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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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