Distributed Tracing Pattern in Spring Boot – Monitor End-to-End Flows

Introduction

In modern microservice architectures, requests span multiple services and components. Tracing a single request across these services can become extremely complex—especially when debugging latency, failures, or bottlenecks. This is where the Distributed Tracing Pattern comes in.

The Distributed Tracing Pattern allows you to track, log, and visualize the lifecycle of a request as it travels across services. This pattern enhances observability and accelerates troubleshooting in production systems.

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Core Intent and Participants

Intent:
To trace and monitor the flow of requests through various distributed components of an application.

Participants:

• Tracer/Agent – A tool like Spring Cloud Sleuth that adds trace and span IDs to logs.
• Collector – A system like Zipkin or Jaeger that collects tracing data.
• Visualizer – Dashboards that display trace timelines.
• Context Propagator – Middleware that ensures tracing metadata is passed downstream.

[ Client ] --> [ API Gateway ] --> [ Service A ] --> [ Service B ] --> [ Database ]
               [TraceID][SpanID]      [TraceID][SpanID]       ...

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Real-World Use Cases

• Debugging issues in multi-service chains
• Monitoring performance bottlenecks
• Identifying the source of failures
• Root cause analysis in incident investigations

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Implementation in Java with Spring Boot

1. Add Dependencies

<!-- Sleuth for Trace Propagation -->
<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>

<!-- Zipkin for Trace Collection -->
<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-zipkin</artifactId>
</dependency>

2. Configuration (application.yml)

spring:
  zipkin:
    base-url: http://localhost:9411
    enabled: true
  sleuth:
    sampler:
      probability: 1.0

3. Sample Service Method with Logging

@RestController
@RequestMapping("/orders")
public class OrderController {

    private final Logger logger = LoggerFactory.getLogger(OrderController.class);

    @GetMapping("/{id}")
    public ResponseEntity<String> getOrder(@PathVariable String id) {
        logger.info("Fetching order with ID: {}", id);
        return ResponseEntity.ok("Order: " + id);
    }
}

Logs will now include traceId and spanId for correlation.

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Pros and Cons

✅ Pros

• End-to-end visibility of requests
• Faster root cause analysis
• Integration with logging and metrics

❌ Cons

• Adds overhead to request processing
• Requires centralized infrastructure (e.g., Zipkin, Jaeger)
• Complexity increases with service count

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Anti-Patterns and Misuse

• Collecting traces without visualizing them (no ROI)
• Ignoring trace IDs in logs (breaks observability)
• Too frequent sampling → performance overhead

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Comparison with Related Patterns

Pattern	Purpose	Key Difference
Logging with Correlation	Attach ID to logs for filtering	Not visual, lacks timing context
Health Check	Detect if a service is up	Doesn't show inter-service call trace
Event Sourcing	Track state changes, not request flow	Different level of trace granularity

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Refactoring Legacy Code

To retrofit distributed tracing in a legacy monolith:

• Extract services incrementally
• Use a proxy or gateway to inject trace context
• Add Sleuth to Spring Boot components gradually

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Best Practices

• Always log traceId and spanId
• Use consistent header propagation (x-b3-traceid)
• Store tracing data for at least 7 days
• Visualize with tools like Zipkin or Grafana Tempo

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Real-World Analogy

Think of tracing like tracking a courier package. Each checkpoint scans the package and logs its status. Similarly, distributed tracing logs request hops across services.

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Java Language Features

• Records – Can be used to model trace metadata.
• Lambdas – Useful for passing tracing-aware callbacks.
• ThreadLocal – Used internally by Sleuth for context propagation.

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Conclusion & Key Takeaways

• Distributed Tracing provides deep visibility into microservice flows.
• Spring Boot + Sleuth + Zipkin is a popular stack.
• It’s essential for debugging, monitoring, and production reliability.

Key Takeaways:

• Use trace IDs to correlate logs.
• Configure a collector (Zipkin, Jaeger).
• Sample traces wisely for performance.

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FAQ – Distributed Tracing Pattern

1. What is distributed tracing?

Tracking the journey of a request across service boundaries.

2. How does Sleuth work in Spring Boot?

It intercepts requests and attaches trace and span IDs to logs.

3. Can I use Jaeger instead of Zipkin?

Yes, Jaeger is another distributed tracing platform.

4. What headers are used in tracing?

Standard B3 headers like X-B3-TraceId, X-B3-SpanId, etc.

5. Is it suitable for monoliths?

Not directly—it's meant for distributed systems, but can be partially adapted.

6. Does tracing affect performance?

Slightly, especially with full sampling—opt for partial sampling.

7. What if a service doesn’t propagate trace IDs?

That breaks the trace chain—make sure all services propagate headers.

8. How to view trace data?

Use UI tools like Zipkin, Jaeger, or Grafana Tempo.

9. Is Sleuth being deprecated?

Yes, as of Spring Cloud 2022, use Micrometer Tracing instead.

10. How is it different from metrics?

Metrics aggregate data; tracing shows per-request details.