Spark RDD Transformations with examples

RDD Transformations are Spark operations when executed on RDD, it results in a single or multiple new RDD’s. Since RDD are immutable in nature, transformations always create new RDD without updating an existing one hence, this creates an RDD lineage.

RDD Lineage is also known as the RDD operator graph or RDD dependency graph.

In this tutorial, you will learn lazy transformations, types of transformations, a complete list of transformation functions using wordcount example in scala.

• What is a lazy transformation
• Transformation types
  • Narrow transformation
  • Wide transformation
• Transformation functions
• Transformation functions with word count examples

RDD Transformations are Lazy

RDD Transformations are lazy operations meaning none of the transformations get executed until you call an action on Spark RDD. Since RDD’s are immutable, any transformations on it result in a new RDD leaving the current one unchanged.

RDD Transformation Types

There are two types are transformations.

Narrow Transformation

Narrow transformations are the result of map() and filter() functions and these compute data that live on a single partition meaning there will not be any data movement between partitions to execute narrow transformations.

Functions such as map(), mapPartition(), flatMap(), filter(), union() are some examples of narrow transformation

Wide Transformation

Wide transformations are the result of groupByKey() and reduceByKey() functions and these compute data that live on many partitions meaning there will be data movements between partitions to execute wide transformations. Since these shuffles the data, they also called shuffle transformations.

Functions such as groupByKey(), aggregateByKey(), aggregate(), join(), repartition() are some examples of a wide transformations.

Note: When compared to Narrow transformations, wide transformations are expensive operations due to shuffling.

Spark RDD Transformation functions

Spark RDD Transformations with Examples

In this section, I will explain a few RDD Transformations with word count example in scala, before we start first, let’s create an RDD by reading a text file. The text file used here is available at the GitHub and, the scala example is available at GitHub project for reference.

// Create RDD by using textFile
val spark:SparkSession = SparkSession.builder()
      .master("local[3]")
      .appName("SparkByExamples.com")
      .getOrCreate()

val sc = spark.sparkContext

val rdd:RDD[String] = sc.textFile("src/main/scala/test.txt")

flatMap() Transformation

flatMap() transformation flattens the RDD after applying the function and returns a new RDD. On the below example, first, it splits each record by space in an RDD and finally flattens it. Resulting RDD consists of a single word on each record.

// Apply flatMap() 
val rdd2 = rdd.flatMap(f=>f.split(" "))

map() Transformation

map() transformation is used the apply any complex operations like adding a column, updating a column e.t.c, the output of map transformations would always have the same number of records as input.

In our word count example, we are adding a new column with value 1 for each word, the result of the RDD is PairRDDFunctions which contains key-value pairs, word of type String as Key and 1 of type Int as value. For your understanding, I’ve defined rdd3 variable with type.

// Apply map()
val rdd3:RDD[(String,Int)]= rdd2.map(m=>(m,1))

filter() Transformation

filter() transformation is used to filter the records in an RDD. In our example we are filtering all words starts with “a”.

// Apply filter()
val rdd4 = rdd3.filter(a=> a._1.startsWith("a"))

reduceByKey() Transformation

reduceByKey() merges the values for each key with the function specified. In our example, it reduces the word string by applying the sum function on value. The result of our RDD contains unique words and their count. 

// Apply reduceByKey()
val rdd5 = rdd3.reduceByKey(_ + _)

sortByKey() Transformation

sortByKey() transformation is used to sort RDD elements on key. In our example, first, we convert RDD[(String,Int]) to RDD[(Int,String]) using map transformation and apply sortByKey which ideally does sort on an integer value. And finally, foreach with println statement prints all words in RDD and their count as key-value pair to console.

// Apply sortByKey()
val rdd6 = rdd5.map(a=>(a._2,a._1)).sortByKey()

//Print rdd6 result to console
rdd6.foreach(println)

Spark RDD Transformations complete example

package com.sparkbyexamples.spark.rdd

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

object WordCountExample {

  def main(args:Array[String]): Unit = {

    val spark:SparkSession = SparkSession.builder()
      .master("local[3]")
      .appName("SparkByExamples.com")
      .getOrCreate()

    val sc = spark.sparkContext

    val rdd:RDD[String] = sc.textFile("src/main/resources/test.txt")
    println("initial partition count:"+rdd.getNumPartitions)

    val reparRdd = rdd.repartition(4)
    println("re-partition count:"+reparRdd.getNumPartitions)

    //rdd.coalesce(3)

    rdd.collect().foreach(println)

    // rdd flatMap transformation
    val rdd2 = rdd.flatMap(f=>f.split(" "))
    rdd2.foreach(f=>println(f))

    //Create a Tuple by adding 1 to each word
    val rdd3:RDD[(String,Int)]= rdd2.map(m=>(m,1))
    rdd3.foreach(println)

    //Filter transformation
    val rdd4 = rdd3.filter(a=> a._1.startsWith("a"))
    rdd4.foreach(println)

    //ReduceBy transformation
    val rdd5 = rdd3.reduceByKey(_ + _)
    rdd5.foreach(println)

    //Swap word,count and sortByKey transformation
    val rdd6 = rdd5.map(a=>(a._2,a._1)).sortByKey()
    println("Final Result")

    //Action - foreach
    rdd6.foreach(println)

    //Action - count
    println("Count : "+rdd6.count())

    //Action - first
    val firstRec = rdd6.first()
    println("First Record : "+firstRec._1 + ","+ firstRec._2)

    //Action - max
    val datMax = rdd6.max()
    println("Max Record : "+datMax._1 + ","+ datMax._2)

    //Action - reduce
    val totalWordCount = rdd6.reduce((a,b) => (a._1+b._1,a._2))
    println("dataReduce Record : "+totalWordCount._1)
    //Action - take
    val data3 = rdd6.take(3)
    data3.foreach(f=>{
      println("data3 Key:"+ f._1 +", Value:"+f._2)
    })

    //Action - collect
    val data = rdd6.collect()
    data.foreach(f=>{
      println("Key:"+ f._1 +", Value:"+f._2)
    })

    //Action - saveAsTextFile
    rdd5.saveAsTextFile("c:/tmp/wordCount")
    
  }
}

Conclusion

In this Spark RDD Transformations tutorial, you have learned different transformation functions and their usage with scala examples and GitHub project for quick reference.

Happy Learning !!

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