I’ve been thinking a lot about how modern applications have evolved. Microservices bring flexibility and scalability, but they also introduce complexity. When a request travels through multiple services, how do we track its journey? How do we pinpoint where things slow down or fail?

Distributed tracing provides that visibility. It helps us understand the complete path of a request across service boundaries. In this guide, I’ll show you how to implement it in Spring Boot using OpenTelemetry and Jaeger.

Let’s start with the basics. Distributed tracing works by creating a trace for each request. This trace contains spans that represent individual operations. Each span has timing information and contextual data. When services communicate, they pass trace context to maintain the chain.

Why choose OpenTelemetry? It’s become the standard for observability instrumentation. It offers vendor-neutral APIs and SDKs. Jaeger complements it as a powerful tracing backend. Together, they provide a robust solution for monitoring microservices.

Setting up your environment is straightforward. You’ll need to add OpenTelemetry dependencies to your Spring Boot projects. The auto-instrumentation agent does most of the work for you. It captures HTTP requests, database calls, and other common operations automatically.

Here’s a simple dependency configuration for Maven:

<dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-spring-boot-starter</artifactId>
    <version>2.1.0</version>
</dependency>

But what about custom operations? Sometimes you need manual instrumentation. OpenTelemetry allows you to create custom spans for specific business logic. This gives you fine-grained control over what gets traced.

Consider this code snippet for manual span creation:

@Autowired
private Tracer tracer;

public void processOrder(Order order) {
    Span span = tracer.spanBuilder("process-order")
                     .setAttribute("order.id", order.getId())
                     .startSpan();
    
    try (Scope scope = span.makeCurrent()) {
        // Your business logic here
        inventoryService.reserveItems(order);
        paymentService.processPayment(order);
    } finally {
        span.end();
    }
}

Trace propagation between services is crucial. When one service calls another, the trace context must be passed along. OpenTelemetry handles this through headers. The receiving service continues the trace seamlessly.

Have you considered how database operations fit into this picture? OpenTelemetry instruments common JDBC drivers. It creates spans for SQL queries, showing their duration and potential errors. This is invaluable for performance analysis.

Message queues present another interesting challenge. How do we maintain trace context across asynchronous communication? OpenTelemetry provides instrumentation for popular messaging systems like Kafka and RabbitMQ. It ensures traces continue even when services don’t communicate directly.

Setting up Jaeger is simple with Docker. The all-in-one image gives you everything needed for development:

version: '3.8'
services:
  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "16686:16686"
      - "14268:14268"

Once running, you can access the Jaeger UI at http://localhost:16686. Here you’ll see visual representations of your traces. You can filter by service, operation, or duration. The flame graph view is particularly useful for identifying bottlenecks.

Performance considerations are important. Tracing every request can generate significant data. Sampling strategies help balance detail with overhead. You might sample only a percentage of requests or use adaptive sampling based on error rates.

What happens when you deploy to production? The configuration changes slightly. You’ll want to use the OpenTelemetry Collector as an intermediary. It can process and export traces to multiple backends. This provides flexibility in your observability strategy.

Troubleshooting common issues is part of the process. Sometimes traces appear incomplete. This often relates to context propagation problems. Ensure all services use compatible OpenTelemetry versions. Verify that headers are being passed correctly between services.

The benefits become clear when you start using traces to solve real problems. You can quickly identify slow database queries. You can see exactly which service in a chain is causing timeouts. You gain insights that would be difficult to obtain otherwise.

Distributed tracing transforms how we understand our systems. It provides the visibility needed to maintain complex microservices architectures. The combination of Spring Boot, OpenTelemetry, and Jaeger makes implementation accessible.

I encourage you to try implementing this in your projects. Start with basic auto-instrumentation and gradually add custom spans. The insights you gain will help you build more reliable systems.

What challenges have you faced with microservices observability? Share your experiences in the comments below. If you found this guide helpful, please like and share it with your team.