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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.scheduler.cluster
import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState}
import org.apache.spark.{SparkException, Logging, SparkContext}
import org.apache.spark.deploy.yarn.{Client, ClientArguments}
import org.apache.spark.scheduler.TaskSchedulerImpl
private[spark] class YarnClientSchedulerBackend(
scheduler: TaskSchedulerImpl,
sc: SparkContext)
extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
with Logging {
var client: Client = null
var appId: ApplicationId = null
override def start() {
super.start()
val defalutWorkerCores = "2"
val defalutWorkerMemory = "512m"
val defaultWorkerNumber = "1"
val userJar = System.getenv("SPARK_YARN_APP_JAR")
var workerCores = System.getenv("SPARK_WORKER_CORES")
var workerMemory = System.getenv("SPARK_WORKER_MEMORY")
var workerNumber = System.getenv("SPARK_WORKER_INSTANCES")
if (userJar == null)
throw new SparkException("env SPARK_YARN_APP_JAR is not set")
if (workerCores == null)
workerCores = defalutWorkerCores
if (workerMemory == null)
workerMemory = defalutWorkerMemory
if (workerNumber == null)
workerNumber = defaultWorkerNumber
val driverHost = System.getProperty("spark.driver.host")
val driverPort = System.getProperty("spark.driver.port")
val hostport = driverHost + ":" + driverPort
val argsArray = Array[String](
"--class", "notused",
"--jar", userJar,
"--args", hostport,
"--worker-memory", workerMemory,
"--worker-cores", workerCores,
"--num-workers", workerNumber,
"--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher"
)
val args = new ClientArguments(argsArray)
client = new Client(args)
appId = client.runApp()
waitForApp()
}
def waitForApp() {
// TODO : need a better way to find out whether the workers are ready or not
// maybe by resource usage report?
while(true) {
val report = client.getApplicationReport(appId)
logInfo("Application report from ASM: \n" +
"\t appMasterRpcPort: " + report.getRpcPort() + "\n" +
"\t appStartTime: " + report.getStartTime() + "\n" +
"\t yarnAppState: " + report.getYarnApplicationState() + "\n"
)
// Ready to go, or already gone.
val state = report.getYarnApplicationState()
if (state == YarnApplicationState.RUNNING) {
return
} else if (state == YarnApplicationState.FINISHED ||
state == YarnApplicationState.FAILED ||
state == YarnApplicationState.KILLED) {
throw new SparkException("Yarn application already ended," +
"might be killed or not able to launch application master.")
}
Thread.sleep(1000)
}
}
override def stop() {
super.stop()
client.stop()
logInfo("Stoped")
}
}
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