To implement distributed tracing using Zipkin in the given microservices architecture, you need to instrument each microservice to send trace data to Zipkin. Below are the steps and code snippets to integrate Zipkin with Microservice A, Microservice B, and Microservice C.
Prerequisites
Zipkin Server: Ensure you have a running Zipkin server. You can run it using Docker:
docker run -d -p 9411:9411 openzipkin/zipkin
Dependencies: Add the necessary dependencies to your microservices. For a Spring Boot application, you can use the Spring Cloud Sleuth and Zipkin dependencies.
Step-by-Step Implementation
1. Add Dependencies
Add the following dependencies to your pom.xml
for each microservice:
<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-sleuth-zipkin</artifactId>
</dependency>
2. Configure Zipkin
Add the following configuration to your application.properties
or application.yml
file for each microservice:
spring.zipkin.base-url=http://localhost:9411
spring.sleuth.sampler.probability=1.0
3. Instrument Microservices
Spring Cloud Sleuth automatically instruments your Spring Boot applications. You just need to ensure that the trace context is propagated across HTTP calls.
Microservice A
@RestController
public class ServiceAController {
@Autowired
private ServiceBClient serviceBClient;
@GetMapping("/serviceA")
public ResponseEntity<String> callServiceB() {
try {
return serviceBClient.callServiceB();
} catch (Exception e) {
log.error("Exception in Service A", e);
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service A");
}
}
}
@FeignClient(name = "serviceB", url = "http://localhost:8081")
public interface ServiceBClient {
@GetMapping("/serviceB")
ResponseEntity<String> callServiceB();
}
Microservice B
@RestController
public class ServiceBController {
@Autowired
private ServiceCClient serviceCClient;
@GetMapping("/serviceB")
public ResponseEntity<String> callServiceC() {
try {
return serviceCClient.callServiceC();
} catch (Exception e) {
log.error("Exception in Service B", e);
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service B");
}
}
}
@FeignClient(name = "serviceC", url = "http://localhost:8082")
public interface ServiceCClient {
@GetMapping("/serviceC")
ResponseEntity<String> callServiceC();
}
Microservice C
@RestController
public class ServiceCController {
@GetMapping("/serviceC")
public ResponseEntity<String> performAction() {
try {
// Some logic
if (errorCondition) {
throw new CustomException("Error in Service C");
}
return ResponseEntity.ok("Success in Service C");
} catch (CustomException e) {
log.error("Exception in Service C", e);
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service C");
}
}
}
4. Run and Test
- Start Zipkin Server: Ensure the Zipkin server is running.
- Start Microservices: Start Microservice A, B, and C.
- Make a Request: Call the endpoint of Microservice A (e.g.,
http://localhost:8080/serviceA
). - View Traces: Open the Zipkin UI (e.g.,
http://localhost:9411
) to view the traces and see how the request flows through the microservices.
Spring Cloud Sleuth, which integrates with Zipkin, automatically captures and propagates trace and span information, including errors.
However, you can enhance the error handling by explicitly adding error tags to the spans. This can be done by using the Tracer
API provided by Spring Cloud Sleuth.
Spring Cloud Sleuth, which integrates with Zipkin, automatically captures and propagates trace and span information, including errors.
However, you can enhance the error handling by explicitly adding error tags to the spans. This can be done by using the Tracer
API provided by Spring Cloud Sleuth.
Step-by-Step Implementation with Error Tagging
Instrument Microservices with Error Tagging
Microservice A
@RestController
public class ServiceAController {
@Autowired
private ServiceBClient serviceBClient;
@Autowired
private Tracer tracer;
@GetMapping("/serviceA")
public ResponseEntity<String> callServiceB() {
try {
return serviceBClient.callServiceB();
} catch (Exception e) {
log.error("Exception in Service A", e);
tracer.currentSpan().tag("error", e.getMessage());
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service A");
}
}
}
@FeignClient(name = "serviceB", url = "http://localhost:8081")
public interface ServiceBClient {
@GetMapping("/serviceB")
ResponseEntity<String> callServiceB();
}
Microservice B
@RestController
public class ServiceBController {
@Autowired
private ServiceCClient serviceCClient;
@Autowired
private Tracer tracer;
@GetMapping("/serviceB")
public ResponseEntity<String> callServiceC() {
try {
return serviceCClient.callServiceC();
} catch (Exception e) {
log.error("Exception in Service B", e);
tracer.currentSpan().tag("error", e.getMessage());
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service B");
}
}
}
@FeignClient(name = "serviceC", url = "http://localhost:8082")
public interface ServiceCClient {
@GetMapping("/serviceC")
ResponseEntity<String> callServiceC();
}
Microservice C
@RestController
public class ServiceCController {
@Autowired
private Tracer tracer;
@GetMapping("/serviceC")
public ResponseEntity<String> performAction() {
try {
// Some logic
if (errorCondition) {
throw new CustomException("Error in Service C");
}
return ResponseEntity.ok("Success in Service C");
} catch (CustomException e) {
log.error("Exception in Service C", e);
tracer.currentSpan().tag("error", e.getMessage());
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("Error in Service C");
}
}
}
Open the Zipkin UI (e.g., http://localhost:9411
) to view the traces and see how the request flows through the microservices. You should see error tags in the spans where exceptions occurred.
Conclusion
By following these steps, you can integrate Zipkin for distributed tracing in your microservices architecture. This setup will help you track the flow of requests and identify where exceptions occur, making it easier to debug and monitor your microservices.
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