Most Frequently Asked PySpark Interview Questions & Answers [For New & Experienced Candidates]

Practice Interview Questions and Solutions

Define PySpark.

Most PySpark interviews will begin with this question.

The Spark Python API is called PySpark. With its help, Spark and Python can work together. PySpark is designed to handle structured and semi-structured datasets, and it offers the option of reading data from a wide variety of sources, each with its unique data format. Furthermore, PySpark allows us to interact with RDDs (Resilient Distributed Datasets), which are an integral part of Spark's functionality. The py4j library is responsible for the implementation of all these features.

What are the pros and cons of using PySpark?

Among the many benefits of utilizing PySpark are:

● PySpark allows us to develop parallel programs in a very straightforward fashion.

● All of the connections and nodes have been eliminated.

● All faults, including synchronization errors, are handled by PySpark.

● PySpark has a lot of handy algorithms already built in.

The following are a handful of drawbacks of PySpark:

● A drawback of PySpark is that it can complicate the expression of problems in MapReduce style.

● PySpark is inefficient in comparison to other programming languages.

Which algorithms can be used with PySpark?

Among the many algorithms that can be run on PySpark, we find:

● spark.mllib

● mllib.clustering

● mllib.clustering

● mllib.regression

● mllib.recommendation

● mllib.linalg

● mllib.fpm

PySpark SparkContext: what is it?

You can think of the SparkContext that PySpark creates as the doorway to all of Spark's features. The SparkContext initiates the Java Virtual Machine (JVM) with the help of the py4j library and then creates the JavaSparkContext. The SparkContext is accessible as sc by default.

What Exactly is PySpark SparkFiles?

Probably the most frequently asked question at PySpark job interviews. Data is loaded into the Apache Spark framework using the Python library PySpark SparkFiles. To add a file to Apache Spark, use the sc.addFile method, which is a SparkContext function. The path can also be retrieved with SparkFile. retrieving or resolving the file paths added by sc.addFile. Class methods getrootdirectory() and get (filename) are available in the SparkFiles directory.

PySpark SparkConf: What is it?

When preparing an application for local or cluster execution, PySpark SparkConf is where most of the heavy lifting takes place.

When we wish to launch SparkConf, we execute the following code:

class pyspark.Sparkconf(

localdefaults = True,

_jvm = None,

_jconf = None

)

Explain PySpark's StorageLevel

By adjusting the RDD's StorageLevel in PySpark, we can decide whether to serialize the RDD's partitions or replicate them across nodes, as well as where the RDD will be kept (memory, disk, or both).

Here is the source for the StorageLevel variable:

class pyspark.

StorageLevel( useDisk, useMemory, useOfHeap, deserialized, replication = 1)

Describe PySpark DataFrames

This is a staple of PySpark data frame-related job interviews. When working with data in PySpark, DataFrames are used to store and arrange data across nodes. Named columns contain these structures, which are the same as those found in relational databases.

Furthermore, PySpark DataFrames are optimized more effectively than those of Python and R. This is because they can be derived from a wide variety of preexisting data structures, including RDDs, Hive tables, external databases, and structured data files.

The data in a PySpark DataFrame has the advantage of being spread out across multiple machines in the cluster. All of the machines will perform the respective tasks simultaneously.

PySpark Join: What Is It?

Joining two DataFrames is a breeze with PySpark. Joining numerous DataFrames is a breeze when using these bindings. It supports the INNER, RIGHT OUTER, LEFT OUTER, LEFT SEMI, LEFT ANTI, SELF JOIN, and CROSS join types, in addition to those available in standard SQL. Joins in PySpark are transformations that involve moving data around within the cluster.

How can you rename the DataFrame column in PySpark?

This is a standard topic for PySpark DataFrame interviews. PySpark's withColumnRenamed() method lets you change the name of a column in a DataFrame.

There are times when you need to keep numerous columns on a PySpark DataFrame, yet you may only need to keep one. There's more than one way to accomplish this. Due to the immutable nature of DataFrame, you cannot modify or rename a column after it has been added using withColumnRenamed().

This occurs because a new DataFrame is constructed, this time with the revised column labels. Common methods for renaming nested columns include renaming all columns or renaming selected multiple columns.

Does Spark mean the same thing as PySpark?

As such, these PySpark code questions are great for gauging a candidate's familiarity with the language's foundations. PySpark was created to facilitate communication between the Python and Apache Spark communities. It's essentially Spark's API written in Python.

PySpark is a library for the Python programming language and the Apache Spark data processing framework that helps you interact with resilient distributed datasets (RDDs).

Explain PySparkSQL

There will be a lot of questions on PySparkSQL in your coding interview, therefore it's important to study it ahead of time. It's a package for the Python framework Spark that mimics SQL and may be used to perform analysis on massive amounts of structured or semi-structured data.

PySparkSQL supports SQL queries as well. Additionally, it supports integration with Apache Hive and the HiveQL query language.

PySparkSQL is an extension of the original PySpark framework. The DataFrame was first introduced by PySparkSQL, and it's a tabular representation of structured data just like a table in an RDBMS (relational database management system).

Also Read:- https://blog.skillslash.com/counting-subarrays

Final Words

These were some of the most common questions you could face in a PySpark Interview. If you go through these, you already have the basic knowledge and understanding. To understand it better, you can enroll in Skillslash's Full Stack Developer Course or Data Science Course in Hyderabad with a placement guarantee and master the core concepts and fundamentals that include PySpark and much more. You even receive a 100% job assurance commitment. Get in Touch with the support team to know more.