Introduction to Big Data and Hadoop/

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Anatomy of a Spark Application

Anatomy of a Spark Application

This lesson explains the constituents of a Spark job.

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Anatomy a Spark Application

In this lesson, we’ll formally look at various components of a Spark job. A Spark application consists of one or several jobs. But a Spark job, unlike MapReduce, is much broader in scope. Each job is made of a directed acyclic graph of stages. A stage is roughly equivalent to a map or reduce phase in MapReduce. A stage is split into tasks by the Spark runtime and executed in parallel on partitions of an RDD across the cluster. The relationship among these various concepts is depicted below:

A single Spark application can run one or more Spark jobs serially or in parallel. Cached RDDs output from one job can be made available to a second without requiring disk I/O in between. This makes certain computations extremely fast. A job always executes in the context of a Spark application. The spark-shell is an instance of a Spark application.

Let’s see an example to better understand jobs, stages and tasks. Consider the example below; it creates two DataFrames, each consisting of integers from 0 to 9. Next, we transform one of the DataFrames to consist of multiples of 3 by multiplying each element with 3. Finally, we compute the intersection of the two DataFrames and sum up the result. The Scala code, presented below can be executed in the terminal.

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    // Start the Spark shell

    spark-shell



    // Create a dataframe consisting of ints from 0 to 9

    val dataFrame1 = spark.range(10).toDF()

    val dataFrame2 = spark.range(10).toDF()



    // Examine the number of partitions

    dataFrame1.rdd.getNumPartitions



    // Repartition the data

    val transformation1 = dataFrame1.repartition(20)

    val transformation2 = dataFrame2.repartition(20)



    // Create multiples of 3 from dataFrame1

    val mulsOf3 = transformation1.selectExpr("id * 3 as id")



    // Perform an inner join between the two dataframes.

    // transformation1.collect() = [0, 1, 2, 3, 4, 5. 6, 7, 8, 9]

    // mulsOf3.collect() = [0, 3, 6, 9, 12, 15, 18, 21, 24, 27]

    val join = mulsOf3.join(transformation2, "id")



    // Sum the intersection of the two dataFrames now

    // join.collect() = [0, 3, 6]

    val sum = join.selectExpr("sum(id)")



    // Perform an action to get the final result

    val result = sum.collect()



    // Examine the physical plan generated

    sum.explain()
Terminal 1
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You need to draw a distinction between transformations and actions in a Spark job. All the statements before line#28 sum.collect() are transformations. Transformations are at the core of expressing your business logic using Spark. ...