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Diffstat (limited to 'docs/running-on-yarn.md')
| -rw-r--r-- | docs/running-on-yarn.md | 54 |
1 files changed, 43 insertions, 11 deletions
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 4056e9c15d..b206270107 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -12,11 +12,12 @@ was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0. We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster. This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows: - SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly + SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly The assembled JAR will be something like this: `./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`. +The build process now also supports new YARN versions (2.2.x). See below. # Preparations @@ -31,27 +32,34 @@ If you want to test out the YARN deployment mode, you can use the current Spark Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those. These are configs that are specific to SPARK on YARN. Environment variables: + * `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`. System Properties: -* 'spark.yarn.applicationMaster.waitTries', property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10. -* 'spark.yarn.submit.file.replication', the HDFS replication level for the files uploaded into HDFS for the application. These include things like the spark jar, the app jar, and any distributed cache files/archives. -* 'spark.yarn.preserve.staging.files', set to true to preserve the staged files(spark jar, app jar, distributed cache files) at the end of the job rather then delete them. -* 'spark.yarn.scheduler.heartbeat.interval-ms', the interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. Default is 5 seconds. -* 'spark.yarn.max.worker.failures', the maximum number of worker failures before failing the application. Default is the number of workers requested times 2 with minimum of 3. + +* `spark.yarn.applicationMaster.waitTries`, property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10. +* `spark.yarn.submit.file.replication`, the HDFS replication level for the files uploaded into HDFS for the application. These include things like the spark jar, the app jar, and any distributed cache files/archives. +* `spark.yarn.preserve.staging.files`, set to true to preserve the staged files(spark jar, app jar, distributed cache files) at the end of the job rather then delete them. +* `spark.yarn.scheduler.heartbeat.interval-ms`, the interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. Default is 5 seconds. +* `spark.yarn.max.worker.failures`, the maximum number of worker failures before failing the application. Default is the number of workers requested times 2 with minimum of 3. # Launching Spark on YARN Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the hadoop cluster. This would be used to connect to the cluster, write to the dfs and submit jobs to the resource manager. +There are two scheduler mode that can be used to launch spark application on YARN. + +## Launch spark application by YARN Client with yarn-standalone mode. + The command to launch the YARN Client is as follows: - SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./spark-class org.apache.spark.deploy.yarn.Client \ + SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar <YOUR_APP_JAR_FILE> \ --class <APP_MAIN_CLASS> \ --args <APP_MAIN_ARGUMENTS> \ --num-workers <NUMBER_OF_WORKER_MACHINES> \ + --master-class <ApplicationMaster_CLASS> --master-memory <MEMORY_FOR_MASTER> \ --worker-memory <MEMORY_PER_WORKER> \ --worker-cores <CORES_PER_WORKER> \ @@ -64,14 +72,14 @@ The command to launch the YARN Client is as follows: For example: # Build the Spark assembly JAR and the Spark examples JAR - $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly + $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly # Configure logging $ cp conf/log4j.properties.template conf/log4j.properties # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ - ./spark-class org.apache.spark.deploy.yarn.Client \ + ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ --class org.apache.spark.examples.SparkPi \ --args yarn-standalone \ @@ -85,11 +93,35 @@ For example: $ cat $YARN_APP_LOGS_DIR/$YARN_APP_ID/container*_000001/stdout Pi is roughly 3.13794 -The above starts a YARN Client programs which periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running. +The above starts a YARN Client programs which start the default Application Master. Then SparkPi will be run as a child thread of Application Master, YARN Client will periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running. + +With this mode, your application is actually run on the remote machine where the Application Master is run upon. Thus application that involve local interaction will not work well, e.g. spark-shell. + +## 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 + +In order to tune worker core/number/memory etc. You need to export SPARK_WORKER_CORES, SPARK_WORKER_MEMORY, SPARK_WORKER_INSTANCES e.g. by ./conf/spark-env.sh + +For example: + + SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ + SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ + ./bin/run-example org.apache.spark.examples.SparkPi yarn-client + + + SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ + SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ + MASTER=yarn-client ./bin/spark-shell + +You can also send extra files to yarn cluster for worker to use by exporting SPARK_YARN_DIST_FILES=file1,file2... etc. + +# Building Spark for Hadoop/YARN 2.2.x + +See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process. # Important Notes -- When your application instantiates a Spark context it must use a special "yarn-standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "yarn-standalone" as an argument to your program, as shown in the example above. - We do not requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed. - The local directories used for spark will be the local directories configured for YARN (Hadoop Yarn config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored. - The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt and your application should use the name as appSees.txt to reference it when running on YARN. |