Recently, I was troubleshooting a performance bottleneck in our microservices setup. A single user request was passing through multiple services, and pinpointing the exact failure point felt like searching for a needle in a haystack. That frustrating experience solidified my belief in distributed tracing. Today, I want to guide you through implementing this powerful observability technique in your Spring Boot applications using OpenTelemetry and Jaeger.

Have you ever wondered what happens to a request after it enters your system? Distributed tracing provides a complete map of every service interaction. It captures the entire journey of a request across service boundaries, giving you visibility into latency, errors, and dependencies. This isn’t just about debugging—it’s about understanding your system’s behavior under real conditions.

Let’s start by setting up our project. We’ll create three simple Spring Boot services: order-service, inventory-service, and payment-service. Each will handle specific business functions while communicating through HTTP calls. I prefer using Maven for dependency management, but Gradle works equally well.

Here’s the core dependency setup for each service’s pom.xml:

<dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-spring-boot-starter</artifactId>
    <version>1.32.0-alpha</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-jaeger</artifactId>
    <version>1.32.0</version>
</dependency>

Next, we need infrastructure. I use Docker Compose to run Jaeger and PostgreSQL locally. This approach keeps the environment consistent and easy to replicate.

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

Configuration is where the magic happens. OpenTelemetry needs to know where to send traces and how to identify your service. I always set the service name explicitly—it makes traces much easier to read later.

@Bean
public OpenTelemetry openTelemetry() {
    return OpenTelemetrySdk.builder()
        .setTracerProvider(SdkTracerProvider.builder()
            .addSpanProcessor(BatchSpanProcessor.builder(
                JaegerGrpcSpanExporter.builder()
                    .setEndpoint(jaegerEndpoint)
                    .build())
                .build())
            .build())
        .build();
}

What if you need to track custom business logic? That’s where manual instrumentation shines. Let me show you how I add custom spans to measure specific operations.

@Autowired
private Tracer tracer;

public void processOrder(Order order) {
    Span span = tracer.spanBuilder("processOrder")
        .setAttribute("order.id", order.getId())
        .startSpan();
    try (Scope scope = span.makeCurrent()) {
        // Business logic here
        span.addEvent("Inventory checked");
    } finally {
        span.end();
    }
}

Instrumenting database calls and HTTP clients automatically captures valuable timing data. Spring’s WebClient and RestTemplate integrate seamlessly with OpenTelemetry when configured properly.

@Bean
public WebClient webClient() {
    return WebClient.builder().build();
}

Have you considered how sampling affects your tracing costs? In production, I use a ratio-based sampler to capture only a percentage of traces. This balances observability with performance overhead.

.setSampler(Sampler.traceIdRatioBased(0.1))

Testing your implementation is crucial. I simulate requests and verify that traces appear in Jaeger’s UI. Look for complete trace graphs and check that timing data makes sense.

In production, remember to secure your Jaeger endpoint and monitor its resource usage. I’ve seen teams overlook this and face availability issues during traffic spikes.

What common mistakes should you watch for? Missing context propagation between services breaks trace continuity. Always verify that headers like traceparent are being passed correctly.

I hope this practical approach helps you implement distributed tracing successfully. Seeing that first complete trace map is incredibly rewarding—it transforms how you understand your system. If this guide clarified things for you, I’d appreciate a like or share. Have questions or experiences to share? Drop a comment below—I’d love to hear how it goes in your projects!