Receiver接收到的数据交由ReceiverSupervisorImpl来管理。
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ReceiverSupervisorImpl接收到数据后，会数据存储并且将数据的元数据报告给ReceiverTracker 。
Executor的数据容错可以有三种方式：
WAL日志
数据副本
接收receiver的数据流回放
/** Store block and report it to driver */
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")
}数据的存储，是借助receiverBlockHandler，它的实现有两种方式：
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)
}
}WriteAheadLogBaseBlockHandler 一方面将数据交由BlockManager管理，另一方面会写WAL日志。
一旦节点崩溃，可以由WAL日志恢复内存中的数据。在WAL开始时，就不在建议数据存储多个副本。
private val effectiveStorageLevel = {
if (storageLevel.deserialized) {
logWarning(s"Storage level serialization ${storageLevel.deserialized} is not supported when" +
s" write ahead log is enabled, change to serialization false")
}
if (storageLevel.replication > 1) {
logWarning(s"Storage level replication ${storageLevel.replication} is unnecessary when " +
s"write ahead log is enabled, change to replication 1")
}
StorageLevel(storageLevel.useDisk, storageLevel.useMemory, storageLevel.useOffHeap, false, 1)
}而BlockManagerBaseBlockHandler直接将数据交由BlockManager管理。
如果不写WAL，当节点崩溃了一定会数据丢失吗？ 这个也不一定。因为在构建WriteAheadLogBaseBlockHandler，和BlockManagerBaseBlockHandler的时候会将receiver的storageLevel传入。storageLevel用来描述数据保存的地方(内存、磁盘)以及副本个数。
class StorageLevel private( private var _useDisk: Boolean, private var _useMemory: Boolean, private var _useOffHeap: Boolean, private var _deserialized: Boolean, private var _replication: Int = 1) extends Externalizable
公有如下种类的StorageLevel：
val NONE = new StorageLevel(false, false, false, false) val DISK_ONLY = new StorageLevel(true, false, false, false) val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2) val MEMORY_ONLY = new StorageLevel(false, true, false, true) val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2) val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false) val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2) val MEMORY_AND_DISK = new StorageLevel(true, true, false, true) val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2) val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false) val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2) val OFF_HEAP = new StorageLevel(false, false, true, false)
默认情况，数据采用MEMORY_AND_DISK_2,也就是说数据会产生两个副本，并且内存不足时会写入磁盘。
数据的最终存储是由BlockManager完成并管理的：
def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
var numRecords = None: Option[Long]
val putResult: Seq[(BlockId, BlockStatus)] = block match {
case ArrayBufferBlock(arrayBuffer) =>
numRecords = Some(arrayBuffer.size.toLong)
blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel,
tellMaster = true)
case IteratorBlock(iterator) =>
val countIterator = new CountingIterator(iterator)
val putResult = blockManager.putIterator(blockId, countIterator, storageLevel,
tellMaster = true)
numRecords = countIterator.count
putResult
case ByteBufferBlock(byteBuffer) =>
blockManager.putBytes(blockId, byteBuffer, storageLevel, tellMaster = true)
case o =>
throw new SparkException(
s"Could not store $blockId to block manager, unexpected block type ${o.getClass.getName}")
}
if (!putResult.map { _._1 }.contains(blockId)) {
throw new SparkException(
s"Could not store $blockId to block manager with storage level $storageLevel")
}
BlockManagerBasedStoreResult(blockId, numRecords)
}对于从kafka中直接读取数据，可以通过记录数据offset的方法来进行容错。如果程序崩溃，下次启动时，从上次未处理数据的offset再次读取数据即可。
备注：
1、DT大数据梦工厂微信公众号DT_Spark
2、IMF晚8点大数据实战YY直播频道号：68917580
3、新浪微博: http://www.weibo.com/ilovepains