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Top 10 PySpark Interview Questions and Answers for 2025

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.

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!