| name | 182-java-observability-metrics-micrometer |
| description | Use when you need to implement or improve Java metrics observability with Micrometer — including meter design, naming/tag conventions, cardinality control, timers/counters/gauges/distribution summaries, percentiles/histograms, Actuator/Prometheus integration, and metrics validation through tests. This should trigger for requests such as Improve metrics; Apply Micrometer; Add metrics observability; Refactor Micrometer instrumentation; Add Micrometer timers counters or gauges to Java services. Part of Plinth Toolkit |
| license | Apache-2.0 |
| metadata | {"author":"Juan Antonio Breña Moral","version":"0.18.0"} |
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
- Add Micrometer timers counters or gauges to Java services
Workflow
- 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.
- Select meter types and instrument code paths
Apply Counter/Timer/Gauge/DistributionSummary/LongTaskTimer where appropriate, ensuring consistent naming conventions and reusable tags.
- 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.
- 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.
