[GitHub] [spark] xinrong-meng commented on a diff in pull request #42284: [SPARK-44629][PYTHON][DOCS] Publish PySpark Test Guidelines webpage

["xinrong-meng (via GitHub)" <gi...@apache.org>]

xinrong-meng commented on code in PR #42284:
URL: https://github.com/apache/spark/pull/42284#discussion_r1284751248


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python/docs/source/getting_started/testing_pyspark.ipynb:
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@@ -0,0 +1,457 @@
+{
+ "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": 5,
+   "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": 7,
+   "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": "c471be1b-c052-4f31-abc9-35668aebc9c1",
+   "metadata": {},
+   "source": [
+    "You can also do this using Spark Connect. Please reference ‘Live Notebook: Spark Connect’ for examples.\n",
+    "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",

Review Comment:
   nit: Shall we put `assertDataFrameEqual` and `assertSchemaEqual` in special fonts?



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