当用户生成sparkcontext是，在读入文件，
可以看出这里直接调用rdd的saveAsTextFile
spark-master\spark-master\core\src\main\scala\org\apache\spark\api\java\JavaRDDLike.scala
def saveAsTextFile(path: String): Unit = {
#触发rdd的action
rdd.saveAsTextFile(path)
}
spark-master\spark-master\core\src\main\scala\org\apache\spark\rdd\RDD.scala
def saveAsTextFile(path: String): Unit = withScope {
val nullWritableClassTag = implicitly[ClassTag[NullWritable]]
val textClassTag = implicitly[ClassTag[Text]]
val r = this.mapPartitions { iter =>
val text = new Text()
iter.map { x =>
text.set(x.toString)
(NullWritable.get(), text)
}
}
#将rdd保存为hadoop支持的文件系统
RDD.rddToPairRDDFunctions(r)(nullWritableClassTag, textClassTag, null)
.saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path)
}
/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
def saveAsHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: OutputFormat[_, _]],
conf: JobConf = new JobConf(self.context.hadoopConfiguration),
codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
// Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
val hadoopConf = conf
hadoopConf.setOutputKeyClass(keyClass)
hadoopConf.setOutputValueClass(valueClass)
conf.setOutputFormat(outputFormatClass)
for (c <- codec) {
hadoopConf.setCompressMapOutput(true)
hadoopConf.set("mapreduce.output.fileoutputformat.compress", "true")
hadoopConf.setMapOutputCompressorClass(c)
hadoopConf.set("mapreduce.output.fileoutputformat.compress.codec", c.getCanonicalName)
hadoopConf.set("mapreduce.output.fileoutputformat.compress.type",
CompressionType.BLOCK.toString)
}
#调用saveAsHadoopDataset
FileOutputFormat.setOutputPath(hadoopConf,
SparkHadoopWriterUtils.createPathFromString(path, hadoopConf))
saveAsHadoopDataset(hadoopConf)
}
在saveAsHadoopDataset 中调用SparkHadoopWriter.write
spark-master\spark-master\core\src\main\scala\org\apache\spark\rdd\PairRDDFunctions.scala
def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
val config = new HadoopMapRedWriteConfigUtil[K, V](new SerializableJobConf(conf))
SparkHadoopWriter.write(
rdd = self,
config = config)
}
spark-master\spark-master\core\src\main\scala\org\apache\spark\internal\io\SparkHadoopWriter.scala
def write[K, V: ClassTag](
rdd: RDD[(K, V)],
config: HadoopWriteConfigUtil[K, V]): Unit = {
// Extract context and configuration from RDD.
val sparkContext = rdd.context
val commitJobId = rdd.id
// Set up a job.
val jobTrackerId = createJobTrackerID(new Date())
val jobContext = config.createJobContext(jobTrackerId, commitJobId)
config.initOutputFormat(jobContext)
// Assert the output format/key/value class is set in JobConf.
config.assertConf(jobContext, rdd.conf)
val committer = config.createCommitter(commitJobId)
committer.setupJob(jobContext)
// Try to write all RDD partitions as a Hadoop OutputFormat.
try {
#最终由调回到sparkcontext的runjob防范
val ret = sparkContext.runJob(rdd, (context: TaskContext, iter: Iterator[(K, V)]) => {
// SPARK-24552: Generate a unique "attempt ID" based on the stage and task attempt numbers.
// Assumes that there won't be more than Short.MaxValue attempts, at least not concurrently.
val attemptId = (context.stageAttemptNumber << 16) | context.attemptNumber
executeTask(
context = context,
config = config,
jobTrackerId = jobTrackerId,
commitJobId = commitJobId,
sparkPartitionId = context.partitionId,
sparkAttemptNumber = attemptId,
committer = committer,
iterator = iter)
})
spark-master\spark-master\core\src\main\scala\org\apache\spark\SparkContext.scala
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit): Unit = {
if (stopped.get()) {
throw new IllegalStateException("SparkContext has been shutdown")
}
val callSite = getCallSite
val cleanedFunc = clean(func)
logInfo("Starting job: " + callSite.shortForm)
if (conf.getBoolean("spark.logLineage", false)) {
logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
}
#调用dagScheduler.runJob
dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
progressBar.foreach(_.finishAll())
rdd.doCheckpoint()
}
spark-master\core\src\main\scala\org\apache\spark\scheduler\DAGScheduler.scala
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
#通过DAG的submit提交job
val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
#等待job执行完成
ThreadUtils.awaitReady(waiter.completionFuture, Duration.Inf)
#判断执行的结果是成功还是失败
waiter.completionFuture.value.get match {
case scala.util.Success(_) =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case scala.util.Failure(exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
// SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
val callerStackTrace = Thread.currentThread().getStackTrace.tail
exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
throw exception
}
}
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): JobWaiter[U] = {
// Check to make sure we are not launching a task on a partition that does not exist.
val maxPartitions = rdd.partitions.length
partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
throw new IllegalArgumentException(
"Attempting to access a non-existent partition: " + p + ". " +
"Total number of partitions: " + maxPartitions)
}
val jobId = nextJobId.getAndIncrement()
if (partitions.size == 0) {
// Return immediately if the job is running 0 tasks
return new JobWaiter[U](this, jobId, 0, resultHandler)
}
assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
#创建JobWaiter对象
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
#将jobsubmit放到event的队列当中
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions.toArray, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}
在EventLoop.scala 中有实现一个thread 会一直从eventProcessLoop的队列中取job来执行
def post(event: E): Unit = {
eventQueue.put(event)
}
spark-master\core\src\main\scala\org\apache\spark\util\EventLoop.scala
private[spark] abstract class EventLoop[E](name: String) extends Logging {
private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()
private val stopped = new AtomicBoolean(false)
// Exposed for testing.
private[spark] val eventThread = new Thread(name) {
setDaemon(true)
override def run(): Unit = {
try {
while (!stopped.get) {
val event = eventQueue.take()
try {
#核心是调用onReceive方法处理
onReceive(event)
} catch {
case NonFatal(e) =>
try {
onError(e)
} catch {
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
} catch {
最终在DAGSchedulerEventProcessLoop 中实现onReceive
spark-master\spark-master\core\src\main\scala\org\apache\spark\scheduler\DAGScheduler.scala
private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler)
extends EventLoop[DAGSchedulerEvent]("dag-scheduler-event-loop") with Logging {
private[this] val timer = dagScheduler.metricsSource.messageProcessingTimer
/**
* The main event loop of the DAG scheduler.
*/
override def onReceive(event: DAGSchedulerEvent): Unit = {
val timerContext = timer.time()
try {
doOnReceive(event)
} finally {
timerContext.stop()
}
}
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
#可见是调用dagScheduler.handleJobSubmitted来完成整个job的提交
case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
case StageCancelled(stageId, reason) =>
dagScheduler.handleStageCancellation(stageId, reason)
case JobCancelled(jobId, reason) =>
dagScheduler.handleJobCancellation(jobId, reason)
}
