Apache Spark Action Examples with Python.ipynb

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     "text": [
      "abeabbyapple\n"
     ]
    }
   ],
   "source": [
    "names1 = sc.parallelize([\"abe\", \"abby\", \"apple\"])\n",
    "print names1.reduce(lambda t1, t2: t1+t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['apple', 5], ['beatty', 6], ['beatrice', 8]]\n"
     ]
    }
   ],
   "source": [
    "names2 = sc.parallelize([\"apple\", \"beatty\", \"beatrice\"]).map(lambda a: [a, len(a)])\n",
    "print names2.collect()"
   ]
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       "19"
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     "execution_count": 36,
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   "source": [
    "names2.flatMap(lambda t: [t[1]]).reduce(lambda t1, t2: t1+t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 2, 2, 2, 3, 3, 3]"
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     },
     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "sc.parallelize([1,2,3]).flatMap(lambda x: [x,x,x]).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "names1 = sc.parallelize([\"abe\", \"abby\", \"apple\"])\n",
    "names1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abe'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abe', 'abby']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1.take(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['brewers', 'brewers', 'twins']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = sc.parallelize((\"twins\", \"brewers\", \"cubs\", \"white sox\", \"indians\", \"bad news bears\"))\n",
    "teams.takeSample(True, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('red wings', 1),\n",
       " ('oilers', 1),\n",
       " ('blackhawks', 1),\n",
       " ('jets', 1),\n",
       " ('wild', 3),\n",
       " ('whalers', 1)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hockeyTeams = sc.parallelize((\"wild\", \"blackhawks\", \"red wings\", \"wild\", \"oilers\", \"whalers\", \"jets\", \"wild\"))\n",
    "hockeyTeams.map(lambda k: (k,1)).countByKey().items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hockeyTeams.saveAsTextFile(\"hockey_teams.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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