Check Set-up.ipynb

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   "source": [
    "# Using Spark - with PySpark\n",
    "\n",
    "## Checking the Set-up\n",
    "\n",
    "This notebook checks if your set-up of the Docker container at http://hub.docker.com/r/gerhardt/pyspark-workshop is OK.\n",
    "\n",
    "Clone this repository first. We'll assume that you will have a local directory called pyspark-workshop. \n",
    "\n",
    "Follow the instructions on Docker hub to pull and run the container using the cloned pyspark-workshop directory. \n",
    "\n",
    "When you see this notebook from your local Jupyter at http://localhost:8888 then run everything using the menu entry Cell > Run all. (On Mac and Windows find the VirtualBox ip address using the command \"docker-machine ip\".)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from operator import add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark.sql import SQLContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyspark.conf.SparkConf at 0x7fe4ef3390b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf = pyspark.SparkConf()\n",
    "conf.setMaster(\"local[1]\")\n",
    "\n",
    "# This master will be available in the workshop.\n",
    "# conf.setMaster(\"spark://bence:7077\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "source": [
    "sc = pyspark.SparkContext(conf= conf, appName=\"test_app\")\n",
    "sqlContext = SQLContext(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20 ms, sys: 0 ns, total: 20 ms\n",
      "Wall time: 1.77 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "result = sc.parallelize(range(1,1000),4).map(lambda x: 1/x).reduce(add)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your set-up is fine :-)\n"
     ]
    }
   ],
   "source": [
    "if result > 1:\n",
    "    print(\"Your set-up is fine :-)\")\n",
    "else:\n",
    "    print(\"Hmm\")"
   ]
  }
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