12 Spark Streaming源码解读之Executor容错安全性

要篇内容介绍Executor的容错，容错方式有WAL、消息重放、其他

1. 首先介绍WAL的方法，就是保存数据前，先把数据写日志。从ReceiverSupervisorImpl的pushAndReportBlock的方法开始看，代码如下

def pushAndReportBlock(
      receivedBlock: ReceivedBlock,
      metadataOption: Option[Any],
      blockIdOption: Option[StreamBlockId]
    ) {
    val blockId = blockIdOption.getOrElse(nextBlockId)
    val time = System.currentTimeMillis
    val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
    logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
    val numRecords = blockStoreResult.numRecords
    val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult)
    trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
    logDebug(s"Reported block $blockId")
}

调用receivedBlockHandler的storeBlock方法，receivedBlockHandler决定了采用哪种方式来存储数据，代码如下

private val receivedBlockHandler: ReceivedBlockHandler = {
    if (WriteAheadLogUtils.enableReceiverLog(env.conf)) {
      if (checkpointDirOption.isEmpty) {
        throw new SparkException(
          "Cannot enable receiver write-ahead log without checkpoint directory set. " +
            "Please use streamingContext.checkpoint() to set the checkpoint directory. " +
            "See documentation for more details.")
      }
      new WriteAheadLogBasedBlockHandler(env.blockManager, receiver.streamId,
        receiver.storageLevel, env.conf, hadoopConf, checkpointDirOption.get)
    } else {
      new BlockManagerBasedBlockHandler(env.blockManager, receiver.storageLevel)
    }
}

如果开启WAL的方式，会将数据保存到checkpoint目录，如果checkpoint目录没有配置，就抛出异常。
先看WriteAheadLogBasedBlockHandler，开启WAL后，采用BlockManager存储数据时就不需要复本了，否则和WAL同时做容错就是重复性工作，降低了系统的性能。
再看BlockManagerBasedBlockHandler，就是将数据交给BlockManager存储，根据用户定义的存储级别来存储，系统一般默认存储级别为MEMORY_AND_DISK_SER_2，如果对数据安全性要求不高也可以不要复本。

2. 消息重放就是一种非常高效的方式，采用kafka的Direct API接口读取数据时首先计算offset的位置，如果job异常，根据消费的offset位置重新指定kafka的offset，从失败的位置读取。kafka直接做为文件存储系统，就像hdfs一样，具体怎么使用以后的章节还会介绍。