Running Spark Applications
You can run Spark applications locally or distributed across a cluster, either by using an interactive shell or by submitting an application. Running Spark applications interactively is commonly performed during the data-exploration phase and for ad-hoc analysis.
Because of a limitation in the way Scala compiles code, some applications with nested definitions running in an interactive shell may encounter a Task not serializable exception. Cloudera recommends submitting these applications.
To run applications distributed across a cluster, Spark requires a cluster manager. Cloudera supports two cluster managers: YARN and Spark Standalone. When run on YARN, Spark application processes are managed by the YARN ResourceManager and NodeManager roles. When run on Spark Standalone, Spark application processes are managed by Spark Master and Worker roles.
- You can dynamically share and centrally configure the same pool of cluster resources among all frameworks that run on YARN.
- You can use all the features of YARN schedulers for categorizing, isolating, and prioritizing workloads.
- You choose the number of executors to use; in contrast, Spark Standalone requires each application to run an executor on every host in the cluster.
- Spark can run against Kerberos-enabled Hadoop clusters and use secure authentication between its processes.
For information on monitoring Spark applications, see Monitoring Spark Applications.
Submitting Spark Applications
To submit an application consisting of a Python file or a compiled and packaged Java or Spark JAR, use the spark-submit script.
spark-submit --option value \ application jar | python file [application arguments]Example: Running SparkPi on YARN demonstrates how to run one of the sample applications, SparkPi, packaged with Spark. It computes an approximation to the value of pi.
|application jar||Path to a JAR file containing a Spark application and all dependencies. For the client deployment mode, the path must point to a local file. For the cluster deployment, the path must be globally visible inside your cluster; see Advanced Dependency Management.|
|python file||Path to a Python file containing a Spark application. For the client deployment mode, the path must point to a local file. For the cluster deployment, the path must be globally visible inside your cluster; see Advanced Dependency Management.|
|application arguments||Arguments to pass to the main method of your main class.|
|class||For Java and Scala applications, the fully-qualified classname of the class containing the main method of the application. For example, org.apache.spark.examples.SparkPi.|
|conf||Spark configuration property in key=value format. For values that contain spaces, surround "key=value" with quotes (as shown).|
|deploy-mode||Deployment mode: cluster and client. In cluster mode, the driver runs
on worker hosts. In client mode, the driver runs locally as an external client. Use cluster mode with production jobs; client mode is more appropriate for interactive and debugging uses, where you
want to see your application output immediately. To see the effect of the deployment mode when running on YARN, see Deployment Modes.
|driver-cores||Number of cores used by the driver, only in cluster mode.
|driver-memory||Maximum heap size (represented as a JVM string; for example 1024m, 2g, and so on) to allocate to the driver. Alternatively, you can use the spark.driver.memory property.|
|files||Comma-separated list of files to be placed in the working directory of each executor. For the client deployment mode, the path must point to a local file. For the cluster deployment, the path must be globally visible inside your cluster; see Advanced Dependency Management.|
|jars||Additional JARs to be loaded in the classpath of drivers and executors in cluster mode or in the executor classpath in client mode. For the client deployment mode, the path must point to a local file. For the cluster deployment, the path must be globally visible inside your cluster; see Advanced Dependency Management.|
|master||The location to run the application.|
|packages||Comma-separated list of Maven coordinates of JARs to include on the driver and executor classpaths. The local Maven, Maven central, and remote repositories specified in repositories are searched in that order. The format for the coordinates is groupId:artifactId:version.|
|py-files||Comma-separated list of .zip, .egg, or .py files to place on PYTHONPATH. For the client deployment mode, the path must point to a local file. For the cluster deployment, the path must be globally visible inside your cluster; see Advanced Dependency Management.|
|repositories||Comma-separated list of remote repositories to search for the Maven coordinates specified in packages.|
|local||Run Spark locally with one worker thread (that is, no parallelism).|
|local[K]||Run Spark locally with K worker threads. (Ideally, set this to the number of cores on your host.)|
|local[*]||Run Spark locally with as many worker threads as logical cores on your host.|
|spark://host:port||Run using the Spark Standalone cluster manager with the Spark Master on the specified host and port (7077 by default).|
|yarn||Run using a YARN cluster manager. The cluster location is determined by HADOOP_CONF_DIR or YARN_CONF_DIR. See Configuring the Environment.|
Cluster Execution Overview
- The driver is launched and invokes the main method in the Spark application.
- The driver requests resources from the cluster manager to launch executors.
- The cluster manager launches executors on behalf of the driver program.
- The driver runs the application. Based on the transformations and actions in the application, the driver sends tasks to executors.
- Tasks are run on executors to compute and save results.
- If dynamic allocation is enabled, after executors are idle for a specified period, they are released.
- When driver's main method exits or calls SparkContext.stop, it terminates any outstanding executors and releases resources from the cluster manager.