I’ve spent countless hours debugging microservices where a single user request travels through multiple services, and pinpointing failures felt like searching for a ghost in the machine. That frustration led me to explore distributed tracing, and today I want to share how OpenTelemetry with Jaeger can transform how you monitor Spring Boot applications. If you’ve ever stared at disconnected logs wondering where things went wrong, this is for you.

Distributed tracing provides a complete story of a request’s journey across your microservices. Each operation becomes a span, and together they form a trace that shows the entire flow. Why is this better than traditional logging? Because it connects related events across service boundaries, giving you context that isolated logs can’t provide.

Setting up OpenTelemetry in Spring Boot starts with adding the right dependencies. Here’s a basic Maven configuration for a microservice:

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

Have you considered what happens when a request moves from your API gateway to an order service? The trace context must propagate seamlessly. OpenTelemetry handles this through HTTP headers, but you need to configure it properly.

I typically use Docker Compose to set up Jaeger alongside my services:

services:
  jaeger:
    image: jaegertracing/all-in-one:1.50
    ports:
      - "16686:16686"

Once running, Jaeger provides a web UI at localhost:16686 where you can visualize traces. But how do we actually generate those traces in our code?

Spring Boot’s auto-configuration does most of the heavy lifting, but sometimes you need custom spans. Here’s how I add detailed operation tracking:

@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.checkStock(order.getItems());
        paymentService.processPayment(order);
        span.addEvent("Order processed successfully");
    } catch (Exception e) {
        span.recordException(e);
        span.setStatus(StatusCode.ERROR);
        throw e;
    } finally {
        span.end();
    }
}

What about database calls? OpenTelemetry automatically instruments Spring Data JPA, but you can enhance it with custom attributes. I often add query-specific details to understand performance bottlenecks.

When services communicate via HTTP, the tracing context must propagate. Using RestTemplate or WebClient? OpenTelemetry instruments them automatically if configured correctly. But did you know you can add custom headers to spans for better context?

@Bean
public RestTemplate restTemplate() {
    return new RestTemplateBuilder()
        .additionalInterceptors(new TracingRestTemplateInterceptor())
        .build();
}

Async processing introduces interesting challenges. If you’re using @Async or CompletableFuture, you must ensure the trace context propagates to new threads. I’ve found that manually capturing and restoring the context prevents broken traces.

Error handling becomes more insightful with distributed tracing. Instead of guessing where an exception originated, you see the exact service and operation that failed. How many times have you wasted hours correlating error logs across services?

Performance overhead is a common concern, but OpenTelemetry’s sampling strategies help. I usually start with recording all traces in development, then move to probabilistic sampling in production. The key is balancing observability with system performance.

Deploying to production requires careful configuration. I set up the Jaeger collector separately from the all-in-one image and use environment variables for endpoint configuration:

OTEL_EXPORTER_JAEGER_ENDPOINT: http://jaeger-collector:14268
OTEL_SERVICE_NAME: order-service

Troubleshooting tracing issues often involves checking if spans are being created and exported. I frequently verify the OpenTelemetry agent logs and Jaeger collector metrics. Have you ever had spans disappear mysteriously?

The beauty of OpenTelemetry is its vendor neutrality. While I prefer Jaeger for its simplicity, you could switch to Zipkin or commercial solutions without changing your instrumentation code. This flexibility has saved me from vendor lock-in multiple times.

Implementing distributed tracing transformed how my team approaches debugging and performance optimization. We now identify bottlenecks faster and understand system behavior at a granular level. The initial setup investment pays back quickly when production issues arise.

I’d love to hear about your experiences with distributed tracing. What challenges have you faced when implementing observability in microservices? If this guide helped clarify OpenTelemetry with Spring Boot, please share it with your team and leave a comment about your implementation journey. Your insights could help others navigating similar paths.