marin-ma opened a new pull request, #53040:
URL: https://github.com/apache/spark/pull/53040
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### What changes were proposed in this pull request?
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Currently, when Spark partitions the input files in a table scan, it first
sorts the input splits, then adjacent splits are coalesced into a single
partition. If the input split size distribution is uneven, some partitions will
have only a few splits while others will have many.
We observed that this file partitioning strategy can slow down the reading
process if some tasks are reading more files than others, especially in
Gluten’s native Parquet reader. To address this performance issue, we are
proposing a new partitioning strategy that takes both partition size and file
count into account and distributes the small files across different partitions
to avoid skew.
The strategy is designed with following steps:
1. Get the number of output partition number from Spark's original logic
FilePartition.getFilePartitions. If `spark.sql.files.maxPartitionNum` is set,
use the smaller one as the output partition number.
2. Assign small files starting from the smallest to the partitions with the
minimum file count + total file size strategy
3. Assign the remaining files from the largest into the partition with the
minimum total file size + file count strategy
The total size of small files can be configured using
spark.gluten.sql.columnar.smallFileThreshold, which specifies the percentage of
the total input file size represented by small files.
### Why are the changes needed?
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As described in the previous section. End users and projects like Apache
Gluten can benefit from this change.
### Does this PR introduce _any_ user-facing change?
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new configurations are added for this enhancement:
- spark.sql.files.partitionStrategy
- spark.sql.files.smallFileThreshold
### How was this patch tested?
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Unit test
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No
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