Spark Programming in Python for Beginners with Apache Spark 3 - Unit Testing Spark Application

This video tutorial covers unit testing in a simple Spark application. It begins with an introduction to unit testing and proceeds to create test cases for two functions: loading data into a DataFrame and applying business rules. The tutorial explains setting up a Spark session for testing, implementing test cases using Python's unit test framework, and validating results by converting DataFrames to Python dictionaries. The video concludes with running the tests and confirming their success.

What is the primary focus of unit testing in the Spark application discussed?

• Testing data loading and business rule functions

• Testing the user interface

• Testing the deployment process

• Testing the network connectivity

Which file is created to implement unit tests in the video?

• test_utils.py

• main.py

• app.py

• config.py

What is the purpose of the setup class method in the unit test?

• To compile the code

• To log test results

• To initialize the Spark session before tests

• To execute after all test cases

Why is the tear down class method commented out in the video?

• To prevent memory leaks

• To avoid a SOCKET error

• To enhance security

• To speed up the tests

What is the first step in implementing a unit test case according to the video?

• Deploy the application

• Compile the code

• Log the test results

• Call the function and get the data frame

How is the data frame validated in the second test case?

• By exporting it to a CSV file

• By comparing it with a list

• By converting it to a Python dictionary

• By printing it to the console

What is the expected count for the United States in the test case?

• 5

• 3

• 2

• 4