I'm facing a severe performance issue on a job that suddenly(no code changes) takes 4x time to complete, after debugging and investigating I found most of the data is read to a single executor(13 GB one executor vs 200 MB the rest). Initially i thought it was an classical uneven partitions issue so i started to test different partition numbers (and criteria) but the issue was not fixed, I did a rows per partition analysis and found all partitions have similar number of rows so this is not the problem, it seems the scheduler assigns most partitions to a single executor instead of evenly, question is how spark decides which partitions go to which executor, and how to control that behavior to make distribution even?  I asked this on SO too: https://stackoverflow.com/questions/67133177/how-spark-distributes-partitions-to-executors ... View more