So basically I have a python spark job that reads some simple json files, and then tries to write them as orc files partitioned by one field. The partition is not very balanced, as some keys are really big, and other really small.
I had memory issues when doing something like this:
events.write.mode('append').partitionBy("type").save("s3n://mybucket/tofolder"), format="orc")
Adding memory to the executors didn't seem to have any effect, but I solved it increasing the driver memory. Does this mean that all the data is being send to the driver for it to write? Can't each executor write its own partition? Im using Spark 2.0.1
Even if you partition dataset and then write it on storage there is no possibility that records are sent to the driver. You should look at logs of memory issues (if they occur on driver on or executors) to figure out exact reason of failing.
Probably your driver has too low memory to handle this write because of previous computations. Try decreasing spark.ui.retainedJobs and spark.ui.retainedStages to save memory on old jobs and stages metadata. If this won't help, connect to driver with jvisualvm to find job/stage than consumes large heap fragments and try to optimize.