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Adopt cc-lib hierarchical JobData/Statistics structs

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cc-lib changed JobData, ScopedJobStats and Job.Statistics from flat maps
to structs (a .Metrics map plus array-valued Groups) to represent
filesystem (and future interconnect) metric groups. Migrate all
construction, indexing and iteration to the new API across the archive
backends, metricstore query path, metric dispatcher, archiver, importer,
tagger, taskmanager, repository and API layers.

Semantics: DecodeJobStats now projects JobData.Groups into
ScopedJobStats.Groups, and the archiver derives per-filesystem node-scope
statistics into Job.Statistics.Groups. deepCopy, resampling and the
metric/scope filter are group-aware. The metricstore internal storage
(buffers, selector tree, checkpoint/parquet) is unchanged; all conversion
stays at the LoadData/archive-codec seam.

Note: requires the corresponding cc-lib release; bump the cc-lib
dependency version once tagged (a local go.mod replace was used during
development and is intentionally not committed).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
moebiusband
2026-07-01 19:14:12 +02:00
co-authored by Claude Opus 4.8
parent 1bd3f25371
commit 0724b2dc60
25 changed files with 303 additions and 196 deletions
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internal/archiver/archiver.go
+42 -4
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@@ -65,11 +65,11 @@ func ArchiveJob(job *schema.Job, ctx context.Context) (*schema.Job, error) {
return nil, err
}
job.Statistics = make(map[string]schema.JobStatistics)
job.Statistics = schema.JobStatisticsSet{Metrics: make(map[string]schema.JobStatistics)}
for metric, data := range jobData {
for metric, data := range jobData.Metrics {
avg, min, max := 0.0, math.MaxFloat32, -math.MaxFloat32
nodeData, ok := data["node"]
nodeData, ok := data[schema.MetricScopeNode]
if !ok {
// This should never happen ?
continue
@@ -82,7 +82,7 @@ func ArchiveJob(job *schema.Job, ctx context.Context) (*schema.Job, error) {
}
// Round AVG Result to 2 Digits
job.Statistics[metric] = schema.JobStatistics{
job.Statistics.Metrics[metric] = schema.JobStatistics{
Unit: schema.Unit{
Prefix: archive.GetMetricConfig(job.Cluster, metric).Unit.Prefix,
Base: archive.GetMetricConfig(job.Cluster, metric).Unit.Base,
@@ -93,5 +93,43 @@ func ArchiveJob(job *schema.Job, ctx context.Context) (*schema.Job, error) {
}
}
// Compute the same node-scope statistics for each array-valued metric group
// instance (e.g. per-filesystem) and attach it as a group in the statistics
// set so it round-trips into the job-meta "statistics" JSON.
for _, group := range jobData.Groups {
for _, inst := range group.Instances {
instStats := make(map[string]schema.JobStatistics)
for metric, data := range inst.Metrics {
avg, min, max := 0.0, math.MaxFloat32, -math.MaxFloat32
nodeData, ok := data[schema.MetricScopeNode]
if !ok {
// This should never happen ?
continue
}
for _, series := range nodeData.Series {
avg += series.Statistics.Avg
min = math.Min(min, series.Statistics.Min)
max = math.Max(max, series.Statistics.Max)
}
// Prefer the unit from a MetricConfig if one exists, otherwise
// fall back to the unit already present on the JobMetric.
unit := nodeData.Unit
if mc := archive.GetMetricConfig(job.Cluster, metric); mc != nil {
unit = schema.Unit{Prefix: mc.Unit.Prefix, Base: mc.Unit.Base}
}
instStats[metric] = schema.JobStatistics{
Unit: unit,
Avg: (math.Round((avg/float64(job.NumNodes))*100) / 100),
Min: min,
Max: max,
}
}
job.Statistics.AddGroupInstance(group.Key, inst.Name, inst.Type, instStats)
}
}
return job, archive.GetHandle().ImportJob(job, &jobData)
}