Java Metrics Observability with Micrometer

Implement effective Java metrics instrumentation with Micrometer by defining meaningful service-level metrics, controlling cardinality, selecting the right meter type, and exposing production-ready telemetry for dashboards and alerting.

What is covered in this Skill?

• Metrics-first observability with Micrometer in Java applications
• Meter selection: Counter, Timer, DistributionSummary, Gauge, LongTaskTimer
• Naming and tagging conventions with low-cardinality dimensions
• Cardinality and meter lifecycle safeguards to prevent time-series explosion
• Histogram/percentile strategy and SLO-oriented metrics design
• Integration guidance for Actuator + Prometheus/OpenTelemetry pipelines
• Testing and verification of metrics registration and values

Scope: Application-level metrics design and instrumentation quality for Java services, with emphasis on operationally useful and cost-efficient telemetry.

Constraints

Metrics instrumentation must be operationally safe, low-cardinality, and validated. Poor tag design or excessive meter creation can degrade observability systems and increase costs.

• LOW CARDINALITY FIRST: Never tag metrics with unbounded values (userId, UUID, raw URL, full exception message)
• RIGHT METER TYPE: Use Counter for monotonically increasing events, Timer for latency, Gauge for point-in-time state, and DistributionSummary for sampled values
• BEFORE APPLYING: Read the reference for good/bad instrumentation examples and anti-patterns
• VERIFY: Run ./mvnw clean verify or mvn clean verify after changes

When to use this skill

• Improve metrics
• Apply Micrometer
• Add metrics observability
• Refactor Micrometer instrumentation

Workflow

1. Define measurement goals and meter contract

Identify key service indicators (throughput, latency, error ratio, saturation) and map each to stable metric names, units, and low-cardinality tags.

2. Select meter types and instrument code paths

Apply Counter/Timer/Gauge/DistributionSummary/LongTaskTimer where appropriate, ensuring consistent naming conventions and reusable tags.

3. Harden instrumentation for production

Control cardinality, avoid dynamic meter churn, configure histogram/percentile strategy only where needed, and align export settings with the telemetry backend.

4. Validate and operationalize metrics

Verify metrics in tests and runtime endpoints, confirm expected labels/units, and ensure dashboards/alerts can consume the emitted series.

Reference

For detailed guidance, examples, and constraints, see references/182-java-observability-metrics-micrometer.md.