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Posted to dev@parquet.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2023/02/01 07:27:00 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=17682892#comment-17682892 ]
ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------
wgtmac commented on code in PR #1011:
URL: https://github.com/apache/parquet-mr/pull/1011#discussion_r1092845969
##########
parquet-generator/src/main/resources/ByteBitPacking512VectorLE:
##########
@@ -0,0 +1,3095 @@
+/*
+ * 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.
+ */
+
+package org.apache.parquet.column.values.bitpacking;
+
+import jdk.incubator.vector.ByteVector;
+import jdk.incubator.vector.IntVector;
+import jdk.incubator.vector.LongVector;
+import jdk.incubator.vector.ShortVector;
+import jdk.incubator.vector.Vector;
+import jdk.incubator.vector.VectorMask;
+import jdk.incubator.vector.VectorOperators;
+import jdk.incubator.vector.VectorShuffle;
+import jdk.incubator.vector.VectorSpecies;
+
+import java.nio.ByteBuffer;
+
+/**
+ * This is an auto-generated source file and should not edit it directly.
+ */
+public abstract class ByteBitPacking512VectorLE {
Review Comment:
Do you have any script to generate the code here? If true, it would be great to commit it as well.
##########
parquet-benchmarks/src/main/java/org/apache/parquet/benchmarks/ByteBitPackingVectorBenchmarks.java:
##########
@@ -0,0 +1,83 @@
+/*
+ * 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.
+ */
+package org.apache.parquet.benchmarks;
+
+import org.apache.parquet.column.values.bitpacking.BytePacker;
+import org.apache.parquet.column.values.bitpacking.Packer;
+import org.openjdk.jmh.annotations.*;
+
+import java.util.concurrent.TimeUnit;
+
+/**
+ * This class uses the java17 vector API, add VM options --add-modules=jdk.incubator.vector
+ */
+
+@State(Scope.Benchmark)
+@BenchmarkMode(Mode.AverageTime)
+@Warmup(iterations = 1, batchSize = 100000)
+@Measurement(iterations = 1, batchSize = 100000)
+@OutputTimeUnit(TimeUnit.MILLISECONDS)
+public class ByteBitPackingVectorBenchmarks {
+
+ /**
+ * The range of bitWidth is 1 ~ 32, change it directly if test other bitWidth.
+ */
+ private static final int bitWidth = 7;
+ private static final int outputValues = 1024;
+ private final byte[] input = new byte[outputValues * bitWidth / 8];
+ private final int[] output = new int[outputValues];
+ private final int[] outputVector = new int[outputValues];
+
+ @Setup(Level.Trial)
+ public void getInputBytes() {
+ for (int i = 0; i < input.length; i++) {
+ input[i] = (byte) i;
+ }
+ }
+
+ @Benchmark
+ public void testUnpack() {
+ BytePacker bytePacker = Packer.LITTLE_ENDIAN.newBytePacker(bitWidth);
+ for (int i = 0, j = 0; i < input.length; i += bitWidth, j += 8) {
+ bytePacker.unpack8Values(input, i, output, j);
+ }
+ }
+
+ @Benchmark
+ public void testUnpackVector() {
+ BytePacker bytePacker = Packer.LITTLE_ENDIAN.newBytePacker(bitWidth);
+ BytePacker bytePackerVector = Packer.LITTLE_ENDIAN.newBytePackerVector(bitWidth);
Review Comment:
Could you elaborate more? @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
> 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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