RDD actions are operations that return the raw values, In other words, any RDD function that returns other than RDD[T] is considered as an action in spark programming. In this tutorial, we will learn RDD actions with Scala examples.
As mentioned in RDD Transformations, all transformations are lazy meaning they do not get executed right away and action functions trigger to execute the transformations.
Complete code I've used in this article is available at GitHub project for quick reference.
Spark RDD Actions
Select a link from the table below to jump to an example.
| RDD ACTION METHODS | METHOD DEFINITION | | --- | --- | | aggregate[U](zeroValue: U)(seqOp: (U, T) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): U | Aggregate the elements of each partition, and then the results for all the partitions. | | collect():Array[T] | Return the complete dataset as an Array. | | count():Long | Return the count of elements in the dataset. | | countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] | Return approximate count of elements in the dataset, this method returns incomplete when execution time meets timeout. | | countApproxDistinct(relativeSD: Double = 0.05): Long | Return an approximate number of distinct elements in the dataset. | | countByValue(): Map[T, Long] | Return Map[T,Long] key representing each unique value in dataset and value represent count each value present. | | countByValueApprox(timeout: Long, confidence: Double = 0.95)(implicit ord: Ordering[T] = null): PartialResult[Map[T, BoundedDouble]] | Same as countByValue() but returns approximate result. | | first():T | Return the first element in the dataset. | | fold(zeroValue: T)(op: (T, T) ⇒ T): T | Aggregate the elements of each partition, and then the results for all the partitions. | | foreach(f: (T) ⇒ Unit): Unit | Iterates all elements in the dataset by applying function f to all elements. | | foreachPartition(f: (Iterator[T]) ⇒ Unit): Unit | Similar to foreach, but applies function f for each partition. | | min()(implicit ord: Ordering[T]): T | Return the minimum value from the dataset. | | max()(implicit ord: Ordering[T]): T | Return the maximum value from the dataset. | | reduce(f: (T, T) ⇒ T): T | Reduces the elements of the dataset using the specified binary operator. | | saveAsObjectFile(path: String): Unit | Saves RDD as a serialized object's to the storage system. | | saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit | Saves RDD as a compressed text file. | | saveAsTextFile(path: String): Unit | Saves RDD as a text file. | | take(num: Int): Array[T] | Return the first num elements of the dataset. | | takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] | Return the first num (smallest) elements from the dataset and this is the opposite of the take() action. Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory. | | takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] | Return the subset of the dataset in an Array. Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory. | | toLocalIterator(): Iterator[T] | Return the complete dataset as an Iterator. Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory. | | top(num: Int)(implicit ord: Ordering[T]): Array[T] | Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory. | | treeAggregate | Aggregates the elements of this RDD in a multi-level tree pattern. | | treeReduce | Reduces the elements of this RDD in a multi-level tree pattern. |
RDD Actions Example
Before we start explaining RDD actions with examples, first, let's create an RDD.
// Create RDD from a List Using parallelize
val spark = SparkSession.builder()
.appName("SparkByExample")
.master("local")
.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
val inputRDD = spark.sparkContext.parallelize(List(("Z", 1),("A", 20),("B", 30),("C", 40),("B", 30),("B", 60)))
val listRdd = spark.sparkContext.parallelize(List(1,2,3,4,5,3,2))
Note that we have created two RDD's in the above code snippet and we use these two as and when necessary to demonstrate the RDD actions.
<a id="aggregate"></a>
aggregate - action
aggregate() the elements of each partition, and then the results for all the partitions.
//aggregate
def param0= (accu:Int, v:Int) => accu + v
def param1= (accu1:Int,accu2:Int) => accu1 + accu2
println("aggregate : "+listRdd.aggregate(0)(param0,param1))
//Output: aggregate : 20
//aggregate
def param3= (accu:Int, v:(String,Int)) => accu + v._2
def param4= (accu1:Int,accu2:Int) => accu1 + accu2
println("aggregate : "+inputRDD.aggregate(0)(param3,param4))
//Output: aggregate : 20
<a id="treeAggregate"></a>
treeAggregate - action
treeAggregate() - Aggregates the elements of this RDD in a multi-level tree pattern. The output of this function will be similar to the aggregate function.
//treeAggregate. This is similar to aggregate
def param8= (accu:Int, v:Int) => accu + v
def param9= (accu1:Int,accu2:Int) => accu1 + accu2
println("treeAggregate : "+listRdd.treeAggregate(0)(param8,param9))
//Output: treeAggregate : 20
<a id="fold"></a>
fold - action
fold() - Aggregate the elements of each partition, and then the results for all the partitions.
//fold
println("fold : "+listRdd.fold(0){ (acc,v) =>
val sum = acc+v
sum
})
//Output: fold : 20
println("fold : "+inputRDD.fold(("Total",0)){(acc:(String,Int),v:(String,Int))=>
val sum = acc._2 + v._2
("Total",sum)
})
//Output: fold : (Total,181)
<a id="reduce"></a>
reduce
reduce() - Reduces the elements of the dataset using the specified binary operator.
//reduce
println("reduce : "+listRdd.reduce(_ + _))
//Output: reduce : 20
println("reduce alternate : "+listRdd.reduce((x, y) => x + y))
//Output: reduce alternate : 20
println("reduce : "+inputRDD.reduce((x, y) => ("Total",x._2 + y._2)))
//Output: reduce : (Total,181)
<a id="treeReduce"></a>
treeReduce
treeReduce() - Reduces the elements of this RDD in a multi-level tree pattern.
//treeReduce. This is similar to reduce
println("treeReduce : "+listRdd.treeReduce(_ + _))
//Output: treeReduce : 20
<a id="collect"></a>
collect
collect() -Return the complete dataset as an Array.
//Collect
val data:Array[Int] = listRdd.collect()
data.foreach(println)
<a id="count"></a>
count, countApprox, countApproxDistinct
count() - Return the count of elements in the dataset.
countApprox() - Return approximate count of elements in the dataset, this method returns incomplete when execution time meets timeout.
countApproxDistinct() - Return an approximate number of distinct elements in the dataset.
//count, countApprox, countApproxDistinct
println("Count : "+listRdd.count)
//Output: Count : 20
println("countApprox : "+listRdd.countApprox(1200))
//Output: countApprox : (final: [7.000, 7.000])
println("countApproxDistinct : "+listRdd.countApproxDistinct())
//Output: countApproxDistinct : 5
println("countApproxDistinct : "+inputRDD.countApproxDistinct())
//Output: countApproxDistinct : 5
<a id="countByValue"></a>
countByValue, countByValueApprox
countByValue() - Return Map[T,Long] key representing each unique value in dataset and value represents count each value present.
countByValueApprox() - Same as countByValue() but returns approximate result.
//countByValue, countByValueApprox
println("countByValue : "+listRdd.countByValue())
//Output: countByValue : Map(5 -> 1, 1 -> 1, 2 -> 2, 3 -> 2, 4 -> 1)
//println(listRdd.countByValueApprox())
<a id="first"></a>
first
first() - Return the first element in the dataset.
//first
println("first : "+listRdd.first())
//Output: first : 1
println("first : "+inputRDD.first())
//Output: first : (Z,1)
<a id="top"></a>
top
top() - Return top n elements from the dataset.
Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory.
//top
println("top : "+listRdd.top(2).mkString(","))
//Output: take : 5,4
println("top : "+inputRDD.top(2).mkString(","))
//Output: take : (Z,1),(C,40)
<a id="min"></a>
min
min() - Return the minimum value from the dataset.
//min
println("min : "+listRdd.min())
//Output: min : 1
println("min : "+inputRDD.min())
//Output: min : (A,20)
<a id="max"></a>
max
max() - Return the maximum value from the dataset.
//max
println("max : "+listRdd.max())
//Output: max : 5
println("max : "+inputRDD.max())
//Output: max : (Z,1)
<a id="take"></a>
take, takeOrdered, takeSample
take() - Return the first num elements of the dataset.
takeOrdered() - Return the first num (smallest) elements from the dataset and this is the opposite of the take() action.
Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory.
takeSample() - Return the subset of the dataset in an Array.
Note: Use this method only when the resulting array is small, as all the data is loaded into the driver's memory.
//take, takeOrdered, takeSample
println("take : "+listRdd.take(2).mkString(","))
//Output: take : 1,2
println("takeOrdered : "+ listRdd.takeOrdered(2).mkString(","))
//Output: takeOrdered : 1,2
//println("take : "+listRdd.takeSample())
Actions - Complete example
package com.aistudylabs.spark.rdd
import com.aistudylabs.spark.rdd.OperationOnPairRDDComplex.kv
import org.apache.spark.sql.SparkSession
import scala.collection.mutable
object RDDActions extends App {
val spark = SparkSession.builder()
.appName("SparkByExample")
.master("local")
.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
val inputRDD = spark.sparkContext.parallelize(List(("Z", 1),("A", 20),("B", 30),("C", 40),("B", 30),("B", 60)))
val listRdd = spark.sparkContext.parallelize(List(1,2,3,4,5,3,2))
//Collect
val data:Array[Int] = listRdd.collect()
data.foreach(println)
//aggregate
def param0= (accu:Int, v:Int) => accu + v
def param1= (accu1:Int,accu2:Int) => accu1 + accu2
println("aggregate : "+listRdd.aggregate(0)(param0,param1))
//Output: aggregate : 20
//aggregate
def param3= (accu:Int, v:(String,Int)) => accu + v._2
def param4= (accu1:Int,accu2:Int) => accu1 + accu2
println("aggregate : "+inputRDD.aggregate(0)(param3,param4))
//Output: aggregate : 20
//treeAggregate. This is similar to aggregate
def param8= (accu:Int, v:Int) => accu + v
def param9= (accu1:Int,accu2:Int) => accu1 + accu2
println("treeAggregate : "+listRdd.treeAggregate(0)(param8,param9))
//Output: treeAggregate : 20
//fold
println("fold : "+listRdd.fold(0){ (acc,v) =>
val sum = acc+v
sum
})
//Output: fold : 20
println("fold : "+inputRDD.fold(("Total",0)){(acc:(String,Int),v:(String,Int))=>
val sum = acc._2 + v._2
("Total",sum)
})
//Output: fold : (Total,181)
//reduce
println("reduce : "+listRdd.reduce(_ + _))
//Output: reduce : 20
println("reduce alternate : "+listRdd.reduce((x, y) => x + y))
//Output: reduce alternate : 20
println("reduce : "+inputRDD.reduce((x, y) => ("Total",x._2 + y._2)))
//Output: reduce : (Total,181)
//treeReduce. This is similar to reduce
println("treeReduce : "+listRdd.treeReduce(_ + _))
//Output: treeReduce : 20
//count, countApprox, countApproxDistinct
println("Count : "+listRdd.count)
//Output: Count : 20
println("countApprox : "+listRdd.countApprox(1200))
//Output: countApprox : (final: [7.000, 7.000])
println("countApproxDistinct : "+listRdd.countApproxDistinct())
//Output: countApproxDistinct : 5
println("countApproxDistinct : "+inputRDD.countApproxDistinct())
//Output: countApproxDistinct : 5
//countByValue, countByValueApprox
println("countByValue : "+listRdd.countByValue())
//Output: countByValue : Map(5 -> 1, 1 -> 1, 2 -> 2, 3 -> 2, 4 -> 1)
//println(listRdd.countByValueApprox())
//first
println("first : "+listRdd.first())
//Output: first : 1
println("first : "+inputRDD.first())
//Output: first : (Z,1)
//top
println("top : "+listRdd.top(2).mkString(","))
//Output: take : 5,4
println("top : "+inputRDD.top(2).mkString(","))
//Output: take : (Z,1),(C,40)
//min
println("min : "+listRdd.min())
//Output: min : 1
println("min : "+inputRDD.min())
//Output: min : (A,20)
//max
println("max : "+listRdd.max())
//Output: max : 5
println("max : "+inputRDD.max())
//Output: max : (Z,1)
//take, takeOrdered, takeSample
println("take : "+listRdd.take(2).mkString(","))
//Output: take : 1,2
println("takeOrdered : "+ listRdd.takeOrdered(2).mkString(","))
//Output: takeOrdered : 1,2
//println("take : "+listRdd.takeSample())
//toLocalIterator
//listRdd.toLocalIterator.foreach(println)
//Output:
}
Conclusion:
RDD actions are operations that return non-RDD values, since RDD's are lazy they do not execute the transformation functions until we call actions. hence, all these functions trigger the transformations to execute and finally returns the value of the action functions to the driver program. and In this tutorial, you have also learned several RDD functions usage and examples in scala language.
<a id="h-related-articles"></a>
Related Articles
- Spark RDD Transformations with examples
- Convert Spark RDD to DataFrame | Dataset
- Different ways to create Spark RDD
- Create a Spark RDD using Parallelize
- Spark RDD aggregate() operation example
- Spark RDD reduce() function example
- Spark RDD fold() function example