The textFile method also takes an optional second argument for controlling the number of partitions of the file. For example, you can use textFile('/my/directory'), textFile('/my/directory/*.txt'), and textFile('/my/directory/*.gz'). Either copy the file to all workers or use a network-mounted shared file system.Īll of Spark’s file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. For example, we can add up the sizes of all the lines using the map and reduce operations as follows: distFile.map(s => s.length).reduce((a, b) => a + b). Once created, distFile can be acted on by dataset operations.

textFile ( 'data.txt' ) distFile : .RDD = data.

It is easiest to followĪlong with if you launch Spark’s interactive shell – either bin/spark-shell for the Scala shell orīin/pyspark for the Python one.

This guide shows each of these features in each of Spark’s supported languages. 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. 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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