victoriametrics-cardinality-analysis（维多利亚计量基数分析）

VictoriaMetrics Cardinality Analysis

Systematic cardinality analysis for VictoriaMetrics. Collects TSDB status, metric usage stats, and label value patterns, then produces a structured report with specific relabeling and stream aggregation configs the user can apply directly.

The goal is to find the highest-impact optimization opportunities — metrics nobody queries, labels that explode cardinality for no monitoring value, and patterns that indicate data hygiene problems (error messages as labels, SQL text as labels, UUIDs as labels).

Environment

Uses the same env vars as the victoriametrics-query skill:

# $VM_METRICS_URL - base URL
#   cluster: export VM_METRICS_URL="https://vmselect.example.com/select/0/prometheus"
#   single: export VM_METRICS_URL="http://localhost:8428"
# $VM_AUTH_HEADER - auth header (empty if no auth is required)

Workflow

Phase 1: Data Collection

Spawn 3 subagents in a single response to collect data in parallel. Each subagent prompt must include the curl auth pattern and environment variable references above.

If the user specified a scope (job, namespace, metric prefix), pass it as match[] parameter to TSDB status queries and as series selectors to label queries.

Subagent 1: TSDB Overview

Agent name: cardinality-tsdb | Description: "Collect TSDB cardinality stats"

Query 1 — Yesterday's series (captures recently churned series):

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/tsdb?topN=50&date=$(date -d 'yesterday' +%Y-%m-%d)" | jq '.data'

Queries yesterday's stats — broader than today (includes series that may have already churned) without scanning the entire TSDB.

Query 2 — Today's active series:

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/tsdb?topN=50" | jq '.data'

Query 3 — Focus on known high-cardinality labels:

for label in pod instance container path url user_id request_id session_id trace_id le name; do
  echo "=== focusLabel=$label ===" && \
  curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
    "$VM_METRICS_URL/api/v1/status/tsdb?topN=20&focusLabel=$label" | \
    jq --arg l "$label" '{label: $l, focus: .data.seriesCountByFocusLabelValue}'
done

Return: All raw JSON preserving structure. Include totalSeries, totalLabelValuePairs, seriesCountByMetricName, seriesCountByLabelName, seriesCountByLabelValuePair from each query.

Subagent 2: Metric Usage Stats

Agent name: cardinality-usage | Description: "Find unused and rarely-queried metrics"

Query 1 — Never-queried metrics:

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/metric_names_stats?le=0&limit=500" | jq '.'

Query 2 — Rarely-queried metrics (≤5 total queries):

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/metric_names_stats?le=5&limit=500" | jq '.'

Query 3 — Stats overview (tracking period):

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/metric_names_stats?limit=1" | \
  jq '{statsCollectedSince: .statsCollectedSince, statsCollectedRecordsTotal: .statsCollectedRecordsTotal}'

If the endpoint returns an error, storage.trackMetricNamesStats may not be enabled on vmstorage. Note this in the return and proceed — the analysis can still work with TSDB status data alone.

Query 4 — Cross-check: are "unused" metrics referenced in alerting rules?

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/rules" | jq '[.data.groups[].rules[].query]'

Extract metric names from rule queries. Any "unused" metric that appears in an alert/recording rule is NOT safe to drop — it's queried indirectly.

Return: Unused metrics with cross-reference against alert rules. Flag each as:

• safe to drop: never queried AND not in any rule
• used by rules only: never queried by dashboards but referenced in rules — verify intent
• rarely used: low query count, may be accessed infrequently (e.g., monthly reports)

Subagent 3: Label Pattern Inspection

Agent name: cardinality-labels | Description: "Inspect label values for problematic patterns"

All data comes from the TSDB status endpoint — do NOT use /api/v1/labels or /api/v1/label/.../values.

Query 1 — Label cardinality overview (unique value counts + series counts):

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/tsdb?topN=50" | \
  jq '{labelValueCountByLabelName: .data.labelValueCountByLabelName, seriesCountByLabelName: .data.seriesCountByLabelName}'

labelValueCountByLabelName returns labels sorted by unique value count (replaces per-label /values counting). seriesCountByLabelName shows how many series each label appears in.

Query 2 — Sample values for high-cardinality labels via focusLabel: For each label with >100 unique values from Query 1, fetch sample values:

for label in <top labels from Query 1>; do
  echo "=== focusLabel=$label ===" && \
  curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
    "$VM_METRICS_URL/api/v1/status/tsdb?topN=20&focusLabel=$label" | \
    jq --arg l "$label" '{label: $l, topValues: .data.seriesCountByFocusLabelValue}'
done

seriesCountByFocusLabelValue returns label values sorted by series count — use the value names to detect problematic patterns.

Query 3 — High-cardinality label-value pairs:

curl -s ${VM_AUTH_HEADER:+-H "$VM_AUTH_HEADER"} \
  "$VM_METRICS_URL/api/v1/status/tsdb?topN=50" | \
  jq '.data.seriesCountByLabelValuePair'

Shows which specific label=value pairs contribute the most series.

Pattern detection — classify label values from focusLabel samples:

Return: Table of labels sorted by unique value count, with detected pattern, sample values from focusLabel, and series impact.

Phase 2: Analysis

After all subagents return, compile and classify findings. This is the analytical core — apply judgment, not mechanical filtering.

Category 1: Unused Metrics (Quick Wins)

Cross-reference metric usage stats with TSDB series counts:

• Drop candidates: queryRequestsCount=0, not in any alert/recording rule, >100 series
• Verify candidates: queryRequestsCount=0 but referenced in rules — check if rule is still needed
• Low-priority: queryRequestsCount≤5 with few series — not worth the config churn

Sort by series count descending — the biggest unused metrics are the biggest wins.

Category 2: High-Cardinality Labels

Labels with excessive unique values that drive series explosion:

For each finding, estimate impact: (series with this label) - (series without) ≈ series saved.

Category 3: Histogram Bloat

Check metrics ending in _bucket:

• How many unique le values?
• Each additional bucket multiplies series by (number of label combinations)
• Look for histograms where most buckets are empty or redundant

Category 4: Series Churn

Compare yesterday's stats vs today:

• Ratio >3:1 suggests significant churn from pod restarts, deployments, short-lived jobs
• Not directly fixable via relabeling, but indicates opportunity for dedup_interval or -search.maxStalenessInterval tuning

Phase 3: Report

Compile into a structured report. Every finding must include impact estimate and specific remedy config.

Use this template:

## VictoriaMetrics Cardinality Report — <date>

### Overview
| Metric | Value |
|--------|-------|
| Total active series (today) | X |
| Total series (yesterday) | X |
| Churn ratio (yesterday / today) | X:1 |
| Unique metric names | X |
| Stats tracking since | <date> |

### 1. Unused Metrics
**Potential savings: ~X series (Y% of total)**

| Metric | Series | Last Queried | In Alert Rules | Action |
|--------|--------|-------------|----------------|--------|
| ... | ... | never | no | Drop |
| ... | ... | never | yes — verify | Check rule |

<details>
<summary>Relabeling config to drop unused metrics</summary>

​```yaml
# Add to vmagent metric_relabel_configs (VMServiceScrape or global)
metric_relabel_configs:
  - source_labels: [__name__]
    regex: "metric1|metric2|metric3"
    action: drop
​```
</details>

### 2. High-Cardinality Labels
**Potential savings: ~X series (Y%)**

| Label | Unique Values | Top Affected Metrics | Pattern | Action |
|-------|--------------|---------------------|---------|--------|
| user_id | 50,000 | http_requests_total | UUID | Drop |
| path | 10,000 | http_request_duration | URL paths | Aggregate |
| error_message | 5,000 | app_errors_total | Long strings | Drop |

<details>
<summary>Drop labels that should never be in metrics</summary>

​```yaml
metric_relabel_configs:
  - regex: "user_id|request_id|session_id|trace_id|error_message|sql_query"
    action: labeldrop
​```
</details>

<details>
<summary>Stream aggregation for high-cardinality HTTP labels</summary>

​```yaml
# vmagent stream aggregation config
- match: '{__name__=~"http_request.*"}'
  interval: 1m
  without: [path, instance, pod]
  outputs: [total]
  # drop_input: true  # enable after verifying aggregated output

- match: '{__name__=~"http_request_duration.*_bucket"}'
  interval: 1m
  without: [pod, instance]
  outputs: [total]
  keep_metric_names: true
​```
</details>

<details>
<summary>Normalize URL paths via relabeling</summary>

​```yaml
metric_relabel_configs:
  - source_labels: [path]
    regex: "/api/v1/users/[^/]+"
    target_label: path
    replacement: "/api/v1/users/:id"
  - source_labels: [path]
    regex: "/api/v1/orders/[^/]+"
    target_label: path
    replacement: "/api/v1/orders/:id"
​```
</details>

### 3. Histogram Optimization
**Potential savings: ~X series (Y%)**

| Metric | Bucket Count | Recommendation |
|--------|-------------|----------------|
| ... | 30 | Reduce to standard 11 buckets |

### 4. Series Churn
| Observation | Value |
|------------|-------|
| Yesterday / today ratio | X:1 |
| Primary driver | Pod restarts / short-lived jobs |

### Summary
| Category | Est. Series Saved | % of Total | Effort |
|----------|------------------|-----------|--------|
| Drop unused metrics | X | Y% | Low — relabeling only |
| Drop bad labels | X | Y% | Low — labeldrop |
| Stream aggregation | X | Y% | Medium — new config |
| Histogram reduction | X | Y% | Low — bucket filtering |
| **Total** | **X** | **Y%** | |

### Implementation Priority
1. **[Low effort]** Drop unused metrics — pure relabeling, no data loss risk
2. **[Low effort]** Drop labels that should never be in metrics (IDs, messages, SQL)
3. **[Medium effort]** Stream aggregation for high-cardinality HTTP/app metrics
4. **[Medium effort]** Histogram bucket reduction

Adapt the template to actual findings — omit sections with no findings, expand sections with significant findings.

Remediation Reference

Relabeling (metric_relabel_configs)

Applied at scrape time or remote write. Changes affect new data immediately.

Drop entire metrics:

metric_relabel_configs:
  - source_labels: [__name__]
    regex: "metric_to_drop|another_metric"
    action: drop

Drop labels:

metric_relabel_configs:
  - regex: "label_to_drop|another_label"
    action: labeldrop

Normalize label values (reduce unique values):

metric_relabel_configs:
  - source_labels: [path]
    regex: "/api/v1/users/[^/]+"
    target_label: path
    replacement: "/api/v1/users/:id"

Stream Aggregation

Applied at vmagent level. Aggregates in-flight before writing to storage. Docs: https://docs.victoriametrics.com/victoriametrics/stream-aggregation/

Remove labels while preserving metric semantics:

- match: '{__name__=~"http_.*"}'
  interval: 1m
  without: [instance, pod]
  outputs: [total]

Aggregate counters (drop high-cardinality dimension):

- match: 'http_requests_total'
  interval: 30s
  without: [path, user_id]
  outputs: [total]

Aggregate histograms:

- match: '{__name__=~".*_bucket"}'
  interval: 1m
  without: [pod, instance]
  outputs: [quantiles(0.5, 0.9, 0.99)]
  keep_metric_names: true

Common output functions:

Important: use total for counters, last/avg/sum_samples for gauges. Using total on gauges produces nonsensical running sums.

Where to Apply in Kubernetes

Common Mistakes