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Table of contents
1.
Introduction
2.
Pyspark interview questions for Freshers
2.1.
Q1. What is PySpark?
2.2.
Q2. What is the difference between RDD, DataFrame, and Dataset in PySpark?
2.3.
Q3. How do you create an RDD in PySpark?
2.4.
Q4. What is lazy evaluation in PySpark?
2.5.
Q5. What is a transformation in PySpark?
2.6.
Q6. What is an action in PySpark?
2.7.
Q7. How do you handle missing data in PySpark?
2.8.
Q8. How do you join two DataFrames in PySpark?
2.9.
Q9. How do you handle skewed data in PySpark?
2.10.
Q10. How do you optimize PySpark performance?
3.
Pyspark interview questions for Experienced
3.1.
Q11. How does PySpark differ from Apache Spark?
3.2.
Q12. What is a SparkSession and why is it important?
3.3.
Q13. How do you cache data in PySpark, and what are the benefits of caching?
3.4.
Q14. How does PySpark handle partitioning, and what is the significance of partitioning?
3.5.
Q15. What is a UDF, and how is it used in PySpark?
3.6.
Q.16 What is a window function, and how is it used in PySpark?
3.7.
Q.17 What is the difference between map() and flatMap() in PySpark?
3.8.
Q.18 What is a pipeline, and how is it used in PySpark?
3.9.
Q.19 What is a checkpoint, and how is it used in PySpark?
3.10.
Q.20 What is a broadcast join, and how is it different from a regular join?
4.
Frequently Asked Questions
4.1.
What topics should I focus on when preparing for a PySpark interview?
4.2.
How can I improve my PySpark coding skills before an interview?
4.3.
What are some common mistakes to avoid during a PySpark interview?
4.4.
What are the capabilities of PySpark?
5.
Conclusion
Last Updated: Mar 27, 2024
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Top PySpark Interview Questions and Answers (2024)

Author Tisha
1 upvote

Introduction

This article will discuss a list of the most asked Top 20 Pyspark interview questions and answers. These questions can help you prepare for your following Pyspark interview. Check out the complete list of questions below.
 

pyspark interview questions

 

Pyspark interview questions for Freshers

Q1. What is PySpark?

PySpark is the Python API for Apache Spark. It is an open-source distributed system that is used for big data processing.

Q2. What is the difference between RDD, DataFrame, and Dataset in PySpark?

Resilient Distributed Datasets is a basic data structure in PySpark. It represents a distributed collection of objects. The Dataset is a high-level abstraction that provides a more organized way of manipulating data. DataFrame is a collection of data organized into named columns.

Q3. How do you create an RDD in PySpark?

We can create an RDD in PySpark by loading data from a file. We can also create it using the parallelize() function from an existing collection.

Q4. What is lazy evaluation in PySpark?

Lazy evaluation is a feature in PySpark that defers the execution of code until it is needed. This is used to optimize the performance of PySpark by decreasing the amount of data that needs to be processed.

Q5. What is a transformation in PySpark?

A transformation is an operation that takes one RDD as input and produces another RDD as output. Some examples of transformations are map(), filter(), and groupBy().

Q6. What is an action in PySpark?

An action is an operation in Pyspark that triggers the execution of transformations and produces a result. Some examples of actions in pyspark are count(), collect(), and saveAsTextFile().

Q7. How do you handle missing data in PySpark?

Missing data can be handled using the dropna() function to drop rows with missing values. We can also handle it by filling in missing values using the fillna() function.

Q8. How do you join two DataFrames in PySpark?

You can join two DataFrames in PySpark using the join() function. It takes the two DataFrames as input and a join condition.

Q9. How do you handle skewed data in PySpark?

Skewed data can be handled using the skew join optimization technique. This technique involves splitting data into multiple partitions based on the join key.

Q10. How do you optimize PySpark performance?

PySpark performance can be optimized by using lazy evaluation, reducing data shuffling. It can also be optimized using the appropriate data structure for the job, for example, RDDs, DataFrames, or Datasets.

 

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Pyspark interview questions for Experienced

Q11. How does PySpark differ from Apache Spark?

PySpark is the Python API for Apache Spark. PySpark differs from Apache Spark because it provides a Python interface for interacting with Spark, while Apache Spark is written in Scala.

Related Article Apache Server

Q12. What is a SparkSession and why is it important?

A SparkSession is the entry point to PySpark. It provides a way to create DataFrames and Datasets. It handles all the configuration and initialization of the Spark runtime. A SparkSession is required for creating a DataFrame or Dataset in PySpark.

Q13. How do you cache data in PySpark, and what are the benefits of caching?

You can cache data in PySpark using the cache() method. Caching can improve performance by reducing the times data needs to be read from disk. Caching can also consume a lot of memory, so it should be used carefully.

Q14. How does PySpark handle partitioning, and what is the significance of partitioning?

Partitioning is dividing data into smaller and manageable chunks called partitions. PySpark can automatically partition data when it reads or create. It can also be repartitioned using the repartition() or coalesce() methods. Partitioning is important because it affects the parallelism and efficiency of data processing in PySpark.

Q15. What is a UDF, and how is it used in PySpark?

User Defined Function is a type of function that is defined by the user and can be used to process data in PySpark. UDFs can be used in PySpark to perform complex data transformations which are not supported by built-in functions.

Q.16 What is a window function, and how is it used in PySpark?

A window function is a function that performs calculations across rows in a DataFrame. Window functions can be used to calculate rolling averages, cumulative sums, and other types of window aggregations in PySpark.

Q.17 What is the difference between map() and flatMap() in PySpark?

The map() method in PySpark is used to implement a function to the elements of an RDD or DataFrame. The flatMap() method is almost similar to the map() but can return multiple elements for each input element.

Q.18 What is a pipeline, and how is it used in PySpark?

A pipeline in PySpark is a series of data processing stages executed in a specific order. Pipelines can be used to process data efficiently. It can be optimized to minimize data movement and maximize parallelism.

Q.19 What is a checkpoint, and how is it used in PySpark?

A checkpoint is a method for storing data to disk during data processing. Checkpoints can improve fault tolerance and optimize data processing. It reduces the data that is required to be recomputed in case of failure.

Q.20 What is a broadcast join, and how is it different from a regular join?

A broadcast join in PySpark is used when one of the data sets is small to fit in memory. The smaller data set is broadcast to all nodes in the cluster. While a regular join involves mixing the data between nodes in the cluster.

Frequently Asked Questions

What topics should I focus on when preparing for a PySpark interview?

It is important to have an understanding of the Spark architecture, RDDs, DataFrames, and Spark SQL. You should also be familiar with PySpark's built-in functions and be able to write custom PySpark code.

How can I improve my PySpark coding skills before an interview?

To improve your PySpark coding skills, you can work through practice problems and examples on PySpark. You can also contribute to open-source PySpark projects or build your own PySpark applications to gain experience. 

What are some common mistakes to avoid during a PySpark interview?

One common mistake to avoid during a PySpark interview is not understanding the problem before attempting to write code. Make sure to ask the questions and break down the problem into smaller parts before starting to write code.

What are the capabilities of PySpark?

PySpark provides a set of libraries and APIs for processing data and provides various data sources, including Hadoop Distributed File System, Apache Cassandra, CSV, and many more. It also provides a graph processing library to perform graph computations.

Conclusion

In conclusion, preparing for a Pyspark interview can be hard. With the proper knowledge and practice, you can prepare easily. Pyspark interview questions and answers in this article will help you prepare. 

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