Percentiles With histogram_quantile

Rate the buckets, keep le, then compute

To estimate a percentile, take the rate of the _bucket series, aggregate while keeping the le label, and pass the result to histogram_quantile(0.95, ...). The result is interpolated within a bucket, so its accuracy depends on bucket boundaries. Average latency is rate(_sum) / rate(_count). The share of requests faster than a threshold uses the bucket at that boundary divided by the total count.

Percentiles, joins and precomputation

Histogram quantiles, vector matching and recording rules make complex queries fast and correct.

Latency queries

PromQL.

# p95 latency per route over 5 minutes
histogram_quantile(0.95,
  sum by (route, le) (rate(http_request_duration_seconds_bucket{job="orders-api"}[5m])))

# average latency
sum(rate(http_request_duration_seconds_sum[5m])) / sum(rate(http_request_duration_seconds_count[5m]))

# fraction of requests faster than 300 ms (needs a 0.3 bucket)
sum(rate(http_request_duration_seconds_bucket{le="0.3"}[5m]))
  / sum(rate(http_request_duration_seconds_count[5m]))

Never drop le before histogram_quantile

Aggregating without the le label destroys the bucket structure and produces meaningless results.

Quick check: Which label must be kept when aggregating buckets for histogram_quantile?

• instance
• le
• job
• __name__