How to use Spark Parallelize

In this tutorial, we will learn how to use Spark Parallelize, especially how to use parallelize to generate RDDs and how to create an empty RDD using PySpark.

How to use Spark Parallelize?

PySpark parallelize() is a SparkContext method that creates an RDD from a list collection. In this article, we will learn how to use parallelize to generate RDDs and how to create an empty RDD using PySpark.

Before we begin, let us understand what are RDDs? Resilient Distributed Datasets (RDD) are a core data structure in PySpark. They are an immutable distributed collection of objects. Each dataset in RDD is separated into logical partitions that can be computed on multiple cluster nodes.

Build Log Analytics Application with Spark Streaming and Kafka  


Let us now parallelize an existing collection in your driver software with PySpark.

Here's an example of how to make an RDD with Sparkcontext's parallelize method.

sparkContext.parallelize([1,2,3,4,5,6,7,8])

Let us now use sparkContext.parallelize in a spark application –

Code:
import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('ParallelizeExample').getOrCreate()
sparkContext=spark.sparkContext

rdd=sparkContext.parallelize([1,2,3,4,5,6,7])
rddCollect = rdd.collect()
print("Number of Partitions: "+str(rdd.getNumPartitions()))
print("Action: First element: "+str(rdd.first()))
print(rddCollect)

Output:
Number of Partitions: 2
Action: First element: 1
[1, 2, 3, 4, 5, 6, 7]

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