[CORE]ZipPartitions for arbitrary number of RDDs. #49659
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| def fn: (Iterator[T], Iterator[U1], Iterator[U2]) => Iterator[V] = | ||
| (t: Iterator[T], u1: Iterator[U1], u2: Iterator[U2]) => | ||
| f.call(t.asJava, u1.asJava, u2.asJava).asScala | ||
| JavaRDD.fromRDD( |
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It can be easily worked around. I wouldn't add this also considering that we're being conservative on RDD API
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Is this comment in regards to the entire PR or just the changes in JavaRDDLike? My long term goal was to add additional cogroup methods for 3, 4, and N number of KeyValueGroupedDatasets. I do not need all the logic from this PR for that goal, but thought it was a good small step in that direction.
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Here is what my long term might look like master...kyle-winkelman:spark:everything (might have some noise in it, but it adds additional cogroup methods and SPARK-42349). If you would prefer I attempt to go straight for the big PR that does all the changes at once, I can repurpose this PR to target those changes.
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Why don't we use Dataset instead? We're promoting it over RDD API actually.
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Closing, will open a new PR with logic for cogroup. |
What changes were proposed in this pull request?
Why are the changes needed?
RDD.zipPartitions currently only allows up to 3 other RDDs to be zipped. This forces users to zipPartitions multiple times to zip more than 4 RDDs.
Also, the Java API only allows zipPartitions with 1 other RDD, so this brings parity between the two.
Does this PR introduce any user-facing change?
Yes, new zipPartitions functions in RDD and JavaRDD.
How was this patch tested?
Added unit tests.
Was this patch authored or co-authored using generative AI tooling?
No