Spark Transformation example in Scala (Part 2)

Updated: Oct 25, 2019

Main menu: Spark Scala Tutorial

In this post I will walk you through groupByKey, reduceByKey, aggregateByKey, sortByKey, join, cartesian, coalesce and repartition Spark transformations.

In previous blog we covered map, flatMap, mapPartitions, mapPartitionsWithIndex, filter, distinct, union, intersection and sample Spark transformations. I would encourage you all to go through these posts before this.


As name says it groups the dataset (K, V) key-value pair based on Key and stores the value as Iterable, (K, V) => (K, Iterable(V)). It's very expensive operation and consumes lot of memory if dataset is huge. For example,

scala> val rdd = sc.parallelize(List("Hello Hello Spark Apache Hello Dataneb Dataneb Dataneb Spark"))

rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[2] at parallelize at <console>:24

scala> rdd.collect

res3: Array[String] = Array(Hello Hello Spark Apache Hello Dataneb Dataneb Dataneb Spark)

// Splitting the array and creating (K, V) pair

scala> val keyValue = rdd.flatMap(words => words.split(" ")).map(x=>(x,1))

keyValue: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[16] at map at <console>:25

// Iterable[Int] Value "1" tells number of occurrences of Key

scala> keyValue.groupByKey.collect

res12: Array[(String, Iterable[Int])] = Array((Spark,CompactBuffer(1, 1)), (Dataneb,CompactBuffer(1, 1, 1)), (Hello,CompactBuffer(1, 1, 1)), (Apache,CompactBuffer(1)))


Operates on (K, V) pair dataset, but reduce func must be of type (V, V) => V. For example, if you want to reduce all the values to get the total number of occurrences.

scala> rdd

.flatMap(words => words.split(" "))


.reduceByKey((x, y)=>x+y)


res14: Array[(String, Int)] = Array((Spark,2), (Dataneb,3), (Hello,3), (Apache,1))



It's similar to reduceByKey(), I hardly use this transformation because you can achieve the same with previous transformation. For example,

scala> rdd

.flatMap(words => words.split(" "))


.aggregateByKey(0)((x, y)=> x+y, (k, v)=> k+v )


res24: Array[(String, Int)] = Array((Spark,2), (Dataneb,3), (Hello,3), (Apache,1))


Called upon key-value pair, returns sorted by keys. For example,

scala> rdd

.flatMap(words => words.split(" "))


.map(x => (x,1))

.sortByKey() -- by default Ascending


res36: Array[(String, Int)] = Array((Apache,1), (Dataneb,1), (Hello,1), (Spark,1))

scala> rdd

.flatMap(words => words.split(" "))


.map(x => (x,1))

.sortByKey(false) -- Ascending order (false)


res37: Array[(String, Int)] = Array((Spark,1), (Hello,1), (Dataneb,1), (Apache,1))



It takes datasets of type key-value pair and works same like sql joins. For no match value will be None. For example,

scala> val rdd1 = sc.parallelize(List("Apple","Orange", "Banana", "Grapes", "Strawberry", "Papaya")).map(words => (words,1))

rdd1: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[96] at map at <console>:24

scala> val rdd2 = sc.parallelize(List("Apple", "Grapes", "Peach", "Fruits")).map(words => (words,1))

rdd2: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[98] at map at <console>:24

scala> rdd1.join(rdd2).collect

res40: Array[(String, (Int, Int))] = Array((Grapes,(1,1)), (Apple,(1,1)))

scala> rdd1.rightOuterJoin(rdd2).collect

res41: Array[(String, (Option[Int], Int))] = Array((Grapes,(Some(1),1)), (Peach,(None,1)), (Apple,(Some(1),1)), (Fruits,(None,1)))

scala> rdd1.leftOuterJoin(rdd2).collect

res43: Array[(String, (Int, Option[Int]))] = Array((Grapes,(1,Some(1))), (Banana,(1,None)), (Papaya,(1,None)), (Orange,(1,None)), (Apple,(1,Some(1))), (Strawberry,(1,None)))

scala> rdd1.fullOuterJoin(rdd2).collect

res44: Array[(String, (Option[Int], Option[Int]))] = Array((Grapes,(Some(1),Some(1))), (Peach,(None,Some(1))), (Banana,(Some(1),None)), (Papaya,(Some(1),None)), (Orange,(Some(1),None)), (Apple,(Some(1),Some(1))), (Fruits,(None,Some(1))), (Strawberry,(Some(1),None)))


Same like cartesian product, return all possible pairs of elements of dataset.

scala> val rdd1 = sc.parallelize(List("Apple","Orange", "Banana", "Grapes", "Strawberry", "Papaya"))

rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[111] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(List("Apple", "Grapes", "Peach", "Fruits"))

rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[112] at parallelize at <console>:24

scala> rdd1.cartesian(rdd2).collect

res46: Array[(String, String)] = Array((Apple,Apple), (Apple,Grapes), (Apple,Peach), (Apple,Fruits), (Orange,Apple), (Banana,Apple), (Orange,Grapes), (Banana,Grapes), (Orange,Peach), (Banana,Peach), (Orange,Fruits), (Banana,Fruits), (Grapes,Apple), (Grapes,Grapes), (Grapes,Peach), (Grapes,Fruits), (Strawberry,Apple), (Papaya,Apple), (Strawberry,Grapes), (Papaya,Grapes), (Strawberry,Peach), (Papaya,Peach), (Strawberry,Fruits), (Papaya,Fruits))



coalesce and repartition both shuffles the data to increase or decrease the partition, but repartition is more costlier operation as it re-shuffles all data and creates new partition. For example,

scala> val distData = sc.parallelize(1 to 16, 4)

distData: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[128] at parallelize at <console>:24

// current partition size

scala> distData.partitions.size

res63: Int = 4

// checking data across each partition

scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect

res64: Array[Int] = Array(1, 2, 3, 4)

scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect

res65: Array[Int] = Array(5, 6, 7, 8)

scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 2) iter else Iterator()).collect

res66: Array[Int] = Array(9, 10, 11, 12)

scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 3) iter else Iterator()).collect

res67: Array[Int] = Array(13, 14, 15, 16)

// decreasing partitions to 2

scala> val coalData = distData.coalesce(2)

coalData: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[133] at coalesce at <console>:25

// see how shuffling occurred. Instead of moving all data it just moved 2 partitions.

scala> coalData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect

res68: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8)

scala> coalData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect

res69: Array[Int] = Array(9, 10, 11, 12, 13, 14, 15, 16)


Notice how it re-shuffled everything to create new partitions as compared to previous RDDs - distData and coalData. Hence repartition is more costlier operation as compared to coalesce.

scala> val repartData = distData.repartition(2)

repartData: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[139] at repartition at <console>:25

// checking data across each partition

scala> repartData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect

res70: Array[Int] = Array(1, 3, 6, 8, 9, 11, 13, 15)

scala> repartData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect

res71: Array[Int] = Array(2, 4, 5, 7, 10, 12, 14, 16)


That's all folks. If you have any question please mention in comments section below. Thank you.

Next: Spark RDD, Transformation and Actions