notebooks/Test PySpark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test PYSPARK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.14136568\n"
     ]
    }
   ],
   "source": [
    "import pyspark\n",
    "import random\n",
    "# Create  Spark Context\n",
    "sc = pyspark.SparkContext()\n",
    "num_samples = 100000000\n",
    "def inside(p):\n",
    "    x, y = random.random(), random.random()\n",
    "    return x*x + y*y < 1\n",
    "count = sc.parallelize(range(0, num_samples)).filter(inside).count()\n",
    "pi = 4 * count / num_samples\n",
    "print(pi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Spark SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+\n",
      "|hello|\n",
      "+-----+\n",
      "|spark|\n",
      "+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "\n",
    "import pyspark # only run after findspark.init()\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.getOrCreate()\n",
    "\n",
    "df = spark.sql('''select 'spark' as hello ''')\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(id=0),\n",
       " Row(id=1),\n",
       " Row(id=2),\n",
       " Row(id=3),\n",
       " Row(id=4),\n",
       " Row(id=5),\n",
       " Row(id=6),\n",
       " Row(id=7),\n",
       " Row(id=8),\n",
       " Row(id=9)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start pyspark via provided command\n",
    "import pyspark\n",
    "\n",
    "# Below code is Spark 2+\n",
    "spark = pyspark.sql.SparkSession.builder.appName('test').getOrCreate()\n",
    "\n",
    "spark.range(10).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.mllib.clustering import KMeans \n",
    "from numpy import array, random \n",
    "from math import sqrt \n",
    "from pyspark import SparkConf, SparkContext \n",
    "from sklearn.preprocessing import scale "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spark Stop Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  }
 ],
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