c21 opened a new pull request #34298:
URL: https://github.com/apache/spark/pull/34298
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### What changes were proposed in this pull request?
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This PR is to add aggregate push down feature for ORC data source v2 reader.
At a high level, the PR does:
* The supported aggregate expression is MIN/MAX/COUNT same as [Parquet
aggregate push down](https://github.com/apache/spark/pull/33639).
* Nested column, partition column, column with Timestamp and Binary type are
disallowed in MIN/MAX aggregate push down. All other columns types are
supported in MIN/MAX aggregate push down.
* All columns types are supported in COUNT aggregate push down.
* Nested column's sub-fields are disallowed in aggregate push down.
* If the file does not have valid statistics, Spark will throw exception and
fail query.
* If aggregate has filter or group-by column, aggregate will not be pushed
down.
At code level, the PR does:
* `OrcScanBuilder`: `pushAggregation()` checks whether the aggregation can
be pushed down. The most checking logic is shared between Parquet and ORC,
extracted into `AggregatePushDownUtils.getSchemaForPushedAggregation()`.
`OrcScanBuilder` will create a `OrcScan` with aggregation and aggregation data
schema.
* `OrcScan`: `createReaderFactory` creates a ORC reader factory with
aggregation and schema. Similar change with `ParquetScan`.
* `OrcPartitionReaderFactory`: `buildReaderWithAggregates` creates a ORC
reader with aggregate push down (i.e. read ORC file footer to process columns
statistics, instead of reading actual data in the file).
`buildColumnarReaderWithAggregates` creates a columnar ORC reader similarly.
Both delegate the real work to read footer in
`OrcUtils.createAggInternalRowFromFooter`.
* `OrcUtils.createAggInternalRowFromFooter`: reads ORC file footer to
process columns statistics (real heavy lift happens here). Similar to
`ParquetUtils.createAggInternalRowFromFooter`. Leverage utility method such as
`OrcFooterReader.readStatistics`.
* `OrcFooterReader`: `readStatistics` reads the ORC `ColumnStatistics[]`
into Spark `OrcColumnsStatistics`. The transformation is needed here, because
ORC `ColumnStatistics[]` stores all columns statistics in a flatten array
style, and hard to process. Spark `OrcColumnsStatistics` stores the statistics
in nested tree structure (e.g. like `StructType`). This is used by
`OrcUtils.createAggInternalRowFromFooter`
* `OrcColumnsStatistics`: the easy-to-manipulate structure for ORC
`ColumnStatistics`. This is used by `OrcFooterReader.readStatistics`.
### Why are the changes needed?
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To improve the performance of query with aggregate.
### Does this PR introduce _any_ user-facing change?
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Yes. A user-facing config `spark.sql.orc.aggregatePushdown` is added to
control enabling/disabling the aggregate push down for ORC. By default the
feature is disabled.
### How was this patch tested?
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Added unit test in `FileSourceAggregatePushDownSuite.scala`. Refactored all
unit tests in https://github.com/apache/spark/pull/33639, and it now works for
both Parquet and ORC.
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