17 PySpark Cognizant Interview Questions and Answers

Introduction

In today's data-driven world, organizations are leveraging Big Data technologies to unlock valuable insights and make informed business decisions. PySpark, the Python API for Apache Spark, has emerged as one of the most popular tools for processing large-scale data and running distributed data processing tasks. For aspiring data engineers, data scientists, and big data enthusiasts, PySpark skills have become increasingly vital in landing a job at top-tier companies like Cognizant.

If you are preparing for a PySpark interview at Cognizant or any other company, this blog is your ultimate guide. Here, we have compiled a list of 20 common PySpark interview questions, ranging from fundamental to advanced topics, along with detailed answers. Whether you are an experienced professional or a fresher, these questions will help you brush up on your PySpark knowledge and boost your chances of acing the interview.

1. What is PySpark, and how does it differ from Apache Spark?

PySpark is the Python library used to interact with Apache Spark, an open-source distributed computing system designed for processing and analyzing large-scale data. PySpark provides a Python interface to Spark's core functionalities, allowing developers and data scientists to write Spark applications using Python programming language. While Apache Spark supports multiple languages like Scala, Java, and R, PySpark caters specifically to Python developers, providing seamless integration with Python's vast ecosystem.

2. Explain the difference between RDD, DataFrame, and Dataset in PySpark.

RDD (Resilient Distributed Dataset): RDD is an immutable distributed collection of objects that can be processed in parallel. It is the fundamental data structure in PySpark, offering fault tolerance and resilience. However, RDDs lack optimization opportunities available in DataFrames and Datasets.
DataFrame: DataFrame is a distributed collection of data organized into named columns. It provides a high-level abstraction built on top of RDDs, offering optimization opportunities due to its schema-aware nature and Tungsten execution engine.
Dataset: Dataset is a distributed collection of data that provides the benefits of RDDs (strong typing, performance, and optimization opportunities) with the benefits of DataFrames (relational querying and optimization).

3. How can you create a DataFrame in PySpark?

To create a DataFrame in PySpark, you can use the createDataFrame method provided by the SparkSession object. Here's an example:


    from pyspark.sql import SparkSession

    spark = SparkSession.builder.appName("example").getOrCreate()
    data = [("Alice", 30), ("Bob", 25), ("Charlie", 28)]
    columns = ["Name", "Age"]

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

4. What is Lazy Evaluation in PySpark?

Lazy Evaluation is a core concept in PySpark that defers the execution of transformations until an action is called. Instead of immediately executing transformations, PySpark builds up a logical execution plan (DAG) and optimizes it before running it. This optimization improves performance by reducing unnecessary computations.

5. How can you select specific columns from a DataFrame in PySpark?

In PySpark, you can use the select method to choose specific columns from a DataFrame. Here's an example:


    selected_df = df.select("Name", "Age")
    selected_df.show()

6. Explain the concept of Partitioning in PySpark.

Partitioning in PySpark refers to dividing the data into smaller chunks or partitions, which can be processed in parallel across a cluster. It helps in achieving parallelism, making distributed data processing faster and efficient. PySpark automatically handles partitioning when reading data from external sources like HDFS or S3.

7. What are the different types of transformations in PySpark?

In PySpark, transformations are operations that create a new DataFrame from an existing one without modifying the original DataFrame. Some common transformations include filter, select, groupBy, orderBy, withColumn, etc.

8. How can you cache a DataFrame in PySpark? Why is caching useful?

To cache a DataFrame in PySpark, you can use the cache() method on the DataFrame. Caching is beneficial when you need to reuse a DataFrame across multiple operations. It helps in improving performance by reducing the need to recompute the same DataFrame multiple times.


    df.cache()

9. Explain the role of the SparkContext in PySpark.

The SparkContext is the entry point of any PySpark application and represents the connection to a Spark cluster. It is responsible for coordinating the execution of tasks on the cluster. However, in Spark 2.0+, you typically use the SparkSession object, which internally manages the SparkContext.

10. How can you handle missing or null values in PySpark?

In PySpark, you can use the na functions to handle missing or null values in DataFrames. Some common methods are drop() to remove rows with null values, fill() to replace null values, and replace() to replace specific values with null.


    // Example: dropping rows with null values
    df.na.drop().show()

11. What is the significance of Broadcast Variables in PySpark?

Broadcast variables in PySpark allow developers to efficiently distribute read-only data to all worker nodes. These variables are cached on each node rather than being sent with tasks, reducing network traffic and speeding up the execution of tasks. Broadcast variables are particularly useful when you have a large dataset or lookup table that needs to be shared across all worker nodes during computation.

You can create a broadcast variable using the broadcast function provided by the pyspark.sql.functions module. Here's an example:


    from pyspark.sql import SparkSession
    from pyspark.sql.functions import broadcast

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

    # Create a DataFrame representing a large lookup table
    lookup_table = spark.createDataFrame([(1, "A"), (2, "B"), (3, "C")], ["id", "value"])

    # Create a broadcast variable from the lookup table
    broadcast_table = broadcast(lookup_table)

    # Use the broadcasted DataFrame in a join operation
    df = spark.createDataFrame([(1, "Data1"), (2, "Data2"), (3, "Data3")], ["id", "data"])
    result = df.join(broadcast_table, "id", "left")

    result.show()

12. What is a Window Function in PySpark?

Window functions in PySpark allow you to perform computations across a range of rows related to the current row. They provide a way to efficiently calculate aggregated values without reducing the DataFrame size. Window functions are often used with the pyspark.sql.Window class, which defines the partitioning and ordering criteria for the window operation.

Here's an example of using a window function to calculate the average age for each person's row over a specific window of rows based on the "Name" column:


    from pyspark.sql import SparkSession
    from pyspark.sql.window import Window
    from pyspark.sql import functions as F

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

    data = [("Alice", 30), ("Bob", 25), ("Alice", 28), ("Bob", 22)]
    df = spark.createDataFrame(data, ["Name", "Age"])

    window_spec = Window.partitionBy("Name")
    df_with_avg_age = df.withColumn("AverageAge", F.avg("Age").over(window_spec))

    df_with_avg_age.show()

13. What are the different types of joins in PySpark, and how can you perform them?

In PySpark, you can perform different types of joins on DataFrames using the join method. The common types of joins include:

Inner Join: Returns only the matching rows from both DataFrames.
Outer (Full) Join: Returns all rows from both DataFrames, filling in null values for non-matching rows.
Left (Outer) Join: Returns all rows from the left DataFrame and matching rows from the right DataFrame, filling in null values for non-matching rows on the right.
Right (Outer) Join: Returns all rows from the right DataFrame and matching rows from the left DataFrame, filling in null values for non-matching rows on the left.
Semi Join: Returns only the rows from the left DataFrame for which there is a match in the right DataFrame.
Anti Join: Returns only the rows from the left DataFrame for which there is no match in the right DataFrame.

Here's an example of performing an inner join between two DataFrames:


    from pyspark.sql import SparkSession

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

    df1 = spark.createDataFrame([(1, "Alice"), (2, "Bob")], ["id", "name"])
    df2 = spark.createDataFrame([(1, 30), (3, 25)], ["id", "age"])

    result_df = df1.join(df2, on="id", how="inner")
    result_df.show()

14. How can you perform aggregations on DataFrames in PySpark?

PySpark provides various aggregation functions like count, sum, avg, max, min, etc., that you can use to perform aggregations on DataFrames. These functions are available in the pyspark.sql.functions module.

Here's an example of calculating the average age from a DataFrame:


    from pyspark.sql import SparkSession
    from pyspark.sql import functions as F

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

    data = [("Alice", 30), ("Bob", 25), ("Charlie", 28)]
    df = spark.createDataFrame(data, ["Name", "Age"])

    avg_age = df.agg(F.avg("Age")).collect()[0][0]
    print("Average Age:", avg_age)

15. How can you handle large datasets that do not fit into memory in PySpark?

PySpark is designed to handle large datasets that don't fit into memory by using its distributed processing capabilities. It automatically breaks down data into partitions and processes them in parallel across a cluster. Additionally, you can perform optimizations like caching, tuning memory settings, and using broadcast variables to improve performance and handle large datasets efficiently.

16. How can you write the output of a DataFrame to an external data source in PySpark?

PySpark provides various methods to write the output of a DataFrame to external data sources like HDFS, S3, JDBC, etc. The most commonly used methods include write.csv, write.parquet, write.jdbc, etc.

Here's an example of writing a DataFrame to a CSV file:


    from pyspark.sql import SparkSession

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

    data = [("Alice", 30), ("Bob", 25), ("Charlie", 28)]
    df = spark.createDataFrame(data, ["Name", "Age"])

    df.write.csv("output.csv", header=True)

17. How can you optimize the performance of PySpark jobs?

To optimize the performance of PySpark jobs, you can consider the following strategies:

Use DataFrames or Datasets over RDDs for better optimization.
Use broadcast variables for large lookup tables that need to be shared across tasks.
Cache or persist intermediate DataFrames to avoid recomputation.
Properly partition data to achieve parallelism.
Enable dynamic resource allocation and tuning cluster resources based on job requirements.
Use appropriate serialization formats like Avro or Parquet for better storage efficiency.
Monitor and analyze job execution plans to identify bottlenecks and optimize accordingly.

20 PySpark Cognizant Interview Questions and Answers for Experienced and Fresher