Getting_Started_With_Spark_DataFrame.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Spark\n",
    "\n",
    "[Spark](https://spark.apache.org/) is a really awesome tool to easily do distributed computations in order to process large-scale data. To be honest, most people probably don't need spark for their own side projects - most of these data will fit in memory or work well in a traditional database like PostgreSQL. That being said, there is a good chance you might need Spark if you are doing data science type work for your job. A lot of companies have a tremendous amount of data and Spark is a great tool to help effectively process these large data.\n",
    "\n",
    "In case you are not familiar with the map reduce structure, here is a very [brief introduction](http://hci.stanford.edu/courses/cs448g/a2/files/map_reduce_tutorial.pdf). Spark is based on this map reduce paradigm, but has made some nice improvements to the open source version Hadoop. A few of these improvements include the ability to cache data to memory, a simpler API supported in multiple languages (scala, python, and java I believe), and some really nice libraries - including a machine learning and SQL library. In my opinion, these additions really make Spark a powerful tool with a realtively easy learning curve.\n",
    "\n",
    "My goal today is to show you how to get started with Spark and get introduced to Spark data frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Installation\n",
    "\n",
    "Fortunately, you don't need a cluster to get started with Spark. You can run it locally as well. This obviously doesn't provide the benefits of a cluster, but is great for learning.\n",
    "\n",
    "Go ahead and install the latest verison of Spark (I would recommend pre-built with latest version of Hadoop) [here](https://spark.apache.org/downloads.html).\n",
    "\n",
    "Once you download and unpack Spark, you can navigate to that directory. From there you can go to bin/pyspark to launch an interactive version of pyspark. This is really useful for experimentation, but let's see if we can't get Spark to work in your ipython notebook.\n",
    "\n",
    "Follow the \"Create PySpark Profile for IPython\" [here](http://ramhiser.com/2015/02/01/configuring-ipython-notebook-support-for-pyspark/) **with the exception of changing PYSPARK_SUBMIT_ARGS to \"--master local[2] pyspark-shell\"** Adding the pyspark-shell to the end is now necessary.\n",
    "\n",
    "You should now be able to run: ipython notebook --profile=pyspark. To test that it works make sure you can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyspark.context.SparkContext at 0x1072eebd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=sc.parallelize([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should also now be able to go to http://localhost:4040/ and see the spark application UI. Hopefully you are now up and running with Spark in your IPython notebook! Also, as a quick side note, if you prefer using an IDE, check out this [post](https://gist.github.com/bigaidream/40fe0f8267a80e7c9cf8) on how to get autocompletion for Spark in PyCharm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#[NY Data](https://data.ny.gov/)\n",
    "New York provides a lot of open data that is easily accessible. A great resource to discovery some really interesting things about NY as well as cities in general. To showcase some of the Spark features, I will be taking a look at salary information for state authorities.\n",
    "\n",
    "We are first going to use their API to grab up to 50,000 observations, convert the data to JSON and save to disk. We can then load the file into a Spark data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib2\n",
    "import json\n",
    "req = urllib2.Request('https://data.ny.gov/resource/unag-2p27.json?$limit=50000')\n",
    "resp = urllib2.urlopen(req)\n",
    "content = json.loads(resp.read())\n",
    "with open(\"data/ny_salaries.json\", \"w\") as f:\n",
    "    json.dump(content, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#[Spark Data Frames](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "sqlContext = SQLContext(sc)\n",
    "df = sqlContext.read.json(\"data/ny_salaries.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a look at the schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- actual_salary_paid: string (nullable = true)\n",
      " |-- authority_name: string (nullable = true)\n",
      " |-- base_annualized_salary: string (nullable = true)\n",
      " |-- department: string (nullable = true)\n",
      " |-- exempt_indicator: string (nullable = true)\n",
      " |-- extra_pay: string (nullable = true)\n",
      " |-- first_name: string (nullable = true)\n",
      " |-- fiscal_year_end_date: string (nullable = true)\n",
      " |-- group: string (nullable = true)\n",
      " |-- has_employees: string (nullable = true)\n",
      " |-- last_name: string (nullable = true)\n",
      " |-- middle_initial: string (nullable = true)\n",
      " |-- other_compensation: string (nullable = true)\n",
      " |-- overtime_paid: string (nullable = true)\n",
      " |-- paid_by_another_entity: string (nullable = true)\n",
      " |-- paid_by_state_or_local_government: string (nullable = true)\n",
      " |-- pay_type: string (nullable = true)\n",
      " |-- performance_bonus: string (nullable = true)\n",
      " |-- title: string (nullable = true)\n",
      " |-- total_compensation: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now a specific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|total_compensation|\n",
      "+------------------+\n",
      "|          28725.55|\n",
      "|          31973.09|\n",
      "|          68654.00|\n",
      "|          25826.95|\n",
      "|          60114.08|\n",
      "|          15257.43|\n",
      "|          85000.00|\n",
      "|          44454.64|\n",
      "|          39474.83|\n",
      "|           2240.00|\n",
      "|          56267.98|\n",
      "|           1817.60|\n",
      "|           4530.33|\n",
      "|          59186.44|\n",
      "|          57664.04|\n",
      "|           1333.50|\n",
      "|          63339.92|\n",
      "|          59782.21|\n",
      "|          48451.11|\n",
      "|          68632.35|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select(\"total_compensation\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily filter by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(actual_salary_paid=u'133040.96', authority_name=u'Battery Park City Authority', base_annualized_salary=u'133041.00', department=u'Administration/BPCPC', exempt_indicator=u'Y', extra_pay=u'0.00', first_name=u'Tessa', fiscal_year_end_date=u'2011-10-31T00:00:00', group=u'Executive', has_employees=None, last_name=u'Huxley', middle_initial=None, other_compensation=u'750.00', overtime_paid=u'0.00', paid_by_another_entity=u'N', paid_by_state_or_local_government=None, pay_type=u'FT', performance_bonus=u'0.00', title=u'Executive Director', total_compensation=u'133790.96')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.filter(df[\"total_compensation\"] > 100000).first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets take a look at the highest paying departments on average. We first make sure to convert the column to float, and then use Spark's groupby and orderby operations to get the data we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import desc\n",
    "df_cast = df.withColumn(\"total_compensation_float\", df.total_compensation.astype(\"float\"))\n",
    "top10 = (df_cast\n",
    "         .groupBy(\"department\")\n",
    "         .mean(\"total_compensation_float\")\n",
    "         .orderBy(desc(\"AVG(total_compensation_float)\"))\n",
    "         .take(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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vcLwT4uiEGDoljk6IoVPi6IQYOiWOToihk+JotihJ4TpJB+RB4PHAPcBdwLGSlgSWAtYG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\nNIWIiBhLU4iIiLE0hYiIGEtTiIiIsf8AZqxFK4vyNDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d592490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "department = np.empty(10, dtype=\"S30\")\n",
    "salary = np.empty(10)\n",
    "for i, row in enumerate(top10):\n",
    "    department[i] = row[0]\n",
    "    salary[i] = row[1]\n",
    "sns.barplot(x=department, y=salary, x_order=department)\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a nice place to be is the Office of Strategic Initative...but I wonder how many people are in that department in our data? Let's utilize the SQL query method on our data frame\n",
    "#To SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(c0=1)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast.registerTempTable(\"df_cast\")\n",
    "sqlContext.sql(\"select count(*) from df_cast where department = 'Office of Strategic Initiative'\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there is only one lucky person :). We could also convert our Spark data frame to a pandas data frame and do the same calculation. Nice.\n",
    "#To Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pandas = df_cast.toPandas()\n",
    "df_pandas.department.value_counts()['Office of Strategic Initiative']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Summary Statistics / Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------------+\n",
      "|summary|total_compensation_float|\n",
      "+-------+------------------------+\n",
      "|  count|                   49997|\n",
      "|   mean|       63374.58037148814|\n",
      "| stddev|       35402.40384989916|\n",
      "|    min|                     0.0|\n",
      "|    max|               735124.75|\n",
      "+-------+------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_cast.describe('total_compensation_float').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow - that is a high max. Lets take a look at the departments by max salary. Excuse the pandas oneliner - would just be the same Spark code as used above to get the average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ca318d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4cOAC4N0F2284EHgf6eLt9cBMoHRSOJpUlPNYSZcD59n+c8H2G1Ozd6NQN2In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\nSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d582990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pandas.groupby('department')[\"total_compensation_float\"].max().order(ascending=False)[:10].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are definitely a few outliers here. Let's just quickly see the distribution for the highest department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115021cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salaries = sqlContext.sql(\"\"\"\n",
    "        select total_compensation_float \n",
    "        from df_cast\n",
    "        where department = '3147 Personnel Human Resources' \"\"\").collect()\n",
    "sns.distplot(salaries, kde=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow - someone is making a killing in this department. Or maybe an error?\n",
    "\n",
    "Spark also has some nifty functions. For example, we can quickly make cross tabulations. Check out the API for a lot more functions available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import DataFrameStatFunctions as sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(paid_by_another_entity_fiscal_year_end_date=u'Y', 2011-03-31T00:00:00=7, 2011-12-31T00:00:00=0, 2012-03-31T00:00:00=5, 2012-12-31T00:00:00=0, 2013-03-31T00:00:00=5, 2012-10-31T00:00:00=0, 2013-12-31T00:00:00=0, 2013-10-31T00:00:00=0, 2011-06-30T00:00:00=0, 2011-10-31T00:00:00=0, 2012-06-30T00:00:00=0, 2013-06-30T00:00:00=0),\n",
       " Row(paid_by_another_entity_fiscal_year_end_date=u'N', 2011-03-31T00:00:00=2376, 2011-12-31T00:00:00=35169, 2012-03-31T00:00:00=2348, 2012-12-31T00:00:00=3548, 2013-03-31T00:00:00=2272, 2012-10-31T00:00:00=183, 2013-12-31T00:00:00=3602, 2013-10-31T00:00:00=208, 2011-06-30T00:00:00=24, 2011-10-31T00:00:00=202, 2012-06-30T00:00:00=24, 2013-06-30T00:00:00=24),\n",
       " Row(paid_by_another_entity_fiscal_year_end_date=u'null', 2011-03-31T00:00:00=1, 2011-12-31T00:00:00=0, 2012-03-31T00:00:00=1, 2012-12-31T00:00:00=0, 2013-03-31T00:00:00=1, 2012-10-31T00:00:00=0, 2013-12-31T00:00:00=0, 2013-10-31T00:00:00=0, 2011-06-30T00:00:00=0, 2011-10-31T00:00:00=0, 2012-06-30T00:00:00=0, 2013-06-30T00:00:00=0)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf(df_cast).crosstab(\"paid_by_another_entity\", \"fiscal_year_end_date\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Learn More\n",
    "* [Databricks Spark SQL blog](https://databricks.com/blog/category/sparksql)\n",
    "* [Spark SQL API](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well I hope this helped you get started with Spark locally and showcased some of the features of data frames. As always, please feel free to email me with questions / comments."
   ]
  }
 ],
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