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

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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).

Read: Spark Project Ideas

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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)

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

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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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10. What is PySpark DataFrames?

This is one of the most common PySpark dataframe interview questions. PySpark DataFrames are the distributed assortment of well-organized data. They are identical to relational database tables and are included in named columns. Moreover, PySpark DataFrames are more efficiently optimized than Python or R programming languages. The reason is they can be created from various sources like Structured Data Files, Hive Tables, external databases, existing RDDs, etc.

The greatest advantage of using PySpark DataFrame is that the data in it is distributed over various machines in the cluster. The corresponding operations will run parallel on all the machines.

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11. What is PySpark Join?

PySpark Join helps combine two DataFrames. By binding these, it is easy to join multiple DataFrames. It enables all fundamental join type operations accessible in traditional SQL like INNER, RIGHT OUTER, LEFT OUTER, LEFT SEMI, LEFT ANTI, SELF JOIN, and CROSS. PySpark Joins are transformations that use data shuffling throughout the network.

12. How to rename a DataFrame column in PySpark?

It is one of the most frequently asked PySpark dataframe interview questions. You can use PySpark withColumnRenamed() to rename a DataFrame column. Frequently, you need to remain single or multiple columns on PySpark DataFrame. It can be done in multiple ways. DataFrame is an immutable collection, so you can’t update or rename a column instead when using withColumnRenamed(). This is because it prepares a new DataFrame with the updated column names. Two common ways to rename nested columns are –renaming all columns or renaming selected multiple columns.

13. Are PySpark and Spark the same?

These types of PySpark coding questions test the candidates’ basic knowledge of the PySpark fundamentals. PySpark has been launched to support the collaboration of Python and Apache Spark. Essentially, it is a Python API for Spark. PySpark assists you in interfacing with Resilient Distributed Datasets (RDDs) in Python programming language and Apache Spark.

14. What is PySparkSQL?

When preparing for PySpark coding interview questions, you must prepare for PySparkSQL. It is a PySpark library to implement SQL-like analysis on a large amount of either structured or semi-structured data. You can also use SQL queries with PySparkSQL. Moreover, it can be connected to Apache Hive, and HiveQL can also be implemented.

PySparkSQL works as a wrapper over the PySpark core. PySparkSQL introduced the DataFrame, a tabular illustration of structured data that is identical to that of a table from an RDBMS (relational database management system).

15. Are there any prerequisites to learning PySpark?

One of the fundamental PySpark coding questions is about the prerequisites to learn PySpark. It is assumed that the readers are aware of what a framework and a programming language are before moving towards different concepts in the PySpark tutorial. It is beneficial if the readers have some knowledge of Python and Spark in advance.

16. What do you understand by PySpark SparkFiles?

It is allowed to upload our files in Apache Spark by using sc.addFile. Here sc is the default SparkContext. It also assists in getting the path on a worker through SparkFiles.get. It also resolves the paths to files that are added via SparkContext.addFile().PySpark SparkFiles includes certain classmethods likeget(filename) and getrootdirectory().

17. What are the key characteristics of PySpark?

Knowing PySpark characteristics is important after you complete preparing for the PySpark coding interview questions. The four key characteristics of PySpark are as below. (i) Nodes are abstracted: You can’t access the individual worker nodes. (ii) APIs for Spark features: PySpark offers APIs for using Spark features. (iii) PySpark is dependent on MapReduce: PySpark is dependent on the MapReduce model of Hadoop. So, it lets a programmer provide the map and the reduced functions. (iv) Abstracted Network: Abstracted networks in PySpark allow implicit communication only.

18. What is SparkCore? What are the major functions of SparkCore?

SparkCore is the Spark platform’s general execution engine that supports all the functionalities. It provides in-memory computing capabilities to offer a decent speed and a universal execution model to support different applications. It also supports Scala, Java, and Python APIs to simplify the development process. The key functions of SparkCore include the basic I/O functions, monitoring, scheduling, effective memory management, fault tolerance, fault recovery, and interaction with storage systems.

19. What it means by PySpark serializers?

One of the mid-level PySpark interview coding questions can be around PySpark serializers. In PySpark, the serialization process is used to perform Spark performance tuning. PySpark incorporates serializers because you must constantly check the data sent or received across the network to the memory or disk. Two types of serializers in PySpark are as below. (i) PickleSerializer: It serializes the objects using Python’s PickleSerializer and class pyspark.PickleSerializer). It supports most of the Python objects. (ii) MarshalSerializer: It performs objects’ serialization. It can be employed through class pyspark.MarshalSerializer. It is faster than the PickleSerializer, but it supports limited types.

20. What is PySpark ArrayType?

PySpark ArrayType is a collection data type that outspreads PySpark’s DataType class (the superclass for all types). It only contains the same types of files. You can use ArraType()to construct an instance of an ArrayType. Two arguments it accepts are discussed below. (i) valueType: The valueType must extend the DataType class in PySpark. (ii) valueContainsNull: It is an optional argument that states whether a value can accept null and it is by default value, is True.

21. What is PySpark Partition? How many partitions can one make in PySpark?

You may be asked a PySpark interview question around PySpark Partition. It is a method that splits a huge dataset into smaller datasets depending on one or multiple partition keys. It improves the execution speed when the transformations on partitioned data operate faster. The reason is that every partition’s transformations run in parallel. PySpark allows two types of partitioning i.e. partitioning on disc (File system) and partitioning in memory (DataFrame). Its syntax is partitionBy (self, *cols) . Including 4x of partitions to the number of cores in the cluster accessible for application is recommended.

22. What is Parquet file in PySpark?

You may be asked PySpark interview coding questions on the file type in PySpark. The Parquet file in PySpark is a column-type format supported by different data processing systems. It helps Spark SQL to perform read and write operations. Its column-type format storage offers the following benefits. (i) It consumes less space. (ii)It allows you to retrieve specific columns for access. (iii)It employs type-specific encoding. (iv)It provides better-summarized data. (v)It supports limited I/O operations.

23. Why is PySpark faster than pandas?

This kind of PySpark interview question tests your in-depth knowledge of PySpark. PySpark is speedier than pandas because it supports parallel execution of statements in a distributed environment. PySpark can be implemented on different machines and cores not supported in Pandas.

Benefits of Using PySpark

Below are the benefits of using PySpark and knowing Pyspark interview questions

  • Accelerated Data Processing

PySpark’s forte lies in its ability to handle mammoth datasets with unparalleled speed. Leveraging parallel processing, it dissects hefty tasks into manageable chunks, executing them simultaneously across diverse nodes in a cluster. This not only slashes processing time but also facilitates real-time data analysis, rendering PySpark indispensable for big data applications, under pyspark questions

  • Seamless Python Integration

One of PySpark’s hallmarks is its seamless integration with Python, a language renowned for its simplicity and versatility. Built upon Python API, PySpark empowers users to wield Python’s familiar syntax effortlessly. This seamless integration is a boon for data scientists well-versed in Python and its arsenal of data analysis libraries like NumPy and Pandas. These can be considered as one of the pyspark coding interview questions for experienced.

  • Scalability at Its Core

It is engineered for scalability that easily accommodate burgeoning data volumes without sacrificing performance. This scalability is pivotal for organizations grappling with expanding datasets, necessitating a tool that can effortlessly adapt to their evolving needs. With PySpark, businesses can effortlessly scale their data processing capabilities up or down as per requirement.

  • Cost-Effective Solution

In a landscape littered with pricey data processing tools, PySpark emerges as a beacon of cost-effectiveness. Riding on the wings of Apache Spark’s open-source framework, PySpark incurs zero licensing costs. This accessibility democratizes data processing, empowering startups and small businesses with limited resources to harness the power of big data analytics and which is also known to be included inpyspark programming interview questions.

  • Advanced Analytics Arsenal

PySpark interview questions data professionals with a formidable array of advanced analytics tools, rendering it a versatile ally in data exploration. Boasting built-in libraries for machine learning, graph processing, and streaming data, PySpark caters to a diverse range of use cases. Moreover, its compatibility with external libraries like TensorFlow and Keras further amplifies its analytical prowess,pyspark coding interview questions and answers.

  • Streamlined Parallel Programming

Navigating the labyrinth of parallel programming can be daunting, especially when grappling with voluminous datasets. PySpark comes to the rescue by furnishing an intuitive API that abstracts away the complexities of parallel operations. This streamlined approach liberates data scientists and analysts to focus on analysis, unencumbered by the intricacies of parallel programming.

  • Vibrant Community Support

Backed by a robust community of developers and enthusiasts, PySpark thrives on collaborative innovation and support. Its open-source ethos fosters a rich ecosystem of resources and documentation, making it a veritable treasure trove for beginners. This abundant support network ensures that aspiring data fans can embark on their PySpark journey with confidence and clarity.

How do I prepare for PySpark interview?

Preparing for a PySpark interview requires strategic planning and diligent study for pyspark interview questions and answers. Here’s a step-by-step guide to help you ace your PySpark interview, also considered as important pyspark interview questions for experienced data engineer

  • Understand the Basics

Begin by familiarizing yourself with the fundamentals of PySpark. Ensure you have a solid grasp of its architecture, RDDs (Resilient Distributed Datasets), DataFrames, transformations, and actions. Brush up on Python basics as well since PySpark is built on top of Python, helpful pyspark coding interview questions.

  • Dive into PySpark APIs

Delve deeper into PySpark APIs to understand their functionalities and usage. Focus on key APIs like SparkContext, DataFrame API, and SQLContext. Practice writing code snippets to perform common tasks such as data manipulation, filtering, aggregation, and joins using PySpark APIs. Tese concepts should  be known for pyspark interview questions and answers for experienced.

  • Data Handling and Transformation

Demonstrate your proficiency in handling and transforming data using PySpark. Understand various data sources supported by PySpark such as CSV, JSON, Parquet, and Hive. Practice loading data from different sources into PySpark DataFrames, performing transformations, and saving results back to storage is available for pyspark interview questions for data engineer.

  • Performance Tuning Techniques

Familiarize yourself with performance tuning techniques in PySpark to optimize query execution and resource utilization. Learn about caching, partitioning, and broadcasting to improve job performance. Understand how to monitor and analyze job execution using Spark UI and Spark logs.

  • Spark SQL and DataFrames

Master Spark SQL and DataFrames, as they are integral parts of PySpark. Practice writing SQL queries on DataFrames using SparkSession’s SQLContext. Understand the benefits of using DataFrames over RDDs and when to leverage each based on the use case and pyspark code interview questions.

  • Machine Learning with PySpark

Gain proficiency in using PySpark for machine learning tasks. Learn about MLlib, PySpark’s machine learning library, and its supported algorithms for classification, regression, clustering, and collaborative filtering. Practice building machine learning pipelines and evaluating model performance.

  • Real-world Projects and Use Cases

To showcase your practical skills, work on real-world PySpark projects and use cases. Implement end-to-end data processing pipelines, from data ingestion to model deployment. Document your projects and be prepared to discuss your approach, challenges faced, and solutions implemented during the interview.

  • Practice Coding and Problem-solving

Practice coding and problem-solving using PySpark. Solve coding challenges and interview questions related to data manipulation, aggregation, and analysis using PySpark. Focus on writing clean, efficient, and optimized code to showcase your programming skills.

  • Stay Updated and Network

Stay updated with the latest advancements in PySpark and big data technologies. Follow relevant blogs, forums, and communities to stay abreast of industry trends and best practices. Network with professionals in the field and participate in PySpark meetups or events to broaden your knowledge and connections.

  • Mock Interviews and Feedback

Conduct mock interviews by to simulate real interview scenarios and receive constructive feedback. Practice explaining your solutions clearly and concisely, emphasizing your problem-solving approach and thought process. Address any weaknesses identified during mock interviews to improve your performance, which will affect pyspark interview questions for 5 years experience.

What skills do you need to learn PySpark?

  • Proficiency in Python

At the heart of PySpark lies Python, a versatile and user-friendly programming language. Thus, a solid grasp of Python fundamentals forms the cornerstone of PySpark mastery. Familiarity with Python syntax, data structures, functions, and libraries like NumPy and Pandas lays a robust foundation for leveraging PySpark’s capabilities.

  • Understanding of Data Processing Concepts

A deep understanding of data processing concepts is paramount for harnessing PySpark’s full potential. Concepts like distributed computing, parallel processing, and data transformations form the bedrock of PySpark’s functionality. Familiarity with these concepts equips learners with the insights needed to optimize data processing workflows and tackle real-world challenges effectively, as important in interview questions on pyspark

  • Knowledge of Apache Spark Architecture

PySpark operates atop Apache Spark, an open-source distributed computing framework. Thus, a comprehensive understanding of Spark’s architecture is indispensable for mastering PySpark. Learners should acquaint themselves with Spark’s core components, such as RDDs (Resilient Distributed Datasets), DataFrames, and SparkSQL, to navigate PySpark’s intricacies with confidence.

  • Proficiency in Data Manipulation and Analysis

PySpark serves as a potent tool for data manipulation and analysis on a massive scale. Hence, proficiency in data manipulation techniques, including filtering, sorting, joining, and aggregating datasets, is essential. Additionally, familiarity with exploratory data analysis (EDA) methodologies empowers learners to glean actionable insights from vast datasets using PySpark.

  • Understanding of Machine Learning Concepts

PySpark boasts built-in libraries for machine learning, making it a formidable ally for predictive analytics tasks. Therefore, a foundational understanding of machine learning concepts, such as regression, classification, clustering, and feature engineering, is beneficial. Proficiency in PySpark’s MLlib library enables learners to develop and deploy machine learning models at scale.

  • Familiarity with SQL

PySpark seamlessly integrates with SQL, enabling users to perform SQL-like queries on distributed datasets using SparkSQL. Thus, a basic understanding of SQL syntax and query execution is advantageous for leveraging PySpark’s SQL capabilities. Proficiency in SQL equips learners with a versatile toolset for data exploration and manipulation in PySpark.

  • Problem-Solving and Critical Thinking Skills

The realm of big data analytics often presents complex challenges that require creative problem-solving and critical thinking skills. Learners should cultivate these skills to devise efficient solutions, optimize data processing workflows, and troubleshoot issues encountered while working with PySpark.

Is PySpark in demand?

Yes, pyspark interview questions are in high demand in today’s data-driven world. As organizations grapple with ever-expanding datasets, the need for efficient data processing and analysis tools has surged. PySpark, with its ability to handle large volumes of data at lightning-fast speeds and its seamless integration with Python, has become a top choice for data professionals. Its scalability, cost-effectiveness, and advanced analytics capabilities further contribute to its popularity, especially when pyspark programming questions

Additionally, the vibrant community support surrounding PySpark ensures that users can access ample resources and assistance. As businesses across various industries recognize the importance of leveraging big data for strategic decision-making, the demand for PySpark expertise continues to grow. Hence, mastering PySpark can open up lucrative opportunities in the job market and propel one’s career in data science and analytics.


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.

If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Do check out his course in order to learn from the best academicians and industry leaders to upgrade your career in this field.

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Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1What 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.

2What 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.

3What 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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