PySpark SparkContext With Examples and Parameters

1. PySpark SparkContext

In our last article, we see PySpark Pros and Cons. In this PySpark tutorial, we will learn the concept of PySpark SparkContext. Moreover, we will see SparkContext parameters. Apart from its Parameters, we will also see its PySpark SparkContext examples, to understand it in depth.
So, let’s start PySpark SparkContext.

PySpark SparkContext

PySpark SparkContext With Examples and Parameters

Let’s explore best PySpark Books

2. What is SparkContext in PySpark?

In simple words, an entry point to any Spark functionality is what we call SparkContext. At the time we run any Spark application, a driver program starts, which has the main function and from this time your SparkContext gets initiated. Afterward, on worker nodes, driver program runs the operations inside the executors.
In addition, to launch a JVM, SparkContext uses Py4J and then creates a JavaSparkContext. However, PySpark has SparkContext available as ‘sc’, by default, thus the creation of a new SparkContext won’t work.

PySpark- SparkContext

what is PySpark SparkContext

Do you know about PySpark RDD Operations

Here is a code block which has the details of a PySpark class as well as the parameters, those a SparkContext can take:

class pyspark.SparkContext (
  master = None,
  appName = None,
  sparkHome = None,
  pyFiles = None,
  environment = None,
  batchSize = 0,
  serializer = PickleSerializer(),
  conf = None,
  gateway = None,
  jsc = None,
  profiler_cls = <class 'pyspark.profiler.BasicProfiler'>
)
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3. Parameters in PySpark SparkContext

Further, we are listing all the parameters of a SparkContext in PySpark:

a. Master

This is the URL of the cluster it connects to.

b. appName

Basically, “appName” parameter refers to the name of your job.

c. SparkHome

Generally, sparkHome is a Spark installation directory.

d. pyFiles

Files like .zip or .py files are to send to the cluster and to add to the PYTHONPATH.

e. Environment

Worker nodes environment variables.

f. BatchSize

Basically, as a single Java object, the number of Python objects represented. However, to disable batching, Set 1, and to automatically choose the batch size based on object sizes set 0, Also, to use an unlimited batch size, set -1.

g. Serializer

So, this parameter tell about Serializer, an RDD serializer.

h. Conf

Moreover, to set all the Spark properties, an object of L{SparkConf} is there.

i. Gateway

Basically, use an existing gateway as well as JVM, else initialize a new JVM.

Have a look at PySpark Broadcast and Accumulator

j. JSC

However, JSC is the JavaSparkContext instance.

k. profiler_cls

Basically, in order to do profiling, a class of custom Profiler is used. Although, make sure the pyspark.profiler.BasicProfiler is the default one.
So, master and appname are mostly used, among the above parameters. However, any PySpark program’s first two lines look as shown below −

from pyspark import SparkContext
sc = SparkContext("local", "First App1")

4. SparkContext Example – PySpark Shell

Since we have learned much about PySpark SparkContext, now let’s understand it with an example. Here we will count the number of the lines with character ‘x’ or ‘y’ in the README.md file. So, let’s assume that there are 5 lines in a file. Hence, 3 lines have the character ‘x’, then the output will be → Line with x: 3. However, for character ‘y’, same will be done .

You must check how much you know about Pyspark
However, make sure in the following PySpark SparkContext example we are not creating any SparkContext object it is because Spark automatically creates the SparkContext object named sc, by default, at the time PySpark shell starts. So, If you try to create another SparkContext object, following error will occur – “ValueError: That says, it is not possible to run multiple SparkContexts at once”.

<<< logFile = "file:///home/hadoop/spark-2.1.0-bin-hadoop2.7/README.md"
<<< logData = sc.textFile(logFile).cache()
<<< numXs = logData.filter(lambda s: 'x' in s).count()
<<< numYs = logData.filter(lambda s: 'y' in s).count()
<<< print "Lines with x: %i, lines with y: %i" % (numXs, numYs)
Lines with x: 62, lines with y: 30

5. SparkContext Example – Python Program

Further, using a Python program, let’s run the same example. So, create a Python file with name firstapp1.py and then enter the following code in that file.

----------------------------------------firstapp1.py---------------------------------------
from pyspark import SparkContext
logFile = "file:///home/hadoop/spark-2.1.0-bin-hadoop2.7/README.md"
sc = SparkContext("local", "first app")
logData = sc.textFile(logFile).cache()
numXs = logData.filter(lambda s: 'x' in s).count()
numYs = logData.filter(lambda s: 'y' in s).count()
print "Lines with x: %i, lines with y: %i" % (numXs, numYs)
----------------------------------------firstapp1.py---------------------------------------

Then to run this Python file, we will execute the following command in the terminal.
Hence, it will give the same output as above:

Let’s discuss PySpark SparkFiles

$SPARK_HOME/bin/spark-submit firstapp1.py

Output: Lines with x: 62, lines with y: 30

6. Conclusion

Hence, we have seen the concept of PySpark SparkContext. Moreover, we have seen all the parameters for in-depth knowledge. Also, we have seen PySpark SparkContext examples to understand it well. However, if any doubt occurs, feel free to ask in the comment tab. Though we assure we will respond.

See also – 

PySpark Interview Questions
For reference

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