Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. In addition, any new classes you define in the shell will automatically be distributed. spark-shell will launch a shell with a.jar and b.jar on its classpath.
This variable should contain a comma-separated list of JARs. If you run spark-shell on a cluster, you can add JARs to it by specifying the ADD_JARS environment variable before you launch it.
Array scala code#
For example, if you’re using SBT, the sbt-assembly plugin is a good way to make a single JAR with your code and dependencies. You’ll need to package your job into a set of JARs using your build system. jars: A list of JAR files on the local machine containing your job’s code and any dependencies, which Spark will deploy to all the worker nodes.sparkHome: The path at which Spark is installed on your worker machines (it should be the same on all of them).If you want to run your job on a cluster, you will need to specify the two optional parameters to SparkContext to let it find your code: The port must be whichever one the master is configured to use,įor running on YARN, Spark launches an instance of the standalone deploy cluster within YARN see running on YARN for details. The host parameter is the hostname of the Mesos master. The port must be whichever one your master is configured to use, which is 7077 by default. Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).Ĭluster master. Run Spark locally with one worker thread (i.e. The master URL passed to Spark can be in one of the following formats: Master URL Add the following lines at the top of your program: In addition, you’ll need to import some Spark classes and implicit conversions. If you use sbt or Maven, Spark is available through Maven Central at: groupId = org.spark-projectįor other build systems or environments, you can run sbt/sbt assembly to build both Spark and its dependencies into one JAR ( core/target/spark-core-assembly-0.6.0.jar), then add this to your CLASSPATH. To write a Spark application, you will need to add both Spark and its dependencies to your CLASSPATH. We highly recommend doing that to follow along! Linking with Spark Note that you can also run Spark interactively using the spark-shell script. It assumes some familiarity with Scala, especially with the syntax for closures. This guide shows each of these features and walks through some samples. Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums.
Array scala driver#
Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Finally, RDDs automatically recover from node failures.Ī second abstraction in Spark is shared variables that can be used in parallel operations. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations.
RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. At a high level, every Spark application consists of a driver program that runs the user’s main function and executes various parallel operations on a cluster.
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