Github user chpritchard-expedia commented on the issue: https://github.com/apache/spark/pull/16347 @rxin - sortBy is somewhat tied in with bucketing, which is also a little difficult to work with. First, bucketing often relies on a column being present, whereas in Hive (and with repartition), I may use a formula, to split the data into appropriate buckets that are evenly distributed. Overall is not well supported throughout the ecosystem. Even with all of that, Spark doesn't particularly support semantics to say that a data set is already sorted. In Hive, I've had to do a lot of PARTITION BY(datefield, bucket) CLUSTERED BY (key) INTO 1 SORTED BY (key) INTO 1 BUCKETS. That gets us stable totally sorted files, for GUIDs. In the case of Spark, this issue of partitionBy destroying sorting is a painful bug. I'm now using some very large data sets, searching for keys, and instead of returning data in a few seconds (thanks to predicate pushdown with Parquet), it has to scan the entire files.
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