How do you implement distributed tracing with Spring Cloud Sleuth?

Table of Contents

• Introduction
• What is Distributed Tracing?
• Steps to Implement Distributed Tracing with Spring Cloud Sleuth
  • 1. Add Dependencies
    • 1.1. Maven Dependencies
    • 1.2. Gradle Dependencies
  • 2. Configure Spring Cloud Sleuth
    • 2.1. Example Configuration for Zipkin
    • 2.2. Example Configuration for OpenTelemetry
  • 3. Enable Tracing in Your Application
    • 3.1. Example of Trace Context in Logs
    • 3.2. Injecting Trace Data in HTTP Requests
  • 4. Visualizing Traces
    • 4.1. Zipkin Setup
    • 4.2. OpenTelemetry Setup
  • 5. Customizing Tracing
    • 5.1. Creating Custom Spans
  • 6. Handling Performance and Sampling
    • 6.1. Adjust Sampling Rate
• Conclusion

Introduction

In modern microservice architectures, distributed tracing is essential for monitoring, debugging, and gaining visibility into how requests flow through multiple microservices. It helps in tracking the lifecycle of a request as it moves across various services and systems, identifying bottlenecks, latencies, and potential errors. Spring Cloud Sleuth is a powerful library that integrates distributed tracing into Spring Boot applications, making it easy to trace requests across microservices.

In this article, we'll explore how to implement distributed tracing in your Spring Boot microservices using Spring Cloud Sleuth, and how to visualize and analyze the traces using tools like Zipkin or OpenTelemetry.

What is Distributed Tracing?

Distributed tracing is a method of tracking the flow of a request as it moves across different services in a distributed system. Each service that handles the request adds its own unique trace data (such as the time it took to process a request). The end result is a trace that provides a visual representation of the journey of the request through the system. This data can be invaluable for debugging, performance analysis, and troubleshooting.

Spring Cloud Sleuth simplifies this by adding trace and span IDs to the logs and HTTP requests, allowing you to follow the request's lifecycle across microservices.

Steps to Implement Distributed Tracing with Spring Cloud Sleuth

1. Add Dependencies

To enable distributed tracing in your Spring Boot applications, you need to include Spring Cloud Sleuth in your project dependencies. Additionally, if you plan to use Zipkin (a popular tracing tool) or OpenTelemetry for visualization, you can include those dependencies as well.

1.1. Maven Dependencies

For Spring Cloud Sleuth and Zipkin, add the following dependencies in your pom.xml:

For OpenTelemetry, which is an alternative to Zipkin, you can add:

1.2. Gradle Dependencies

For Gradle, add these dependencies in your build.gradle file:

2. Configure Spring Cloud Sleuth

Spring Cloud Sleuth automatically adds tracing to your application, but you may want to configure it according to your specific needs. You can configure Sleuth through the application.yml (or application.properties) file.

2.1. Example Configuration for Zipkin

If you're using Zipkin to visualize the traces, you need to provide the Zipkin server URL in your application.yml:

• sampler.probability: Defines the percentage of requests to trace. Set to 1.0 for 100% sampling, or adjust based on your needs.
• zipkin.baseUrl: The URL of the Zipkin server where traces will be sent.

2.2. Example Configuration for OpenTelemetry

If you're using OpenTelemetry for tracing, the configuration would look slightly different. For example:

3. Enable Tracing in Your Application

Once Spring Cloud Sleuth is added and configured, it automatically adds tracing functionality to your application. It will inject trace and span IDs into the logs, HTTP headers, and other relevant places.

3.1. Example of Trace Context in Logs

By default, Spring Cloud Sleuth adds trace information to your logs. This includes the trace ID and span ID, allowing you to correlate logs from different services:

Here, the log includes:

• Trace ID: 12345abcde
• Span ID: 12345xyz

This helps you correlate logs from different services for the same request.

3.2. Injecting Trace Data in HTTP Requests

Spring Cloud Sleuth also automatically adds trace information (such as trace ID and span ID) to the HTTP headers of outgoing requests. This allows the downstream services to continue the trace.

For example, if service A makes an HTTP request to service B, Spring Cloud Sleuth will add X-B3-TraceId, X-B3-SpanId, and X-B3-ParentSpanId headers to the HTTP request. Service B can then use these headers to join the same trace.

Here is an example of the request headers:

4. Visualizing Traces

Once distributed tracing is enabled, the next step is to visualize and analyze the traces. You can use a tracing tool like Zipkin or OpenTelemetry to see the traces and spans for requests.

4.1. Zipkin Setup

If you’re using Zipkin, it’s typically running on localhost:9411 by default. After you start your application and generate some traffic, you can visit http://localhost:9411 to view the traces.

Zipkin will show you a timeline of traces with details about each request, including which service processed it and the time taken for each service. You can drill down into individual spans to see more granular details about specific operations.

4.2. OpenTelemetry Setup

With OpenTelemetry, you can send traces to any compatible backend. You can use an OpenTelemetry-compatible trace visualization tool or use open-source options like Jaeger or Prometheus to analyze the traces.

5. Customizing Tracing

You can also customize tracing behavior by using Spring Cloud Sleuth’s API. For instance, you can create custom spans, or add tags to an existing span.

5.1. Creating Custom Spans

You can manually create a custom span in your service using the Tracer bean:

This custom span can be visualized in Zipkin or OpenTelemetry as part of the trace.

6. Handling Performance and Sampling

Distributed tracing can add overhead to your application, especially if the tracing sample rate is set to 100%. You can adjust the sampling rate based on the environment (production vs. development) to balance between tracing coverage and performance.

6.1. Adjust Sampling Rate

In your application.yml, you can configure the sampling rate:

For production environments, you might choose a lower sampling rate, while in development or staging, you might use a higher rate for more detailed tracing.

Conclusion

Distributed tracing with Spring Cloud Sleuth is an invaluable tool for monitoring and troubleshooting microservices-based applications. By automatically adding trace and span data to logs and HTTP requests, Spring Cloud Sleuth makes it easy to follow the journey of a request through various services, detect bottlenecks, and understand system performance.

With integration options for tracing tools like Zipkin and OpenTelemetry, you can visualize the traces, analyze request lifecycles, and optimize your microservices for better reliability and performance. Using Spring Cloud Sleuth for distributed tracing enhances observability and helps you better manage complex distributed systems.