mosche opened a new pull request, #24288:
URL: https://github.com/apache/beam/pull/24288
The Spark dataset runner currently materializes and broadcasts
`PCollections` used as side-input per consuming `ParDo`.
If a `PCollection` is used as side-input for multiple `ParDos`, it would be
evaluated, collected (on the driver) and finally be broadcasted repeatedly for
every such `ParDo` (#24035).
To avoid this the broadcast variable is added to `TranslationResult` and
will be reused if needed.
Additionally, on the executor side, memory was wasted because we kept both
the broadcasted binary data as well as a cached representation of the
deserialized values in memory.
This implementation uses Kryo to broadcast the binary data. During
deserialization the binary shape is dropped and only deserialized values are
kept. Caching is done per partition for `multimap` materialization (using a lru
cache). `iterable` materialization is a no-op as serialization is was already
done.
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