从数据层面，ReceivedBlockTracker为整个SparkStreaming应用程序记录元数据信息。
从调度层面，DStreamGraph和JobGenerator是Spark Streaming调度的核心，记录当前调度到哪一进度，和业务有关。
ReceivedBlockTracker在接收到元数据信息后调用addBlock方法，先写入磁盘中，然后在写入内存中。
ReceivedBlockTracker:
/** Add received block. This event will get written to the write ahead log (if enabled). */
defaddBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
try {
val writeResult =writeToLog(BlockAdditionEvent(receivedBlockInfo))
if (writeResult) {
synchronized {
getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
}
logDebug(s"Stream ${receivedBlockInfo.streamId} received " +
s"block ${receivedBlockInfo.blockStoreResult.blockId}")
} else {
logDebug(s"Failed to acknowledge stream ${receivedBlockInfo.streamId} receiving " +
s"block ${receivedBlockInfo.blockStoreResult.blockId} in the Write Ahead Log.")
}
writeResult
} catch {
case NonFatal(e) =>
logError(s"Error adding block $receivedBlockInfo", e)
false
}
}
ReceivedBlockTracker:
private type ReceivedBlockQueue = mutable.Queue[ReceivedBlockInfo]
// 为分配的ReceivedBlock
private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue]
// 已分配的ReceivedBlock
private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks]
private val writeAheadLogOption = createWriteAheadLog()
根据batchTime分配属于当前BatchDuration要处理的数据到timToAllocatedBlocks数据结构中。
ReceivedBlockTracker：
/**
* Allocate all unallocated blocks to the given batch.
* This event will get written to the write ahead log (if enabled).
*/
def allocateBlocksToBatch(batchTime: Time): Unit = synchronized {
if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) {
val streamIdToBlocks = streamIds.map { streamId =>
(streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true))
}.toMap
val allocatedBlocks = AllocatedBlocks(streamIdToBlocks)
if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) {
timeToAllocatedBlocks.put(batchTime, allocatedBlocks)
lastAllocatedBatchTime = batchTime
} else {
...
Time类的是一个case Class，记录时间，重载了操作符，隐式转换。
case class Time(private val millis: Long) {
def milliseconds: Long = millis
def < (that: Time): Boolean = (this.millis < that.millis)
def <= (that: Time): Boolean = (this.millis <= that.millis)
def > (that: Time): Boolean = (this.millis > that.millis)
def >= (that: Time): Boolean = (this.millis >= that.millis)
def + (that: Duration): Time = new Time(millis + that.milliseconds)
def - (that: Time): Duration = new Duration(millis - that.millis)
def - (that: Duration): Time = new Time(millis - that.milliseconds)
// Java-friendlier versions of the above.
def less(that: Time): Boolean = this < that
def lessEq(that: Time): Boolean = this <= that
def greater(that: Time): Boolean = this > that
def greaterEq(that: Time): Boolean = this >= that
def plus(that: Duration): Time = this + that
def minus(that: Time): Duration = this - that
def minus(that: Duration): Time = this - that
def floor(that: Duration): Time = {
val t = that.milliseconds
new Time((this.millis / t) * t)
}
def floor(that: Duration, zeroTime: Time): Time = {
val t = that.milliseconds
new Time(((this.millis - zeroTime.milliseconds) / t) * t + zeroTime.milliseconds)
}
def isMultipleOf(that: Duration): Boolean =
(this.millis % that.milliseconds == 0)
def min(that: Time): Time = if (this < that) this else that
def max(that: Time): Time = if (this > that) this else that
def until(that: Time, interval: Duration): Seq[Time] = {
(this.milliseconds) until (that.milliseconds) by (interval.milliseconds) map (new Time(_))
}
def to(that: Time, interval: Duration): Seq[Time] = {
(this.milliseconds) to (that.milliseconds) by (interval.milliseconds) map (new Time(_))
}
override def toString: String = (millis.toString + " ms")
}
object Time {
implicit val ordering = Ordering.by((time: Time) => time.millis)
}
跟踪Time对象，ReceiverTracker的allocateBlocksToBatch方法中的入参batchTime是被JobGenerator的generateJobs方法调用的。
ReceiverTracker:
/** Allocate all unallocated blocks to the given batch. */
def allocateBlocksToBatch(batchTime: Time): Unit = {
if (receiverInputStreams.nonEmpty) {
receivedBlockTracker.allocateBlocksToBatch(batchTime)
}
}
JobGenerator的generateJobs方法是被定时器发送GenerateJobs消息调用的。
JobGenerator:
/** Generate jobs and perform checkpoint for the given `time`. */
private defgenerateJobs(time: Time) {
// Set the SparkEnv in this thread, so that job generation code can access the environment
// Example: BlockRDDs are created in this thread, and it needs to access BlockManager
// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
SparkEnv.set(ssc.env)
Try {
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
graph.generateJobs(time) // generate jobs using allocated block
} match {
case Success(jobs) =>
val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
case Failure(e) =>
jobScheduler.reportError("Error generating jobs for time " + time, e)
}
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}
JobGenerator:
/** Processes all events */
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
caseGenerateJobs(time) =>generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
JobGenerator:
private val timer = newRecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
GenerateJobs中的时间参数就是nextTime，而nextTime+=period，这个period就是ssc.graph.batchDuration.milliseconds。
RecurringTimer:
private def triggerActionForNextInterval(): Unit = {
clock.waitTillTime(nextTime)
callback(nextTime)
prevTime = nextTime
nextTime += period
logDebug("Callback for " + name + " called at time " + prevTime)
}
nextTime的初始值是在start方法中传入的startTime赋值的，即RecurringTimer的getStartTime方法的返回值，是当前时间period的(整数倍+1)。
RecurringTimer:
/**
* Start at the given start time.
*/
def start(startTime: Long): Long = synchronized {
nextTime = startTime
thread.start()
logInfo("Started timer for " + name + " at time " + nextTime)
nextTime
}
JobGenerator:
/** Starts the generator for the first time */
private def startFirstTime() {
val startTime = newTime(timer.getStartTime())
graph.start(startTime - graph.batchDuration)
timer.start(startTime.milliseconds)
logInfo("Started JobGenerator at " + startTime)
}
RecurringTimer:
/**
* Get the time when this timer will fire if it is started right now.
* The time will be a multiple of this timer's period and more than
* current system time.
*/
def getStartTime(): Long = {
(math.floor(clock.getTimeMillis().toDouble / period) + 1).toLong * period
}
Period这个值是我们调用new StreamingContext来构造StreamingContext时传入的Duration值。
DStreamGraph:
def setBatchDuration(duration: Duration) {
this.synchronized {
require(batchDuration == null,
s"Batch duration already set as $batchDuration. Cannot set it again.")
batchDuration = duration
}
}
StreamingContext:
private[streaming] val graph: DStreamGraph = {
if (isCheckpointPresent) {
cp_.graph.setContext(this)
cp_.graph.restoreCheckpointData()
cp_.graph
} else {
require(batchDur_ != null, "Batch duration for StreamingContext cannot be null")
val newGraph = new DStreamGraph()
newGraph.setBatchDuration(batchDur_)
newGraph
}
}
ReceivedBlockTracker会清除过期的元数据信息，从HashMap中移除，也是先写入磁盘，然后在写入内存。
StreamingContext:
class StreamingContext private[streaming] (
sc_ : SparkContext,
cp_ : Checkpoint,
batchDur_ : Duration
) extends Logging {
/**
* Create a StreamingContext using an existing SparkContext.
* @param sparkContext existing SparkContext
* @param batchDuration the time interval at which streaming data will be divided into batches
*/
def this(sparkContext: SparkContext, batchDuration: Duration) = {
this(sparkContext, null, batchDuration)
}
元数据的生成，消费和销毁都有WAL，所以失败时就可以从日志中恢复。从源码分析中得出只有设置了checkpoint目录，才进行WAL机制。
ReceiverTracker:
class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false) extends Logging {
private val receiverInputStreams = ssc.graph.getReceiverInputStreams()
private val receiverInputStreamIds = receiverInputStreams.map { _.id }
private val receivedBlockTracker = newReceivedBlockTracker(
ssc.sparkContext.conf,
ssc.sparkContext.hadoopConfiguration,
receiverInputStreamIds,
ssc.scheduler.clock,
ssc.isCheckpointPresent,
Option(ssc.checkpointDir)
)
private val listenerBus = ssc.scheduler.listenerBus
对传入的checkpoint目录来创建日志目录进行WAL。
ReceivedBlockTracker:
/** Optionally create the write ahead log manager only if the feature is enabled */
private defcreateWriteAheadLog(): Option[WriteAheadLog] = {
checkpointDirOption.map { checkpointDir =>
val logDir = ReceivedBlockTracker.checkpointDirToLogDir(checkpointDirOption.get)
WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf)
}
}
这里是在checkpoint目录下创建文件夹名为receivedBlockMetadata的文件夹来保存WAL记录的数据。
ReceivedBlockTracker:
private[streaming] object ReceivedBlockTracker {
def checkpointDirToLogDir(checkpointDir: String): String = {
new Path(checkpointDir, "receivedBlockMetadata").toString
}
}
ReceivedBlockTracker:
/** Write an update to the tracker to the write ahead log */
private def writeToLog(record: ReceivedBlockTrackerLogEvent): Boolean = {
if (isWriteAheadLogEnabled) {
logTrace(s"Writing record: $record")
try {
writeAheadLogOption.get.write(ByteBuffer.wrap(Utils.serialize(record)),
clock.getTimeMillis())
true
} catch {
case NonFatal(e) =>
logWarning(s"Exception thrown while writing record: $record to the WriteAheadLog.", e)
false
}
} else {
true
}
}
把当前的DStream和JobGenerator的状态进行checkpoint，该方法是在generateJobs方法最后通过发送DoCheckpoint消息，来调用的。
JobGenerator:
/** Perform checkpoint for the give `time`. */
private defdoCheckpoint(time: Time, clearCheckpointDataLater: Boolean) {
if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) {
logInfo("Checkpointing graph for time " + time)
ssc.graph.updateCheckpointData(time)
checkpointWriter.write(new Checkpoint(ssc, time), clearCheckpointDataLater)
}
}
JobGenerator:
/** Processes all events */
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
case GenerateJobs(time) => generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
JobGenerator:
/** Generate jobs and perform checkpoint for the given `time`. */
private defgenerateJobs(time: Time) {
// Set the SparkEnv in this thread, so that job generation code can access the environment
// Example: BlockRDDs are created in this thread, and it needs to access BlockManager
// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
SparkEnv.set(ssc.env)
Try {
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
graph.generateJobs(time) // generate jobs using allocated block
} match {
case Success(jobs) =>
val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
case Failure(e) =>
jobScheduler.reportError("Error generating jobs for time " + time, e)
}
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}
总结：
ReceivedBlockTracker是通过WAL方式来进行数据容错的。
DStreamGraph和JobGenerator是通过checkpoint方式来进行数据容错的。
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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