\n   360→\n   361→\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n   362→\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n   363→\t\tcclog.Warn(\"Error while scanning rows\")\n   364→\t\treturn nil, err\n   365→\t}\n   366→\n   367→\tif jobs.Valid {\n   368→\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n   369→\n   370→\t\tif nodeHours.Valid {\n   371→\t\t\ttotalNodeHours = int(nodeHours.Int64)\n   372→\t\t}\n   373→\t\tif coreHours.Valid {\n   374→\t\t\ttotalCoreHours = int(coreHours.Int64)\n   375→\t\t}\n   376→\t\tif accHours.Valid {\n   377→\t\t\ttotalAccHours = int(accHours.Int64)\n   378→\t\t}\n   379→\t\tstats = append(stats,\n   380→\t\t\t&model.JobsStatistics{\n   381→\t\t\t\tTotalJobs:      int(jobs.Int64),\n   382→\t\t\t\tTotalUsers:     int(users.Int64),\n   383→\t\t\t\tTotalWalltime:  int(walltime.Int64),\n   384→\t\t\t\tTotalNodeHours: totalNodeHours,\n   385→\t\t\t\tTotalCoreHours: totalCoreHours,\n   386→\t\t\t\tTotalAccHours:  totalAccHours,\n   387→\t\t\t})\n   388→\t}\n   389→\n   390→\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n   391→\treturn stats, nil\n   392→}\n   393→\n   394→// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n   395→// Returns 0.0 if the metric is not found or statType is invalid.\n   396→//\n   397→// Parameters:\n   398→//   - job: Job struct with populated Statistics field\n   399→//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n   400→//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n   401→//\n   402→// Returns the requested statistic value or 0.0 if not found.\n   403→func LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n   404→\tif stats, ok := job.Statistics[metric]; ok {\n   405→\t\tswitch statType {\n   406→\t\tcase \"avg\":\n   407→\t\t\treturn stats.Avg\n   408→\t\tcase \"max\":\n   409→\t\t\treturn stats.Max\n   410→\t\tcase \"min\":\n   411→\t\t\treturn stats.Min\n   412→\t\tdefault:\n   413→\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n   414→\t\t}\n   415→\t}\n   416→\n   417→\treturn 0.0\n   418→}\n   419→\n   420→// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n   421→//\n   422→// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n   423→// avoiding the overhead of calculating walltime and resource usage metrics.\n   424→//\n   425→// Parameters:\n   426→//   - ctx: Context for security checks\n   427→//   - filter: Filters to apply\n   428→//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n   429→//\n   430→// Returns JobsStatistics with only ID and TotalJobs populated for each group.\n   431→func (r *JobRepository) JobCountGrouped(\n   432→\tctx context.Context,\n   433→\tfilter []*model.JobFilter,\n   434→\tgroupBy *model.Aggregate,\n   435→) ([]*model.JobsStatistics, error) {\n   436→\tstart := time.Now()\n   437→\tcol := groupBy2column[*groupBy]\n   438→\tquery := r.buildCountQuery(filter, \"\", col)\n   439→\tquery, err := SecurityCheck(ctx, query)\n   440→\tif err != nil {\n   441→\t\treturn nil, err\n   442→\t}\n   443→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n   444→\tif err != nil {\n   445→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n   446→\t\treturn nil, err\n   447→\t}\n   448→\tdefer rows.Close()\n   449→\n   450→\tstats := make([]*model.JobsStatistics, 0, 100)\n   451→\n   452→\tfor rows.Next() {\n   453→\t\tvar id sql.NullString\n   454→\t\tvar cnt sql.NullInt64\n   455→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n   456→\t\t\tcclog.Warn(\"Error while scanning rows\")\n   457→\t\t\treturn nil, err\n   458→\t\t}\n   459→\t\tif id.Valid {\n   460→\t\t\tstats = append(stats,\n   461→\t\t\t\t&model.JobsStatistics{\n   462→\t\t\t\t\tID:        id.String,\n   463→\t\t\t\t\tTotalJobs: int(cnt.Int64),\n   464→\t\t\t\t})\n   465→\t\t}\n   466→\t}\n   467→\n   468→\tif err := rows.Err(); err != nil {\n   469→\t\treturn nil, err\n   470→\t}\n   471→\n   472→\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n   473→\treturn stats, nil\n   474→}\n   475→\n   476→// AddJobCountGrouped augments existing statistics with additional job counts by category.\n   477→//\n   478→// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n   479→// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n   480→//\n   481→// Parameters:\n   482→//   - ctx: Context for security checks\n   483→//   - filter: Filters to apply\n   484→//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n   485→//   - stats: Existing statistics to augment (modified in-place by ID matching)\n   486→//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n   487→//\n   488→// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n   489→// Groups without matching jobs will have 0 for the added field.\n   490→func (r *JobRepository) AddJobCountGrouped(\n   491→\tctx context.Context,\n   492→\tfilter []*model.JobFilter,\n   493→\tgroupBy *model.Aggregate,\n   494→\tstats []*model.JobsStatistics,\n   495→\tkind string,\n   496→) ([]*model.JobsStatistics, error) {\n   497→\tstart := time.Now()\n   498→\tcol := groupBy2column[*groupBy]\n   499→\tquery := r.buildCountQuery(filter, kind, col)\n   500→\tquery, err := SecurityCheck(ctx, query)\n   501→\tif err != nil {\n   502→\t\treturn nil, err\n   503→\t}\n   504→\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n   505→\tif err != nil {\n   506→\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n   507→\t\treturn nil, err\n   508→\t}\n   509→\tdefer rows.Close()\n   510→\n   511→\tcounts := make(map[string]int)\n   512→\n   513→\tfor rows.Next() {\n   514→\t\tvar id sql.NullString\n   515→\t\tvar cnt sql.NullInt64\n   516→\t\tif err := rows.Scan(&id, &cnt); err != nil {\n   517→\t\t\tcclog.Warn(\"Error while scanning rows\")\n   518→\t\t\treturn nil, err\n   519→\t\t}\n   520→\t\tif id.Valid {\n   521→\t\t\tcounts[id.String] = int(cnt.Int64)\n   522→\t\t}\n   523→\t}\n   524→\n   525→\tif err := rows.Err(); err != nil {\n   526→\t\treturn nil, err\n   527→\t}\n   528→\n   529→\tswitch kind {\n   530→\tcase \"running\":\n   531→\t\tfor _, s := range stats {\n   532→\t\t\ts.RunningJobs = counts[s.ID]\n   533→\t\t}\n   534→\tcase \"short\":\n   535→\t\tfor _, s := range stats {\n   536→\t\t\ts.ShortJobs = counts[s.ID]\n   537→\t\t}\n   538→\t}\n   539→\n   540→\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n   541→\treturn stats, nil\n   542→}\n   543→\n   544→// AddJobCount augments existing overall statistics with additional job counts by category.\n   545→//\n   546→// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n   547→// to all statistics entries (typically just one).\n   548→//\n   549→// Parameters:\n   550→//   - ctx: Context for security checks\n   551→//   - filter: Filters to apply\n   552→//   - stats: Existing statistics to augment (modified in-place)\n   553→//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n   554→//\n   555→// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\n   556→func (r *JobRepository) AddJobCount(\n   557→\tctx context.Context,\n   558→\tfilter []*model.JobFilter,\n   559→\tstats []*model.JobsStatistics,\n   560→\tkind string,\n   561→) ([]*model.JobsStatistics, error) {\n   562→\tstart := time.Now()\n   563→\tquery := r.buildCountQuery(filter, kind, \"\")\n   564→\tquery, err := SecurityCheck(ctx, query)\n   565→\tif err != nil {\n   566→\t\treturn nil, err\n   567→\t}\n   568→\tvar cnt sql.NullInt64\n   569→\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n   570→\t\tcclog.Warn(\"Error while querying DB for job count\")\n   571→\t\treturn nil, err\n   572→\t}\n   573→\n   574→\tcount := int(cnt.Int64)\n   575→\n   576→\tswitch kind {\n   577→\tcase \"running\":\n   578→\t\tfor _, s := range stats {\n   579→\t\t\ts.RunningJobs = count\n   580→\t\t}\n   581→\tcase \"short\":\n   582→\t\tfor _, s := range stats {\n   583→\t\t\ts.ShortJobs = count\n   584→\t\t}\n   585→\t}\n   586→\n   587→\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n   588→\treturn stats, nil\n   589→}\n   590→\n   591→// AddHistograms augments statistics with distribution histograms for job properties.\n   592→//\n   593→// Generates histogram data for visualization of job duration, node count, core count,\n   594→// and accelerator count distributions. Duration histogram uses intelligent binning based\n   595→// on the requested resolution.\n   596→//\n   597→// Parameters:\n   598→//   - ctx: Context for security checks\n   599→//   - filter: Filters to apply to jobs included in histograms\n   600→//   - stat: Statistics struct to augment (modified in-place)\n   601→//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n   602→//\n   603→// Populates these fields in stat:\n   604→//   - HistDuration: Job duration distribution (zero-padded bins)\n   605→//   - HistNumNodes: Node count distribution\n   606→//   - HistNumCores: Core (hwthread) count distribution\n   607→//   - HistNumAccs: Accelerator count distribution\n   608→//\n   609→// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n   610→// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\n   611→func (r *JobRepository) AddHistograms(\n   612→\tctx context.Context,\n   613→\tfilter []*model.JobFilter,\n   614→\tstat *model.JobsStatistics,\n   615→\tdurationBins *string,\n   616→) (*model.JobsStatistics, error) {\n   617→\tstart := time.Now()\n   618→\n   619→\tvar targetBinCount int\n   620→\tvar targetBinSize int\n   621→\tswitch *durationBins {\n   622→\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n   623→\t\ttargetBinCount = 60\n   624→\t\ttargetBinSize = 60\n   625→\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n   626→\t\ttargetBinCount = 72\n   627→\t\ttargetBinSize = 600\n   628→\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n   629→\t\ttargetBinCount = 48\n   630→\t\ttargetBinSize = 3600\n   631→\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n   632→\t\ttargetBinCount = 12\n   633→\t\ttargetBinSize = 21600\n   634→\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n   635→\t\ttargetBinCount = 14\n   636→\t\ttargetBinSize = 43200\n   637→\tdefault: // 24h\n   638→\t\ttargetBinCount = 24\n   639→\t\ttargetBinSize = 3600\n   640→\t}\n   641→\n   642→\tvar err error\n   643→\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n   644→\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n   645→\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n   646→\tif err != nil {\n   647→\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n   648→\t\treturn nil, err\n   649→\t}\n   650→\n   651→\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n   652→\tif err != nil {\n   653→\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n   654→\t\treturn nil, err\n   655→\t}\n   656→\n   657→\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n   658→\tif err != nil {\n   659→\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n   660→\t\treturn nil, err\n   661→\t}\n   662→\n   663→\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n   664→\tif err != nil {\n   665→\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n   666→\t\treturn nil, err\n   667→\t}\n   668→\n   669→\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n   670→\treturn stat, nil\n   671→}\n   672→\n   673→// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n   674→//\n   675→// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n   676→// and completed jobs differently: running jobs load data from metric backend, completed jobs\n   677→// use footprint data from database.\n   678→//\n   679→// Parameters:\n   680→//   - ctx: Context for security checks\n   681→//   - filter: Filters to apply (MUST contain State filter for running jobs)\n   682→//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n   683→//   - stat: Statistics struct to augment (modified in-place)\n   684→//   - targetBinCount: Number of histogram bins (default: 10)\n   685→//\n   686→// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n   687→//\n   688→// Binning algorithm:\n   689→//   - Values normalized to metric's peak value from cluster configuration\n   690→//   - Bins evenly distributed from 0 to peak\n   691→//   - Pre-initialized with zeros for consistent visualization\n   692→//\n   693→// Limitations:\n   694→//   - Running jobs: Limited to 5000 jobs for performance\n   695→//   - Requires valid cluster configuration with metric peak values\n   696→//   - Uses footprint statistic (avg/max/min) configured per metric\n   697→func (r *JobRepository) AddMetricHistograms(\n   698→\tctx context.Context,\n   699→\tfilter []*model.JobFilter,\n   700→\tmetrics []string,\n   701→\tstat *model.JobsStatistics,\n   702→\ttargetBinCount *int,\n   703→) (*model.JobsStatistics, error) {\n   704→\tstart := time.Now()\n   705→\n   706→\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n   707→\tfor _, f := range filter {\n   708→\t\tif f.State != nil {\n   709→\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n   710→\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n   711→\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n   712→\t\t\t\treturn stat, nil\n   713→\t\t\t}\n   714→\t\t}\n   715→\t}\n   716→\n   717→\t// All other cases: Query and make bins in sqlite directly\n   718→\tfor _, m := range metrics {\n   719→\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n   720→\t\tif err != nil {\n   721→\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n   722→\t\t\tcontinue\n   723→\t\t}\n   724→\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n   725→\t}\n   726→\n   727→\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n   728→\treturn stat, nil\n   729→}\n   730→\n   731→// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n   732→//\n   733→// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n   734→// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n   735→//\n   736→// Parameters:\n   737→//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n   738→//   - filters: Job filters to apply\n   739→//\n   740→// Returns histogram points with Value (from column) and Count (number of jobs).\n   741→func (r *JobRepository) jobsStatisticsHistogram(\n   742→\tctx context.Context,\n   743→\tvalue string,\n   744→\tfilters []*model.JobFilter,\n   745→) ([]*model.HistoPoint, error) {\n   746→\tstart := time.Now()\n   747→\tquery, qerr := SecurityCheck(ctx,\n   748→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n   749→\n   750→\tif qerr != nil {\n   751→\t\treturn nil, qerr\n   752→\t}\n   753→\n   754→\tfor _, f := range filters {\n   755→\t\tquery = BuildWhereClause(f, query)\n   756→\t}\n   757→\n   758→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n   759→\tif err != nil {\n   760→\t\tcclog.Error(\"Error while running query\")\n   761→\t\treturn nil, err\n   762→\t}\n   763→\tdefer rows.Close()\n   764→\n   765→\tpoints := make([]*model.HistoPoint, 0)\n   766→\t// is it possible to introduce zero values here? requires info about bincount\n   767→\tfor rows.Next() {\n   768→\t\tpoint := model.HistoPoint{}\n   769→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n   770→\t\t\tcclog.Warn(\"Error while scanning rows\")\n   771→\t\t\treturn nil, err\n   772→\t\t}\n   773→\n   774→\t\tpoints = append(points, &point)\n   775→\t}\n   776→\n   777→\tif err := rows.Err(); err != nil {\n   778→\t\treturn nil, err\n   779→\t}\n   780→\n   781→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n   782→\treturn points, nil\n   783→}\n   784→\n   785→// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n   786→//\n   787→// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n   788→// histograms which only return bins with data. The value parameter should compute\n   789→// the bin number from job duration.\n   790→//\n   791→// Parameters:\n   792→//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n   793→//   - filters: Job filters to apply\n   794→//   - binSizeSeconds: Width of each bin in seconds\n   795→//   - targetBinCount: Number of bins to pre-initialize\n   796→//\n   797→// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n   798→// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n   799→//\n   800→// Algorithm:\n   801→//  1. Pre-initialize targetBinCount bins with zero counts\n   802→//  2. Query database for actual counts per bin\n   803→//  3. Match query results to pre-initialized bins by value\n   804→//  4. Bins without matches remain at zero\n   805→func (r *JobRepository) jobsDurationStatisticsHistogram(\n   806→\tctx context.Context,\n   807→\tvalue string,\n   808→\tfilters []*model.JobFilter,\n   809→\tbinSizeSeconds int,\n   810→\ttargetBinCount *int,\n   811→) ([]*model.HistoPoint, error) {\n   812→\tstart := time.Now()\n   813→\tquery, qerr := SecurityCheck(ctx,\n   814→\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n   815→\n   816→\tif qerr != nil {\n   817→\t\treturn nil, qerr\n   818→\t}\n   819→\n   820→\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n   821→\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n   822→\tpoints := make([]*model.HistoPoint, 0)\n   823→\tfor i := 1; i <= *targetBinCount; i++ {\n   824→\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n   825→\t\tpoints = append(points, &point)\n   826→\t}\n   827→\n   828→\tfor _, f := range filters {\n   829→\t\tquery = BuildWhereClause(f, query)\n   830→\t}\n   831→\n   832→\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n   833→\tif err != nil {\n   834→\t\tcclog.Error(\"Error while running query\")\n   835→\t\treturn nil, err\n   836→\t}\n   837→\tdefer rows.Close()\n   838→\n   839→\t// Match query results to pre-initialized bins.\n   840→\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n   841→\tfor rows.Next() {\n   842→\t\tpoint := model.HistoPoint{}\n   843→\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n   844→\t\t\tcclog.Warn(\"Error while scanning rows\")\n   845→\t\t\treturn nil, err\n   846→\t\t}\n   847→\n   848→\t\tfor _, e := range points {\n   849→\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n   850→\t\t\t\te.Count = point.Count\n   851→\t\t\t\tbreak\n   852→\t\t\t}\n   853→\t\t}\n   854→\t}\n   855→\n   856→\tif err := rows.Err(); err != nil {\n   857→\t\treturn nil, err\n   858→\t}\n   859→\n   860→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n   861→\treturn points, nil\n   862→}\n   863→\n   864→// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n   865→//\n   866→// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n   867→// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n   868→// and computing bin assignments in SQL.\n   869→//\n   870→// Parameters:\n   871→//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n   872→//   - filters: Job filters to apply\n   873→//   - bins: Number of bins to generate\n   874→//\n   875→// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n   876→//\n   877→// Algorithm:\n   878→//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n   879→//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n   880→//  3. Pre-initialize bins with min/max ranges based on peak value\n   881→//  4. Query database for counts per bin\n   882→//  5. Match results to pre-initialized bins\n   883→//\n   884→// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n   885→// peak by 0.999999999 to avoid creating an extra bin.\n   886→func (r *JobRepository) jobsMetricStatisticsHistogram(\n   887→\tctx context.Context,\n   888→\tmetric string,\n   889→\tfilters []*model.JobFilter,\n   890→\tbins *int,\n   891→) (*model.MetricHistoPoints, error) {\n   892→\t// Peak value defines the upper bound for binning: values are distributed across\n   893→\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n   894→\t// scan all clusters to find the maximum peak value.\n   895→\tvar metricConfig *schema.MetricConfig\n   896→\tvar peak float64\n   897→\tvar unit string\n   898→\tvar footprintStat string\n   899→\n   900→\t// Try to get metric config from filtered cluster\n   901→\tfor _, f := range filters {\n   902→\t\tif f.Cluster != nil {\n   903→\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n   904→\t\t\tpeak = metricConfig.Peak\n   905→\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n   906→\t\t\tfootprintStat = metricConfig.Footprint\n   907→\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n   908→\t\t}\n   909→\t}\n   910→\n   911→\t// If no cluster filter or peak not found, find largest peak across all clusters\n   912→\t// This ensures histogram can accommodate all possible values\n   913→\tif peak == 0.0 {\n   914→\t\tfor _, c := range archive.Clusters {\n   915→\t\t\tfor _, m := range c.MetricConfig {\n   916→\t\t\t\tif m.Name == metric {\n   917→\t\t\t\t\tif m.Peak > peak {\n   918→\t\t\t\t\t\tpeak = m.Peak\n   919→\t\t\t\t\t}\n   920→\t\t\t\t\tif unit == \"\" {\n   921→\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n   922→\t\t\t\t\t}\n   923→\t\t\t\t\tif footprintStat == \"\" {\n   924→\t\t\t\t\t\tfootprintStat = m.Footprint\n   925→\t\t\t\t\t}\n   926→\t\t\t\t}\n   927→\t\t\t}\n   928→\t\t}\n   929→\t}\n   930→\n   931→\t// Construct SQL histogram bins using normalized values.\n   932→\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n   933→\tstart := time.Now()\n   934→\n   935→\t// Bin calculation formula:\n   936→\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n   937→\t// Special case: value == peak would create bin N+1, so we test for equality\n   938→\t// and multiply peak by 0.999999999 to force it into bin N.\n   939→\tbinQuery := fmt.Sprintf(`CAST(\n   940→\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n   941→\t\t* %v as INTEGER )`,\n   942→\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n   943→\n   944→\tmainQuery := sq.Select(\n   945→\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n   946→\t\t`count(*) as count`,\n   947→\t).From(\"job\").Where(\n   948→\t\t\"JSON_VALID(footprint)\",\n   949→\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n   950→\n   951→\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n   952→\tif qerr != nil {\n   953→\t\treturn nil, qerr\n   954→\t}\n   955→\n   956→\tfor _, f := range filters {\n   957→\t\tmainQuery = BuildWhereClause(f, mainQuery)\n   958→\t}\n   959→\n   960→\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n   961→\n   962→\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n   963→\tif err != nil {\n   964→\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n   965→\t\treturn nil, err\n   966→\t}\n   967→\tdefer rows.Close()\n   968→\n   969→\t// Pre-initialize bins with calculated min/max ranges.\n   970→\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n   971→\tpoints := make([]*model.MetricHistoPoint, 0)\n   972→\tbinStep := int(peak) / *bins\n   973→\tfor i := 1; i <= *bins; i++ {\n   974→\t\tbinMin := (binStep * (i - 1))\n   975→\t\tbinMax := (binStep * i)\n   976→\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n   977→\t\tpoints = append(points, &epoint)\n   978→\t}\n   979→\n   980→\t// Match query results to pre-initialized bins.\n   981→\tfor rows.Next() {\n   982→\t\trpoint := model.MetricHistoPoint{}\n   983→\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n   984→\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n   985→\t\t\treturn nil, err\n   986→\t\t}\n   987→\n   988→\t\tfor _, e := range points {\n   989→\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n   990→\t\t\t\te.Count = rpoint.Count\n   991→\t\t\t\tbreak\n   992→\t\t\t}\n   993→\t\t}\n   994→\t}\n   995→\n   996→\tif err := rows.Err(); err != nil {\n   997→\t\treturn nil, err\n   998→\t}\n   999→\n  1000→\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n  1001→\n  1002→\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n  1003→\treturn &result, nil\n  1004→}\n  1005→\n  1006→// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n  1007→//\n  1008→// Unlike completed jobs which use footprint data from the database, running jobs require\n  1009→// fetching current metric averages from the metric backend (via metricdispatch).\n  1010→//\n  1011→// Parameters:\n  1012→//   - metrics: List of metric names\n  1013→//   - filters: Job filters (should filter to running jobs only)\n  1014→//   - bins: Number of histogram bins\n  1015→//\n  1016→// Returns slice of MetricHistoPoints, one per metric.\n  1017→//\n  1018→// Limitations:\n  1019→//   - Maximum 5000 jobs (returns nil if more jobs match)\n  1020→//   - Requires metric backend availability\n  1021→//   - Bins based on metric peak values from cluster configuration\n  1022→//\n  1023→// Algorithm:\n  1024→//  1. Query first 5001 jobs to check count limit\n  1025→//  2. Load metric averages for all jobs via metricdispatch\n  1026→//  3. For each metric, create bins based on peak value\n  1027→//  4. Iterate averages and count jobs per bin\n  1028→func (r *JobRepository) runningJobsMetricStatisticsHistogram(\n  1029→\tctx context.Context,\n  1030→\tmetrics []string,\n  1031→\tfilters []*model.JobFilter,\n  1032→\tbins *int,\n  1033→) []*model.MetricHistoPoints {\n  1034→\t// Get Jobs\n  1035→\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n  1036→\tif err != nil {\n  1037→\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n  1038→\t\treturn nil\n  1039→\t}\n  1040→\tif len(jobs) > 5000 {\n  1041→\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n  1042→\t\treturn nil\n  1043→\t}\n  1044→\n  1045→\t// Get AVGs from metric repo\n  1046→\tavgs := make([][]schema.Float, len(metrics))\n  1047→\tfor i := range avgs {\n  1048→\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n  1049→\t}\n  1050→\n  1051→\tfor _, job := range jobs {\n  1052→\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n  1053→\t\t\tcontinue\n  1054→\t\t}\n  1055→\n  1056→\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n  1057→\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n  1058→\t\t\treturn nil\n  1059→\t\t}\n  1060→\t}\n  1061→\n  1062→\t// Iterate metrics to fill endresult\n  1063→\tdata := make([]*model.MetricHistoPoints, 0)\n  1064→\tfor idx, metric := range metrics {\n  1065→\t\t// Get specific Peak or largest Peak\n  1066→\t\tvar metricConfig *schema.MetricConfig\n  1067→\t\tvar peak float64\n  1068→\t\tvar unit string\n  1069→\n  1070→\t\tfor _, f := range filters {\n  1071→\t\t\tif f.Cluster != nil {\n  1072→\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n  1073→\t\t\t\tpeak = metricConfig.Peak\n  1074→\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n  1075→\t\t\t}\n  1076→\t\t}\n  1077→\n  1078→\t\tif peak == 0.0 {\n  1079→\t\t\tfor _, c := range archive.Clusters {\n  1080→\t\t\t\tfor _, m := range c.MetricConfig {\n  1081→\t\t\t\t\tif m.Name == metric {\n  1082→\t\t\t\t\t\tif m.Peak > peak {\n  1083→\t\t\t\t\t\t\tpeak = m.Peak\n  1084→\t\t\t\t\t\t}\n  1085→\t\t\t\t\t\tif unit == \"\" {\n  1086→\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n  1087→\t\t\t\t\t\t}\n  1088→\t\t\t\t\t}\n  1089→\t\t\t\t}\n  1090→\t\t\t}\n  1091→\t\t}\n  1092→\n  1093→\t\t// Make and fill bins\n  1094→\t\tpeakBin := int(peak) / *bins\n  1095→\n  1096→\t\tpoints := make([]*model.MetricHistoPoint, 0)\n  1097→\t\tfor b := 0; b < *bins; b++ {\n  1098→\t\t\tcount := 0\n  1099→\t\t\tbindex := b + 1\n  1100→\t\t\tbmin := peakBin * b\n  1101→\t\t\tbmax := peakBin * (b + 1)\n  1102→\n  1103→\t\t\t// Iterate AVG values for indexed metric and count for bins\n  1104→\t\t\tfor _, val := range avgs[idx] {\n  1105→\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n  1106→\t\t\t\t\tcount += 1\n  1107→\t\t\t\t}\n  1108→\t\t\t}\n  1109→\n  1110→\t\t\t// Append Bin to Metric Result Array\n  1111→\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n  1112→\t\t\tpoints = append(points, &point)\n  1113→\t\t}\n  1114→\n  1115→\t\t// Append Metric Result Array to final results array\n  1116→\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n  1117→\t\tdata = append(data, &result)\n  1118→\t}\n  1119→\n  1120→\treturn data\n  1121→}\n  1122→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"59b4aa7e-acd9-4b89-b74a-0b94e111b930","timestamp":"2026-03-11T13:38:58.246Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped queries: Also select grouping column and user's display name from hpc_user table\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).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)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","numLines":1122,"startLine":1,"totalLines":1122}},"sourceToolAssistantUUID":"8a467d4d-173b-4734-9903-3b0f5eefcf7d"}
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{"parentUuid":"86583df4-d48b-41c8-b221-77b9d4794c80","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H9xfsUUxjYWoP1Niwb93u6","type":"tool_result","content":"   640→\t\thasNextPage = len(nextJobs) == 1\n   641→\t}\n   642→\n   643→\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n   644→}\n   645→\n   646→// JobsStatistics is the resolver for the jobsStatistics field.\n   647→func (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n   648→\tvar err error\n   649→\tvar stats []*model.JobsStatistics\n   650→\n   651→\t// Top Level Defaults\n   652→\tdefaultDurationBins := \"1h\"\n   653→\tdefaultMetricBins := 10\n   654→\n   655→\tif requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n   656→\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {\n   657→\t\tif groupBy == nil {\n   658→\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n   659→\t\t} else {\n   660→\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n   661→\t\t}\n   662→\t} else {\n   663→\t\tstats = make([]*model.JobsStatistics, 0, 1)\n   664→\t\tstats = append(stats, &model.JobsStatistics{})\n   665→\t}\n   666→\n   667→\tif groupBy != nil {\n   668→\t\tif requireField(ctx, \"shortJobs\") {\n   669→\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n   670→\t\t}\n   671→\t\tif requireField(ctx, \"runningJobs\") {\n   672→\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n   673→\t\t}\n   674→\t} else {\n   675→\t\tif requireField(ctx, \"shortJobs\") {\n   676→\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n   677→\t\t}\n   678→\t\tif requireField(ctx, \"runningJobs\") {\n   679→\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n   680→\t\t}\n   681→\t}\n   682→\n   683→\tif err != nil {\n   684→\t\treturn nil, err\n   685→\t}\n   686→\n   687→\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n   688→\n   689→\t\tif numDurationBins == nil {\n   690→\t\t\tnumDurationBins = &defaultDurationBins\n   691→\t\t}\n   692→\n   693→\t\tif groupBy == nil {\n   694→\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n   695→\t\t\tif err != nil {\n   696→\t\t\t\treturn nil, err\n   697→\t\t\t}\n   698→\t\t} else {\n   699→\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n   700→\t\t}\n   701→\t}\n   702→\n   703→\tif requireField(ctx, \"histMetrics\") {\n   704→\n   705→\t\tif numMetricBins == nil {\n   706→\t\t\tnumMetricBins = &defaultMetricBins\n   707→\t\t}\n   708→\n   709→\t\tif groupBy == nil {\n   710→\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n   711→\t\t\tif err != nil {\n   712→\t\t\t\treturn nil, err\n   713→\t\t\t}\n   714→\t\t} else {\n   715→\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n   716→\t\t}\n   717→\t}\n   718→\n   719→\treturn stats, nil\n   720→}\n   721→\n   722→// JobsMetricStats is the resolver for the jobsMetricStats field.\n   723→func (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n   724→\t// No Paging, Fixed Order by StartTime ASC\n   725→\torder := &model.OrderByInput{\n   726→\t\tField: \"startTime\",\n   727→\t\tType:  \"col\",\n   728→\t\tOrder: \"ASC\",\n   729→\t}\n   730→\n   731→\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n   732→\tif err != nil {\n   733→\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n   734→\t\treturn nil, err\n   735→\t}\n   736→\n   737→\tres := []*model.JobStats{}\n   738→\tfor _, job := range jobs {\n   739→\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"5d917c1e-c946-4054-b4f7-8fb8a45116b0","timestamp":"2026-03-11T13:38:58.675Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go","content":"\t\thasNextPage = len(nextJobs) == 1\n\t}\n\n\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n}\n\n// JobsStatistics is the resolver for the jobsStatistics field.\nfunc (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n\tvar err error\n\tvar stats []*model.JobsStatistics\n\n\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tif requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\tif groupBy != nil {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t}\n\t} else {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t}\n\t}\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n\n\t\tif numDurationBins == nil {\n\t\t\tnumDurationBins = &defaultDurationBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\tif requireField(ctx, \"histMetrics\") {\n\n\t\tif numMetricBins == nil {\n\t\t\tnumMetricBins = &defaultMetricBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// JobsMetricStats is the resolver for the jobsMetricStats field.\nfunc (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n\t// No Paging, Fixed Order by StartTime ASC\n\torder := &model.OrderByInput{\n\t\tField: \"startTime\",\n\t\tType:  \"col\",\n\t\tOrder: \"ASC\",\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobStats{}\n\tfor _, job := range jobs {\n\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)","numLines":100,"startLine":640,"totalLines":1041}},"sourceToolAssistantUUID":"86583df4-d48b-41c8-b221-77b9d4794c80"}
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{"parentUuid":"7f0c0e66-8092-47d9-9816-9e8de42108de","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01R6M1srv8sSUGdNL36HJcPN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BmDzkbWGPbgqCqLRtnpfhU","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped queries: Also select grouping column and user's display name from hpc_user table\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}","new_string":"// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4231,"cache_read_input_tokens":29873,"output_tokens":3261,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":4231,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPPZxMSowr7B4rTeV4p","type":"assistant","uuid":"07b68e92-25e3-420d-ab6d-5168bde87b2d","timestamp":"2026-03-11T13:40:23.259Z"}
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{"parentUuid":"07b68e92-25e3-420d-ab6d-5168bde87b2d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BmDzkbWGPbgqCqLRtnpfhU","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"6030ba8f-0157-4e17-9246-78648cdd2d3a","timestamp":"2026-03-11T13:40:23.277Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped queries: Also select grouping column and user's display name from hpc_user table\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}","newString":"// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped queries: Also select grouping column and user's display name from hpc_user table\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(\n\t\t\tcol,\n\t\t\t\"name\",\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),\n\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).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)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":129,"oldLines":6,"newStart":129,"newLines":7,"lines":[" // Parameters:"," //   - filter: Job filters to apply (cluster, user, time range, etc.)"," //   - col: Column name to GROUP BY; empty string for overall statistics without grouping","+//   - shortThreshold: Duration threshold in seconds for counting short-running jobs"," //"," // Returns a SelectBuilder that produces comprehensive statistics:"," //   - totalJobs: Count of jobs"]},{"oldStart":140,"oldLines":42,"newStart":141,"newLines":69,"lines":[" //   - totalCoreHours: Sum of (duration × num_hwthreads) in hours"," //   - totalAccs: Sum of accelerators used across all jobs"," //   - totalAccHours: Sum of (duration × num_acc) in hours","+//   - runningJobs: Count of jobs with job_state = 'running'","+//   - shortJobs: Count of jobs with duration < shortThreshold"," //"," // Special handling:"," //   - Running jobs: Duration calculated as (now - start_time) instead of stored duration","-//   - Grouped queries: Also select grouping column and user's display name from hpc_user table","+//   - Grouped by user: Also joins hpc_user table to select display name","+//   - Grouped by other dimensions: Selects empty string for name column (no join)"," //   - All time values converted from seconds to hours (÷ 3600) and rounded"," func (r *JobRepository) buildStatsQuery(","   filter []*model.JobFilter,","   col string,","+  shortThreshold int,"," ) sq.SelectBuilder {","+  now := time.Now().Unix()","   var query sq.SelectBuilder"," ","   if col != \"\" {","-    query = sq.Select(","-      col,","-      \"name\",","-      \"COUNT(job.id) as totalJobs\",","-      \"COUNT(DISTINCT job.hpc_user) AS totalUsers\",","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, time.Now().Unix()),","-      `CAST(SUM(job.num_nodes) as int) as totalNodes`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, time.Now().Unix()),","-      `CAST(SUM(job.num_hwthreads) as int) as totalCores`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, time.Now().Unix()),","-      `CAST(SUM(job.num_acc) as int) as totalAccs`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, time.Now().Unix()),","-    ).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)","+    if col == \"job.hpc_user\" {","+      query = sq.Select(","+        col,","+        \"name\",","+        \"COUNT(job.id) as totalJobs\",","+        \"COUNT(DISTINCT job.hpc_user) AS totalUsers\",","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),","+        `CAST(SUM(job.num_nodes) as int) as totalNodes`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),","+        `CAST(SUM(job.num_hwthreads) as int) as totalCores`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),","+        `CAST(SUM(job.num_acc) as int) as totalAccs`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),","+        `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,","+        fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),","+      ).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)","+    } else {","+      query = sq.Select(","+        col,","+        \"'' as name\",","+        \"COUNT(job.id) as totalJobs\",","+        \"COUNT(DISTINCT job.hpc_user) AS totalUsers\",","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),","+        `CAST(SUM(job.num_nodes) as int) as totalNodes`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),","+        `CAST(SUM(job.num_hwthreads) as int) as totalCores`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),","+        `CAST(SUM(job.num_acc) as int) as totalAccs`,","+        fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),","+        `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,","+        fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),","+      ).From(\"job\").GroupBy(col)","+    }","   } else {","     query = sq.Select(","       \"COUNT(job.id) as totalJobs\",","       \"COUNT(DISTINCT job.hpc_user) AS totalUsers\",","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, time.Now().Unix()),","+      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),","       `CAST(SUM(job.num_nodes) as int)`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, time.Now().Unix()),","+      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),","       `CAST(SUM(job.num_hwthreads) as int)`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, time.Now().Unix()),","+      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),","       `CAST(SUM(job.num_acc) as int)`,","-      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, time.Now().Unix()),","+      fmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),","+      `COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,","+      fmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),","     ).From(\"job\")","   }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"07b68e92-25e3-420d-ab6d-5168bde87b2d"}
{"parentUuid":"6030ba8f-0157-4e17-9246-78648cdd2d3a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01TdQi7q1r8ByaZRM5ax2VMv","type":"message","role":"assistant","content":[{"type":"text","text":"Now update `JobsStatsGrouped` to pass `shortThreshold` and scan the new columns:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3323,"cache_read_input_tokens":34104,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3323},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwPRnzEVSecjt65W5JKZ","type":"assistant","uuid":"ee7ed0ef-f081-4489-8685-fd47e4dc84d0","timestamp":"2026-03-11T13:40:26.496Z"}
{"parentUuid":"ee7ed0ef-f081-4489-8685-fd47e4dc84d0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01TdQi7q1r8ByaZRM5ax2VMv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SwAptqrhS3kRzJdMCDTmBV","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}","new_string":"\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &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\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3323,"cache_read_input_tokens":34104,"output_tokens":2273,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3323,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPRnzEVSecjt65W5JKZ","type":"assistant","uuid":"c5eeefe9-8b61-4687-b651-cb7cd7682ec4","timestamp":"2026-03-11T13:40:46.169Z"}
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{"parentUuid":"c5eeefe9-8b61-4687-b651-cb7cd7682ec4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SwAptqrhS3kRzJdMCDTmBV","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"73c9b615-87a0-4e73-bcfe-2fc3c78d6bdd","timestamp":"2026-03-11T13:40:46.186Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}","newString":"\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &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\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows: %s\", err.Error())\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif id.Valid {\n\t\t\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\t// name := r.getUserName(ctx, id.String)\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).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)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":242,"oldLines":7,"newStart":242,"newLines":7,"lines":[" ) ([]*model.JobsStatistics, error) {","   start := time.Now()","   col := groupBy2column[*groupBy]","-  query := r.buildStatsQuery(filter, col)","+  query := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)"," ","   query, err := SecurityCheck(ctx, query)","   if err != nil {"]},{"oldStart":270,"oldLines":8,"newStart":270,"newLines":8,"lines":["   for rows.Next() {","     var id sql.NullString","     var name sql.NullString","-    var jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64","-    if err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {","+    var jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64","+    if err := rows.Scan(&id, &name, &jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {","       cclog.Warnf(\"Error while scanning rows: %s\", err.Error())","       return nil, err","     }"]},{"oldStart":317,"oldLines":7,"newStart":317,"newLines":6,"lines":["       }"," ","       if col == \"job.hpc_user\" {","-        // name := r.getUserName(ctx, id.String)","         stats = append(stats,","           &model.JobsStatistics{","             ID:             id.String,"]},{"oldStart":330,"oldLines":6,"newStart":329,"newLines":8,"lines":["             TotalCoreHours: totalCoreHours,","             TotalAccs:      totalAccs,","             TotalAccHours:  totalAccHours,","+            RunningJobs:    int(runningJobs.Int64),","+            ShortJobs:      int(shortJobs.Int64),","           })","       } else {","         stats = append(stats,"]},{"oldStart":344,"oldLines":6,"newStart":345,"newLines":8,"lines":["             TotalCoreHours: totalCoreHours,","             TotalAccs:      totalAccs,","             TotalAccHours:  totalAccHours,","+            RunningJobs:    int(runningJobs.Int64),","+            ShortJobs:      int(shortJobs.Int64),","           })","       }","     }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c5eeefe9-8b61-4687-b651-cb7cd7682ec4"}
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{"parentUuid":"36c588b8-e847-4efa-b653-b77dbbebb746","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_011NzuRn3SxfTfhphdfP1uih","type":"message","role":"assistant","content":[{"type":"text","text":"Need to fix the remaining two call sites. Let me check what's there now:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2555,"cache_read_input_tokens":37427,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2555},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwPTUxLMPsthgRti2Wnz","type":"assistant","uuid":"056eaafe-699f-48d1-9495-6c388cc49e73","timestamp":"2026-03-11T13:40:51.731Z"}
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{"parentUuid":"42885a1c-20aa-4975-b8a3-950353d88f6d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01YXfntdEerWzsKgyLNZs7T1","toolUseID":"toolu_01YXfntdEerWzsKgyLNZs7T1","timestamp":"2026-03-11T13:40:52.645Z","uuid":"677aa390-63a1-4a97-a5f8-9475b51dd1ae"}
{"parentUuid":"42885a1c-20aa-4975-b8a3-950353d88f6d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YXfntdEerWzsKgyLNZs7T1","type":"tool_result","content":"   240→\tsortBy *model.SortByAggregate,\n   241→\tgroupBy *model.Aggregate,\n   242→) ([]*model.JobsStatistics, error) {\n   243→\tstart := time.Now()\n   244→\tcol := groupBy2column[*groupBy]\n   245→\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n   246→\n   247→\tquery, err := SecurityCheck(ctx, query)\n   248→\tif err != nil {\n   249→\t\treturn nil, err\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"f5aa86e7-fbc6-409b-9212-4079ba7ca3ea","timestamp":"2026-03-11T13:40:52.644Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","content":"\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err","numLines":10,"startLine":240,"totalLines":1153}},"sourceToolAssistantUUID":"42885a1c-20aa-4975-b8a3-950353d88f6d"}
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{"parentUuid":"7d850a74-8c70-4b9b-9553-3984a2693a40","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JMa1RE8uA56tVnKGiKPotD","type":"tool_result","content":"   375→//\n   376→// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n   377→// Security checks are applied via SecurityCheck to respect user access levels.\n   378→func (r *JobRepository) JobsStats(\n   379→\tctx context.Context,\n   380→\tfilter []*model.JobFilter,\n   381→) ([]*model.JobsStatistics, error) {\n   382→\tstart := time.Now()\n   383→\tquery := r.buildStatsQuery(filter, \"\")\n   384→\tquery, err := SecurityCheck(ctx, query)\n   385→\tif err != nil {\n   386→\t\treturn nil, err\n   387→\t}\n   388→\n   389→\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"b5069e09-2683-468e-8548-9142379c923e","timestamp":"2026-03-11T13:40:53.440Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","content":"//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)","numLines":15,"startLine":375,"totalLines":1153}},"sourceToolAssistantUUID":"7d850a74-8c70-4b9b-9553-3984a2693a40"}
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{"parentUuid":"7e1241db-e663-439c-a888-32140b0d47b6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01YEe4pcquazUUhrGbQqcDMQ","toolUseID":"toolu_01YEe4pcquazUUhrGbQqcDMQ","timestamp":"2026-03-11T13:40:53.998Z","uuid":"c5ec6e68-43ec-40e8-8679-e2478e05d97e"}
{"parentUuid":"7e1241db-e663-439c-a888-32140b0d47b6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YEe4pcquazUUhrGbQqcDMQ","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→package repository\n     7→\n     8→import (\n     9→\t\"fmt\"\n    10→\t\"testing\"\n    11→\n    12→\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n    13→)\n    14→\n    15→func TestBuildJobStatsQuery(t *testing.T) {\n    16→\tr := setup(t)\n    17→\tq := r.buildStatsQuery(nil, \"USER\")\n    18→\n    19→\tsql, _, err := q.ToSql()\n    20→\tnoErr(t, err)\n    21→\n    22→\tfmt.Printf(\"SQL: %s\\n\", sql)\n    23→}\n    24→\n    25→func TestJobStats(t *testing.T) {\n    26→\tr := setup(t)\n    27→\n    28→\tvar expectedCount int\n    29→\terr := r.DB.QueryRow(`SELECT COUNT(*) FROM job`).Scan(&expectedCount)\n    30→\tnoErr(t, err)\n    31→\n    32→\tstats, err := r.JobsStats(getContext(t), []*model.JobFilter{})\n    33→\tnoErr(t, err)\n    34→\n    35→\tif stats[0].TotalJobs != expectedCount {\n    36→\t\tt.Fatalf(\"Want %d, Got %d\", expectedCount, stats[0].TotalJobs)\n    37→\t}\n    38→}\n    39→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"4234c6a4-254e-4711-8520-a9b75706d387","timestamp":"2026-03-11T13:40:53.998Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats_test.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"fmt\"\n\t\"testing\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n)\n\nfunc TestBuildJobStatsQuery(t *testing.T) {\n\tr := setup(t)\n\tq := r.buildStatsQuery(nil, \"USER\")\n\n\tsql, _, err := q.ToSql()\n\tnoErr(t, err)\n\n\tfmt.Printf(\"SQL: %s\\n\", sql)\n}\n\nfunc TestJobStats(t *testing.T) {\n\tr := setup(t)\n\n\tvar expectedCount int\n\terr := r.DB.QueryRow(`SELECT COUNT(*) FROM job`).Scan(&expectedCount)\n\tnoErr(t, err)\n\n\tstats, err := r.JobsStats(getContext(t), []*model.JobFilter{})\n\tnoErr(t, err)\n\n\tif stats[0].TotalJobs != expectedCount {\n\t\tt.Fatalf(\"Want %d, Got %d\", expectedCount, stats[0].TotalJobs)\n\t}\n}\n","numLines":39,"startLine":1,"totalLines":39}},"sourceToolAssistantUUID":"7e1241db-e663-439c-a888-32140b0d47b6"}
{"parentUuid":"4234c6a4-254e-4711-8520-a9b75706d387","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01EJpcD6UduHXx6BmVhJHHBf","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me fix `JobsStats` and the test, and also update the scan in `JobsStats` to handle the new columns:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1846,"cache_read_input_tokens":39982,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1846},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwPU4WBwbaXDy6pzQ9nV","type":"assistant","uuid":"a17c1747-d84d-4392-8e87-dc35f16d1811","timestamp":"2026-03-11T13:40:57.044Z"}
{"parentUuid":"a17c1747-d84d-4392-8e87-dc35f16d1811","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01EJpcD6UduHXx6BmVhJHHBf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TUm6mMQEtJT9aWy6urY6T7","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}","new_string":"\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration)\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}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1846,"cache_read_input_tokens":39982,"output_tokens":1050,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1846,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPU4WBwbaXDy6pzQ9nV","type":"assistant","uuid":"902d8462-b4df-4d92-bdfb-32453570aae7","timestamp":"2026-03-11T13:41:05.371Z"}
{"parentUuid":"902d8462-b4df-4d92-bdfb-32453570aae7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01TUm6mMQEtJT9aWy6urY6T7","toolUseID":"toolu_01TUm6mMQEtJT9aWy6urY6T7","timestamp":"2026-03-11T13:41:05.377Z","uuid":"18e3b8c4-9652-4ea2-a3ea-2a9badb82cdb"}
{"parentUuid":"902d8462-b4df-4d92-bdfb-32453570aae7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TUm6mMQEtJT9aWy6urY6T7","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"999fca6c-9186-4b04-bbf6-34109f6775cd","timestamp":"2026-03-11T13:41:05.377Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}","newString":"\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration)\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}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &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\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).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)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":380,"oldLines":7,"newStart":380,"newLines":7,"lines":["   filter []*model.JobFilter,"," ) ([]*model.JobsStatistics, error) {","   start := time.Now()","-  query := r.buildStatsQuery(filter, \"\")","+  query := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration)","   query, err := SecurityCheck(ctx, query)","   if err != nil {","     return nil, err"]},{"oldStart":389,"oldLines":8,"newStart":389,"newLines":8,"lines":["   row := query.RunWith(r.DB).QueryRowContext(ctx)","   stats := make([]*model.JobsStatistics, 0, 1)"," ","-  var jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours sql.NullInt64","-  if err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours); err != nil {","+  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\")","     return nil, err","   }"]},{"oldStart":415,"oldLines":6,"newStart":415,"newLines":8,"lines":["         TotalNodeHours: totalNodeHours,","         TotalCoreHours: totalCoreHours,","         TotalAccHours:  totalAccHours,","+        RunningJobs:    int(runningJobs.Int64),","+        ShortJobs:      int(shortJobs.Int64),","       })","   }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"902d8462-b4df-4d92-bdfb-32453570aae7"}
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{"parentUuid":"c91bcc50-8213-4417-9f90-339be18c90db","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01R31A8DGhUBRZhrZn8upxWS","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats_test.go has been updated successfully."}]},"uuid":"5e836fad-8b2d-48b1-b69b-974778297e8c","timestamp":"2026-03-11T13:41:08.497Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats_test.go","oldString":"\tq := r.buildStatsQuery(nil, \"USER\")","newString":"\tq := r.buildStatsQuery(nil, \"USER\", 300)","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\npackage repository\n\nimport (\n\t\"fmt\"\n\t\"testing\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n)\n\nfunc TestBuildJobStatsQuery(t *testing.T) {\n\tr := setup(t)\n\tq := r.buildStatsQuery(nil, \"USER\")\n\n\tsql, _, err := q.ToSql()\n\tnoErr(t, err)\n\n\tfmt.Printf(\"SQL: %s\\n\", sql)\n}\n\nfunc TestJobStats(t *testing.T) {\n\tr := setup(t)\n\n\tvar expectedCount int\n\terr := r.DB.QueryRow(`SELECT COUNT(*) FROM job`).Scan(&expectedCount)\n\tnoErr(t, err)\n\n\tstats, err := r.JobsStats(getContext(t), []*model.JobFilter{})\n\tnoErr(t, err)\n\n\tif stats[0].TotalJobs != expectedCount {\n\t\tt.Fatalf(\"Want %d, Got %d\", expectedCount, stats[0].TotalJobs)\n\t}\n}\n","structuredPatch":[{"oldStart":14,"oldLines":7,"newStart":14,"newLines":7,"lines":[" "," func TestBuildJobStatsQuery(t *testing.T) {","   r := setup(t)","-  q := r.buildStatsQuery(nil, \"USER\")","+  q := r.buildStatsQuery(nil, \"USER\", 300)"," ","   sql, _, err := q.ToSql()","   noErr(t, err)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c91bcc50-8213-4417-9f90-339be18c90db"}
{"parentUuid":"5e836fad-8b2d-48b1-b69b-974778297e8c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01LfA9XLCBdjnmwWh2tgaZ6N","type":"message","role":"assistant","content":[{"type":"text","text":"Now update the resolver to skip the separate count queries when main stats were already fetched:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":211,"cache_read_input_tokens":42940,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":211},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwPV8W5BUm2mLxfu6NDW","type":"assistant","uuid":"7818fe63-a22d-4710-9cc4-3895effc36d5","timestamp":"2026-03-11T13:41:11.146Z"}
{"parentUuid":"7818fe63-a22d-4710-9cc4-3895effc36d5","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01LfA9XLCBdjnmwWh2tgaZ6N","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TFDXxorerPBjaC57rann6s","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go","old_string":"\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tif requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\tif groupBy != nil {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t}\n\t} else {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t}\n\t}","new_string":"\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tfetchedMainStats := requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\")\n\n\tif fetchedMainStats {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n\t// Only run separate count queries if main stats were not fetched.\n\tif !fetchedMainStats {\n\t\tif groupBy != nil {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t\t}\n\t\t} else {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t\t}\n\t\t}\n\t}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":211,"cache_read_input_tokens":42940,"output_tokens":1092,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":211,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPV8W5BUm2mLxfu6NDW","type":"assistant","uuid":"47593f2c-9fa3-44a0-84bd-cec49891cc8f","timestamp":"2026-03-11T13:41:20.902Z"}
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{"parentUuid":"47593f2c-9fa3-44a0-84bd-cec49891cc8f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_01TFDXxorerPBjaC57rann6s","toolUseID":"toolu_01TFDXxorerPBjaC57rann6s","timestamp":"2026-03-11T13:41:20.913Z","uuid":"fd661dac-b52e-4c7c-9b49-f171e8def92f"}
{"parentUuid":"47593f2c-9fa3-44a0-84bd-cec49891cc8f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TFDXxorerPBjaC57rann6s","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go has been updated successfully."}]},"uuid":"2e838443-c66b-463e-9401-93045906df58","timestamp":"2026-03-11T13:41:20.913Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go","oldString":"\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tif requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\tif groupBy != nil {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t}\n\t} else {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t}\n\t}","newString":"\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tfetchedMainStats := requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\")\n\n\tif fetchedMainStats {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\t// runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.\n\t// Only run separate count queries if main stats were not fetched.\n\tif !fetchedMainStats {\n\t\tif groupBy != nil {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t\t}\n\t\t} else {\n\t\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t\t}\n\t\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t\t}\n\t\t}\n\t}","originalFile":"package graph\n\n// This file will be automatically regenerated based on the schema, any resolver\n// implementations\n// will be copied through when generating and any unknown code will be moved to the end.\n// Code generated by github.com/99designs/gqlgen version v0.17.87\n\nimport (\n\t\"context\"\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\t\"regexp\"\n\t\"slices\"\n\t\"strconv\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/generated\"\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/internal/repository\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\tccunit \"github.com/ClusterCockpit/cc-lib/v2/ccUnits\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// Partitions is the resolver for the partitions field.\nfunc (r *clusterResolver) Partitions(ctx context.Context, obj *schema.Cluster) ([]string, error) {\n\treturn r.Repo.Partitions(obj.Name)\n}\n\n// StartTime is the resolver for the startTime field.\nfunc (r *jobResolver) StartTime(ctx context.Context, obj *schema.Job) (*time.Time, error) {\n\ttimestamp := time.Unix(obj.StartTime, 0)\n\treturn &timestamp, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *jobResolver) Tags(ctx context.Context, obj *schema.Job) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), obj.ID)\n}\n\n// ConcurrentJobs is the resolver for the concurrentJobs field.\nfunc (r *jobResolver) ConcurrentJobs(ctx context.Context, obj *schema.Job) (*model.JobLinkResultList, error) {\n\t// FIXME: Make the hardcoded duration configurable\n\tif obj.Shared != \"none\" && obj.Duration > 600 {\n\t\treturn r.Repo.FindConcurrentJobs(ctx, obj)\n\t}\n\n\treturn nil, nil\n}\n\n// Footprint is the resolver for the footprint field.\nfunc (r *jobResolver) Footprint(ctx context.Context, obj *schema.Job) ([]*model.FootprintValue, error) {\n\trawFootprint, err := r.Repo.FetchFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.FootprintValue{}\n\tfor name, value := range rawFootprint {\n\n\t\tparts := strings.Split(name, \"_\")\n\t\tstatPart := parts[len(parts)-1]\n\t\tnameParts := parts[:len(parts)-1]\n\n\t\tres = append(res, &model.FootprintValue{\n\t\t\tName:  strings.Join(nameParts, \"_\"),\n\t\t\tStat:  statPart,\n\t\t\tValue: value,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// EnergyFootprint is the resolver for the energyFootprint field.\nfunc (r *jobResolver) EnergyFootprint(ctx context.Context, obj *schema.Job) ([]*model.EnergyFootprintValue, error) {\n\trawEnergyFootprint, err := r.Repo.FetchEnergyFootprint(obj)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching job energy footprint data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.EnergyFootprintValue{}\n\tfor name, value := range rawEnergyFootprint {\n\t\t// Suboptimal: Nearly hardcoded metric name expectations\n\t\tmatchCPU := regexp.MustCompile(`cpu|Cpu|CPU`)\n\t\tmatchAcc := regexp.MustCompile(`acc|Acc|ACC`)\n\t\tmatchMem := regexp.MustCompile(`mem|Mem|MEM`)\n\t\tmatchCore := regexp.MustCompile(`core|Core|CORE`)\n\n\t\thwType := \"\"\n\t\tswitch test := name; { // NOtice ';' for var declaration\n\t\tcase matchCPU.MatchString(test):\n\t\t\thwType = \"CPU\"\n\t\tcase matchAcc.MatchString(test):\n\t\t\thwType = \"Accelerator\"\n\t\tcase matchMem.MatchString(test):\n\t\t\thwType = \"Memory\"\n\t\tcase matchCore.MatchString(test):\n\t\t\thwType = \"Core\"\n\t\tdefault:\n\t\t\thwType = \"Other\"\n\t\t}\n\n\t\tres = append(res, &model.EnergyFootprintValue{\n\t\t\tHardware: hwType,\n\t\t\tMetric:   name,\n\t\t\tValue:    value,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *jobResolver) MetaData(ctx context.Context, obj *schema.Job) (any, error) {\n\treturn r.Repo.FetchMetadata(obj)\n}\n\n// UserData is the resolver for the userData field.\nfunc (r *jobResolver) UserData(ctx context.Context, obj *schema.Job) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, obj.User)\n}\n\n// Name is the resolver for the name field.\nfunc (r *metricValueResolver) Name(ctx context.Context, obj *schema.MetricValue) (*string, error) {\n\tpanic(fmt.Errorf(\"not implemented: Name - name\"))\n}\n\n// CreateTag is the resolver for the createTag field.\nfunc (r *mutationResolver) CreateTag(ctx context.Context, typeArg string, name string, scope string) (*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\tif user.HasRole(schema.RoleAdmin) && scope == \"admin\" ||\n\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && scope == \"global\" ||\n\t\tuser.Username == scope {\n\t\t// Create in DB\n\t\tid, err := r.Repo.CreateTag(typeArg, name, scope)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while creating tag\")\n\t\t\treturn nil, err\n\t\t}\n\t\treturn &schema.Tag{ID: id, Type: typeArg, Name: name, Scope: scope}, nil\n\t} else {\n\t\tcclog.Warnf(\"Not authorized to create tag with scope: %s\", scope)\n\t\treturn nil, fmt.Errorf(\"not authorized to create tag with scope: %s\", scope)\n\t}\n}\n\n// DeleteTag is the resolver for the deleteTag field.\nfunc (r *mutationResolver) DeleteTag(ctx context.Context, id string) (string, error) {\n\t// This Uses ID string <-> ID string, removeTagFromList uses []string <-> []int\n\tpanic(fmt.Errorf(\"not implemented: DeleteTag - deleteTag\"))\n}\n\n// AddTagsToJob is the resolver for the addTagsToJob field.\nfunc (r *mutationResolver) AddTagsToJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while adding tag to job\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Add to Job\n\t\t\tif tags, err = r.Repo.AddTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while adding tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to add tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to add tag: %d\", tid)\n\t\t}\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagsFromJob is the resolver for the removeTagsFromJob field.\nfunc (r *mutationResolver) RemoveTagsFromJob(ctx context.Context, job string, tagIds []string) ([]*schema.Tag, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\tjid, err := strconv.ParseInt(job, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\ttags := []*schema.Tag{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Support/Admin and Global Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && tscope == \"admin\" ||\n\t\t\tuser.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) && tscope == \"global\" ||\n\t\t\tuser.Username == tscope {\n\t\t\t// Remove from Job\n\t\t\tif tags, err = r.Repo.RemoveTag(user, jid, tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\n\t}\n\n\treturn tags, nil\n}\n\n// RemoveTagFromList is the resolver for the removeTagFromList field.\nfunc (r *mutationResolver) RemoveTagFromList(ctx context.Context, tagIds []string) ([]int, error) {\n\t// Needs Contextuser\n\tuser := repository.GetUserFromContext(ctx)\n\tif user == nil {\n\t\treturn nil, fmt.Errorf(\"no user in context\")\n\t}\n\n\ttags := []int{}\n\tfor _, tagID := range tagIds {\n\t\t// Get ID\n\t\ttid, err := strconv.ParseInt(tagID, 10, 64)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while parsing tag id for removal\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\t// Test Exists\n\t\t_, _, tscope, exists := r.Repo.TagInfo(tid)\n\t\tif !exists {\n\t\t\tcclog.Warnf(\"Tag does not exist (ID): %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"tag does not exist (ID): %d\", tid)\n\t\t}\n\n\t\t// Test Access: Admins && Admin Tag OR Everyone && Private Tag\n\t\tif user.HasRole(schema.RoleAdmin) && (tscope == \"global\" || tscope == \"admin\") || user.Username == tscope {\n\t\t\t// Remove from DB\n\t\t\tif err = r.Repo.RemoveTagByID(tid); err != nil {\n\t\t\t\tcclog.Warn(\"Error while removing tag\")\n\t\t\t\treturn nil, err\n\t\t\t} else {\n\t\t\t\ttags = append(tags, int(tid))\n\t\t\t}\n\t\t} else {\n\t\t\tcclog.Warnf(\"Not authorized to remove tag: %d\", tid)\n\t\t\treturn nil, fmt.Errorf(\"not authorized to remove tag: %d\", tid)\n\t\t}\n\t}\n\treturn tags, nil\n}\n\n// UpdateConfiguration is the resolver for the updateConfiguration field.\nfunc (r *mutationResolver) UpdateConfiguration(ctx context.Context, name string, value string) (*string, error) {\n\tif err := repository.GetUserCfgRepo().UpdateConfig(name, value, repository.GetUserFromContext(ctx)); err != nil {\n\t\tcclog.Warn(\"Error while updating user config\")\n\t\treturn nil, err\n\t}\n\n\treturn nil, nil\n}\n\n// ID is the resolver for the id field.\nfunc (r *nodeResolver) ID(ctx context.Context, obj *schema.Node) (string, error) {\n\tpanic(fmt.Errorf(\"not implemented: ID - id\"))\n}\n\n// SchedulerState is the resolver for the schedulerState field.\nfunc (r *nodeResolver) SchedulerState(ctx context.Context, obj *schema.Node) (schema.SchedulerState, error) {\n\tif obj.NodeState != \"\" {\n\t\treturn obj.NodeState, nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no SchedulerState (NodeState) on node object\")\n\t}\n}\n\n// HealthState is the resolver for the healthState field.\nfunc (r *nodeResolver) HealthState(ctx context.Context, obj *schema.Node) (string, error) {\n\tif obj.HealthState != \"\" {\n\t\treturn string(obj.HealthState), nil\n\t} else {\n\t\treturn \"\", fmt.Errorf(\"resolver: no HealthState (NodeState) on node object\")\n\t}\n}\n\n// MetaData is the resolver for the metaData field.\nfunc (r *nodeResolver) MetaData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.MetaData != nil {\n\t\treturn obj.MetaData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no MetaData (NodeState) on node object\")\n\t\temptyMeta := make(map[string]string, 0)\n\t\treturn emptyMeta, nil\n\t}\n}\n\n// HealthData is the resolver for the healthData field.\nfunc (r *nodeResolver) HealthData(ctx context.Context, obj *schema.Node) (any, error) {\n\tif obj.HealthData != nil {\n\t\treturn obj.HealthData, nil\n\t} else {\n\t\tcclog.Debug(\"resolver: no HealthData (NodeState) on node object\")\n\t\temptyHealth := make(map[string][]string, 0)\n\t\treturn emptyHealth, nil\n\t}\n}\n\n// Clusters is the resolver for the clusters field.\nfunc (r *queryResolver) Clusters(ctx context.Context) ([]*schema.Cluster, error) {\n\treturn archive.Clusters, nil\n}\n\n// Tags is the resolver for the tags field.\nfunc (r *queryResolver) Tags(ctx context.Context) ([]*schema.Tag, error) {\n\treturn r.Repo.GetTags(repository.GetUserFromContext(ctx), nil)\n}\n\n// GlobalMetrics is the resolver for the globalMetrics field.\nfunc (r *queryResolver) GlobalMetrics(ctx context.Context) ([]*schema.GlobalMetricListItem, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\n\tif user != nil {\n\t\tif user.HasRole(schema.RoleUser) || user.HasRole(schema.RoleManager) {\n\t\t\treturn archive.GlobalUserMetricList, nil\n\t\t}\n\t}\n\n\treturn archive.GlobalMetricList, nil\n}\n\n// User is the resolver for the user field.\nfunc (r *queryResolver) User(ctx context.Context, username string) (*model.User, error) {\n\treturn repository.GetUserRepository().FetchUserInCtx(ctx, username)\n}\n\n// AllocatedNodes is the resolver for the allocatedNodes field.\nfunc (r *queryResolver) AllocatedNodes(ctx context.Context, cluster string) ([]*model.Count, error) {\n\tdata, err := r.Repo.AllocatedNodes(cluster)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while fetching allocated nodes\")\n\t\treturn nil, err\n\t}\n\n\tcounts := make([]*model.Count, 0, len(data))\n\tfor subcluster, hosts := range data {\n\t\tcounts = append(counts, &model.Count{\n\t\t\tName:  subcluster,\n\t\t\tCount: len(hosts),\n\t\t})\n\t}\n\n\treturn counts, nil\n}\n\n// Node is the resolver for the node field.\nfunc (r *queryResolver) Node(ctx context.Context, id string) (*schema.Node, error) {\n\trepo := repository.GetNodeRepository()\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\treturn repo.GetNodeByID(numericID, false)\n}\n\n// Nodes is the resolver for the nodes field.\nfunc (r *queryResolver) Nodes(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodes(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodesWithMeta is the resolver for the nodesWithMeta field.\nfunc (r *queryResolver) NodesWithMeta(ctx context.Context, filter []*model.NodeFilter, order *model.OrderByInput) (*model.NodeStateResultList, error) {\n\t// Why Extra Handler? -> graphql.CollectAllFields(ctx) only returns toplevel fields (i.e.: items, count), and not subfields like item.metaData\n\trepo := repository.GetNodeRepository()\n\tnodes, err := repo.QueryNodesWithMeta(ctx, filter, nil, order) // Ignore Paging, Order Unused\n\tcount := len(nodes)\n\treturn &model.NodeStateResultList{Items: nodes, Count: &count}, err\n}\n\n// NodeStates is the resolver for the nodeStates field.\nfunc (r *queryResolver) NodeStates(ctx context.Context, filter []*model.NodeFilter) ([]*model.NodeStates, error) {\n\trepo := repository.GetNodeRepository()\n\n\tstateCounts, serr := repo.CountStates(ctx, filter, \"node_state\")\n\tif serr != nil {\n\t\tcclog.Warnf(\"Error while counting nodeStates: %s\", serr.Error())\n\t\treturn nil, serr\n\t}\n\n\thealthCounts, herr := repo.CountStates(ctx, filter, \"health_state\")\n\tif herr != nil {\n\t\tcclog.Warnf(\"Error while counting healthStates: %s\", herr.Error())\n\t\treturn nil, herr\n\t}\n\n\tallCounts := append(stateCounts, healthCounts...)\n\n\treturn allCounts, nil\n}\n\n// NodeStatesTimed is the resolver for the nodeStatesTimed field.\nfunc (r *queryResolver) NodeStatesTimed(ctx context.Context, filter []*model.NodeFilter, typeArg string) ([]*model.NodeStatesTimed, error) {\n\trepo := repository.GetNodeRepository()\n\n\tif typeArg == \"node\" {\n\t\tstateCounts, serr := repo.CountStatesTimed(ctx, filter, \"node_state\")\n\t\tif serr != nil {\n\t\t\tcclog.Warnf(\"Error while counting nodeStates in time: %s\", serr.Error())\n\t\t\treturn nil, serr\n\t\t}\n\t\treturn stateCounts, nil\n\t}\n\n\tif typeArg == \"health\" {\n\t\thealthCounts, herr := repo.CountStatesTimed(ctx, filter, \"health_state\")\n\t\tif herr != nil {\n\t\t\tcclog.Warnf(\"Error while counting healthStates in time: %s\", herr.Error())\n\t\t\treturn nil, herr\n\t\t}\n\t\treturn healthCounts, nil\n\t}\n\n\treturn nil, errors.New(\"unknown Node State Query Type\")\n}\n\n// Job is the resolver for the job field.\nfunc (r *queryResolver) Job(ctx context.Context, id string) (*schema.Job, error) {\n\tnumericID, err := strconv.ParseInt(id, 10, 64)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while parsing job id\")\n\t\treturn nil, err\n\t}\n\n\tjob, err := r.Repo.FindByID(ctx, numericID)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while finding job by id\")\n\t\treturn nil, err\n\t}\n\n\tif user := repository.GetUserFromContext(ctx); user != nil &&\n\t\tjob.User != user.Username &&\n\t\tuser.HasNotRoles([]schema.Role{schema.RoleAdmin, schema.RoleSupport, schema.RoleManager}) {\n\t\treturn nil, errors.New(\"you are not allowed to see this job\")\n\t}\n\n\treturn job, nil\n}\n\n// JobMetrics is the resolver for the jobMetrics field.\nfunc (r *queryResolver) JobMetrics(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope, resolution *int) ([]*model.JobMetricWithName, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying job for metrics\")\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadData(job, metrics, scopes, ctx, *resolution)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job data\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobMetricWithName{}\n\tfor name, md := range data {\n\t\tfor scope, metric := range md {\n\t\t\tres = append(res, &model.JobMetricWithName{\n\t\t\t\tName:   name,\n\t\t\t\tScope:  scope,\n\t\t\t\tMetric: metric,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, err\n}\n\n// JobStats is the resolver for the jobStats field.\nfunc (r *queryResolver) JobStats(ctx context.Context, id string, metrics []string) ([]*model.NamedStats, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading jobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := []*model.NamedStats{}\n\tfor name, md := range data {\n\t\tres = append(res, &model.NamedStats{\n\t\t\tName: name,\n\t\t\tData: &md,\n\t\t})\n\t}\n\n\treturn res, err\n}\n\n// ScopedJobStats is the resolver for the scopedJobStats field.\nfunc (r *queryResolver) ScopedJobStats(ctx context.Context, id string, metrics []string, scopes []schema.MetricScope) ([]*model.NamedStatsWithScope, error) {\n\tjob, err := r.Query().Job(ctx, id)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while querying job %s for metadata\", id)\n\t\treturn nil, err\n\t}\n\n\tdata, err := metricdispatch.LoadScopedJobStats(job, metrics, scopes, ctx)\n\tif err != nil {\n\t\tcclog.Warnf(\"Error while loading scopedJobStats data for job id %s\", id)\n\t\treturn nil, err\n\t}\n\n\tres := make([]*model.NamedStatsWithScope, 0)\n\tfor name, scoped := range data {\n\t\tfor scope, stats := range scoped {\n\n\t\t\tmdlStats := make([]*model.ScopedStats, 0)\n\t\t\tfor _, stat := range stats {\n\t\t\t\tmdlStats = append(mdlStats, &model.ScopedStats{\n\t\t\t\t\tHostname: stat.Hostname,\n\t\t\t\t\tID:       stat.ID,\n\t\t\t\t\tData:     stat.Data,\n\t\t\t\t})\n\t\t\t}\n\n\t\t\tres = append(res, &model.NamedStatsWithScope{\n\t\t\t\tName:  name,\n\t\t\t\tScope: scope,\n\t\t\t\tStats: mdlStats,\n\t\t\t})\n\t\t}\n\t}\n\n\treturn res, nil\n}\n\n// Jobs is the resolver for the jobs field.\nfunc (r *queryResolver) Jobs(ctx context.Context, filter []*model.JobFilter, page *model.PageRequest, order *model.OrderByInput) (*model.JobResultList, error) {\n\tif page == nil {\n\t\tpage = &model.PageRequest{\n\t\t\tItemsPerPage: 50,\n\t\t\tPage:         1,\n\t\t}\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, page, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs\")\n\t\treturn nil, err\n\t}\n\n\tcount, err := r.Repo.CountJobs(ctx, filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while counting jobs\")\n\t\treturn nil, err\n\t}\n\n\t// Note: Even if App-Default 'config.Keys.UiDefaults[\"job_list_usePaging\"]' is set, always return hasNextPage boolean.\n\t// Users can decide in frontend to use continuous scroll, even if app-default is paging!\n\t// Skip if page.ItemsPerPage == -1 (\"Load All\" -> No Next Page required, Status Dashboards)\n\t/*\n\t  Example Page 4 @ 10 IpP : Does item 41 exist?\n\t  Minimal Page 41 @ 1 IpP : If len(result) is 1, Page 5 @ 10 IpP exists.\n\t*/\n\thasNextPage := false\n\tif page.ItemsPerPage != -1 {\n\t\tnextPage := &model.PageRequest{\n\t\t\tItemsPerPage: 1,\n\t\t\tPage:         ((page.Page * page.ItemsPerPage) + 1),\n\t\t}\n\t\tnextJobs, err := r.Repo.QueryJobs(ctx, filter, nextPage, order)\n\t\tif err != nil {\n\t\t\tcclog.Warn(\"Error while querying next jobs\")\n\t\t\treturn nil, err\n\t\t}\n\t\thasNextPage = len(nextJobs) == 1\n\t}\n\n\treturn &model.JobResultList{Items: jobs, Count: &count, HasNextPage: &hasNextPage}, nil\n}\n\n// JobsStatistics is the resolver for the jobsStatistics field.\nfunc (r *queryResolver) JobsStatistics(ctx context.Context, filter []*model.JobFilter, metrics []string, page *model.PageRequest, sortBy *model.SortByAggregate, groupBy *model.Aggregate, numDurationBins *string, numMetricBins *int) ([]*model.JobsStatistics, error) {\n\tvar err error\n\tvar stats []*model.JobsStatistics\n\n\t// Top Level Defaults\n\tdefaultDurationBins := \"1h\"\n\tdefaultMetricBins := 10\n\n\tif requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||\n\t\trequireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {\n\t\tif groupBy == nil {\n\t\t\tstats, err = r.Repo.JobsStats(ctx, filter)\n\t\t} else {\n\t\t\tstats, err = r.Repo.JobsStatsGrouped(ctx, filter, page, sortBy, groupBy)\n\t\t}\n\t} else {\n\t\tstats = make([]*model.JobsStatistics, 0, 1)\n\t\tstats = append(stats, &model.JobsStatistics{})\n\t}\n\n\tif groupBy != nil {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")\n\t\t}\n\t} else {\n\t\tif requireField(ctx, \"shortJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")\n\t\t}\n\t\tif requireField(ctx, \"runningJobs\") {\n\t\t\tstats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")\n\t\t}\n\t}\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif requireField(ctx, \"histDuration\") || requireField(ctx, \"histNumNodes\") || requireField(ctx, \"histNumCores\") || requireField(ctx, \"histNumAccs\") {\n\n\t\tif numDurationBins == nil {\n\t\t\tnumDurationBins = &defaultDurationBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddHistograms(ctx, filter, stats[0], numDurationBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\tif requireField(ctx, \"histMetrics\") {\n\n\t\tif numMetricBins == nil {\n\t\t\tnumMetricBins = &defaultMetricBins\n\t\t}\n\n\t\tif groupBy == nil {\n\t\t\tstats[0], err = r.Repo.AddMetricHistograms(ctx, filter, metrics, stats[0], numMetricBins)\n\t\t\tif err != nil {\n\t\t\t\treturn nil, err\n\t\t\t}\n\t\t} else {\n\t\t\treturn nil, errors.New(\"metric histograms only implemented without groupBy argument\")\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// JobsMetricStats is the resolver for the jobsMetricStats field.\nfunc (r *queryResolver) JobsMetricStats(ctx context.Context, filter []*model.JobFilter, metrics []string) ([]*model.JobStats, error) {\n\t// No Paging, Fixed Order by StartTime ASC\n\torder := &model.OrderByInput{\n\t\tField: \"startTime\",\n\t\tType:  \"col\",\n\t\tOrder: \"ASC\",\n\t}\n\n\tjobs, err := r.Repo.QueryJobs(ctx, filter, nil, order)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying jobs for comparison\")\n\t\treturn nil, err\n\t}\n\n\tres := []*model.JobStats{}\n\tfor _, job := range jobs {\n\t\tdata, err := metricdispatch.LoadJobStats(job, metrics, ctx)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading comparison jobStats data for job id %d\", job.JobID)\n\t\t\tcontinue\n\t\t\t// return nil, err\n\t\t}\n\n\t\tsres := []*model.NamedStats{}\n\t\tfor name, md := range data {\n\t\t\tsres = append(sres, &model.NamedStats{\n\t\t\t\tName: name,\n\t\t\t\tData: &md,\n\t\t\t})\n\t\t}\n\n\t\tnumThreadsInt := int(job.NumHWThreads)\n\t\tnumAccsInt := int(job.NumAcc)\n\t\tres = append(res, &model.JobStats{\n\t\t\tID:              int(*job.ID),\n\t\t\tJobID:           strconv.Itoa(int(job.JobID)),\n\t\t\tStartTime:       int(job.StartTime),\n\t\t\tDuration:        int(job.Duration),\n\t\t\tCluster:         job.Cluster,\n\t\t\tSubCluster:      job.SubCluster,\n\t\t\tNumNodes:        int(job.NumNodes),\n\t\t\tNumHWThreads:    &numThreadsInt,\n\t\t\tNumAccelerators: &numAccsInt,\n\t\t\tStats:           sres,\n\t\t})\n\t}\n\treturn res, err\n}\n\n// JobsFootprints is the resolver for the jobsFootprints field.\nfunc (r *queryResolver) JobsFootprints(ctx context.Context, filter []*model.JobFilter, metrics []string) (*model.Footprints, error) {\n\t// NOTE: Legacy Naming! This resolver is for normalized histograms in analysis view only - *Not* related to DB \"footprint\" column!\n\treturn r.jobsFootprints(ctx, filter, metrics)\n}\n\n// RooflineHeatmap is the resolver for the rooflineHeatmap field.\nfunc (r *queryResolver) RooflineHeatmap(ctx context.Context, filter []*model.JobFilter, rows int, cols int, minX float64, minY float64, maxX float64, maxY float64) ([][]float64, error) {\n\treturn r.rooflineHeatmap(ctx, filter, rows, cols, minX, minY, maxX, maxY)\n}\n\n// NodeMetrics is the resolver for the nodeMetrics field.\nfunc (r *queryResolver) NodeMetrics(ctx context.Context, cluster string, nodes []string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time) ([]*model.NodeMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tdefaultMetrics := make([]string, 0)\n\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\tdefaultMetrics = append(defaultMetrics, mc.Name)\n\t}\n\tif metrics == nil {\n\t\tmetrics = defaultMetrics\n\t} else {\n\t\tmetrics = slices.DeleteFunc(metrics, func(metric string) bool {\n\t\t\treturn !slices.Contains(defaultMetrics, metric) // Remove undefined metrics.\n\t\t})\n\t}\n\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nodes, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\tstateMap, _ := nodeRepo.MapNodes(cluster)\n\n\tnodeMetrics := make([]*model.NodeMetrics, 0, len(data))\n\tfor hostname, metrics := range data {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost:    hostname,\n\t\t\tState:   stateMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0, len(metrics)*len(scopes)),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName:   metric,\n\t\t\t\t\tScope:  schema.MetricScopeNode,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetrics = append(nodeMetrics, host)\n\t}\n\n\treturn nodeMetrics, nil\n}\n\n// NodeMetricsList is the resolver for the nodeMetricsList field.\nfunc (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int) (*model.NodesResultList, error) {\n\tif resolution == nil { // Load from Config\n\t\tif config.Keys.EnableResampling != nil {\n\t\t\tdefaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)\n\t\t\tresolution = &defaultRes\n\t\t} else { // Set 0 (Loads configured metric timestep)\n\t\t\tdefaultRes := 0\n\t\t\tresolution = &defaultRes\n\t\t}\n\t}\n\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tnodeRepo := repository.GetNodeRepository()\n\t// nodes -> array hostname\n\tnodes, stateMap, countNodes, hasNextPage, nerr := nodeRepo.GetNodesForList(ctx, cluster, subCluster, stateFilter, nodeFilter, page)\n\tif nerr != nil {\n\t\treturn nil, errors.New(\"could not retrieve node list required for resolving NodeMetricsList\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// data -> map hostname:jobdata\n\tdata, err := metricdispatch.LoadNodeListData(cluster, subCluster, nodes, metrics, scopes, *resolution, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data (Resolver.NodeMetricsList\")\n\t\treturn nil, err\n\t}\n\n\tnodeMetricsList := make([]*model.NodeMetrics, 0, len(data))\n\tfor _, hostname := range nodes {\n\t\thost := &model.NodeMetrics{\n\t\t\tHost:    hostname,\n\t\t\tState:   stateMap[hostname],\n\t\t\tMetrics: make([]*model.JobMetricWithName, 0),\n\t\t}\n\t\thost.SubCluster, err = archive.GetSubClusterByNode(cluster, hostname)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"error in nodeMetrics resolver: %s\", err)\n\t\t}\n\n\t\tfor metric, scopedMetrics := range data[hostname] {\n\t\t\tfor scope, scopedMetric := range scopedMetrics {\n\t\t\t\thost.Metrics = append(host.Metrics, &model.JobMetricWithName{\n\t\t\t\t\tName:   metric,\n\t\t\t\t\tScope:  scope,\n\t\t\t\t\tMetric: scopedMetric,\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\n\t\tnodeMetricsList = append(nodeMetricsList, host)\n\t}\n\n\tnodeMetricsListResult := &model.NodesResultList{\n\t\tItems: nodeMetricsList,\n\t\t// TotalNodes depends on sum of nodes grouped on latest timestamp, see repo/node.go:357\n\t\tTotalNodes:  &countNodes,\n\t\tHasNextPage: &hasNextPage,\n\t}\n\n\treturn nodeMetricsListResult, nil\n}\n\n// ClusterMetrics is the resolver for the clusterMetrics field.\nfunc (r *queryResolver) ClusterMetrics(ctx context.Context, cluster string, metrics []string, from time.Time, to time.Time) (*model.ClusterMetrics, error) {\n\tuser := repository.GetUserFromContext(ctx)\n\tif user != nil && !user.HasAnyRole([]schema.Role{schema.RoleAdmin, schema.RoleSupport}) {\n\t\treturn nil, errors.New(\"you need to be administrator or support staff for this query\")\n\t}\n\n\tif metrics == nil {\n\t\tfor _, mc := range archive.GetCluster(cluster).MetricConfig {\n\t\t\tmetrics = append(metrics, mc.Name)\n\t\t}\n\t}\n\n\t// 'nodes' == nil -> Defaults to all nodes of cluster for existing query workflow\n\tscopes := []schema.MetricScope{\"node\"}\n\tdata, err := metricdispatch.LoadNodeData(cluster, metrics, nil, scopes, from, to, ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"error while loading node data\")\n\t\treturn nil, err\n\t}\n\n\tclusterMetricData := make([]*model.ClusterMetricWithName, 0)\n\tclusterMetrics := model.ClusterMetrics{NodeCount: 0, Metrics: clusterMetricData}\n\n\tcollectorTimestep := make(map[string]int)\n\tcollectorUnit := make(map[string]schema.Unit)\n\tcollectorData := make(map[string][]schema.Float)\n\n\tfor _, metrics := range data {\n\t\tclusterMetrics.NodeCount += 1\n\t\tfor metric, scopedMetrics := range metrics {\n\t\t\tfor _, scopedMetric := range scopedMetrics {\n\t\t\t\t// Collect Info Once\n\t\t\t\t_, okTimestep := collectorTimestep[metric]\n\t\t\t\tif !okTimestep {\n\t\t\t\t\tcollectorTimestep[metric] = scopedMetric.Timestep\n\t\t\t\t}\n\t\t\t\t_, okUnit := collectorUnit[metric]\n\t\t\t\tif !okUnit {\n\t\t\t\t\tcollectorUnit[metric] = scopedMetric.Unit\n\t\t\t\t}\n\t\t\t\t// Collect Data\n\t\t\t\tfor _, ser := range scopedMetric.Series {\n\t\t\t\t\t_, okData := collectorData[metric]\n\t\t\t\t\t// Init With Datasize > 0\n\t\t\t\t\tif !okData && len(ser.Data) != 0 {\n\t\t\t\t\t\tcollectorData[metric] = make([]schema.Float, len(ser.Data))\n\t\t\t\t\t} else if !okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip init: no data -> %s at %s; size %d\", metric, ser.Hostname, len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t\t// Sum if init'd and matching size\n\t\t\t\t\tif okData && len(ser.Data) == len(collectorData[metric]) {\n\t\t\t\t\t\tfor i, val := range ser.Data {\n\t\t\t\t\t\t\tif val.IsNaN() {\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcollectorData[metric][i] += val\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} else if okData {\n\t\t\t\t\t\tcclog.Debugf(\"[SCHEMARESOLVER] clusterMetrics skip sum: data diff -> %s at %s; want size %d, have size %d\", metric, ser.Hostname, len(collectorData[metric]), len(ser.Data))\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tfor metricName, data := range collectorData {\n\t\t// use ccUnits for backend normalization to \"Tera\"\n\t\tp_old := ccunit.NewPrefix(collectorUnit[metricName].Prefix)\n\t\tp_new := ccunit.NewPrefix(\"T\")\n\t\tconvFunc := ccunit.GetPrefixPrefixFactor(p_old, p_new)\n\t\tu_new := schema.Unit{Prefix: p_new.Prefix(), Base: collectorUnit[metricName].Base}\n\n\t\troundedData := make([]schema.Float, 0)\n\t\tfor _, v_old := range data {\n\t\t\tv_new := math.Round(convFunc(float64(v_old)).(float64)*100.0) / 100.0\n\t\t\troundedData = append(roundedData, schema.Float(v_new))\n\t\t}\n\n\t\tcm := model.ClusterMetricWithName{\n\t\t\tName:     metricName,\n\t\t\tUnit:     &u_new,\n\t\t\tTimestep: collectorTimestep[metricName],\n\t\t\tData:     roundedData,\n\t\t}\n\n\t\tclusterMetrics.Metrics = append(clusterMetrics.Metrics, &cm)\n\t}\n\n\treturn &clusterMetrics, nil\n}\n\n// NumberOfNodes is the resolver for the numberOfNodes field.\nfunc (r *subClusterResolver) NumberOfNodes(ctx context.Context, obj *schema.SubCluster) (int, error) {\n\tnodeList, err := archive.ParseNodeList(obj.Nodes)\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\treturn nodeList.NodeCount(), nil\n}\n\n// Cluster returns generated.ClusterResolver implementation.\nfunc (r *Resolver) Cluster() generated.ClusterResolver { return &clusterResolver{r} }\n\n// Job returns generated.JobResolver implementation.\nfunc (r *Resolver) Job() generated.JobResolver { return &jobResolver{r} }\n\n// MetricValue returns generated.MetricValueResolver implementation.\nfunc (r *Resolver) MetricValue() generated.MetricValueResolver { return &metricValueResolver{r} }\n\n// Mutation returns generated.MutationResolver implementation.\nfunc (r *Resolver) Mutation() generated.MutationResolver { return &mutationResolver{r} }\n\n// Node returns generated.NodeResolver implementation.\nfunc (r *Resolver) Node() generated.NodeResolver { return &nodeResolver{r} }\n\n// Query returns generated.QueryResolver implementation.\nfunc (r *Resolver) Query() generated.QueryResolver { return &queryResolver{r} }\n\n// SubCluster returns generated.SubClusterResolver implementation.\nfunc (r *Resolver) SubCluster() generated.SubClusterResolver { return &subClusterResolver{r} }\n\ntype clusterResolver struct{ *Resolver }\ntype jobResolver struct{ *Resolver }\ntype metricValueResolver struct{ *Resolver }\ntype mutationResolver struct{ *Resolver }\ntype nodeResolver struct{ *Resolver }\ntype queryResolver struct{ *Resolver }\ntype subClusterResolver struct{ *Resolver }\n","structuredPatch":[{"oldStart":652,"oldLines":8,"newStart":652,"newLines":10,"lines":["   defaultDurationBins := \"1h\"","   defaultMetricBins := 10"," ","-  if requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||","-    requireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\") {","+  fetchedMainStats := requireField(ctx, \"totalJobs\") || requireField(ctx, \"totalUsers\") || requireField(ctx, \"totalWalltime\") || requireField(ctx, \"totalNodes\") || requireField(ctx, \"totalCores\") ||","+    requireField(ctx, \"totalAccs\") || requireField(ctx, \"totalNodeHours\") || requireField(ctx, \"totalCoreHours\") || requireField(ctx, \"totalAccHours\")","+","+  if fetchedMainStats {","     if groupBy == nil {","       stats, err = r.Repo.JobsStats(ctx, filter)","     } else {"]},{"oldStart":664,"oldLines":20,"newStart":666,"newLines":24,"lines":["     stats = append(stats, &model.JobsStatistics{})","   }"," ","-  if groupBy != nil {","-    if requireField(ctx, \"shortJobs\") {","-      stats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")","+  // runningJobs and shortJobs are already inlined in JobsStats/JobsStatsGrouped.","+  // Only run separate count queries if main stats were not fetched.","+  if !fetchedMainStats {","+    if groupBy != nil {","+      if requireField(ctx, \"shortJobs\") {","+        stats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"short\")","+      }","+      if requireField(ctx, \"runningJobs\") {","+        stats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")","+      }","+    } else {","+      if requireField(ctx, \"shortJobs\") {","+        stats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")","+      }","+      if requireField(ctx, \"runningJobs\") {","+        stats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")","+      }","     }","-    if requireField(ctx, \"runningJobs\") {","-      stats, err = r.Repo.AddJobCountGrouped(ctx, filter, groupBy, stats, \"running\")","-    }","-  } else {","-    if requireField(ctx, \"shortJobs\") {","-      stats, err = r.Repo.AddJobCount(ctx, filter, stats, \"short\")","-    }","-    if requireField(ctx, \"runningJobs\") {","-      stats, err = r.Repo.AddJobCount(ctx, filter, stats, \"running\")","-    }","   }"," ","   if err != nil {"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"47593f2c-9fa3-44a0-84bd-cec49891cc8f"}
{"parentUuid":"2e838443-c66b-463e-9401-93045906df58","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01H3fQTxzL6gyWp3EU6Zb5iQ","type":"message","role":"assistant","content":[{"type":"text","text":"Now apply Fix 3 — map-based bin lookup in `jobsDurationStatisticsHistogram`:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1138,"cache_read_input_tokens":43151,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1138},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwPW3S1AHBwtExwrQj7J","type":"assistant","uuid":"40ac47fe-8b3e-4663-a108-85bd5663e10b","timestamp":"2026-03-11T13:41:23.796Z"}
{"parentUuid":"40ac47fe-8b3e-4663-a108-85bd5663e10b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01H3fQTxzL6gyWp3EU6Zb5iQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014HAWTMqxbNWKfYBbYX98S1","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}","new_string":"\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}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1138,"cache_read_input_tokens":43151,"output_tokens":979,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1138,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPW3S1AHBwtExwrQj7J","type":"assistant","uuid":"ac0a813c-16e8-4230-a908-3a727d68516b","timestamp":"2026-03-11T13:41:33.710Z"}
{"parentUuid":"ac0a813c-16e8-4230-a908-3a727d68516b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Edit","command":"callback"},"parentToolUseID":"toolu_014HAWTMqxbNWKfYBbYX98S1","toolUseID":"toolu_014HAWTMqxbNWKfYBbYX98S1","timestamp":"2026-03-11T13:41:33.717Z","uuid":"1071ba69-f8f9-4f9e-9a0c-066085743ccc"}
{"parentUuid":"ac0a813c-16e8-4230-a908-3a727d68516b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014HAWTMqxbNWKfYBbYX98S1","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"aff1b5e5-9732-40c5-97de-2b448c90cc0e","timestamp":"2026-03-11T13:41:33.717Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}","newString":"\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}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &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\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Value == (point.Value * binSizeSeconds) {\n\t\t\t\te.Count = point.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).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)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":852,"oldLines":12,"newStart":852,"newLines":18,"lines":[" ","   // Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)","   // Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.","-  points := make([]*model.HistoPoint, 0)","+  points := make([]*model.HistoPoint, 0, *targetBinCount)","   for i := 1; i <= *targetBinCount; i++ {","     point := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}","     points = append(points, &point)","   }"," ","+  // Build a map from bin value (seconds) to slice index for O(1) lookup.","+  binMap := make(map[int]int, len(points))","+  for i, p := range points {","+    binMap[p.Value] = i","+  }","+","   for _, f := range filters {","     query = BuildWhereClause(f, query)","   }"]},{"oldStart":869,"oldLines":8,"newStart":875,"newLines":8,"lines":["   }","   defer rows.Close()"," ","-  // Match query results to pre-initialized bins.","-  // point.Value from query is the bin number; multiply by binSizeSeconds to match bin.Value.","+  // Match query results to pre-initialized bins using map lookup.","+  // point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.","   for rows.Next() {","     point := model.HistoPoint{}","     if err := rows.Scan(&point.Value, &point.Count); err != nil {"]},{"oldStart":878,"oldLines":11,"newStart":884,"newLines":8,"lines":["       return nil, err","     }"," ","-    for _, e := range points {","-      if e.Value == (point.Value * binSizeSeconds) {","-        e.Count = point.Count","-        break","-      }","+    if idx, ok := binMap[point.Value*binSizeSeconds]; ok {","+      points[idx].Count = point.Count","     }","   }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"ac0a813c-16e8-4230-a908-3a727d68516b"}
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{"parentUuid":"5719aa01-168f-435c-bbfc-6fd4e5d1390e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01KNZFZztWUPACkpmMqsuRi5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GBMPvPnLkCriQkD8mFDkoe","name":"Edit","input":{"replace_all":false,"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","old_string":"\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}","new_string":"\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}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1022,"cache_read_input_tokens":44289,"output_tokens":880,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1022,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPWzG2nfjTR8JaE4PvN","type":"assistant","uuid":"072e2c6c-85a4-4d43-870f-1244ed32a0c5","timestamp":"2026-03-11T13:41:44.324Z"}
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{"parentUuid":"072e2c6c-85a4-4d43-870f-1244ed32a0c5","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GBMPvPnLkCriQkD8mFDkoe","type":"tool_result","content":"The file /Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go has been updated successfully."}]},"uuid":"6a7aef22-1281-4138-8dd9-27ebf32767ac","timestamp":"2026-03-11T13:41:44.338Z","toolUseResult":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/repository/stats.go","oldString":"\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}","newString":"\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}","originalFile":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains job statistics and histogram generation functionality for the JobRepository.\n//\n// # Job Statistics\n//\n// The statistics methods provide aggregated metrics about jobs including total jobs, users,\n// walltime, and resource usage (nodes, cores, accelerators). Statistics can be computed:\n//   - Overall (JobsStats): Single aggregate across all matching jobs\n//   - Grouped (JobsStatsGrouped): Aggregated by user, project, cluster, or subcluster\n//   - Counts (JobCountGrouped, AddJobCount): Simple job counts with optional filtering\n//\n// All statistics methods support filtering via JobFilter and respect security contexts.\n//\n// # Histograms\n//\n// Histogram methods generate distribution data for visualization:\n//   - Duration, nodes, cores, accelerators (AddHistograms)\n//   - Job metrics like CPU load, memory usage (AddMetricHistograms)\n//\n// Histograms use intelligent binning:\n//   - Duration: Variable bin sizes (1m, 10m, 1h, 6h, 12h, 24h) with zero-padding\n//   - Resources: Natural value-based bins\n//   - Metrics: Normalized to peak values with configurable bin counts\n//\n// # Running vs. Completed Jobs\n//\n// Statistics handle running jobs specially:\n//   - Duration calculated as (now - start_time) for running jobs\n//   - Metric histograms for running jobs load data from metric backend instead of footprint\n//   - Job state filtering distinguishes running/completed jobs\n//\n// # Performance Considerations\n//\n// - All queries use prepared statements via stmtCache\n// - Complex aggregations use SQL for efficiency\n// - Histogram pre-initialization ensures consistent bin ranges\n// - Metric histogram queries limited to 5000 jobs for running job analysis\n\npackage repository\n\nimport (\n\t\"context\"\n\t\"database/sql\"\n\t\"fmt\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/graph/model\"\n\t\"github.com/ClusterCockpit/cc-backend/internal/metricdispatch\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\tsq \"github.com/Masterminds/squirrel\"\n)\n\n// groupBy2column maps GraphQL Aggregate enum values to their corresponding database column names.\n// Used by JobsStatsGrouped and JobCountGrouped to translate user-facing grouping dimensions\n// into SQL GROUP BY clauses. GraphQL validation ensures only valid enum values are accepted.\nvar groupBy2column = map[model.Aggregate]string{\n\tmodel.AggregateUser:       \"job.hpc_user\",\n\tmodel.AggregateProject:    \"job.project\",\n\tmodel.AggregateCluster:    \"job.cluster\",\n\tmodel.AggregateSubcluster: \"job.subcluster\",\n}\n\n// sortBy2column maps GraphQL SortByAggregate enum values to their corresponding computed column names.\n// Used by JobsStatsGrouped to translate sort preferences into SQL ORDER BY clauses.\n// Column names match the AS aliases used in buildStatsQuery.\nvar sortBy2column = map[model.SortByAggregate]string{\n\tmodel.SortByAggregateTotaljobs:      \"totalJobs\",\n\tmodel.SortByAggregateTotalusers:     \"totalUsers\",\n\tmodel.SortByAggregateTotalwalltime:  \"totalWalltime\",\n\tmodel.SortByAggregateTotalnodes:     \"totalNodes\",\n\tmodel.SortByAggregateTotalnodehours: \"totalNodeHours\",\n\tmodel.SortByAggregateTotalcores:     \"totalCores\",\n\tmodel.SortByAggregateTotalcorehours: \"totalCoreHours\",\n\tmodel.SortByAggregateTotalaccs:      \"totalAccs\",\n\tmodel.SortByAggregateTotalacchours:  \"totalAccHours\",\n}\n\n// buildCountQuery constructs a SQL query to count jobs with optional grouping and filtering.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - kind: Special filter - \"running\" for running jobs only, \"short\" for jobs under threshold\n//   - col: Column name to GROUP BY; empty string for total count without grouping\n//\n// Returns a SelectBuilder that produces either:\n//   - Single count: COUNT(job.id) when col is empty\n//   - Grouped counts: col, COUNT(job.id) when col is specified\n//\n// The kind parameter enables counting specific job categories:\n//   - \"running\": Only jobs with job_state = 'running'\n//   - \"short\": Only jobs with duration < ShortRunningJobsDuration config value\n//   - empty: All jobs matching filters\nfunc (r *JobRepository) buildCountQuery(\n\tfilter []*model.JobFilter,\n\tkind string,\n\tcol string,\n) sq.SelectBuilder {\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tquery = sq.Select(col, \"COUNT(job.id)\").From(\"job\").GroupBy(col)\n\t} else {\n\t\tquery = sq.Select(\"COUNT(job.id)\").From(\"job\")\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tquery = query.Where(\"job.job_state = ?\", \"running\")\n\tcase \"short\":\n\t\tquery = query.Where(\"job.duration < ?\", config.Keys.ShortRunningJobsDuration)\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// buildStatsQuery constructs a SQL query to compute comprehensive job statistics with optional grouping.\n//\n// Parameters:\n//   - filter: Job filters to apply (cluster, user, time range, etc.)\n//   - col: Column name to GROUP BY; empty string for overall statistics without grouping\n//   - shortThreshold: Duration threshold in seconds for counting short-running jobs\n//\n// Returns a SelectBuilder that produces comprehensive statistics:\n//   - totalJobs: Count of jobs\n//   - totalUsers: Count of distinct users (always 0 when grouping by user)\n//   - totalWalltime: Sum of job durations in hours\n//   - totalNodes: Sum of nodes used across all jobs\n//   - totalNodeHours: Sum of (duration × num_nodes) in hours\n//   - totalCores: Sum of hardware threads used across all jobs\n//   - totalCoreHours: Sum of (duration × num_hwthreads) in hours\n//   - totalAccs: Sum of accelerators used across all jobs\n//   - totalAccHours: Sum of (duration × num_acc) in hours\n//   - runningJobs: Count of jobs with job_state = 'running'\n//   - shortJobs: Count of jobs with duration < shortThreshold\n//\n// Special handling:\n//   - Running jobs: Duration calculated as (now - start_time) instead of stored duration\n//   - Grouped by user: Also joins hpc_user table to select display name\n//   - Grouped by other dimensions: Selects empty string for name column (no join)\n//   - All time values converted from seconds to hours (÷ 3600) and rounded\nfunc (r *JobRepository) buildStatsQuery(\n\tfilter []*model.JobFilter,\n\tcol string,\n\tshortThreshold int,\n) sq.SelectBuilder {\n\tnow := time.Now().Unix()\n\tvar query sq.SelectBuilder\n\n\tif col != \"\" {\n\t\tif col == \"job.hpc_user\" {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").LeftJoin(\"hpc_user ON hpc_user.username = job.hpc_user\").GroupBy(col)\n\t\t} else {\n\t\t\tquery = sq.Select(\n\t\t\t\tcol,\n\t\t\t\t\"'' as name\",\n\t\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int) as totalWalltime`, now),\n\t\t\t\t`CAST(SUM(job.num_nodes) as int) as totalNodes`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int) as totalNodeHours`, now),\n\t\t\t\t`CAST(SUM(job.num_hwthreads) as int) as totalCores`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int) as totalCoreHours`, now),\n\t\t\t\t`CAST(SUM(job.num_acc) as int) as totalAccs`,\n\t\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int) as totalAccHours`, now),\n\t\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t\t).From(\"job\").GroupBy(col)\n\t\t}\n\t} else {\n\t\tquery = sq.Select(\n\t\t\t\"COUNT(job.id) as totalJobs\",\n\t\t\t\"COUNT(DISTINCT job.hpc_user) AS totalUsers\",\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END)) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_nodes) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_nodes) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_hwthreads) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_hwthreads) / 3600) as int)`, now),\n\t\t\t`CAST(SUM(job.num_acc) as int)`,\n\t\t\tfmt.Sprintf(`CAST(ROUND(SUM((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) * job.num_acc) / 3600) as int)`, now),\n\t\t\t`COUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs`,\n\t\t\tfmt.Sprintf(`COUNT(CASE WHEN job.duration < %d THEN 1 END) as shortJobs`, shortThreshold),\n\t\t).From(\"job\")\n\t}\n\n\tfor _, f := range filter {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\treturn query\n}\n\n// JobsStatsGrouped computes comprehensive job statistics grouped by a dimension (user, project, cluster, or subcluster).\n//\n// This is the primary method for generating aggregated statistics views in the UI, providing\n// metrics like total jobs, walltime, and resource usage broken down by the specified grouping.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//   - page: Optional pagination (ItemsPerPage: -1 disables pagination)\n//   - sortBy: Optional sort column (totalJobs, totalWalltime, totalCoreHours, etc.)\n//   - groupBy: Required grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns a slice of JobsStatistics, one per group, with:\n//   - ID: The group identifier (username, project name, cluster name, etc.)\n//   - Name: Display name (for users, from hpc_user.name; empty for other groups)\n//   - Statistics: totalJobs, totalUsers, totalWalltime, resource usage metrics\n//\n// Security: Respects user roles via SecurityCheck - users see only their own data unless admin/support.\n// Performance: Results are sorted in SQL and pagination applied before scanning rows.\nfunc (r *JobRepository) JobsStatsGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tpage *model.PageRequest,\n\tsortBy *model.SortByAggregate,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildStatsQuery(filter, col, config.Keys.ShortRunningJobsDuration)\n\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tif sortBy != nil {\n\t\tsortBy := sortBy2column[*sortBy]\n\t\tquery = query.OrderBy(fmt.Sprintf(\"%s DESC\", sortBy))\n\t}\n\tif page != nil && page.ItemsPerPage != -1 {\n\t\tlimit := uint64(page.ItemsPerPage)\n\t\tquery = query.Offset((uint64(page.Page) - 1) * limit).Limit(limit)\n\t}\n\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar name sql.NullString\n\t\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\t\tif err := rows.Scan(&id, &name, &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\tvar totalJobs, totalUsers, totalWalltime, totalNodes, totalNodeHours, totalCores, totalCoreHours, totalAccs, totalAccHours int\n\t\t\tvar personName string\n\n\t\t\tif name.Valid {\n\t\t\t\tpersonName = name.String\n\t\t\t}\n\n\t\t\tif jobs.Valid {\n\t\t\t\ttotalJobs = int(jobs.Int64)\n\t\t\t}\n\n\t\t\tif users.Valid {\n\t\t\t\ttotalUsers = int(users.Int64)\n\t\t\t}\n\n\t\t\tif walltime.Valid {\n\t\t\t\ttotalWalltime = int(walltime.Int64)\n\t\t\t}\n\n\t\t\tif nodes.Valid {\n\t\t\t\ttotalNodes = int(nodes.Int64)\n\t\t\t}\n\t\t\tif cores.Valid {\n\t\t\t\ttotalCores = int(cores.Int64)\n\t\t\t}\n\t\t\tif accs.Valid {\n\t\t\t\ttotalAccs = int(accs.Int64)\n\t\t\t}\n\n\t\t\tif nodeHours.Valid {\n\t\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t\t}\n\t\t\tif coreHours.Valid {\n\t\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t\t}\n\t\t\tif accHours.Valid {\n\t\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t\t}\n\n\t\t\tif col == \"job.hpc_user\" {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tName:           personName,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t} else {\n\t\t\t\tstats = append(stats,\n\t\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\t\tID:             id.String,\n\t\t\t\t\t\tTotalJobs:      totalJobs,\n\t\t\t\t\t\tTotalUsers:     totalUsers,\n\t\t\t\t\t\tTotalWalltime:  totalWalltime,\n\t\t\t\t\t\tTotalNodes:     totalNodes,\n\t\t\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\t\t\tTotalCores:     totalCores,\n\t\t\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\t\t\tTotalAccs:      totalAccs,\n\t\t\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobsStatsGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// JobsStats computes overall job statistics across all matching jobs without grouping.\n//\n// This method provides a single aggregate view of job metrics, useful for dashboard\n// summaries and overall system utilization reports.\n//\n// Parameters:\n//   - ctx: Context for security checks and cancellation\n//   - filter: Filters to apply (time range, cluster, job state, etc.)\n//\n// Returns a single-element slice containing aggregate statistics:\n//   - totalJobs, totalUsers, totalWalltime\n//   - totalNodeHours, totalCoreHours, totalAccHours\n//\n// Unlike JobsStatsGrouped, this returns overall totals without breaking down by dimension.\n// Security checks are applied via SecurityCheck to respect user access levels.\nfunc (r *JobRepository) JobsStats(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildStatsQuery(filter, \"\", config.Keys.ShortRunningJobsDuration)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\trow := query.RunWith(r.DB).QueryRowContext(ctx)\n\tstats := make([]*model.JobsStatistics, 0, 1)\n\n\tvar jobs, users, walltime, nodes, nodeHours, cores, coreHours, accs, accHours, runningJobs, shortJobs sql.NullInt64\n\tif err := row.Scan(&jobs, &users, &walltime, &nodes, &nodeHours, &cores, &coreHours, &accs, &accHours, &runningJobs, &shortJobs); err != nil {\n\t\tcclog.Warn(\"Error while scanning rows\")\n\t\treturn nil, err\n\t}\n\n\tif jobs.Valid {\n\t\tvar totalNodeHours, totalCoreHours, totalAccHours int\n\n\t\tif nodeHours.Valid {\n\t\t\ttotalNodeHours = int(nodeHours.Int64)\n\t\t}\n\t\tif coreHours.Valid {\n\t\t\ttotalCoreHours = int(coreHours.Int64)\n\t\t}\n\t\tif accHours.Valid {\n\t\t\ttotalAccHours = int(accHours.Int64)\n\t\t}\n\t\tstats = append(stats,\n\t\t\t&model.JobsStatistics{\n\t\t\t\tTotalJobs:      int(jobs.Int64),\n\t\t\t\tTotalUsers:     int(users.Int64),\n\t\t\t\tTotalWalltime:  int(walltime.Int64),\n\t\t\t\tTotalNodeHours: totalNodeHours,\n\t\t\t\tTotalCoreHours: totalCoreHours,\n\t\t\t\tTotalAccHours:  totalAccHours,\n\t\t\t\tRunningJobs:    int(runningJobs.Int64),\n\t\t\t\tShortJobs:      int(shortJobs.Int64),\n\t\t\t})\n\t}\n\n\tcclog.Debugf(\"Timer JobStats %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// LoadJobStat retrieves a specific statistic for a metric from a job's statistics.\n// Returns 0.0 if the metric is not found or statType is invalid.\n//\n// Parameters:\n//   - job: Job struct with populated Statistics field\n//   - metric: Name of the metric to query (e.g., \"cpu_load\", \"mem_used\")\n//   - statType: Type of statistic: \"avg\", \"min\", or \"max\"\n//\n// Returns the requested statistic value or 0.0 if not found.\nfunc LoadJobStat(job *schema.Job, metric string, statType string) float64 {\n\tif stats, ok := job.Statistics[metric]; ok {\n\t\tswitch statType {\n\t\tcase \"avg\":\n\t\t\treturn stats.Avg\n\t\tcase \"max\":\n\t\t\treturn stats.Max\n\t\tcase \"min\":\n\t\t\treturn stats.Min\n\t\tdefault:\n\t\t\tcclog.Errorf(\"Unknown stat type %s\", statType)\n\t\t}\n\t}\n\n\treturn 0.0\n}\n\n// JobCountGrouped counts jobs grouped by a dimension without computing detailed statistics.\n//\n// This is a lightweight alternative to JobsStatsGrouped when only job counts are needed,\n// avoiding the overhead of calculating walltime and resource usage metrics.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (User, Project, Cluster, or SubCluster)\n//\n// Returns JobsStatistics with only ID and TotalJobs populated for each group.\nfunc (r *JobRepository) JobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, \"\", col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tstats := make([]*model.JobsStatistics, 0, 100)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tstats = append(stats,\n\t\t\t\t&model.JobsStatistics{\n\t\t\t\t\tID:        id.String,\n\t\t\t\t\tTotalJobs: int(cnt.Int64),\n\t\t\t\t})\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer JobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCountGrouped augments existing statistics with additional job counts by category.\n//\n// This method enriches JobsStatistics returned by JobsStatsGrouped or JobCountGrouped\n// with counts of running or short-running (based on ShortRunningJobsDuration) jobs, matched by group ID.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - groupBy: Grouping dimension (must match the dimension used for stats parameter)\n//   - stats: Existing statistics to augment (modified in-place by ID matching)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields populated per group.\n// Groups without matching jobs will have 0 for the added field.\nfunc (r *JobRepository) AddJobCountGrouped(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tgroupBy *model.Aggregate,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tcol := groupBy2column[*groupBy]\n\tquery := r.buildCountQuery(filter, kind, col)\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\trows, err := query.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job statistics\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tcounts := make(map[string]int)\n\n\tfor rows.Next() {\n\t\tvar id sql.NullString\n\t\tvar cnt sql.NullInt64\n\t\tif err := rows.Scan(&id, &cnt); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\t\tif id.Valid {\n\t\t\tcounts[id.String] = int(cnt.Int64)\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = counts[s.ID]\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = counts[s.ID]\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCountGrouped %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddJobCount augments existing overall statistics with additional job counts by category.\n//\n// Similar to AddJobCountGrouped but for ungrouped statistics. Applies the same count\n// to all statistics entries (typically just one).\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply\n//   - stats: Existing statistics to augment (modified in-place)\n//   - kind: \"running\" to add RunningJobs count, \"short\" to add ShortJobs count\n//\n// Returns the same stats slice with RunningJobs or ShortJobs fields set to the total count.\nfunc (r *JobRepository) AddJobCount(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstats []*model.JobsStatistics,\n\tkind string,\n) ([]*model.JobsStatistics, error) {\n\tstart := time.Now()\n\tquery := r.buildCountQuery(filter, kind, \"\")\n\tquery, err := SecurityCheck(ctx, query)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\tvar cnt sql.NullInt64\n\tif err := query.RunWith(r.DB).QueryRowContext(ctx).Scan(&cnt); err != nil {\n\t\tcclog.Warn(\"Error while querying DB for job count\")\n\t\treturn nil, err\n\t}\n\n\tcount := int(cnt.Int64)\n\n\tswitch kind {\n\tcase \"running\":\n\t\tfor _, s := range stats {\n\t\t\ts.RunningJobs = count\n\t\t}\n\tcase \"short\":\n\t\tfor _, s := range stats {\n\t\t\ts.ShortJobs = count\n\t\t}\n\t}\n\n\tcclog.Debugf(\"Timer AddJobCount %s\", time.Since(start))\n\treturn stats, nil\n}\n\n// AddHistograms augments statistics with distribution histograms for job properties.\n//\n// Generates histogram data for visualization of job duration, node count, core count,\n// and accelerator count distributions. Duration histogram uses intelligent binning based\n// on the requested resolution.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply to jobs included in histograms\n//   - stat: Statistics struct to augment (modified in-place)\n//   - durationBins: Bin size - \"1m\", \"10m\", \"1h\", \"6h\", \"12h\", or \"24h\" (default)\n//\n// Populates these fields in stat:\n//   - HistDuration: Job duration distribution (zero-padded bins)\n//   - HistNumNodes: Node count distribution\n//   - HistNumCores: Core (hwthread) count distribution\n//   - HistNumAccs: Accelerator count distribution\n//\n// Duration bins are pre-initialized with zeros to ensure consistent ranges for visualization.\n// Bin size determines both the width and maximum duration displayed (e.g., \"1h\" = 48 bins × 1h = 48h max).\nfunc (r *JobRepository) AddHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tstat *model.JobsStatistics,\n\tdurationBins *string,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\tvar targetBinCount int\n\tvar targetBinSize int\n\tswitch *durationBins {\n\tcase \"1m\": // 1 Minute Bins + Max 60 Bins -> Max 60 Minutes\n\t\ttargetBinCount = 60\n\t\ttargetBinSize = 60\n\tcase \"10m\": // 10 Minute Bins + Max 72 Bins -> Max 12 Hours\n\t\ttargetBinCount = 72\n\t\ttargetBinSize = 600\n\tcase \"1h\": // 1 Hour Bins + Max 48 Bins -> Max 48 Hours\n\t\ttargetBinCount = 48\n\t\ttargetBinSize = 3600\n\tcase \"6h\": // 6 Hour Bins + Max 12 Bins -> Max 3 Days\n\t\ttargetBinCount = 12\n\t\ttargetBinSize = 21600\n\tcase \"12h\": // 12 hour Bins + Max 14 Bins -> Max 7 Days\n\t\ttargetBinCount = 14\n\t\ttargetBinSize = 43200\n\tdefault: // 24h\n\t\ttargetBinCount = 24\n\t\ttargetBinSize = 3600\n\t}\n\n\tvar err error\n\t// Return X-Values always as seconds, will be formatted into minutes and hours in frontend\n\tvalue := fmt.Sprintf(`CAST(ROUND(((CASE WHEN job.job_state = \"running\" THEN %d - job.start_time ELSE job.duration END) / %d) + 1) as int) as value`, time.Now().Unix(), targetBinSize)\n\tstat.HistDuration, err = r.jobsDurationStatisticsHistogram(ctx, value, filter, targetBinSize, &targetBinCount)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: job duration\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumNodes, err = r.jobsStatisticsHistogram(ctx, \"job.num_nodes as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num nodes\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumCores, err = r.jobsStatisticsHistogram(ctx, \"job.num_hwthreads as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num hwthreads\")\n\t\treturn nil, err\n\t}\n\n\tstat.HistNumAccs, err = r.jobsStatisticsHistogram(ctx, \"job.num_acc as value\", filter)\n\tif err != nil {\n\t\tcclog.Warn(\"Error while loading job statistics histogram: num acc\")\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer AddHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// AddMetricHistograms augments statistics with distribution histograms for job metrics.\n//\n// Generates histogram data for metrics like CPU load, memory usage, etc. Handles running\n// and completed jobs differently: running jobs load data from metric backend, completed jobs\n// use footprint data from database.\n//\n// Parameters:\n//   - ctx: Context for security checks\n//   - filter: Filters to apply (MUST contain State filter for running jobs)\n//   - metrics: List of metric names to histogram (e.g., [\"cpu_load\", \"mem_used\"])\n//   - stat: Statistics struct to augment (modified in-place)\n//   - targetBinCount: Number of histogram bins (default: 10)\n//\n// Populates HistMetrics field in stat with MetricHistoPoints for each metric.\n//\n// Binning algorithm:\n//   - Values normalized to metric's peak value from cluster configuration\n//   - Bins evenly distributed from 0 to peak\n//   - Pre-initialized with zeros for consistent visualization\n//\n// Limitations:\n//   - Running jobs: Limited to 5000 jobs for performance\n//   - Requires valid cluster configuration with metric peak values\n//   - Uses footprint statistic (avg/max/min) configured per metric\nfunc (r *JobRepository) AddMetricHistograms(\n\tctx context.Context,\n\tfilter []*model.JobFilter,\n\tmetrics []string,\n\tstat *model.JobsStatistics,\n\ttargetBinCount *int,\n) (*model.JobsStatistics, error) {\n\tstart := time.Now()\n\n\t// Running Jobs Only: First query jobdata from sqlite, then query data and make bins\n\tfor _, f := range filter {\n\t\tif f.State != nil {\n\t\t\tif len(f.State) == 1 && f.State[0] == \"running\" {\n\t\t\t\tstat.HistMetrics = r.runningJobsMetricStatisticsHistogram(ctx, metrics, filter, targetBinCount)\n\t\t\t\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\t\t\t\treturn stat, nil\n\t\t\t}\n\t\t}\n\t}\n\n\t// All other cases: Query and make bins in sqlite directly\n\tfor _, m := range metrics {\n\t\tmetricHisto, err := r.jobsMetricStatisticsHistogram(ctx, m, filter, targetBinCount)\n\t\tif err != nil {\n\t\t\tcclog.Warnf(\"Error while loading job metric statistics histogram: %s\", m)\n\t\t\tcontinue\n\t\t}\n\t\tstat.HistMetrics = append(stat.HistMetrics, metricHisto)\n\t}\n\n\tcclog.Debugf(\"Timer AddMetricHistograms %s\", time.Since(start))\n\treturn stat, nil\n}\n\n// jobsStatisticsHistogram generates a simple histogram by grouping on a column value.\n//\n// Used for histograms where the column value directly represents the bin (e.g., node count, core count).\n// Unlike duration/metric histograms, this doesn't pre-initialize bins with zeros.\n//\n// Parameters:\n//   - value: SQL expression that produces the histogram value, aliased as \"value\"\n//   - filters: Job filters to apply\n//\n// Returns histogram points with Value (from column) and Count (number of jobs).\nfunc (r *JobRepository) jobsStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\tpoints := make([]*model.HistoPoint, 0)\n\t// is it possible to introduce zero values here? requires info about bincount\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tpoints = append(points, &point)\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsDurationStatisticsHistogram generates a duration histogram with pre-initialized bins.\n//\n// Bins are zero-padded to provide consistent ranges for visualization, unlike simple\n// histograms which only return bins with data. The value parameter should compute\n// the bin number from job duration.\n//\n// Parameters:\n//   - value: SQL expression computing bin number from duration, aliased as \"value\"\n//   - filters: Job filters to apply\n//   - binSizeSeconds: Width of each bin in seconds\n//   - targetBinCount: Number of bins to pre-initialize\n//\n// Returns histogram points with Value (bin_number × binSizeSeconds) and Count.\n// All bins from 1 to targetBinCount are returned, with Count=0 for empty bins.\n//\n// Algorithm:\n//  1. Pre-initialize targetBinCount bins with zero counts\n//  2. Query database for actual counts per bin\n//  3. Match query results to pre-initialized bins by value\n//  4. Bins without matches remain at zero\nfunc (r *JobRepository) jobsDurationStatisticsHistogram(\n\tctx context.Context,\n\tvalue string,\n\tfilters []*model.JobFilter,\n\tbinSizeSeconds int,\n\ttargetBinCount *int,\n) ([]*model.HistoPoint, error) {\n\tstart := time.Now()\n\tquery, qerr := SecurityCheck(ctx,\n\t\tsq.Select(value, \"COUNT(job.id) AS count\").From(\"job\"))\n\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\t// Each bin represents a duration range: bin N = [N*binSizeSeconds, (N+1)*binSizeSeconds)\n\t// Example: binSizeSeconds=3600 (1 hour), bin 1 = 0-1h, bin 2 = 1-2h, etc.\n\tpoints := make([]*model.HistoPoint, 0, *targetBinCount)\n\tfor i := 1; i <= *targetBinCount; i++ {\n\t\tpoint := model.HistoPoint{Value: i * binSizeSeconds, Count: 0}\n\t\tpoints = append(points, &point)\n\t}\n\n\t// Build a map from bin value (seconds) to slice index for O(1) lookup.\n\tbinMap := make(map[int]int, len(points))\n\tfor i, p := range points {\n\t\tbinMap[p.Value] = i\n\t}\n\n\tfor _, f := range filters {\n\t\tquery = BuildWhereClause(f, query)\n\t}\n\n\trows, err := query.GroupBy(\"value\").RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Error(\"Error while running query\")\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Match query results to pre-initialized bins using map lookup.\n\t// point.Value from query is the bin number; multiply by binSizeSeconds to get bin key.\n\tfor rows.Next() {\n\t\tpoint := model.HistoPoint{}\n\t\tif err := rows.Scan(&point.Value, &point.Count); err != nil {\n\t\t\tcclog.Warn(\"Error while scanning rows\")\n\t\t\treturn nil, err\n\t\t}\n\n\t\tif idx, ok := binMap[point.Value*binSizeSeconds]; ok {\n\t\t\tpoints[idx].Count = point.Count\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn points, nil\n}\n\n// jobsMetricStatisticsHistogram generates a metric histogram using footprint data from completed jobs.\n//\n// Values are normalized to the metric's peak value and distributed into bins. The algorithm\n// is based on SQL histogram generation techniques, extracting metric values from JSON footprint\n// and computing bin assignments in SQL.\n//\n// Parameters:\n//   - metric: Metric name (e.g., \"cpu_load\", \"mem_used\")\n//   - filters: Job filters to apply\n//   - bins: Number of bins to generate\n//\n// Returns MetricHistoPoints with metric name, unit, footprint stat type, and binned data.\n//\n// Algorithm:\n//  1. Determine peak value from cluster configuration (filtered cluster or max across all)\n//  2. Generate SQL that extracts footprint value, normalizes to [0,1], multiplies by bin count\n//  3. Pre-initialize bins with min/max ranges based on peak value\n//  4. Query database for counts per bin\n//  5. Match results to pre-initialized bins\n//\n// Special handling: Values exactly equal to peak are forced into the last bin by multiplying\n// peak by 0.999999999 to avoid creating an extra bin.\nfunc (r *JobRepository) jobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetric string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) (*model.MetricHistoPoints, error) {\n\t// Peak value defines the upper bound for binning: values are distributed across\n\t// bins from 0 to peak. First try to get peak from filtered cluster, otherwise\n\t// scan all clusters to find the maximum peak value.\n\tvar metricConfig *schema.MetricConfig\n\tvar peak float64\n\tvar unit string\n\tvar footprintStat string\n\n\t// Try to get metric config from filtered cluster\n\tfor _, f := range filters {\n\t\tif f.Cluster != nil {\n\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\tpeak = metricConfig.Peak\n\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\tfootprintStat = metricConfig.Footprint\n\t\t\tcclog.Debugf(\"Cluster %s filter found with peak %f for %s\", *f.Cluster.Eq, peak, metric)\n\t\t}\n\t}\n\n\t// If no cluster filter or peak not found, find largest peak across all clusters\n\t// This ensures histogram can accommodate all possible values\n\tif peak == 0.0 {\n\t\tfor _, c := range archive.Clusters {\n\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\tif m.Name == metric {\n\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t}\n\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t}\n\t\t\t\t\tif footprintStat == \"\" {\n\t\t\t\t\t\tfootprintStat = m.Footprint\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Construct SQL histogram bins using normalized values.\n\t// Algorithm based on: https://jereze.com/code/sql-histogram/ (modified)\n\tstart := time.Now()\n\n\t// Bin calculation formula:\n\t//   bin_number = CAST( (value / peak) * num_bins AS INTEGER ) + 1\n\t// Special case: value == peak would create bin N+1, so we test for equality\n\t// and multiply peak by 0.999999999 to force it into bin N.\n\tbinQuery := fmt.Sprintf(`CAST(\n\t\t((case when json_extract(footprint, \"$.%s\") = %f then %f*0.999999999 else json_extract(footprint, \"$.%s\") end) / %f)\n\t\t* %v as INTEGER )`,\n\t\t(metric + \"_\" + footprintStat), peak, peak, (metric + \"_\" + footprintStat), peak, *bins)\n\n\tmainQuery := sq.Select(\n\t\tfmt.Sprintf(`%s + 1 as bin`, binQuery),\n\t\t`count(*) as count`,\n\t).From(\"job\").Where(\n\t\t\"JSON_VALID(footprint)\",\n\t).Where(fmt.Sprintf(`json_extract(footprint, \"$.%s\") is not null and json_extract(footprint, \"$.%s\") <= %f`, (metric + \"_\" + footprintStat), (metric + \"_\" + footprintStat), peak))\n\n\tmainQuery, qerr := SecurityCheck(ctx, mainQuery)\n\tif qerr != nil {\n\t\treturn nil, qerr\n\t}\n\n\tfor _, f := range filters {\n\t\tmainQuery = BuildWhereClause(f, mainQuery)\n\t}\n\n\tmainQuery = mainQuery.GroupBy(\"bin\").OrderBy(\"bin\")\n\n\trows, err := mainQuery.RunWith(r.DB).QueryContext(ctx)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while running mainQuery: %s\", err)\n\t\treturn nil, err\n\t}\n\tdefer rows.Close()\n\n\t// Pre-initialize bins with calculated min/max ranges.\n\t// Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]\n\tpoints := make([]*model.MetricHistoPoint, 0)\n\tbinStep := int(peak) / *bins\n\tfor i := 1; i <= *bins; i++ {\n\t\tbinMin := (binStep * (i - 1))\n\t\tbinMax := (binStep * i)\n\t\tepoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}\n\t\tpoints = append(points, &epoint)\n\t}\n\n\t// Match query results to pre-initialized bins.\n\tfor rows.Next() {\n\t\trpoint := model.MetricHistoPoint{}\n\t\tif err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {\n\t\t\tcclog.Warnf(\"Error while scanning rows for %s\", metric)\n\t\t\treturn nil, err\n\t\t}\n\n\t\tfor _, e := range points {\n\t\t\tif e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {\n\t\t\t\te.Count = rpoint.Count\n\t\t\t\tbreak\n\t\t\t}\n\t\t}\n\t}\n\n\tif err := rows.Err(); err != nil {\n\t\treturn nil, err\n\t}\n\n\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Stat: &footprintStat, Data: points}\n\n\tcclog.Debugf(\"Timer jobsStatisticsHistogram %s\", time.Since(start))\n\treturn &result, nil\n}\n\n// runningJobsMetricStatisticsHistogram generates metric histograms for running jobs using live data.\n//\n// Unlike completed jobs which use footprint data from the database, running jobs require\n// fetching current metric averages from the metric backend (via metricdispatch).\n//\n// Parameters:\n//   - metrics: List of metric names\n//   - filters: Job filters (should filter to running jobs only)\n//   - bins: Number of histogram bins\n//\n// Returns slice of MetricHistoPoints, one per metric.\n//\n// Limitations:\n//   - Maximum 5000 jobs (returns nil if more jobs match)\n//   - Requires metric backend availability\n//   - Bins based on metric peak values from cluster configuration\n//\n// Algorithm:\n//  1. Query first 5001 jobs to check count limit\n//  2. Load metric averages for all jobs via metricdispatch\n//  3. For each metric, create bins based on peak value\n//  4. Iterate averages and count jobs per bin\nfunc (r *JobRepository) runningJobsMetricStatisticsHistogram(\n\tctx context.Context,\n\tmetrics []string,\n\tfilters []*model.JobFilter,\n\tbins *int,\n) []*model.MetricHistoPoints {\n\t// Get Jobs\n\tjobs, err := r.QueryJobs(ctx, filters, &model.PageRequest{Page: 1, ItemsPerPage: 5000 + 1}, nil)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while querying jobs for footprint: %s\", err)\n\t\treturn nil\n\t}\n\tif len(jobs) > 5000 {\n\t\tcclog.Errorf(\"too many jobs matched (max: %d)\", 5000)\n\t\treturn nil\n\t}\n\n\t// Get AVGs from metric repo\n\tavgs := make([][]schema.Float, len(metrics))\n\tfor i := range avgs {\n\t\tavgs[i] = make([]schema.Float, 0, len(jobs))\n\t}\n\n\tfor _, job := range jobs {\n\t\tif job.MonitoringStatus == schema.MonitoringStatusDisabled || job.MonitoringStatus == schema.MonitoringStatusArchivingFailed {\n\t\t\tcontinue\n\t\t}\n\n\t\tif err := metricdispatch.LoadAverages(job, metrics, avgs, ctx); err != nil {\n\t\t\tcclog.Errorf(\"Error while loading averages for histogram: %s\", err)\n\t\t\treturn nil\n\t\t}\n\t}\n\n\t// Iterate metrics to fill endresult\n\tdata := make([]*model.MetricHistoPoints, 0)\n\tfor idx, metric := range metrics {\n\t\t// Get specific Peak or largest Peak\n\t\tvar metricConfig *schema.MetricConfig\n\t\tvar peak float64\n\t\tvar unit string\n\n\t\tfor _, f := range filters {\n\t\t\tif f.Cluster != nil {\n\t\t\t\tmetricConfig = archive.GetMetricConfig(*f.Cluster.Eq, metric)\n\t\t\t\tpeak = metricConfig.Peak\n\t\t\t\tunit = metricConfig.Unit.Prefix + metricConfig.Unit.Base\n\t\t\t}\n\t\t}\n\n\t\tif peak == 0.0 {\n\t\t\tfor _, c := range archive.Clusters {\n\t\t\t\tfor _, m := range c.MetricConfig {\n\t\t\t\t\tif m.Name == metric {\n\t\t\t\t\t\tif m.Peak > peak {\n\t\t\t\t\t\t\tpeak = m.Peak\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif unit == \"\" {\n\t\t\t\t\t\t\tunit = m.Unit.Prefix + m.Unit.Base\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Make and fill bins\n\t\tpeakBin := int(peak) / *bins\n\n\t\tpoints := make([]*model.MetricHistoPoint, 0)\n\t\tfor b := 0; b < *bins; b++ {\n\t\t\tcount := 0\n\t\t\tbindex := b + 1\n\t\t\tbmin := peakBin * b\n\t\t\tbmax := peakBin * (b + 1)\n\n\t\t\t// Iterate AVG values for indexed metric and count for bins\n\t\t\tfor _, val := range avgs[idx] {\n\t\t\t\tif int(val) >= bmin && int(val) < bmax {\n\t\t\t\t\tcount += 1\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Append Bin to Metric Result Array\n\t\t\tpoint := model.MetricHistoPoint{Bin: &bindex, Count: count, Min: &bmin, Max: &bmax}\n\t\t\tpoints = append(points, &point)\n\t\t}\n\n\t\t// Append Metric Result Array to final results array\n\t\tresult := model.MetricHistoPoints{Metric: metric, Unit: unit, Data: points}\n\t\tdata = append(data, &result)\n\t}\n\n\treturn data\n}\n","structuredPatch":[{"oldStart":1004,"oldLines":16,"newStart":1004,"newLines":25,"lines":[" ","   // Pre-initialize bins with calculated min/max ranges.","   // Example: peak=1000, bins=10 -> bin 1=[0,100), bin 2=[100,200), ..., bin 10=[900,1000]","-  points := make([]*model.MetricHistoPoint, 0)","+  points := make([]*model.MetricHistoPoint, 0, *bins)","   binStep := int(peak) / *bins","   for i := 1; i <= *bins; i++ {","     binMin := (binStep * (i - 1))","     binMax := (binStep * i)","-    epoint := model.MetricHistoPoint{Bin: &i, Count: 0, Min: &binMin, Max: &binMax}","+    idx := i","+    epoint := model.MetricHistoPoint{Bin: &idx, Count: 0, Min: &binMin, Max: &binMax}","     points = append(points, &epoint)","   }"," ","-  // Match query results to pre-initialized bins.","+  // Build a map from bin number to slice index for O(1) lookup.","+  binMap := make(map[int]int, len(points))","+  for i, p := range points {","+    if p.Bin != nil {","+      binMap[*p.Bin] = i","+    }","+  }","+","+  // Match query results to pre-initialized bins using map lookup.","   for rows.Next() {","     rpoint := model.MetricHistoPoint{}","     if err := rows.Scan(&rpoint.Bin, &rpoint.Count); err != nil {"]},{"oldStart":1021,"oldLines":10,"newStart":1030,"newLines":9,"lines":["       return nil, err","     }"," ","-    for _, e := range points {","-      if e.Bin != nil && rpoint.Bin != nil && *e.Bin == *rpoint.Bin {","-        e.Count = rpoint.Count","-        break","+    if rpoint.Bin != nil {","+      if idx, ok := binMap[*rpoint.Bin]; ok {","+        points[idx].Count = rpoint.Count","       }","     }","   }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"072e2c6c-85a4-4d43-870f-1244ed32a0c5"}
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{"parentUuid":"4870dcdb-4be4-4477-afc5-87613fbcfa4e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01DzuwWB2VsTHCEefR9KADws","type":"message","role":"assistant","content":[{"type":"text","text":"All tests pass. Here's a summary of what was implemented:\n\n### Fix 1: Merged 3 queries into 1 (biggest impact)\n`buildStatsQuery` now gains a `shortThreshold int` parameter and always emits two extra columns:\n```sql\nCOUNT(CASE WHEN job.job_state = 'running' THEN 1 END) as runningJobs,\nCOUNT(CASE WHEN job.duration < <threshold> THEN 1 END) as shortJobs\n```\n`JobsStatsGrouped` and `JobsStats` now scan these columns and populate `RunningJobs`/`ShortJobs` directly.\n\nIn `schema.resolvers.go`, a `fetchedMainStats` bool gates the separate `AddJobCountGrouped`/`AddJobCount` calls — they only run if the main stats query was skipped entirely.\n\n### Fix 2: Removed unnecessary LEFT JOIN for non-user grouping\n`buildStatsQuery` now only adds `.LeftJoin(\"hpc_user ON ...\")` and selects `name` when `col == \"job.hpc_user\"`. For project/cluster/subcluster grouping, it selects `'' as name` with no join.\n\n### Fix 3: O(1) map lookup for histogram bin matching\nBoth `jobsDurationStatisticsHistogram` and `jobsMetricStatisticsHistogram` now pre-build a `binMap` after initializing the points slice, replacing the O(n×bins) inner loop with a single map lookup per result row.\n\n### Fix 4: Single `time.Now().Unix()` call per query build\n`now := time.Now().Unix()` is computed once at the top of `buildStatsQuery` and reused in all CASE WHEN expressions."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":119,"cache_read_input_tokens":49969,"output_tokens":418,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":119,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwPZPGwJC5y9kpbLPr96","type":"assistant","uuid":"3119bfd9-f134-456e-a3bc-58fed958121e","timestamp":"2026-03-11T13:42:17.764Z"}
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{"parentUuid":"56f11da5-20f9-4066-bee5-84082166f523","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01DsfaL1wJoSMdqUv2UNuo6T","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EpoCCkYICxgCKkDhgf+c8nRjimnmw84pRTkCTwoQ4+feLStpLdxgp7jF9vwB7jRGMWsOq/M7ZzfSjMhCJYs+82XXE8hzozXh8Jx6EgzlSFFYNHXXrRkne3UaDNvach1wtaKUII6jKiIwD8Ct/NjSebiFYc09fDUKoiLbzKq/HiNcIbAmfWm0fcqG4iRTHcKW6QnpT/6AW85fKoEB8xsRK8a9QDypYUJOjbxbzvpAZS9Tvy+jq3f1nPE0cym+W07kIjA94Eh8GJ3xGyFQ3ouj2G0ZgbD373InGlv9zpvdminkAQluacIUQf5IEmS3cLoHwARA0DqvuOcLWYAeXQnK6Mi3bcQtZ9QZ4lSS43ZZOv3FbLM0e7KoSsxGGXhVGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":491,"cache_read_input_tokens":50088,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":491},"output_tokens":0,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYwRUJur2VkCyUXpuvc9o","type":"assistant","uuid":"bff53c51-8126-465e-bb14-048a3b02876a","timestamp":"2026-03-11T14:07:13.913Z"}
{"parentUuid":"bff53c51-8126-465e-bb14-048a3b02876a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"c85f0d9d-5ec7-4e08-a07a-be075090ec2d","version":"2.1.72","gitBranch":"hotfix","slug":"rosy-churning-spindle","message":{"model":"claude-sonnet-4-6","id":"msg_01DsfaL1wJoSMdqUv2UNuo6T","type":"message","role":"assistant","content":[{"type":"text","text":"### The problem: O(n × bins) inner loop\n\nAfter querying the database, the code needs to match each query result row to its pre-initialized bin. The original code did this with a nested loop:\n\n```go\n// For each row returned by the query...\nfor rows.Next() {\n    rows.Scan(&point.Value, &point.Count)\n\n    // ...scan every bin to find the matching one\n    for _, e := range points {\n        if e.Value == (point.Value * binSizeSeconds) {\n            e.Count = point.Count\n            break\n        }\n    }\n}\n```\n\nIf the query returns `R` rows and there are `B` bins, this is **O(R × B)** — every result row triggers a full scan of the `points` slice until it finds its match. For the duration histogram with 48 bins, each of the up to 48 result rows scans up to 48 entries = up to 2,304 comparisons. Small here, but the pattern is still wasteful.\n\n### The fix: build a reverse index first\n\n```go\n// Build map once: bin value (seconds) → slice index\nbinMap := make(map[int]int, len(points))\nfor i, p := range points {\n    binMap[p.Value] = i  // e.g. 3600 → 0, 7200 → 1, ...\n}\n\n// Then during row scan: one map lookup per row\nfor rows.Next() {\n    rows.Scan(&point.Value, &point.Count)\n    if idx, ok := binMap[point.Value * binSizeSeconds]; ok {\n        points[idx].Count = point.Count\n    }\n}\n```\n\nBuilding the map is **O(B)** once. Each result row lookup is **O(1)** average. Total: **O(B + R)** instead of **O(R × B)**.\n\n### Why it also fixes a correctness subtlety\n\nThe original loop used `break` to stop after the first match — relying on the assumption that bin values are unique (which they are, since they're `1×size, 2×size, ...`). The map makes this uniqueness structural rather than implicit.\n\n### Practical impact\n\nFor histograms the bins are small (max ~72), so the raw speedup here is modest. The more significant benefit is in `jobsMetricStatisticsHistogram`, which is called once per metric per stats request — if someone requests 10 metrics, that's 10 × O(R×B) inner loops replaced with 10 × O(B+R). It also makes the code clearly correct on inspection rather than requiring mental simulation of the loop."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":491,"cache_read_input_tokens":50088,"output_tokens":673,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":491,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYwRUJur2VkCyUXpuvc9o","type":"assistant","uuid":"cc70951b-99f4-4ae6-9b6d-a7ec9e9bfbaa","timestamp":"2026-03-11T14:07:26.672Z"}
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