How to Use Spring Cloud Sleuth for Distributed Tracing

Table of Contents

1. Core Principles of Spring Cloud Sleuth
2. Design Philosophies behind Distributed Tracing
3. Performance Considerations
4. Idiomatic Patterns in Spring Cloud Sleuth
5. Java Code Examples
6. Common Trade - offs and Pitfalls
7. Best Practices and Design Patterns
8. Real - World Case Studies
9. Conclusion
10. References

Core Principles of Spring Cloud Sleuth

Trace and Span

Spring Cloud Sleuth is based on the concepts of traces and spans. A trace represents the entire journey of a request through multiple services. It is identified by a unique traceId. A span, on the other hand, is a single unit of work within a trace. Each span has its own spanId and is associated with a particular operation, such as a database call or an API call.

Propagation

Spring Cloud Sleuth propagates trace and span information across different services. It does this by injecting special headers into the HTTP requests. These headers carry the traceId and spanId, allowing the receiving service to continue the trace.

Sampling

Since tracing can generate a large amount of data, Spring Cloud Sleuth supports sampling. Sampling determines which requests should be traced. By default, it samples a certain percentage of requests to balance the need for tracing with the overhead it incurs.

Design Philosophies behind Distributed Tracing

End - to - End Visibility

The primary goal of distributed tracing is to provide end - to - end visibility of a request. With Spring Cloud Sleuth, developers can see how a request flows through different services, which operations are taking the most time, and where potential bottlenecks might be.

Decoupling

Spring Cloud Sleuth is designed to be decoupled from the business logic of the application. It uses aspect - oriented programming (AOP) to inject tracing code without modifying the core business code significantly. This allows developers to add tracing capabilities to existing applications with minimal effort.

Interoperability

Spring Cloud Sleuth is designed to work with other distributed tracing systems such as Zipkin. It can export trace data to these systems for further analysis and visualization.

Performance Considerations

Overhead

Tracing adds some overhead to the application. The injection of headers, the creation of spans, and the logging of tracing information all consume resources. However, the overhead can be minimized through proper sampling. By sampling only a subset of requests, the performance impact can be kept to a minimum.

Memory and CPU Usage

The creation and management of traces and spans require memory. If the tracing volume is too high, it can lead to increased memory usage and potentially cause performance issues. Additionally, the CPU is used to perform operations such as generating unique IDs and injecting headers.

Idiomatic Patterns in Spring Cloud Sleuth

Custom Spans

Developers can create custom spans to trace specific parts of the application. For example, if there is a complex business logic that involves multiple steps, a custom span can be created to measure the time taken by this logic.

Tagging Spans

Spans can be tagged with additional information. This information can be used for filtering and analysis. For example, a span can be tagged with the user ID or the type of operation being performed.

Error Handling

When an error occurs within a span, it should be properly marked as an error. This allows for easy identification of failed requests in the tracing data.

Java Code Examples

1. Adding Spring Cloud Sleuth to a Spring Boot Application

// First, add the Spring Cloud Sleuth dependency to your pom.xml
// <dependency>
//     <groupId>org.springframework.cloud</groupId>
//     <artifactId>spring-cloud-starter-sleuth</artifactId>
// </dependency>

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class MyApp {
    public static void main(String[] args) {
        SpringApplication.run(MyApp.class, args);
    }
}

In this example, we simply add the Spring Cloud Sleuth dependency to a Spring Boot application. Spring Cloud Sleuth will automatically start tracing requests.

2. Creating a Custom Span

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import brave.Tracer;
import brave.Span;

@RestController
public class MyController {

    @Autowired
    private Tracer tracer;

    @GetMapping("/customSpan")
    public String customSpan() {
        // Create a custom span
        Span customSpan = tracer.nextSpan().name("customBusinessLogic").start();
        try (Tracer.SpanInScope ws = tracer.withSpanInScope(customSpan)) {
            // Simulate some business logic
            try {
                Thread.sleep(200);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        } finally {
            // Finish the span
            customSpan.finish();
        }
        return "Custom span executed";
    }
}

In this code, we create a custom span to measure the time taken by a simulated business logic. We use the Tracer to create and manage the span.

3. Tagging a Span

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import brave.Tracer;
import brave.Span;

@RestController
public class TaggingController {

    @Autowired
    private Tracer tracer;

    @GetMapping("/taggedSpan")
    public String taggedSpan() {
        Span span = tracer.nextSpan().name("taggedOperation").start();
        try (Tracer.SpanInScope ws = tracer.withSpanInScope(span)) {
            // Tag the span
            span.tag("operation.type", "complexCalculation");
            // Simulate some work
            try {
                Thread.sleep(150);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        } finally {
            span.finish();
        }
        return "Tagged span executed";
    }
}

Here, we create a span and tag it with the type of operation being performed.

Common Trade - offs and Pitfalls

Sampling Rate

Choosing the right sampling rate is a trade - off. A low sampling rate may miss important requests, while a high sampling rate can lead to excessive overhead.

Incorrect Error Handling

If errors are not properly marked in the spans, it can be difficult to identify failed requests in the tracing data.

Inconsistent Tagging

If spans are not tagged consistently, it can make filtering and analysis difficult.

Best Practices and Design Patterns

Use Sampling Wisely

Choose a sampling rate based on the application’s traffic and the importance of tracing. For production applications, a lower sampling rate may be appropriate to minimize overhead.

Standardize Tagging

Develop a standard set of tags for spans. This will make it easier to analyze and filter the tracing data.

Error Reporting

Ensure that errors are properly marked in the spans. This will help in quickly identifying and debugging issues.

Real - World Case Studies

E - commerce Application

In an e - commerce application, Spring Cloud Sleuth can be used to trace the flow of a customer order. From the moment the order is placed to the time it is fulfilled, each step can be traced. This helps in identifying bottlenecks in the order processing system, such as slow database queries or long - running business logic.

Financial Services

In a financial services application, distributed tracing can be used to ensure compliance and security. For example, a request to transfer funds can be traced to ensure that all the necessary authorization steps are followed and that there are no security breaches.

Conclusion

Spring Cloud Sleuth is a powerful tool for distributed tracing in Java applications. It provides end - to - end visibility of requests, is designed with decoupling and interoperability in mind, and can be used with minimal performance impact through proper sampling. By following best practices and using idiomatic patterns, developers can effectively use Spring Cloud Sleuth to build robust and maintainable applications.

References

1. Spring Cloud Sleuth Documentation: https://spring.io/projects/spring-cloud-sleuth
2. Brave Documentation: https://github.com/openzipkin/brave
3. Zipkin Documentation: https://zipkin.io/pages/quickstart.html