Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 1

1. What is PySpark and how is it different from Pandas?

PySpark is the Python API for Apache Spark, an open-source distributed computing framework designed to process large-scale data across multiple nodes in a cluster. It provides an interface for leveraging Spark’s capabilities (such as distributed data processing, fault tolerance, and in-memory computation) using Python syntax.

✅Key Differences Between PySpark and Pandas

Feature	PySpark	Pandas
Data Size	Handles massive datasets (terabytes or petabytes) distributed across multiple machines	Works with small to medium datasets that fit in a single machine’s memory
Execution Model	Lazy evaluation — transformations are only executed when an action (like .count() or .show()) is called	Eager evaluation — operations execute immediately
Performance	Optimized for parallel, distributed processing via Spark’s execution engine	Single-threaded and runs on a single machine
Environment	Requires a Spark cluster or local Spark setup	Runs locally with no cluster requirement
Use Cases	Big Data ETL, Data Engineering, Machine Learning Pipelines on distributed data	Data analysis, exploration, and quick prototyping

Example:

# Pandas example
import pandas as pd
df = pd.read_csv("sales.csv")
df.groupby("region")["revenue"].sum()

# PySpark example
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SalesApp").getOrCreate()
df = spark.read.csv("sales.csv", header=True, inferSchema=True)
df.groupBy("region").sum("revenue").show()

💬 Summary:

• Pandas is like a powerful notebook — excellent for data that fits in memory.
• PySpark is like a distributed supercomputer — designed to handle big data efficiently and fault-tolerantly.

2. What is SparkSession in PySpark?

SparkSession is the entry point to programming with PySpark. It provides a unified interface to interact with all Spark functionalities, including DataFrame and SQL APIs, streaming, and machine learning.

2.1 How it replaced older contexts?

• Introduced in Spark 2.0, replacing older contexts (SparkContext, SQLContext, and HiveContext).
• Acts as the main gateway to access Spark’s core features.
• Handles the configuration, resource management, and session state for the Spark application.

2.2 What is the Core Responsibilities of SparkSession?

1. Create and manage DataFrames

   df = spark.read.csv("data.csv", header=True, inferSchema=True)

2. Run SQL queries

   spark.sql("SELECT * FROM customers WHERE age > 30").show()

3. Access the Spark Context

   sc = spark.sparkContext

4. Manage configurations and catalogs

   spark.conf.set("spark.sql.shuffle.partitions", 50)
   spark.catalog.listTables()

2.3 Explain the internal architecture of SparkSession in PySpark. How does it interact with SparkContext and SQLContext?

• SparkSession internally creates or uses a SparkContext to connect with the cluster.

• It also manages:

  • The SQLContext (for structured queries)
  • The Catalog (for metadata and table management)

🧾 Example:

from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder \
    .appName("ExampleSession") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

# Load data
df = spark.read.json("data.json")

# Perform SQL operations
df.createOrReplaceTempView("people")
spark.sql("SELECT name FROM people WHERE age > 30").show()

💬 Summary:

• SparkSession = Unified Entry Point for all Spark operations.
• Simplifies working with structured data, SQL, and configuration.
• Without it, no PySpark job can run.

3. What is DataFrame and How do you create a DataFrame in PySpark?

In PySpark, a DataFrame is a distributed collection of data organized into named columns, similar to a relational table in SQL or a DataFrame in Pandas — but capable of handling data at scale across a cluster.

✅You can create a DataFrame in PySpark in three main ways:

1️⃣ From a Structured Data Source (like CSV, JSON, Parquet, etc.)

Using the spark.read API:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DataFrameExample").getOrCreate()

# Read from CSV
df_csv = spark.read.csv("orders.csv", header=True, inferSchema=True)

# Read from JSON
df_json = spark.read.json("orders.json")

df_csv.show(5)

Explanation:

• header=True → tells Spark to use the first row as column names
• inferSchema=True → automatically detects data types
• These files are distributed and loaded in parallel across worker nodes

---

2️⃣ From a Python Collection (List, Dictionary, or RDD)

You can manually create a small DataFrame using local Python data — useful for testing or demos:

data = [("Alice", 25), ("Bob", 30), ("Catherine", 29)]
columns = ["Name", "Age"]

df_manual = spark.createDataFrame(data, columns)
df_manual.show()

Or from an RDD:

rdd = spark.sparkContext.parallelize(data)
df_rdd = rdd.toDF(columns)

---

3️⃣ From an External Database or Data Warehouse

You can connect to JDBC sources such as MySQL, PostgreSQL, or Snowflake:

df_db = spark.read.format("jdbc") \
    .option("url", "jdbc:mysql://localhost:3306/sales_db") \
    .option("dbtable", "orders") \
    .option("user", "root") \
    .option("password", "mypassword") \
    .load()

4. Explain the difference between RDD and DataFrame?

In PySpark, both RDDs and DataFrames are fundamental abstractions for working with data — but they differ significantly in abstraction level, performance, and usability.

Feature	RDD (Resilient Distributed Dataset)	DataFrame
Definition	A low-level distributed collection of objects; the fundamental Spark data structure.	A distributed collection of rows with named columns — built on top of RDDs.
Abstraction Level	Low-level API — requires manual transformations and actions.	High-level API — designed for structured data operations.
Data Structure Type	Unstructured — can hold any type of Python, Scala, or Java objects.	Structured — data is organized in tabular form like a SQL table.
Schema	No schema; user must manage data types manually.	Has an explicit schema (column names and data types).
Ease of Use	Complex to code; requires more lines and transformations.	User-friendly — supports SQL-like operations and DataFrame APIs.
Optimization	No automatic optimization; user must handle logic.	Automatically optimized by Spark’s Catalyst Optimizer.
Performance	Slower due to lack of optimization and serialization overhead.	Faster due to Tungsten execution engine and optimized query planning.
Data Representation	Distributed collection of Java/Python objects.	Distributed collection of Rows with named columns.
Use Case	Best for low-level transformations, custom computations, or when schema is unknown.	Best for structured data processing, ETL, and SQL operations.
Interoperability	Harder to integrate with SQL or MLlib directly.	Integrates seamlessly with Spark SQL, MLlib, and GraphFrames.
API Type	Functional (map, flatMap, filter, reduce).	Declarative (select, filter, groupBy, join).
Example Syntax	rdd.filter(lambda x: x > 5)	df.filter(df.value > 5) or spark.sql("SELECT * FROM table WHERE value > 5")

💬Summary:

• “RDDs give you full control but demand more effort.
• DataFrames give you structured power and performance optimization with minimal code.”

5. How do you check the version of PySpark?

You can check the PySpark version using any of the following methods, depending on your environment.

1️⃣ Check version using PySpark shell or script

import pyspark
print(pyspark.__version__)

Output Example:

3.5.2

This prints the version of the installed PySpark package in your environment.

2️⃣ Check version from SparkSession

If you’ve already created a SparkSession:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("VersionCheck").getOrCreate()
print(spark.version)

Output Example:

3.5.2

Here, spark.version gives the version of the Spark runtime (the engine your code is actually running on).

Sometimes, the installed PySpark version (pyspark.__version__) and Spark runtime version (spark.version) may differ slightly if your environment is misconfigured — checking both is a good practice.

3️⃣ Check via Command Line (CLI)

If you’re using a local or cluster terminal:

pyspark --version

Output Example:

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.5.2
      /_/
Using Scala version 2.12.15, Java HotSpot(TM) 64-Bit Server VM, 11.0.22

This shows both the Spark version and Scala version used by your PySpark installation.

📋 Quick Summary Table

Method	Command	Output Example	Checks
Python Package	pyspark.__version__	3.5.2	Installed PySpark package version
SparkSession	spark.version	3.5.2	Running Spark engine version
Command Line	pyspark --version	Version details + Scala info	CLI environment setup