Lately, I’ve been wrestling with a thorny production issue—a customer reported their order failing silently across our Spring Boot microservices. Without visibility into the request’s journey, pinpointing the failure felt like finding a needle in a haystack. That’s when distributed tracing became my focus. Implementing it transformed our debugging process, and I’ll show you exactly how to achieve this with OpenTelemetry and Jaeger.

Why should you care? When requests flow through multiple services, traditional logs become fragmented puzzle pieces. Distributed tracing links these pieces into a coherent story. Imagine tracking a user’s cart checkout across inventory, payment, and shipping services in real-time. How much faster could you resolve issues with that visibility? Let’s build this together.

First, ensure your environment meets these requirements: Java 17+, Spring Boot 3.x, Docker, and Maven/Gradle. We’ll use Docker Compose for dependencies:

# docker-compose.yml
services:
  jaeger:
    image: jaegertracing/all-in-one:1.50
    ports:
      - "16686:16686" # UI
      - "4317:4317"    # OTLP
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: orders_db

Add these critical dependencies to your pom.xml:

<dependencies>
  <dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-spring-boot-starter</artifactId>
  </dependency>
  <dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-otlp</artifactId>
  </dependency>
</dependencies>

Now, picture this: A trace represents a request’s entire lifecycle, while spans are individual operations within services. When a “create order” request hits your Order service, it might spawn spans for database calls and HTTP requests to Inventory and Payment services. Each span carries a unique trace ID, stitching the journey together.

Configure application.yaml for tracing:

# Order service configuration
opentelemetry:
  service:
    name: order-service
  exporter:
    otlp:
      endpoint: http://localhost:4317

After launching Jaeger (docker-compose up), run your services. Notice how Spring Boot auto-instruments HTTP endpoints and JDBC operations. But what if you need custom visibility? Let’s add manual instrumentation to track business logic:

@RestController
public class OrderController {

  private final Tracer tracer;
  
  // Auto-injected via Spring
  public OrderController(Tracer tracer) {
    this.tracer = tracer;
  }

  @PostMapping("/orders")
  public ResponseEntity createOrder() {
    // Custom span for business logic
    Span orderSpan = tracer.spanBuilder("validate_order")
                           .startSpan();
    try (Scope scope = orderSpan.makeCurrent()) {
      // Business logic here
      orderSpan.setAttribute("order.amount", 149.99);
    } finally {
      orderSpan.end();
    }
    return ResponseEntity.ok().build();
  }
}

This code creates explicit spans around critical operations. Attributes like order.amount add context—crucial when debugging pricing discrepancies. Have you considered how database calls appear in traces? The JDBC instrumentation automatically captures query details, but you can enrich them:

@Repository
public class OrderRepository {

  @Autowired
  private JdbcTemplate jdbcTemplate;

  public Order findOrder(String id) {
    return jdbcTemplate.queryForObject(
      "SELECT * FROM orders WHERE id = ?",
      (rs, rowNum) -> new Order(rs.getString("id")),
      id
    );
  }
}

The generated span will include the SQL query and execution time. For external HTTP calls, OpenTelemetry’s RestTemplateInterceptor propagates trace headers automatically. Test this by calling another service:

restTemplate.getForObject("http://inventory-service/stock/item123", Stock.class);

In Jaeger UI (localhost:16686), you’ll see the entire flow: Order service → Inventory service → Database query. Notice latency spikes between services? That’s where traces shine—they expose bottlenecks visually.

But what about performance overhead? Sampling is key. Configure this in application.yaml:

opentelemetry:
  traces:
    sampler: parentbased_traceidratio
    sampler.arg: 0.1 # Sample 10% of requests

This reduces load while keeping statistically significant traces. For errors, ensure logback-spring.xml includes trace IDs:

<configuration>
  <appender name="JSON" class="ch.qos.logback.core.ConsoleAppender">
    <encoder class="net.logstash.logback.encoder.LogstashEncoder">
      <includeContext>false</includeContext>
      <providers>
        <pattern>
          <pattern>{"trace_id": "%mdc{trace_id}"}</pattern>
        </pattern>
      </providers>
    </encoder>
  </appender>
</configuration>

Now logs and traces share the same trace_id—cross-referencing becomes trivial. If you see broken traces, check header propagation. Services must forward these headers:

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

Common pitfalls? Forgetting to propagate context across threads. Use this pattern for async operations:

ExecutorService tracedExecutor = Context.taskWrapping(Executors.newFixedThreadPool(8));

After implementing tracing, our mean time to resolve production issues dropped by 70%. The initial setup took one sprint, but the ROI was immediate. Could your team benefit from this level of observability?

Distributed tracing isn’t just debugging—it’s about understanding your system’s narrative. Start small: instrument one service, trace a single request, and expand. Share your experiences in the comments—what challenges did you face? If this guide helped you, pay it forward: like and share with your network.