Support for running on YARN (Hadoop NextGen) was added to Spark in version 0.6.0, and improved in subsequent releases.
Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Please see Spark Security and the specific security sections in this doc before running Spark.
Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster. These configs are used to write to HDFS and connect to the YARN ResourceManager. The configuration contained in this directory will be distributed to the YARN cluster so that all containers used by the application use the same configuration. If the configuration references Java system properties or environment variables not managed by YARN, they should also be set in the Spark application's configuration (driver, executors, and the AM when running in client mode).
There are two deploy modes that can be used to launch Spark applications on YARN. In cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
Unlike other cluster managers supported by Spark in which the master‘s address is specified in the --master parameter, in YARN mode the ResourceManager’s address is picked up from the Hadoop configuration. Thus, the --master parameter is yarn.
To launch a Spark application in cluster mode:
$ ./bin/spark-submit --class path.to.your.Class --master yarn --deploy-mode cluster [options] <app jar> [app options]
For example:
$ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 4g \
--executor-memory 2g \
--executor-cores 1 \
--queue thequeue \
examples/jars/spark-examples*.jar \
10
The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running. Refer to the Debugging your Application section below for how to see driver and executor logs.
To launch a Spark application in client mode, do the same, but replace cluster with client. The following shows how you can run spark-shell in client mode:
$ ./bin/spark-shell --master yarn --deploy-mode client
In cluster mode, the driver runs on a different machine than the client, so SparkContext.addJar won't work out of the box with files that are local to the client. To make files on the client available to SparkContext.addJar, include them with the --jars option in the launch command.
$ ./bin/spark-submit --class my.main.Class \
--master yarn \
--deploy-mode cluster \
--jars my-other-jar.jar,my-other-other-jar.jar \
my-main-jar.jar \
app_arg1 app_arg2
Running Spark on YARN requires a binary distribution of Spark which is built with YARN support. Binary distributions can be downloaded from the downloads page of the project website. To build Spark yourself, refer to Building Spark.
To make Spark runtime jars accessible from YARN side, you can specify spark.yarn.archive or spark.yarn.jars. For details please refer to Spark Properties. If neither spark.yarn.archive nor spark.yarn.jars is specified, Spark will create a zip file with all jars under $SPARK_HOME/jars and upload it to the distributed cache.
Most of the configs are the same for Spark on YARN as for other deployment modes. See the configuration page for more information on those. These are configs that are specific to Spark on YARN.
In YARN terminology, executors and application masters run inside “containers”. YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the yarn logs command.
yarn logs -applicationId <app ID>
will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (yarn.nodemanager.remote-app-log-dir and yarn.nodemanager.remote-app-log-dir-suffix). The logs are also available on the Spark Web UI under the Executors Tab. You need to have both the Spark history server and the MapReduce history server running and configure yarn.log.server.url in yarn-site.xml properly. The log URL on the Spark history server UI will redirect you to the MapReduce history server to show the aggregated logs.
When log aggregation isn‘t turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory. Subdirectories organize log files by application ID and container ID. The logs are also available on the Spark Web UI under the Executors Tab and doesn’t require running the MapReduce history server.
To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a large value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirs on the nodes on which containers are launched. This directory contains the launch script, JARs, and all environment variables used for launching each container. This process is useful for debugging classpath problems in particular. (Note that enabling this requires admin privileges on cluster settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
To use a custom log4j configuration for the application master or executors, here are the options:
log4j.properties using spark-submit, by adding it to the --files list of files to be uploaded with the application.-Dlog4j.configuration=<location of configuration file> to spark.driver.extraJavaOptions (for the driver) or spark.executor.extraJavaOptions (for executors). Note that if using a file, the file: protocol should be explicitly provided, and the file needs to exist locally on all the nodes.$SPARK_CONF_DIR/log4j.properties file and it will be automatically uploaded along with the other configurations. Note that other 2 options has higher priority than this option if multiple options are specified.Note that for the first option, both executors and the application master will share the same log4j configuration, which may cause issues when they run on the same node (e.g. trying to write to the same log file).
If you need a reference to the proper location to put log files in the YARN so that YARN can properly display and aggregate them, use spark.yarn.app.container.log.dir in your log4j.properties. For example, log4j.appender.file_appender.File=${spark.yarn.app.container.log.dir}/spark.log. For streaming applications, configuring RollingFileAppender and setting file location to YARN‘s log directory will avoid disk overflow caused by large log files, and logs can be accessed using YARN’s log utility.
To use a custom metrics.properties for the application master and executors, update the $SPARK_CONF_DIR/metrics.properties file. It will automatically be uploaded with other configurations, so you don't need to specify it manually with --files.
cluster mode, the local directories used by the Spark executors and the Spark driver 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. In client mode, the Spark executors will use the local directories configured for YARN while the Spark driver will use those defined in spark.local.dir. This is because the Spark driver does not run on the YARN cluster in client mode, only the Spark executors do.--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.--jars option allows the SparkContext.addJar function to work if you are using it with local files and running in cluster mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.Standard Kerberos support in Spark is covered in the Security page.
In YARN mode, when accessing Hadoop filesystems, Spark will automatically obtain delegation tokens for:
spark.yarn.stagingDir is not set);If an application needs to interact with other secure Hadoop filesystems, their URIs need to be explicitly provided to Spark at launch time. This is done by listing them in the spark.yarn.access.hadoopFileSystems property, described in the configuration section below.
The YARN integration also supports custom delegation token providers using the Java Services mechanism (see java.util.ServiceLoader). Implementations of org.apache.spark.deploy.yarn.security.ServiceCredentialProvider can be made available to Spark by listing their names in the corresponding file in the jar's META-INF/services directory. These providers can be disabled individually by setting spark.security.credentials.{service}.enabled to false, where {service} is the name of the credential provider.
(Works also with the “local” master.)
(Works also with the “local” master.)
Debugging Hadoop/Kerberos problems can be “difficult”. One useful technique is to enable extra logging of Kerberos operations in Hadoop by setting the HADOOP_JAAS_DEBUG environment variable.
export HADOOP_JAAS_DEBUG=true
The JDK classes can be configured to enable extra logging of their Kerberos and SPNEGO/REST authentication via the system properties sun.security.krb5.debug and sun.security.spnego.debug=true
-Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
All these options can be enabled in the Application Master:
spark.yarn.appMasterEnv.HADOOP_JAAS_DEBUG true spark.yarn.am.extraJavaOptions -Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
Finally, if the log level for org.apache.spark.deploy.yarn.Client is set to DEBUG, the log will include a list of all tokens obtained, and their expiry details
To start the Spark Shuffle Service on each NodeManager in your YARN cluster, follow these instructions:
spark-<version>-yarn-shuffle.jar. This should be under $SPARK_HOME/common/network-yarn/target/scala-<version> if you are building Spark yourself, and under yarn if you are using a distribution.NodeManagers in your cluster.yarn-site.xml on each node, add spark_shuffle to yarn.nodemanager.aux-services, then set yarn.nodemanager.aux-services.spark_shuffle.class to org.apache.spark.network.yarn.YarnShuffleService.NodeManager's heap size by setting YARN_HEAPSIZE (1000 by default) in etc/hadoop/yarn-env.sh to avoid garbage collection issues during shuffle.NodeManagers in your cluster.The following extra configuration options are available when the shuffle service is running on YARN:
Apache Oozie can launch Spark applications as part of a workflow. In a secure cluster, the launched application will need the relevant tokens to access the cluster's services. If Spark is launched with a keytab, this is automatic. However, if Spark is to be launched without a keytab, the responsibility for setting up security must be handed over to Oozie.
The details of configuring Oozie for secure clusters and obtaining credentials for a job can be found on the Oozie web site in the “Authentication” section of the specific release's documentation.
For Spark applications, the Oozie workflow must be set up for Oozie to request all tokens which the application needs, including:
To avoid Spark attempting —and then failing— to obtain Hive, HBase and remote HDFS tokens, the Spark configuration must be set to disable token collection for the services.
The Spark configuration must include the lines:
spark.security.credentials.hive.enabled false spark.security.credentials.hbase.enabled false
The configuration option spark.yarn.access.hadoopFileSystems must be unset.
It is possible to use the Spark History Server application page as the tracking URL for running applications when the application UI is disabled. This may be desirable on secure clusters, or to reduce the memory usage of the Spark driver. To set up tracking through the Spark History Server, do the following:
Be aware that the history server information may not be up-to-date with the application's state.