I’ve been thinking about distributed tracing a lot lately, especially as our team’s microservices architecture grows more complex. When something goes wrong in production, finding the root cause often feels like searching for a needle in a haystack. That’s why implementing proper distributed tracing isn’t just nice to have—it’s essential for maintaining system reliability and performance.

Distributed tracing gives you complete visibility into how requests flow through your services. Imagine being able to see exactly where latency occurs, which services communicate with each other, and what happens when things break. This visibility transforms how we understand our systems.

Have you ever wondered why some requests take much longer than others, even with similar payloads?

Let me show you how to set this up with Spring Boot. First, add Spring Cloud Sleuth to your dependencies:

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>

That’s it for basic setup. Sleuth automatically instruments your Spring Boot applications, adding trace and span IDs to your logs. You’ll start seeing entries like this:

2024-01-15 10:30:45.123 INFO [order-service,abc123def456,789ghi012jkl] Creating order for user

But logs alone aren’t enough. We need a way to collect and visualize these traces. That’s where Zipkin comes in. Setting up Zipkin is straightforward with Docker:

# docker-compose.yml
version: '3.8'
services:
  zipkin:
    image: openzipkin/zipkin
    ports:
      - "9411:9411"

Now configure your services to send traces to Zipkin:

# application.yml
spring:
  zipkin:
    base-url: http://localhost:9411
  sleuth:
    sampler:
      probability: 1.0

What happens when you need more control over your tracing data?

That’s where OpenTelemetry comes in. It’s becoming the standard for observability data collection. Migrating from Sleuth to OpenTelemetry is straightforward:

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-tracing-bridge-otel</artifactId>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-zipkin</artifactId>
</dependency>

Sometimes automatic instrumentation isn’t enough. You might want to add custom spans to track specific business logic:

@Autowired
private Tracer tracer;

public void processOrder(Order order) {
    Span customSpan = tracer.nextSpan().name("processOrder");
    try (Scope scope = customSpan.start()) {
        // Your business logic here
        customSpan.tag("order.amount", order.getAmount().toString());
    } finally {
        customSpan.end();
    }
}

Database calls and external service communications are critical to trace. Spring Data and RestTemplate are automatically instrumented, but you can add more context:

@Autowired
private ObservationRegistry observationRegistry;

public Payment processPayment(Order order) {
    return Observation.createNotStarted("process-payment", observationRegistry)
        .lowCardinalityKeyValue("payment.method", order.getPaymentMethod())
        .observe(() -> paymentService.charge(order));
}

When deploying to production, consider sampling rates carefully. You don’t want to trace every request, but you need enough data to be useful:

spring:
  sleuth:
    sampler:
      probability: 0.1

Performance impact is minimal when implemented correctly. The overhead is typically less than 3% for most applications, which is a small price to pay for the insights gained.

Have you considered how tracing data can help you optimize your service dependencies?

Troubleshooting becomes much easier with distributed tracing. Instead of guessing where problems occur, you can see exactly which service or database call is causing issues. This saves countless hours of debugging and makes incident response much faster.

The beauty of this approach is that it works across different programming languages and platforms. Once you establish tracing in your Spring Boot services, you can extend it to other parts of your infrastructure.

I’d love to hear about your experiences with distributed tracing. What challenges have you faced? What insights have you gained? Share your thoughts in the comments below, and if you found this useful, please like and share with your team!