import unittest

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)


import user_code as uc


class TestRDDChallenge(unittest.TestCase):

    def test_total_flights(self):
        _dynamic_test(self, isinstance(uc.total_flights, int) and uc.total_flights > 0,
            "The `total_flights` is a positive integer.",
            "Expected `total_flights` to be a positive integer.")

    def test_delayed_count(self):
        _dynamic_test(self, isinstance(uc.delayed_count, int) and 0 < uc.delayed_count < uc.total_flights,
            "The `delayed_count` is positive and less than `total_flights`.",
            "Expected `delayed_count` to be positive and less than `total_flights`.")

    def test_delayed_airlines(self):
        _dynamic_test(self, isinstance(uc.delayed_airlines, list) and len(uc.delayed_airlines) > 0 and uc.delayed_airlines == sorted(uc.delayed_airlines),
            "The `delayed_airlines` is a non-empty sorted list.",
            "Expected `delayed_airlines` to be a non-empty sorted list.")

    def test_max_distance(self):
        _dynamic_test(self, isinstance(uc.max_distance, int) and uc.max_distance > 0,
            "The `max_distance` is a positive integer.",
            "Expected `max_distance` to be a positive integer.")


if __name__ == "__main__":
    unittest.main()

test_main.py

Dive into the fundamentals of big data processing with PySpark – from Spark's distributed architecture and RDDs to the DataFrame API for scalable, real-world data analysis.

Explore the foundations of PySpark, from understanding big data and Spark's architecture to hands-on practice with RDDs and DataFrames.

Challenge: Analyzing a Text File with RDDs

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