PySpark Interview Questions and Answers for 2025

There are some key advantages of PySpark RDD. Here is the detail explanation of those: 

1. Immutability: In PySpark, RDDs are immutable. Once created, it cannot be changed later. Every time I apply a transform operation to an RDD, I must create a new RDD. 
2. Fault Tolerance: PySpark RDD provides fault tolerance capabilities. If the operation fails, the data is automatically reloaded from other available partitions. This allows us to run PySpark applications seamlessly. 
3. Partitioning: By default, when we create an RDD from arbitrary data, the elements of the RDD are split among the available cores. 
4. Lazy Evolution: PySpark RDDs follow the Lazy Evolution process. In PySpark RDDs, transform operations are not executed as soon as they occur. Operations are stored in a DAG and evaluated when the first RDD action is found. 
5. In-memory processing: PySpark RDDs are used to load data from disk into memory. We can keep the RDD in memory and reuse the computation.

Nowadays, every industry makes use of big data to evaluate where they stand and grow. When we hear the term big data, Apache Spark comes to our mind. There are the industry benefits of using PySpark that supports Spark. These benefits can be listed as: 

1. Media streaming: Spark can be used to achieve real-time streaming to provide personalized recommendations to subscribers. Netflix is one such example that uses Apache Spark. It processes around 450 billion events every day to flow to its server-side apps. 
2. Finance: Bank uses Spark to access and analyze social media profiles. In return, we gain insight into which strategies can help us make the right decisions in terms of customer segmentation, credit risk assessment, fraud detection, and more. 
3. Healthcare: Healthcare provider uses Spark to analyze patient historical records to identify health issues patients may face after discharge. Spark is also used to perform genomic sequencing, reducing the time required to process genomic data. 
4. The Travel Industry: Companies like TripAdvisor use Spark to help users plan the perfect trip, comparing data and reviews from hundreds of websites about places, hotels, and more to make travel decisions. We offer personalized recommendations to enthusiasts.  
5. Retail and E-Commerce: This is a key industry segment that requires big data analytics for targeted advertising. Companies like Alibaba run Spark jobs to analyze petabytes of data to improve customer experience, deliver targeted offers and sales, and optimize overall performance.

PySpark SparkConf is used to set the configuration and parameters required to run the application on the cluster or on the local system. We can run SparkConf by running the following class: 

class PySpark.SparkConf ( 
localdefaults = True, 
_jvm = None, 
_jconf = None 
) 

Where: 

1. localdefaults – It is of type boolean and it indicates whether we require loading values from Java System Properties. Its value is True by default.  
2. _jvm – This belongs to the class py4j.java_gateway.JVMView. It is an internal parameter that is used for passing the handle to JVM. This need not be set by the users. 
3. _jconf – This belongs to the class py4j.java_gateway.JavaObject. This parameter is optional and can be used to pass an existing SparkConf handle to use the parameter.  

We can create DataFrame in PySpark by making use of the createDataFrame() method of the SparkSession. Let me explain with an example. 

For example: 

data = [(‘Raj’, 20), 
(‘Rahul’, 20), 
(‘Simran’, 20)] 
columns = [“Name”, “Age”] 
df = spark.createDataFrame(data = data, schema = columns) 

This will create a dataframe where columns will be Name and Age. The data will be filled in the columns accordingly. 

Now, we can get the schema of dataframe by using the method df.printSchema(). This will look like: 

>> df.printSchema() 
root 
|-- Name: string (nullable = true) 
|-- Age: integer (nullable = true) 

Yes, we can create PySpark DataFrame from the external data source. Real-time applications leverage external file systems such as local systems, HDFS, HBase, MySQL tables, and S3 Azure. The following example shows how to read data from a CSV file residing on your local system and create a DataFrame. PySpark supports many file extensions like csv, text, avro, parquet, tsv, etc. 

For example: 

df = spark.read.csv(“path/to/filename.csv”) 

Expect to come across this popular question in PySpark Interview Questions. PySpark SQL is an organized data library for Spark. PySpark SQL provides more details about data structures and operations than the PySpark RDD API. It comes with the "DataFrame" programming paradigm. 

A DataFrame is a fixed distributed collection of data. DataFrame can handle large amounts of organized data such as relational databases, and semi-structured data such as JavaScript Object Notation or JSON. After creating the DataFrame, we can handle the data using SQL syntax/queries. 

In PySpark SQL, the first step is to create a temporary table in the DataFrame using the createOrReplaceTempView() function. Tables are available in the SparkSession via the sql() method. These temporary tables can be dropped by closing the SparkSession. 

The example of PySpark SQL: 

import findspark 
findspark.init() 
import PySpark 
from PySpark.sql import SparkSession 
spark = SparkSession.builder.getOrCreate() 
df = spark.sql(“select ‘spark’ as hello”) 
df.show() 

The Lineage Graph is a collection of RDD dependencies. There are separate lineage graphs for every Spark application. The lineage graph recompiles RDDs on-demand and restores misplaced data from persisted RDDs. An RDD lineage graph lets us assemble a new RDD or restore data from a lost persisted RDD. It was created by using changes to the RDD and generating a regular execution plan.

Catalyst Optimizer plays a very important role in Apache Spark. It helps to improve structural queries in SQL or expressed through DataFrame or DataSet APIs by reducing the program execution time and cost. The Spark Catalyst Optimizer supports both cost-based and rule-based optimization. Rule-based optimization contains a set of rules that define how a query is executed. Cost-based optimization uses rules to create multiple plans and calculate their costs. Catalyst optimizer also manages various big data challenges like semi-structured data and advanced analytics.

PySpark is a Python API. It was created and distributed by the Apache Spark agency to make working with Spark less complicated for Python programmers. Scala is the programming language utilized by Apache Spark. It can work with different languages like Java, R, and Python.

Because Scala is a compile-time, type-secure language, Apache Spark has many skills that PySpark does not. One of which is incorporating Datasets. Datasets are a series of domain-specific objects that can be used to execute concurrent calculations.

In PySpark, the Parquet file is a column-type format supported by various data processing systems. Using a Parquet file, Spark SQL can perform both read and write operations.

A Parquet file contains column-type format storage that provides some benefits. The benefits are: It is small and takes up less space. It makes it easier for us to access specific columns. It follows type-specific encoding. It offers better summarized data. It contains very limited Input/Output operations.

We can use some steps to connect Spark with Mesos.

First, configure the sparkle driver program to associate with Mesos. The paired Spark volume must be in an open Mesos region. Then install Apache Spark in a similar region as Apache Mesos and design the "spark.mesos.executor.home" property to point to the region where it is installed. This is how we can associate Spark with Apache Mesos. 

In PySpark, DStream is an acronym for Discretized Stream. It is a collection of groups or RDDs of information divided into small clusters. It is also called Apache Spark Discretized Stream and is used as a collection of RDDs in the cluster. DStreams are grounded in Spark RDDs. They are used to allow uninterrupted coordination of streaming with some other Apache Spark options such as Spark MLlib and Spark SQL.

The code to see the specific keyword exists or not will be: 

lines = sc.textFile(“hdfs://Hadoop/user/test_file.txt”); 
def isFound(line): 
if line.find(“my_keyword”) > -1 
return 1 
return 0 
foundBits = lines.map(isFound); 
sum = foundBits.reduce(sum); 
if sum > 0: 
print “Found” 
else: 
print “Not Found”; 

If the keyword is found it will print as “Found” else if it not found it will print “Not Found”. 

There are two types of errors in Python: syntax errors and exceptions.

Syntax errors are often referred to as parsing errors. Bugs are errors in a program that can cause it to crash or terminate unexpectedly. When the parser detects an error, it repeats the problematic line and then displays an arrow pointing to the beginning of the line.

Exceptions occur in a program when the program's normal flow is disrupted by an external event. Even if the program's syntax is accurate, there is a chance that an error will be encountered during execution; however, this error is an exception. ZeroDivisionError, TypeError, and NameError are some examples of exceptions.

Receivers are unique objects in Apache Spark Streaming whose sole purpose is to consume data from various data sources and then push it to Spark. By streaming contexts as long-running tasks on different executors, we can generate receiver objects.  

There are two different types of receivers which are as follows: 

• Reliable Receiver: When data is received and properly copied to Apache Spark Storage, this receiver validates the data sources. 
• Unreliable receiver: When receiving or replicating data in Apache Spark Storage, these receivers do not recognize data sources. 

When data is unevenly distributed, PySpark has a few tricks up its sleeve to deal with it. It can shuffle data around to balance things out, sort of like how a teacher might rearrange students in a classroom to balance the teams. This helps everything run more smoothly and evenly.

Tungsten is all about making Spark run faster and use less memory. It does this by being really smart about how it handles data behind the scenes—think of it as the wizard who fine-tunes the engine of a car for better performance and efficiency.

In PySpark, the Catalyst optimizer takes care of making our queries run faster. It works like a smart planner that tweaks and rearranges the steps of our query to make it more efficient. It's like having an expert optimise our travel route, avoiding traffic jams and roadblocks.

Broadcasting is like giving a cheat sheet to all parts of our Spark program. Instead of sending data to each worker node every time it's needed, PySpark sends it once, and each node holds onto it. This is super handy for data that we need to reference often across many nodes because it saves a lot of time and network traffic.

Accumulators are like counters or tally marks that PySpark uses to keep track of information across tasks. For example, if we wanted to count how many times a certain word appears in documents across all nodes, we could use an accumulator to keep a running total even as our data is processed in parallel.

PySpark SQL is the most famous PySpark module used to process structured columnar data. Once the DataFrame is created, we can work with the data using SQL syntax. Spark SQL is used to pass native raw SQL queries to Spark using select, where group by, join, union, etc. To use PySpark SQL, the first step is to create a temporary table on a DataFrame using the createOrReplaceTempView() method. Once created, the table is accessible within a SparkSession using the sql() function. When the SparkSession is terminated, the temporary table will get dropped. 

For example, consider that we have a DataFrame assigned to the variable df which contains Name, Age and Gender of Students as the columns. Now, we will create a temporary table of the DataFrame that gets access to the SparkSession by using the sql() function. The SQL queries can be run within the function.   

df.createOrReplaceTempView("STUDENTS")  
df_new = spark.sql("SELECT * from STUDENTS")  
df_new.printSchema() 
The schema will be shown as: 
>> df.printSchema()  
root  
|-- Name: string (nullable = true)  
|-- Age: integer (nullable = true)  
|-- Gender: string (nullable = true) 

We can use join() method that is present in PySpark SQL. The syntax of the method looks like:

join(self, other, on = None, how = None) 

Where,

• other – It is the right side of the join.
• on – It is the column name string used for joining.
• how – It is the type of join. Default type is inner. The values of the type can be inner, left, right, cross, full, outer, left_outer, right_outer, left_anti and left_semi.

where() and filter() methods can be attached to the join expression to filter rows. We can also have multiple joins using the chaining join() method.

For example, consider we have two dataframes named Employee and Department. Both have columns named as emp_id, emp_name, empdept_id and dept_id, dept_name respectively. We can internally join the Employee DataFrame with the Department DataFrame to get the department information along with the employee information. The code will look like:

emp_dept_df = empDF.join(deptDF,empDF.empdept_id==deptDF.dept_id,"inner").show(truncate = False) 

PySpark Streaming is a scalable, fault-tolerant, high-throughput process streaming system that supports both streaming and batch loading to support real-time data from data sources such as TCP Socket, S3, Kafka, Twitter, file system folders, etc. The processed data can be sent to live dashboards, Kafka, databases, HDFS, etc.

To stream from a TCP socket, we can use the readStream.format("socket") method of the Spark session object to read data from the TCP socket and provide the host and port of the streaming source as options. The code will look like: 

from PySpark import SparkContext  
from PySpark.streaming  
import StreamingContext from PySpark.sql  
import SQLContext from PySpark.sql.functions  
import desc  
sc = SparkContext()  
ssc = StreamingContext(sc, 10)  
sqlContext = SQLContext(sc)  
socket_stream = ssc.socketTextStream("127.0.0.1", 5555)  
lines = socket_stream.window(20) df.printSchema()  

Spark retrieves the data from the socket and represents it in the value column of the DataFrame object. After processing the data, the DataFrame can be streamed to the console or other destinations on demand such as Kafka, dashboards, databases, etc.  

Spark automatically stores intermediate data from various shuffle processes. However, it is recommended to use RDD's persist() function. There are many levels of persistence for storing RDDs in memory, disk, or both, with varying levels of replication. The following persistence levels are available in Spark:  

1. MEMORY ONLY: This is the default persistence level and is used to store RDDs on the JVM as deserialized Java objects. In case the RDDs are too large to fit in memory, the partitions are not cached and must be recomputed as needed. 
2. MEMORY AND DISK: On the JVM, RDDs are stored as deserialized Java objects. In case of insufficient memory, partitions that do not fit in memory will be left on disk and data will be read from the drive as needed. 
3. MEMORY ONLY SER: RDD is stored as one byte per partition of serialized Java objects. 
4. DISK ONLY: RDD partitions are stored on disk only. 
5. OFF HEAP: This level is like MEMORY ONLY SER, except that the data is stored in off-heap memory. 

The persist() function has the following syntax for using persistence levels:  

df.persist(StorageLevel.)  

The streaming application must be available 24/7 and tolerant of errors outside the application code (e.g., system crashes, JVM crashes, etc.). The checkpointing process makes streaming applications more fault tolerant. We can store data and metadata in the checkpoint directory.

Checkpoint can be of two types – metadata check and data check.

A metadata checkpoint allows you to store the information that defines a streaming computation in a fault-tolerant storage system such as HDFS. This helps to recover data after a streaming application controller node failure.

Data checkpointing means saving the created RDDs to a safe place. This type of checkpoint requires several state calculations that combine data from different batches.

We can determine the total number of unique words by following certain steps in PySpark. The steps to follow can be listed as: 

• Open the text file in RDD mode.  

sc.textFile(“hdfs://Hadoop/user/test_file.txt”); 

• Then write a function that will convert each line into a single word. 

def toWords(line): 
return line.split(); 

• Now, run the toWords function on every member of the RDD in Spark. 

words = line.flatMap(toWords); 

• Next, generate a (key, value) pair for every word. 

def toTuple(word): 
return (word, 1); 
wordTuple = words.map(toTuple); 

• Run the reduceByKey() command. 

def sum(x, y): 
return x+y: 
counts = wordsTuple.reduceByKey(sum) 

• Then print it out. 

counts.collect() 

• Property Operators – These operators create a new graph with a user-defined map function modifying the characteristics of a vertex or edge. For these operators, the graph structure is unchanged. This is an important property of these operators because it allows the generated graph to preserve the structural indices of the original graph. 
• Structural Operators - GraphX currently supports only a few widely used structural operators. The opposite operator creates a new graph with the directions of the edges reversed. The subgraph operator returns a graph with only vertices and edges that meet the vertex predicate. The mask operator creates a subgraph by returning a graph with all vertices and edges found in the input graph. The groupEdges operator merges parallel edges. 
• Join operators – Join operators allow you to join data from external collections (RDDs) to existing graphs. For example, we may want to combine new custom attributes with an existing graph or drag vertex properties from one graph to another. 

• Avoid dictionaries: If we use Python data types such as dictionaries, our code may not be able to run in a distributed fashion. We should consider adding an extra column to the dataframe that can be used as a filter instead of using keys to index items in the dictionary. This suggestion also applies to Python types that are not PySpark distributable, such as lists. 
• Limit the use of Pandas: Using toPandas causes all data to be loaded into memory on the driver node, preventing operations from running in a distributed fashion. If the data has been previously aggregated and we want to use regular Python plotting tools, this method is fine, but should not be used for larger data frames. 
• Minimize eager operations: If we want our pipeline to be as scalable as possible, it's best to avoid heavy operations that pull entire data frames into memory. Reading in CSV, for example, is an eager activity, so before using the dataframe I set it to S3 as Parquet before using it in the next steps.  

The basis of Spark Streaming is dividing the content of the data stream into batches of X seconds, known as DStreams. These DStreams allow developers to cache data, which can be especially useful if data from a DStream is used multiple times. The cache() function or the persist() method can be used to cache data with the correct persistence settings. For input streams receiving data over networks such as Kafka, Flume, and others, the default persistence level is configured to achieve data replication across two nodes to achieve fault tolerance.  

• Cache method -  

val cacheDf = dframe.cache()  

• Persist method -  

val persistDf = dframe.persist(StorageLevel.MEMORY_ONLY) 

There are some benefits of Caching. The key benefits are time saving and cost efficiency. Since Spark computations are expensive, caching helps in data reuse, which leads to reuse of computations and reduces the cost of operations. By reusing calculations, we can save a lot of time. Worker nodes can execute/run more tasks by reducing the computation execution time. Hence more tasks can be achieved. 

Spark RDD is extended with a robust API called GraphX that supports graphs and graph-based computations. The Resilient Distributed Property Graph is an enhanced property of Spark RDD, which is a directed multigraph with many parallel edges. User-defined characteristics are associated with each edge and vertex. Multiple connections between the same set of vertices are represented by parallel edges. GraphX offers a collection of operators that enable graph computations such as subgraph, mapReduceTriplets, joinVertices, and so on. It also offers many chart builders and algorithms to facilitate chart analysis.

According to the UNIX Standard Streams, Apache Spark supports the pipe() function on RDDs, which allows us to assemble different parts of jobs that can use any language. An RDD transformation can be created using the pipe() function and can be used to read each RDD element as a string. These can be changed as needed and the results can be presented as strings.

import findspark 
findspark.init() 
from PySpark.sql import SparkSession, types 
spark = SparkSession.builder.master("local").appName('Modes of Dataframereader')\ 
.getorCreate() 
sc = spark.sparkContext 
df1 = spark.read.option("delimiter","|").csv('input.csv') 
df2 = spark.read.option("delimiter","|").csv("input2.csv",header=True) 
from PySpark.sql.functions import lit 
df_add = df1.withColumn("Gender",lit("null")) 
df_add. union(df2).show() 

For the Union-

from PySpark.sql.types import * 
schema = StructType( 
    [ 
        StructField("Name", StringType(), True), 
        StructField("Age", StringType(), True), 
        StructField("Gender", StringType(),True), 
    ] 
) 
df3 = spark.read.option("delimiter","|").csv("input.csv", header = True, schema = schema) 
df4 = spark.read.option("delimiter","|").csv("input2.csv", header = True, schema = schema) 
df3.union(df4).show() 

PySpark is great for big text projects like analysing tons of reviews or processing huge documents. We use things like RDDs or DataFrames to break down the text and analyze it—think word counts or searching for specific phrases. It's like having a super-powered text editor that can instantly handle and analyse entire libraries. Using functions like flatMap() for tokenization and reduceByKey() for counting words ensures efficient processing over clusters.

Window functions in PySpark help us make sense of data that's ordered or grouped in some way. For example, if we’re looking at sales data, we could use a window function to calculate a running total of sales or average sales over a specific period directly within our dataset. It's a bit like having a spreadsheet that automatically updates those figures as we add new data. We can use the over() function to specify a window partitioned by one column, ordered by another, and then apply aggregation functions like sum() or avg() over this window.

Optimising PySpark applications involves several strategies like choosing the right data abstraction (RDD, DataFrame, or DataSet), tuning resource allocation (e.g., memory and cores), and optimising data partitioning. Additionally, minimising data shuffling and using broadcast variables and accumulators wisely can significantly improve performance.

GraphFrames are an enhanced version of DataFrames specifically designed for graph processing in PySpark. They allow us to perform advanced graph operations like finding the shortest paths, calculating PageRank, or running breadth-first searches. Since GraphFrames are integrated with DataFrames, we can seamlessly create, query, and manage graphs while also working with regular tabular data. Imagine trying to figure out the shortest path in a network of roads or ranking websites by importance. GraphFrames build on DataFrames to let us handle these tasks efficiently in PySpark, combining graph algorithms with the ease of SQL-like operations.

Dynamic resource allocation allows PySpark to automatically adjust the number of executors based on workload. This is particularly beneficial in cloud environments where computing resources are elastic. By scaling up resources when demand spikes and scaling down when less compute power is needed, it optimises costs and ensures efficient processing. Dynamic resource allocation is like having an elastic band that stretches resources when needed and contracts when things are quiet. In cloud environments, where we pay for what we use, PySpark can automatically scale up to handle big jobs and scale down when the job's done, saving us money and improving processing efficiency.

MLlib in PySpark is like a treasure chest of machine learning tools. It’s built right into PySpark, so it lets us run algorithms like classification, regression, clustering, and collaborative filtering, as well as feature transformation. MLlib is PySpark's machine learning library that makes practical machine learning scalable and easy. Using MLlib in PySpark allows data scientists to leverage Spark’s distributed computing power to run algorithms faster across clusters. It's perfect for when we need to uncover insights from large datasets quickly.

While PySpark is powerful, it has limitations such as higher memory consumption for certain operations and less efficiency for complex iterative algorithms that do not map well to its execution model. Additionally, its Python API can run slower than the Scala API due to the overhead of Py4J that translates Python API calls to JVM operations. PySpark isn’t perfect—it can be a bit of a memory hog and sometimes slower, especially if we’re using Python instead of Scala. It's also not the best fit for very complex iterative algorithms where we keep looping over the same data. But for many big data tasks, it's still a solid choice.

Keeping our data clean and consistent in PySpark involves setting up checks right from the start. I would use built-in tools to filter out duplicates, correct errors, and make sure everything looks right. Utilising DataFrame API’s built-in functions like dropDuplicates(), filter(), and where() help maintain data consistency. Additionally, writing unit tests and using data validation frameworks can further ensure the quality and consistency of the data processed. Think of it as proofreading our data before it goes out into the world.

Speculative execution is like having a backup plan for slow tasks in PySpark. Speculative execution in PySpark involves launching duplicate tasks for those that are running slower than expected. If certain tasks are straggling due to issues like slow nodes, PySpark will launch duplicate tasks on other nodes. Whichever task finishes first is used, and this can significantly improve performance by reducing delays caused by slow nodes.

StatFunctions in PySpark are part of the DataFrame API and provide statistical methods to quickly summarise data. For example, wecan use df.stat.corr('column1', 'column2') to calculate the correlation between two columns. It’s handy for exploratory data analysis, like when we’re trying to find relationships between variables or just get a quick overview of our data’s distribution and trends. It’s like having a fast, built-in data scientist to help us understand large datasets more intuitively.