msan694_disco/MSAN694-Lec6-Notes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "### This week Spark Cluster , Spark standalone cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More multiple choice this time. \n",
    "\n",
    "#### Which is supporting the spark, which is supporting the yarn. How did you setup your cluster?\n",
    "Need to setup slave notes, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running on a YARN CLuster\n",
    "#### Running on a Mesos Cluster\n",
    "#### Loading and Saving Data - JSV, TSV, CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## YARN - Yet another resource negotiatior\n",
    "\n",
    "This is compared to spark standalone\n",
    "\n",
    "Its an execution system for hadoop. Most HDFS has YARN configured. \n",
    "- You dont have to install spark on all nodes in the cluster. \n",
    "- You can run types of java applications, not just spark (default was only spark)\n",
    "- Can isolate and priority applications (management)\n",
    "- its the only that supports kerberos-secured HDFS (IAM)\n",
    "- Runs in a container - JVM - java virtual machine\n",
    "- YARN supports Hadoop, Spark doesn't\n",
    "- Takes longer time, because you are creating containers JVM that take more time\n",
    "\n",
    "\n",
    "Analogy to the standalone spark items\n",
    "\n",
    "- Resource manager ( similar to the spakrs master process)\n",
    "- Node Manager similar to worker process\n",
    "- Application mastetr ( similar to cluster manager)\n",
    "\n",
    "\n",
    "In Client mode \n",
    "- starts the driver\n",
    "- starts the driver\n",
    "\n",
    "\n",
    "\n",
    "#### Driver / executor / Master / Cluster manager\n",
    "\n",
    "Slightly different terminology. Client will launch the driver. Driver will talk to other ocmputers to launch the executor. The executor will run and come back.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon EMR\n",
    "\n",
    "Yarn is supported on amazon EMR. EMR will automatically makes instances. \n",
    "\n",
    "#### Review of key pairs (pem) , set permissions\n",
    "\n",
    "#### Walkthrough of setting up the emr\n",
    "- EMR\n",
    "- create cluster\n",
    "- Spark: Spark 2.2.0 on Hadoop 2.7.3 YARN with Ganglia 3.7.2 and Zeppelin 0.7.3\n",
    "- Zeppelin - apache notebook\n",
    "- Ganglia - a manager\n",
    "- instance type (depending on the application)\n",
    "- choose 2 instances ( 1 master, 1 node)\n",
    "- set your key\n",
    "\n",
    "#### All done!\n",
    "\n",
    "\n",
    "- let it start up and you should be able to see the SSH.\n",
    "- Click Enable Web Connection (zeppelin notebook) and other management dashboards\n",
    "- Have to setup this web connection everytime we start a cluster\n",
    "- note that ND815 is for web applications not SSH\n",
    "- double check that you are using the correct location of your pem file\n",
    "\n",
    "#### how to SSH\n",
    "- make sure you add TCP port 22 to the security group of your AWS instance otherwise you wont be able to access your instance by SSH. This should be done on the master.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mesos\n",
    "\n",
    "- Spark was originally started to demonstrate mesoes.\n",
    "- Supports C/C++, java, python\n",
    "- run docker images\n",
    "- Docker : software wrap that contains a complete file system.\n",
    "- Used by : Apple Siri, ebay, netflix, twitter, uber"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Saving Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "\n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://10.1.156.130:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.2.0</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>PySparkShell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<SparkContext master=local[*] appName=PySparkShell>"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structured and unstructured.\n",
    "\n",
    "- Text File\n",
    "- JSON\n",
    "- CSV / TSV\n",
    "- Sequence File - Hadoop File (key / value)\n",
    "- Object File - Spark Job (sharing data between different spark applications)\n",
    "\n",
    "### Text file methods\n",
    "\n",
    "- `.textFile(file_name)`\n",
    "- `.wholeTextFiles(dir_name)`\n",
    "- `.saveAsTextFile(new_subdir_name)`\n",
    "\n",
    "#### When loaded the key value will look like this:\n",
    "\n",
    "```\n",
    "filename1.txt | full contents....\n",
    "filename2.txt | full contents....\n",
    "filename3.txt | full contents....\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1 load a directory of files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = sc.wholeTextFiles('../MSAN_694_DistrComputing/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/output2.txt',\n",
       "  u\"jupyter: '/Users/tlee010/Desktop/MSAN-pywork/MSAN_604_DistrComputing/quiz1_Lee_Tim.py' is not a Jupyter command\\n\"),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/MSAN694-Lec5-Notes.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## MSAN-694-Lec5-Notes\\\\n\",\\n    \"\\\\n\",\\n    \"Gonna have some fun with AWS\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"source\": [\\n    \"### Week 4 Material: Recapping The Pair RDD\\\\n\",\\n    \"- countByKey()\\\\n\",\\n    \"- lookupKey()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"PATH2 = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/filtered_registered_business_sf.csv\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"From \\u201dfiltered_registered_business_sf.csv\\u201d, create a pair RDD of (zip, (store name, city))\\\\n\",\\n    \"- Countpairswhichdonothaveakey.\\\\n\",\\n    \"- Filterpairsthatdonotinclude\\u201cSan Francisco\\u201d in the city value.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Count Pairs which don\\'t have a key\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 13,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"92\"\\n      ]\\n     },\\n     \"execution_count\": 13,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(PATH2)\\\\n\",\\n    \"lines.collect()\\\\n\",\\n    \"\\\\n\",\\n    \"parse = lines.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"parse = parse.map(lambda x : (x[0],(x[1],x[2])))\\\\n\",\\n    \"\\\\n\",\\n    \"len(parse.lookup(\\'\\'))\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 16,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"92\"\\n      ]\\n     },\\n     \"execution_count\": 16,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"parse.countByKey()[\\'\\']\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Filter pairs that do not include \\u201cSan Francisco\\u201d in the city value.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 14,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"198561\"\\n      ]\\n     },\\n     \"execution_count\": 14,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(PATH2)\\\\n\",\\n    \"lines.collect()\\\\n\",\\n    \"\\\\n\",\\n    \"parse = lines.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"parse = parse.map(lambda x : (x[0],(x[1],x[2])))\\\\n\",\\n    \"parse.filter(lambda x : x[1][1]).count()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week 4: About them RDDs (resilient distributed dataset)\\\\n\",\\n    \"\\\\n\",\\n    \"### 4.1 - how to add some data redundancy for data/system failures\\\\n\",\\n    \"\\\\n\",\\n    \"RDDs are by default **recomputed each time**. calls:\\\\n\",\\n    \"\\\\n\",\\n    \"---\\\\n\",\\n    \"\\\\n\",\\n    \"To cache the data for repeat:\\\\n\",\\n    \" \\\\n\",\\n    \"**`line_with_spark.persist(StorageLevel.persistency_level)`**\\\\n\",\\n    \"\\\\n\",\\n    \"**`persistency_level`** - storage levels can be the following:\\\\n\",\\n    \"\\\\n\",\\n    \"- **`MEMORY_ONLY`** : put as much possible in RAM, recompute the rest\\\\n\",\\n    \"\\\\n\",\\n    \"- **`MEMORY_AND_DISK`** : put as much as possible in MEM, remainder on DISK\\\\n\",\\n    \"\\\\n\",\\n    \"- **`MEMORY_ONLY_SER`** : turn data into a stream of bytes ( reduction AKA serialized) but needs to conver the data back later\\\\n\",\\n    \"\\\\n\",\\n    \"- **`MEMORY_AND_DISK_ONLY_SER`** : turn data into a stream of bytes ( reduction AKA serialized) but needs to conver the da\\\\\"ta back later\\\\n\",\\n    \"    \\\\n\",\\n    \"- **`DISK_ONLY`** - keeps RDD data only on disk\\\\n\",\\n    \"    \\\\n\",\\n    \"- **`MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc`** - replicate each partition on two different nodes\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"\\\\n\",\\n    \"### Why Replicate?\\\\n\",\\n    \"\\\\n\",\\n    \"If a server fails, then you will have much faster recovery during computation. And for repeat calls, \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example\\\\n\",\\n    \"\\\\n\",\\n    \"Python example \\\\n\",\\n    \"```python\\\\n\",\\n    \"             .persist(storage_label)\\\\n\",\\n    \"    .cache()=.persist(MEMORY_ONLY)\\\\n\",\\n    \"             .persist()\\\\n\",\\n    \"             \\\\n\",\\n    \"             .unpersist() # restore the data\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"**`.cache()`** is the same thing as **`.persist(MEMORY_ONLY)`**\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 6 - comparison\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"#### No persist management\\\\n\",\\n    \"```python\\\\n\",\\n    \"start = time.time()\\\\n\",\\n    \"print \\\\\"first \\\\\",\\\\n\",\\n    \"print time.time() - start\\\\n\",\\n    \"\\\\n\",\\n    \"start = time.time()\\\\n\",\\n    \"lines.count()\\\\n\",\\n    \"print \\\\\"second \\\\\",\\\\n\",\\n    \"print time.time() - start\\\\n\",\\n    \"```\\\\n\",\\n    \"#### Check the time\\\\n\",\\n    \"```{r}\\\\n\",\\n    \"first 59.7 secs\\\\n\",\\n    \"second 56l72 secs\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### No persist management\\\\n\",\\n    \"```python\\\\n\",\\n    \"lines.persist(StorageLevel.MEMORY_AND_DISK)\\\\n\",\\n    \"start = time.time()\\\\n\",\\n    \"print \\\\\"first \\\\\",\\\\n\",\\n    \"print time.time() - start\\\\n\",\\n    \"\\\\n\",\\n    \"start = time.time()\\\\n\",\\n    \"lines.count()\\\\n\",\\n    \"print \\\\\"second \\\\\",\\\\n\",\\n    \"print time.time() - start\\\\n\",\\n    \"\\\\n\",\\n    \"start = time.time()\\\\n\",\\n    \"lines.count()\\\\n\",\\n    \"print \\\\\"third \\\\\",\\\\n\",\\n    \"print time.time() - start\\\\n\",\\n    \"```\\\\n\",\\n    \"#### Check the time, repeat times have faster processing\\\\n\",\\n    \"```{r}\\\\n\",\\n    \"first 79.07\\\\n\",\\n    \"second 20.86\\\\n\",\\n    \"third 20.4\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Spark Cluster\\\\n\",\\n    \"\\\\n\",\\n    \"- Hadoop YARN\\\\n\",\\n    \"- Mesos\\\\n\",\\n    \"- Spark standalone\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Get storage level\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"**`getStorageLevel()`** \\u2013 returns different storage option flags set for an RDD.`\\\\n\",\\n    \"\\\\n\",\\n    \"- **`StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication = 1)`** \\\\n\",\\n    \"    - **`useDisk`** : If set, partitions that do not fit in memory will be written to disk.\\\\n\",\\n    \"    - **`useMemory`** : If set, the RDDs will be stored in-memory.\\\\n\",\\n    \"    - **`useOffHeap`** : If set, the RDD will be stored outside of the Spark executor in an external system such as Tachyon.\\\\n\",\\n    \"    - **`deserialization`** : If set, the RDD will be stored as deserialized Java objects.\\\\n\",\\n    \"    - **`replication`** : An integer that controls the number of copies of the persisted data to be stored.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Storage Guidance when designing a Spark Program\\\\n\",\\n    \"\\\\n\",\\n    \"- **`\\'MEMORY_ONLY\\'`** - Best\\\\n\",\\n    \"- **`\\'DISK\\'`** - only if recomputation is expensive and cannot fit in memory\\\\n\",\\n    \"- **Off-Heap** - stored outside of the spark cluster in an external system. Use if there are memory issues\\\\n\",\\n    \"- **serialization** - if there is memory issues, or if its too big to fit into memory\\\\n\",\\n    \"- **replication** - faster fault recovery, but the trade off is taking more storage space on your cluster. Use when there is a bad connection to your cluster, or you have live web app\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Sample:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"    Output[1]:\\\\n\",\\n    \"        StorageLevel(False,False,False,1)\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 19,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"PATH2 = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/USF_Mission.txt\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 21,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"from pyspark.storagelevel import StorageLevel\\\\n\",\\n    \"lines = sc.textFile(PATH2)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 7\\\\n\",\\n    \"\\\\n\",\\n    \"Try persisting some of the data\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 22,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/USF_Mission.txt MapPartitionsRDD[35] at textFile at NativeMethodAccessorImpl.java:0\"\\n      ]\\n     },\\n     \"execution_count\": 22,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines.persist(StorageLevel.DISK_ONLY)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 28,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"24\"\\n      ]\\n     },\\n     \"execution_count\": 28,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines.count()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 29,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"# will error, no java\\\\n\",\\n    \"#lines.persist(StorageLevel.MEMORY_AND_DISK_SER)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 30,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"# will error\\\\n\",\\n    \"#lines.persist(StorageLevel.MEMORY_ONLY_2)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### One an action has been performed, the persisted data will show in the `storage` tab\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"\\\\n\",\\n    \"![](http://apache-spark-user-list.1001560.n3.nabble.com/attachment/6487/0/Cloudera-Training-Spark-Developer-VM-cdh5.0_ALPHA.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week 5 Material : Running Spark on a Cluster\\\\n\",\\n    \"\\\\n\",\\n    \"### Spark Cluster\\\\n\",\\n    \"\\\\n\",\\n    \"### Spark Standalone Cluster - using spark instead of Mesos / YARN\\\\n\",\\n    \"\\\\n\",\\n    \"### Spark Cluster Types:\\\\n\",\\n    \"- Spark Standalone\\\\n\",\\n    \"- Hadoop YARN\\\\n\",\\n    \"- Mesos\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Components (when running)\\\\n\",\\n    \"\\\\n\",\\n    \"![](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/images/driver-sparkcontext-clustermanager-workers-executors.png)\\\\n\",\\n    \"\\\\n\",\\n    \"**Client** \\\\n\",\\n    \"- starts the driver program.\\\\n\",\\n    \"- prepares config + class options\\\\n\",\\n    \"- `spark-submit`, `pyspark`, or `spark-shell` scripts\\\\n\",\\n    \"\\\\n\",\\n    \"**Driver** \\\\n\",\\n    \"- 1 driver per application\\\\n\",\\n    \"- Monitors executor of spark\\\\n\",\\n    \"- distributes tasks to each work executor\\\\n\",\\n    \"\\\\n\",\\n    \"**Executor** \\\\n\",\\n    \"- stores spark ask with variable amount of cores\\\\n\",\\n    \"- stores and caches data in memory\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Deploy Mode\\\\n\",\\n    \"\\\\n\",\\n    \"#### Example: Driver in the cluser\\\\n\",\\n    \"![](https://cdn-images-1.medium.com/max/568/1*-d0reAUnrkZQhL72ks8paA.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"source\": [\\n    \"#### Example: Driver outside the cluster\\\\n\",\\n    \"![](http://freecontent.manning.com/wp-content/uploads/bonaci_runtimeArch_02.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Spark Cluster \\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"![](https://i.stack.imgur.com/KHekf.png)\\\\n\",\\n    \"\\\\n\",\\n    \"**Cluster Manager **\\\\n\",\\n    \"- monitors worker and allocates and reserves resources (processing, storage, memory). Kinda like my old boss at a restaurant\\\\n\",\\n    \"\\\\n\",\\n    \"**Master**\\\\n\",\\n    \"- Takes in applications\\\\n\",\\n    \"- Requests resources\\\\n\",\\n    \"- Sometimes acts as a resource manager\\\\n\",\\n    \"\\\\n\",\\n    \"**Worker**\\\\n\",\\n    \"- Instructions from master\\\\n\",\\n    \"- launches executors on worker\\\\n\",\\n    \"- Spark must be on every node\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Good Linkedin Article\\\\n\",\\n    \"\\\\n\",\\n    \"https://www.linkedin.com/pulse/spark-architecture-ashish-kumar/\\\\n\",\\n    \"\\\\n\",\\n    \"![](https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAs-AAAAJGIzNmIyOWFmLTVkODEtNDBlYS1hNGE4LTI1ZGE2NTQyZTQzYg.jpg)\\\\n\",\\n    \"\\\\n\",\\n    \"Spark Application Workflow in Standalone Mode\\\\n\",\\n    \"\\\\n\",\\n    \"1.     Client connect to master.\\\\n\",\\n    \"\\\\n\",\\n    \"2.     Master start driver on one of node.\\\\n\",\\n    \"\\\\n\",\\n    \"3.     Driver connect to master and request for Executors to run the tasks.\\\\n\",\\n    \"\\\\n\",\\n    \"4.     Master connect to worker node and request to create executors.\\\\n\",\\n    \"\\\\n\",\\n    \"5.     Each Worker node create one executor for each Application.\\\\n\",\\n    \"\\\\n\",\\n    \"6.     Driver connect to executors and schedule tasks on it.\\\\n\",\\n    \"\\\\n\",\\n    \"7.     Update the status of task to driver.\\\\n\",\\n    \"\\\\n\",\\n    \"8.     Driver send application output to client.\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"anaconda-cloud\": {},\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 0\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/quiz1.py',\n",
       "  u'from pyspark import SparkContext, SparkConf\\nfrom user_definition import *\\n#Do not add any other libraries\\n\\nconf = SparkConf().setMaster(\"local[*]\").setAppName(app_name)\\nsc = SparkContext(conf=conf)\\nlines = sc.textFile(input_file)\\nnums = lines.map(lambda x : float(x))\\nfor x in nums.collect():\\n\\tprint x\\nmean = nums.mean()\\nprint mean\\nstdev = nums.stdev()\\nprint stdev\\nfor x in nums:\\n\\tprint (x-mean)/stdev\\n\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/user_definition.pyc',\n",
       "  u'\\x03\\ufffd\\r\\n\\x0e\\ufffd\\ufffdYc\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00@\\x00\\x00\\x00s\\x10\\x00\\x00\\x00d\\x00\\x00Z\\x00\\x00d\\x01\\x00Z\\x01\\x00d\\x02\\x00S(\\x03\\x00\\x00\\x00t\\x05\\x00\\x00\\x00quiz1s\\x11\\x00\\x00\\x00input_2/input.txtN(\\x02\\x00\\x00\\x00t\\x08\\x00\\x00\\x00app_namet\\n\\x00\\x00\\x00input_file(\\x00\\x00\\x00\\x00(\\x00\\x00\\x00\\x00(\\x00\\x00\\x00\\x00sM\\x00\\x00\\x00/Users/tlee010/Desktop/MSAN-pywork/MSAN_604_DistrComputing/user_definition.pyt\\x08\\x00\\x00\\x00<module>\\x01\\x00\\x00\\x00s\\x02\\x00\\x00\\x00\\x06\\x01'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/DistComp-HW2.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://192.168.1.72:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 1,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"\\\\n\",\\n    \"\\'\\'\\'\\\\n\",\\n    \"1. Print the distinct words with alphabetical characters in alphabetical order (lowercase). o Ignore/Remove special characters including : ; . , [] {} () \\u201c \\u2018 ? !\\\\n\",\\n    \"    - Assume a hyphenated word are 2 or more words. (off-campus\\xe8off campus)\\\\n\",\\n    \"    - Assume different forms of a same word (plural vs singular nouns, present vs past tense vs past participle verbs, etc.) are different. (study1studies, do1did)\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"2. Print the sum of all numerical values in the text as a float. Some things to keep in mind:\\\\n\",\\n    \"    - o Numbers may or may not include commas after every third digit from right to left (e.g., 3,000 and 3000 are valid and equal). You may ignore numbers that mix comma and non-comma formatting (i.e., 3000,000 can be ignored since it\\u2019s not properly formatted).\\\\n\",\\n    \"    - o Numbers may be floats and may or may not have a leading 0 (e.g., .25 and 0.25 are valid and equal)\\\\n\",\\n    \"    - o Don\\u2019t evaluate equations and remove operations (3*3\\xe83 3) 3. Print an empty line between step 1 and 2 (example below).\\\\n\",\\n    \"\\\\n\",\\n    \"\\'\\'\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 2,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[u\\'In 2013, the project was donated to the Apache Software Foundation and switched its license to Apache 2.0. In February 2014, Spark became a Top-Level Apache Project.[28]\\', u\\'\\', u\\'[28] \\\\\"The Apache Software Foundation Announces Apache Spark as a Top-Level Project\\\\\". Apache Software Foundation. 27 February 2014. Retrieved 4 March 2014.\\']\\\\n\",\\n      \"[\\'in\\', \\'2013\\', \\'the\\', \\'project\\', \\'was\\', \\'donated\\', \\'to\\', \\'the\\', \\'apache\\', \\'software\\', \\'foundation\\', \\'and\\', \\'switched\\', \\'its\\', \\'license\\', \\'to\\', \\'apache\\', \\'20\\', \\'in\\', \\'february\\', \\'2014\\', \\'spark\\', \\'became\\', \\'a\\', \\'top\\', \\'level\\', \\'apache\\', \\'project28\\', \\'28\\', \\'the\\', \\'apache\\', \\'software\\', \\'foundation\\', \\'announces\\', \\'apache\\', \\'spark\\', \\'as\\', \\'a\\', \\'top\\', \\'level\\', \\'project\\', \\'apache\\', \\'software\\', \\'foundation\\', \\'27\\', \\'february\\', \\'2014\\', \\'retrieved\\', \\'4\\', \\'march\\', \\'2014\\']\\\\n\",\\n      \"a\\\\n\",\\n      \"and\\\\n\",\\n      \"announces\\\\n\",\\n      \"apache\\\\n\",\\n      \"as\\\\n\",\\n      \"became\\\\n\",\\n      \"donated\\\\n\",\\n      \"february\\\\n\",\\n      \"foundation\\\\n\",\\n      \"in\\\\n\",\\n      \"its\\\\n\",\\n      \"level\\\\n\",\\n      \"license\\\\n\",\\n      \"march\\\\n\",\\n      \"project\\\\n\",\\n      \"project28\\\\n\",\\n      \"retrieved\\\\n\",\\n      \"software\\\\n\",\\n      \"spark\\\\n\",\\n      \"switched\\\\n\",\\n      \"the\\\\n\",\\n      \"to\\\\n\",\\n      \"top\\\\n\",\\n      \"was\\\\n\",\\n      \"\\\\n\",\\n      \"\\\\n\",\\n      \"8144.0\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"import re\\\\n\",\\n    \"lines = sc.textFile(\\'/Users/tlee010/PycharmProjects/MSAN/DistC_HW2/input_2/input.txt\\')\\\\n\",\\n    \"print lines.collect()\\\\n\",\\n    \"\\\\n\",\\n    \"lines = lines.map(lambda x : x.encode(\\'ascii\\',\\'ignore\\'))\\\\n\",\\n    \"\\\\n\",\\n    \"def clean_string(x):\\\\n\",\\n    \"    regex = re.compile(\\'[\\\\\":;.,\\\\\\\\[\\\\\\\\]\\\\\\\\{\\\\\\\\}\\\\\\\\(\\\\\\\\)\\\\\\\\\\u2018\\\\\\\\?\\\\\\\\!\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\t\\\\\\\\\\\\\\\\r]\\')\\\\n\",\\n    \"    nopunct = regex.sub(\\\\\"\\\\\", x)  # delete stuff but leave at least a space to avoid clumping together\\\\n\",\\n    \"    nopunct = nopunct.replace(\\'-\\', \\' \\')\\\\n\",\\n    \"    return nopunct\\\\n\",\\n    \"\\\\n\",\\n    \"clean_lines = lines.map(lambda x : clean_string(x))\\\\n\",\\n    \"words = clean_lines.flatMap(lambda x : x.split())\\\\n\",\\n    \"words = words.map(lambda x : x.lower())\\\\n\",\\n    \"print words.collect()\\\\n\",\\n    \"words_only = words.filter(lambda x : x.isdigit()==False)\\\\n\",\\n    \"unique = words_only.countByValue()\\\\n\",\\n    \"for x in sorted(unique.keys()):\\\\n\",\\n    \"    print x \\\\n\",\\n    \"print \\'\\\\\\\\n\\'\\\\n\",\\n    \"\\\\n\",\\n    \"def clean_string2(x):\\\\n\",\\n    \"    regex = re.compile(\\'[\\\\\":;\\\\\\\\[\\\\\\\\]\\\\\\\\{\\\\\\\\}\\\\\\\\(\\\\\\\\)\\\\\\\\\\u2018\\\\\\\\?\\\\\\\\!\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\t\\\\\\\\\\\\\\\\r]\\')\\\\n\",\\n    \"    nopunct = regex.sub(\\\\\" \\\\\", x)  # delete stuff but leave at least a space to avoid clumping together\\\\n\",\\n    \"    nopunct = nopunct.replace(\\'-\\', \\' \\')\\\\n\",\\n    \"    return nopunct\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"def getNumbers(x):\\\\n\",\\n    \"    regex = re.compile(\\'[*/+=]\\')\\\\n\",\\n    \"    x = regex.sub(\\\\\" \\\\\", x)\\\\n\",\\n    \"    regex = re.compile(r\\'[0-9,]*[.]?[0-9]+\\')\\\\n\",\\n    \"    return regex.findall(x)\\\\n\",\\n    \"\\\\n\",\\n    \"clean_lines = lines.map(lambda x : clean_string2(x))\\\\n\",\\n    \"words = clean_lines.flatMap(lambda x : x.split())\\\\n\",\\n    \"words = words.map(lambda x : x.lower())\\\\n\",\\n    \"num_only = words.map(getNumbers)\\\\n\",\\n    \"num_only = num_only.filter(lambda x : x !=[])\\\\n\",\\n    \"num_only = num_only.map(lambda x: float(x[0].replace(\\',\\', \\'\\')))\\\\n\",\\n    \"\\\\n\",\\n    \"print num_only.reduce(lambda x,y : x + y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 3,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[u\\'In 2013, the project was donated to the Apache Software Foundation and switched its license to Apache 2.0. In February 2014, Spark became a Top-Level Apache Project.[28]\\', u\\'\\', u\\'[28] \\\\\"The Apache Software Foundation Announces Apache Spark as a Top-Level Project\\\\\". Apache Software Foundation. 27 February 2014. Retrieved 4 March 2014.\\']\\\\n\",\\n      \"[[\\'2013\\'], [\\'2.0\\'], [\\'2014\\'], [\\'28\\'], [\\'28\\'], [\\'27\\'], [\\'2014\\'], [\\'4\\'], [\\'2014\\']]\\\\n\",\\n      \"[2013.0, 2.0, 2014.0, 28.0, 28.0, 27.0, 2014.0, 4.0, 2014.0]\\\\n\",\\n      \"8144.0\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(\\'/Users/tlee010/PycharmProjects/MSAN/DistC_HW2/input_2/input.txt\\')\\\\n\",\\n    \"print lines.collect()\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"def clean_string2(x):\\\\n\",\\n    \"    regex = re.compile(\\'[\\\\\":;\\\\\\\\[\\\\\\\\]\\\\\\\\{\\\\\\\\}\\\\\\\\(\\\\\\\\)\\\\\\\\\\u2018\\\\\\\\?\\\\\\\\!\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\t\\\\\\\\\\\\\\\\r]\\')\\\\n\",\\n    \"    nopunct = regex.sub(\\\\\" \\\\\", x)  # delete stuff but leave at least a space to avoid clumping together\\\\n\",\\n    \"    nopunct = nopunct.replace(\\'-\\', \\' \\')\\\\n\",\\n    \"    return nopunct\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"def getNumbers(x):\\\\n\",\\n    \"    regex = re.compile(\\'[*/+=]\\')\\\\n\",\\n    \"    x = regex.sub(\\\\\" \\\\\", x)\\\\n\",\\n    \"    regex = re.compile(\\'[0-9,]*[.]?[0-9]+\\')\\\\n\",\\n    \"    return regex.findall(x)\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"lines = lines.map(lambda x : x.encode(\\'ascii\\',\\'ignore\\'))\\\\n\",\\n    \"lines = lines.map(lambda x : clean_string2(x))\\\\n\",\\n    \"words = lines.flatMap(lambda x : x.split())\\\\n\",\\n    \"words = words.map(lambda x : x.lower())\\\\n\",\\n    \"\\\\n\",\\n    \"num_only = words.map(getNumbers)\\\\n\",\\n    \"num_only = num_only.filter(lambda x : x !=[])\\\\n\",\\n    \"print num_only.collect()\\\\n\",\\n    \"num_only = num_only.map(lambda x: float(x[0].replace(\\',\\', \\'\\')))\\\\n\",\\n    \"print num_only.collect()\\\\n\",\\n    \"print num_only.reduce(lambda x,y : x + y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 4,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[\\'3,000,000\\']\\\\n\",\\n      \"[\\'3000\\']\\\\n\",\\n      \"[\\'2.0\\']\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"def getNumbers(x):\\\\n\",\\n    \"    regex = re.compile(\\'[0-9,]*[.]?[0-9]+\\')\\\\n\",\\n    \"    clean = regex.findall(x)\\\\n\",\\n    \"    return clean\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"print getNumbers(\\'3,000,000\\')\\\\n\",\\n    \"print getNumbers(\\'3000\\')\\\\n\",\\n    \"print getNumbers(\\'2.0.\\')\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 5,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"\\'3,000,000\\'\"\\n      ]\\n     },\\n     \"execution_count\": 5,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"regex = re.compile(\\'[^0-9,.]\\')\\\\n\",\\n    \"regex.sub(\\'\\',\\'we found 3,000,000\\')\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 10,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[u\\'In 2013, the project was donated to the Apache Software Foundation and switched its license to Apache 2.0. In February 2014, Spark became a Top-Level Apache Project.[28]\\', u\\'\\', u\\'[28] \\\\\"The Apache Software Foundation Announces Apache Spark as a Top-Level Project\\\\\". Apache Software Foundation. 27 February 2014. Retrieved 4 March 2014.\\']\\\\n\",\\n      \"a\\\\n\",\\n      \"and\\\\n\",\\n      \"announces\\\\n\",\\n      \"apache\\\\n\",\\n      \"as\\\\n\",\\n      \"became\\\\n\",\\n      \"donated\\\\n\",\\n      \"february\\\\n\",\\n      \"foundation\\\\n\",\\n      \"in\\\\n\",\\n      \"its\\\\n\",\\n      \"level\\\\n\",\\n      \"license\\\\n\",\\n      \"march\\\\n\",\\n      \"project\\\\n\",\\n      \"retrieved\\\\n\",\\n      \"software\\\\n\",\\n      \"spark\\\\n\",\\n      \"switched\\\\n\",\\n      \"the\\\\n\",\\n      \"to\\\\n\",\\n      \"top\\\\n\",\\n      \"was\\\\n\",\\n      \"\\\\n\",\\n      \"8144.0\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"import re\\\\n\",\\n    \"lines = sc.textFile(\\'/Users/tlee010/PycharmProjects/MSAN/DistC_HW2/input_2/input.txt\\')\\\\n\",\\n    \"print lines.collect()\\\\n\",\\n    \"\\\\n\",\\n    \"def replaceSymbols(x):\\\\n\",\\n    \"    regex = re.compile(\\'[\\\\\\\\+\\\\\\\\-\\\\\\\\*\\\\\\\\/\\\\\\\\=]\\')\\\\n\",\\n    \"    clean = regex.sub(\\\\\" \\\\\",x)\\\\n\",\\n    \"    return clean\\\\n\",\\n    \"\\\\n\",\\n    \"def letters_only(x):\\\\n\",\\n    \"    regex = re.compile(\\'[^A-Za-z]\\')\\\\n\",\\n    \"    clean = regex.sub(\\\\\"\\\\\",x)\\\\n\",\\n    \"    return clean\\\\n\",\\n    \"\\\\n\",\\n    \"def numbers_only(x):\\\\n\",\\n    \"    regex = re.compile(\\'[0-9,]*[.]?[0-9]+\\')\\\\n\",\\n    \"    clean = regex.findall(x)\\\\n\",\\n    \"    return clean\\\\n\",\\n    \"\\\\n\",\\n    \"def comma_test(x):\\\\n\",\\n    \"    if x[0] == \\',\\':\\\\n\",\\n    \"        x = x[1:]\\\\n\",\\n    \"    if (\\',\\' in x) & (len(x)>3):\\\\n\",\\n    \"        regex = re.compile(\\'^\\\\\\\\d{1,3}(?:,\\\\\\\\d{3})+(?:\\\\\\\\.\\\\\\\\d+)?$\\')\\\\n\",\\n    \"        num_found = regex.findall(x)\\\\n\",\\n    \"        if len(num_found) > 0:\\\\n\",\\n    \"            if len(num_found[0])==len(x):\\\\n\",\\n    \"                return num_found[0].replace(\\',\\',\\'\\')\\\\n\",\\n    \"            else:\\\\n\",\\n    \"                return \\'0\\'\\\\n\",\\n    \"        else:\\\\n\",\\n    \"            return \\'0\\'\\\\n\",\\n    \"    elif x.count(\\'.\\') > 1:\\\\n\",\\n    \"        return \\'0\\'\\\\n\",\\n    \"    else:\\\\n\",\\n    \"        return x\\\\n\",\\n    \"\\\\n\",\\n    \"# replacing all math symbols\\\\n\",\\n    \"lines = lines.map(replaceSymbols)\\\\n\",\\n    \"\\\\n\",\\n    \"# splitting on spaces\\\\n\",\\n    \"words = lines.flatMap(lambda x : x.lower().split(\\' \\'))\\\\n\",\\n    \"\\\\n\",\\n    \"# replacing non alpha chars\\\\n\",\\n    \"clean_words = words.map(letters_only)\\\\n\",\\n    \"\\\\n\",\\n    \"# filtered down to words that only have phrases\\\\n\",\\n    \"clean_words = clean_words.filter(lambda x : x!=\\'\\')\\\\n\",\\n    \"\\\\n\",\\n    \"# getting distinct words\\\\n\",\\n    \"clean_words = clean_words.distinct()\\\\n\",\\n    \"\\\\n\",\\n    \"# ordereding for final step\\\\n\",\\n    \"for x in sorted(clean_words.collect()):\\\\n\",\\n    \"    print x\\\\n\",\\n    \"\\\\n\",\\n    \"num_only = words.flatMap(numbers_only)\\\\n\",\\n    \"num_only = num_only.filter(lambda x : x not in (\\'.\\',\\'\\'))\\\\n\",\\n    \"num_only = num_only.map(comma_test)\\\\n\",\\n    \"num_only = num_only.map(lambda x : float(x))\\\\n\",\\n    \"print \\'\\'\\\\n\",\\n    \"print num_only.sum()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 7,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"3000\\\\n\",\\n      \"0\\\\n\",\\n      \"3000\\\\n\",\\n      \"30000\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"300000\\\\n\",\\n      \"0\\\\n\",\\n      \"200000\\\\n\",\\n      \"20000\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"def comma_test(x):\\\\n\",\\n    \"    \\\\n\",\\n    \"    if (\\',\\' in x) & (len(x)>3):        \\\\n\",\\n    \"        regex = re.compile(\\'^\\\\\\\\d{1,3}\\\\\\\\,?\\\\\\\\d{3}\\')\\\\n\",\\n    \"        num_found = regex.findall(x)\\\\n\",\\n    \"        if len(num_found) > 0:\\\\n\",\\n    \"            if len(num_found[0])==len(x):\\\\n\",\\n    \"                return num_found[0].replace(\\',\\',\\'\\')\\\\n\",\\n    \"            else:\\\\n\",\\n    \"                return \\'0\\'\\\\n\",\\n    \"        else:\\\\n\",\\n    \"            return \\'0\\'\\\\n\",\\n    \"    else:\\\\n\",\\n    \"        return x\\\\n\",\\n    \"\\\\n\",\\n    \"print comma_test(\\'3000\\')\\\\n\",\\n    \"print comma_test(\\'300,0\\')\\\\n\",\\n    \"print comma_test(\\'3,000\\')\\\\n\",\\n    \"print comma_test(\\'30000\\')\\\\n\",\\n    \"print comma_test(\\'300,00\\')\\\\n\",\\n    \"print comma_test(\\'300,00\\')\\\\n\",\\n    \"print comma_test(\\'300,000\\')\\\\n\",\\n    \"print comma_test(\\'3000,000\\')\\\\n\",\\n    \"print comma_test(\\'200,000\\')\\\\n\",\\n    \"print comma_test(\\'20,000\\')\\\\n\",\\n    \"print comma_test(\\'3000,000,000\\')\\\\n\",\\n    \"print comma_test(\\'3,000,000,000\\')\\\\n\",\\n    \"print comma_test(\\'300,000.02\\')\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 2,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"1000\\\\n\",\\n      \"0\\\\n\",\\n      \"3000\\\\n\",\\n      \"40000\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"700000\\\\n\",\\n      \"0\\\\n\",\\n      \"900000\\\\n\",\\n      \"10000\\\\n\",\\n      \"0\\\\n\",\\n      \"1200000000\\\\n\",\\n      \"130000.02\\\\n\",\\n      \"140000.012455122\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"180000000\\\\n\",\\n      \"100\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"0\\\\n\",\\n      \"3.444\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"import re\\\\n\",\\n    \"def comma_test(x):\\\\n\",\\n    \"    if x[0] == \\',\\':\\\\n\",\\n    \"        x = x[1:]\\\\n\",\\n    \"    if (\\',\\' in x) & (len(x)>3):\\\\n\",\\n    \"        regex = re.compile(\\'^\\\\\\\\d{1,3}(?:,\\\\\\\\d{3})+(?:\\\\\\\\.\\\\\\\\d+)?$\\')\\\\n\",\\n    \"        num_found = regex.findall(x)\\\\n\",\\n    \"        if len(num_found) > 0:\\\\n\",\\n    \"            if len(num_found[0])==len(x):\\\\n\",\\n    \"                return num_found[0].replace(\\',\\',\\'\\')\\\\n\",\\n    \"            else:\\\\n\",\\n    \"                return \\'0\\'\\\\n\",\\n    \"        else:\\\\n\",\\n    \"            return \\'0\\'\\\\n\",\\n    \"    elif x.count(\\'.\\') > 1:\\\\n\",\\n    \"        return \\'0\\'\\\\n\",\\n    \"    else:\\\\n\",\\n    \"        return x\\\\n\",\\n    \"\\\\n\",\\n    \"print comma_test(\\'1000\\')\\\\n\",\\n    \"print comma_test(\\'200,0\\')\\\\n\",\\n    \"print comma_test(\\'3,000\\')\\\\n\",\\n    \"print comma_test(\\'40000\\')\\\\n\",\\n    \"print comma_test(\\'500,00\\')\\\\n\",\\n    \"print comma_test(\\'600,00\\')\\\\n\",\\n    \"print comma_test(\\'700,000\\')\\\\n\",\\n    \"print comma_test(\\'8000,000\\')\\\\n\",\\n    \"print comma_test(\\'900,000\\')\\\\n\",\\n    \"print comma_test(\\'10,000\\')\\\\n\",\\n    \"print comma_test(\\'1100,000,000\\')\\\\n\",\\n    \"print comma_test(\\'1,200,000,000\\')\\\\n\",\\n    \"print comma_test(\\'130,000.02\\')\\\\n\",\\n    \"print comma_test(\\'140,000.012455122\\')\\\\n\",\\n    \"print comma_test(\\'15,000,00\\')\\\\n\",\\n    \"print comma_test(\\'16000,000\\')\\\\n\",\\n    \"print comma_test(\\'17,000,000,00\\')\\\\n\",\\n    \"print comma_test(\\',180,000,00\\')\\\\n\",\\n    \"print comma_test(\\',180,000,000\\')\\\\n\",\\n    \"print comma_test(\\',100\\')\\\\n\",\\n    \"print comma_test(\\'2.2.2\\')\\\\n\",\\n    \"print comma_test(\\'..2\\')\\\\n\",\\n    \"print comma_test(\\'12,22,2\\')\\\\n\",\\n    \"print comma_test(\\'2.2.2\\')\\\\n\",\\n    \"print comma_test(\\'3000,333\\')\\\\n\",\\n    \"print comma_test(\\'3.444\\')\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"\\'^(?:0|[1-9][0-9]{0,2}(?:\\\\\\\\.[0-9]{3})*)$\\'\"\\n   ]\\n  }\\n ],\\n \"metadata\": {\\n  \"anaconda-cloud\": {},\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 0\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/derby.log',\n",
       "  u\"----------------------------------------------------------------\\nFri Dec 01 14:30:58 PST 2017:\\nBooting Derby version The Apache Software Foundation - Apache Derby - 10.12.1.1 - (1704137): instance a816c00e-0160-1434-479d-000011b63728 \\non database directory /Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/metastore_db with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@50b3bb42 \\nLoaded from file:/usr/local/Cellar/apache-spark/2.2.0/libexec/jars/derby-10.12.1.1.jar\\njava.vendor=Oracle Corporation\\njava.runtime.version=1.8.0_152-b16\\nuser.dir=/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing\\nos.name=Mac OS X\\nos.arch=x86_64\\nos.version=10.13.1\\nderby.system.home=null\\nDatabase Class Loader started - derby.database.classpath=''\\n\"),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/ex6.py',\n",
       "  u'from pyspark import SparkConf, SparkContext\\n\\n#Create SparkContext\\nconf = SparkConf().setMaster(\"local[*]\").setAppName(\"read_lines\")\\nsc = SparkContext(conf = conf)\\n\\n#Load Data.\\nlines=sc.textFile(\"../Data/README.md\")\\nprint(lines.count())\\nprint(lines.first())\\n\\nsc.stop()\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/MSAN694-Lec3-notes.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://192.168.1.72:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 1,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"**flatmap** vs. **map** only affects how the data is collected.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Fold, Aggregate, countByValue, takeSample, and foreach(n)\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Fold - similiar to a reduce function\\\\n\",\\n    \"**fold** is similar to reduce with a default value. \\\\n\",\\n    \"- takes function with same design as needed for reduce()\\\\n\",\\n    \"- Takes a default value to be used for the intial call on each partition\\\\n\",\\n    \"- Returns a new value of hte same type\\\\n\",\\n    \"\\\\n\",\\n    \"---\\\\n\",\\n    \"\\\\n\",\\n    \"If there\\'s 3 partitions\\\\n\",\\n    \"\\\\n\",\\n    \"`.fold(0,lambda x,y : x+y)`\\\\n\",\\n    \"\\\\n\",\\n    \"Step 1 : **0** 1 3 5 | **0** 7 9 | **0**\\\\n\",\\n    \"\\\\n\",\\n    \"Step 2 : **0** 9 | **0** 16\\\\n\",\\n    \"\\\\n\",\\n    \"Step 3 :25\\\\n\",\\n    \"\\\\n\",\\n    \"---\\\\n\",\\n    \"\\\\n\",\\n    \"`.fold(1,lambda x,y : x+y)`\\\\n\",\\n    \"\\\\n\",\\n    \"If there\\'s 3 partitions\\\\n\",\\n    \"\\\\n\",\\n    \"Step 1 : **1** 1 3 5 | **1** 7 9 | **1**\\\\n\",\\n    \"\\\\n\",\\n    \"Step 2 : **0** 9 | **0** 16\\\\n\",\\n    \"\\\\n\",\\n    \"Step 3 : 25\\\\n\",\\n    \"\\\\n\",\\n    \"Fold is often used for error handling. Handle error on your own. The 0 value will automatically handle the error. \\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Lets look at odd numbers, want to calculate (sum / count)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 2,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"odd_nums = sc.parallelize([1,3,5,7,9],2)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 3,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1, 4, 5, 7, 9]\"\\n      ]\\n     },\\n     \"execution_count\": 3,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.map(lambda x : x+1 if x == 3 else x).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 4,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[0, 4, 7, 0, 0]\"\\n      ]\\n     },\\n     \"execution_count\": 4,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.map(lambda x : x+1 if x == 3 else x+2 if x==5 else 0).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 5,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1, 3, 5, 7, 9]\"\\n      ]\\n     },\\n     \"execution_count\": 5,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"We will make a 2-value tuple `(0,0)` which will be considered as `x`.\\\\n\",\\n    \"\\\\n\",\\n    \"- `x[0]` will be our sum\\\\n\",\\n    \"- `x[1]` will be our count\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \".aggregate((0,0),  # default value\\\\n\",\\n    \"    lambda x,y : (x[0],x[1]+y),  # within a partition\\\\n\",\\n    \"    lambda x,y: (x[0]+y[0],x[1]+y[1])) # condensing partitions\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Lets look at the parts\\\\n\",\\n    \"\\\\n\",\\n    \"##### Default Value\\\\n\",\\n    \"Will default to (0,0) as a starting point. Then will continually add. \\\\n\",\\n    \"\\\\n\",\\n    \"##### Within partition \\\\n\",\\n    \"\\\\n\",\\n    \"`lambda x,y : (x[0]+1,x[1]+y)`\\\\n\",\\n    \"\\\\n\",\\n    \"Let\\'s work through this. \\\\n\",\\n    \"\\\\n\",\\n    \"1. We start with `(0,0) = x`\\\\n\",\\n    \"2. We encounter our first value 1 so `y=1`\\\\n\",\\n    \"3. We want (0,0) to be => (1,1)\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"##### Gathering Partitions results\\\\n\",\\n    \"\\\\n\",\\n    \"`lambda x,y: (x[0]+y[0],x[1]+y[1]))`\\\\n\",\\n    \"\\\\n\",\\n    \"Let\\'s work through this step by step. Lets say the partition break is `[1,3] [5,7,9]`\\\\n\",\\n    \"\\\\n\",\\n    \"Then after applying the function within the partition we should have `(4,2) (21,3)`\\\\n\",\\n    \"\\\\n\",\\n    \"So to reiterate:\\\\n\",\\n    \"\\\\n\",\\n    \"1. `(4,2) = x`\\\\n\",\\n    \"2. `(21,3) = y`\\\\n\",\\n    \"3. so the lambda function should add these pair-wise together\\\\n\",\\n    \"4. `x[0]+y[0] = 25`\\\\n\",\\n    \"5. `x[1]+y[1] = 5`\\\\n\",\\n    \"6. Returns `(25,5)`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 4-1 for the numbers between 1 and 9 calculate sum of odd numbers\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 6,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"25\"\\n      ]\\n     },\\n     \"execution_count\": 6,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.reduce(lambda x,y : x + y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 4-2 for the numbers between 1 and 9 calculate the sum of the odd numbers using fold\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 7,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"25\"\\n      ]\\n     },\\n     \"execution_count\": 7,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.fold(0,lambda x,y : x + y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 4-3 using aggregate () return (sum # of elements ) of odd numbers\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 8,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"(5, 25)\"\\n      ]\\n     },\\n     \"execution_count\": 8,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.aggregate((0,0),lambda x,y:(x[0]+1,+x[1]+y),lambda x,y:(x[0]+y[0],x[1]+y[1]))\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 9,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[1, 3], [5, 7, 9]]\"\\n      ]\\n     },\\n     \"execution_count\": 9,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Whats the difference between take() and first()?\\\\n\",\\n    \"\\\\n\",\\n    \"One returns values, the other returns a string\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 10,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"odd_nums = sc.parallelize([1,3,5,7,9],2)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 11,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1]\"\\n      ]\\n     },\\n     \"execution_count\": 11,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.take(1)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 12,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"1\"\\n      ]\\n     },\\n     \"execution_count\": 12,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"odd_nums.first()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Whats the difference between sample() and takeSample()?\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"### Sample\\\\n\",\\n    \"Sample(withReplacement=True, fraction=0.5, seed=1) **transformation**\\\\n\",\\n    \"- **CREATES A NEW RDD** with random elements form the calling RDD\\\\n\",\\n    \"- with replacement (with repeats)\\\\n\",\\n    \"- fraction - expected positive\\\\n\",\\n    \"- expected probability element is used\\\\n\",\\n    \"- seed (for standardizing randomiztion) - otherwise default based on millisecond time\\\\n\",\\n    \"\\\\n\",\\n    \"### takeSample\\\\n\",\\n    \"takesSample (with replacement , num, seed) : ** action **\\\\n\",\\n    \"- returns fixed size sample subset of an RDD as an **ARRAY**\\\\n\",\\n    \"- with replacement allow sample multiple times\\\\n\",\\n    \"- num - example number of sampled element\\\\n\",\\n    \"- seed\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 5-1\\\\n\",\\n    \"\\\\n\",\\n    \"Try collect(), count(), countByValue(), top(n), take(n), first(), takeSample(), operations on Z\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 13,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"x = sc.parallelize([3,4,1,2])\\\\n\",\\n    \"y = sc.parallelize(range(2,6))\\\\n\",\\n    \"z = x.union(y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 14,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 4, 1, 2, 2, 3, 4, 5]\"\\n      ]\\n     },\\n     \"execution_count\": 14,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 15,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"8\"\\n      ]\\n     },\\n     \"execution_count\": 15,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.count()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 16,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"defaultdict(int, {1: 1, 2: 2, 3: 2, 4: 2, 5: 1})\"\\n      ]\\n     },\\n     \"execution_count\": 16,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.countByValue()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 17,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[5, 4, 4]\"\\n      ]\\n     },\\n     \"execution_count\": 17,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.top(3)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 18,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 4, 1]\"\\n      ]\\n     },\\n     \"execution_count\": 18,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.take(3)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 19,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"3\"\\n      ]\\n     },\\n     \"execution_count\": 19,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.first()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 20,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[2, 1, 3]\"\\n      ]\\n     },\\n     \"execution_count\": 20,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.takeSample(withReplacement=False,num=3,seed=None)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 21,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 2, 3, 2, 4, 3, 5, 2, 4, 1, 2, 4, 2, 4, 2, 3, 2, 2, 3, 4]\"\\n      ]\\n     },\\n     \"execution_count\": 21,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.takeSample(withReplacement=True,num=20,seed=None)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 22,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[], [3], [], [4], [], [1], [], [2], [], [2], [], [3], [], [4], [], [5]]\"\\n      ]\\n     },\\n     \"execution_count\": 22,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 23,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[], [], [], [], [], [], [], [], [], [2], [], [], [], [], [], [5]]\"\\n      ]\\n     },\\n     \"execution_count\": 23,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"res = z.sample(True,0.5)\\\\n\",\\n    \"res.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 24,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"b = z.foreach(lambda x : x+1)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week 3 - Pair RDDs\\\\n\",\\n    \"\\\\n\",\\n    \"Pair RDDs are a **key value pair**. \\\\n\",\\n    \"\\\\n\",\\n    \"|Key|value|\\\\n\",\\n    \"|---|------------------------------|\\\\n\",\\n    \"|001| \\'some data that i have added\\'|\\\\n\",\\n    \"\\\\n\",\\n    \"Very common structure for schemaless data or NoSQL. In NoSQL - key is unique. Spark, key could be duplicated. Key could show up in multiple places\\\\n\",\\n    \"\\\\n\",\\n    \"#### Keys \\\\n\",\\n    \"Could be simple, or complex objects (tuples)\\\\n\",\\n    \"Could be simple, or a complex json\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Exercise 1 - get words from readme file\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 25,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"path = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/README.md\\'\\\\n\",\\n    \"rdm = sc.textFile(path)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 26,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'#\\', 1),\\\\n\",\\n       \" (u\\'Apache\\', 1),\\\\n\",\\n       \" (u\\'Spark\\', 1),\\\\n\",\\n       \" (u\\'Spark\\', 1),\\\\n\",\\n       \" (u\\'is\\', 1),\\\\n\",\\n       \" (u\\'a\\', 1),\\\\n\",\\n       \" (u\\'fast\\', 1),\\\\n\",\\n       \" (u\\'and\\', 1),\\\\n\",\\n       \" (u\\'general\\', 1),\\\\n\",\\n       \" (u\\'cluster\\', 1),\\\\n\",\\n       \" (u\\'computing\\', 1),\\\\n\",\\n       \" (u\\'system\\', 1),\\\\n\",\\n       \" (u\\'for\\', 1),\\\\n\",\\n       \" (u\\'Big\\', 1),\\\\n\",\\n       \" (u\\'Data.\\', 1),\\\\n\",\\n       \" (u\\'It\\', 1),\\\\n\",\\n       \" (u\\'provides\\', 1),\\\\n\",\\n       \" (u\\'high-level\\', 1),\\\\n\",\\n       \" (u\\'APIs\\', 1),\\\\n\",\\n       \" (u\\'in\\', 1)]\"\\n      ]\\n     },\\n     \"execution_count\": 26,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (x,1))\\\\n\",\\n    \"words.collect()[:20]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Transformations designed for key value pairs\\\\n\",\\n    \"\\\\n\",\\n    \"|function | description |\\\\n\",\\n    \"|-----|------------------|\\\\n\",\\n    \"|keys()| gets the keys|\\\\n\",\\n    \"|values()| value|\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Do the same but do value as length of word and sort by value\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 27,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'[IntelliJ](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-IntelliJ).\\',\\\\n\",\\n       \"  115),\\\\n\",\\n       \" (u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\',\\\\n\",\\n       \"  112),\\\\n\",\\n       \" (u\\'Version\\\\\"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version)\\',\\\\n\",\\n       \"  96),\\\\n\",\\n       \" (u\\'3\\\\\"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3).\\',\\\\n\",\\n       \"  82),\\\\n\",\\n       \" (u\\'tests](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools).\\',\\\\n\",\\n       \"  81),\\\\n\",\\n       \" (u\\'Spark\\\\\"](http://spark.apache.org/docs/latest/building-spark.html).\\', 65),\\\\n\",\\n       \" (u\\'Guide](http://spark.apache.org/docs/latest/configuration.html)\\', 62),\\\\n\",\\n       \" (u\\'wiki](https://cwiki.apache.org/confluence/display/SPARK).\\', 57),\\\\n\",\\n       \" (u\\'page](http://spark.apache.org/documentation.html)\\', 49),\\\\n\",\\n       \" (u\\'sc.parallelize(range(1000)).count()\\', 35),\\\\n\",\\n       \" (u\\'Maven](http://maven.apache.org/).\\', 33),\\\\n\",\\n       \" (u\\'<http://spark.apache.org/>\\', 26),\\\\n\",\\n       \" (u\\'MASTER=spark://host:7077\\', 24),\\\\n\",\\n       \" (u\\'Spark](#building-spark).\\', 24),\\\\n\",\\n       \" (u\\'`./bin/run-example\\', 18),\\\\n\",\\n       \" (u\\'./bin/spark-shell\\', 17),\\\\n\",\\n       \" (u\\'./bin/run-example\\', 17),\\\\n\",\\n       \" (u\\'./bin/run-example\\', 17),\\\\n\",\\n       \" (u\\'sc.parallelize(1\\', 16),\\\\n\",\\n       \" (u\\'Hadoop-supported\\', 16)]\"\\n      ]\\n     },\\n     \"execution_count\": 27,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (x,len(x)))\\\\n\",\\n    \"sorted_list = words.sortBy(lambda x: x[1],ascending=False)\\\\n\",\\n    \"sorted_list.collect()[:20]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 3 \\\\n\",\\n    \"\\\\n\",\\n    \"Create a pair RDD with (length of a word, list of words) from README.md\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 34,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"(96, [u\\'Version\\\\\"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version)\\'])\\\\n\",\\n      \"----------------\\\\n\",\\n      \"(112, [u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\'])\\\\n\",\\n      \"----------------\\\\n\",\\n      \"(2, [u\\'is\\', u\\'It\\', u\\'in\\', u\\'R,\\', u\\'an\\', u\\'It\\', u\\'of\\', u\\'##\\', u\\'on\\', u\\'##\\', u\\'is\\', u\\'To\\', u\\'do\\', u\\'to\\', u\\'do\\', u\\'if\\', u\\'by\\', u\\'-T\\', u\\'in\\', u\\'is\\', u\\'at\\', u\\'an\\', u\\'##\\', u\\'to\\', u\\'is\\', u\\'to\\', u\\'##\\', u\\'if\\', u\\'##\\', u\\'in\\', u\\'To\\', u\\'of\\', u\\'Pi\\', u\\'to\\', u\\'to\\', u\\'be\\', u\\'or\\', u\\'to\\', u\\'on\\', u\\'to\\', u\\'or\\', u\\'to\\', u\\'an\\', u\\'if\\', u\\'is\\', u\\'in\\', u\\'of\\', u\\'if\\', u\\'no\\', u\\'##\\', u\\'is\\', u\\'be\\', u\\'on\\', u\\'to\\', u\\'or\\', u\\'##\\', u\\'to\\', u\\'to\\', u\\'in\\', u\\'of\\', u\\'to\\', u\\'at\\', u\\'on\\', u\\'of\\', u\\'##\\', u\\'to\\', u\\'in\\', u\\'an\\', u\\'on\\', u\\'to\\'])\\\\n\",\\n      \"----------------\\\\n\",\\n      \"(4, [u\\'fast\\', u\\'APIs\\', u\\'that\\', u\\'data\\', u\\'also\\', u\\'rich\\', u\\'find\\', u\\'This\\', u\\'file\\', u\\'only\\', u\\'run:\\', u\\'(You\\', u\\'need\\', u\\'this\\', u\\'more\\', u\\'than\\', u\\'with\\', u\\'More\\', u\\'from\\', u\\'IDE,\\', u\\'also\\', u\\'also\\', u\\'with\\', u\\'will\\', u\\'when\\', u\\'This\\', u\\'URL,\\', u\\'with\\', u\\'with\\', u\\'also\\', u\\'name\\', u\\'Many\\', u\\'help\\', u\\'Once\\', u\\'[run\\', u\\'Note\\', u\\'uses\\', u\\'core\\', u\\'talk\\', u\\'HDFS\\', u\\'have\\', u\\'must\\', u\\'same\\', u\\'that\\', u\\'your\\', u\\'Hive\\', u\\'Hive\\'])\\\\n\",\\n      \"----------------\\\\n\",\\n      \"(6, [u\\'Apache\\', u\\'system\\', u\\'Scala,\\', u\\'engine\\', u\\'graphs\\', u\\'GraphX\\', u\\'stream\\', u\\'Online\\', u\\'latest\\', u\\'guide,\\', u\\'README\\', u\\'thread\\', u\\'option\\', u\\'Maven,\\', u\\'builds\\', u\\'shell:\\', u\\'should\\', u\\'return\\', u\\'scala>\\', u\\'Python\\', u\\'prefer\\', u\\'Python\\', u\\'shell:\\', u\\'should\\', u\\'return\\', u\\'sample\\', u\\'MASTER\\', u\\'submit\\', u\\'\\\\\"yarn\\\\\"\\', u\\'params\\', u\\'given.\\', u\\'built,\\', u\\'using:\\', u\\'Please\\', u\\'Hadoop\\', u\\'Hadoop\\', u\\'Please\\', u\\'Hadoop\\', u\\'Please\\', u\\'online\\', u\\'Spark.\\'])\\\\n\",\\n      \"----------------\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (len(x),x))\\\\n\",\\n    \"res = words.groupByKey().map(lambda x: (x[0],list(x[1])))\\\\n\",\\n    \"res.sortByKey()\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"for x in res.collect()[:5]:\\\\n\",\\n    \"    print x[:2]\\\\n\",\\n    \"    print \\'----------------\\'\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### mapValues\\\\n\",\\n    \"\\\\n\",\\n    \"Pass each value in the key value pair RDD through a map function wihtout changing the keys\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 4\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 35,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'when\\', <pyspark.resultiterable.ResultIterable at 0x1039c2910>),\\\\n\",\\n       \" (u\\'R,\\', <pyspark.resultiterable.ResultIterable at 0x10391ed10>),\\\\n\",\\n       \" (u\\'including\\', <pyspark.resultiterable.ResultIterable at 0x10391e690>)]\"\\n      ]\\n     },\\n     \"execution_count\": 35,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (x,1))\\\\n\",\\n    \"res = words.groupByKey()\\\\n\",\\n    \"res.collect()[:3]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 36,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'the\\', 22),\\\\n\",\\n       \" (u\\'Spark\\', 15),\\\\n\",\\n       \" (u\\'to\\', 14),\\\\n\",\\n       \" (u\\'for\\', 11),\\\\n\",\\n       \" (u\\'and\\', 11),\\\\n\",\\n       \" (u\\'##\\', 8),\\\\n\",\\n       \" (u\\'a\\', 8),\\\\n\",\\n       \" (u\\'run\\', 7),\\\\n\",\\n       \" (u\\'can\\', 7),\\\\n\",\\n       \" (u\\'is\\', 6)]\"\\n      ]\\n     },\\n     \"execution_count\": 36,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"res2 = res.mapValues(sum)\\\\n\",\\n    \"res2.sortBy(lambda x: x[1], ascending = False).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### flatMapValues\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Example 5 - similiar to before, but using flatMapValues\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 48,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, [u\\'#\\']),\\\\n\",\\n       \" (6, [u\\'Apache\\']),\\\\n\",\\n       \" (5, [u\\'Spark\\']),\\\\n\",\\n       \" (5, [u\\'Spark\\']),\\\\n\",\\n       \" (2, [u\\'is\\']),\\\\n\",\\n       \" (1, [u\\'a\\']),\\\\n\",\\n       \" (4, [u\\'fast\\']),\\\\n\",\\n       \" (3, [u\\'and\\']),\\\\n\",\\n       \" (7, [u\\'general\\']),\\\\n\",\\n       \" (7, [u\\'cluster\\'])]\"\\n      ]\\n     },\\n     \"execution_count\": 48,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (len(x),x))\\\\n\",\\n    \"words.mapValues(lambda x : list([x])).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### reduceByKey\\\\n\",\\n    \"\\\\n\",\\n    \"similar to reduce ()\\\\n\",\\n    \"reuns in parallel reduce operations\\\\n\",\\n    \"\\\\n\",\\n    \"#### Example 6 - do the word counts - generate word / occurance again\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 49,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'the\\', 22),\\\\n\",\\n       \" (u\\'Spark\\', 15),\\\\n\",\\n       \" (u\\'to\\', 14),\\\\n\",\\n       \" (u\\'for\\', 11),\\\\n\",\\n       \" (u\\'and\\', 11),\\\\n\",\\n       \" (u\\'##\\', 8),\\\\n\",\\n       \" (u\\'a\\', 8),\\\\n\",\\n       \" (u\\'run\\', 7),\\\\n\",\\n       \" (u\\'can\\', 7),\\\\n\",\\n       \" (u\\'is\\', 6)]\"\\n      ]\\n     },\\n     \"execution_count\": 49,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = rdm.flatMap(lambda x: x.split())\\\\n\",\\n    \"words = words.map(lambda x: (x,1))\\\\n\",\\n    \"res = words.reduceByKey(lambda x,y: x+y).sortBy(lambda x: x[1], ascending=False)\\\\n\",\\n    \"res.collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Which requires less shuffles?\\\\n\",\\n    \"\\\\n\",\\n    \"    .groupByKey().mapValues(lambda x : sum(x))\\\\n\",\\n    \"    - Sends all the values over first, then condenses 2nd with the mapValues\\\\n\",\\n    \"\\\\n\",\\n    \"    .reduceByKey(lambda x,y: x+y)\\\\n\",\\n    \"    - Does partition accumulation first, then send pair to be combined\\\\n\",\\n    \"    - FEWER SHUFFLES\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 1\\n}\\n'),\n",
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       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"# Lecture Notes\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Slides 1-3 : About Diane\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Contents \\\\n\",\\n    \"\\\\n\",\\n    \"1. Class Overview\\\\n\",\\n    \"2. Motivation \\\\n\",\\n    \"3. What is Distributed Computing?\\\\n\",\\n    \"4. Spark\\\\n\",\\n    \"5. RDD Creation\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Part 1: Class Overview\\\\n\",\\n    \"#### Slides 6-11 : about the course \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### 1.1 Spark Interview Questions \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"1. What is Apache Spark?\\\\n\",\\n    \"2. Explain the key features of Spark. What is RDD?\\\\n\",\\n    \"3. How to create RDD.\\\\n\",\\n    \"4. What is \\u201dpartitions\\u201d?\\\\n\",\\n    \"5. Types of RDD operations?\\\\n\",\\n    \"6. What is \\u201ctransformation\\u201d?\\\\n\",\\n    \"7. What is \\u201caction\\u201d?\\\\n\",\\n    \"8. Functions of \\u201cspark core\\u201d?\\\\n\",\\n    \"9. What is \\u201cspark context\\u201d?\\\\n\",\\n    \"10. What is an \\u201cRDD lineage\\u201d?\\\\n\",\\n    \"11. Which file systems does Spark support?\\\\n\",\\n    \"12. List the various types of \\u201cCluster Managers\\u201d in Spark.\\\\n\",\\n    \"13. What is \\u201cYARN\\u201d?\\\\n\",\\n    \"14. What is \\u201cMesos\\u201d?\\\\n\",\\n    \"15. What is a \\u201cworker node\\u201d? \\\\n\",\\n    \"16. What is an \\u201caccumulator\\u201d? \\\\n\",\\n    \"17. What is \\u201cSpark SQL\\u201d (Shark)? What is \\u201cSparkStreaming\\u201d? \\\\n\",\\n    \"18. What is \\u201cGraphX\\u201d?\\\\n\",\\n    \"19. What is \\u201cMLlib\\u201d?\\\\n\",\\n    \"20. What are the advantages of using Apache Spark over Hadoop MapReduce for big data processing?\\\\n\",\\n    \"21. What are the languages supported by Apache Spark for developing big data applications?\\\\n\",\\n    \"22. Can you use Spark to access and analyze data stored in Cassandra databases?\\\\n\",\\n    \"23. Is it possible to run Apache Spark on Apache Mesos?\\\\n\",\\n    \"24. How can you minimize data transfers when working with Spark?\\\\n\",\\n    \"25. Why is there a need for broadcast variables?\\\\n\",\\n    \"26. Name a few companies that use Apache Spark in production.\\\\n\",\\n    \"27. What are the various data sources available in SparkSQL?\\\\n\",\\n    \"28. What is the advantage of a Parquet file?\\\\n\",\\n    \"29. What do you understand by Pair RDD?\\\\n\",\\n    \"30. Is Apache Spark a good fit for Reinforcement learning?\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Part 2: Motivation: Why Distributed Computing? (Slide 14)\\\\n\",\\n    \"- How to make a line move faster\\\\n\",\\n    \"- Counting or Ordering Cards\\\\n\",\\n    \"- Searching for the max numbers in multiple sets of numbers\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Part 3: What is Distributed Computing? (Slide 19)\\\\n\",\\n    \"\\\\n\",\\n    \"** cluster ** : collection of systems that work together to perform functions\\\\n\",\\n    \"\\\\n\",\\n    \"** node ** : individual servers within a cluster\\\\n\",\\n    \"\\\\n\",\\n    \"** scale up ** : getting a faster performing complex machine\\\\n\",\\n    \"\\\\n\",\\n    \"** scale out **: getting more simple machines to work in parallel \\\\n\",\\n    \"\\\\n\",\\n    \"** why out instead of up **?\\\\n\",\\n    \"1. ** Cheaper**: Easier to collect many cheaper machines instead of a super high performing machine\\\\n\",\\n    \"2. ** Reliable**: If a node fails, another node can assume the workload or the responsibility of the faulty node\\\\n\",\\n    \"2. ** Faster** : with current tech, a single machine can only run fast, in contrast, an infinite number of parallel computers can be added via networking\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## 3.1 Map Reduce\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"**Big Idea** - the data will be split up over multiple nodes (computers), and tasks will be **mapped** and run on each data subsets. Afterwards, the data will be shffled and collected and **reduced**.\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"** map **: apply a function to each key-value pair over a subset of the data. This happens in parallel on different nodes, simultaneously. *Example: filtering for even numbers in each node\\'s data subset*\\\\n\",\\n    \"\\\\n\",\\n    \"** reduce **: return only 1 key-value pair from multiple pairs of data. This is usually done with an aggregate function such as sum, count, etc.\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Walmart Order\\\\n\",\\n    \"OrderID: 1\\\\n\",\\n    \"Customer : Diane\\\\n\",\\n    \"Timestamp : 2017-08-15 05:04:32 PST\\\\n\",\\n    \"Items:\\\\n\",\\n    \"\\\\n\",\\n    \"#### Step 1: Orders distributed over multiple nodes\\\\n\",\\n    \"\\\\n\",\\n    \"|Order No| Node | item number | data |\\\\n\",\\n    \"|--------|--|---|-------------|\\\\n\",\\n    \"|101|A|1 |{ProductName : San Francisco Giants Hat M, {Qty: 1, UnitPrice : 10, Price : 10}}|\\\\n\",\\n    \"|102|B|1 |{ProductName : San Francisco Giants Hat M, {Qty: 2, UnitPrice : 10, Price : 20}}|\\\\n\",\\n    \"|102|B|2|{ProductName : San Francisco Giants Hat S,{ Qty: 3, UnitPrice : 8, Price : 24}} Shipping: Corte Madera|\\\\n\",\\n    \"\\\\n\",\\n    \"#### Step 2: Map a function - pull out only the product and quantity information\\\\n\",\\n    \"\\\\n\",\\n    \"|Node| item number | data |\\\\n\",\\n    \"|-|---|-------------|\\\\n\",\\n    \"|A|1 |{San Francisco Giants Hat M, {Qty:1, Price : 10} |\\\\n\",\\n    \"|B|1 |{San Francisco Giants Hat M, {Qty:2, Price : 20} |\\\\n\",\\n    \"|B|2 |{San Francisco Giants Hat S, {Qty:3, Price : 24}|\\\\n\",\\n    \"\\\\n\",\\n    \"#### Step 3: Reduce the Key Value pairs (condense M hats)\\\\n\",\\n    \"\\\\n\",\\n    \"| item number | data |\\\\n\",\\n    \"|---|-------------|\\\\n\",\\n    \"|1 |{San Francisco Giants Hat M, {Qty:3, Price : 30} |\\\\n\",\\n    \"|2 |{San Francisco Giants Hat S, {Qty:3, Price : 24}|\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## 3.2 Hadoop Map Reduce\\\\n\",\\n    \"\\\\n\",\\n    \"**Description**: Open source, distributed, Java computation framework consisting of\\\\n\",\\n    \"- Hadoop common\\\\n\",\\n    \"- Hadoop Distributed File System (HDFS)\\\\n\",\\n    \"- YARN\\\\n\",\\n    \"- MapReduce\\\\n\",\\n    \"\\\\n\",\\n    \"Solved issues of:\\\\n\",\\n    \"- Distribution\\\\n\",\\n    \"- Parallelism\\\\n\",\\n    \"- Fault Tolerance\\\\n\",\\n    \"\\\\n\",\\n    \"Limitations:\\\\n\",\\n    \"- **Slow**: MapReduce Jobs need to be stored in disk before used by another job. Slow with iterative algorithms\\\\n\",\\n    \"- **MapReduce not always a good fit** many kinds of problems dont easily fit Map Reduce\\'s two step paradigm\\\\n\",\\n    \"- **Low-level framework**: other tools have been made to work with it, but leads to tool fragmentation and increased complexity to use\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"# 4. Spark (Slide 24)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"** A quick comparison**\\\\n\",\\n    \"\\\\n\",\\n    \"||HadoopMapReuce|Spark|\\\\n\",\\n    \"|-------|---------|-------------------|\\\\n\",\\n    \"|Speed|Decently fast|100 times faster than Hadoop|\\\\n\",\\n    \"|Ease of Use|No interactive modes and Hard to learn|Provides interactive modes and Easy to learn|\\\\n\",\\n    \"|$Costs|Open source|Open source|\\\\n\",\\n    \"|Data Processing|Batch Processing|Batch Processing + Streaming|\\\\n\",\\n    \"|Fault Tolerence|Fault Torelent |Fault Torelent|\\\\n\",\\n    \"|Security|Kerberos authentication|Password authentification|\\\\n\",\\n    \"\\\\n\",\\n    \"||HadoopMapReuce|Spark|\\\\n\",\\n    \"|-------|---------|-------------------|\\\\n\",\\n    \"|Operations|Will keep datasubsets on Disk|will keep data subsets in RAM|\\\\n\",\\n    \"\\\\n\",\\n    \"![](https://www.packtpub.com/sites/default/files/Article-Images/B05195_4.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### 4.1 Spark Benefits & Similiarities to Mapreduce\\\\n\",\\n    \"\\\\n\",\\n    \"- Write distributed programs in a similiar way to writing local programs (in python, R, java .. etc)\\\\n\",\\n    \"- Spark combines the following:\\\\n\",\\n    \"    - batch processing\\\\n\",\\n    \"    - real time data processing\\\\n\",\\n    \"    - SQL-like handling\\\\n\",\\n    \"    - graph algorithms\\\\n\",\\n    \"    - machine learning\\\\n\",\\n    \"    \\\\n\",\\n    \"** Main data object used in Spark: RDD, Resilient Distributed Datasets **\\\\n\",\\n    \"\\\\n\",\\n    \"| Aspect | Structure |\\\\n\",\\n    \"|------|------------|\\\\n\",\\n    \"|Speed | Runs in memory, uses DAG, and cyclical data flow |\\\\n\",\\n    \"|Ease of use | Offers 80+ high level operators (treated like libraries, that are developer friendly). Also has shells for Scala, Python and R.\\\\n\",\\n    \"| Code lines | Spark code often only requires 1/3 # of lines\\\\n\",\\n    \"| Generality| <ul><li>Spark SQL</li><li>Spark Streaming</li><li> MLib (machine learning)</li> <li>GraphX</li></ul>\\\\n\",\\n    \"| Runs On| <ul><li>Standalone</li><li>YARN</li><li>Mesos</li></ul>|\\\\n\",\\n    \"| Can Access| <ul><li>HDFS</li><li>Cassandra</li><li>HBASE</li><li>HIVE</li><li>Tachyon</li></ul>|\\\\n\",\\n    \"    \\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### 4.2 Spark Stack (components)\\\\n\",\\n    \"\\\\n\",\\n    \"![](https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/assets/lnsp_0101.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Note on common data objects in spark: DataFrames vs. RDD\\\\n\",\\n    \"So, a `DataFrame` has additional metadata due to its tabular format, which allows Spark to run certain optimizations on the finalized query. ** Added in Spark 2.0 **\\\\n\",\\n    \"\\\\n\",\\n    \"An `RDD`, on the other hand, is merely a Resilient Distributed Dataset that is more of a blackbox of data that cannot be optimized as the operations that can be performed against it, are not as constrained.  ** Since Spark 1.0 **\\\\n\",\\n    \"\\\\n\",\\n    \"#### Spark Core:\\\\n\",\\n    \"\\\\n\",\\n    \"1. **Data abstraction & logic:** Connects RDDs to underlying distributed file system, such as S3, HDFS, GlusterFS\\\\n\",\\n    \"2. **Fundamental Functions : ** Networking, security, job scheduling, data shuffling\\\\n\",\\n    \"\\\\n\",\\n    \"#### Spark SQL:\\\\n\",\\n    \"\\\\n\",\\n    \"1. **Provides Data Manipulation** for structured datasets\\\\n\",\\n    \"2. **Operates on DataFrames + Datasets** - transforms these operations into operations on RDDs ( see spark core)\\\\n\",\\n    \"3. **Database compatibility : ** Hive, JSON stores, relational databases, NoSQL, and Parquet ( for additional datasets)\\\\n\",\\n    \"\\\\n\",\\n    \"#### Spark Streaming:\\\\n\",\\n    \"\\\\n\",\\n    \"1. **Ingest real-time data compatibility**: \\\\n\",\\n    \"    - HDFS\\\\n\",\\n    \"    - Kafka\\\\n\",\\n    \"    - Flume\\\\n\",\\n    \"    - Twitter\\\\n\",\\n    \"    - ZeroMQ\\\\n\",\\n    \"    - many more...\\\\n\",\\n    \"2. ** Automatic recovery **\\\\n\",\\n    \"3. ** Provides DStreams = periodic RDDs per timing window **\\\\n\",\\n    \"4. ** Compatible with other Spark components **\\\\n\",\\n    \"    - Core\\\\n\",\\n    \"    - ML\\\\n\",\\n    \"    - Mllib\\\\n\",\\n    \"    - GraphX\\\\n\",\\n    \"    - SQL\\\\n\",\\n    \"    \\\\n\",\\n    \"#### Spark MLlib + ML\\\\n\",\\n    \"\\\\n\",\\n    \"1. **MLlib** : RDD-based APIs (spark core)\\\\n\",\\n    \"2. **Spark ML** : DataFrame-based APIs (spark SQL)\\\\n\",\\n    \"3. Library of machine learning algorithms designed to run on a distributed dataset.\\\\n\",\\n    \"    - logistic regression\\\\n\",\\n    \"    - naive bayes\\\\n\",\\n    \"    - support vector machines\\\\n\",\\n    \"    - decision trees\\\\n\",\\n    \"    - random forests\\\\n\",\\n    \"    - linear regression\\\\n\",\\n    \"    - k-mean clustering\\\\n\",\\n    \"\\\\n\",\\n    \"#### Spark GraphX\\\\n\",\\n    \"\\\\n\",\\n    \"1. ** Graph functions: ** such as EdgeRDD and VertexRDD\\\\n\",\\n    \"2. ** Graph Algos: ** including\\\\n\",\\n    \"    - page rank\\\\n\",\\n    \"    - connected components\\\\n\",\\n    \"    - shortest paths\\\\n\",\\n    \"    - SVD++\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### 4.3 Spark Example Operations\\\\n\",\\n    \"\\\\n\",\\n    \"- Extract-transformation-load (ETL) operations\\\\n\",\\n    \"- Predictive analytics\\\\n\",\\n    \"- Machine learning\\\\n\",\\n    \"- Data access operation (SQL queries and visualizations) Text mining and text processing\\\\n\",\\n    \"- Real-time event processing Graph applications\\\\n\",\\n    \"- Pattern Recognition Recommendation engines\\\\n\",\\n    \"- And many more..\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### 4.4 Spark installation - slide 43-48\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 1\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/MSAN694-Lec4-Notes.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Lecture Notes MSAN694\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://10.1.45.27:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 1,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"source\": [\\n    \"## Quiz 3: Covers Material from Week 2-3-4\\\\n\",\\n    \"\\\\n\",\\n    \"Data - create the basic RDD and common python. Harder than last time, because more materials have been covered. Practice with the examples provided. \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## From Week 3:\\\\n\",\\n    \"\\\\n\",\\n    \"### CombineByKey\\\\n\",\\n    \"\\\\n\",\\n    \"Similiar to aggregate(), but also includes the kkey and the values.\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"**createCombiner** - initial value\\\\n\",\\n    \"\\\\n\",\\n    \"**mergeValue** - if not initial, then aggregator Partition\\\\n\",\\n    \"\\\\n\",\\n    \"**mergeCombiner** - this is adding across partitions\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"\\'I love tacos\\'\\\\n\",\\n    \"\\'I love coffee\\'\\\\n\",\\n    \"\\'I love coffee\\'\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"**Target Output** \\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"(len, (occurence, word))\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"** Step by Step **\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"\\\\n\",\\n    \".textFile(\\' \\')\\\\n\",\\n    \".flatMap(lambda x ; x.split())\\\\n\",\\n    \".map(lambda x: (len(x), x)\\\\n\",\\n    \"\\\\n\",\\n    \"output -> partition 1: (1,I) (4,love) (5,tacos), (1,I) (4,love) (6,coffee)\\\\n\",\\n    \"output -> partition 2: (1,I) (4, love) (6, coffee)\\\\n\",\\n    \"\\\\n\",\\n    \"# SAMPLE FUNCTIONS (WHATS HAPPENING IN THE BACKGROUND)\\\\n\",\\n    \"# will nest the tuple in another key\\\\n\",\\n    \"createCombiner: lambda x: (1,x)\\\\n\",\\n    \"mergevalue : lambda x,y : x[0] + 1, x[1] +\\', \\'+ y\\\\n\",\\n    \"\\\\n\",\\n    \"(1,I) (4,love) (5,tacos), (1,I) (4,love) (6,coffee)\\\\n\",\\n    \"\\\\n\",\\n    \"```\\\\n\",\\n    \"#### Partition 1:\\\\n\",\\n    \"```python\\\\n\",\\n    \"(5, (1, tacos))\\\\n\",\\n    \"(1, (2, I, I)\\\\n\",\\n    \"(4, (1+1, love, love))\\\\n\",\\n    \"(6, (1, coffee))\\\\n\",\\n    \"```\\\\n\",\\n    \"#### Partition 2:\\\\n\",\\n    \"```python\\\\n\",\\n    \"(1, (1, I))\\\\n\",\\n    \"(4, (1, love))\\\\n\",\\n    \"(6, (1, coffee))\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### How do you combine between partitions? \\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"mergeCombiner: lambda x,y : x[0] + y[0], x[1] + \\\\\",\\\\\" + y[1]\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### All together\\\\n\",\\n    \"\\\\n\",\\n    \"Before the data comes in, its already in (length, word) format\\\\n\",\\n    \"```python\\\\n\",\\n    \"(2, hi)\\\\n\",\\n    \"(4, love)\\\\n\",\\n    \"...\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"Then run this function\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"comp = words.combineByKey(\\\\n\",\\n    \"                    (lambda x : (1,x)), \\\\n\",\\n    \"                   (lambda x,y : (x[0] +1 , x[1] +\\', \\'+ y)), \\\\n\",\\n    \"                   (lambda x,y : (x[0] + y[0], x[1] +\\', \\'+y[1])))\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"##### First it will nest the base value\\\\n\",\\n    \"```python\\\\n\",\\n    \"(key, value)\\\\n\",\\n    \"lambda x : (1,x)\\\\n\",\\n    \"(key, (occurance(default=1), growing list of words))\\\\n\",\\n    \"\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"(2 (1, hi))\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"##### Let\\'s add (2,to), it has the same key so it will combine:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"(2 (1, hi))\\\\n\",\\n    \"lambda x,y : (x[0] +1 , x[1] +\\', \\'+ y)\\\\n\",\\n    \"(2 (2, hi, to))\\\\n\",\\n    \"```\\\\n\",\\n    \"##### Let\\'s add (3,bye), it has the same key so it will combine:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"(2 (2, hi, to))\\\\n\",\\n    \"(3 (1, bye))\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"##### And so on\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week 3: Example 7\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 6,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"I love tacos\\\\n\",\\n       \"I love coffee\\\\n\",\\n       \"I love coffee\\\\n\",\\n       \" MapPartitionsRDD[5] at textFile at NativeMethodAccessorImpl.java:0\"\\n      ]\\n     },\\n     \"execution_count\": 6,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(text)\\\\n\",\\n    \"lines\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 28,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"PATH = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/README.md\\'\\\\n\",\\n    \"lines = sc.textFile(PATH)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 45,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"words = lines.flatMap(lambda x : x. split(\\' \\'))\\\\n\",\\n    \"words = words.map(lambda x : (len(x),x))\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 46,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, u\\'#\\'),\\\\n\",\\n       \" (6, u\\'Apache\\'),\\\\n\",\\n       \" (5, u\\'Spark\\'),\\\\n\",\\n       \" (0, u\\'\\'),\\\\n\",\\n       \" (5, u\\'Spark\\'),\\\\n\",\\n       \" (2, u\\'is\\'),\\\\n\",\\n       \" (1, u\\'a\\'),\\\\n\",\\n       \" (4, u\\'fast\\'),\\\\n\",\\n       \" (3, u\\'and\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 46,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words.collect()[:9]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 48,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(0,\\\\n\",\\n       \"  (68,\\\\n\",\\n       \"   u\\', , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , \\')),\\\\n\",\\n       \" (112,\\\\n\",\\n       \"  (1,\\\\n\",\\n       \"   u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\')),\\\\n\",\\n       \" (2,\\\\n\",\\n       \"  (70,\\\\n\",\\n       \"   u\\'is, It, in, R,, an, It, of, ##, on, ##, is, To, do, to, do, if, by, -T, in, is, at, an, ##, to, is, to, ##, if, ##, in, To, of, Pi, to, to, be, or, to, on, to, or, to, an, if, is, in, of, if, no, ##, is, be, on, to, or, ##, to, to, in, of, to, at, on, of, ##, to, in, an, on, to\\')),\\\\n\",\\n       \" (4,\\\\n\",\\n       \"  (47,\\\\n\",\\n       \"   u\\'fast, APIs, that, data, also, rich, find, This, file, only, run:, (You, need, this, more, than, with, More, from, IDE,, also, also, with, will, when, This, URL,, with, with, also, name, Many, help, Once, [run, Note, uses, core, talk, HDFS, have, must, same, that, your, Hive, Hive\\')),\\\\n\",\\n       \" (6,\\\\n\",\\n       \"  (41,\\\\n\",\\n       \"   u\\'Apache, system, Scala,, engine, graphs, GraphX, stream, Online, latest, guide,, README, thread, option, Maven,, builds, shell:, should, return, scala>, Python, prefer, Python, shell:, should, return, sample, MASTER, submit, \\\\\"yarn\\\\\", params, given., built,, using:, Please, Hadoop, Hadoop, Please, Hadoop, Please, online, Spark.\\')),\\\\n\",\\n       \" (8,\\\\n\",\\n       \"  (33,\\\\n\",\\n       \"   u\\'provides, supports, supports, [project, [project, contains, Building, detailed, command,, command,, Programs, programs, example:, locally., variable, examples, examples, cluster., mesos://, spark://, threads., package., programs, requires, guidance, Versions, systems., versions, detailed, guidance, building, building, overview\\'))]\"\\n      ]\\n     },\\n     \"execution_count\": 48,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"comp = words.combineByKey(\\\\n\",\\n    \"                    (lambda x : (1,x)), \\\\n\",\\n    \"                   (lambda x,y : (x[0] +1 , x[1] +\\', \\'+ y)), \\\\n\",\\n    \"                   (lambda x,y : (x[0] + y[0], x[1] +\\', \\'+y[1])))\\\\n\",\\n    \"\\\\n\",\\n    \"comp.collect()[:6]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Question 8:\\\\n\",\\n    \"\\\\n\",\\n    \"Which operations don\\'t  shuffle data?\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"    - mapValues() <- O WITHIN the partition applies\\\\n\",\\n    \"    - groupByKey() <-- X will shuffle ( up front)\\\\n\",\\n    \"    - reduceByKey() <-- X will shuffle (at the end) \\\\n\",\\n    \"    - combineBykey() <-- X will shuffle (at the end)\\\\n\",\\n    \"    - sortByKey() <-- X will shuffle\\\\n\",\\n    \"    - flatMapValues() <- O WITHIN the partition applies\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week4 : Pair RDD\\\\n\",\\n    \"\\\\n\",\\n    \"- Pair RDD\\\\n\",\\n    \"- Pair RDD Operations - Actions\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Actions \\\\n\",\\n    \"- subtractByKey\\\\n\",\\n    \"- join\\\\n\",\\n    \"- rightOuterJoin\\\\n\",\\n    \"- leftOuterJoin\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Subtract by Key\\\\n\",\\n    \"- Only compares the keys\\\\n\",\\n    \"\\\\n\",\\n    \"#### Example:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"A = [(2,3), (1,2), (1,3)]\\\\n\",\\n    \"B = [(1,2)]\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Note that it ignores the actual values in the pairs, only compares the keys\\\\n\",\\n    \"```python\\\\n\",\\n    \"A.subtract(B) = [(2,3), (1,3)]\\\\n\",\\n    \"A.subtractByKey(B) = [(2,3)]\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Week4 : Example 1\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"List business names wihtout a district supervisor\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 79,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"PATH1 = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/supervisor_sf.csv\\'\\\\n\",\\n    \"PATH2 = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/filtered_registered_business_sf.csv\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 80,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"lines1 = sc.textFile(PATH1)\\\\n\",\\n    \"lines2 = sc.textFile(PATH2)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 86,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94102\\', u\\'8\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'6\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'3\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'5\\'),\\\\n\",\\n       \" (u\\'94103\\', u\\'8\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 86,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"# split by word\\\\n\",\\n    \"zippairs = lines1.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"\\\\n\",\\n    \"# then turn into tuple \\\\n\",\\n    \"zippairs = zippairs.map(lambda x : (x[0],x[1]))\\\\n\",\\n    \"\\\\n\",\\n    \"# look at data\\\\n\",\\n    \"zippairs.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 87,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94123\\', u\\'Tournahu George L\\'),\\\\n\",\\n       \" (u\\'94124\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94105\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94108\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94107\\', u\\'Stephens Institute Inc\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 87,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"# split the 2nd file by commas\\\\n\",\\n    \"zipbus = lines2.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"\\\\n\",\\n    \"# split the 2nd file by commas\\\\n\",\\n    \"zipbus = zipbus.map(lambda x : (x[0], x[1]))\\\\n\",\\n    \"zipbus.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 88,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'Precision Communication Serv\\',\\\\n\",\\n       \" u\\'Schefer Thomas R\\',\\\\n\",\\n       \" u\\'Tyrrell Thomas\\',\\\\n\",\\n       \" u\\'Lucid Systems\\',\\\\n\",\\n       \" u\\'Jacob Abraham\\']\"\\n      ]\\n     },\\n     \"execution_count\": 88,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"res = zipbus.subtractByKey(zippairs).values().distinct()\\\\n\",\\n    \"res.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Week 4 : Join , rightOuterjoin, LeftOuterjoin\\\\n\",\\n    \"## Week 4 : cogroup(otherdataset)\\\\n\",\\n    \"\\\\n\",\\n    \"Group data from both RDDs sharing the same key. Go over two RDDs sharing the same key. Return the key and respective lists from two RDD values.\\\\n\",\\n    \"\\\\n\",\\n    \"##### Group by Illustration\\\\n\",\\n    \"```python\\\\n\",\\n    \".groupByKey()\\\\n\",\\n    \"\\\\n\",\\n    \"a = [(1,2),(1,3)] -- > group by key --> [1, <iterable>] = [1, [2,3]]\\\\n\",\\n    \"\\\\n\",\\n    \"b = [(2,2),(2,3)] --> group by key -- > [2, <iterable>] = [2, [2,3]]\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"##### Cogroup sample\\\\n\",\\n    \"```python\\\\n\",\\n    \".cogroup(1 other dataset)\\\\n\",\\n    \"\\\\n\",\\n    \"(1, (<iterable>,<iterable>)) = (1, ([2,3],[3]))\\\\n\",\\n    \"\\\\n\",\\n    \"(2, (<iterable>,<iterable>)) = (2, ([],[2,3])) \\\\n\",\\n    \"\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Week4 : Example 2\\\\n\",\\n    \"\\\\n\",\\n    \"Try out the join commands\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"first_num_pairs = sc.parallelize({(2,3), (1,2),(1,3),(2,4),(3,6)})\\\\n\",\\n    \"\\\\n\",\\n    \"second_num_pairs = sc.parallelize({(1,3),(2,2)})\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 2,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, 2), (1, 3), (2, 3), (2, 4), (3, 6)]\"\\n      ]\\n     },\\n     \"execution_count\": 2,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 3,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, 3), (2, 2)]\"\\n      ]\\n     },\\n     \"execution_count\": 3,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"second_num_pairs.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 4,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(2, 3), (3, 6), (1, 2), (2, 4)]\"\\n      ]\\n     },\\n     \"execution_count\": 4,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.subtract(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 5,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(3, 6)]\"\\n      ]\\n     },\\n     \"execution_count\": 5,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.subtractByKey(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 6,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, (2, 3)), (1, (3, 3)), (2, (3, 2)), (2, (4, 2))]\"\\n      ]\\n     },\\n     \"execution_count\": 6,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.join(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### RIGHT = keeping keys from the 2nd\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 7,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, (2, 3)), (1, (3, 3)), (2, (3, 2)), (2, (4, 2))]\"\\n      ]\\n     },\\n     \"execution_count\": 7,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.rightOuterJoin(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### LEFT = keeping the keys from the first\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 8,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1, (2, 3)), (1, (3, 3)), (2, (3, 2)), (2, (4, 2)), (3, (6, None))]\"\\n      ]\\n     },\\n     \"execution_count\": 8,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.leftOuterJoin(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 102,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(1,\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x117073790>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x115c6bc50>)),\\\\n\",\\n       \" (2,\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x115c6b850>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x115c6b890>)),\\\\n\",\\n       \" (3,\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x115c6b310>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x115c6b050>))]\"\\n      ]\\n     },\\n     \"execution_count\": 102,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"first_num_pairs.cogroup(second_num_pairs).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Week4 : Example 3\\\\n\",\\n    \"\\\\n\",\\n    \"Using Example 1 list (zip (business_name, supervisor_id) pairs ordered  y supervisor_id\\\\n\",\\n    \"- only if both business and supervisor exist\\\\n\",\\n    \"- if a business exists\\\\n\",\\n    \"- if a supervisor exists\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 103,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'94102,8\\', u\\'94102,6\\', u\\'94102,3\\', u\\'94102,5\\', u\\'94103,8\\']\"\\n      ]\\n     },\\n     \"execution_count\": 103,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines1.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 104,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'94123,Tournahu George L,3301 Broderick St,San Francisco,CA\\',\\\\n\",\\n       \" u\\'94124,Stephens Institute Inc,2225 Jerrold Ave,San Francisco,CA\\',\\\\n\",\\n       \" u\\'94105,Stephens Institute Inc,180 New Montgomery St,San Francisco,CA\\',\\\\n\",\\n       \" u\\'94108,Stephens Institute Inc,540 Powell St,San Francisco,CA\\',\\\\n\",\\n       \" u\\'94107,Stephens Institute Inc,460 Townsend St,San Francisco,CA\\']\"\\n      ]\\n     },\\n     \"execution_count\": 104,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines2.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 106,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94123\\', u\\'Tournahu George L\\'),\\\\n\",\\n       \" (u\\'94124\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94105\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94108\\', u\\'Stephens Institute Inc\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 106,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"zipbus = lines2.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"zipbus = zipbus.map(lambda x : (x[0],x[1]))\\\\n\",\\n    \"zipbus.take(4)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 110,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94102\\', u\\'8\\'), (u\\'94102\\', u\\'6\\'), (u\\'94102\\', u\\'3\\'), (u\\'94102\\', u\\'5\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 110,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"zips_super = lines1.map(lambda x : x.split(\\',\\'))\\\\n\",\\n    \"zips_super = zips_super.map(lambda x : (x[0],x[1]))\\\\n\",\\n    \"zips_super.take(4)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### If both business and supervisor exist (inner join)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 123,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": true\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94117\\', (u\\'Alouis Auto Radiator Inc\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Atlantic Richfield Co\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Breall William Etal\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Cole Hardware Inc\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Cole Hardware Inc\\', u\\'1\\'))]\"\\n      ]\\n     },\\n     \"execution_count\": 123,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"zip_comb = zipbus.join(zips_super).sortBy(lambda x : x[1][1])\\\\n\",\\n    \"zip_comb.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### If a business exists\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 126,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94117\\', (u\\'Alouis Auto Radiator Inc\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Atlantic Richfield Co\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Breall William Etal\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Cole Hardware Inc\\', u\\'1\\')),\\\\n\",\\n       \" (u\\'94117\\', (u\\'Cole Hardware Inc\\', u\\'1\\'))]\"\\n      ]\\n     },\\n     \"execution_count\": 126,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"zip_comb = zipbus.rightOuterJoin(zips_super).sortBy(lambda x : x[1][1])\\\\n\",\\n    \"zip_comb.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### If a supervisor exists\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 127,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'\\', (u\\'Hartmann Studios Incorporated\\', None)),\\\\n\",\\n       \" (u\\'\\', (u\\'Cardno Entrix\\', None)),\\\\n\",\\n       \" (u\\'\\', (u\\'Bond Blacktop Inc\\', None)),\\\\n\",\\n       \" (u\\'\\', (u\\'Moonka Nishi\\', None)),\\\\n\",\\n       \" (u\\'\\', (u\\'Cooper Jim B\\', None))]\"\\n      ]\\n     },\\n     \"execution_count\": 127,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"zip_comb = zipbus.leftOuterJoin(zips_super).sortBy(lambda x : x[1][1])\\\\n\",\\n    \"zip_comb.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Week4 : Example 4\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"Genearte Key(zip) and value pair RDDs from \\u201cfiltered_registered_business_sf.csv\\u201d and \\u201csupervisor_sf.csv\\u201d and cogroup() the RDDs.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 130,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94102\\', u\\'8\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'6\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'3\\'),\\\\n\",\\n       \" (u\\'94102\\', u\\'5\\'),\\\\n\",\\n       \" (u\\'94103\\', u\\'8\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 130,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"superzips = lines1.map(lambda x: x.split(\\',\\'))\\\\n\",\\n    \"superzips = superzips.map(lambda x : (x[0],x[1]))\\\\n\",\\n    \"superzips.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 131,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'94123\\', u\\'Tournahu George L\\'),\\\\n\",\\n       \" (u\\'94124\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94105\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94108\\', u\\'Stephens Institute Inc\\'),\\\\n\",\\n       \" (u\\'94107\\', u\\'Stephens Institute Inc\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 131,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"businesszips = lines2.map(lambda x: x.split(\\',\\'))\\\\n\",\\n    \"businesszips = businesszips.map(lambda x : (x[0],x[1]))\\\\n\",\\n    \"businesszips.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 134,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163a5ed0>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab0d0>)),\\\\n\",\\n       \" (u\\'85716\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163ab310>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab350>)),\\\\n\",\\n       \" (u\\'28036\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163ab390>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab3d0>)),\\\\n\",\\n       \" (u\\'97302\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163ab410>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab450>)),\\\\n\",\\n       \" (u\\'55344\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163ab510>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab550>)),\\\\n\",\\n       \" (u\\'94035\\',\\\\n\",\\n       \"  (<pyspark.resultiterable.ResultIterable at 0x1163ab590>,\\\\n\",\\n       \"   <pyspark.resultiterable.ResultIterable at 0x1163ab5d0>))]\"\\n      ]\\n     },\\n     \"execution_count\": 134,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"superzips.cogroup(businesszips).take(6)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 136,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"defaultdict(int,\\\\n\",\\n       \"            {u\\'94102\\': 4,\\\\n\",\\n       \"             u\\'94103\\': 6,\\\\n\",\\n       \"             u\\'94104\\': 2,\\\\n\",\\n       \"             u\\'94105\\': 2,\\\\n\",\\n       \"             u\\'94107\\': 2,\\\\n\",\\n       \"             u\\'94108\\': 2,\\\\n\",\\n       \"             u\\'94109\\': 4,\\\\n\",\\n       \"             u\\'94110\\': 4,\\\\n\",\\n       \"             u\\'94111\\': 2,\\\\n\",\\n       \"             u\\'94112\\': 5,\\\\n\",\\n       \"             u\\'94114\\': 3,\\\\n\",\\n       \"             u\\'94115\\': 3,\\\\n\",\\n       \"             u\\'94116\\': 2,\\\\n\",\\n       \"             u\\'94117\\': 4,\\\\n\",\\n       \"             u\\'94118\\': 3,\\\\n\",\\n       \"             u\\'94121\\': 2,\\\\n\",\\n       \"             u\\'94122\\': 4,\\\\n\",\\n       \"             u\\'94123\\': 1,\\\\n\",\\n       \"             u\\'94124\\': 2,\\\\n\",\\n       \"             u\\'94127\\': 3,\\\\n\",\\n       \"             u\\'94129\\': 1,\\\\n\",\\n       \"             u\\'94130\\': 1,\\\\n\",\\n       \"             u\\'94131\\': 3,\\\\n\",\\n       \"             u\\'94132\\': 3,\\\\n\",\\n       \"             u\\'94133\\': 2,\\\\n\",\\n       \"             u\\'94134\\': 3,\\\\n\",\\n       \"             u\\'94158\\': 2})\"\\n      ]\\n     },\\n     \"execution_count\": 136,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"superzips.countByKey()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 137,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'7\\', u\\'8\\', u\\'11\\', u\\'9\\', u\\'10\\']\"\\n      ]\\n     },\\n     \"execution_count\": 137,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"superzips.lookup(\\'94112\\')\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 1\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/quiz1_Lee_Tim.py',\n",
       "  u'from pyspark import SparkContext, SparkConf\\nfrom user_definition import *\\n#Do not add any other libraries\\n\\nconf = SparkConf().setMaster(\"local[*]\").setAppName(app_name)\\nsc = SparkContext(conf=conf)\\nlines = sc.textFile(input_file)\\nnums = lines.map(lambda x : float(x))\\nfor x in nums.collect():\\n\\tprint x\\nmean = nums.mean()\\nprint mean\\nstdev = nums.stdev()\\nprint stdev\\n\\n\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/user_definition.py',\n",
       "  u'app_name = \"quiz1\"\\ninput_file = \"input_2/input.txt\"\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/output.txt',\n",
       "  u'13.2\\n24.0\\n35.3\\n1.4\\n2.0\\n3.0\\n19.0\\n13.9857142857\\n11.970729608\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/DisCo Week 3.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"# DisCo Week 3\\\\n\",\\n    \"Office Hours  \\\\n\",\\n    \"\\u2022 Tuesday 12:00 - 1:00 PM (Room 522)  \\\\n\",\\n    \"Teaching Assistant  \\\\n\",\\n    \"\\u2022 Jinxin Ma (email - jma33@dons.usfca.edu )  \\\\n\",\\n    \"\\u2022 TA office hours : Thursday 4:00 - 5:00 PM (Room 515)  \\\\n\",\\n    \"Example Data  \\\\n\",\\n    \"\\u2022 https://github.com/dianewoodbridge/2017-msan694-example.git  \\\\n\",\\n    \"Poll   \\\\n\",\\n    \"\\u2022 https://pollev.com/dianewoodbri311 \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"from pyspark import SparkContext, SparkConf\\\\n\",\\n    \"conf = SparkConf().setMaster(\\\\\"local[*]\\\\\").setAppName(app_name)\\\\n\",\\n    \"sc = SparkContext(conf = conf)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 3,\\n   \"metadata\": {},\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://10.2.3.178:4041\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 3,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 2,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"terry_path= \\\\\"PycharmProjects/MSAN_692_mine/files_to_cloudify/terry_folds.txt\\\\\"\\\\n\",\\n    \"x_path = \\\\\"PycharmProjects/MSAN_692_mine/files_to_cloudify/x_gon.txt\\\\\"\\\\n\",\\n    \"\\\\n\",\\n    \"terry = sc.textFile(terry_path)\\\\n\",\\n    \"x_gon = sc.textFile(x_path)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Contents:\\\\n\",\\n    \"Pair RDDs  \\\\n\",\\n    \"Pair RDD Operations -Transformation\"\\n   ]\\n  },\\n  {\\n   \"attachments\": {\\n    \"image.png\": {\\n     \"image/png\": 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\"\\n    }\\n   },\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDD\\'s - Definition\\\\n\",\\n    \"-Key-value pairs - Commonly used for operations like aggregations, ETL (Extract, Transform, and load) in Spark  \\\\n\",\\n    \"-Allow operations on each key in parallel or regroup data across the network such as reduceByKey(), join(), etc.\\\\n\",\\n    \"![image.png](attachment:image.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDD\\'s - Creation   \\\\n\",\\n    \"\\\\n\",\\n    \"- Apply a map function with a lambda or user-defined function to have a pair of (Key, Value).  \\\\n\",\\n    \"-- Key: can be any thing simple (int, string) or complex (tuples, lists)  \\\\n\",\\n    \"-- Value: could be a simple objects, like the above, or more complex data structures (lists, dicts, sets)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 1\\\\n\",\\n    \"From the \\u201cREADME.md\\u201d file, or any other :  \\\\n\",\\n    \"\\u2022Extract all the words. (space separated)  \\\\n\",\\n    \"\\u2022Generate key-value pairs of (Word, 1).\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"terry.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDD\\'s - Operations\\\\n\",\\n    \"\\\\n\",\\n    \"Transformations on Pair RDD\\'s\\\\n\",\\n    \"\\\\n\",\\n    \"|Fucntion name | Purpose |  \\\\n\",\\n    \"| ---: | ---:|  \\\\n\",\\n    \"|keys() | Return RDD of just keys |  \\\\n\",\\n    \"|values() | Return RDD of just values |  \\\\n\",\\n    \"|sortByKey() | Return whole RDD sorted by the key |  \\\\n\",\\n    \"|groupByKey() | Group values with the same key |  \\\\n\",\\n    \"|mapValues(*func*) | Apply a function to each value of a pair RDD without changing the key |  \\\\n\",\\n    \"|flatMapValues(*func*) | Apply function that returns iterator to each value of pair RDD. for each element returned, produce kay/value entry with old key. Often used for \\\\\"tokenization\\\\\"|  \\\\n\",\\n    \"| reduceByKey(*func*) | Combine values with the same key useing *func* |  \\\\n\",\\n    \"|combineByKey(createComb, mergeVal, mergeComb) | Combine values with the same key using a different resukt type. |  \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 2  \\\\n\",\\n    \"From the \\u201cREADME.md\\u201d file,   \\\\n\",\\n    \"\\u2022Extract all the words. (space separated)  \\\\n\",\\n    \"\\u2022Generate key-value pairs of (Length of Word, Word).  \\\\n\",\\n    \"\\u2022Try the following :  \\\\n\",\\n    \"- \\u2022keys()  \\\\n\",\\n    \"- \\u2022values()  \\\\n\",\\n    \"- \\u2022sortByKey()  \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"attachments\": {\\n    \"image.png\": {\\n     \"image/png\": 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\"\\n    }\\n   },\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"source\": [\\n    \"### Example 2\\\\n\",\\n    \"Using Example 4, try the following.  \\\\n\",\\n    \"\\u2022keys()  \\\\n\",\\n    \"\\u2022values()  \\\\n\",\\n    \"\\u2022sortByKey()  \\\\n\",\\n    \"\\\\n\",\\n    \"![image.png](attachment:image.png)  \\\\n\",\\n    \"  \\\\n\",\\n    \"http://spark.apache.org/docs/latest/programming-guide.html  \\\\n\",\\n    \"  \\\\n\",\\n    \"https://spark.apache.org/docs/0.6.2/api/core/spark/PairRDDFunctions.html\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDDs - Operations \\\\n\",\\n    \"**Transformation on Pair RDDs**   \\\\n\",\\n    \"\\u2022groupByKey()  \\\\n\",\\n    \"- Group data using the key.  \\\\n\",\\n    \"- Return an RDD of (Key, ResultIterable) pairs.  \\\\n\",\\n    \"  \\\\n\",\\n    \"### Example 3  \\\\n\",\\n    \"Create a pair RDD with (length of a word, list of words) from \\u201cREADME.md\\u201d.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDDs -Operations  \\\\n\",\\n    \"**Transformation on Pair RDDs**  \\\\n\",\\n    \"\\u2022mapValues(func)  \\\\n\",\\n    \"\\u2022Pass each value in the key-value pair RDD through a map function without changing the keys.  \\\\n\",\\n    \"\\u2022Retain the original RDD\\u2019s partitioning.  \\\\n\",\\n    \"  \\\\n\",\\n    \"### Example 4\\\\n\",\\n    \"From the \\u201cREADME.md\\u201d file,  \\\\n\",\\n    \"\\u2022Extract all the words. (space separated)  \\\\n\",\\n    \"\\u2022Generate key-value pairs of (Word, Occurrence).  \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDDs -Operations  \\\\n\",\\n    \"**Transformation on Pair RDDs**  \\\\n\",\\n    \"\\u2022flatMapValues(func)  \\\\n\",\\n    \"\\u2022Pass each value in the key-value pair RDD through a flatMap function without changing the keys.  \\\\n\",\\n    \"\\u2022Retain the original RDD\\u2019s partitioning.  \\\\n\",\\n    \"  \\\\n\",\\n    \"### Example 5 \\\\n\",\\n    \"Create a list of (len, word) pairs from len_word_pair_groupin Example 3.  \\\\n\",\\n    \"`len_word_pair_group=sc.textFile(file)\\\\n\",\\n    \".flatMap(lambda x : x.split(\\u201d \\\\\"))\\\\n\",\\n    \".map(lambda x : (len(x),x))`\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDDs -Operations  \\\\n\",\\n    \"**Transformation on Pair RDDs**  \\\\n\",\\n    \"\\u2022reduceByKey(func)  \\\\n\",\\n    \"- Similar to reduce().  \\\\n\",\\n    \"- Run several parallel reduce operations, one for each key in the data set.  \\\\n\",\\n    \"- When called on a dataset of (Key, Val) pairs, returns a dataset of (Key, Val) pairs where the values for each key are aggregated using the given reduce function func.  \\\\n\",\\n    \"- **Transformation** (Not an action) : Returns a new RDD consisting of each key and the reduced value for that key.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example6   \\\\n\",\\n    \"From the \\u201cREADME.md\\u201d file,  \\\\n\",\\n    \"\\u2022Extract all the words. (space separated)  \\\\n\",\\n    \"\\u2022Generate key-value pairs of (Word, Occurrence)using reduceByKey().\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 4 vs Example 6  \\\\n\",\\n    \"1) word_rdd.groupByKey().mapValues(lambda x : sum(x))  \\\\n\",\\n    \"2) word_rdd.reduceByKey(lambda x,y : x+y) \\\\n\",\\n    \"  \\\\n\",\\n    \"To group values for the purpose of aggregation (such as sum or count for each key), using reduceByKey(), foldByKey() or combineByKey() will provide better performance.  \\\\n\",\\n    \"\\u2022They combines the aggregation function before the shuffle.  \\\\n\",\\n    \"-> Result in a reduced amount of data shuffled.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Pair RDDs -Operations  \\\\n\",\\n    \"**Transformation on Pair RDDs**   \\\\n\",\\n    \"\\u2022combineByKey(createCombiner, mergeValue, mergeCombiners)  \\\\n\",\\n    \"- Similar to aggregate(). \\\\n\",\\n    \"- createCombiner - If it is new in a partition, create the initial value for the accumulator on the key.  \\\\n\",\\n    \"- mergeValue - If it is not new, apply the mergeValue function.  \\\\n\",\\n    \"- mergeCombiners - When merging the results from each partition, apply the mergeCombiners to merge the accumulators for the same key.  \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 7  \\\\n\",\\n    \"Using combineByKey(), create pairs (Length of words, (Frequency, a list of words))from \\u201cREADME.md\\u201d\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 8  \\\\n\",\\n    \"Which operations don\\u2019t shuffle data?  \\\\n\",\\n    \"\\u2022mapValues()  \\\\n\",\\n    \"\\u2022groupByKey()  \\\\n\",\\n    \"\\u2022reduceByKey()  \\\\n\",\\n    \"\\u2022combineBykey()  \\\\n\",\\n    \"\\u2022sortByKey()  \\\\n\",\\n    \"\\u2022flatMapValues()  \\\\n\",\\n    \"\\u2022...\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"ReferencesDistributed Computing with Spark, Reza Zadeh,   http://stanford.edu/~rezab/slides/bayacm_spark.pdfSpark  \\\\n\",\\n    \"Online Documentation :  \\\\n\",\\n    \"http://spark.apache.org/docs/latest/   \\\\n\",\\n    \"Karau, Holden, et al. Learning spark: lightning-fast big data analysis.  O\\'Reilly Media, Inc., 2015.Zecevic, Petar, et al. Spark in Action, Manning, 2016.  \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 2\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/Quiz-Scratch.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://10.1.45.27:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 1,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 4,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[u\\'5\\', u\\'4\\'], [u\\'2\\', u\\'1\\'], []]\"\\n      ]\\n     },\\n     \"execution_count\": 4,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(\\\\\"input_1/input.txt\\\\\")\\\\n\",\\n    \"lines.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 5,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[5.0, 4.0, 2.0, 1.0]\"\\n      ]\\n     },\\n     \"execution_count\": 5,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"nums = lines.map(lambda x : float(x))\\\\n\",\\n    \"nums_only = nums.collect()\\\\n\",\\n    \"nums_only\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 6,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"3.0\"\\n      ]\\n     },\\n     \"execution_count\": 6,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"mean = nums.mean()\\\\n\",\\n    \"mean\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 7,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"1.5811388300841898\"\\n      ]\\n     },\\n     \"execution_count\": 7,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"stdev = nums.stdev()\\\\n\",\\n    \"stdev\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 8,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"def z_s(x):    \\\\n\",\\n    \"    return (x-mean)/stdev\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 9,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"0.63245553203367588\"\\n      ]\\n     },\\n     \"execution_count\": 9,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z_s(4)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 12,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"ename\": \"Py4JJavaError\",\\n     \"evalue\": \"An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.\\\\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 6.0 failed 1 times, most recent failure: Lost task 1.0 in stage 6.0 (TID 19, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 177, in main\\\\n    process()\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 172, in process\\\\n    serializer.dump_stream(func(split_index, iterator), outfile)\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/serializers.py\\\\\", line 268, in dump_stream\\\\n    vs = list(itertools.islice(iterator, batch))\\\\n  File \\\\\"<ipython-input-12-60953b2e6539>\\\\\", line 1, in <lambda>\\\\nNameError: global name \\'zs\\' is not defined\\\\n\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\\\\n\\\\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\\\\n\\\\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\\\\n\\\\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\\\\n\\\\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\\\\n\\\\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\\\\n\\\\tat java.lang.Thread.run(Thread.java:748)\\\\n\\\\nDriver stacktrace:\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\\\\n\\\\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\\\\n\\\\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\\\\n\\\\tat scala.Option.foreach(Option.scala:257)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\\\\n\\\\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)\\\\n\\\\tat org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)\\\\n\\\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\\\n\\\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\\\\n\\\\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\\\\n\\\\tat org.apache.spark.rdd.RDD.collect(RDD.scala:935)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:458)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)\\\\n\\\\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\\\n\\\\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\\\n\\\\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\\\n\\\\tat java.lang.reflect.Method.invoke(Method.java:498)\\\\n\\\\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\\\\n\\\\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\\\\n\\\\tat py4j.Gateway.invoke(Gateway.java:280)\\\\n\\\\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\\\\n\\\\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\\\\n\\\\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\\\\n\\\\tat java.lang.Thread.run(Thread.java:748)\\\\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 177, in main\\\\n    process()\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 172, in process\\\\n    serializer.dump_stream(func(split_index, iterator), outfile)\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/serializers.py\\\\\", line 268, in dump_stream\\\\n    vs = list(itertools.islice(iterator, batch))\\\\n  File \\\\\"<ipython-input-12-60953b2e6539>\\\\\", line 1, in <lambda>\\\\nNameError: global name \\'zs\\' is not defined\\\\n\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\\\\n\\\\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\\\\n\\\\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\\\\n\\\\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\\\\n\\\\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\\\\n\\\\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\\\\n\\\\t... 1 more\\\\n\",\\n     \"output_type\": \"error\",\\n     \"traceback\": [\\n      \"\\\\u001b[0;31m---------------------------------------------------------------------------\\\\u001b[0m\",\\n      \"\\\\u001b[0;31mPy4JJavaError\\\\u001b[0m                             Traceback (most recent call last)\",\\n      \"\\\\u001b[0;32m<ipython-input-12-60953b2e6539>\\\\u001b[0m in \\\\u001b[0;36m<module>\\\\u001b[0;34m()\\\\u001b[0m\\\\n\\\\u001b[0;32m----> 1\\\\u001b[0;31m \\\\u001b[0mnums\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mmap\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;32mlambda\\\\u001b[0m \\\\u001b[0mx\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m \\\\u001b[0mzs\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0mx\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mcollect\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0m\",\\n      \"\\\\u001b[0;32m/usr/local/Cellar/apache-spark/2.2.0/libexec/python/pyspark/rdd.pyc\\\\u001b[0m in \\\\u001b[0;36mcollect\\\\u001b[0;34m(self)\\\\u001b[0m\\\\n\\\\u001b[1;32m    807\\\\u001b[0m         \\\\\"\\\\\"\\\\\"\\\\n\\\\u001b[1;32m    808\\\\u001b[0m         \\\\u001b[0;32mwith\\\\u001b[0m \\\\u001b[0mSCCallSiteSync\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0mself\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mcontext\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m \\\\u001b[0;32mas\\\\u001b[0m \\\\u001b[0mcss\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0;32m--> 809\\\\u001b[0;31m             \\\\u001b[0mport\\\\u001b[0m \\\\u001b[0;34m=\\\\u001b[0m \\\\u001b[0mself\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mctx\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0m_jvm\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mPythonRDD\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mcollectAndServe\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0mself\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0m_jrdd\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mrdd\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0m\\\\u001b[1;32m    810\\\\u001b[0m         \\\\u001b[0;32mreturn\\\\u001b[0m \\\\u001b[0mlist\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0m_load_from_socket\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0mport\\\\u001b[0m\\\\u001b[0;34m,\\\\u001b[0m \\\\u001b[0mself\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0m_jrdd_deserializer\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m    811\\\\u001b[0m \\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\",\\n      \"\\\\u001b[0;32m/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py\\\\u001b[0m in \\\\u001b[0;36m__call__\\\\u001b[0;34m(self, *args)\\\\u001b[0m\\\\n\\\\u001b[1;32m   1131\\\\u001b[0m         \\\\u001b[0manswer\\\\u001b[0m \\\\u001b[0;34m=\\\\u001b[0m \\\\u001b[0mself\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mgateway_client\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0msend_command\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0mcommand\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m   1132\\\\u001b[0m         return_value = get_return_value(\\\\n\\\\u001b[0;32m-> 1133\\\\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\\\\n\\\\u001b[0m\\\\u001b[1;32m   1134\\\\u001b[0m \\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m   1135\\\\u001b[0m         \\\\u001b[0;32mfor\\\\u001b[0m \\\\u001b[0mtemp_arg\\\\u001b[0m \\\\u001b[0;32min\\\\u001b[0m \\\\u001b[0mtemp_args\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\",\\n      \"\\\\u001b[0;32m/usr/local/Cellar/apache-spark/2.2.0/libexec/python/pyspark/sql/utils.pyc\\\\u001b[0m in \\\\u001b[0;36mdeco\\\\u001b[0;34m(*a, **kw)\\\\u001b[0m\\\\n\\\\u001b[1;32m     61\\\\u001b[0m     \\\\u001b[0;32mdef\\\\u001b[0m \\\\u001b[0mdeco\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;34m*\\\\u001b[0m\\\\u001b[0ma\\\\u001b[0m\\\\u001b[0;34m,\\\\u001b[0m \\\\u001b[0;34m**\\\\u001b[0m\\\\u001b[0mkw\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m     62\\\\u001b[0m         \\\\u001b[0;32mtry\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0;32m---> 63\\\\u001b[0;31m             \\\\u001b[0;32mreturn\\\\u001b[0m \\\\u001b[0mf\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;34m*\\\\u001b[0m\\\\u001b[0ma\\\\u001b[0m\\\\u001b[0;34m,\\\\u001b[0m \\\\u001b[0;34m**\\\\u001b[0m\\\\u001b[0mkw\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0m\\\\u001b[1;32m     64\\\\u001b[0m         \\\\u001b[0;32mexcept\\\\u001b[0m \\\\u001b[0mpy4j\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mprotocol\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mPy4JJavaError\\\\u001b[0m \\\\u001b[0;32mas\\\\u001b[0m \\\\u001b[0me\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m     65\\\\u001b[0m             \\\\u001b[0ms\\\\u001b[0m \\\\u001b[0;34m=\\\\u001b[0m \\\\u001b[0me\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mjava_exception\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0mtoString\\\\u001b[0m\\\\u001b[0;34m(\\\\u001b[0m\\\\u001b[0;34m)\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\",\\n      \"\\\\u001b[0;32m/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py\\\\u001b[0m in \\\\u001b[0;36mget_return_value\\\\u001b[0;34m(answer, gateway_client, target_id, name)\\\\u001b[0m\\\\n\\\\u001b[1;32m    317\\\\u001b[0m                 raise Py4JJavaError(\\\\n\\\\u001b[1;32m    318\\\\u001b[0m                     \\\\u001b[0;34m\\\\\"An error occurred while calling {0}{1}{2}.\\\\\\\\n\\\\\"\\\\u001b[0m\\\\u001b[0;34m.\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[0;32m--> 319\\\\u001b[0;31m                     format(target_id, \\\\\".\\\\\", name), value)\\\\n\\\\u001b[0m\\\\u001b[1;32m    320\\\\u001b[0m             \\\\u001b[0;32melse\\\\u001b[0m\\\\u001b[0;34m:\\\\u001b[0m\\\\u001b[0;34m\\\\u001b[0m\\\\u001b[0m\\\\n\\\\u001b[1;32m    321\\\\u001b[0m                 raise Py4JError(\\\\n\",\\n      \"\\\\u001b[0;31mPy4JJavaError\\\\u001b[0m: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.\\\\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 6.0 failed 1 times, most recent failure: Lost task 1.0 in stage 6.0 (TID 19, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 177, in main\\\\n    process()\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 172, in process\\\\n    serializer.dump_stream(func(split_index, iterator), outfile)\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/serializers.py\\\\\", line 268, in dump_stream\\\\n    vs = list(itertools.islice(iterator, batch))\\\\n  File \\\\\"<ipython-input-12-60953b2e6539>\\\\\", line 1, in <lambda>\\\\nNameError: global name \\'zs\\' is not defined\\\\n\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\\\\n\\\\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\\\\n\\\\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\\\\n\\\\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\\\\n\\\\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\\\\n\\\\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\\\\n\\\\tat java.lang.Thread.run(Thread.java:748)\\\\n\\\\nDriver stacktrace:\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\\\\n\\\\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\\\\n\\\\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\\\\n\\\\tat scala.Option.foreach(Option.scala:257)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\\\\n\\\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\\\\n\\\\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\\\\n\\\\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)\\\\n\\\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)\\\\n\\\\tat org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)\\\\n\\\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\\\n\\\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\\\\n\\\\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\\\\n\\\\tat org.apache.spark.rdd.RDD.collect(RDD.scala:935)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:458)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)\\\\n\\\\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\\\n\\\\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\\\n\\\\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\\\n\\\\tat java.lang.reflect.Method.invoke(Method.java:498)\\\\n\\\\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\\\\n\\\\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\\\\n\\\\tat py4j.Gateway.invoke(Gateway.java:280)\\\\n\\\\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\\\\n\\\\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\\\\n\\\\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\\\\n\\\\tat java.lang.Thread.run(Thread.java:748)\\\\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 177, in main\\\\n    process()\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/worker.py\\\\\", line 172, in process\\\\n    serializer.dump_stream(func(split_index, iterator), outfile)\\\\n  File \\\\\"/usr/local/Cellar/apache-spark/2.2.0/libexec/python/lib/pyspark.zip/pyspark/serializers.py\\\\\", line 268, in dump_stream\\\\n    vs = list(itertools.islice(iterator, batch))\\\\n  File \\\\\"<ipython-input-12-60953b2e6539>\\\\\", line 1, in <lambda>\\\\nNameError: global name \\'zs\\' is not defined\\\\n\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\\\\n\\\\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\\\\n\\\\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\\\\n\\\\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\\\\n\\\\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\\\\n\\\\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\\\\n\\\\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\\\\n\\\\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\\\\n\\\\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\\\\n\\\\t... 1 more\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"nums.map(lambda x: zs(x)).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 11,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1.2649110640673518,\\\\n\",\\n       \" 0.63245553203367588,\\\\n\",\\n       \" -0.63245553203367588,\\\\n\",\\n       \" -1.2649110640673518]\"\\n      ]\\n     },\\n     \"execution_count\": 11,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"nums.map(lambda x: (x - mean)/stdev).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 73,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"def count_sum(nums):\\\\n\",\\n    \"    \\\\n\",\\n    \"    # making our own list to hold two things\\\\n\",\\n    \"    # first the count\\\\n\",\\n    \"    # the sum\\\\n\",\\n    \"    count_sum = [0,0]\\\\n\",\\n    \"    \\\\n\",\\n    \"    for num in nums:\\\\n\",\\n    \"        # will count the quantity of floats\\\\n\",\\n    \"        count_sum[0] += 1\\\\n\",\\n    \"        # will sum the actual values of the floats\\\\n\",\\n    \"        count_sum[1] += num\\\\n\",\\n    \"        \\\\n\",\\n    \"    # returning a nested list\\\\n\",\\n    \"    return [count_sum]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 75,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[2, 9.0], [2, 3.0], [0, 0]]\"\\n      ]\\n     },\\n     \"execution_count\": 75,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"nums.mapPartitions(count_sum).collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 77,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[5.0, 4.0, 2.0, 1.0]\"\\n      ]\\n     },\\n     \"execution_count\": 77,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"nums.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 84,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"res = nums.cartesian(nums)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"res.aggregateByKey((0,0)\"\\n   ]\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 0\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/DiscoLecture7.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### This week Spark Cluster , Spark standalone cluster\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"More multiple choice this time. \\\\n\",\\n    \"\\\\n\",\\n    \"#### Which is supporting the spark, which is supporting the yarn. How did you setup your cluster?\\\\n\",\\n    \"Need to setup slave notes, etc.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Running on a YARN CLuster\\\\n\",\\n    \"#### Running on a Mesos Cluster\\\\n\",\\n    \"#### Loading and Saving Data - JSV, TSV, CSV\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## YARN - Yet another resource negotiatior\\\\n\",\\n    \"\\\\n\",\\n    \"This is compared to spark standalone\\\\n\",\\n    \"\\\\n\",\\n    \"Its an execution system for hadoop. Most HDFS has YARN configured. \\\\n\",\\n    \"- You dont have to install spark on all nodes in the cluster. \\\\n\",\\n    \"- You can run types of java applications, not just spark (default was only spark)\\\\n\",\\n    \"- Can isolate and priority applications (management)\\\\n\",\\n    \"- its the only that supports kerberos-secured HDFS (IAM)\\\\n\",\\n    \"- Runs in a container - JVM - java virtual machine\\\\n\",\\n    \"- YARN supports Hadoop, Spark doesn\\'t\\\\n\",\\n    \"- Takes longer time, because you are creating containers JVM that take more time\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Analogy to the standalone spark items\\\\n\",\\n    \"\\\\n\",\\n    \"- Resource manager ( similar to the spakrs master process)\\\\n\",\\n    \"- Node Manager similar to worker process\\\\n\",\\n    \"- Application mastetr ( similar to cluster manager)\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"In Client mode \\\\n\",\\n    \"- starts the driver\\\\n\",\\n    \"- starts the driver\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"#### Driver / executor / Master / Cluster manager\\\\n\",\\n    \"\\\\n\",\\n    \"Slightly different terminology. Client will launch the driver. Driver will talk to other ocmputers to launch the executor. The executor will run and come back.\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Amazon EMR\\\\n\",\\n    \"\\\\n\",\\n    \"Yarn is supported on amazon EMR. EMR will automatically makes instances. \\\\n\",\\n    \"\\\\n\",\\n    \"#### Review of key pairs (pem) , set permissions\\\\n\",\\n    \"\\\\n\",\\n    \"#### Walkthrough of setting up the emr\\\\n\",\\n    \"- EMR\\\\n\",\\n    \"- create cluster\\\\n\",\\n    \"- Spark: Spark 2.2.0 on Hadoop 2.7.3 YARN with Ganglia 3.7.2 and Zeppelin 0.7.3\\\\n\",\\n    \"- Zeppelin - apache notebook\\\\n\",\\n    \"- Ganglia - a manager\\\\n\",\\n    \"- instance type (depending on the application)\\\\n\",\\n    \"- choose 2 instances ( 1 master, 1 node)\\\\n\",\\n    \"- set your key\\\\n\",\\n    \"\\\\n\",\\n    \"#### All done!\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"- let it start up and you should be able to see the SSH.\\\\n\",\\n    \"- Click Enable Web Connection (zeppelin notebook) and other management dashboards\\\\n\",\\n    \"- Have to setup this web connection everytime we start a cluster\\\\n\",\\n    \"- note that ND815 is for web applications not SSH\\\\n\",\\n    \"- double check that you are using the correct location of your pem file\\\\n\",\\n    \"\\\\n\",\\n    \"#### how to SSH\\\\n\",\\n    \"- make sure you add TCP port 22 to the security group of your AWS instance otherwise you wont be able to access your instance by SSH. This should be done on the master.\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Mesos\\\\n\",\\n    \"\\\\n\",\\n    \"- Spark was originally started to demonstrate mesoes.\\\\n\",\\n    \"- Supports C/C++, java, python\\\\n\",\\n    \"- run docker images\\\\n\",\\n    \"- Docker : software wrap that contains a complete file system.\\\\n\",\\n    \"- Used by : Apple Siri, ebay, netflix, twitter, uber\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Loading and Saving Data\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 3,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://10.1.156.130:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 3,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Structured and unstructured.\\\\n\",\\n    \"\\\\n\",\\n    \"- Text File\\\\n\",\\n    \"- JSON\\\\n\",\\n    \"- CSV / TSV\\\\n\",\\n    \"- Sequence File - Hadoop File (key / value)\\\\n\",\\n    \"- Object File - Spark Job (sharing data between different spark applications)\\\\n\",\\n    \"\\\\n\",\\n    \"### Text file methods\\\\n\",\\n    \"\\\\n\",\\n    \"- `.textFile(file_name)`\\\\n\",\\n    \"- `.wholeTextFiles(dir_name)`\\\\n\",\\n    \"- `.saveAsTextFile(new_subdir_name)`\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 1\\n}\\n'),\n",
       " (u'file:/Users/tlee010/Desktop/MSAN-pywork/MSAN_694_DistrComputing/MSAN694-Lec2-Notes.ipynb',\n",
       "  u'{\\n \"cells\": [\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"# MSAN 604 Sec2Lec2 Notes: RDDs, Transformations, Actions\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"Note: to run the code, please note where your dataset is and also that you have started the notebook at the command line using:\\\\n\",\\n    \"\\\\n\",\\n    \"```bash\\\\n\",\\n    \"$pyspark\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"which should open jupyter notebook instance\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 1,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/html\": [\\n       \"\\\\n\",\\n       \"        <div>\\\\n\",\\n       \"            <p><b>SparkContext</b></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <p><a href=\\\\\"http://10.1.45.27:4040\\\\\">Spark UI</a></p>\\\\n\",\\n       \"\\\\n\",\\n       \"            <dl>\\\\n\",\\n       \"              <dt>Version</dt>\\\\n\",\\n       \"                <dd><code>v2.2.0</code></dd>\\\\n\",\\n       \"              <dt>Master</dt>\\\\n\",\\n       \"                <dd><code>local[*]</code></dd>\\\\n\",\\n       \"              <dt>AppName</dt>\\\\n\",\\n       \"                <dd><code>PySparkShell</code></dd>\\\\n\",\\n       \"            </dl>\\\\n\",\\n       \"        </div>\\\\n\",\\n       \"        \"\\n      ],\\n      \"text/plain\": [\\n       \"<SparkContext master=local[*] appName=PySparkShell>\"\\n      ]\\n     },\\n     \"execution_count\": 1,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"sc\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## What is a RDD?\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"![alt text](http://backtobazics.com/wp-content/themes/twentyfourteen/images/spark/spark-rdd.png)\\\\n\",\\n    \"\\\\n\",\\n    \"RDD is a distributed file set split over partitions in a data cluster. This is represented in spark as a single dataset called RD.\\\\n\",\\n    \"\\\\n\",\\n    \"**Partition**: a \\\\\"subset\\\\\". In computer terms, you may have 1TB, but you can put some walls up to subdivide the drive into 2 drives of 500 GB, or 3 drives of 250, 250, 500 GB.\\\\n\",\\n    \"\\\\n\",\\n    \"** By default, max number of partitions is number of threads that you computer has **\\\\n\",\\n    \"** Example: if your computer has a quad core intel, each has 2 threads so 4 x2 = 8 available cores, 8 partitions**\\\\n\",\\n    \"\\\\n\",\\n    \"Some features:\\\\n\",\\n    \"\\\\n\",\\n    \"1. Distributed -> over different partitions\\\\n\",\\n    \"2. Immutable -> read only\\\\n\",\\n    \"3. Resilient -> if one node dies, cluster will be rebuilt. (Not replication). The data will rebuilt by the instructions supplied by the master note\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"# Data Manipulations - Transformations + Actions\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"![](https://i.stack.imgur.com/3QiV8.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### RDD - Transformations: change RDD\\'s and return RDDs\\\\n\",\\n    \"\\\\n\",\\n    \"### RDD - Actions: take RDD\\'s and return values\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"![](https://trongkhoanguyen.com/assets/post-images/2014/rdd-operations.png)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Get data \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 11,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/README.md\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Read in data from readme markdown file\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 12,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/README.md MapPartitionsRDD[8] at textFile at NativeMethodAccessorImpl.java:0\"\\n      ]\\n     },\\n     \"execution_count\": 12,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"lines\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Read in data parallel\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 13,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"ParallelCollectionRDD[9] at parallelize at PythonRDD.scala:480\"\\n      ]\\n     },\\n     \"execution_count\": 13,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.parallelize([\\\\\"spark\\\\\",\\\\\"spark is fun!\\\\\"])\\\\n\",\\n    \"lines\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 14,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"8\"\\n      ]\\n     },\\n     \"execution_count\": 14,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines.getNumPartitions()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 15,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"### Shows all the partitions\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Note - **`collect`** is a mapping function that is forcing a evaluation\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 16,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[], [], [], [\\'spark\\'], [], [], [], [\\'spark is fun!\\']]\"\\n      ]\\n     },\\n     \"execution_count\": 16,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 17,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"### Shows \"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 10,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[\\'spark\\', \\'spark is fun!\\']\"\\n      ]\\n     },\\n     \"execution_count\": 10,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Lets add more data\\\\n\",\\n    \"\\\\n\",\\n    \"- add an option for how many partitions it needs to be divided by\\\\n\",\\n    \"- sc.parallelize( data, no_of_partitions)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 23,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[\\'spark\\', \\'spark is fun!\\'], [\\'1\\', \\'2\\'], [\\'3\\', \\'4\\'], [\\'5\\', \\'6\\', \\'7\\', \\'8\\']]\"\\n      ]\\n     },\\n     \"execution_count\": 23,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"lines = sc.parallelize([\\\\\"spark\\\\\",\\\\\"spark is fun!\\\\\",\\\\\"1\\\\\",\\\\\"2\\\\\",\\\\\"3\\\\\",\\\\\"4\\\\\",\\\\\"5\\\\\",\\\\\"6\\\\\",\\\\\"7\\\\\",\\\\\"8\\\\\"],4)\\\\n\",\\n    \"lines.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Lambda functions\\\\n\",\\n    \"\\\\n\",\\n    \"These are one-time functions are executed at run-time. These are also considered anonymus functions. A lot of times you don\\'t have to name the functions.\\\\n\",\\n    \"\\\\n\",\\n    \"Example of a regular function\\\\n\",\\n    \"```python\\\\n\",\\n    \"def f(x):\\\\n\",\\n    \"    return x+2\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"Inline equivalent\\\\n\",\\n    \"```python\\\\n\",\\n    \"lambda x : x+2\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## RDD Operations\\\\n\",\\n    \"\\\\n\",\\n    \"1. Transformations\\\\n\",\\n    \"\\\\n\",\\n    \"**RDDs when changed:** Make a new RDD when manipulating an old RDD (this is because all RDDs are immutable). So any changes must mean a new data set has to be stored to a new dataset.\\\\n\",\\n    \"    \\\\n\",\\n    \"    \\\\n\",\\n    \"    ```python\\\\n\",\\n    \"    # only want lines that have the word \\'spark\\\\\" in it\\\\n\",\\n    \"    lines_with_spark = lines.filter(lambda lines : \\'spark\\' in lines)\\\\n\",\\n    \"    ```\\\\n\",\\n    \"    \\\\n\",\\n    \"**Lazy evaluation**: the above filtering function has been setup BUT HAS NOT BEEN EXECUTED. This will only be executed when the `lines_with_spark` is called in another function\\\\n\",\\n    \"\\\\n\",\\n    \"**Sample Transformation functions:** \\\\n\",\\n    \"\\\\n\",\\n    \"- map\\\\n\",\\n    \"- filter\\\\n\",\\n    \"- flatMAP\\\\n\",\\n    \"- mapPartitions\\\\n\",\\n    \"\\\\n\",\\n    \"** Example: Map (on Elements)**\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Typical methods that take a function and apply element wise\\\\n\",\\n    \"- map(func)\\\\n\",\\n    \"\\\\n\",\\n    \"- flatmap(func)\\\\n\",\\n    \"\\\\n\",\\n    \"- filter(func)\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Element wise transformation methods, even across partitions. \\\\n\",\\n    \"\\\\n\",\\n    \"**Example**, lets say you have a dataset [1,2,3,4] split over 2 partitions:\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 1 \\\\n\",\\n    \"    1, 2\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 2\\\\n\",\\n    \"    3, 4\\\\n\",\\n    \"\\\\n\",\\n    \"`map(f(x))` will do the following:\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 1 \\\\n\",\\n    \"    f(1), f(2)\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 2\\\\n\",\\n    \"    f(3), f(4)\\\\n\",\\n    \"    \\\\n\",\\n    \"**Example** let\\'s say you have a dataset:\\\\n\",\\n    \"```python\\\\n\",\\n    \"\\'I love tacos\\'\\\\n\",\\n    \"\\'I love coffee\\'\\\\n\",\\n    \"```\\\\n\",\\n    \"**map function under** `.map(lambda x : x.split())`\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"[[\\'I\\',\\'love\\',\\'taco\\']\\\\n\",\\n    \"  [\\'I\\',\\'love\\',\\'coffee\\']]\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"Note the nested list structure\\\\n\",\\n    \"\\\\n\",\\n    \"**flat map** under `.flatmap(lambda x : x.split())`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"[\\'I\\',\\'love\\',\\'taco\\',\\'I\\',\\'love\\',\\'coffee\\']\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"Note the flat list structure\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Inclass Tutorial\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 25,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Read in data from disk\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 30,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'= Ignatian Values =\\',\\\\n\",\\n       \" u\\'The University of San Francisco enjoys a distinguished heritage and Jesuit tradition.  At the core of this tradition are transcendent values, including the integration of learning, faith and service; care for the whole person; character and conviction; religious truth and interfaith understanding; and a commitment to building a more just world.  The key values of this Jesuit tradition are as follows:\\',\\\\n\",\\n       \" u\\'***********************************************************************************\\',\\\\n\",\\n       \" u\\\\\"1. Contemplative in Action - St. Ignatius Loyola believed that prayer and reflectivity should so guide our choices and actions that our activity itself becomes a way of entering into union with and praising God.  Being a contemplative in action also means seeing beyond the superficial in life to appreciate the mystery, beauty, and sacredness of all life.  It is a means of seeing God in all things and in everyone.  Contemplation is a critical dimension of the spiritual life and it is reflected in USF\\'s commitment to prayer and spiritual growth.  Analogously, in the academic life, a spirit of reflectivity is a critical aspect of intellectual inquiry.\\\\\"]\"\\n      ]\\n     },\\n     \"execution_count\": 30,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"# reads in a CSV flat text file\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"\\\\n\",\\n    \"# collects all the different terms\\\\n\",\\n    \"lines.collect()[:4]\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Split the words using the map function (should have nested lists)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 37,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[u\\'=\\', u\\'Ignatian\\', u\\'Values\\', u\\'=\\'],\\\\n\",\\n       \" [u\\'The\\',\\\\n\",\\n       \"  u\\'University\\',\\\\n\",\\n       \"  u\\'of\\',\\\\n\",\\n       \"  u\\'San\\',\\\\n\",\\n       \"  u\\'Francisco\\',\\\\n\",\\n       \"  u\\'enjoys\\',\\\\n\",\\n       \"  u\\'a\\',\\\\n\",\\n       \"  u\\'distinguished\\',\\\\n\",\\n       \"  u\\'heritage\\',\\\\n\",\\n       \"  u\\'and\\',\\\\n\",\\n       \"  u\\'Jesuit\\',\\\\n\",\\n       \"  u\\'tradition.\\',\\\\n\",\\n       \"  u\\'At\\',\\\\n\",\\n       \"  u\\'the\\',\\\\n\",\\n       \"  u\\'core\\',\\\\n\",\\n       \"  u\\'of\\',\\\\n\",\\n       \"  u\\'this\\',\\\\n\",\\n       \"  u\\'tradition\\',\\\\n\",\\n       \"  u\\'are\\',\\\\n\",\\n       \"  u\\'transcendent\\',\\\\n\",\\n       \"  u\\'values,\\',\\\\n\",\\n       \"  u\\'including\\',\\\\n\",\\n       \"  u\\'the\\',\\\\n\",\\n       \"  u\\'integration\\',\\\\n\",\\n       \"  u\\'of\\',\\\\n\",\\n       \"  u\\'learning,\\',\\\\n\",\\n       \"  u\\'faith\\',\\\\n\",\\n       \"  u\\'and\\',\\\\n\",\\n       \"  u\\'service;\\',\\\\n\",\\n       \"  u\\'care\\',\\\\n\",\\n       \"  u\\'for\\',\\\\n\",\\n       \"  u\\'the\\',\\\\n\",\\n       \"  u\\'whole\\',\\\\n\",\\n       \"  u\\'person;\\',\\\\n\",\\n       \"  u\\'character\\',\\\\n\",\\n       \"  u\\'and\\',\\\\n\",\\n       \"  u\\'conviction;\\',\\\\n\",\\n       \"  u\\'religious\\',\\\\n\",\\n       \"  u\\'truth\\',\\\\n\",\\n       \"  u\\'and\\',\\\\n\",\\n       \"  u\\'interfaith\\',\\\\n\",\\n       \"  u\\'understanding;\\',\\\\n\",\\n       \"  u\\'and\\',\\\\n\",\\n       \"  u\\'a\\',\\\\n\",\\n       \"  u\\'commitment\\',\\\\n\",\\n       \"  u\\'to\\',\\\\n\",\\n       \"  u\\'building\\',\\\\n\",\\n       \"  u\\'a\\',\\\\n\",\\n       \"  u\\'more\\',\\\\n\",\\n       \"  u\\'just\\',\\\\n\",\\n       \"  u\\'world.\\',\\\\n\",\\n       \"  u\\'The\\',\\\\n\",\\n       \"  u\\'key\\',\\\\n\",\\n       \"  u\\'values\\',\\\\n\",\\n       \"  u\\'of\\',\\\\n\",\\n       \"  u\\'this\\',\\\\n\",\\n       \"  u\\'Jesuit\\',\\\\n\",\\n       \"  u\\'tradition\\',\\\\n\",\\n       \"  u\\'are\\',\\\\n\",\\n       \"  u\\'as\\',\\\\n\",\\n       \"  u\\'follows:\\'],\\\\n\",\\n       \" [u\\'***********************************************************************************\\']]\"\\n      ]\\n     },\\n     \"execution_count\": 37,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = lines.map(lambda line: line.split())\\\\n\",\\n    \"\\\\n\",\\n    \"# each of the lines is essentially a paragraph\\\\n\",\\n    \"words.collect()[:3]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Inclass Problem - do the same above exercise, but do it using a flatMap command`\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 62,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'=\\',\\\\n\",\\n       \" u\\'Ignatian\\',\\\\n\",\\n       \" u\\'Values\\',\\\\n\",\\n       \" u\\'=\\',\\\\n\",\\n       \" u\\'The\\',\\\\n\",\\n       \" u\\'University\\',\\\\n\",\\n       \" u\\'of\\',\\\\n\",\\n       \" u\\'San\\',\\\\n\",\\n       \" u\\'Francisco\\',\\\\n\",\\n       \" u\\'enjoys\\']\"\\n      ]\\n     },\\n     \"execution_count\": 62,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = lines.flatMap(lambda line: line.split())\\\\n\",\\n    \"words.collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### part 2 : filter by \\'USF\\'\\\\n\",\\n    \"\\\\n\",\\n    \"#### using a list comprehension\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 61,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\\\\"USF\\'s\\\\\", u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\'USF\\', u\\'USF\\', u\\'USF\\']\"\\n      ]\\n     },\\n     \"execution_count\": 61,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = lines.flatMap(lambda line: [x for x in line.split() if \\'USF\\' in x])\\\\n\",\\n    \"words.collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 56,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\\\\"USF\\'s\\\\\", u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\'USF\\', u\\'USF\\', u\\'USF\\']\"\\n      ]\\n     },\\n     \"execution_count\": 56,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = lines.flatMap(lambda line: [x for x in line.split() if \\'USF\\' in x])\\\\n\",\\n    \"filtered_words = words.filter(lambda x: \\'USF\\' in x )\\\\n\",\\n    \"filtered_words.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Partition Based Operations\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"**Remember the Example**, lets say you have a dataset [1,2,3,4] split over 2 partitions:\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 1 \\\\n\",\\n    \"    1, 2\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 2\\\\n\",\\n    \"    3, 4\\\\n\",\\n    \"\\\\n\",\\n    \"`map(f(x))` will do the following:\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 1 \\\\n\",\\n    \"    f(1), f(2)\\\\n\",\\n    \"\\\\n\",\\n    \"    Partition 2\\\\n\",\\n    \"    f(3), f(4)\\\\n\",\\n    \"    \\\\n\",\\n    \"Creates a connection & function per element, very computationally expensive. **What about doing it at a partition level ( level above)?**\\\\n\",\\n    \"\\\\n\",\\n    \"This is done by creating a **iterator**. A iterator is a data structure that works like a cursor. It starts at the beginning of a list and only feeds the next element when the process is ready. Simplest python example is as follows:\\\\n\",\\n    \"\\\\n\",\\n    \"`[0,1,2,3,4,5,6]` vs. `xrange(6)`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"### Inclass example: parallelize numbers between 1 and 16. Calculate the count and sum in each partition.\\\\n\",\\n    \"\\\\n\",\\n    \"In 4 partitions:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"\\\\n\",\\n    \"[1,2] [3,4] [ 5,6], [7,8]\\\\n\",\\n    \"\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"\\\\n\",\\n    \"(2,3) (2,7)\\\\n\",\\n    \"\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"    \\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### Let\\'s make a function for map partioins()\\\\n\",\\n    \"\\\\n\",\\n    \"lets make a sample numbers dataset for 16 numbers\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 80,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]\"\\n      ]\\n     },\\n     \"execution_count\": 80,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"numbers = sc.parallelize(range(1,17))\\\\n\",\\n    \"numbers.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 81,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"def count_sum(nums):\\\\n\",\\n    \"    \\\\n\",\\n    \"    # making our own list to hold two things\\\\n\",\\n    \"    # first the count\\\\n\",\\n    \"    # the sum\\\\n\",\\n    \"    count_sum = [0,0]\\\\n\",\\n    \"    \\\\n\",\\n    \"    for num in nums:\\\\n\",\\n    \"        # will count the quantity of floats\\\\n\",\\n    \"        count_sum[0] += 1\\\\n\",\\n    \"        # will sum the actual values of the floats\\\\n\",\\n    \"        count_sum[1] += num\\\\n\",\\n    \"        \\\\n\",\\n    \"    # returning a nested list\\\\n\",\\n    \"    return [count_sum]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### the answer is for each partition\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 88,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[2, 3], [2, 7], [2, 11], [2, 15], [2, 19], [2, 23], [2, 27], [2, 31]]\"\\n      ]\\n     },\\n     \"execution_count\": 88,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"parse = numbers.mapPartitions(count_sum)\\\\n\",\\n    \"parse.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### how to get the total sum overall partitions\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 93,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[16, 136]\"\\n      ]\\n     },\\n     \"execution_count\": 93,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"total_count_sum = parse.reduce(lambda x,y: [x[0]+y[0],x[1]+y[1]])\\\\n\",\\n    \"total_count_sum\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Run Spark program in a Python script (instead of notebooks)\\\\n\",\\n    \"\\\\n\",\\n    \"Know this for the test. The submissions are going to be in .py\\\\n\",\\n    \"\\\\n\",\\n    \"## Within your python file\\\\n\",\\n    \"\\\\n\",\\n    \"#### import your libraries\\\\n\",\\n    \"```python\\\\n\",\\n    \"from pyspark import SparkConf, SparkContext\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Set the configuration files. \\\\n\",\\n    \"\\\\n\",\\n    \"- **Appname** = the name program or \\'job\\' when its sent to the cluster to be run.\\\\n\",\\n    \"- **local[\\\\\\\\*]** = the cluster or program that you are connecting to. Would be a URL if connecting to a system\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"conf = SparkConf().setMaster(\\\\\"local[*]\\\\\").setAppName(\\\\\"AppName\\\\\")\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### start your pyspark context (program envir)\\\\n\",\\n    \"```python\\\\n\",\\n    \"sc = SparkContext(conf = conf)\\\\n\",\\n    \"\\\\n\",\\n    \"# when done call\\\\n\",\\n    \"sc.stop()\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"## At the command line\\\\n\",\\n    \"\\\\n\",\\n    \"#### Check current setup\\\\n\",\\n    \"\\\\n\",\\n    \"```bash\\\\n\",\\n    \"$echo $PYSPARK_DRIVER_PYTHON\\\\n\",\\n    \"jupyter\\\\n\",\\n    \"\\\\n\",\\n    \"$echo $PYSPARK_DRIVER_PYTHON_OPTS\\\\n\",\\n    \"notebook\\\\n\",\\n    \"\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Unset your environment variables\\\\n\",\\n    \"\\\\n\",\\n    \"```bash\\\\n\",\\n    \"$unset PYSPARK_DRIVER_PYTHON\\\\n\",\\n    \"$echo PYSPARK_DRIER_PYTHON\\\\n\",\\n    \"\\\\n\",\\n    \"$unset PYSPARK_DRIVER_PYTHON_OPTS\\\\n\",\\n    \"$echo PYSPARK_DRIVER_PYTHON_OPTS\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Run your standalone program\\\\n\",\\n    \"```bash\\\\n\",\\n    \"# too much output! but will print to screen\\\\n\",\\n    \"$spark-submit ex6.py\\\\n\",\\n    \"\\\\n\",\\n    \"# will still print to console, but the ouput only will write to a file\\\\n\",\\n    \"$spark-submit ex6.py > output.txt\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Reset the environment variables\\\\n\",\\n    \"\\\\n\",\\n    \"```bash\\\\n\",\\n    \"$ nano ~/.bash_profile\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"#### Within your bash profile - reset the defaults back to jupyter notebook\\\\n\",\\n    \"\\\\n\",\\n    \"```bash\\\\n\",\\n    \"export PYSPARK_DRIVER_PYTHON=jupyter\\\\n\",\\n    \"export PYSPARK_DRIVER_PYTHON=\\\\\"notebook\\\\\"\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Review of HW1 assignment, and submission\\\\n\",\\n    \"\\\\n\",\\n    \"Split by word, but if number, add the values together. Going over the assignment PDF\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## RDD Operation-Transformations\\\\n\",\\n    \"\\\\n\",\\n    \"- distinct()\\\\n\",\\n    \"- union()\\\\n\",\\n    \"- intersection()\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## RDD Operation-Action\\\\n\",\\n    \"\\\\n\",\\n    \"Compute on RDD, but return non-RDD answers. \\\\n\",\\n    \"\\\\n\",\\n    \"- only those covered in class will be on quiz (see below)\\\\n\",\\n    \"```python\\\\n\",\\n    \"reduce()\\\\n\",\\n    \"collect()\\\\n\",\\n    \"count()\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"others\\\\n\",\\n    \"```\\\\n\",\\n    \"fold()\\\\n\",\\n    \"aggregate()\\\\n\",\\n    \"```\\\\n\",\\n    \"**Example**:\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \">>> num = [1, 2] [3, 4] [5, 6] [7, 8]\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \">>> num.reduce(lambda x,y: x + y)\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"What happens under the hood\\\\n\",\\n    \"\\\\n\",\\n    \"`[1 + 2] [3 + 4] [5 + 6] [7 + 8]`\\\\n\",\\n    \"\\\\n\",\\n    \"`[x + y] [x + y] [x + y] [x + y]`\\\\n\",\\n    \"\\\\n\",\\n    \"Reduces x\\'s and y\\'s by partition\\\\n\",\\n    \"\\\\n\",\\n    \"`[3] [7] [11] [15]`\\\\n\",\\n    \"\\\\n\",\\n    \"`[3]+[7] <-[11]<- [15]`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Then condenses (there\\'s a lot of options here, so not guaranteed to be the exact process)\\\\n\",\\n    \"\\\\n\",\\n    \"    `[10]<-[11]`\\\\n\",\\n    \"\\\\n\",\\n    \"        `[21]<- [15]`\\\\n\",\\n    \"\\\\n\",\\n    \"            `[36]`\\\\n\",\\n    \"\\\\n\",\\n    \"** Fold example **\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \">>> num.fold(0)(lambda x,y: x + y)\\\\n\",\\n    \"```\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"What happens under the hood, note the empty partition on the right. The fold(0) will give a default value and prevent a computation error\\\\n\",\\n    \"\\\\n\",\\n    \"`[1 + 2] [3 + 4] [5 + 6] [7 + 8] []`\\\\n\",\\n    \"\\\\n\",\\n    \"`[0+ x + y] [0 + x + y] [0 + x + y] [0 + x + y] [0]`\\\\n\",\\n    \"\\\\n\",\\n    \"Reduces x\\'s and y\\'s by partition\\\\n\",\\n    \"\\\\n\",\\n    \"`[3] [7] [11] [15] [0]`\\\\n\",\\n    \"\\\\n\",\\n    \"`[3]+[7] <-[11]<- [15] <- [0]`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"Then condenses\\\\n\",\\n    \"\\\\n\",\\n    \"    `[10]<-[11]`\\\\n\",\\n    \"\\\\n\",\\n    \"        `[21]<- [15]`\\\\n\",\\n    \"\\\\n\",\\n    \"            `[36]`\\\\n\",\\n    \"\\\\n\",\\n    \"\\\\n\",\\n    \"** In class Example 4-1 calculate sum of odd nubmers **\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 94,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]\"\\n      ]\\n     },\\n     \"execution_count\": 94,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"numbers = sc.parallelize(range(1,17))\\\\n\",\\n    \"numbers.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 99,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"def getOdd(x):\\\\n\",\\n    \"    if x % 2 == 1:\\\\n\",\\n    \"        return x\\\\n\",\\n    \"    else:\\\\n\",\\n    \"        return 0\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 110,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"64\"\\n      ]\\n     },\\n     \"execution_count\": 110,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"temp1 = numbers.map(lambda x: x if x %2==1 else 0) \\\\n\",\\n    \"temp1 = numbers.filter(lambda x: x%2==1) \\\\n\",\\n    \"temp2 = temp1.reduce(lambda x,y : x+y)\\\\n\",\\n    \"temp2\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"lambda x,y : 0 if x&2 == 0 else 0 if y\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## RDD operation-actions (to be used with reduce)\\\\n\",\\n    \"\\\\n\",\\n    \"Numeric RDD action Types\\\\n\",\\n    \"\\\\n\",\\n    \"- count()\\\\n\",\\n    \"- collect()\\\\n\",\\n    \"- countByValue()\\\\n\",\\n    \"- top(n)\\\\n\",\\n    \"- take(n)\\\\n\",\\n    \"- first()\\\\n\",\\n    \"- takeSample()\\\\n\",\\n    \"- foreach()\\\\n\",\\n    \"- mean()\\\\n\",\\n    \"- sum()\\\\n\",\\n    \"- max()\\\\n\",\\n    \"- min()\\\\n\",\\n    \"- variance()\\\\n\",\\n    \"- stdev()\\\\n\",\\n    \"\\\\n\",\\n    \"Sample for a RDD\\\\n\",\\n    \"\\\\n\",\\n    \"```python\\\\n\",\\n    \"rdd_variable.sum()\\\\n\",\\n    \"rdd_variable.top()\\\\n\",\\n    \"rdd_variable.mean()\\\\n\",\\n    \"```\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"## Inclass exercise - the number of distinct values \\\\n\",\\n    \"\\\\n\",\\n    \"from a pedagogy file in the data folder\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 123,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 124,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [],\\n   \"source\": [\\n    \"pedagog = sc.textFile(filepath)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 132,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'all\\', 3),\\\\n\",\\n       \" (u\\'enrollment.\\', 1),\\\\n\",\\n       \" (u\\'themes\\', 1),\\\\n\",\\n       \" (u\\'religious\\', 2),\\\\n\",\\n       \" (u\\'Today\\', 1),\\\\n\",\\n       \" (u\\'relationships\\', 1),\\\\n\",\\n       \" (u\\'young\\', 1),\\\\n\",\\n       \" (u\\'to\\', 17),\\\\n\",\\n       \" (u\\'Reflecting\\', 1),\\\\n\",\\n       \" (u\\'discovering\\', 1)]\"\\n      ]\\n     },\\n     \"execution_count\": 132,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"words = pedagog.flatMap(lambda x : x.split())\\\\n\",\\n    \"summary = words.countByValue()\\\\n\",\\n    \"summary.items()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"source\": [\\n    \"---\\\\n\",\\n    \"\\\\n\",\\n    \"# Additional Exercises from the Slides\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 1-1 Load Text file and split line by space\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 93,\\n   \"metadata\": {\\n    \"collapsed\": false,\\n    \"scrolled\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[u\\'=\\', u\\'Ignatian\\', u\\'Values\\', u\\'=\\'],\\\\n\",\\n       \" [u\\'The\\', u\\'University\\', u\\'of\\', u\\'San\\'],\\\\n\",\\n       \" [u\\'***********************************************************************************\\'],\\\\n\",\\n       \" [u\\'1.\\', u\\'Contemplative\\', u\\'in\\', u\\'Action\\'],\\\\n\",\\n       \" [],\\\\n\",\\n       \" [u\\'2.\\', u\\'Academic\\', u\\'Excellence\\', u\\'-\\'],\\\\n\",\\n       \" [],\\\\n\",\\n       \" [u\\'3.\\', u\\'Educating\\', u\\'the\\', u\\'Whole\\'],\\\\n\",\\n       \" [],\\\\n\",\\n       \" [u\\'4.\\', u\\'\\\\\"Cura\\', u\\'Personalis\\\\\"\\', u\\'-\\']]\"\\n      ]\\n     },\\n     \"execution_count\": 93,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.map(lambda line : line.split())\\\\n\",\\n    \"\\\\n\",\\n    \"# print sample results, will only print top 4 of every list \\\\n\",\\n    \"results = words.glom().collect() # limited to 2 items for viewing ease\\\\n\",\\n    \"map( lambda x: x[:4],results[0])\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 1-2 Generate a list of words within one level structure\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 66,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[u\\'=\\', u\\'Ignatian\\', u\\'Values\\', u\\'=\\', u\\'The\\']\\\\n\",\\n      \"[u\\'5.\\', u\\'Women\\', u\\'and\\', u\\'Men\\', u\\'for\\']\\\\n\",\\n      \"\\\\n\",\\n      \"[u\\'=\\', u\\'Ignatian\\', u\\'Values\\', u\\'=\\', u\\'The\\', u\\'University\\', u\\'of\\', u\\'San\\', u\\'Francisco\\', u\\'enjoys\\']\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.flatMap(lambda line : line.split())\\\\n\",\\n    \"\\\\n\",\\n    \"# print sample results, will only print top 5 of every list \\\\n\",\\n    \"results = words.glom().collect()\\\\n\",\\n    \"for x in map( lambda x: x[:5],results):\\\\n\",\\n    \"    print x\\\\n\",\\n    \"\\\\n\",\\n    \"# or make flat with collect()\\\\n\",\\n    \"results = words.collect()\\\\n\",\\n    \"print \\'\\\\\\\\n\\', results[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 1-3 Find words USF\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"##### note that if we use `map` when breaking lines, it will filter which LIST has USF in the collection\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 67,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"----------------- original split into words\\\\n\",\\n      \"[u\\'=\\', u\\'Ignatian\\', u\\'Values\\', u\\'=\\']\\\\n\",\\n      \"[u\\'The\\', u\\'University\\', u\\'of\\', u\\'San\\']\\\\n\",\\n      \"[u\\'***********************************************************************************\\']\\\\n\",\\n      \"[u\\'1.\\', u\\'Contemplative\\', u\\'in\\', u\\'Action\\']\\\\n\",\\n      \"[]\\\\n\",\\n      \"[u\\'2.\\', u\\'Academic\\', u\\'Excellence\\', u\\'-\\']\\\\n\",\\n      \"[]\\\\n\",\\n      \"[u\\'3.\\', u\\'Educating\\', u\\'the\\', u\\'Whole\\']\\\\n\",\\n      \"[]\\\\n\",\\n      \"[u\\'4.\\', u\\'\\\\\"Cura\\', u\\'Personalis\\\\\"\\', u\\'-\\']\\\\n\",\\n      \"----------------- only collections with USF\\\\n\",\\n      \"[u\\'2.\\', u\\'Academic\\', u\\'Excellence\\', u\\'-\\']\\\\n\"\\n     ]\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.map(lambda line : line.split())\\\\n\",\\n    \"usf_words = words.filter(lambda w: \\'USF\\' in w)\\\\n\",\\n    \"\\\\n\",\\n    \"# print sample results, will only print top 5 of every list \\\\n\",\\n    \"results = words.glom().collect() # limited to 2 items for viewing ease\\\\n\",\\n    \"print \\'----------------- original split into words\\'\\\\n\",\\n    \"for x in map( lambda x: x[:4],results[0]):\\\\n\",\\n    \"    print x\\\\n\",\\n    \"\\\\n\",\\n    \"# print sample results, will only print top 4 of every list \\\\n\",\\n    \"results = usf_words.glom().collect() # limited to 2 items for viewing ease\\\\n\",\\n    \"print \\'----------------- only collections with USF\\'\\\\n\",\\n    \"for x in map( lambda x: x[:4],results[0]):\\\\n\",\\n    \"    print x\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"##### note that if we use `mapFlat` when breaking lines, it will filter WORDS in the LIST that have USF\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 54,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\\\\"USF\\'s\\\\\"], [u\\\\\"USF\\'s\\\\\", u\\'USF\\', u\\'USF\\', u\\'USF\\', u\\'USF\\']]\"\\n      ]\\n     },\\n     \"execution_count\": 54,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.flatMap(lambda line : line.split())\\\\n\",\\n    \"usf_words = words.filter(lambda w: \\'USF\\' in w)\\\\n\",\\n    \"\\\\n\",\\n    \"usf_words.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 2: Parallelize numbers between 1 and 16\\\\n\",\\n    \"\\\\n\",\\n    \"Calculate the count and sum in each partition\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 75,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]\"\\n      ]\\n     },\\n     \"execution_count\": 75,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"numbers = sc.parallelize(range(1,17),4)\\\\n\",\\n    \"numbers.glom().collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"##### instead, if we return a 1 value list, we can get sums by partition\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 94,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[10, 26, 42, 58]\"\\n      ]\\n     },\\n     \"execution_count\": 94,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"results = numbers.mapPartitions(lambda x: [sum(x)])\\\\n\",\\n    \"results.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 76,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"136\"\\n      ]\\n     },\\n     \"execution_count\": 76,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"numbers.reduce(lambda x,y: x + y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 3-1 \\\\n\",\\n    \"Find distinct words in ignatian pedagogy\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 100,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'when\\',\\\\n\",\\n       \" u\\'R,\\',\\\\n\",\\n       \" u\\'including\\',\\\\n\",\\n       \" u\\'computation\\',\\\\n\",\\n       \" u\\'using:\\',\\\\n\",\\n       \" u\\'guidance\\',\\\\n\",\\n       \" u\\'Scala,\\',\\\\n\",\\n       \" u\\'environment\\',\\\\n\",\\n       \" u\\'only\\',\\\\n\",\\n       \" u\\'rich\\',\\\\n\",\\n       \" u\\'Apache\\',\\\\n\",\\n       \" u\\'sc.parallelize(range(1000)).count()\\',\\\\n\",\\n       \" u\\'Building\\',\\\\n\",\\n       \" u\\'And\\',\\\\n\",\\n       \" u\\'guide,\\',\\\\n\",\\n       \" u\\'return\\',\\\\n\",\\n       \" u\\'Please\\',\\\\n\",\\n       \" u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\',\\\\n\",\\n       \" u\\'Try\\',\\\\n\",\\n       \" u\\'not\\']\"\\n      ]\\n     },\\n     \"execution_count\": 100,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/ignatian_pedagogy\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.flatMap(lambda x: x.split())\\\\n\",\\n    \"distinct31 = words.distinct()\\\\n\",\\n    \"distinct.collect()[:20]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 3-2\\\\n\",\\n    \"Create a flatmap of distinct words from \\u201cREADME.md\\u201d\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 101,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'when\\',\\\\n\",\\n       \" u\\'R,\\',\\\\n\",\\n       \" u\\'including\\',\\\\n\",\\n       \" u\\'computation\\',\\\\n\",\\n       \" u\\'using:\\',\\\\n\",\\n       \" u\\'guidance\\',\\\\n\",\\n       \" u\\'Scala,\\',\\\\n\",\\n       \" u\\'environment\\',\\\\n\",\\n       \" u\\'only\\',\\\\n\",\\n       \" u\\'rich\\',\\\\n\",\\n       \" u\\'Apache\\',\\\\n\",\\n       \" u\\'sc.parallelize(range(1000)).count()\\',\\\\n\",\\n       \" u\\'Building\\',\\\\n\",\\n       \" u\\'And\\',\\\\n\",\\n       \" u\\'guide,\\',\\\\n\",\\n       \" u\\'return\\',\\\\n\",\\n       \" u\\'Please\\',\\\\n\",\\n       \" u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\',\\\\n\",\\n       \" u\\'Try\\',\\\\n\",\\n       \" u\\'not\\']\"\\n      ]\\n     },\\n     \"execution_count\": 101,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"filepath = \\'/Users/tlee010/Desktop/github_repos/2017-msan694-example/Data/README.md\\'\\\\n\",\\n    \"lines = sc.textFile(filepath)\\\\n\",\\n    \"words = lines.flatMap(lambda x: x.split())\\\\n\",\\n    \"distinct32 = words.distinct()\\\\n\",\\n    \"distinct.collect()[:20]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 3-3\\\\n\",\\n    \"What is union, intersection, subtract and cartesian product of the sets from Example 3-1 and Example 3-2?\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### union: joining 2 sets together\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 107,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'when\\',\\\\n\",\\n       \" u\\'R,\\',\\\\n\",\\n       \" u\\'including\\',\\\\n\",\\n       \" u\\'computation\\',\\\\n\",\\n       \" u\\'using:\\',\\\\n\",\\n       \" u\\'guidance\\',\\\\n\",\\n       \" u\\'Scala,\\',\\\\n\",\\n       \" u\\'environment\\',\\\\n\",\\n       \" u\\'only\\',\\\\n\",\\n       \" u\\'rich\\']\"\\n      ]\\n     },\\n     \"execution_count\": 107,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"distinct32.union(distinct31).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### intesection: only the common\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 106,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'through\\',\\\\n\",\\n       \" u\\'including\\',\\\\n\",\\n       \" u\\'This\\',\\\\n\",\\n       \" u\\'and\\',\\\\n\",\\n       \" u\\'from\\',\\\\n\",\\n       \" u\\'building\\',\\\\n\",\\n       \" u\\'with\\',\\\\n\",\\n       \" u\\'this\\',\\\\n\",\\n       \" u\\'a\\',\\\\n\",\\n       \" u\\'to\\']\"\\n      ]\\n     },\\n     \"execution_count\": 106,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"distinct32.intersection(distinct31).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### subtract: differences\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 108,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[u\\'when\\',\\\\n\",\\n       \" u\\'environment\\',\\\\n\",\\n       \" u\\'Apache\\',\\\\n\",\\n       \" u\\'Building\\',\\\\n\",\\n       \" u\\'IDE,\\',\\\\n\",\\n       \" u\\'return\\',\\\\n\",\\n       \" u\\'Please\\',\\\\n\",\\n       \" u\\'[Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse)\\',\\\\n\",\\n       \" u\\'\\\\\"yarn\\\\\"\\',\\\\n\",\\n       \" u\\'\\\\\"local\\\\\"\\']\"\\n      ]\\n     },\\n     \"execution_count\": 108,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"distinct32.subtract(distinct31).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"#### cartesian multiply words\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 109,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[(u\\'when\\', u\\'1981,\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'all\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'just\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'Father\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'actions\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'discovered\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'schools\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'including\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'ecumenical\\'),\\\\n\",\\n       \" (u\\'when\\', u\\'human\\')]\"\\n      ]\\n     },\\n     \"execution_count\": 109,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"distinct32.cartesian(distinct31).collect()[:10]\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Exercise 4-1\\\\n\",\\n    \"For the numbers between 1 and 9, calculate sum of the odd numbers.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 127,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[1, 3, 5, 7, 9]\\\\n\"\\n     ]\\n    },\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"25\"\\n      ]\\n     },\\n     \"execution_count\": 127,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"numbers = sc.parallelize(range(1,10))\\\\n\",\\n    \"oddnum = numbers.filter(lambda x : x%2==1)\\\\n\",\\n    \"print oddnum.collect()\\\\n\",\\n    \"oddnum.reduce(lambda x,y:x+y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Exercise 4-2\\\\n\",\\n    \"For the numbers between 1 and 9, calculate sum of the odd numbers using fold().\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 132,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1, 3, 5, 7, 9]\"\\n      ]\\n     },\\n     \"execution_count\": 132,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"oddnum.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 128,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"25\"\\n      ]\\n     },\\n     \"execution_count\": 128,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"oddnum.reduce(lambda x,y: x+y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 133,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"34\"\\n      ]\\n     },\\n     \"execution_count\": 133,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"oddnum.fold(1,lambda x,y: x+y)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Exercise 4-3\\\\n\",\\n    \"\\\\n\",\\n    \"Using aggregate(), return (sum, # of elements) of odd numbers.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 134,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[1, 3, 5, 7, 9]\"\\n      ]\\n     },\\n     \"execution_count\": 134,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"oddnum.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 135,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"(5, 25)\"\\n      ]\\n     },\\n     \"execution_count\": 135,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"oddnum.aggregate((0,0),(lambda x,y: (x[0]+1,x[1]+y)),(lambda x,y : (x[0]+y[0],x[1]+y[1])))\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"markdown\",\\n   \"metadata\": {},\\n   \"source\": [\\n    \"### Example 5-1\\\\n\",\\n    \"Try collect(), count(), countByValue(), top(n), take(n), first(), takeSample() operations on z.\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 145,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"name\": \"stdout\",\\n     \"output_type\": \"stream\",\\n     \"text\": [\\n      \"[[], [3], [], [4], [], [1], [], [2], [], [2], [], [3], [], [4], [], [5]]\\\\n\"\\n     ]\\n    },\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 4, 1, 2, 2, 3, 4, 5]\"\\n      ]\\n     },\\n     \"execution_count\": 145,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"x = sc.parallelize([3,4,1,2])\\\\n\",\\n    \"y = sc.parallelize(range(2,6))\\\\n\",\\n    \"z = x.union(y)\\\\n\",\\n    \"print z.glom().collect()\\\\n\",\\n    \"z.collect()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 140,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"8\"\\n      ]\\n     },\\n     \"execution_count\": 140,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.count()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 141,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"defaultdict(int, {1: 1, 2: 2, 3: 2, 4: 2, 5: 1})\"\\n      ]\\n     },\\n     \"execution_count\": 141,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.countByValue()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 142,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[5, 4, 4, 3]\"\\n      ]\\n     },\\n     \"execution_count\": 142,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.top(4)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 144,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 4, 1, 2, 2]\"\\n      ]\\n     },\\n     \"execution_count\": 144,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.take(5)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 146,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"3\"\\n      ]\\n     },\\n     \"execution_count\": 146,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.first()\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": 150,\\n   \"metadata\": {\\n    \"collapsed\": false\\n   },\\n   \"outputs\": [\\n    {\\n     \"data\": {\\n      \"text/plain\": [\\n       \"[3, 1, 2, 4, 5, 2]\"\\n      ]\\n     },\\n     \"execution_count\": 150,\\n     \"metadata\": {},\\n     \"output_type\": \"execute_result\"\\n    }\\n   ],\\n   \"source\": [\\n    \"z.takeSample(False,6,1)\"\\n   ]\\n  },\\n  {\\n   \"cell_type\": \"code\",\\n   \"execution_count\": null,\\n   \"metadata\": {\\n    \"collapsed\": true\\n   },\\n   \"outputs\": [],\\n   \"source\": []\\n  }\\n ],\\n \"metadata\": {\\n  \"anaconda-cloud\": {},\\n  \"kernelspec\": {\\n   \"display_name\": \"Python 2\",\\n   \"language\": \"python\",\\n   \"name\": \"python2\"\\n  },\\n  \"language_info\": {\\n   \"codemirror_mode\": {\\n    \"name\": \"ipython\",\\n    \"version\": 2\\n   },\\n   \"file_extension\": \".py\",\\n   \"mimetype\": \"text/x-python\",\\n   \"name\": \"python\",\\n   \"nbconvert_exporter\": \"python\",\\n   \"pygments_lexer\": \"ipython2\",\\n   \"version\": \"2.7.13\"\\n  }\\n },\\n \"nbformat\": 4,\\n \"nbformat_minor\": 0\\n}\\n')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2\n",
    "- import supervisior filter 94103 and save under supervisor 94103\n",
    "\n",
    "The number of files created when saving, is dependent on the number of partitions. You should have a single file for each partition that you have. Though some of the files might be empty, since there is no data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tf = sc.textFile('../../github_repos/2017-msan694-example/Data/supervisor_sf.csv')\n",
    "tf = tf.map(lambda x : x.split(','))\n",
    "tf = tf.map(lambda x: (x[0],x[1]))\n",
    "tf = tf.filter(lambda x : x[0]=='94103')\n",
    "tf.saveAsTextFile('supervisor_94103')\n",
    "#tf.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Saving data\n",
    "\n",
    "File formats\n",
    "- JSON, use json package\n",
    "- load one JSON per row\n",
    "- use spark SQL\n",
    "- use JSON.dumps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3 - import convert and save JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'{\"ID\":\"id_1\",\"array\":[1,2,3],\"dict\": {\"key\": \"value1\"}}',\n",
       " u'{\"ID\":\"id_2\",\"array\":[2,4,6],\"dict\": {\"key\": \"value2\"}}',\n",
       " u'{\"ID\":\"id_3\",\"array\":[3,6,9],\"dict\": {\"key\": \"value3\", \"extra_key\": \"extra_value3\"}}']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "tf = sc.textFile('data/example.json')\n",
    "data = tf.map(lambda x: json.loads(x))\n",
    "json_output = data.map(lambda x: json.dumps(x))\n",
    "json_output.saveAsTextFile('json_data_with_3')\n",
    "tf.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4 - using CSV methods, import and save CSV\n",
    "\n",
    "- Use textFile()/wholeTextFiles() and parse the csv\n",
    "- StringIO.StringIO()\n",
    "- csv.DictReader(input, fieldnames ) - > converts CSV to dictionaries\n",
    "\n",
    "#### Examples\n",
    "\n",
    "- stringIO()\n",
    "- DictWriter()\n",
    "- WriteRow()\n",
    "- Save the file using saveAsTextFile()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import StringIO\n",
    "tf = sc.textFile('../../github_repos/2017-msan694-example/Data/supervisor_sf.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[u'94102,8',\n",
       "  u'94102,6',\n",
       "  u'94102,3',\n",
       "  u'94102,5',\n",
       "  u'94103,8',\n",
       "  u'94103,9',\n",
       "  u'94103,10',\n",
       "  u'94103,6',\n",
       "  u'94103,3',\n",
       "  u'94103,5',\n",
       "  u'94104,6',\n",
       "  u'94104,3',\n",
       "  u'94105,6',\n",
       "  u'94105,3',\n",
       "  u'94107,10',\n",
       "  u'94107,6',\n",
       "  u'94108,6',\n",
       "  u'94108,3',\n",
       "  u'94109,2',\n",
       "  u'94109,6',\n",
       "  u'94109,3',\n",
       "  u'94109,5',\n",
       "  u'94110,8',\n",
       "  u'94110,11',\n",
       "  u'94110,9',\n",
       "  u'94110,10',\n",
       "  u'94111,6',\n",
       "  u'94111,3',\n",
       "  u'94112,7',\n",
       "  u'94112,8',\n",
       "  u'94112,11',\n",
       "  u'94112,9',\n",
       "  u'94112,10',\n",
       "  u'94114,7',\n",
       "  u'94114,8',\n",
       "  u'94114,5',\n",
       "  u'94115,2',\n",
       "  u'94115,1'],\n",
       " [u'94115,5',\n",
       "  u'94116,7',\n",
       "  u'94116,4',\n",
       "  u'94117,1',\n",
       "  u'94117,7',\n",
       "  u'94117,8',\n",
       "  u'94117,5',\n",
       "  u'94118,2',\n",
       "  u'94118,1',\n",
       "  u'94118,5',\n",
       "  u'94121,2',\n",
       "  u'94121,1',\n",
       "  u'94122,1',\n",
       "  u'94122,7',\n",
       "  u'94122,5',\n",
       "  u'94122,4',\n",
       "  u'94123,2',\n",
       "  u'94124,9',\n",
       "  u'94124,10',\n",
       "  u'94127,7',\n",
       "  u'94127,8',\n",
       "  u'94127,11',\n",
       "  u'94129,2',\n",
       "  u'94130,6',\n",
       "  u'94131,7',\n",
       "  u'94131,8',\n",
       "  u'94131,5',\n",
       "  u'94132,7',\n",
       "  u'94132,11',\n",
       "  u'94132,4',\n",
       "  u'94133,2',\n",
       "  u'94133,3',\n",
       "  u'94134,11',\n",
       "  u'94134,9',\n",
       "  u'94134,10',\n",
       "  u'94158,10',\n",
       "  u'94158,6']]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.glom().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parseLine(line):\n",
    "    string_as_a_file = StringIO.StringIO(line)\n",
    "    pd.read_csv(string_as_a_file)\n",
    "    #reader = csv.DictReader(input_line, fieldnames=['Zip','Supervisor'])\n",
    "    return reader.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "csv_data = tf.map(parseLine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Supervisor': '8', 'Zip': '94102'},\n",
       " {'Supervisor': '6', 'Zip': '94102'},\n",
       " {'Supervisor': '3', 'Zip': '94102'},\n",
       " {'Supervisor': '5', 'Zip': '94102'},\n",
       " {'Supervisor': '8', 'Zip': '94103'},\n",
       " {'Supervisor': '9', 'Zip': '94103'},\n",
       " {'Supervisor': '10', 'Zip': '94103'},\n",
       " {'Supervisor': '6', 'Zip': '94103'},\n",
       " {'Supervisor': '3', 'Zip': '94103'},\n",
       " {'Supervisor': '5', 'Zip': '94103'},\n",
       " {'Supervisor': '6', 'Zip': '94104'},\n",
       " {'Supervisor': '3', 'Zip': '94104'},\n",
       " {'Supervisor': '6', 'Zip': '94105'},\n",
       " {'Supervisor': '3', 'Zip': '94105'},\n",
       " {'Supervisor': '10', 'Zip': '94107'},\n",
       " {'Supervisor': '6', 'Zip': '94107'},\n",
       " {'Supervisor': '6', 'Zip': '94108'},\n",
       " {'Supervisor': '3', 'Zip': '94108'},\n",
       " {'Supervisor': '2', 'Zip': '94109'},\n",
       " {'Supervisor': '6', 'Zip': '94109'},\n",
       " {'Supervisor': '3', 'Zip': '94109'},\n",
       " {'Supervisor': '5', 'Zip': '94109'},\n",
       " {'Supervisor': '8', 'Zip': '94110'},\n",
       " {'Supervisor': '11', 'Zip': '94110'},\n",
       " {'Supervisor': '9', 'Zip': '94110'},\n",
       " {'Supervisor': '10', 'Zip': '94110'},\n",
       " {'Supervisor': '6', 'Zip': '94111'},\n",
       " {'Supervisor': '3', 'Zip': '94111'},\n",
       " {'Supervisor': '7', 'Zip': '94112'},\n",
       " {'Supervisor': '8', 'Zip': '94112'},\n",
       " {'Supervisor': '11', 'Zip': '94112'},\n",
       " {'Supervisor': '9', 'Zip': '94112'},\n",
       " {'Supervisor': '10', 'Zip': '94112'},\n",
       " {'Supervisor': '7', 'Zip': '94114'},\n",
       " {'Supervisor': '8', 'Zip': '94114'},\n",
       " {'Supervisor': '5', 'Zip': '94114'},\n",
       " {'Supervisor': '2', 'Zip': '94115'},\n",
       " {'Supervisor': '1', 'Zip': '94115'},\n",
       " {'Supervisor': '5', 'Zip': '94115'},\n",
       " {'Supervisor': '7', 'Zip': '94116'},\n",
       " {'Supervisor': '4', 'Zip': '94116'},\n",
       " {'Supervisor': '1', 'Zip': '94117'},\n",
       " {'Supervisor': '7', 'Zip': '94117'},\n",
       " {'Supervisor': '8', 'Zip': '94117'},\n",
       " {'Supervisor': '5', 'Zip': '94117'},\n",
       " {'Supervisor': '2', 'Zip': '94118'},\n",
       " {'Supervisor': '1', 'Zip': '94118'},\n",
       " {'Supervisor': '5', 'Zip': '94118'},\n",
       " {'Supervisor': '2', 'Zip': '94121'},\n",
       " {'Supervisor': '1', 'Zip': '94121'},\n",
       " {'Supervisor': '1', 'Zip': '94122'},\n",
       " {'Supervisor': '7', 'Zip': '94122'},\n",
       " {'Supervisor': '5', 'Zip': '94122'},\n",
       " {'Supervisor': '4', 'Zip': '94122'},\n",
       " {'Supervisor': '2', 'Zip': '94123'},\n",
       " {'Supervisor': '9', 'Zip': '94124'},\n",
       " {'Supervisor': '10', 'Zip': '94124'},\n",
       " {'Supervisor': '7', 'Zip': '94127'},\n",
       " {'Supervisor': '8', 'Zip': '94127'},\n",
       " {'Supervisor': '11', 'Zip': '94127'},\n",
       " {'Supervisor': '2', 'Zip': '94129'},\n",
       " {'Supervisor': '6', 'Zip': '94130'},\n",
       " {'Supervisor': '7', 'Zip': '94131'},\n",
       " {'Supervisor': '8', 'Zip': '94131'},\n",
       " {'Supervisor': '5', 'Zip': '94131'},\n",
       " {'Supervisor': '7', 'Zip': '94132'},\n",
       " {'Supervisor': '11', 'Zip': '94132'},\n",
       " {'Supervisor': '4', 'Zip': '94132'},\n",
       " {'Supervisor': '2', 'Zip': '94133'},\n",
       " {'Supervisor': '3', 'Zip': '94133'},\n",
       " {'Supervisor': '11', 'Zip': '94134'},\n",
       " {'Supervisor': '9', 'Zip': '94134'},\n",
       " {'Supervisor': '10', 'Zip': '94134'},\n",
       " {'Supervisor': '10', 'Zip': '94158'},\n",
       " {'Supervisor': '6', 'Zip': '94158'}]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_data.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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