sunchao opened a new pull request #34659: URL: https://github.com/apache/spark/pull/34659
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If you want to add or modify an error type or message, please read the guideline first in 'core/src/main/resources/error/README.md'. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> 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? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> 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? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> 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? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Added new unit tests. -- This is an automated message from the Apache Git Service. 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