Implementing Distributed Tracing with OpenTelemetry in Java Microservices

As microservices scale in complexity, debugging issues across multiple services becomes increasingly difficult. Traditional logging falls short when tracking a request across boundaries. This is where distributed tracing plays a crucial role.

In this blog, we’ll explore how to implement distributed tracing in Java microservices using OpenTelemetry, an open-source observability framework backed by the Cloud Native Computing Foundation (CNCF). You’ll learn how to capture spans, export traces, and integrate with popular tracing tools like Jaeger or Zipkin.

---

What Is Distributed Tracing?

Distributed tracing provides visibility into the flow of requests across service boundaries. It answers key questions such as:

• Where is latency introduced?
• Which service failed?
• What is the full path of a request?

Each trace is composed of spans, which represent units of work (e.g., HTTP calls, DB queries). When linked, these spans form a full picture of the request’s journey.

📚 Reference: OpenTelemetry Overview

---

Why Use OpenTelemetry?

OpenTelemetry is a vendor-neutral standard for collecting telemetry (traces, metrics, logs). Key benefits include:

• Unified instrumentation across languages
• Works with Jaeger, Zipkin, Prometheus, and others
• Integrates easily with Java libraries and Spring Boot
• Supported by CNCF and cloud providers (AWS, GCP, Azure)

---

Key OpenTelemetry Components

• SDK: Client-side instrumentation
• Collector: Aggregates and exports data
• Exporter: Sends telemetry to observability platforms
• Auto-Instrumentation: Adds spans without code changes

---

Setting Up OpenTelemetry in Java Microservices

Step 1: Add Dependencies (Maven)

xmlCopyEdit<dependency>
  <groupId>io.opentelemetry</groupId>
  <artifactId>opentelemetry-api</artifactId>
  <version>1.32.0</version>
</dependency>
<dependency>
  <groupId>io.opentelemetry</groupId>
  <artifactId>opentelemetry-sdk</artifactId>
  <version>1.32.0</version>
</dependency>
<dependency>
  <groupId>io.opentelemetry</groupId>
  <artifactId>opentelemetry-exporter-otlp</artifactId>
  <version>1.32.0</version>
</dependency>

---

Step 2: Configure the Tracer

javaCopyEditOpenTelemetry openTelemetry = OpenTelemetrySdk.builder()
    .setTracerProvider(SdkTracerProvider.builder()
        .addSpanProcessor(SimpleSpanProcessor.create(OtlpGrpcSpanExporter.builder().build()))
        .build())
    .build();

Tracer tracer = openTelemetry.getTracer("my-service");

---

Step 3: Create Custom Spans

javaCopyEditSpan span = tracer.spanBuilder("order-service-call").startSpan();
try (Scope scope = span.makeCurrent()) {
    // business logic
    orderService.call();
} finally {
    span.end();
}

You can trace downstream HTTP clients, Kafka producers, DB calls, and more.

---

Step 4: Run the OpenTelemetry Collector

Use Docker to spin up the collector:

bashCopyEditdocker run -p 4317:4317 -p 55681:55681 otel/opentelemetry-collector-contrib

Configure the collector to export to Jaeger or Zipkin.

---

Auto-Instrumentation for Spring Boot

OpenTelemetry Java Agent offers zero-code instrumentation for Spring Boot apps:

Usage:

bashCopyEditjava -javaagent:opentelemetry-javaagent.jar \
     -Dotel.service.name=payment-service \
     -Dotel.exporter.otlp.endpoint=http://localhost:4317 \
     -jar your-spring-boot-app.jar

This automatically traces:

• REST controllers
• WebClient or RestTemplate
• Kafka producers/consumers
• JDBC queries

📚 Reference: OpenTelemetry Java Agent Docs

---

Exporting Traces

OpenTelemetry supports exporters like:

• OTLP (OpenTelemetry Protocol)
• Jaeger
• Zipkin
• Prometheus (for metrics)

You can visualize traces in Jaeger:

bashCopyEditdocker run -d -p 16686:16686 -p 6831:6831/udp jaegertracing/all-in-one

Visit: http://localhost:16686

---

Best Practices

1. Name spans meaningfully (e.g., get-user, checkout)
2. Use baggage and context propagation to correlate requests
3. Avoid sampling everything in production—control trace volume
4. Instrument third-party libraries using interceptors
5. Secure trace data if it includes sensitive user info

---

Benefits of Distributed Tracing

• Faster debugging and root-cause analysis
• Service-level latency and error tracking
• Performance optimization insights
• Better SLAs and SLO visibility

---

Conclusion

Implementing distributed tracing with OpenTelemetry in Java microservices unlocks deep observability, accelerates troubleshooting, and boosts reliability. Whether you’re debugging a production issue or optimizing service performance, tracing provides the visibility needed in modern cloud-native systems.

With Spring Boot, OpenTelemetry setup is straightforward—especially with auto-instrumentation and the Java agent. Embrace distributed tracing early to build resilient, transparent systems.

---

<> “Happy developing, one line at a time!” </>