Observability for Java Microservices: Metrics, Tracing, and Logging in Cloud‑Native Systems
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Abstract
Distributed Java microservices require comprehensive observability to maintain reliability, diagnose performance degradation, and support operational continuity across diverse cloud native and hybrid environments. This paper presents a unified framework for monitoring Spring Boot based microservices through metrics instrumentation, distributed tracing, structured log aggregation, and infrastructure level telemetry. The study identifies essential JVM, application, and platform indicators required for effective performance analysis, including garbage collection behavior, thread pool utilization, connection pool saturation, request latency, and error propagation patterns. Techniques for instrumenting Spring Boot applications using Micrometer, OpenTelemetry, and structured logging are examined, along with strategies for exporting telemetry to platforms such as Dynatrace, Prometheus, Datadog, New Relic, Splunk, and Elastic Stack. The framework extends to asynchronous processing pipelines by outlining critical Kafka producer, consumer, broker, and topic level metrics such as consumer lag, partition health, request latency, and under replicated partitions that enable early detection of backpressure and message flow disruptions. Infrastructure level signals, including container resource limits, node pressure, CPU throttling, pod restarts, and autoscaling activity, are incorporated to support SRE driven diagnostics and correlation. The paper defines recommended dashboards, alert thresholds, and telemetry correlation models that reduce mean time to resolution and provide deep visibility into Java microservices operating at scale.
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