BryanCutler · February 17, 2022 13:57 · ghost · Apr 19, 2018 · rajatdeshpande · Oct 17, 2018
diff --git a/PySpark_Vectorized_UDFs.ipynb b/PySpark_Vectorized_UDFs.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PySpark Vectorized UDFs using Arrow\n",
    "Using Arrow, it is possible to perform vectorized evaluation of Python UDFs that will accept one or more `Pandas.Series` as input and return a single `Pandas.Series` of equal length. Using vectorized functions will offer a performance boost over the current way PySpark evaluates using a loop that iterates over 1 row at a time.\n",
    "\n",
    "The following assumes you have started a Jupyter Notebook with a PySpark kernel that creates a default SparkSession `spark`. For a quick method to start this up, see [here](https://gist.github.com/BryanCutler/b7f10167c4face19e03330a07b24ce21).\n",
    "\n",
    "\n",
    "## Where to get it\n",
    "This functionality is currently pending review and has not yet been merged into Spark, see [SPARK-21404](https://issues.apache.org/jira/browse/SPARK-21404). Until then, a patch for this can be downloaded from the branch in the PR [here](https://patch-diff.githubusercontent.com/raw/apache/spark/pull/18659.diff)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PySpark API\n",
    "A new API has been added in pyspark to declare a vectorized UDF.  As with normal UDFs you can wrap a function or use a decorator:\n",
    "\n",
    "```python\n",
    "# Wrap the function \"func\"\n",
    "pandas_udf(func, DoubleType())\n",
    "\n",
    "# Use a decorator\n",
    "@pandas(returnType=DoubleType())\n",
    "def func(x):\n",
    "    # do something with \"x\" (pandas.Series) and return \"y\" (also a pandas.Series)\n",
    "    return y\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Usage\n",
    "Let's go through a simple example with first evaluating a UDF without vectorization, then the same UDF with vectorization enabled. This will define some sample data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import col, udf, mean, rand\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "df = spark.range(1 << 24, numPartitions=16).toDF(\"id\") \\\n",
    "        .withColumn(\"p1\", rand()).withColumn(\"p2\", rand())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First define the function *without vectorization*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import log, exp\n",
    "\n",
    "def my_func(p1, p2):\n",
    "    w = 0.5\n",
    "    return exp(log(p1) + log(p2) - log(w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and evaluate it as a UDF (using `filter()` to force evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 13.6 s per loop\n"
     ]
    }
   ],
   "source": [
    "my_udf = udf(my_func, DoubleType())\n",
    "\n",
    "result = df.withColumn(\"p\", my_udf(col(\"p1\"), col(\"p2\")))\n",
    "\n",
    "%timeit result.filter(\"p < 1.0\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now define the function *with vectorization*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import log, exp\n",
    "\n",
    "def my_func(p1, p2):\n",
    "    w = 0.5\n",
    "    return exp(log(p1) + log(p2) - log(w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and evaluate the UDF again, this time making use of Arrow to evaluate `my_func` with `p1` and `p2` as `Pandas.Series`, which will also cause the expression to return a `Pandas.Series` of the same size.\n",
    "\n",
    "NOTE: Spark will not accept Numpy types as return values, which is why we need to redefine the function.  This is an known issue from [SPARK-12157](https://issues.apache.org/jira/browse/SPARK-12157)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 2 s per loop\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import pandas_udf\n",
    "\n",
    "my_udf = pandas_udf(my_func, DoubleType())\n",
    "\n",
    "result = df.withColumn(\"p\", my_udf(col(\"p1\"), col(\"p2\")))\n",
    "\n",
    "%timeit result.filter(\"p < 1.0\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Make better use of Pandas and Numpy\n",
    "\n",
    "Since the inputs to your UDF are `Pandas.Series`, you can use Pandas and Numpy operations on the data and also return a series or numpy array. For example, say we want to draw samples from a random distribution for data points with a specific label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------+\n",
      "|label|counts|\n",
      "+-----+------+\n",
      "|    0|   7.0|\n",
      "|    1|   0.0|\n",
      "|    2|   0.0|\n",
      "|    0|   8.0|\n",
      "|    1|   0.0|\n",
      "|    2|   0.0|\n",
      "|    0|   7.0|\n",
      "+-----+------+\n",
      "only showing top 7 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = spark.range(1 << 20).toDF(\"id\") \\\n",
    "        .selectExpr(\"(id % 3) AS label\")\n",
    "\n",
    "def sample(label):\n",
    "    \"\"\" \n",
    "    Sample selected data from a Poisson distribution\n",
    "    :param label: Pandas.Series of data labels\n",
    "    \"\"\"\n",
    "\n",
    "    # use numpy to initialze an empty array\n",
    "    p = pd.Series(np.zeros(len(label)))\n",
    "\n",
    "    # use pandas to select data matching label \"0\"\n",
    "    idx0 = label == 0\n",
    "\n",
    "    # sample from numpy and assign to the selected data\n",
    "    p[idx0] = np.random.poisson(7, len(idx0))\n",
    "\n",
    "    # return the pandas series\n",
    "    return p\n",
    "\n",
    "sample_udf = pandas_udf(sample, DoubleType())\n",
    "\n",
    "result = df.withColumn(\"counts\", sample_udf(col(\"label\")))\n",
    "result.show(n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# PySpark Vectorized UDFs using Arrow\n",
	"Using Arrow, it is possible to perform vectorized evaluation of Python UDFs that will accept one or more `Pandas.Series` as input and return a single `Pandas.Series` of equal length. Using vectorized functions will offer a performance boost over the current way PySpark evaluates using a loop that iterates over 1 row at a time.\n",
	"\n",
	"The following assumes you have started a Jupyter Notebook with a PySpark kernel that creates a default SparkSession `spark`. For a quick method to start this up, see [here](https://gist.github.com/BryanCutler/b7f10167c4face19e03330a07b24ce21).\n",
	"\n",
	"\n",
	"## Where to get it\n",
	"This functionality is currently pending review and has not yet been merged into Spark, see [SPARK-21404](https://issues.apache.org/jira/browse/SPARK-21404). Until then, a patch for this can be downloaded from the branch in the PR [here](https://patch-diff.githubusercontent.com/raw/apache/spark/pull/18659.diff)."
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## PySpark API\n",
	"A new API has been added in pyspark to declare a vectorized UDF. As with normal UDFs you can wrap a function or use a decorator:\n",
	"\n",
	"```python\n",
	"# Wrap the function \"func\"\n",
	"pandas_udf(func, DoubleType())\n",
	"\n",
	"# Use a decorator\n",
	"@pandas(returnType=DoubleType())\n",
	"def func(x):\n",
	" # do something with \"x\" (pandas.Series) and return \"y\" (also a pandas.Series)\n",
	" return y\n",
	"```"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Example Usage\n",
	"Let's go through a simple example with first evaluating a UDF without vectorization, then the same UDF with vectorization enabled. This will define some sample data:"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from pyspark.sql.functions import col, udf, mean, rand\n",
	"from pyspark.sql.types import *\n",
	"\n",
	"df = spark.range(1 << 24, numPartitions=16).toDF(\"id\") \\\n",
	" .withColumn(\"p1\", rand()).withColumn(\"p2\", rand())"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### First define the function without vectorization"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from math import log, exp\n",
	"\n",
	"def my_func(p1, p2):\n",
	" w = 0.5\n",
	" return exp(log(p1) + log(p2) - log(w))"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"and evaluate it as a UDF (using `filter()` to force evaluation)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"1 loop, best of 3: 13.6 s per loop\n"
	]
	}
	],
	"source": [
	"my_udf = udf(my_func, DoubleType())\n",
	"\n",
	"result = df.withColumn(\"p\", my_udf(col(\"p1\"), col(\"p2\")))\n",
	"\n",
	"%timeit result.filter(\"p < 1.0\").count()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Now define the function with vectorization"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from numpy import log, exp\n",
	"\n",
	"def my_func(p1, p2):\n",
	" w = 0.5\n",
	" return exp(log(p1) + log(p2) - log(w))"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"and evaluate the UDF again, this time making use of Arrow to evaluate `my_func` with `p1` and `p2` as `Pandas.Series`, which will also cause the expression to return a `Pandas.Series` of the same size.\n",
	"\n",
	"NOTE: Spark will not accept Numpy types as return values, which is why we need to redefine the function. This is an known issue from [SPARK-12157](https://issues.apache.org/jira/browse/SPARK-12157)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"1 loop, best of 3: 2 s per loop\n"
	]
	}
	],
	"source": [
	"from pyspark.sql.functions import pandas_udf\n",
	"\n",
	"my_udf = pandas_udf(my_func, DoubleType())\n",
	"\n",
	"result = df.withColumn(\"p\", my_udf(col(\"p1\"), col(\"p2\")))\n",
	"\n",
	"%timeit result.filter(\"p < 1.0\").count()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"collapsed": true
	},
	"source": [
	"## Make better use of Pandas and Numpy\n",
	"\n",
	"Since the inputs to your UDF are `Pandas.Series`, you can use Pandas and Numpy operations on the data and also return a series or numpy array. For example, say we want to draw samples from a random distribution for data points with a specific label."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"scrolled": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"+-----+------+\n",
	"\|label\|counts\|\n",
	"+-----+------+\n",
	"\| 0\| 7.0\|\n",
	"\| 1\| 0.0\|\n",
	"\| 2\| 0.0\|\n",
	"\| 0\| 8.0\|\n",
	"\| 1\| 0.0\|\n",
	"\| 2\| 0.0\|\n",
	"\| 0\| 7.0\|\n",
	"+-----+------+\n",
	"only showing top 7 rows\n",
	"\n"
	]
	}
	],
	"source": [
	"import numpy as np\n",
	"import pandas as pd\n",
	"\n",
	"df = spark.range(1 << 20).toDF(\"id\") \\\n",
	" .selectExpr(\"(id % 3) AS label\")\n",
	"\n",
	"def sample(label):\n",
	" \"\"\" \n",
	" Sample selected data from a Poisson distribution\n",
	" :param label: Pandas.Series of data labels\n",
	" \"\"\"\n",
	"\n",
	" # use numpy to initialze an empty array\n",
	" p = pd.Series(np.zeros(len(label)))\n",
	"\n",
	" # use pandas to select data matching label \"0\"\n",
	" idx0 = label == 0\n",
	"\n",
	" # sample from numpy and assign to the selected data\n",
	" p[idx0] = np.random.poisson(7, len(idx0))\n",
	"\n",
	" # return the pandas series\n",
	" return p\n",
	"\n",
	"sample_udf = pandas_udf(sample, DoubleType())\n",
	"\n",
	"result = df.withColumn(\"counts\", sample_udf(col(\"label\")))\n",
	"result.show(n=7)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
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