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Posted to reviews@spark.apache.org by "HyukjinKwon (via GitHub)" <gi...@apache.org> on 2023/08/05 04:01:32 UTC
[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42284: [SPARK-44629][PYTHON][DOCS] Publish PySpark Test Guidelines webpage
HyukjinKwon commented on code in PR #42284:
URL: https://github.com/apache/spark/pull/42284#discussion_r1284959655
##########
python/docs/source/getting_started/testing_pyspark.ipynb:
##########
@@ -0,0 +1,486 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4ee2125b-f889-47e6-9c3d-8bd63a253683",
+ "metadata": {},
+ "source": [
+ "# Testing PySpark\n",
+ "\n",
+ "This guide is a reference for writing robust tests for PySpark code.\n",
+ "\n",
+ "To view the docs for PySpark test utils, see here. To see the code for PySpark built-in test utils, check out the Spark repository here. To see the JIRA board tickets for the PySpark test framework, see here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e8ee4b6-9544-45e1-8a91-e71ed8ef8b9d",
+ "metadata": {},
+ "source": [
+ "## Build a PySpark Application\n",
+ "Here is an example for how to start a PySpark application. Feel free to skip to the next section, “Testing your PySpark Application,” if you already have an application you’re ready to test.\n",
+ "\n",
+ "First, start your Spark Session."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9af4a35b-17e8-4e45-816b-34c14c5902f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.sql import SparkSession \n",
+ "from pyspark.sql.functions import col \n",
+ "\n",
+ "# Create a SparkSession \n",
+ "spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a4c6efe-91f5-4e18-b4b2-b0401c2368e4",
+ "metadata": {},
+ "source": [
+ "Next, create a DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3b483dd8-3a76-41c6-9206-301d7ef314d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ "\n",
+ "df = spark.createDataFrame(sample_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0f44333-0e08-470b-9fa2-38f59e3dbd63",
+ "metadata": {},
+ "source": [
+ "Now, let’s define and apply a transformation function to our DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a6c0b766-af5f-4e1d-acf8-887d7cf0b0b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+--------+\n",
+ "|age| name|\n",
+ "+---+--------+\n",
+ "| 30| John D.|\n",
+ "| 25|Alice G.|\n",
+ "| 35| Bob T.|\n",
+ "| 28| Eve A.|\n",
+ "+---+--------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pyspark.sql.functions import col, regexp_replace\n",
+ "\n",
+ "# Remove additional spaces in name\n",
+ "def remove_extra_spaces(df, column_name):\n",
+ " # Remove extra spaces from the specified column\n",
+ " df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), \"\\\\s+\", \" \"))\n",
+ " \n",
+ " return df_transformed\n",
+ "\n",
+ "transformed_df = remove_extra_spaces(df, \"name\")\n",
+ "\n",
+ "transformed_df.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "530beaa6-aabf-43a1-ad2b-361f267e9608",
+ "metadata": {},
+ "source": [
+ "## Testing your PySpark Application\n",
+ "Now let’s test our PySpark transformation function. \n",
+ "\n",
+ "One option is to simply eyeball the resulting DataFrame. However, this can be impractical for large DataFrame or input sizes.\n",
+ "\n",
+ "A better way is to write tests. Here are some examples of how we can test our code. The examples below apply for Spark 3.5 and above versions.\n",
+ "\n",
+ "Note that these examples are not exhaustive, as there are many other test framework alternatives which you can use instead of `unittest` or `pytest`. The built-in PySpark testing util functions are standalone, meaning they can be compatible with any test framework or CI test pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d84a9fc1-9768-4af4-bfbf-e832f23334dc",
+ "metadata": {},
+ "source": [
+ "### Option 1: Using Only PySpark Built-in Test Utility Functions\n",
+ "\n",
+ "For simple ad-hoc validation cases, PySpark testing utils like `assertDataFrameEqual` and `assertSchemaEqual` can be used in a standalone context.\n",
+ "You could easily test PySpark code in a notebook session. For example, say you want to assert equality between two DataFrames:\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8e533732-ee40-4cd0-9669-8eb92973908a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyspark.testing\n",
+ "\n",
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "# Example 1\n",
+ "df1 = spark.createDataFrame(data=[(\"1\", 1000), (\"2\", 3000)], schema=[\"id\", \"amount\"])\n",
+ "df2 = spark.createDataFrame(data=[(\"1\", 1000), (\"2\", 3000)], schema=[\"id\", \"amount\"])\n",
+ "assertDataFrameEqual(df1, df2) # pass, DataFrames are identical"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "2d77a6be-1e50-4c1a-8a44-85cf7dcec3f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Example 2\n",
+ "df1 = spark.createDataFrame(data=[(\"1\", 0.1), (\"2\", 3.23)], schema=[\"id\", \"amount\"])\n",
+ "df2 = spark.createDataFrame(data=[(\"1\", 0.109), (\"2\", 3.23)], schema=[\"id\", \"amount\"])\n",
+ "assertDataFrameEqual(df1, df2, rtol=1e-1) # pass, DataFrames are approx equal by rtol"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76ade5f2-4a1f-4601-9d2a-80da9da950ff",
+ "metadata": {},
+ "source": [
+ "You can also simply compare two DataFrame schemas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "74393af5-40fb-4d04-87cb-265971ffe6d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.testing.utils import assertSchemaEqual\n",
+ "from pyspark.sql.types import StructType, StructField, ArrayType, DoubleType\n",
+ "\n",
+ "s1 = StructType([StructField(\"names\", ArrayType(DoubleType(), True), True)])\n",
+ "s2 = StructType([StructField(\"names\", ArrayType(DoubleType(), True), True)])\n",
+ "\n",
+ "assertSchemaEqual(s1, s2) # pass, schemas are identical"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c67be105-f6b1-4083-ad11-9e819331eae8",
+ "metadata": {},
+ "source": [
+ "### Option 2: Using [Unit Test](https://docs.python.org/3/library/unittest.html)\n",
+ "For more complex testing scenarios, you may want to use a testing framework.\n",
+ "\n",
+ "One of the most popular testing framework options is unit tests. Let’s walk through how you can use the built-in Python `unittest` library to write PySpark tests. For more information about the `unittest` library, see here: https://docs.python.org/3/library/unittest.html. \n",
+ "\n",
+ "First, you will need a Spark session. You can use the `@classmethod` decorator from the `unittest` package to take care of setting up and tearing down a Spark session."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "54093761-0b49-4aee-baec-2d29bcf13f9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import unittest\n",
+ "\n",
+ "class PySparkTestCase(unittest.TestCase):\n",
+ " @classmethod\n",
+ " def setUpClass(cls):\n",
+ " cls.spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate() \n",
+ "\n",
+ " @classmethod\n",
+ " def tearDownClass(cls):\n",
+ " cls.spark.stop()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3de27500-8526-412e-bf09-6927a760c5d7",
+ "metadata": {},
+ "source": [
+ "Now let’s write a `unittest` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "34feb5e1-944f-4f6b-9c5f-3b0bf68c7d05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "class TestTranformation(PySparkTestCase):\n",
+ " def test_single_space(self):\n",
+ " sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ " \n",
+ " # create a Spark DataFrame\n",
+ " original_df = spark.createDataFrame(sample_data)\n",
+ " \n",
+ " # apply the transformation function from before\n",
+ " transformed_df = remove_extra_spaces(original_df, \"name\")\n",
+ " \n",
+ " expected_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}]\n",
+ " \n",
+ " expected_df = spark.createDataFrame(expected_data)\n",
+ " \n",
+ " assertDataFrameEqual(transformed_df, expected_df)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "319a690f-71bd-4886-bd3a-424e866525c2",
+ "metadata": {},
+ "source": [
+ "When run, `unittest` will pick up all functions with a name beginning with “test.”"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d79e53d-cc1e-4fdf-a069-478337bed83d",
+ "metadata": {},
+ "source": [
+ "### Option 3: Using [Pytest](https://docs.pytest.org/en/7.1.x/contents.html)\n",
+ "\n",
+ "We can also write our tests with `pytest`, which is one of the most popular Python testing frameworks. For more information about 'pytest', see the docs here: https://docs.pytest.org/en/7.1.x/contents.html.\n",
+ "\n",
+ "Using a `pytest` fixture allows us to share a spark session across tests, tearing it down when the tests are complete."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "60a4f304-1911-4b4d-8ed9-00ecc8b0890b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pytest\n",
+ "\n",
+ "@pytest.fixture\n",
+ "def spark_fixture():\n",
+ " spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate()\n",
+ " yield spark"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcb4e26a-9bfc-48a5-8aca-538697d66642",
+ "metadata": {},
+ "source": [
+ "We can then define our tests like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "fa5db3a1-7305-44b7-ab84-f5ed55fd2ba9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pytest\n",
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "def test_single_space(spark_fixture):\n",
+ " sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
Review Comment:
indentation
##########
python/docs/source/getting_started/testing_pyspark.ipynb:
##########
@@ -0,0 +1,486 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4ee2125b-f889-47e6-9c3d-8bd63a253683",
+ "metadata": {},
+ "source": [
+ "# Testing PySpark\n",
+ "\n",
+ "This guide is a reference for writing robust tests for PySpark code.\n",
+ "\n",
+ "To view the docs for PySpark test utils, see here. To see the code for PySpark built-in test utils, check out the Spark repository here. To see the JIRA board tickets for the PySpark test framework, see here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e8ee4b6-9544-45e1-8a91-e71ed8ef8b9d",
+ "metadata": {},
+ "source": [
+ "## Build a PySpark Application\n",
+ "Here is an example for how to start a PySpark application. Feel free to skip to the next section, “Testing your PySpark Application,” if you already have an application you’re ready to test.\n",
+ "\n",
+ "First, start your Spark Session."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9af4a35b-17e8-4e45-816b-34c14c5902f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.sql import SparkSession \n",
+ "from pyspark.sql.functions import col \n",
+ "\n",
+ "# Create a SparkSession \n",
+ "spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a4c6efe-91f5-4e18-b4b2-b0401c2368e4",
+ "metadata": {},
+ "source": [
+ "Next, create a DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3b483dd8-3a76-41c6-9206-301d7ef314d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ "\n",
+ "df = spark.createDataFrame(sample_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0f44333-0e08-470b-9fa2-38f59e3dbd63",
+ "metadata": {},
+ "source": [
+ "Now, let’s define and apply a transformation function to our DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a6c0b766-af5f-4e1d-acf8-887d7cf0b0b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+--------+\n",
+ "|age| name|\n",
+ "+---+--------+\n",
+ "| 30| John D.|\n",
+ "| 25|Alice G.|\n",
+ "| 35| Bob T.|\n",
+ "| 28| Eve A.|\n",
+ "+---+--------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pyspark.sql.functions import col, regexp_replace\n",
+ "\n",
+ "# Remove additional spaces in name\n",
+ "def remove_extra_spaces(df, column_name):\n",
+ " # Remove extra spaces from the specified column\n",
+ " df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), \"\\\\s+\", \" \"))\n",
+ " \n",
+ " return df_transformed\n",
+ "\n",
+ "transformed_df = remove_extra_spaces(df, \"name\")\n",
+ "\n",
+ "transformed_df.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "530beaa6-aabf-43a1-ad2b-361f267e9608",
+ "metadata": {},
+ "source": [
+ "## Testing your PySpark Application\n",
+ "Now let’s test our PySpark transformation function. \n",
+ "\n",
+ "One option is to simply eyeball the resulting DataFrame. However, this can be impractical for large DataFrame or input sizes.\n",
+ "\n",
+ "A better way is to write tests. Here are some examples of how we can test our code. The examples below apply for Spark 3.5 and above versions.\n",
+ "\n",
+ "Note that these examples are not exhaustive, as there are many other test framework alternatives which you can use instead of `unittest` or `pytest`. The built-in PySpark testing util functions are standalone, meaning they can be compatible with any test framework or CI test pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d84a9fc1-9768-4af4-bfbf-e832f23334dc",
+ "metadata": {},
+ "source": [
+ "### Option 1: Using Only PySpark Built-in Test Utility Functions\n",
+ "\n",
+ "For simple ad-hoc validation cases, PySpark testing utils like `assertDataFrameEqual` and `assertSchemaEqual` can be used in a standalone context.\n",
+ "You could easily test PySpark code in a notebook session. For example, say you want to assert equality between two DataFrames:\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8e533732-ee40-4cd0-9669-8eb92973908a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyspark.testing\n",
+ "\n",
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "# Example 1\n",
+ "df1 = spark.createDataFrame(data=[(\"1\", 1000), (\"2\", 3000)], schema=[\"id\", \"amount\"])\n",
+ "df2 = spark.createDataFrame(data=[(\"1\", 1000), (\"2\", 3000)], schema=[\"id\", \"amount\"])\n",
+ "assertDataFrameEqual(df1, df2) # pass, DataFrames are identical"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "2d77a6be-1e50-4c1a-8a44-85cf7dcec3f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Example 2\n",
+ "df1 = spark.createDataFrame(data=[(\"1\", 0.1), (\"2\", 3.23)], schema=[\"id\", \"amount\"])\n",
+ "df2 = spark.createDataFrame(data=[(\"1\", 0.109), (\"2\", 3.23)], schema=[\"id\", \"amount\"])\n",
+ "assertDataFrameEqual(df1, df2, rtol=1e-1) # pass, DataFrames are approx equal by rtol"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76ade5f2-4a1f-4601-9d2a-80da9da950ff",
+ "metadata": {},
+ "source": [
+ "You can also simply compare two DataFrame schemas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "74393af5-40fb-4d04-87cb-265971ffe6d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.testing.utils import assertSchemaEqual\n",
+ "from pyspark.sql.types import StructType, StructField, ArrayType, DoubleType\n",
+ "\n",
+ "s1 = StructType([StructField(\"names\", ArrayType(DoubleType(), True), True)])\n",
+ "s2 = StructType([StructField(\"names\", ArrayType(DoubleType(), True), True)])\n",
+ "\n",
+ "assertSchemaEqual(s1, s2) # pass, schemas are identical"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c67be105-f6b1-4083-ad11-9e819331eae8",
+ "metadata": {},
+ "source": [
+ "### Option 2: Using [Unit Test](https://docs.python.org/3/library/unittest.html)\n",
+ "For more complex testing scenarios, you may want to use a testing framework.\n",
+ "\n",
+ "One of the most popular testing framework options is unit tests. Let’s walk through how you can use the built-in Python `unittest` library to write PySpark tests. For more information about the `unittest` library, see here: https://docs.python.org/3/library/unittest.html. \n",
+ "\n",
+ "First, you will need a Spark session. You can use the `@classmethod` decorator from the `unittest` package to take care of setting up and tearing down a Spark session."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "54093761-0b49-4aee-baec-2d29bcf13f9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import unittest\n",
+ "\n",
+ "class PySparkTestCase(unittest.TestCase):\n",
+ " @classmethod\n",
+ " def setUpClass(cls):\n",
+ " cls.spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate() \n",
+ "\n",
+ " @classmethod\n",
+ " def tearDownClass(cls):\n",
+ " cls.spark.stop()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3de27500-8526-412e-bf09-6927a760c5d7",
+ "metadata": {},
+ "source": [
+ "Now let’s write a `unittest` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "34feb5e1-944f-4f6b-9c5f-3b0bf68c7d05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "class TestTranformation(PySparkTestCase):\n",
+ " def test_single_space(self):\n",
+ " sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ " \n",
+ " # create a Spark DataFrame\n",
+ " original_df = spark.createDataFrame(sample_data)\n",
+ " \n",
+ " # apply the transformation function from before\n",
+ " transformed_df = remove_extra_spaces(original_df, \"name\")\n",
+ " \n",
+ " expected_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}]\n",
+ " \n",
+ " expected_df = spark.createDataFrame(expected_data)\n",
+ " \n",
+ " assertDataFrameEqual(transformed_df, expected_df)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "319a690f-71bd-4886-bd3a-424e866525c2",
+ "metadata": {},
+ "source": [
+ "When run, `unittest` will pick up all functions with a name beginning with “test.”"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d79e53d-cc1e-4fdf-a069-478337bed83d",
+ "metadata": {},
+ "source": [
+ "### Option 3: Using [Pytest](https://docs.pytest.org/en/7.1.x/contents.html)\n",
+ "\n",
+ "We can also write our tests with `pytest`, which is one of the most popular Python testing frameworks. For more information about 'pytest', see the docs here: https://docs.pytest.org/en/7.1.x/contents.html.\n",
+ "\n",
+ "Using a `pytest` fixture allows us to share a spark session across tests, tearing it down when the tests are complete."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "60a4f304-1911-4b4d-8ed9-00ecc8b0890b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pytest\n",
+ "\n",
+ "@pytest.fixture\n",
+ "def spark_fixture():\n",
+ " spark = SparkSession.builder.appName(\"Testing PySpark Example\").getOrCreate()\n",
+ " yield spark"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcb4e26a-9bfc-48a5-8aca-538697d66642",
+ "metadata": {},
+ "source": [
+ "We can then define our tests like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "fa5db3a1-7305-44b7-ab84-f5ed55fd2ba9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pytest\n",
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "def test_single_space(spark_fixture):\n",
+ " sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ " \n",
+ " # create a Spark DataFrame\n",
+ " original_df = spark.createDataFrame(sample_data)\n",
+ " \n",
+ " # apply the transformation function from before\n",
+ " transformed_df = remove_extra_spaces(original_df, \"name\")\n",
+ " \n",
+ " expected_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}]\n",
+ " \n",
+ " expected_df = spark.createDataFrame(expected_data)\n",
+ " \n",
+ " assertDataFrameEqual(transformed_df, expected_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0fc3f394-3260-4e42-82cf-1a7edc859151",
+ "metadata": {},
+ "source": [
+ "When you run your test file with the `pytest` command, it will pick up all functions that have their name beginning with “test.”"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8f50eee-5d0b-4719-b505-1b3ff05c16e8",
+ "metadata": {},
+ "source": [
+ "## Putting It All Together!\n",
+ "\n",
+ "Let’s see all the steps together, in a Unit Test example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "a2ea9dec-0ac0-4c23-8770-d6cc226d2e97",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# src/etl.py\n",
+ "\n",
+ "from pyspark.sql import SparkSession \n",
+ "from pyspark.sql.functions import col\n",
+ "import pyspark.sql.functions as F \n",
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\n",
+ "import unittest\n",
+ "\n",
+ "# Create a SparkSession \n",
+ "spark = SparkSession.builder.appName(\"Sample PySpark ETL\").getOrCreate() \n",
+ "\n",
+ "sample_data = [{\"name\": \"John D.\", \"age\": 30}, \n",
+ " {\"name\": \"Alice G.\", \"age\": 25}, \n",
+ " {\"name\": \"Bob T.\", \"age\": 35}, \n",
+ " {\"name\": \"Eve A.\", \"age\": 28}] \n",
+ "\n",
+ "df = spark.createDataFrame(sample_data)\n",
+ "\n",
+ "# define DataFrame transformation function\n",
Review Comment:
```suggestion
"# Define DataFrame transformation function\n",
```
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