A Step‑by‑Step Debugging Journey of Data Drop After a Feature Release

1. Confirm the problem’s authenticity? The data team reported a severe drop in a specific metric after a new feature launch, prompting an immediate investigation of request and actual delivery volumes.

2. Review code and compare with existing functionality. Initial checks found no obvious code errors, but a hidden bug was eventually discovered and fixed, though the issue persisted.

3. Involve the DBA to monitor data changes. DBA‑generated statistics showed little variation, leading to further manual testing that produced intermittent results.

4. Perform local debugging. Local tests required VPN access but ran successfully, highlighting the need to start troubleshooting from a controlled environment.

5. Compare production and test environment configurations. Extensive checks for file differences and configuration mismatches yielded no evidence of a production‑only problem.

6. Add comprehensive logging. Enhanced logs revealed occasional mis‑logged parameters (e.g., memory addresses) but still did not resolve the inconsistency.

7. Capture network traffic. Using tcpdump and lsof , the team 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 application and Kafka services produced mixed success, with some requests still failing.

9. Switch asynchronous calls to synchronous. Converting to synchronous requests made the system slower but allowed Kafka calls to succeed; reverting back restored performance but re‑introduced failures.

10. Conduct load testing in the test environment. A shell script ( for i in $(seq 1 N); do nohup a.sh > /dev/null 2>&1 &; done ) simulated high concurrency without reproducing the issue, suggesting the problem was not pure load.

11. Re‑examine the code collaboratively. Multiple code reviews yielded no further clues.

12. Issue direct command‑line requests to Kafka. Direct Kafka client requests succeeded instantly, while the application‑level requests failed, reinforcing doubts about the code path.

13. Re‑inspect the data. Unexpectedly, the metric recovered after a Kafka restart, indicating a Kafka‑related cause.

14. Identify the root cause. The final analysis revealed that an oversized topic request volume combined with an undersized partition count caused throughput degradation; increasing the partition count restored normal data flow.