diff options
4 files changed, 7 insertions, 12 deletions
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index cd4509ede7..ee1d892a3b 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -99,13 +99,12 @@ With this mode, your application is actually run on the remote machine where the ## Launch spark application with yarn-client mode. -With yarn-client mode, the application will be launched locally. Just like running application or spark-shell on Local / Mesos / Standalone mode. The launch method is also the similar with them, just make sure that when you need to specify a master url, use "yarn-client" instead. And you also need to export the env value for SPARK_JAR and SPARK_YARN_APP_JAR +With yarn-client mode, the application will be launched locally. Just like running application or spark-shell on Local / Mesos / Standalone mode. The launch method is also the similar with them, just make sure that when you need to specify a master url, use "yarn-client" instead. And you also need to export the env value for SPARK_JAR. Configuration in yarn-client mode: In order to tune worker core/number/memory etc. You need to export environment variables or add them to the spark configuration file (./conf/spark_env.sh). The following are the list of options. -* `SPARK_YARN_APP_JAR`, Path to your application's JAR file (required) * `SPARK_WORKER_INSTANCES`, Number of workers to start (Default: 2) * `SPARK_WORKER_CORES`, Number of cores for the workers (Default: 1). * `SPARK_WORKER_MEMORY`, Memory per Worker (e.g. 1000M, 2G) (Default: 1G) @@ -118,12 +117,11 @@ In order to tune worker core/number/memory etc. You need to export environment v For example: SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ - SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ ./bin/run-example org.apache.spark.examples.SparkPi yarn-client +or SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ - SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ MASTER=yarn-client ./bin/spark-shell diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 1419f215c7..fe37168e5a 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -108,7 +108,7 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) { args = tail case Nil => - if (userJar == null || userClass == null) { + if (userClass == null) { printUsageAndExit(1) } @@ -129,7 +129,7 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) { System.err.println( "Usage: org.apache.spark.deploy.yarn.Client [options] \n" + "Options:\n" + - " --jar JAR_PATH Path to your application's JAR file (required)\n" + + " --jar JAR_PATH Path to your application's JAR file (required in yarn-standalone mode)\n" + " --class CLASS_NAME Name of your application's main class (required)\n" + " --args ARGS Arguments to be passed to your application's main class.\n" + " Mutliple invocations are possible, each will be passed in order.\n" + diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala index 2db5744be1..24520bd21b 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala @@ -68,7 +68,8 @@ trait ClientBase extends Logging { def validateArgs() = { Map( (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", - (args.userJar == null) -> "Error: You must specify a user jar!", + ((args.userJar == null && args.amClass == classOf[ApplicationMaster].getName) -> + "Error: You must specify a user jar when running in standalone mode!"), (args.userClass == null) -> "Error: You must specify a user class!", (args.numWorkers <= 0) -> "Error: You must specify at least 1 worker!", (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be" + diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 22e55e0c60..e7130d2407 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -44,10 +44,6 @@ private[spark] class YarnClientSchedulerBackend( override def start() { super.start() - val userJar = System.getenv("SPARK_YARN_APP_JAR") - if (userJar == null) - throw new SparkException("env SPARK_YARN_APP_JAR is not set") - val driverHost = conf.get("spark.driver.host") val driverPort = conf.get("spark.driver.port") val hostport = driverHost + ":" + driverPort @@ -55,7 +51,7 @@ private[spark] class YarnClientSchedulerBackend( val argsArrayBuf = new ArrayBuffer[String]() argsArrayBuf += ( "--class", "notused", - "--jar", userJar, + "--jar", null, "--args", hostport, "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher" ) |