[
https://issues.apache.org/jira/browse/PARQUET-2159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17695405#comment-17695405
]
ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------
jiangjiguang commented on code in PR #1011:
URL: https://github.com/apache/parquet-mr/pull/1011#discussion_r1121226362
##########
.github/workflows/vector-plugins.yml:
##########
@@ -0,0 +1,56 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+name: Vector-plugins
+
+on: [push, pull_request]
+
+jobs:
+ build:
+
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ java: [ '17' ]
+ codes: [ 'uncompressed,brotli', 'gzip,snappy' ]
+ name: Build Parquet with JDK ${{ matrix.java }} and ${{ matrix.codes }}
+
+ steps:
+ - uses: actions/checkout@master
+ - name: Set up JDK ${{ matrix.java }}
+ uses: actions/setup-java@v1
+ with:
+ java-version: ${{ matrix.java }}
+ - name: before_install
+ env:
+ CI_TARGET_BRANCH: $GITHUB_HEAD_REF
+ run: |
+ bash dev/ci-before_install.sh
+ - name: install
+ run: |
+ EXTRA_JAVA_TEST_ARGS=$(mvn help:evaluate
-Dexpression=extraJavaTestArgs -q -DforceStdout)
+ export MAVEN_OPTS="$MAVEN_OPTS $EXTRA_JAVA_TEST_ARGS"
+ mvn install --batch-mode -Pvector-plugins -DskipTests=true
-Dmaven.javadoc.skip=true -Dsource.skip=true -Djava.version=${{ matrix.java }}
-pl
-parquet-hadoop,-parquet-arrow,-parquet-avro,-parquet-benchmarks,-parquet-cli,-parquet-column,-parquet-hadoop-bundle,-parquet-jackson,-parquet-pig,-parquet-pig-bundle,-parquet-protobuf,-parquet-thrift
Review Comment:
because these module have been run in the Test workflow. I think
vector-plugins should run only the modules associated with vector
> 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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