Prometheus gauges track values that can increase or decrease (like memory usage, queue depth, active connections), unlike counters which only increment. Common mistakes include treating gauges like counters, relying on stale values, and using high-cardinality labels. Gauges require explicit value setting and proper lifecycle management to avoid misleading dashboards and failed alerts. The guide covers implementation patterns across different programming languages, performance implications, and migration strategies from gauges to counters when appropriate.
Table of contents
Common Mistakes That Break Gauge Metrics in ProductionMetric Types in PrometheusGauges vs. Counters: Behavior and Reset SemanticsHow to Instrument Gauges in Your Application (By Language)How to Monitor System and Application Resources with GaugesPerformance Implications of Gauge vs Counter MetricsMonitor Queue Depth and Backpressure with GaugesWorkflow State Tracking with Labeled GaugesSet vs Add: Choose the Correct Gauge Update MethodGauge Cardinality Limits and Label DesignHow to Migrate from Gauges to CountersQuick Start: 5-Minute Gauge SetupFinal ThoughtsFAQsSort: