As a developer who has spent countless hours debugging complex microservices architectures, I often found myself lost in a maze of logs and metrics without a clear path to follow. The turning point came when a critical production issue took days to resolve because we couldn’t trace a single user request across our dozen services. That experience led me to discover distributed tracing, and today I want to share how you can implement it in your Spring Boot applications using Micrometer and Zipkin.
Have you ever wondered what happens to a user request after it enters your system? Distributed tracing answers this by providing a complete map of request flow across service boundaries. It’s like having GPS navigation for your microservices – you can see exactly where requests go, how long they spend at each stop, and where bottlenecks occur.
Let me show you how to set this up. First, we need to add the necessary dependencies to our Spring Boot project. Here’s the essential setup for your Maven configuration:
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-tracing-bridge-brave</artifactId>
</dependency>
<dependency>
<groupId>io.zipkin.reporter2</groupId>
<artifactId>zipkin-reporter-brave</artifactId>
</dependency>
</dependencies>But what makes this actually work across services? The magic happens through trace context propagation. When one service calls another, it automatically passes tracing headers that connect all the pieces together. Here’s a simple example of how you might make an inter-service call with tracing enabled:
@RestController
public class OrderController {
private final InventoryClient inventoryClient;
private final Tracer tracer;
public OrderController(InventoryClient inventoryClient, Tracer tracer) {
this.inventoryClient = inventoryClient;
this.tracer = tracer;
}
@PostMapping("/orders")
public Order createOrder(@RequestBody OrderRequest request) {
Span orderSpan = tracer.nextSpan().name("create-order").start();
try (Tracer.SpanInScope ws = tracer.withSpan(orderSpan)) {
// Check inventory
inventoryClient.checkInventory(request.getProductId());
// Process order logic
return processOrder(request);
} finally {
orderSpan.end();
}
}
}Did you notice how the trace continues seamlessly between services? This happens because Micrometer automatically injects headers like traceId and spanId into HTTP requests. The receiving service picks up these headers and continues the trace without any manual intervention.
Now, what if you want to add custom information to your traces? You can create custom spans to track specific operations. Imagine you’re processing a payment and want to track how long it takes:
@NewSpan("process-payment")
public PaymentResult processPayment(@SpanTag("payment.amount") BigDecimal amount) {
// Your payment processing logic
return paymentService.charge(amount);
}The @NewSpan annotation automatically creates a new span, while @SpanTag adds important contextual information that appears in your traces. This is incredibly useful when you’re trying to understand why certain operations are slow.
But how do you actually see these traces? That’s where Zipkin comes in. You can run it locally using Docker:
# docker-compose.yml
version: '3.8'
services:
zipkin:
image: openzipkin/zipkin:latest
ports:
- "9411:9411"After starting Zipkin, your application configuration needs to point to it:
spring:
tracing:
zipkin:
endpoint: http://localhost:9411/api/v2/spans
sampling:
probability: 1.0What happens when things go wrong? Distributed tracing shines in error scenarios. If a service call fails, the trace shows exactly where and when the failure occurred, along with error details. This eliminates the guesswork from debugging distributed systems.
Here’s a practical question: how much tracing data should you collect in production? While it’s tempting to trace everything, you’ll want to implement sampling to manage overhead. A common approach is to sample only a percentage of requests:
spring:
tracing:
sampling:
probability: 0.1 # Sample 10% of requestsFor high-traffic applications, you might use rate-based sampling or even dynamic sampling based on request characteristics. The key is finding the right balance between observability and performance.
What about database calls and cache operations? Micrometer can automatically trace these too. When you use Spring Data JPA or Redis templates, spans are created automatically for database queries and cache operations. This gives you complete visibility into your data layer performance.
As your system grows, you’ll appreciate how distributed tracing connects the dots between logs and metrics. When you see a slow trace, you can immediately jump to the relevant logs using the trace ID, and correlate with metrics to understand the broader impact.
I’ve found that the real power emerges when you combine tracing with your existing monitoring stack. It transforms how you understand system behavior and respond to incidents. The investment in setting up distributed tracing pays for itself the first time you solve a complex issue in minutes instead of days.
If you’re working with microservices, distributed tracing isn’t just nice to have – it’s essential for maintaining system reliability and performance. The combination of Spring Boot, Micrometer, and Zipkin makes implementation straightforward and powerful.
I hope this guide helps you bring clarity to your microservices architecture. What challenges have you faced with distributed systems monitoring? Share your experiences in the comments below – I’d love to hear how you’re implementing observability in your projects. If this article helped you, please like and share it with others who might benefit from better tracing implementation.
