chapter_05_1_UserDefinedFunctions.ipynb

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   "source": [
    "# Chapter 5 - User Defined Functions\n",
    "Christoph Windheuser    \n",
    "May, 2022   \n",
    "Python examples of chapter 5 in the book *Learning Spark*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required python spark libraries\n",
    "import findspark\n",
    "import pyspark\n",
    "\n",
    "from pyspark.conf import SparkConf\n",
    "from pyspark.context import SparkContext\n",
    "\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import col, expr, when, concat, lit, avg, desc\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import Row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect Jupyter Notebook with the Spark application and create Spark Context\n",
    "findspark.init()\n",
    "sc = pyspark.SparkContext(appName=\"chapter_5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a SparkSession\n",
    "\n",
    "spark = (SparkSession \\\n",
    "         .builder \\\n",
    "         .enableHiveSupport() \\\n",
    "         .config(\"spark.sql.catalogImplementation\",\"hive\") \\\n",
    "         .appName(\"Chapter_5_Examples\") \\\n",
    "         .getOrCreate())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User-Defined Functions\n",
    "page 114 ff."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spark SQL UDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define cubed function:\n",
    "def cubed(s):\n",
    "    return s * s * s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Register UDF\n",
    "spark.udf.register(\"cubed\", cubed, LongType())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate temporary view:\n",
    "spark.range(1, 9).createOrReplaceTempView(\"udf_test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.sql(\"SELECT * FROM udf_test\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.sql(\"SELECT id, cubed(id) AS id_cubed FROM udf_test\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas_UDF Functions\n",
    "Page 115 ff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pyspark.sql.functions import pandas_udf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a pandas functon cubed\n",
    "def cubed (a: pd.Series) -> pd.Series:\n",
    "    return a * a * a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pandas UDF for the cubed function:\n",
    "cubed_udf = pandas_udf(cubed, returnType=LongType())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    2\n",
      "3    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a pandas Series \n",
    "x = pd.Series([0, 1, 2, 3])\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     0\n",
      "1     1\n",
      "2     8\n",
      "3    27\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Run the cubed function on the pandas series:\n",
    "print (cubed(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a Spark DataFrame:\n",
    "df = spark.range(0, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+\n",
      "| id|\n",
      "+---+\n",
      "|  0|\n",
      "|  1|\n",
      "|  2|\n",
      "|  3|\n",
      "+---+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+\n",
      "| id|cubed(id)|\n",
      "+---+---------+\n",
      "|  0|        0|\n",
      "|  1|        1|\n",
      "|  2|        8|\n",
      "|  3|       27|\n",
      "+---+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Execute the cube function as a vectorized Pandas UDF function:\n",
    "df.select(\"id\", cubed_udf(col(\"id\"))).show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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