{"id":529,"date":"2026-08-08T05:55:00","date_gmt":"2026-08-08T05:55:00","guid":{"rendered":"https:\/\/harshad-sonawane.com\/blog\/?p=529"},"modified":"2026-07-19T10:18:53","modified_gmt":"2026-07-19T10:18:53","slug":"spring-boot-observability-opentelemetry-collector","status":"publish","type":"post","link":"https:\/\/harshad-sonawane.com\/blog\/spring-boot-observability-opentelemetry-collector\/","title":{"rendered":"Spring Boot Observability: Metrics, Traces & Logs with OpenTelemetry Collector"},"content":{"rendered":"

Modern distributed systems<\/a> are becoming increasingly complex. Microservices<\/a> communicate across networks, asynchronous event-driven workflows span multiple services, and cloud-native environments scale dynamically. In such architectures, traditional debugging methods like checking application logs manually are no longer sufficient.<\/p>\n

This is where observability becomes essential.<\/p>\n

Observability helps engineering teams understand the internal state of systems by analyzing telemetry data such as metrics, traces, and logs. Combined with Spring Boot<\/a><\/span><\/span> and OpenTelemetry<\/span><\/span>, organizations can build highly observable systems capable of detecting performance bottlenecks, troubleshooting failures, and improving reliability in production environments.<\/p>\n

In this blog, we will explore how to implement end-to-end observability in Spring Boot applications using OpenTelemetry Collector, metrics, distributed tracing, centralized logging, Prometheus<\/a>, Grafana, and Jaeger.<\/p>\n

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What is Observability?<\/h1>\n

Observability refers to the ability to understand a system\u2019s behavior using telemetry data generated by applications and infrastructure.<\/p>\n

The three pillars of observability are:<\/p>\n

    \n
  1. Metrics<\/li>\n
  2. Traces<\/li>\n
  3. Logs<\/li>\n<\/ol>\n

    Together, these provide visibility into system performance, failures, and request flows.<\/p>\n

    \n

    Why Observability Matters<\/h1>\n

    Modern applications face several operational challenges.<\/p>\n

    Distributed Architectures<\/h2>\n

    Microservices communicate across multiple services and networks.<\/p>\n

    Dynamic Infrastructure<\/h2>\n

    Containers and Kubernetes<\/a> environments scale continuously.<\/p>\n

    Asynchronous Communication<\/h2>\n

    Event-driven systems create non-linear request flows.<\/p>\n

    Faster Release Cycles<\/h2>\n

    Frequent deployments require rapid troubleshooting capabilities.<\/p>\n

    Without observability, diagnosing production issues becomes extremely difficult.<\/p>\n

    \n

    Understanding Metrics<\/h1>\n

    Metrics are numerical measurements collected over time.<\/p>\n

    Examples include:<\/p>\n