This is bread and butter Spark stuff but I still fumbled the ordering a bit.
Start by explaining how you would confirm data skew through Spark UI metrics and logs, then outline a systematic diagnosis process. Next, present a prioritized set of fixes, from tuning to algorithmic changes, and discuss trade-offs for each.
Pro tip: Mention that skew often stems from null keys or hot keys; handling nulls separately and salting hot keys are quick wins. Also, emphasize monitoring and iterative testing to validate fixes.
Use Spark UI to inspect stage/task metrics: look for tasks with disproportionately large shuffle read/write, long durations, or high GC time. Check executor logs for OOM errors or spills.
Determine which key(s) are skewed by sampling data or examining key distribution. Common causes: null keys, hot keys (e.g., popular user IDs), or uneven partitioning.
For null keys, filter or replace with random salts. For hot keys, use salting (add random prefix) to distribute load, or increase parallelism via repartitioning.
If skew persists, consider broadcast joins for small tables, custom partitioners, or splitting the job into two stages (e.g., pre-aggregate skewed keys).
After applying fixes, re-run the job and verify even task distribution. Set up monitoring/alerting for future skew detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.