c21 opened a new pull request #32198:
URL: https://github.com/apache/spark/pull/32198
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### What changes were proposed in this pull request?
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This is a re-proposal of https://github.com/apache/spark/pull/23163.
Currently spark always requires a [local
sort](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala#L188)
before writing to output table with dynamic partition/bucket columns. The sort
can be unnecessary if cardinality of partition/bucket values is small, and can
be avoided by keeping multiple output writers concurrently.
This PR introduces a config `spark.sql.maxConcurrentOutputWriters` (which
disables this feature by default), where user can tune the maximal number of
concurrent writers. The config is needed here as we cannot keep arbitrary
number of writers in task memory which can cause OOM (especially for
Parquet/ORC vectorization writer).
The feature is to first use concurrent writers to write rows. If the number
of writers exceeds the above config specified limit. Sort rest of rows and
write rows one by one (See `DynamicPartitionDataWriter.writeWithIterator()`).
In addition, interface `WriteTaskStatsTracker` and its implementation
`BasicWriteTaskStatsTracker` are also changed because previously they are
relying on the assumption that only one writer is active for writing dynamic
partitions and bucketed table.
### Why are the changes needed?
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Avoid the sort before writing output for dynamic partitioned query and
bucketed table.
Help improve CPU and IO performance for these queries.
### Does this PR introduce _any_ user-facing change?
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No.
### How was this patch tested?
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Added unit test in `DataFrameReaderWriterSuite.scala`.
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