Create a Spark RDD using Parallelize

Let’s see how to create Spark RDD using sparkContext.parallelize() method and using Spark shell and Scala example.

Before we start let me explain what is RDD, Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark, It is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster.

Note: Spark Parallelizes an existing collection in your driver program.

Below is an example of how to create an RDD using a parallelize method from Sparkcontext. For example, sparkContext.parallelize(Array(1,2,3,4,5,6,7,8,9,10)) creates an RDD with an Array of Integers.

Using sc.parallelize on Spark Shell or REPL

Spark shell provides SparkContext variable “sc”, use sc.parallelize() to create an RDD.


scala> val rdd = sc.parallelize(Array(1,2,3,4,5,6,7,8,9,10))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at
:24

Using Spark sparkContext.parallelize in Scala

If you are using scala, get SparkContext object from SparkSession and use sparkContext.parallelize() to create rdd. This function also has another signature which additionally takes an integer argument to specify the number of partitions. Partitions are basic units of parallelism in Apache Spark. RDDs in Apache Spark are a collection of partitions that are executed by processors to achieve parallelism. The parallelize method takes a collection of objects, such as a list, tuple, or set, and creates an RDD from them. The RDD is then distributed across the nodes in the Spark cluster for parallel processing.


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

object RDDParallelize {

  def main(args: Array[String]): Unit = {
      val spark:SparkSession = SparkSession.builder().master("local[1]")
          .appName("SparkByExamples.com")
          .getOrCreate()
      val rdd:RDD[Int] = spark.sparkContext.parallelize(List(1,2,3,4,5))
      val rddCollect:Array[Int] = rdd.collect()
      println("Number of Partitions: "+rdd.getNumPartitions)
      println("Action: First element: "+rdd.first())
      println("Action: RDD converted to Array[Int] : ")
      rddCollect.foreach(println)
  }
}

By executing the above program you should see the below output.


Number of Partitions: 1
Action: First element: 1
Action: RDD converted to Array[Int] : 
1
2
3
4
5

Create empty RDD by using sparkContext.parallelize

We can create emptyRdd in two ways , one is using sparkContext.parallelize() and the other is using sparkContext.emptyRDD() method. The difference is being creating between these two ways is the former one creates no part files on disk , whereas the latter one creates the part files on Disk.

Using sparkContext.parallelize


// Create emptyRdd using parallelize
sparkContext.parallelize(Seq.empty[String])

Using sparkContext.emptyRDD


// Create emptyRdd using emptyRDD
val emptyRDD = sparkContext.emptyRDD[String]
emptyRDD.saveAsTextFile("src/main/output2/EmptyRDD2")

When above code with emptyRDD is executed it creates multiple part files which are empty.

The complete code can be downloaded from GitHub – Spark Scala Examples project

Naveen (NNK)

Naveen (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ LinkedIn and Medium

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This Post Has 27 Comments

  1. Mohan

    My Heart Felt Thanks to the creator , Author of this site. By this site, i got an opportunity to get a job
    from an MNC

    1. NNK

      Thank you Mohan for the comment. I am glad it helped you to get the job.

  2. mohammed

    i really appreciate what you have done ..i cleared my interview because of you only ..again thank you

    1. NNK

      Congratulation Mohammed. I am glad this site helped you to clear the interview.

  3. mohammed

    i really appreciate what you have done ..i cleared my interview because of you only ..again thank you

    1. NNK

      Congratulation Mohammed. I am glad this site helped you to clear the interview.

  4. Anonymous

    the information is very helpful to beginners
    good work

  5. Tejas

    Hi . Just an appreciation post. you are doing great , this blog of Spark by examples is good for learning and look at the coding part & examples in Spark-Scala , which i did not find anywhere else. Keep it up.

  6. Tejas

    Hi . Just an appreciation post. you are doing great , this blog of Spark by examples is good for learning and look at the coding part & examples in Spark-Scala , which i did not find anywhere else. Keep it up.

  7. Ashwathy

    I tried implementing this piece of code in IntelliJ mavin.
    The number of partitions created there was = 1 .
    How is the number of partitions decided in spark?

  8. Ashwathy

    I tried implementing this piece of code in IntelliJ mavin.
    The number of partitions created there was = 1 .
    How is the number of partitions decided in spark?

  9. Anonymous

    Thank you

  10. Anonymous

    Thank you

  11. Siraj

    Hi,
    Thanks for the detailed analysis of Spark RDD.
    I had one request to make. Can you please do a post on how to implement SCD#2.0 in Spark-scala.!!
    Thanks.

  12. Siraj

    Hi,
    Thanks for the detailed analysis of Spark RDD.
    I had one request to make. Can you please do a post on how to implement SCD#2.0 in Spark-scala.!!
    Thanks.

  13. rah

    one humble request, can you please give example with Java also .

  14. rah

    one humble request, can you please give example with Java also .

  15. Anonymous

    how to stop this moving text
    i am uanble to read it

    1. NNK

      Hi Apologies for inconvenience. scrolling issue has been fixed.

  16. Anonymous

    how to stop this moving text
    i am uanble to read it

    1. NNK

      Hi Apologies for inconvenience. scrolling issue has been fixed.

  17. Akash

    when we use sparkSession or sqlContext or hiveContext,it will be converted into RDD which follows SparkContext means every time when you will use any spark functionality,you have to create SparkContext (mandatory).

  18. Akash

    when we use sparkSession or sqlContext or hiveContext,it will be converted into RDD which follows SparkContext means every time when you will use any spark functionality,you have to create SparkContext (mandatory).

  19. rachana

    hello,
    just a small qn, in spark 2.x we are using spark.session insterd of spark/sql context but while defining the rdd why we are using sc insterd of spark session?

    1. NNK

      Hi Rachana, Not every method of SparkContext is defined in SparkSession. for example, all operations on RDD are still present in SparkContext hence you have to use sc to create RDD. hope this helps.

  20. rachana

    hello,
    just a small qn, in spark 2.x we are using spark.session insterd of spark/sql context but while defining the rdd why we are using sc insterd of spark session?

    1. NNK

      Hi Rachana, Not every method of SparkContext is defined in SparkSession. for example, all operations on RDD are still present in SparkContext hence you have to use sc to create RDD. hope this helps.