Spark ShuffleManager内存缓冲器BypassMergeSortShuffleWriter设计思路剖析-Spark商业环境实战

时间 2019-11-17

标签 spark shufflemanager 内存缓冲器 bypassmergesortshufflewriter 设计思路剖析商业环境实战栏目 Spark 繁體版

原文原文链接

1 从ShuffeManager讲起

一张图我已经用过屡次了，不要见怪，由于毕竟都是一个主题，有关shuffle的。英文注释已经很详细了，这里简单介绍一下：数组

目前只有一个实现 SortShuffleManager。
SortShuffleManager依赖于ShuffleWriter提供服务，经过ShuffleWriter定义的规范，能够将MapTask的任务中间结果按照约束的规范持久化到磁盘。
SortShuffleManager总共有三个子类， UnsafeShuffleWriter，SortShuffleWriter ，BypassMergeSortShuffleWriter。
SortShuffleManager依赖于ShuffleHandle样例类，主要仍是负责向Task传递Shuffle信息。一个是序列化，一个是肯定什么时候绕开合并和排序的Shuffle路径。

官方英文介绍以下：缓存

* Pluggable interface for shuffle systems. A ShuffleManager is created in SparkEnv on the 
     * driver and on each executor, based on the spark.shuffle.manager setting. The driver 
     * registers shuffles with it, and executors (or tasks running locally in the driver) can ask * to read and write data.
     
     * NOTE: this will be instantiated by SparkEnv so its constructor can take a SparkConf and
     * boolean isDriver as parameters.
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1 华山论剑之BypassMergeSortShuffleWriter

从命名来看，绝对是投机取巧，绕开合并和排序的ShuffleWriter，姑且称之为投机侠吧。架构

* This class implements sort-based shuffle's hash-style shuffle fallback path. This write path
 * writes incoming records to separate files, one file per reduce partition, then concatenates these
 * per-partition files to form a single output file, regions of which are served to reducers.
 * Records are not buffered in memory. It writes output in a format
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2 华山论剑之成员力量

BypassMergeSortShuffleWriter但是直接开挂的节奏，彻底没有什么排序器啊，我来承担一切。我最屌，我承担一切，心声，嘿嘿。app

2.1 BypassMergeSortShuffleWriter的孩子：

partitionWriters : 看看初始化为数组 ==> private DiskBlockObjectWriter[] partitionWriters，每个DiskBlockObjectWriter负责处理一个分区的数据。框架
private final int fileBufferSize ==>文件缓冲大小，经过Spark.shuffle.file.buffer属性配置，默认是32KB。oop
private final boolean transferToEnabled => 是否采用NIO的从文件流待文件流的复制方式，spark.file.transferTo属性配置，默认是true。post
private final int numPartitions => 分区数学习
private final BlockManager blockManager优化
private final Partitioner partitioner => 分区计算器
private final ShuffleWriteMetrics writeMetrics
private final int shuffleId;
private final int mapId ==>map任务的身份标识。
private final Serializer serializer;
private final IndexShuffleBlockResolver shuffleBlockResolver
private FileSegment[] partitionWriterSegments ==>FileSegment数组，每个DiskBlockObjectWriter对应一个分区，也所以对应一个处理的文件片。
@Nullable private MapStatus mapStatus;
private long[] partitionLengths;

2 BypassMergeSortShuffleWriter核心实现方法Writer

先欣赏代码段：

public void write(Iterator<Product2<K, V>> records) throws IOException {
        assert (partitionWriters == null);
        if (!records.hasNext()) {
          partitionLengths = new long[numPartitions];
          shuffleBlockResolver.writeIndexFileAndCommit(shuffleId, mapId, partitionLengths, null);
          mapStatus = MapStatus$.MODULE$.apply(blockManager.shuffleServerId(), partitionLengths);
          return;
        }
        final SerializerInstance serInstance = serializer.newInstance();
        final long openStartTime = System.nanoTime();
        
        partitionWriters = new DiskBlockObjectWriter[numPartitions];    <=点睛之笔
        partitionWriterSegments = new FileSegment[numPartitions];       <=点睛之笔
        
        for (int i = 0; i < numPartitions; i++) {              <=点睛之笔（按照分区来写片断）
          final Tuple2<TempShuffleBlockId, File> tempShuffleBlockIdPlusFile =
            blockManager.diskBlockManager().createTempShuffleBlock();
          final File file = tempShuffleBlockIdPlusFile._2();
          final BlockId blockId = tempShuffleBlockIdPlusFile._1();
          
          partitionWriters[i] =                     <=点睛之笔（获得不一样分区的DiskBlockObjectWriter）
            blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, writeMetrics);
        }                                           
        
        
        // Creating the file to write to and creating a disk writer both involve interacting with
        // the disk, and can take a long time in aggregate when we open many files, so should be
        // included in the shuffle write time.
        writeMetrics.incWriteTime(System.nanoTime() - openStartTime);
    
        while (records.hasNext()) {
          final Product2<K, V> record = records.next();
          final K key = record._1();
          partitionWriters[partitioner.getPartition(key)].write(key, record._2());
        }
    
        for (int i = 0; i < numPartitions; i++) {
          final DiskBlockObjectWriter writer = partitionWriters[i];
          partitionWriterSegments[i] = writer.commitAndGet();  <= 生成一堆临时文件，写入到磁盘
          writer.close();
        }
    
        File output = shuffleBlockResolver.getDataFile(shuffleId, mapId);  <==获取一堆临时文件
        File tmp = Utils.tempFileWith(output);
        try {
        
        
          partitionLengths = writePartitionedFile(tmp);   <==多个分区文件合并
          shuffleBlockResolver.writeIndexFileAndCommit(shuffleId, mapId, partitionLengths, tmp);
                                                          <==生成索引
                                                          
                                                          
        } finally {
          if (tmp.exists() && !tmp.delete()) {
            logger.error("Error while deleting temp file {}", tmp.getAbsolutePath());
          }
        }
        mapStatus = MapStatus$.MODULE$.apply(blockManager.shuffleServerId(), partitionLengths);
      }
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2 BypassMergeSortShuffleWriter核心实现方法writePartitionedFile

聚合每个分区文件为正式的Block文件

Concatenate all of the per-partition files into a single combined file.

    private long[] writePartitionedFile(File outputFile) throws IOException {
        // Track location of the partition starts in the output file
        final long[] lengths = new long[numPartitions];
        if (partitionWriters == null) {
          // We were passed an empty iterator
          return lengths;
        }
    
        final FileOutputStream out = new FileOutputStream(outputFile, true);
        final long writeStartTime = System.nanoTime();
        boolean threwException = true;
        try {
          for (int i = 0; i < numPartitions; i++) {
            final File file = partitionWriterSegments[i].file();
            if (file.exists()) {
              final FileInputStream in = new FileInputStream(file);
              boolean copyThrewException = true;
              try {
                lengths[i] = Utils.copyStream(in, out, false, transferToEnabled);
                copyThrewException = false;
              } finally {
                Closeables.close(in, copyThrewException);
              }
              if (!file.delete()) {
                logger.error("Unable to delete file for partition {}", i);
              }
            }
          }
          threwException = false;
        } finally {
          Closeables.close(out, threwException);
          writeMetrics.incWriteTime(System.nanoTime() - writeStartTime);
        }
        partitionWriters = null;
        return lengths;
      }
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3 BypassMergeSortShuffleWriter核心shuffle write流程

根据分区ID，为每个分区建立DiskBlockObjectWriter
按照分区ID升序写入正式的Shuffle数据文件
最终经过writeIndexFileAndCommit创建MapTask输出的数据索引

不废话，这张图简直画的太好了，望原图做者看到留言于我。

4 总结

本节内容是做者投入大量时间优化后的内容，采用最平实的语言来剖析 ShuffeManager之统一存储服务BypassMergeSortShuffleWriter设计思路。

秦凯新于深圳 0:53分