Debugging a Kafka Data Drop: A Step‑by‑Step Troubleshooting Case Study

After a recent feature release caused a sharp decline in a key data metric, the team embarked on a systematic, fourteen‑step troubleshooting process that can serve as a reference methodology.

1. Verify the problem’s reality – The data team reported a severe drop, prompting us to compare request volumes and actual landed data to confirm the issue.

2. Review code differences with experienced colleagues – Initial checks did not prove a code fault, but a hidden bug was eventually discovered and fixed.

3. Involve the DBA to monitor data changes – DBA‑generated statistics showed little variation, suggesting the issue wasn’t simply time‑related.

4. Perform local debugging – Despite the inconvenience of VPN access, local tests ran successfully, indicating the problem was not purely online.

5. Compare online and test environment configurations – We examined every file and timestamp difference, but could not find evidence of a configuration error; moving everything to the test environment worked fine.

6. Adjust code for online debugging – Added comprehensive logging, but initial logs printed memory addresses instead of useful parameters.

7. Capture network traffic – Using tcpdump and lsof, we confirmed that client requests reached the server and that long‑living connections were healthy, shifting suspicion to the server side.

8. Restart services – Multiple restarts of the service and Kafka yielded inconsistent results, with some requests still failing.

9. Switch asynchronous requests to synchronous – Converting to sync made requests slower but allowed Kafka calls to succeed; however, the performance impact was unacceptable, so we reverted.

10. Conduct load testing in the test environment – A simple shell script (

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

) simulated high concurrency without reproducing the failure, suggesting concurrency was not the root cause.

11. Re‑examine the code thoroughly – Multiple code reviews by several developers still did not reveal the issue.

12. Issue direct command‑line requests to Kafka – Using both the application code and Kafka’s native request tool produced different results; the native tool responded in milliseconds, casting further doubt on the application code.

13. Re‑inspect the data – While waiting, the data metric unexpectedly recovered.

14. Identify the fundamental cause – The recovery was traced to a Kafka restart; the real problem was that the topic’s request volume exceeded the capacity of its small partition count, causing throughput throttling. Enlarging the partition size restored normal operation.