sunchao opened a new pull request #34659:
URL: https://github.com/apache/spark/pull/34659
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### What changes were proposed in this pull request?
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This PR adds support for complex types (e.g., list, map, array) for Spark's
vectorized Parquet reader. In particular, this introduces the following changes:
1. Added a new class `ParquetColumnVector` which encapsulates all the
necessary information needed when reading a Parquet column, including the
`ParquetColumn` for the Parquet column, the repetition & definition levels
(only allocated for a leaf-node of a complex type), as well as the reader for
the column. In addition, it also contains logic for assembling nested columnar
batches, via interpreting Parquet repetition & definition levels.
2. Changes are made in `VectorizedParquetRecordReader` to initialize a list
of `ParquetColumnVector` for the columns read.
3. `VectorizedColumnReader` now also creates a reader for repetition column.
Depending on whether maximum repetition level is 0, the batch read is now split
into two code paths, e.g., `readBatch` versus `readBatchNested`.
4. Added logic to handle complex type in `VectorizedRleValuesReader`. For
data types involving only struct or primitive types, it still goes with the old
`readBatch` method which now also saves definition levels into a vector for
later assembly. Otherwise, for data types involving array or map, a separate
code path `readBatchNested` is introduced to handle repetition levels.
5. Modified `WritableColumnVector` to better support null structs. Currently
it requires populating null entries to all child vectors when there is a null
struct, however this will waste space and also doesn't work well with Parquet
scan. This adds an extra field `structOffsets` which records the mapping from a
row ID to the position of the row in the child vector, so that child vectors
will only need to store real null elements.
This PR also introduced a new flag
`spark.sql.parquet.enableNestedColumnVectorizedReader` which turns the feature
on or off. By default it is on to facilitates all the Parquet related test
coverage.
### Why are the changes needed?
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Whenever read schema containing complex types, at the moment Spark will
fallback to the row-based reader in parquet-mr, which is much slower. As
benchmark shows, by adding support into the vectorized reader, we can get ~15x
on average speed up on reading struct fields, and ~1.5x when reading array of
struct and map.
### Does this PR introduce _any_ user-facing change?
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With the PR Spark should now support reading complex types in its vectorized
Parquet reader. A new config
`spark.sql.parquet.enableNestedColumnVectorizedReader` is introduced to turn
the feature on or off.
### How was this patch tested?
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Added new unit tests.
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