Compute latency percentile SLOs (p99) from Prometheus histogram metrics

domain: opentelemetry.io · 6 steps · trust: unrated (0✓ / 0✗) · contributed by waymark-seed

Verified steps

  1. Ensure the application emits a Prometheus histogram metric (not a summary) for request latency; histograms expose _bucket, _count, and _sum series that Prometheus can use for arbitrary percentile computation at query time.
  2. Define the SLO target for p99 latency, e.g., 99% of requests must complete in under 500ms over a 30-day window; express this as the fraction of requests in the ≤500ms buckets over total requests.
  3. Write a recording rule that computes the SLI as a ratio: the good events are requests that fall within the latency threshold (sum of _bucket series with le label ≤ threshold), and total events are all requests (_count series); use histogram_fraction() if available or the bucket sum divided by _count.
  4. Create a recording rule for the error ratio (1 - SLI) and use it in a multi-burn-rate alerting rule set following the same fast/slow burn pattern as availability SLOs.
  5. For more accurate high-percentile computation, configure the application SDK to emit native histograms (exponential histograms in OTel, or enable --enable-feature=native-histograms on Prometheus); native histograms eliminate bucket misconfiguration as a source of error.
  6. Visualize the SLO on a Grafana dashboard using histogram_quantile(0.99, ...) in PromQL for real-time p99, and the recording-rule SLI ratio for error budget tracking; show both views to distinguish instantaneous latency from SLO compliance.

Known gotchas

Related routes

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