See how much time Spring Boot services spend on an http request.
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README.md

Basic example showing distributed tracing across Spring Boot apps

This is an example app where two Spring Boot (Java) services collaborate on an http request. Notably, timing of these requests are recorded into Zipkin, a distributed tracing system. This allows you to see the how long the whole operation took, as well how much time was spent in each service.

Here's an example of what it looks like zipkin screen shot

This example was initially made for a Distributed Tracing Webinar on June 30th, 2016. There's probably room to enroll if it hasn't completed, yet, and you are interested in the general topic.

Implementation Overview

Web requests are served by Spring MVC controllers, and tracing is automatically performed for you by Spring Cloud Sleuth.

This example intentionally avoids advanced topics like async and load balancing, eventhough Spring Cloud Sleuth supports that, too. Once you get familiar with things, you can play with more interesting Spring Cloud components.

Running the example

This example has two services: frontend and backend. They both report trace data to zipkin. To setup the demo, you need to start Frontend, Backend and Zipkin.

Once the services are started, open http://localhost:8081/

Next, you can view traces that went through the backend via http://localhost:9411/?serviceName=backend

  • This is a locally run zipkin service which keeps traces in memory

Starting the Services

In a separate tab or window, start each of sleuth.webmvc.Frontend and sleuth.webmvc.Backend:

$ ./mvnw compile exec:java -Dexec.mainClass=sleuth.webmvc.Backend
$ ./mvnw compile exec:java -Dexec.mainClass=sleuth.webmvc.Frontend

Next, run Zipkin, which stores and queries traces reported by the above services.

curl -sSL https://zipkin.io/quickstart.sh | bash -s
java -jar zipkin.jar

Configuration tips

  • The service name in the Zipkin UI defaults to the application name
    • spring.application.name=frontend
  • All incoming requests are sampled and that decision is honored downstream.
    • spring.sleuth.sampler.probability=1.0
  • The below pattern adds trace and span identifiers into log output
    • logging.pattern.level=%d{ABSOLUTE} [%X{X-B3-TraceId}/%X{X-B3-SpanId}] %-5p [%t] %C{2} - %m%n

Going further

Sleuth layers on the Brave project, so can re-use any code that works with brave. It can also use transports besides http to send data to a Zipkin compatible service. Here are a few small examples that showcase commonly requested features:

Apache Http Client Tracing

This changes the example to use Apache HttpClient instead of RestTemplate to make the call from the frontend to the backend.

https://github.com/openzipkin/brave/tree/master/instrumentation/httpclient

MySQL Tracing

This changes the example to read the timestamp from MySQL instead of the Spring Boot Process. It adds a brave tracing interceptor to add details to the existing trace.

https://github.com/openzipkin/brave/tree/master/instrumentation/mysql

RabbitMQ Tracing

This changes the example to invoke the backend with RabbitMQ instead of WebMVC. Sleuth automatically configures Brave's spring-rabbit to add trace details.

https://github.com/openzipkin/brave/tree/master/instrumentation/spring-rabbit

Dubbo Tracing

This changes the example to call a Dubbo backend instead of WebMVC. It uses Brave's RPC filter to add details to the existing trace.

https://github.com/openzipkin/brave/tree/master/instrumentation/dubbo-rpc

Customizing with OpenTracing

This changes the example to add a lookup tag using the default SpanCustomizer and OpenTracing's Tracer api. Users can choose which api makes most sense for them to expose to business code.

Under the covers, this uses the brave-opentracing bridge:

https://github.com/openzipkin-contrib/brave-opentracing