HyukjinKwon commented on code in PR #42284:
URL: https://github.com/apache/spark/pull/42284#discussion_r1283881332
##########
python/docs/source/getting_started/testing_pyspark.ipynb:
##########
@@ -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",
Review Comment:
separate cell?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
