[jira] [Commented] (PARQUET-2159) Parquet bit-packing de/encode optimization

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/PARQUET-2159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17679821#comment-17679821 ] 

ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------

jiangjiguang commented on PR #1011:
URL: https://github.com/apache/parquet-mr/pull/1011#issuecomment-1400441832

   > Thank @gszadovszky a lot for helping with this PR!
   > 
   > +1 for what @gszadovszky said. The mainstream runtime JDK is still 1.8. Parquet is one of the underlying building blocks for many big data applications. The bare minimum, for now, is to keep java8 compatible. Otherwise forcing applications to upgrade to jdk17 because of Parquet is disruptive and impacts adoptions.
   > 
   > @jiangjiguang, I am very happy to see you have this PR to help the Parquet community. Would you mind starting an email discussion to [dev@parquet.apache.org](mailto:dev@parquet.apache.org) for this topic?
   > 
   > cc @ggershinsky @wgtmac
   
   @shangxinli 
   
   > 
   
   @gszadovszky @shangxinli @wgtmac I have started the discussion about how to upgrade java17 over a month, but nobody involved!  So I have updated the PR, it does not  involve how to upgrade java17.
   The default compilation is java8
   Just add maven build parameters  -P java17-target -P vector and get the expected jars  when people want to use java17 vector to speed up parquet decode




> 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
>            Assignee: 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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