khakhlyuk opened a new pull request, #52973:
URL: https://github.com/apache/spark/pull/52973
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### What changes were proposed in this pull request?
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This PR adds several fixes and improvements over
https://github.com/apache/spark/pull/52613 which added support for 2GB+ local
relations in Spark Connect.
#### Uploading batches of chunks
Currently, before caching the local relation on the server via
`ChunkedCachedLocalRelation`, the client materializes all chunks in memory (1
schema chunk and N data chunks). This can lead to high memory pressure on the
client when uploading very large local relations.
In this PR, I'm changing how the client uploads the local relation. Instead
of materializing all chunks in memory, the client will materialize a batch of
chunks in memory, upload the batch of chunks to the server, and proceed to
collecting the next batch of chunks. The size of the batch of chunks is
controlled via `spark.sql.session.localRelationBatchOfChunksSizeBytes` (1GB by
default). This way, the uploading mechanism only consumes 1GB of memory at each
point in time. Alternatives to this approach are:
a) uploading each chunk separately - would require one pair of
`ArtifactStatuses` and `AddArtifactsRequest` RPC calls for each chunk, which is
inefficient.
b) (current implementation) materializing all chunks in memory and uploading
them via a single pair of `ArtifactStatuses` and `AddArtifactsRequest` RPC
calls. This can lead to high memory pressure on the client.
Uploading batches of chunks is a middle-ground solution.
Changes are implemented both for the python and scala clients.
#### Minor fixes and improvements
- Replace `ArraySeq.unsafeWrapArray(data.map(_.copy()).toArray))` with
`data.map(_.copy()).toArray.toImmutableArraySeq` in
`SparkConnectPlanner.scala`. The latter is compatible with both scala 2.13 and
scala 2.12 and is consistent with how arrays are converted to sequences in
other places in the code base.
- Improved asserts and tests in the python client.
### Why are the changes needed?
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Reduce memory pressure in the spark connect python and scala clients when
uploading very large local relations to the server.
### Does this PR introduce _any_ user-facing change?
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No
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Existing tests.
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No
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