promql-validator

PromQL validation checks syntax correctness and applies best-practice audits to identify anti-patterns like missing rate() on counters or subquery inefficiencies. It returns structured errors, warnings, and concrete optimization recommendations to improve query reliability.

Common PromQL anti-patterns include missing rate() on counters, applying rate() to gauges, subquery inefficiencies, and quantile misuse. Identifying these issues helps optimize query performance and ensures accurate metric analysis across classic and native histograms.

Yes, PromQL validation supports both classic histograms and native histograms. It checks metric names, label matchers, and structures, while identifying specific histogram anti-patterns like quantile misuse to ensure accurate and efficient monitoring queries.

You should extract a PromQL query into a recording rule when the query is complex or expensive to compute repeatedly. Validation tools can detect these opportunities and provide guidance to pre-compute expressions for reuse and improved dashboard performance.

Applying rate() to a gauge metric is a common PromQL anti-pattern that causes unexpected results because gauges represent instantaneous values, not monotonically increasing counters. Validation identifies this misuse and recommends appropriate functions for your metric type.