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  • Post last modified:March 27, 2024
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You are currently viewing PySpark SparkContext Explained

pyspark.SparkContext is an entry point to the PySpark functionality that is used to communicate with the cluster and to create an RDD, accumulator, and broadcast variables. In this article, you will learn how to create PySpark SparkContext with examples. Note that you can create only one SparkContext per JVM, in order to create another first you need to stop the existing one using stop() method.

spark sparkcontext
Source: spark.apache.org

The Spark driver program creates and uses SparkContext to connect to the cluster manager to submit PySpark jobs, and know what resource manager (YARN, Mesos, or Standalone) to communicate to. It is the heart of the PySpark application.

Related: How to get current SparkContext & its configurations in Spark

1. SparkContext in PySpark shell

Be default PySpark shell creates and provides sc object, which is an instance of SparkContext class. We can directly use this object where required without the need of creating.


Similar to the PySpark shell, in most of the tools, notebooks, and Azure Databricks, the environment itself creates a default SparkContext object for us to use so you don’t have to worry about creating a PySpark context.

2. Create SparkContext in PySpark

Since PySpark 2.0, Creating a SparkSession creates a SparkContext internally and exposes the sparkContext variable to use.

At any given time only one SparkContext instance should be active per JVM. In case you want to create another you should stop existing SparkContext using stop() before creating a new one.

# Create SparkSession from builder
from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[1]") \
                    .appName('SparkByExamples.com') \
print("Spark App Name : "+ spark.sparkContext.appName)

# Outputs
#<SparkContext master=local[1] appName=SparkByExamples.com>
#Spark App Name : SparkByExamples.com

As I explained in the SparkSession article, you can create any number of SparkSession objects however, for all those objects underlying there will be only one SparkContext.

3. Stop PySpark SparkContext

You can stop the SparkContext by calling the stop() method. As explained above you can have only one SparkContext per JVM. If you wanted to create another, you need to shutdown it first by using stop() method and create a new SparkContext.

# SparkContext stop() method

When PySpark executes this statement, it logs the message INFO SparkContext: Successfully stopped SparkContext to console or to a log file.

When you try to create multiple SparkContext you will get the below error.

ValueError: Cannot run multiple SparkContexts at once;

4. Creating SparkContext prior to PySpark 2.0

You can create SparkContext by programmatically using its constructor, and pass parameters like master and appName at least as these are mandatory params. The below example creates context with a master as local and app name as Spark_Example_App.

# Create SparkContext
from pyspark import SparkContext
sc = SparkContext("local", "Spark_Example_App")

You can also create it using SparkContext.getOrCreate(). It actually returns an existing active SparkContext otherwise creates one with a specified master and app name.

# Create Spark Context
from pyspark import SparkConf, SparkContext
conf = SparkConf()
conf.setMaster("local").setAppName("Spark Example App")
sc = SparkContext.getOrCreate(conf)

5. Create PySpark RDD

Once you have a SparkContext object, you can create a PySpark RDD in several ways, below I have used the range() function.

# Create RDD
rdd = spark.sparkContext.range(1, 5)

# Output
#[1, 2, 3, 4]

6. SparkContext Commonly Used Variables

applicationId – Returns a unique ID of a PySpark application.

version – Version of PySpark cluster where your job is running.

uiWebUrl – Provides the Spark Web UI url that started by SparkContext.

7. SparkContext Commonly Used Methods

accumulator(value[, accum_param]) – It creates an pyspark accumulator variable with initial specified value. Only a driver can access accumulator variables.

broadcast(value) – read-only PySpark broadcast variable. This will be broadcast to the entire cluster. You can broadcast a variable to a PySpark cluster only once.

emptyRDD() – Creates an empty RDD

getOrCreate() – Creates or returns a SparkContext

hadoopFile() – Returns an RDD of a Hadoop file

newAPIHadoopFile() – Creates an RDD for a Hadoop file with a new API InputFormat.

sequenceFile() – Get an RDD for a Hadoop SequenceFile with given key and value types.

setLogLevel() – Change log level to debug, info, warn, fatal, and error

textFile()Reads a text file from HDFS, local or any Hadoop supported file systems and returns an RDD

union() – Union two RDDs

wholeTextFiles()Reads a text file in the folder from HDFS, local or any Hadoop supported file systems and returns an RDD of Tuple2. The first element of the tuple consists file name and the second element consists context of the text file.

8. Conclusion

In this PySpark Context article, you have learned what is SparkContext, how to create it, stop it, and usage with a few basic examples. As you learned SparkContext is an entry point to the PySpark execution engine which communicates with the cluster. Using this you can create a RDD, Accumulators and Broadcast variables.


Happy Learning !!