---name: promqllicense: Apache-2.0description: Write, validate, and optimize PromQL for Prometheus / Grafana Mimir / Grafana Cloud Metrics. Covers `rate` vs `irate` vs `increase`, label matchers and regex, `sum / avg / topk / by / without` aggregation, classic + native `histogram_quantile`, ratios with divide-by-zero guards, `absent` / `changes` for staleness, time offsets and `predict_linear`, recording-rule naming, SLO + burn-rate math, and a cardinality-hunting playbook. Use when writing a metric query, fixing wrong p95s, building an error-budget alert, debugging "query is slow", finding the noisy label that blew up cardinality, or migrating a dashboard query to a recording rule — even when the user says "calculate the error rate", "p99 latency", "sum by service", "why is this query slow", or "what's filling Mimir" without naming PromQL.---

PromQL Query Patterns

Docs: https://prometheus.io/docs/prometheus/latest/querying/basics/

PromQL returns either an instant vector, a range vector, or a scalar.

Golden rule: rate() / increase() require a range vector ≥ 4× the scrape interval. 60s scrape → use [5m] minimum.

Prerequisites

• A Prometheus / Mimir / Grafana Cloud endpoint to query (/api/v1/query or via Grafana Explore)
• The PromQL pattern library in references/patterns.md

Common Workflows

1. Write + validate a query

# 0. Point at your Prometheus/Mimir. For Grafana Cloud, use the metrics endpoint
#    and add basic auth (-u "<metrics_user>:<token>") to each curl below.
PROM=http://localhost:9090   # or https://prometheus-prod-XX.grafana.net/api/prom

# 1. Sketch the query — for "5xx error rate per service":
EXPR='sum(rate(http_requests_total{status_code=~"5.."}[5m])) by (service)'

# 2. Validate syntax + that the metric/labels exist
curl -sG --data-urlencode "query=${EXPR}" \
  "$PROM/api/v1/query" | jq '.status, (.data.result|length)'
# Expect: "success" and result count > 0. If 0 — check label spelling and scrape activity:
curl -sG --data-urlencode "match[]=http_requests_total" "$PROM/api/v1/series" | jq '.data | length'

# 3. Sanity-check the magnitude — open Grafana Explore, paste the expr,
#    confirm the values look right against a known ground truth (k6 run, log count, etc.)

2. Common patterns to copy

Per-status request rate (aggregate AFTER rate):

sum(rate(http_requests_total{job="api"}[5m])) by (status_code)

p95 latency (must keep le in the inner aggregation):

histogram_quantile(0.95,
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))

Error rate with divide-by-zero guard:

sum(rate(http_requests_total{status_code=~"5.."}[5m]))
  / (sum(rate(http_requests_total[5m])) > 0)

Full library (recording rules, SLO burn-rate, offsets, cardinality hunt, native histograms): references/patterns.md.

3. Convert a slow dashboard query into a recording rule

# 1. Pick the slow expression, give it a recording-rule name
groups:
  - name: http_request_rates
    interval: 1m
    rules:
      - record: job:http_request_duration_p95:rate5m
        expr: |
          histogram_quantile(0.95,
            sum(rate(http_request_duration_seconds_bucket[5m])) by (le, job))

# 2. After rules load, verify the new metric exists
curl -sG --data-urlencode "query=job:http_request_duration_p95:rate5m" \
  "$PROM/api/v1/query" | jq '.data.result | length'   # → > 0

# 3. Verify it matches the original expression for at least one sample window
# (Both queries should produce the same value at the same timestamp.)

# 4. Replace the dashboard panel expression with the recording-rule metric.

Common bugs

• histogram_quantile returns NaN → forgot by (le) in the inner aggregation
• "No data" → check the metric exists (/api/v1/series) and the window ≥ 4× scrape interval
• Wrong rate magnitude → counter was aggregated before rate() (always rate() first)
• Query timeout → series count too high; use topk(...) + a recording rule + drop high-cardinality labels (see references/patterns.md)

Resources

• PromQL basics
• Operators
• Functions
• Grafana Mimir