What to Monitor in Kafka Production Kafka applications need visibility into: Consumer lag — how many records are unprocessed per partition Throughput — records produced and consumed per second Error rates — listener exceptions, DLT records, retry counts Producer latency — time from send() to broker acknowledgment Rebalance frequency — high rebalance rate signals consumer instability Dependencies <!-- Micrometer Prometheus registry --> <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-registry-prometheus</artifactId> </dependency> <!-- Spring Boot Actuator --> <dependency> <groupId>org.

Observability means being able to answer “what’s wrong and why” from the outside — without modifying the code. The three pillars: metrics (what happened), logs (what the code did), and traces (how a request flowed). This article wires them all together. The Stack Spring Boot App ├── Metrics → Micrometer → Prometheus scrape → Grafana dashboards ├── Traces → Micrometer Tracing → OTLP → Tempo → Grafana trace view └── Logs → Logback → Loki4j → Loki → Grafana log explorer All three converge in Grafana — click a metric spike to see the correlated logs and traces for that exact time window.

Spring Boot Actuator exposes production-ready operational endpoints — health checks, metrics, environment info, thread dumps — out of the box. Combined with Prometheus and Grafana, you get a full monitoring stack with minimal configuration. This guide covers everything from initial setup to Kubernetes health probes, custom metrics, and securing your management endpoints. Setup Dependencies <dependencies> <!-- Actuator --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> <!-- Micrometer Prometheus registry --> <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-registry-prometheus</artifactId> <scope>runtime</scope> </dependency> </dependencies> Basic configuration # application.

Observability means you can answer “what is wrong and why” from your system’s outputs alone — without adding new instrumentation after an incident. It requires three types of data: metrics (what happened), traces (why it happened), and logs (the details). Spring Boot 4 ships a single OpenTelemetry starter that covers all three. This guide shows how to wire up the complete observability stack: Prometheus + Grafana for metrics, Grafana Tempo for distributed tracing, and Grafana Loki for logs.