How would you isolate and fix a skewed join in a PySpark job using Spark UI and AWS CloudWatch?

To isolate a skewed join in PySpark, start by inspecting the Spark UI. Look at the stage graph and identify the stage with the longest task. Drill down to the task details to see the partition sizes; a skewed join will show one partition with a much larger number of rows. Next, check CloudWatch logs for the executor that ran that task. Look for OOM errors or high memory usage spikes. Once you confirm the skew, you can mitigate it by salting the join key: add a random hash prefix to the key before the join, then perform the join and drop the salt after. Alternatively, you can broadcast the smaller dataset or repartition the data to balance the load. You can also use Spark’s skewed hint or partitionBy to force a more even distribution. After applying the fix, re‑run the job and verify that the task times are balanced and no OOM errors appear. This approach ensures the join is distributed evenly and the job completes successfully.