Question

A data engineer notices that a Spark job processing a large aggregation is running much slower than expected. In the Spark UI, they open the Stages tab and see that several tasks in the aggregation stage show large values under the 'Spill (Memory)' and 'Spill (Disk)' columns, while task durations vary only slightly. What does this most likely indicate, and what is an appropriate first action?

Accepted Answer

The executors do not have enough memory to hold shuffle/aggregation data, so it is being written to disk; increasing executor memory or reducing per-task data volume can reduce spill.

Answer

There is severe data skew, so the correct fix is to add a broadcast join hint to the aggregation.

Answer

The Delta table has too many small files, so running OPTIMIZE will eliminate the spill.

Answer

The job is failing due to a corrupted checkpoint, so the checkpoint directory must be deleted and the job restarted.