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Posted to dev@parquet.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2022/11/15 07:33:00 UTC
[jira] [Commented] (PARQUET-2159) Parquet bit-packing de/encode optimization
[ https://issues.apache.org/jira/browse/PARQUET-2159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17634206#comment-17634206 ]
ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------
jiangjiguang opened a new pull request, #1011:
URL: https://github.com/apache/parquet-mr/pull/1011
The PR includes 3 aspects：
1. Use java17 vector api to decode bit-packing , the performance gain is 4x ~ 8x according to the microbenchmark
2. Upgrade the project to java17 to support java vector api
3. Add ParquetReadRouter to compatible with different platform when computing engines(such as spark) read parquet.
### Jira
- https://issues.apache.org/jira/projects/PARQUET/issues/PARQUET-2159?filter=allopenissues
### Tests
- Add unit tests org.apache.parquet.column.values.bitpacking.TestByteBitPackingVectorLE
### Commits
- [ ] My commits all reference Jira issues in their subject lines. In addition, my commits follow the guidelines from "[How to write a good git commit message](http://chris.beams.io/posts/git-commit/)":
1. Subject is separated from body by a blank line
1. Subject is limited to 50 characters (not including Jira issue reference)
1. Subject does not end with a period
1. Subject uses the imperative mood ("add", not "adding")
1. Body wraps at 72 characters
1. Body explains "what" and "why", not "how"
### Documentation
- The PR adds maven profile vector to enable generate java17 vector bit-packing decode opt. code, and run junit tests: mvn clean install -P vector
- The PR needs Intel Ice Lake CPU to run junit tests
- The contributors are jiangjiguang jiyu1021 guangzegu Fang-Xie , and co-contributor is jatin-bhateja
> Parquet bit-packing de/encode optimization
> ------------------------------------------
>
> Key: PARQUET-2159
> URL: https://issues.apache.org/jira/browse/PARQUET-2159
> Project: Parquet
> Issue Type: Improvement
> Components: parquet-mr
> Affects Versions: 1.13.0
> Reporter: Fang-Xie
> Priority: Major
> Fix For: 1.13.0
>
> Attachments: image-2022-06-15-22-56-08-396.png, image-2022-06-15-22-57-15-964.png, image-2022-06-15-22-58-01-442.png, image-2022-06-15-22-58-40-704.png
>
>
> Current Spark use Parquet-mr as parquet reader/writer library, but the built-in bit-packing en/decode is not efficient enough.
> Our optimization for Parquet bit-packing en/decode with jdk.incubator.vector in Open JDK18 brings prominent performance improvement.
> Due to Vector API is added to OpenJDK since 16, So this optimization request JDK16 or higher.
> *Below are our test results*
> Functional test is based on open-source parquet-mr Bit-pack decoding function: *_public final void unpack8Values(final byte[] in, final int inPos, final int[] out, final int outPos)_* __
> compared with our implementation with vector API *_public final void unpack8Values_vec(final byte[] in, final int inPos, final int[] out, final int outPos)_*
> We tested 10 pairs (open source parquet bit unpacking vs ours optimized vectorized SIMD implementation) decode function with bit width=\{1,2,3,4,5,6,7,8,9,10}, below are test results:
> !image-2022-06-15-22-56-08-396.png|width=437,height=223!
> We integrated our bit-packing decode implementation into parquet-mr, tested the parquet batch reader ability from Spark VectorizedParquetRecordReader which get parquet column data by the batch way. We construct parquet file with different row count and column count, the column data type is Int32, the maximum int value is 127 which satisfies bit pack encode with bit width=7, the count of the row is from 10k to 100 million and the count of the column is from 1 to 4.
> !image-2022-06-15-22-57-15-964.png|width=453,height=229!
> !image-2022-06-15-22-58-01-442.png|width=439,height=217!
> !image-2022-06-15-22-58-40-704.png|width=415,height=208!
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