Most Common PySpark Interview Questions & Answers [For Freshers & Experienced]

Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s better to have an idea about the types of PySpark interview questions that will be asked so that you can mentally prepare answers for them.

To help you out, I have created the top PySpark interview question and answers guide to understand the depth and real-intend of PySpark interview questions. Let’s get started.

As the name suggests, PySpark is an integration of Apache Spark and the Python programming language. Apache Spark is a widely used open-source framework that is used for cluster-computing and is developed to provide an easy-to-use and faster experience. Python is a high-level general-purpose programming language. It is mainly used for Data Science, Machine Learning and Real-Time Streaming Analytics, apart from its many other uses.

Originally, Apache spark is written in the Scala programming language, and PySpark is actually the Python API for Apache Spark. In this article, we will take a glance at the most frequently asked PySpark interview questions and their answers to help you get prepared for your next interview. If you are a beginner and interested to learn more about data science, check out our data analytics certification from top universities.

Read: Dataframe in Apache PySpark



PySpark Interview Questions and Answers

1. What is PySpark?

This is almost always the first PySpark interview question you will face.

PySpark is the Python API for Spark. It is used to provide collaboration between Spark and Python. PySpark focuses on processing structured and semi-structured data sets and also provides the facility to read data from multiple sources which have different data formats. Along with these features, we can also interface with RDDs (Resilient Distributed Datasets ) using PySpark. All these features are implemented using the py4j library. 

2. List the advantages and disadvantages of PySpark? (Frequently asked PySpark Interview Question)

The advantages of using PySpark are: 

  • Using the PySpark, we can write a parallelized code in a very simple way.
  • All the nodes and networks are abstracted.
  • PySpark handles all the errors as well as synchronization errors.
  • PySpark contains many useful in-built algorithms.

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The disadvantages of using PySpark are:

  • PySpark can often make it difficult to express problems in MapReduce fashion.
  • When compared with other programming languages, PySpark is not efficient. 

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3. What are the various algorithms supported in PySpark?

The different algorithms supported by PySpark are:

  1. spark.mllib
  2. mllib.clustering
  3. mllib.classification
  4. mllib.regression
  5. mllib.recommendation
  6. mllib.linalg
  7. mllib.fpm

4. What is PySpark SparkContext?

PySpark SparkContext can be seen as the initial point for entering and using any Spark functionality. The SparkContext uses py4j library to launch the JVM, and then create the JavaSparkContext. By default, the SparkContext is available as ‘sc’. 

5. What is PySpark SparkFiles?

One of the most common PySpark interview questions. PySpark SparkFiles is used to load our files on the Apache Spark application. It is one of the functions under SparkContext and can be called using sc.addFile to load the files on the Apache Spark. SparkFIles can also be used to get the path using SparkFile.get or resolve the paths to files that were added from sc.addFile. The class methods present in the SparkFiles directory are getrootdirectory() and get(filename).

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6. What is PySpark SparkConf?

PySpark SparkConf is mainly used to set the configurations and the parameters when we want to run the application on the local or the cluster.
We run the following code whenever we want to run SparkConf:

class pyspark.Sparkconf(

localdefaults = True,

_jvm = None,

_jconf = None


7. What is PySpark StorageLevel?

PySpark StorageLevel is used to control how the RDD is stored, take decisions on where the RDD will be stored (on memory or over the disk or both), and whether we need to replicate the RDD partitions or to serialize the RDD. The code for StorageLevel is as follows: 

class pyspark.StorageLevel( useDisk, useMemory, useOfHeap, deserialized, replication = 1)

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8. What is PySpark SparkJobinfo?

One of the most common questions in any PySpark interview. PySpark SparkJobinfo is used to gain information about the SparkJobs that are in execution. The code for using the SparkJobInfo is as follows: 

class SparkJobInfo(namedtuple(“SparkJobInfo”, “jobId stageIds status ”)):

pyspark interview questions

9. What is PySpark SparkStageinfo?

One of the most common question in any PySpark interview question and answers guide. PySpark SparkStageInfo is used to gain information about the SparkStages that are present at that time. The code used fo SparkStageInfo is as follows: 

class SparkStageInfo(namedtuple(“SparkStageInfo”, “stageId currentAttemptId name numTasks unumActiveTasks” “numCompletedTasks numFailedTasks” )):

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We hope you went through all the frequently asked PySpark Interview Questions. Apache Spark is mainly used to handle BigData and is in very high demand as companies move forward to use the latest technologies to drive their businesses.

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What is Cluster Computing?

Cluster Computing consists of loosely coupled systems that interact, work, and perform operations as a single system. The various cluster nodes are connected via LAN (Local Area Network). Cluster computing ensures scalability, speed, resource management, and continuous availability of computing power. Clusters are of two types: Open and closed. Open clusters are those through which nodes can be accessed only via the Internet. In closed clusters, the nodes are hidden and secure. Each cluster computer consists of cluster nodes, cluster operating system, switches, and network-switching hardware.

What is the average salary of an Apache PySpark Developer in India?

A PySpark developer ensures that data is available for query processing. An Apache PySpark developer should be good at Python, Apache Spark, Java, and Scala. The demand for Apache Spark developers has been increasing. One can get more than 60000 search results of job opportunities for these roles. The salary, however, depends on many factors. These include work experience, skill set, demand in the market, organisation, location, etc. Based on these, the salary could range from INR 8 LPA to INR 20 LPA. The average wages for people with less than two years of experience range from INR 4.5 LPA to INR 15.7 LPA.

What is meant by RDD?

RDD stands for Resilient Distributed Dataset (RDD). It is a data structure that stores immutable objects. It supports the storage of objects of any language, like Python, Java, Scala, and other user-defined objects. MapReduce is used for massively parallel processing of data quickly. Spark uses RDD to perform MapReduce operations. RDDs can be created in 2 ways: either by parallelising a data set in your system or by referencing an external data storage system. RDD is fault-tolerant and supports parallel processing. It is mainly used to process and manipulate unstructured data. RDD is a distributed system. It follows the Lazy Evaluation Principle, i.e. transformations are applied only when we call it and not when the data is loaded.


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