Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20414 > Not quite - coalesce will not combine partitions across executors (aka shuffle) so you could still end up having many many files. I'm not sure if I follow here. For `coalesce(1)` Spark just launches a single task to handle all the data partitions. If the final action is saving file, we still have only one file at the end. Compared to `repartition(1)`, I think the only difference is the cost of task retry. I think we can special case `repartition(1)`, if there is only one reducer, we don't need to add the local sort.
--- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org