Spark RDD Operations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkConf, SparkContext\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = SparkConf().setMaster(\"local\").setAppName(\"RDD-Ops\")\n",
    "sc = SparkContext(conf = conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find total count of movie ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file:///C:/Users/dennis/Spark/Spark/resources/u.data MapPartitionsRDD[1] at textFile at NativeMethodAccessorImpl.java:0\n"
     ]
    }
   ],
   "source": [
    "lines = sc.textFile(\"file:///C:/Users/dennis/Spark/Spark/resources/u.data\")\n",
    "print(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PythonRDD[2] at RDD at PythonRDD.scala:53\n"
     ]
    }
   ],
   "source": [
    "# Extract the required data \n",
    "ratings = lines.map(lambda x:x.split()[2])\n",
    "print(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "1\n",
      "2\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# Perform action on RDD\n",
    "results = ratings.countByValue()\n",
    "for result in results:   \n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 6110\n",
      "2 11370\n",
      "3 27145\n",
      "4 34174\n",
      "5 21201\n"
     ]
    }
   ],
   "source": [
    "# Sort and display the results\n",
    "sortedResults = collections.OrderedDict(sorted(results.items()))\n",
    "for key,value in sortedResults.items():   \n",
    "    print(key,value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find average number of friends by age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demo on key pair "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file:///C:/Users/dennis/Spark/Spark/resources/fakefriends.csv MapPartitionsRDD[5] at textFile at NativeMethodAccessorImpl.java:0\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "lines = sc.textFile(\"file:///C:/Users/dennis/Spark/Spark/resources/fakefriends.csv\")\n",
    "print(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseLine(line):\n",
    "    fields = line.split(',')\n",
    "    age = int(fields[2])\n",
    "    numFriends = int(fields[3])\n",
    "    return (age, numFriends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the input data to get ootput in form key/value pair of (age, numFriends) \n",
    "rdd=lines.map(parseLine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mappedRDD = rdd.mapValues(lambda x: (x,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PythonRDD[10] at RDD at PythonRDD.scala:53\n"
     ]
    }
   ],
   "source": [
    "totalsByAge= mappedRDD.reduceByKey(lambda x,y:(x[0] + y[0],x[1] + y[1]))\n",
    "print(totalsByAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "averageByAge = totalsByAge.mapValues(lambda x:x[0]/x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 325.3333333333333)\n",
      "(26, 242.05882352941177)\n",
      "(55, 295.53846153846155)\n",
      "(40, 250.8235294117647)\n",
      "(68, 269.6)\n",
      "(59, 220.0)\n",
      "(37, 249.33333333333334)\n",
      "(54, 278.0769230769231)\n",
      "(38, 193.53333333333333)\n",
      "(27, 228.125)\n",
      "(53, 222.85714285714286)\n",
      "(57, 258.8333333333333)\n",
      "(56, 306.6666666666667)\n",
      "(43, 230.57142857142858)\n",
      "(36, 246.6)\n",
      "(22, 206.42857142857142)\n",
      "(35, 211.625)\n",
      "(45, 309.53846153846155)\n",
      "(60, 202.71428571428572)\n",
      "(67, 214.625)\n",
      "(19, 213.27272727272728)\n",
      "(30, 235.8181818181818)\n",
      "(51, 302.14285714285717)\n",
      "(25, 197.45454545454547)\n",
      "(21, 350.875)\n",
      "(42, 303.5)\n",
      "(49, 184.66666666666666)\n",
      "(48, 281.4)\n",
      "(50, 254.6)\n",
      "(39, 169.28571428571428)\n",
      "(32, 207.9090909090909)\n",
      "(58, 116.54545454545455)\n",
      "(64, 281.3333333333333)\n",
      "(31, 267.25)\n",
      "(52, 340.6363636363636)\n",
      "(24, 233.8)\n",
      "(20, 165.0)\n",
      "(62, 220.76923076923077)\n",
      "(41, 268.55555555555554)\n",
      "(44, 282.1666666666667)\n",
      "(69, 235.2)\n",
      "(65, 298.2)\n",
      "(61, 256.22222222222223)\n",
      "(28, 209.1)\n",
      "(66, 276.44444444444446)\n",
      "(46, 223.69230769230768)\n",
      "(29, 215.91666666666666)\n",
      "(18, 343.375)\n",
      "(47, 233.22222222222223)\n",
      "(34, 245.5)\n",
      "(63, 384.0)\n",
      "(23, 246.3)\n"
     ]
    }
   ],
   "source": [
    "results = averageByAge.collect()\n",
    "for result in results:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find min and max temperature in a year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demo for filtering in RDD. Filtering is for removing unwanted data from RDD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "lines = sc.textFile(\"file:///C:/Users/dennis/Spark/Spark/resources/1800.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseLine(line):\n",
    "    fields = line.split(',')\n",
    "    stationID = fields[0]\n",
    "    entryType = fields[2]\n",
    "    temperature = float(fields[3]) * 0.1 * (9.0/5.0) + 32.0\n",
    "    return (stationID, entryType, temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the input data to get output in form (stationID, entryType, temperature) \n",
    "rdd=lines.map(parseLine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply filter\n",
    "minTemps = rdd.filter(lambda x: \"TMIN\" in x[1])\n",
    "maxTemps = rdd.filter(lambda x: \"TMAX\" in x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create key value pair for stationID, temperature\n",
    "minStationTemps = minTemps.map(lambda x:(x[0],x[2]))\n",
    "maxStationTemps = maxTemps.map(lambda x:(x[0],x[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find min temp by stationID\n",
    "minTemp = minStationTemps.reduceByKey(lambda x,y:min(x,y))\n",
    "maxTemp = maxStationTemps.reduceByKey(lambda x,y:max(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITE00100554\t5.36F\n",
      "EZE00100082\t7.70F\n"
     ]
    }
   ],
   "source": [
    "# Collect and print min results \n",
    "minResults = minTemp.collect()\n",
    "for result in minResults:\n",
    "    print(result[0]+\"\\t{:.2f}F\".format(result[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITE00100554\t90.14F\n",
      "EZE00100082\t90.14F\n"
     ]
    }
   ],
   "source": [
    "# Collect and print max results \n",
    "maxResults = maxTemp.collect()\n",
    "for result in maxResults:\n",
    "    print(result[0]+\"\\t{:.2f}F\".format(result[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count number of word occurrence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map vs Flatmap\n",
    "Map transforms each element of an RDD into one new element. \n",
    "Flatmap can transform each element of an RDD into many new elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = sc.textFile(\"file:///C:/Users/dennis/Spark/Spark/resources/book.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def normalizeWords(text):\n",
    "    return re.compile(r'\\W+',re.UNICODE).split(text.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split to words\n",
    "words = data.flatMap(normalizeWords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count each word occurrence\n",
    "wordCounts = words.map( lambda x:(x, 1)).reduceByKey(lambda x, y: x + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle and Sort\n",
    "wordCountSorted = sorted((v,k) for k,v in wordCounts.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "b'advocates' 1\n",
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      "b'cat' 1\n",
      "b'catches' 1\n",
      "b'catching' 1\n",
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    },
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     "text": [
      "b'throughout' 3\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "b'old' 8\n",
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      "b'that' 747\n",
      "b'and' 934\n",
      "b'of' 970\n",
      "b'a' 1191\n",
      "b'the' 1292\n",
      "b'your' 1420\n",
      "b'to' 1828\n",
      "b'you' 1878\n"
     ]
    }
   ],
   "source": [
    "# Print results \n",
    "for result in wordCountSorted:\n",
    "    count=str(result[0])\n",
    "    word = result[1].encode('ascii','ignore')\n",
    "    if(word):\n",
    "        print(word , count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the Total Amount Spent by Customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = sc.textFile(\"file:///C:/Users/dennis/Spark/Spark/resources/customer-orders.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseLine(data):\n",
    "    fields = data.split(',')\n",
    "    custId = int(fields[0])\n",
    "    itemId = int(fields[1])\n",
    "    amount = float(fields[2])\n",
    "    return (custId, itemId, amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the input data to get output in form (custId, itemId, amount) \n",
    "rdd=data.map(parseLine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalByCustomer = rdd.map(lambda x:(x[0],x[2])).reduceByKey(lambda x, y: x + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle and Sort\n",
    "totalByCustomerSorted = totalByCustomer.sortByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 5524.949999999998\n",
      "1 4958.600000000001\n",
      "2 5994.59\n",
      "3 4659.63\n",
      "4 4815.050000000002\n",
      "5 4561.069999999999\n",
      "6 5397.879999999998\n",
      "7 4755.070000000001\n",
      "8 5517.240000000001\n",
      "9 5322.649999999999\n",
      "10 4819.700000000001\n",
      "11 5152.290000000002\n",
      "12 4664.589999999998\n",
      "13 4367.62\n",
      "14 4735.030000000001\n",
      "15 5413.510000000001\n",
      "16 4979.06\n",
      "17 5032.679999999999\n",
      "18 4921.27\n",
      "19 5059.4299999999985\n",
      "20 4836.859999999999\n",
      "21 4707.41\n",
      "22 5019.449999999999\n",
      "23 4042.6499999999987\n",
      "24 5259.920000000003\n",
      "25 5057.610000000001\n",
      "26 5250.4\n",
      "27 4915.889999999999\n",
      "28 5000.709999999998\n",
      "29 5032.529999999999\n",
      "30 4990.72\n",
      "31 4765.05\n",
      "32 5496.050000000004\n",
      "33 5254.659999999998\n",
      "34 5330.8\n",
      "35 5155.419999999999\n",
      "36 4278.049999999997\n",
      "37 4735.200000000002\n",
      "38 4898.460000000002\n",
      "39 6193.109999999999\n",
      "40 5186.429999999999\n",
      "41 5637.62\n",
      "42 5696.840000000003\n",
      "43 5368.83\n",
      "44 4756.8899999999985\n",
      "45 3309.38\n",
      "46 5963.109999999999\n",
      "47 4316.299999999999\n",
      "48 4384.33\n",
      "49 4394.599999999999\n",
      "50 4517.27\n",
      "51 4975.22\n",
      "52 5245.059999999999\n",
      "53 4945.299999999999\n",
      "54 6065.389999999999\n",
      "55 5298.090000000002\n",
      "56 4701.019999999999\n",
      "57 4628.4\n",
      "58 5437.7300000000005\n",
      "59 5642.89\n",
      "60 5040.709999999999\n",
      "61 5497.479999999998\n",
      "62 5253.3200000000015\n",
      "63 5415.150000000001\n",
      "64 5288.689999999996\n",
      "65 5140.3499999999985\n",
      "66 4681.919999999999\n",
      "67 4505.79\n",
      "68 6375.449999999997\n",
      "69 5123.010000000001\n",
      "70 5368.249999999999\n",
      "71 5995.660000000003\n",
      "72 5337.44\n",
      "73 6206.199999999999\n",
      "74 4647.129999999999\n",
      "75 4178.500000000001\n",
      "76 4904.209999999999\n",
      "77 4327.729999999999\n",
      "78 4524.509999999999\n",
      "79 3790.570000000001\n",
      "80 4727.860000000001\n",
      "81 5112.709999999999\n",
      "82 4812.489999999998\n",
      "83 4635.799999999997\n",
      "84 4652.939999999999\n",
      "85 5503.43\n",
      "86 4908.81\n",
      "87 5206.4\n",
      "88 4830.549999999999\n",
      "89 4851.479999999999\n",
      "90 5290.409999999998\n",
      "91 4642.259999999999\n",
      "92 5379.280000000002\n",
      "93 5265.750000000001\n",
      "94 4475.569999999999\n",
      "95 4876.840000000002\n",
      "96 3924.230000000001\n",
      "97 5977.189999999995\n",
      "98 4297.260000000001\n",
      "99 4172.289999999998\n"
     ]
    }
   ],
   "source": [
    "# Collect results \n",
    "results = totalByCustomerSorted.collect()\n",
    "for result in results:\n",
    "    print(result[0],result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  }
 ],
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