HyukjinKwon commented on code in PR #42284:
URL: https://github.com/apache/spark/pull/42284#discussion_r1283880817
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python/docs/source/getting_started/testing_pyspark.ipynb:
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@@ -0,0 +1,525 @@
+{
+ "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": 1,
+ "id": "64e82e7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'4.0.0.dev0'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyspark\n",
+ "pyspark.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "9af4a35b-17e8-4e45-816b-34c14c5902f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Setting default log level to \"WARN\".\n",
+ "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
setLogLevel(newLevel).\n",
+ "23/08/01 19:05:21 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable\n"
+ ]
+ }
+ ],
+ "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": 3,
+ "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": 4,
+ "id": "a6c0b766-af5f-4e1d-acf8-887d7cf0b0b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "
\r"
+ ]
+ },
+ {
+ "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": "c471be1b-c052-4f31-abc9-35668aebc9c1",
+ "metadata": {},
+ "source": [
+ "You can also do this using Spark Connect. The only difference is using
remote() when you create your SparkSession, for example:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ba5f56e-dbfb-442f-a9ac-e0d0ad2d0931",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spark = SparkSession.builder.remote(\"sc://localhost\").appName(\"Sample
PySpark ETL\").getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ad40cf4-f1fb-460d-a3b1-68fb6a2ad9f6",
+ "metadata": {},
+ "source": [
+ "For more information on how to use Spark Connect and its benefits, see:
https://spark.apache.org/docs/latest/spark-connect-overview.html";
+ ]
+ },
+ {
+ "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.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d84a9fc1-9768-4af4-bfbf-e832f23334dc",
+ "metadata": {},
+ "source": [
+ "### Option 1: No test framework\n",
+ "\n",
+ "For simple ad-hoc validation cases, PySpark testing utils like
assertDataFrameEqual and assertSchemaEqual can also 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": 5,
+ "id": "8e533732-ee40-4cd0-9669-8eb92973908a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+
"/Users/amanda.liu/Documents/Databricks/spark/python/pyspark/pandas/__init__.py:50:
UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is
required to set this environment variable to '1' in both driver and executor
sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it
does not work if there is a Spark context already launched.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "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\n",
+ "\n",
+ "# 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": 6,
+ "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: Unit Test\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. \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": 7,
+ "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(\"Sample PySpark
ETL\").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": 8,
+ "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: pytest\n",
+ "\n",
+ "We can also write our tests with pytest, which is one of the most popular
Python testing frameworks. \n",
+ "\n",
+ "Using the pytest fixture allows us to share a spark session across tests,
tearing it down when the tests are complete."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "60a4f304-1911-4b4d-8ed9-00ecc8b0890b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pytest\n",
+ "\n",
+ "@pytest.fixture(scope=\"session\")\n",
+ "def spark():\n",
+ " spark = SparkSession.builder.appName(\"Sample PySpark
ETL\").getOrCreate()\n",
+ " yield spark\n",
+ " spark.stop()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcb4e26a-9bfc-48a5-8aca-538697d66642",
+ "metadata": {},
+ "source": [
+ "We can then define our tests like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fa5db3a1-7305-44b7-ab84-f5ed55fd2ba9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.testing.utils import assertDataFrameEqual\n",
+ "\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)"
+ ]
+ },
+ {
+ "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": "code",
+ "execution_count": null,
+ "id": "ea1ee67d-db57-48c7-ba55-a32f64723e29",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pytest /path/to/file"
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