Effective Debugging Tips for Java Developers(Spring Boot in a Microservices Architecture)

Microservices bring agility and scalability to application development, but they also come with their own set of challenges in debugging due to the distributed nature of the architecture. Here, we’ll explore practical debugging techniques with real-world scenarios and step-by-step solutions.

Scenario 1: A Service Is Timing Out

Problem: One microservice calls another, but the request frequently times out.

Steps to Debug:

1. Identify the Cause: Check the logs for timeout errors in the calling service and verify the latency using Actuator’s /metrics endpoint.
2. Network Check: Use curl or Postman to directly call the downstream service and measure the response time.
3. Check Thread Pools: Monitor thread pool usage in the downstream service (e.g., @Async thread pools or Tomcat thread pools).
4. Fix: Optimize thread pool configurations and increase timeout settings in application.properties (spring.web.client.connect-timeout).
5. Validation: Retest the endpoint after making changes.

Scenario 2: Inconsistent Data Between Microservices

Problem: Data retrieved from one service is inconsistent with another.

Steps to Debug:

1. Trace the Flow: Enable distributed tracing (e.g., Sleuth/Zipkin) to follow the data journey.
2. Check for Cache Issues: Inspect if the data inconsistency stems from stale cache.
3. Validate Database: Query the databases of both services to verify the data.
4. Fix: Implement proper cache invalidation or use @CacheEvict annotations where necessary.
5. Retest: Run end-to-end tests to ensure consistency.

Scenario 3: Circuit Breaker Is Not Opening

Problem: Circuit breaker configuration isn’t working, leading to cascading failures.

Steps to Debug:

1. Verify Configuration: Check the circuit breaker properties in the application configuration.
2. Test Scenarios: Simulate failure in the downstream service to test circuit breaker behavior.
3. Inspect Fallbacks: Ensure fallback methods are properly implemented.
4. Fix: Adjust thresholds (failureRateThreshold, slidingWindowSize) in Resilience4j or Hystrix configuration.
5. Monitor: Use Actuator or Resilience4j’s metrics to confirm the circuit breaker triggers as expected.

Scenario 4: Service Discovery Failure

Problem: Services fail to register or discover other services in Eureka/Consul.

Steps to Debug:

1. Verify Registration: Check the service registration status on the discovery server.
2. Inspect Network: Ensure that the discovery server is accessible from the service.
3. Check Configuration: Review eureka.client.service-url.defaultZone or equivalent configuration for typos or incorrect URLs.
4. Fix: Correct configurations or restart the discovery service if necessary.
5. Validate: Use the discovery server UI or API to confirm the service is listed.

Scenario 5: API Gateway Not Forwarding Requests

Problem: The API gateway fails to route requests to downstream services.

Steps to Debug:

1. Check Routing Configurations: Validate routes in application.yml for Spring Cloud Gateway.
2. Inspect Logs: Look for error logs in the gateway service.
3. Test Downstream Services: Manually call downstream services to ensure they are operational.
4. Fix: Update routing configurations or add missing predicates.
5. Test Again: Use Postman to confirm the gateway routes requests correctly.

Scenario 6: High CPU Usage in One Service

Problem: A microservice consumes unusually high CPU, affecting performance.

Steps to Debug:

1. Check Logs: Look for repeated exceptions or expensive operations.
2. Use a Profiler: Attach a profiler like VisualVM or JProfiler to identify hot spots.
3. Inspect Thread Dumps: Analyze thread dumps for threads stuck in a loop.
4. Fix: Optimize the code causing the issue or adjust resource-heavy queries.
5. Retest: Verify CPU usage returns to normal under load.

Scenario 7: Authentication Errors Across Services

Problem: Services using OAuth2 or JWT tokens fail to authenticate.

Steps to Debug:

1. Verify Token: Decode the JWT token (e.g., with jwt.io) and check expiration or claims.
2. Inspect Configurations: Ensure spring.security.oauth2.resourceserver or token verification configurations are correct.
3. Validate Key: Verify the public key/certificate used to validate the token.
4. Fix: Correct the configurations or update the token issuer settings.
5. Test: Retry authentication and validate token exchange.

Scenario 8: Service Scaling Causes Failures

Problem: Errors arise when scaling services (e.g., duplicate messages or missed events).

Steps to Debug:

1. Inspect Load Balancer: Check how requests are distributed among instances.
2. Validate Idempotency: Ensure that endpoints and database operations are idempotent.
3. Review Event Processing: Check Kafka/Message Broker configurations for duplicate delivery handling.
4. Fix: Add idempotent keys or deduplication logic.
5. Monitor: Test with scaled-up instances and ensure stability.

Scenario 9: Logs Missing for Critical Errors

Problem: Critical errors are not logged, making debugging harder.

Steps to Debug:

1. Review Log Levels: Ensure log levels in logback-spring.xml or application.properties are set appropriately.
2. Enable Stack Traces: Verify that stack traces are included in the logs.
3. Check External Systems: Ensure centralized logging tools like ELK/Graylog are configured correctly.
4. Fix: Add proper error-handling logic and logging in @ExceptionHandler.
5. Retest: Simulate errors and confirm logs are recorded.

Scenario 10: Integration Tests Fail Randomly

Problem: Integration tests fail intermittently due to test data or service dependencies.

Steps to Debug:

1. Inspect Test Logs: Look for common patterns in failed tests.
2. Check Dependencies: Verify mocked dependencies or test containers are starting correctly.
3. Validate Data: Ensure test data is correctly set up and isolated.
4. Fix: Use transactional tests or reset the state after each test.
5. Monitor: Run tests multiple times to ensure stability.

Takeaway Tips for Effective Debugging

1. Use Spring Boot Actuator: Expose and monitor health checks, metrics, and logs in real time.
2. Centralize Logs: Use ELK, Graylog, or Splunk for consolidated logging and error tracking.
3. Enable Distributed Tracing: Integrate tools like Sleuth, Zipkin, or Jaeger to trace requests across services.
4. Invest in Monitoring: Tools like Prometheus and Grafana can provide insights into resource usage and service health.
5. Test Resilience: Simulate failures using chaos testing tools like Chaos Monkey for Spring Boot.
6. Automate Testing: Use integration and contract testing to prevent regression issues.
7. Document Debugging Steps: Maintain a playbook of common scenarios and debugging strategies.

By following these practical tips and approaches, debugging in a Spring Boot microservices environment becomes more efficient and systematic.

— — — -

I hope you enjoyed reading this practical guide, please do share thoughts/feedback about it.