Hi all, I'm trying to understand the impact of coalesce operation on spark job performance.
As a side note: were are using emrfs (i.e. aws s3) as source and a target for the job. Omitting unnecessary details job can be explained as: join 200M records Dataframe stored in orc format on emrfs with another 200M records cached Dataframe, the result of the join put back to emrfs. First DF is a set of wide rows (Spark UI shows 300 GB) and the second is relatively small (Spark shows 20 GB). I have enough resources in my cluster to perform the job but I don't like the fact that output datasource contains 200 part orc files (as spark.sql.shuffle.partitions defaults to 200) so before saving orc to emrfs I'm doing .coalesce(10). >From documentation coalesce looks like a quite harmless operations: no repartitioning etc. But with such setup my job fails to write dataset on the last stage. Right now the error is OOM: GC overhead. When I change .coalesce(10) to .coalesce(100) the job runs much faster and finishes without errors. So what's the impact of .coalesce in this case? And how to do in place concatenation of files (not involving hive) to end up with smaller amount of bigger files, as with .coalesce(100) job generates 100 orc snappy encoded files ~300MB each. Thanks, Andrii
