Top 10 PySpark Interview Questions and Answers for 2025

PySpark has become a go-to framework for big data processing, and companies are increasingly testing candidates on their ability to manipulate large datasets using Apache Spark’s Python API. If you're preparing for a PySpark interview, this guide will help you understand the key topics and practice commonly asked questions.

Why PySpark?

PySpark is widely used because it combines the scalability of Apache Spark with the simplicity of Python. It enables distributed computing for large-scale data processing, making it essential for data engineers and big data professionals.

Common PySpark Interview Topics

To excel in a PySpark interview, focus on these core concepts:

• RDDs (Resilient Distributed Datasets): The building blocks of Spark that allow fault-tolerant parallel computations.
• DataFrames: Higher-level APIs built on top of RDDs that provide powerful data manipulation features similar to Pandas.
• Spark SQL: A module for processing structured data using SQL queries.
• Transformations & Actions: Understanding lazy evaluations and Spark operations is crucial.
• Joins & Aggregations: Efficiently joining datasets and performing group-wise computations.
• Performance Optimization: Techniques like partitioning, caching, and broadcast joins.
• Handling CSV and JSON Data: Loading and processing structured data formats in PySpark.

Apart from these concepts, practice coding questions for PySpark interviews here: PySpark Coding Interview Questions

Top PySpark Interview Questions

1. What are RDDs, and how do they differ from DataFrames?

Answer:
RDDs (Resilient Distributed Datasets) are the fundamental data structure in Spark, allowing parallel processing. DataFrames, on the other hand, provide higher-level optimizations and use a schema similar to tables in a database.

2. How do you create a DataFrame in PySpark?

Answer:
You can create a DataFrame from a list, dictionary, or an external file like CSV or JSON.

1from pyspark.sql import SparkSession
2
3spark = SparkSession.builder.appName("Example").getOrCreate()
4
5data = [("Alice", 25), ("Bob", 30)]
6columns = ["Name", "Age"]
7
8df = spark.createDataFrame(data, columns)
9df.show()

3. What is the difference between transformations and actions in PySpark?

Answer:
Transformations (like map(), filter(), groupBy()) create new RDDs but are lazily evaluated.
Actions (like collect(), count(), show()) trigger computation and return results.

4. How do you optimize a PySpark job?

Answer:
Some common techniques include:

• Using broadcast joins for small datasets.
• Repartitioning data effectively to avoid data skew.
• Caching frequently used DataFrames using .cache() or .persist().
• Avoiding shuffling by using coalesce() where needed.

5. How do you read and write CSV files in PySpark?

Answer:

1# Reading a CSV file
2df = spark.read.csv("path/to/file.csv", header=True, inferSchema=True)
3
4# Writing to a CSV file
5df.write.csv("output/path", header=True)

6. How do you perform a join between two DataFrames in PySpark?

Answer:

1df1 = spark.createDataFrame([(1, "Alice")], ["id", "name"])
2df2 = spark.createDataFrame([(1, "NYC")], ["id", "city"])
3
4result = df1.join(df2, on="id", how="inner")
5result.show()

7. What is a broadcast join and when would you use it?

Answer:
A broadcast join copies a small DataFrame to all nodes in the cluster to avoid shuffling large datasets. Use it when one DataFrame is small enough to fit in memory.

1from pyspark.sql.functions import broadcast
2
3result = df1.join(broadcast(df2), on="id")

8. Explain the difference between repartition() and coalesce().

Answer:

• repartition() increases or decreases partitions by shuffling the data.
• coalesce() decreases the number of partitions without a full shuffle, making it more efficient for reducing partitions.

9. How do you handle missing or null values in a DataFrame?

Answer:

1df.na.drop()            # Drops rows with any null values
2df.na.fill(0)           # Replaces nulls with 0
3df.na.replace("NA", None)  # Replaces 'NA' string with None

10. What are some common performance bottlenecks in PySpark?

Answer:

• Inefficient joins
• Skewed data
• Excessive shuffling
• Lack of caching
• Small partition sizes or too many partitions

Final Tips to Ace Your PySpark Interview

• Practice coding challenges on real-world datasets here.
• Understand distributed computing concepts and how Spark executes tasks.
• Be comfortable with SQL queries in Spark.
• Know how to debug PySpark jobs and handle performance bottlenecks.
• Use Spark Playground to test your PySpark skills online.

Preparing for a PySpark interview requires hands-on practice and a solid understanding of Spark's core concepts. Keep coding and refining your approach to common problems. Happy learning!