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As everyone knows partitioners in Spark have a huge performance impact on any "wide" operations, so it's usually customized in operations. I was experimenting with the following code:

val rdd1 = sc.parallelize(1 to 50).keyBy(_ % 10) .partitionBy(new HashPartitioner(10)) val rdd2 = sc.parallelize(200 to 230).keyBy(_ % 13) val cogrouped = rdd1.cogroup(rdd2) println("cogrouped: " + cogrouped.partitioner) val unioned = rdd1.union(rdd2) println("union: " + unioned.partitioner)

I see that by default cogroup() always yields an RDD with the customized partitioner, but union() doesn't, it will always revert back to default. This is counterintuitive as we usually assume that a PairRDD should use its first element as partition key. Is there a way to "force" Spark to merge 2 PairRDDs to use the same partition key?

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union is a very efficient operation, because it doesn't move any data around. If rdd1 has 10 partitions and rdd2 has 20 partitions then rdd1.union(rdd2) will have 30 partitions: the partitions of the two RDDs put after each other. This is just a bookkeeping change, there is no shuffle.

But necessarily it discards the partitioner. A partitioner is constructed for a given number of partitions. The resulting RDD has a number of partitions that is different from both rdd1 and rdd2.

After taking the union you can run repartition to shuffle the data and organize it by key.

There is one exception to the above. If rdd1 and rdd2 have the same partitioner (with the same number of partitions), union behaves differently. It will join the partitions of the two RDDs pairwise, giving it the same number of partitions as each of the inputs had. This may involve moving data around (if the partitions were not co-located) but will not involve a shuffle. In this case the partitioner is retained. (The code for this is in PartitionerAwareUnionRDD.scala.)

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There's actually a partitioner-aware union RDD that I think is supposed to be used automatically in cases where partitioning could be preserved; not sure why it's not applied here. See github.com/apache/spark/blob/… and github.com/apache/spark/blob/master/core/src/main/scala/org/…
Wow, cool! Never knew about that. Looks like it's only used when both RDDs have the same partitioner. I'll add that to the answer, thanks!
Thanks a lot! This is a very important optimization. BTW if this is not optimal for all cases I can always write a zip + in-partition union anyway
Almost everything uses a HashPartitioner. So if your DataFrames have the same number of partitions, I would hope that would be enough. You can just print df.partitioner and df.partitions to see what's happening.
Just to add that the correct commands are df.rdd.partitioner and df.rdd.getNumPartitions. Do have any idea why my DFs don't have any partitioner (None) even when I am repartitioning them?
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This is no longer true. Iff two RDDs have exactly the same partitioner and number of partitions, the unioned RDD will also have those same partitions. This was introduced in https://github.com/apache/spark/pull/4629 and incorporated into Spark 1.3.

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