Prometheus Cardinality Troubleshooter

Prometheus Cardinality Troubleshooter

You are an expert in diagnosing live Prometheus cardinality problems. When a user reports a Prometheus performance, memory, or cost issue that smells like cardinality, use this guide to triage systematically.

This skill is diagnostic and operational. For schema design and prevention, route to prometheus-label-strategy.

Before You Remediate: The One Rule

Under pressure, the tempting move is to labeldrop the high-cardinality label at scrape time. Do not. You cannot remove, at scrape time, any label that makes a series unique — not pod, not instance, not anything that distinguishes one real series from another. It looks like it stops the bleeding; it actually breaks the data:

• Counter resets from different series get merged → rate() and increase() return garbage, often absurdly high values.
• Multiple samples land on the same series per scrape → duplicate-sample / out-of-order errors and inflated DPM, not reduced.
• The breakage is silent (no config error) and leaves no evidence in the data of where it went wrong. Weeks later someone asks "why is my DPM so high / why is rate() absurd?" and there's nothing to point to.

The only safe remediations are:

1. Drop an entire unwanted metric (action: drop on __name__) — you're discarding the whole metric, not merging distinct series.
2. Fix the source — stop the application emitting the bad label (the real fix for unbounded path, user_id, etc.).
3. Adaptive Metrics — for structural cardinality on series you can't fix at the source. It aggregates correctly (counter-reset-aware, audited, reversible). This is the right way to reduce the cost of a label like pod. Route to adaptive-metrics.

Everywhere below that says "drop a label," read it through this rule: drop whole metrics, fix the source, or use Adaptive Metrics — never labeldrop a distinguishing label.

Symptom → Likely Cause

Step 1: Active Series Triage

Get the headline number

# Total active series in the local Prometheusprometheus_tsdb_head_series
# Or for Mimir / Grafana Cloud Metrics (per tenant)cortex_ingester_memory_series{user="<tenant>"}

Compare to recent history:

# Growth over the last 7 daysderiv(prometheus_tsdb_head_series[7d]) * 86400

A growth rate > a few % per day on a stable application set is a red flag.

Use the TSDB status endpoint

Prometheus exposes a built-in cardinality breakdown:

curl -s http://prometheus:9090/api/v1/status/tsdb | jq

Returns:

• seriesCountByMetricName — top metrics by series count
• labelValueCountByLabelName — top labels by unique value count
• memoryInBytesByLabelName — top labels by memory footprint
• seriesCountByLabelValuePair — top label-value pairs by series count

This is usually the fastest path to "which metric / which label is the problem."

For Grafana Cloud:

# Same endpoint, authenticated against the per-tenant Mimircurl -s -u "<user>:<token>" \  "https://prometheus-prod-XX.grafana.net/api/prom/api/v1/status/tsdb" | jq

Step 2: Read the Output

Top metrics by series count

"seriesCountByMetricName": [  { "name": "http_request_duration_seconds_bucket", "value": 184320 },  { "name": "go_gc_duration_seconds",               "value": 80 },  ...]

Heuristics:

• A histogram (_bucket) at the top is almost always the answer — those have a 14× multiplier (bucket count + 3). The fix is usually reducing the labels on the underlying histogram at the source (in instrumentation code), not stripping them at scrape and not touching the buckets themselves.
• A metric in the top 5 you don't recognize → grep the codebase for it; it's likely a new feature flag or a debug metric that shipped to prod
• The same metric showing up under multiple variants (_total, _count, _sum) — that's a histogram or summary, count all variants together for the true impact

Top labels by unique value count

"labelValueCountByLabelName": [  { "name": "url",       "value": 84210 },  { "name": "trace_id",  "value": 41000 },  { "name": "pod",       "value": 1820 }]

Red flags:

• Any label with >10K unique values is almost certainly a bug. The only exceptions are intentional per-target labels in massive fleets.
• trace_id, request_id, session_id, query, email, path, url — these should never be labels. They belong in exemplars, logs, or traces.
• pod with thousands of values — see Churn diagnosis; recent churn often inflates this number

Step 3: Per-Metric Drill-Down

Once you've identified a suspect metric, find which label is responsible.

Count distinct label values per label, for one metric

# How many unique values does each label have on this metric?count by (__name__) (  count by (__name__, label_name_here) (    http_request_duration_seconds_bucket  ))

Repeat per label, or use the helper:

# Via the Prometheus HTTP APIcurl -s "http://prometheus:9090/api/v1/labels?match[]=http_request_duration_seconds_bucket" | jq -r '.data[]' | \  while read label; do    count=$(curl -s "http://prometheus:9090/api/v1/label/${label}/values?match[]=http_request_duration_seconds_bucket" | jq '.data | length')    echo "${count}  ${label}"  done | sort -rn | head -20

Find the top label values for one label

# Top 20 path values for http_requests_totaltopk(20,  count by (path) (http_requests_total))

If you see UUIDs, hashes, timestamps, or numeric IDs in the top values → that label has unbounded values from the source.

Per-metric series count, grouped

# Series-per-instance breakdown — if uneven, one instance is misbehavingsum by (job, instance) ({__name__=~"my_metric.*"})

Step 4: Recent Change Diff

If the cardinality fire started recently, the cause is almost always a recent change. Diff what's there now against what was there before.

List of metrics, current vs. yesterday

Via Grafana Cloud cardinality dashboard, or:

# Current metricsgroup by (__name__) ({__name__!=""})
# Compare to last week (offset)group by (__name__) ({__name__!=""} offset 7d)

Diff externally. A new metric near the top of seriesCountByMetricName that wasn't there a week ago → that's your offender.

Correlate with deploys

# Active series correlated with build_infoprometheus_tsdb_head_series# Overlay with:changes(app_build_info[1d])

A vertical step in series count aligned with a deploy is conclusive.

Step 5: Churn Diagnosis

High churn means series are being created and abandoned faster than they age out. Symptoms: series count keeps climbing, then drops sharply on Prometheus restart.

Churn signal

# Series created vs. removed per secondrate(prometheus_tsdb_head_series_created_total[5m])rate(prometheus_tsdb_head_series_removed_total[5m])
# Ratio of churned to liveprometheus_tsdb_head_series_created_total / prometheus_tsdb_head_series

A creation rate that materially exceeds the removal rate, sustained, means cardinality is on a one-way trip up. Common causes:

Memory impact of churn

# A churn-driven head block carries old series until tsdb compactionprometheus_tsdb_head_chunksgo_memstats_heap_inuse_bytes{job="prometheus"}

Restarting Prometheus drops churned series but is not a fix. The fix is at the source.

Common-Culprit Gallery

Histogram blowup

Tell: *_bucket metric at the top of seriesCountByMetricName. Multiplier ≈ 14×.

Fix:

1. First, reduce labels on the histogram at the source — every label removed saves 14× series. Trim path, method, or status_code in the instrumentation code (don't labeldrop them at scrape — that merges distinct histograms and corrupts the buckets). For series already in Grafana Cloud you can't change, aggregate them with Adaptive Metrics.
2. Then, reduce bucket count if appropriate (custom buckets vs. defaults).
3. For high-resolution latency tracking, consider native histograms (Prometheus 2.40+) — single sparse series replaces the bucket family.

kube-state-metrics label explosion

Tell: kube_pod_labels or kube_pod_annotations at the top, with label_* or annotation_* labels driving cardinality.

Fix: configure kube-state-metrics with --metric-labels-allowlist and --metric-annotations-allowlist. By default it emits all labels and annotations as series.

# kube-state-metrics flags--metric-labels-allowlist=pods=[app,team,version]--metric-annotations-allowlist=pods=[checksum/config]

Path / route blowup from a new endpoint

Tell: http_requests_total (or framework equivalent) grew 10×+ overnight. topk(20, count by (path) (http_requests_total)) shows hundreds of /users/123456-style values.

Fix: the real fix is to template the path in application code (/users/:id) — route the user to prometheus-label-strategy. For series already in Grafana Cloud, Adaptive Metrics can aggregate path away correctly — route to adaptive-metrics.

Do not "normalize" path with a relabel replacement rule — collapsing /users/123, /users/456, … into one /users/:id value at scrape merges distinct series and produces duplicate-sample errors and broken rate(). The merge has to happen at the source (templating) or post-ingest (Adaptive Metrics), never at scrape.

If you must stop a production fire right now and templating isn't deployable yet, the only safe scrape-time action is to drop the entire offending metric (you lose it completely until the code fix lands — a deliberate trade, not a silent corruption):

# Emergency: drop the whole metric until the source is templatedmetric_relabel_configs:  - source_labels: [__name__]    regex: http_requests_total    action: drop

Application emitting a debug metric in prod

Tell: A metric you don't recognize in the top 10. Grep the source — often a _details or _per_request debug metric the developer forgot to gate.

Fix: drop entirely at scrape:

metric_relabel_configs:  - source_labels: [__name__]    regex: my_app_request_details    action: drop

Open a ticket against the team to remove it from the code.

App-emitted labels colliding with target labels

Tell: Series count for one job is several × what it should be. Looking at one series, you see both an app-emitted instance=... AND the target instance=... collided into something weird (Prometheus renames the conflicting one to exported_instance).

Fix: the right fix is in the application — stop emitting instance/node/host from code; they belong to the scrape target. Confirm honor_labels is false (the default) so the target labels win.

If you need a scrape-time stopgap, you may remove a label only where it exactly duplicates a target label — that's the one safe labeldrop, because the target label still provides uniqueness. Scope it tightly to the duplicated names and never include pod (or any other label that is the source of uniqueness):

# Stopgap ONLY for app-emitted duplicates of target labels.# Drops the `exported_*` collisions — NOT pod, which makes K8s series unique.metric_relabel_configs:  - regex: exported_(instance|node|host)    action: labeldrop

Then the target labels from relabel_configs apply cleanly. Prefer fixing the app.

Federation amplifying cardinality

Tell: A federated Prometheus or Mimir global view has way more series than expected. Each source has its own cluster / region label, multiplying.

Fix: this is usually expected — federation by design preserves source labels. If the series count is too high, federate only aggregated recording rules, not raw metrics:

- job_name: federate  honor_labels: true  metrics_path: /federate  params:    'match[]':      - '{__name__=~".*:.*"}'  # Recording-rule naming convention only

Remediation Decision Tree

Cardinality fire confirmed│├── Need to stop the bleeding NOW (production OOM, ingest 429s)│   └── Drop the ENTIRE offending metric via metric_relabel_configs (action: drop on __name__)│       (also applies to Alloy/Agent — same syntax)│       Do NOT labeldrop a distinguishing label — it breaks the data, see "The One Rule".│       Then schedule the proper fix.│├── It's a Grafana Cloud Active Series bill issue, not a perf issue│   ├── Cardinality is structural and you can't fix the app│   │   └── Route to `adaptive-metrics` skill (post-ingest aggregation rules — the safe way)│   └── You want metric-by-metric DPM breakdown│       └── Route to `dpm-finder` skill│├── It's a fixable application bug (unbounded label, debug metric in prod)│   ├── Short-term: drop the whole metric at scrape, OR aggregate via Adaptive Metrics│   └── Long-term: fix in code; route to `prometheus-label-strategy` for design guidance│├── It's histogram cardinality│   ├── Reduce labels on the underlying histogram AT THE SOURCE (14× win per label)│   ├── Reduce bucket count if appropriate│   └── Consider native histograms for high-resolution latency│└── It's churn (deploy-driven)    ├── Stop EMITTING `version`/`git_sha`/`instance` from app code (use info-metric for version)    ├── Keep `pod` — never drop it; if pod-level series are too costly, use Adaptive Metrics    └── Verify K8s SD relabel rules aren't mapping in `uid` or other ephemeral fields

Emergency Drop Patterns (copy-paste ready)

These are the safe scrape-time emergency actions: dropping an entire unwanted metric. They do not merge distinct series, so they don't corrupt the data.

⚠️ There is intentionally no labeldrop of a distinguishing label and no value-normalizing relabel here. Both merge distinct series and break rate()/DPM (see The One Rule). To reduce cardinality without dropping the whole metric, fix the source or use Adaptive Metrics (route to adaptive-metrics). The only safe labeldrop is removing a label that exactly duplicates a target label (e.g. exported_instance) — see App-emitted labels colliding with target labels.

For Prometheus scrape_configs:

metric_relabel_configs:  # Drop a specific bad metric entirely  - source_labels: [__name__]    regex: bad_metric_name    action: drop
  # Drop a set of debug/temporary metrics by name prefix  - source_labels: [__name__]    regex: debug_.*    action: drop

For Grafana Alloy (prometheus.relabel component):

prometheus.relabel "drop_bad_metric" {  forward_to = [prometheus.remote_write.default.receiver]
  rule {    source_labels = ["__name__"]    regex = "bad_metric_name"    action = "drop"  }}

Always test in staging first, and prefer fixing the source or using Adaptive Metrics over any scrape-time drop.

When to Hand Off

• "Now design a label strategy so this doesn't happen again" → prometheus-label-strategy
• "We need to keep these metrics but reduce cost" → adaptive-metrics
• "Which metric is the most expensive in DPM terms?" → dpm-finder
• "Write the PromQL to find this" → promql
• "Configure this in Alloy" → alloy
• "Why is my Loki slow?" → loki-label-analyzer (different system, same family of problems)

This skill's lane is diagnosis under pressure. Prevention, design, and post-ingest cost optimization live elsewhere.