Debugging Kafka Data Drop: A Step‑by‑Step Troubleshooting Story

After a feature release caused a sharp decline in data volume, the team embarked on a systematic investigation to identify the root cause.

1. Verify the problem's reality – Confirmed by the data team that the drop was severe and coincided with the new release, prompting a thorough data‑volume comparison.

2. Review code and compare with existing functionality – Initial blind checks revealed a hidden bug that was quickly fixed, yet the issue persisted.

3. Consult the DBA – Requested real‑time data statistics, but observed no significant change, leading to further suspicion.

4. Perform local debugging – Tested locally via VPN, confirming the code behaved correctly in a controlled environment.

5. Compare production and test environment configurations – Searched for any file or setting differences; none provided conclusive evidence.

6. Add detailed logging in production – Enhanced logs to capture full request details, but discovered that some parameters were logged as memory addresses, complicating analysis.

7. Capture network traffic – Used tcpdump and lsof to verify that client requests reached the server and that long‑living connections were healthy, indicating the issue lay on the server side.

8. Restart services – Restarted the application and Kafka services; intermittent successes suggested the problem persisted.

9. Switch asynchronous calls to synchronous – Modified the request flow to synchronous, which slowed user experience but allowed Kafka requests to succeed, confirming the async path was problematic.

10. Conduct load testing in the test environment – Executed a shell script to simulate concurrent requests (e.g.,

for i in $(seq 1 N); do nohup a.sh > /dev/null 2>&1 &; done

); no failures were observed, ruling out concurrency as the cause.

11. Re‑inspect the code thoroughly – Multiple reviews by several developers yielded no additional findings.

12. Issue direct command‑line requests to Kafka – Compared results from the application code versus Kafka’s native request tool; the native tool responded in milliseconds while the code failed, deepening suspicion of the application layer.

13. Re‑examine data trends – Noted that data volume recovered unexpectedly after a Kafka restart.

14. Identify the root cause – Determined that excessive topic request volume combined with an undersized partition count caused throughput degradation; increasing the number of partitions restored normal operation.