notebook pyspark

test_pyspark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example IPython Notebook running with PySpark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After completing the setup [here](http://example.com), this is what an example IPython Notebook session could look like.\n",
    "\n",
    "You will first notice that when a notebook first connects to the IPython kernel, the notebook server will spit out all the usual PySpark output about initializing Spark.\n",
    "\n",
    "First start by seeing that there does exist a `SparkContext` object in the `sc` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "import os\n",
    "findspark.init()\n",
    "\n",
    "import pyspark\n",
    "sc = pyspark.SparkContext()\n",
    "print sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load an RDD with some interesting data.  We have the GDELT event data set on our cluster as a collection of tab-delimited text files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_events = sc.textFile('/Users/m2shad0w/Desktop/daily.CSV')\n",
    "# raw_events.cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how we've cached the event data in distributed memory.\n",
    "\n",
    "Let's see what an object in the RDD looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "print raw_events.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's count the number of events we have.  You can follow the progress of the computation using the Spark application web server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13535\n"
     ]
    }
   ],
   "source": [
    "print raw_events.count()"
   ]
  }
 ],
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