Modern distributed systems are becoming increasingly complex. Microservices 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.

This is where observability becomes essential.

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

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, Grafana, and Jaeger.

What is Observability?

Observability refers to the ability to understand a system’s behavior using telemetry data generated by applications and infrastructure.

The three pillars of observability are:

Metrics
Traces
Logs

Together, these provide visibility into system performance, failures, and request flows.

Why Observability Matters

Modern applications face several operational challenges.

Distributed Architectures

Microservices communicate across multiple services and networks.

Dynamic Infrastructure

Containers and Kubernetes environments scale continuously.

Asynchronous Communication

Event-driven systems create non-linear request flows.

Faster Release Cycles

Frequent deployments require rapid troubleshooting capabilities.

Without observability, diagnosing production issues becomes extremely difficult.

Understanding Metrics

Metrics are numerical measurements collected over time.

Examples include:

CPU usage
Memory consumption
Request count
Response time
Error rates

Metrics help answer questions like:

Is the application healthy?

or:

Did latency increase after deployment?

Understanding Distributed Tracing

Tracing tracks requests across multiple services.

Example request flow:

Gateway → Order Service → Payment Service → Inventory Service

Tracing helps identify:

Slow services
Network bottlenecks
Failed downstream dependencies
Cascading failures

Understanding Logs

Logs provide detailed event-level information.

Examples:

Exceptions
Business events
Audit activities
Debug messages

Logs help investigate application-specific issues deeply.

Why OpenTelemetry?

OpenTelemetry is the industry-standard observability framework for collecting telemetry data.

It supports:

Metrics
Traces
Logs
Multiple programming languages
Vendor-neutral telemetry pipelines

Benefits include:

Unified instrumentation
Standardized telemetry
Vendor independence
Cloud-native compatibility

What is OpenTelemetry Collector?

The OpenTelemetry Collector acts as a centralized telemetry pipeline.

Responsibilities include:

Receiving telemetry
Processing data
Filtering telemetry
Exporting to monitoring platforms

Instead of applications sending data directly to multiple tools, they send everything to the collector.

High-Level Observability Architecture

A modern Spring Boot observability stack typically includes:

Spring Boot Application
OpenTelemetry SDK
OpenTelemetry Collector
Prometheus
Grafana
Jaeger
Log Aggregation Platform

Technology Stack

For this implementation, we will use:

Java 21
Spring Boot
OpenTelemetry
Prometheus
Grafana
Jaeger
Docker
Kubernetes
Micrometer

Adding Spring Boot Dependencies

Maven Dependencies

<dependencies>

    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>
            spring-boot-starter-actuator
        </artifactId>
    </dependency>

    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>
            micrometer-registry-prometheus
        </artifactId>
    </dependency>

</dependencies>

Enabling Spring Boot Actuator

Spring Boot Actuator exposes operational metrics automatically.

Configuration

management:
  endpoints:
    web:
      exposure:
        include: health,prometheus,metrics,info

Metrics endpoint:

/actuator/prometheus

Configuring OpenTelemetry Java Agent

The easiest way to instrument Spring Boot applications is using the OpenTelemetry Java agent.

Download Agent

opentelemetry-javaagent.jar

Running Spring Boot with Agent

java -javaagent:opentelemetry-javaagent.jar \
     -jar app.jar

Setting Environment Variables

export OTEL_SERVICE_NAME=order-service
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

Automatic Instrumentation

The Java agent automatically instruments:

HTTP requests
JDBC calls
Kafka messaging
Redis
Spring MVC
WebClient
REST templates

No code changes are required.

Configuring OpenTelemetry Collector

The collector receives telemetry data from applications.

Collector Configuration

receivers:
  otlp:
    protocols:
      grpc:
      http:

exporters:
  prometheus:
    endpoint: "0.0.0.0:8889"

  jaeger:
    endpoint: jaeger:14250
    tls:
      insecure: true

service:
  pipelines:

    traces:
      receivers: [otlp]
      exporters: [jaeger]

    metrics:
      receivers: [otlp]
      exporters: [prometheus]

Running OpenTelemetry Collector

Docker example:

docker run \
  -p 4317:4317 \
  -p 8889:8889 \
  otel/opentelemetry-collector

Prometheus Integration

Prometheus scrapes metrics from the OpenTelemetry Collector.

Prometheus Configuration

scrape_configs:

  - job_name: 'otel-collector'

    static_configs:
      - targets: ['otel-collector:8889']

Grafana Dashboards

Grafana visualizes telemetry data.

Common dashboards include:

JVM memory usage
Request latency
API throughput
Error rates
Database performance
Kafka consumer lag

Distributed Tracing with Jaeger

Jaeger visualizes distributed traces.

Example trace flow:

API Gateway
   ↓
Order Service
   ↓
Payment Service
   ↓
Inventory Service

Each request receives a trace ID.

Adding Custom Traces

Custom spans help trace business logic.

Example

Span span = tracer.spanBuilder("payment-processing")
                  .startSpan();

try {

    processPayment();

} finally {

    span.end();
}

Structured Logging

Logs become more powerful when structured properly.

Recommended format:

{
  "timestamp": "2026-05-17T10:00:00",
  "traceId": "abc123",
  "service": "order-service",
  "message": "Payment successful"
}

Correlating Logs and Traces

Include trace IDs in logs.

Benefits:

Faster debugging
End-to-end request visibility
Easier root cause analysis

Log Aggregation

Centralized logging platforms include:

Elasticsearch
Loki
Splunk

Kubernetes Observability

Kubernetes environments require additional telemetry visibility.

Monitor:

Pod restarts
Node resource usage
Network latency
Container crashes

OpenTelemetry in Kubernetes

Collector deployment example:

apiVersion: apps/v1
kind: Deployment

metadata:
  name: otel-collector

Metrics Best Practices

Avoid High Cardinality

Bad example:

userId as metric label

This creates excessive memory usage.

Use Meaningful Metrics

Good examples:

request_latency
active_users
failed_payments

Tracing Best Practices

Trace Critical Paths

Focus on business-critical workflows.

Avoid Excessive Spans

Too many spans increase overhead.

Sample Traces Carefully

Production systems should use sampling.

Logging Best Practices

Use Structured JSON Logs

Avoid plain text logs.

Avoid Sensitive Data

Never log:

Passwords
Tokens
API keys

Use Log Levels Correctly

INFO for business events
WARN for recoverable issues
ERROR for failures

Monitoring SLIs and SLOs

Modern observability focuses heavily on reliability metrics.

Examples:

Service Level Indicator (SLI)

99.95% successful requests

Service Level Objective (SLO)

API latency below 200ms

Common Observability Challenges

Telemetry Overload

Excessive metrics and logs increase storage costs.

Missing Correlation

Without trace IDs, debugging becomes fragmented.

High Storage Costs

Logs and traces scale rapidly in large systems.

Sampling Complexity

Improper sampling may hide critical issues.

Security Considerations

Observability systems must also remain secure.

Important practices:

Encrypt telemetry traffic
Restrict dashboard access
Mask sensitive logs
Apply RBAC controls
Secure exporters

Production Best Practices

Centralize Telemetry Collection

Avoid direct application-to-vendor coupling.

Standardize Telemetry Formats

Use OpenTelemetry conventions consistently.

Monitor the Monitoring Stack

Observability infrastructure itself requires monitoring.

Automate Dashboard Provisioning

Use Infrastructure as Code for dashboards and alerts.

When Should You Implement Full Observability?

Comprehensive observability becomes essential when:

Applications use microservices
Systems scale dynamically
Multiple teams share ownership
High availability is required
Incident response speed matters

Final Thoughts

Observability has become a foundational requirement for modern cloud-native systems. Metrics alone are no longer sufficient for troubleshooting distributed architectures. Engineering teams need unified visibility into metrics, traces, and logs to understand application behavior effectively.

Using Spring Boot together with OpenTelemetry and the OpenTelemetry Collector enables organizations to build scalable, vendor-neutral observability pipelines capable of supporting complex distributed systems.

The key to successful observability implementation lies in standardization, trace correlation, centralized telemetry pipelines, and continuous monitoring improvements.

Reference URLs

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Spring Boot Observability: Metrics, Traces & Logs with OpenTelemetry Collector

Published by HS on August 8, 2026August 8, 2026