Building and managing microservices can be a wild ride, but nailing the whole process becomes a lot smoother when you can monitor and troubleshoot the way these tiny services interact with each other. That’s where distributed tracing comes into play. If you’ve ever found yourself scratching your head over how to get a clear visual of the request flow across your microservices, you’re in the right place. Micronaut’s capabilities combined with popular tracing tools like Zipkin and Jaeger can make that seemingly Herculean task a lot more manageable.

Understanding Distributed Tracing

Think of distributed tracing as your map and compass in the vast wilderness of microservices. It tracks the flow of requests as they dart between various services. It’s like having a bird’s-eye view that helps you spot bottlenecks, understand dependencies, and get to the root of problems quicker than ever. Zipkin and Jaeger are like your trusty binoculars here, each with its own set of superpowers.

Setting Up Micronaut with Zipkin

Zipkin is a reliable buddy when it comes to visualizing the journey of request data. Ideal for investigations into your system’s ins and outs, getting started with Zipkin is a breeze, especially with Docker.

Crack open your terminal and type this:

$ docker run -d -p 9411:9411 openzipkin/zipkin

Just like that, Zipkin is up and running, and you can see it in action at http://localhost:9411.

Now, time to add Zipkin to your Micronaut project. You’ll need to tweak your build.gradle file just a bit:

implementation("io.micronaut.tracing:micronaut-tracing-brave-http")
runtime("io.zipkin.brave:brave-instrumentation-http")
runtime("io.zipkin.reporter2:zipkin-reporter")
compile("io.opentracing.brave:brave-opentracing")

With the dependencies sorted, it’s time to talk configs. Add these lines to your application.yml file to get your app chatting with Zipkin:

tracing:
  zipkin:
    http:
      url: http://localhost:9411
    enabled: true
    sampler:
      probability: 1

Setting the sampling probability to 1 means every request gets traced. You might want to dial this down in production for performance reasons.

Running tests? You probably don’t want tracer overhead there, so it’s smart to turn off tracing in your application-test.yml like so:

tracing:
  zipkin:
    enabled: false

Fire up your application, and you’ll start seeing those spans pop up in the Zipkin UI at http://localhost:9411.

Using Micronaut with Jaeger

Jaeger is another robust player in the tracing game, particularly good at handling big, complex environments. It’s no sweat to get Jaeger up and running with Docker, either:

$ docker run -d -p 16686:16686 jaegertracing/all-in-one:latest

You’ll find Jaeger doing its thing at http://localhost:16686.

Next, let’s sprinkle in the required Jaeger dependencies into your build.gradle file:

implementation("io.micronaut.tracing:micronaut-tracing")
runtimeOnly("io.jaegertracing:jaeger-thrift")

With the groundwork laid, move on to configuring your Micronaut application by updating your application.yml:

tracing:
  jaeger:
    enabled: true
    sampler:
      type: probabilistic
      param: 1.0

Fire up your app, and you’ll start seeing those tracing spans lighting up in Jaeger’s UI at http://localhost:16686.

Tracing Annotations in Micronaut

Micronaut’s got some handy annotations to make span management a walk in the park.

• @NewSpan: Wraps method calls or reactive types, creating a new span.

  @NewSpan
  public void doSomething() {
      // Method implementation
  }
  

• @ContinueSpan: Continues an existing span, perfect for adding more tags or logs.

  @ContinueSpan
  public void doSomethingElse() {
      // Method implementation
  }
  

• @SpanTag: Includes method argument values as tags within the span.

  @NewSpan
  public void doSomething(@SpanTag("argument") String arg) {
      // Method implementation
  }
  

Instrumentation and Propagation

Micronaut thoughtfully includes various instrumentations to make sure the span context travels smoothly between threads and across your service boundaries. Check out the io.micronaut.tracing.instrument package for filters that propagate the necessary headers through HTTP.

Choosing Between Zipkin and Jaeger

Picking between Zipkin and Jaeger boils down to your project’s needs and the skills of your team.

• Zipkin: Great for smaller projects or those sticking to Java and Spring Boot. Simple to set up and supports many languages, though it might lack some of Jaeger’s more advanced features.
• Jaeger: The heavyweight champ for large-scale projects. It brings advanced capabilities like distributed context propagation and dependency analysis to the table. Seamlessly integrates with modern infrastructure like Kubernetes and the CNCF.

When making your choice, think about your service architecture’s complexity, what your team is comfortable with, and the features that are must-haves for your project.

Conclusion

Distributed tracing is absolutely essential for keeping your microservices in check. By leveraging Micronaut’s integrations with both Zipkin and Jaeger, you can dramatically improve your monitoring and troubleshooting capabilities. Whether you roll with Zipkin for its straightforward simplicity or opt for Jaeger’s advanced features, Micronaut’s robust tracing support has your back. Happy tracing!