Github user CodingCat commented on the pull request:
https://github.com/apache/incubator-spark/pull/602#issuecomment-35157378
Hi, @mridulm , I think it will be used in local, mesos, and standalone mode
1. local
case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
// Check to make sure memory requested <= memoryPerSlave. Otherwise
Spark will just hang.
val memoryPerSlaveInt = memoryPerSlave.toInt
if (sc.executorMemory > memoryPerSlaveInt) {
throw new SparkException(
"Asked to launch cluster with %d MB RAM / worker but requested
%d MB/worker".format(
memoryPerSlaveInt, sc.executorMemory))
}
val scheduler = new TaskSchedulerImpl(sc)
val localCluster = new LocalSparkCluster(
numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt)
val masterUrls = localCluster.start()
val backend = new SparkDeploySchedulerBackend(scheduler, sc,
masterUrls, appName)
scheduler.initialize(backend)
backend.shutdownCallback = (backend: SparkDeploySchedulerBackend)
=> {
localCluster.stop()
}
scheduler
2. standalone (SparkDeployClusterBackend.scala)
override def start() {
super.start()
// The endpoint for executors to talk to us
val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
conf.get("spark.driver.host"), conf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}",
"{{CORES}}", "{{WORKER_URL}}")
val command = Command(
"org.apache.spark.executor.CoarseGrainedExecutorBackend", args,
sc.executorEnvs)
val sparkHome = sc.getSparkHome()
val appDesc = new ApplicationDescription(appName, maxCores,
sc.executorMemory, command,
sparkHome, "http://"; + sc.ui.appUIAddress)
client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
client.start()
}
3. CoarseMesosSchedulerBackend.scala
resourceOffers(d: SchedulerDriver, offers: JList[Offer])
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