本套系列博客从真实商业环境抽取案例进行总结和分享,并给出Spark源码解读及商业实战指导,请持续关注本套博客。版权声明:本套Spark源码解读及商业实战归作者(秦凯新)所有,禁止转载,欢迎学习。
- Spark商业环境实战-Spark内置框架rpc通讯机制及RpcEnv基础设施
- Spark商业环境实战-Spark事件监听总线流程分析
- Spark商业环境实战-Spark存储体系底层架构剖析
- Spark商业环境实战-Spark底层多个MessageLoop循环线程执行流程分析
- Spark商业环境实战-Spark二级调度系统Stage划分算法和最佳任务调度细节剖析
- Spark商业环境实战-Spark任务延迟调度及调度池Pool架构剖析
- Spark商业环境实战-Task粒度的缓存聚合排序结构AppendOnlyMap详细剖析
- Spark商业环境实战-ExternalSorter 外部排序器在Spark Shuffle过程中设计思路剖析
- Spark商业环境实战-ShuffleExternalSorter外部排序器在Spark Shuffle过程中的设计思路剖析
- Spark商业环境实战-Spark ShuffleManager内存缓冲器SortShuffleWriter设计思路剖析
- Spark商业环境实战-Spark ShuffleManager内存缓冲器UnsafeShuffleWriter设计思路剖析
- Spark商业环境实战-Spark ShuffleManager内存缓冲器BypassMergeSortShuffleWriter设计思路剖析
- [Spark商业环境实战-Spark Shuffle 聚合拉取读数据(Reduce Task)过程深入剖析]
- Spark商业环境实战-StreamingContext启动流程及Dtream 模板源码剖析
- Spark商业环境实战-ReceiverTracker与BlockGenerator数据流接收过程剖析
1 从ShuffeManager讲起
一张图我已经用过多次了,不要见怪,因为毕竟都是一个主题,有关shuffle的。英文注释已经很详细了,这里简单介绍一下:
- 目前只有一个实现 SortShuffleManager。
- SortShuffleManager依赖于ShuffleWriter提供服务,通过ShuffleWriter定义的规范,可以将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,主要还是负责向Task传递Shuffle信息。一个是序列化,一个是确定何时绕开合并和排序的Shuffle路径。
官方英文介绍如下:
* 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.
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。
* 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
2 华山论剑之成员力量
BypassMergeSortShuffleWriter可是直接开挂的节奏,完全没有什么排序器啊,我来承担一切。我最屌,我承担一切,心声,嘿嘿。
2.1 BypassMergeSortShuffleWriter的孩子:
-
partitionWriters : 看看初始化为数组 ==> private DiskBlockObjectWriter[] partitionWriters,每一个DiskBlockObjectWriter负责处理一个分区的数据。
-
private final int fileBufferSize ==>文件缓冲大小,通过Spark.shuffle.file.buffer属性配置,默认是32KB。
-
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。
-
private final int numPartitions => 分区数
-
private final BlockManager blockManager
-
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,每一个DiskBlockObjectWriter对应一个分区,也因此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(按照分区来写片段)
final Tuple2<TempShuffleBlockId, File> tempShuffleBlockIdPlusFile =
blockManager.diskBlockManager().createTempShuffleBlock();
final File file = tempShuffleBlockIdPlusFile._2();
final BlockId blockId = tempShuffleBlockIdPlusFile._1();
partitionWriters[i] = <=点睛之笔(得到不同分区的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);
}
2 BypassMergeSortShuffleWriter核心实现方法writePartitionedFile
聚合每一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;
}
3 BypassMergeSortShuffleWriter核心shuffle write流程
- 根据分区ID,为每一个分区创建DiskBlockObjectWriter
- 按照分区ID升序写入正式的Shuffle数据文件
- 最终通过writeIndexFileAndCommit建立MapTask输出的数据索引
不废话,这张图简直画的太好了,望原图作者看到留言于我。
4 总结
本节内容是作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