I was debugging a complex issue in our microservices architecture last week where a user’s request was timing out, and it took me hours to pinpoint the exact service causing the delay. That frustrating experience made me realize how crucial visibility is in distributed systems. If you’ve ever felt lost in a maze of service calls, this guide will show you how distributed tracing can light the way. By the end, you’ll have a clear path to implement tracing in your Spring Boot applications using OpenTelemetry and Jaeger.

Distributed tracing tracks requests as they travel through multiple services. Think of it as leaving breadcrumbs across your system. Each service adds a span to the trace, showing what happened and how long it took. This helps you see the full picture of a request’s journey, making it easier to find slow spots or failures.

Have you ever wondered how to connect the dots when an error occurs in one service but originates from another? Context propagation carries trace information between services, ensuring all spans are linked under one trace ID. This connection is vital for understanding dependencies and interactions.

Let’s start by setting up our project. I’ll use a simple order processing system with multiple services. First, add OpenTelemetry dependencies to your pom.xml. These libraries handle the instrumentation and export of traces.

<dependency>
    <groupId>io.opentelemetry</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, configure OpenTelemetry to send data to Jaeger. This setup defines how traces are collected and where they’re sent. I prefer using a properties file to keep things flexible.

@Bean
public OpenTelemetry openTelemetry() {
    return OpenTelemetrySdk.builder()
        .setTracerProvider(SdkTracerProvider.builder()
            .addSpanProcessor(BatchSpanProcessor.builder(
                JaegerGrpcSpanExporter.builder()
                    .setEndpoint("http://localhost:14250")
                    .build())
                .build())
            .build())
        .build();
}

Now, run Jaeger using Docker. It’s straightforward and gives you a UI to visualize traces. I use this in development to quickly see what’s happening.

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

Start it with docker-compose up -d. Open http://localhost:16686 to access the Jaeger UI. You’ll see traces appear here once your services are running.

In your service code, inject the Tracer and create spans for key operations. For example, when processing an order, I add a span to track each step. This helps me see how long database queries or external calls take.

public Order createOrder(CreateOrderRequest request) {
    Span span = tracer.spanBuilder("order.process")
        .setAttribute("customer.id", request.getCustomerId())
        .startSpan();
    try (Scope scope = span.makeCurrent()) {
        // Business logic here
        Order order = orderRepository.save(new Order(...));
        span.addEvent("Order saved to database");
        return order;
    } finally {
        span.end();
    }
}

What happens when your service calls another over HTTP? OpenTelemetry automatically propagates the trace context if you use a configured HTTP client. This ensures the next service continues the same trace.

For database operations, add spans around your queries. I often wrap repository calls to see if slow queries are the bottleneck. You can even add custom attributes like query parameters for more detail.

@Autowired
private Tracer tracer;

public List<Order> findOrdersByCustomer(String customerId) {
    Span span = tracer.spanBuilder("database.query")
        .setAttribute("db.operation", "SELECT")
        .setAttribute("db.table", "orders")
        .startSpan();
    try (Scope scope = span.makeCurrent()) {
        return orderRepository.findByCustomerId(customerId);
    } finally {
        span.end();
    }
}

Message queues are common in microservices. When sending a message, include the trace context in the headers. The consuming service can pick it up and link spans. This way, asynchronous processes don’t break the trace.

How do you handle errors in spans? I add status and error messages when exceptions occur. This makes it clear in Jaeger where things went wrong without digging through logs.

Performance is key, so I set sampling rates to control how many traces are collected. In production, you might sample only a percentage of requests to reduce overhead. OpenTelemetry makes this easy with ratio-based sampling.

Troubleshooting tip: If traces aren’t appearing, check your Jaeger endpoint and network connectivity. I’ve spent time debugging only to find a typo in the configuration.

Imagine being able to see exactly how a request flows from the user through authentication, order processing, payment, and notification. Distributed tracing turns that imagination into reality. It transforms debugging from a guessing game into a precise science.

I encourage you to start small—add tracing to one service and expand from there. The insights you gain will save countless hours in the long run. If this guide helps you implement tracing, please like, share, and comment with your experiences. Your feedback helps others learn and improve their systems too.