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Extension to unittest for pySpark

Project description


Extensions for testing pyspark with unittest and doctest.

These utils can be used in standalone Python or in Databricks notebooks.

Usage With Doctest

from unittest_pyspark import get_spark
spark = get_spark()

def go_spark():
    >>> spark.sql("SELECT 'hello world'").show()
    |hello world|
    |hello world|
    >>> spark.createDataFrame([{'hello':'world'}], 'hello:string').show()

import doctest

Usage With Unittest

Here is a simple unittest test case, which can be used as template for pySpark test case.

import unittest
from unittest_pyspark import as_list, get_spark
import pyspark.sql.types as pst

class Test_Spark(unittest.TestCase):
  def setUp(self):
      self.spark = get_spark()

  def test_i_can_fly(self):
    input = [ pst.Row(a=1, b=2)]
    input_df = self.spark.createDataFrame(input)

    expect = [{'a':1}]

    actual_df ="a")
    actual = as_list(actual_df)

    self.assertEqual(actual, expect)

You can find this entire example in the tests.test_sample module. To execute it from the command line:

python -m unittest tests.test_sample

Usage With Unittest and Databricks

To execute the unittest test cases in Databricks, add following cell:

from unittest_pyspark.unittest import *
if __name__ == "__main__":

Above code will automatically discover all test cases (unittest.TestCase sub classes) defined in the global scope and execute them.

Build package

You will need setuptools and twine:

pip install --upgrade setuptools
pip install --upgrade wheel

Build and upload:

python sdist bdist_wheel
python -m twine upload dist/*

Project details

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