[
https://issues.apache.org/jira/browse/PARQUET-2159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17693637#comment-17693637
]
ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------
jiangjiguang commented on code in PR #1011:
URL: https://github.com/apache/parquet-mr/pull/1011#discussion_r1118075418
##########
parquet-column/src/main/java/org/apache/parquet/column/values/bitpacking/ParquetReadRouter.java:
##########
@@ -0,0 +1,133 @@
+/*
+ * 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 org.apache.parquet.bytes.ByteBufferInputStream;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.EOFException;
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+/**
+ * Utility class for big data applications (such as Apache Spark and Apache
Flink).
+ * For Intel CPU, Flags containing avx512vbmi and avx512_vbmi2 can have better
performance gains.
+ */
+public class ParquetReadRouter {
+ private static final Logger LOG =
LoggerFactory.getLogger(ParquetReadRouter.class);
+
+ private static final int BITS_PER_BYTE = 8;
+
+ // register of avx512 are 512 bits, and can load up to 64 bytes
+ private static final int BYTES_PER_VECTOR_512 = 64;
+
+ // values are bit packed 8 at a time, so reading bitWidth will always work
+ private static final int NUM_VALUES_TO_PACK = 8;
+
+ private static final VectorSupport vectorSupport;
+
+ static {
+ vectorSupport = getSupportVectorFromCPUFlags();
+ }
+
+ // Dispatches to use vector when available. Directly call
readBatchUsing512Vector() if you are sure about it.
+ public static void read(int bitWidth, ByteBufferInputStream in, int
currentCount, int[] currentBuffer) throws IOException {
+ switch (vectorSupport) {
+ case VECTOR_512:
+ readBatchUsing512Vector(bitWidth, in, currentCount, currentBuffer);
+ break;
+ default:
+ readBatch(bitWidth, in, currentCount, currentBuffer);
+ }
+ }
+
+ // Call the method directly if your computer system contains avx512vbmi and
avx512_vbmi2 CPU Flags
+ public static void readBatchUsing512Vector(int bitWidth,
ByteBufferInputStream in, int currentCount, int[] currentBuffer) throws
IOException {
+ BytePacker packer = Packer.LITTLE_ENDIAN.newBytePacker(bitWidth);
+ BytePacker packerVector =
Packer.LITTLE_ENDIAN.newBytePackerVector(bitWidth);
+ int valueIndex = 0;
+ int byteIndex = 0;
+ int unpackCount = packerVector.getUnpackCount();
+ int inputByteCountPerVector = packerVector.getUnpackCount() /
BITS_PER_BYTE * bitWidth;
+ int totalByteCount = currentCount * bitWidth / BITS_PER_BYTE;
+ int totalByteCountVector = totalByteCount - BYTES_PER_VECTOR_512;
+ ByteBuffer buffer = in.slice(totalByteCount);
+ if (buffer.hasArray()) {
+ for (; byteIndex < totalByteCountVector; byteIndex +=
inputByteCountPerVector, valueIndex += unpackCount) {
+ packerVector.unpackValuesUsingVector(buffer.array(),
buffer.arrayOffset() + buffer.position() + byteIndex, currentBuffer,
valueIndex);
+ }
+ // If the remaining bytes size <= {BYTES_PER_512VECTOR}, the remaining
bytes are unpacked by packer
+ for (; byteIndex < totalByteCount; byteIndex += bitWidth, valueIndex +=
NUM_VALUES_TO_PACK) {
+ packer.unpack8Values(buffer.array(), buffer.arrayOffset() +
buffer.position() + byteIndex, currentBuffer, valueIndex);
+ }
+ } else {
+ for (; byteIndex < totalByteCountVector; byteIndex +=
inputByteCountPerVector, valueIndex += unpackCount) {
+ packerVector.unpackValuesUsingVector(buffer, buffer.position() +
byteIndex, currentBuffer, valueIndex);
+ }
+ for (; byteIndex < totalByteCount; byteIndex += bitWidth, valueIndex +=
NUM_VALUES_TO_PACK) {
+ packer.unpack8Values(buffer, buffer.position() + byteIndex,
currentBuffer, valueIndex);
+ }
+ }
+ }
+
+ // Call the method directly if your computer system doesn't contain
avx512vbmi and avx512_vbmi2 CPU Flags
+ public static void readBatch(int bitWidth, ByteBufferInputStream in, int
currentCount, int[] currentBuffer) throws EOFException {
+ BytePacker packer = Packer.LITTLE_ENDIAN.newBytePacker(bitWidth);
+ int valueIndex = 0;
+ while (valueIndex < currentCount) {
+ ByteBuffer buffer = in.slice(bitWidth);
+ packer.unpack8Values(buffer, buffer.position(), currentBuffer,
valueIndex);
+ valueIndex += NUM_VALUES_TO_PACK;
+ }
+ }
+
+ private static VectorSupport getSupportVectorFromCPUFlags() {
+ try {
+ String os = System.getProperty("os.name");
+ if (os == null || !os.toLowerCase().startsWith("linux")) {
+ return VectorSupport.NONE;
+ }
+ List<String> allLines = Files.readAllLines(Paths.get("/proc/cpuinfo"),
StandardCharsets.UTF_8);
+ for (String line : allLines) {
+ if (line != null && line.startsWith("flags")) {
+ int index = line.indexOf(":");
+ if (index < 0) {
+ continue;
+ }
+ line = line.substring(index + 1);
+ Set<String> flagsSet = Arrays.stream(line.split("
")).collect(Collectors.toSet());
+ if (flagsSet.contains("avx512vbmi") &&
flagsSet.contains("avx512_vbmi2")) {
+ return VectorSupport.VECTOR_512;
+ }
+ }
+ }
Review Comment:
@gszadovszky I got it. As far as I know, there is no such library available
for java. I think we can try to achieve implement a library like that with JNI
in the future. But there is no time line. If I have finished it, I will update
here.
> 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!
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
