I’ve been building microservices for years, and there’s one question that always comes up when things go wrong: what exactly happened to that user request as it traveled through our system? Last month, I spent three days chasing a bug that disappeared into the maze of our service architecture. That experience convinced me we needed better visibility. Today, I want to share how distributed tracing transformed our ability to understand what’s happening inside our applications.

Distributed tracing lets you follow a single request as it moves through multiple services. Think of it as putting a tracking number on every user request. When something slows down or fails, you can see exactly where it happened and why. This isn’t just about fixing problems—it’s about understanding how your system actually behaves under real conditions.

Have you ever tried to debug a performance issue that involved multiple services? Without proper tracing, you’re essentially working blind. Each service might have its own logs, but connecting them into a coherent story is nearly impossible. Distributed tracing solves this by creating a unified view of request flow.

Let’s start with the basics. A trace represents the entire journey of a request. Within that trace, spans represent individual operations—like database calls or HTTP requests between services. Context propagation ensures trace information gets passed along as the request moves between services. This creates a complete picture of what happened and when.

I chose OpenTelemetry because it’s become the standard for observability data collection. It works across different programming languages and doesn’t lock you into specific vendors. Paired with Jaeger for visualization, you get a powerful combination that’s both flexible and production-ready.

Setting up our project requires a few key dependencies. Here’s what I typically include in my Spring Boot applications:

<dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-spring-boot-starter</artifactId>
    <version>2.0.0</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-jdbc</artifactId>
</dependency>

Configuration happens mostly through application properties. This setup automatically instruments web requests, database calls, and HTTP clients:

otel:
  service:
    name: order-service
  exporter:
    jaeger:
      endpoint: http://localhost:14250
  traces:
    exporter: jaeger
management:
  tracing:
    sampling:
      probability: 1.0

Did you know that automatic instrumentation can capture most of what you need without changing your code? Spring MVC endpoints, JDBC database calls, and REST template requests all get traced automatically. This immediate visibility surprised me when I first tried it—I could see complex request patterns without writing any tracing code.

But sometimes you need more control. That’s where custom spans come in. Imagine you have a complex business process that spans multiple method calls. You can create custom spans to highlight important operations:

@Autowired
private Tracer tracer;

public void processOrder(Order order) {
    Span span = tracer.spanBuilder("process-order")
        .setAttribute("order.id", order.getId())
        .setAttribute("customer.id", order.getCustomerId())
        .startSpan();
    
    try (Scope scope = span.makeCurrent()) {
        // Business logic here
        validateOrder(order);
        reserveInventory(order);
        sendConfirmation(order);
    } finally {
        span.end();
    }
}

What happens when your trace needs to cross service boundaries? This is where context propagation becomes crucial. When one service calls another, trace context gets passed through HTTP headers. The receiving service continues the same trace, creating a seamless view across your architecture.

Setting up Jaeger is straightforward with Docker. Here’s a simple compose file to get you started:

services:
  jaeger:
    image: jaegertracing/all-in-one:1.51
    ports:
      - "16686:16686"
      - "14250:14250"

Once running, you can access the Jaeger UI at http://localhost:16686. The first time I saw a complete trace visualized there, it felt like someone had turned on the lights in a dark room. Suddenly, I could see exactly how requests flowed between services, where time was being spent, and what dependencies existed.

In production, you’ll want to adjust sampling rates. Capturing every trace can be expensive. I typically start with 100% sampling in development, then move to lower rates in production based on traffic volume. The key is capturing enough data to be useful without overwhelming your storage.

One common mistake I’ve seen is adding too many attributes to spans. While it’s tempting to log everything, focus on information that actually helps with debugging—like user IDs, request types, and error codes. Too much noise makes traces harder to understand.

Another pitfall is forgetting about log correlation. You can enrich your application logs with trace IDs, making it easier to connect traditional logging with distributed traces. This combination becomes incredibly powerful when investigating issues.

What if you’re already using other tracing systems? OpenTelemetry’s strength is its ability to work with existing tools. You can export data to multiple backends simultaneously, giving you flexibility during migration periods.

The real value of distributed tracing emerges when you have multiple teams working on different services. It creates a common language for discussing performance and reliability. In my team, we now start every performance investigation by looking at traces rather than guessing where bottlenecks might be.

Implementing distributed tracing changed how we think about our system’s behavior. It moved us from reactive debugging to proactive understanding. We can now identify performance patterns before they become problems and make data-driven decisions about architectural changes.

I hope this guide helps you bring the same clarity to your microservices. The initial setup might take a few hours, but the time you’ll save debugging complex issues makes it absolutely worthwhile. If you found this useful or have questions about your specific use case, I’d love to hear from you—please share your thoughts in the comments below, and if this helped you see your systems more clearly, consider sharing it with your team!