dask-hdfs-parquet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dask HDFS Parquet\n",
    "\n",
    "- https://github.com/dask/dask-yarn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas 1.0.5\n",
      "dask 2.19.0\n",
      "numpy 1.18.5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import dask\n",
    "import dask.dataframe as dd\n",
    "from dask.distributed import Client\n",
    "\n",
    "HDFS_HOME = \"hdfs://node-master:54310/user/hadoop\"\n",
    "\n",
    "for module in [pd, dask, np]:\n",
    "    print(module.__name__, module.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33405</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>3</li>\n",
       "  <li><b>Cores: </b>6</li>\n",
       "  <li><b>Memory: </b>42.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:33405' processes=3 threads=6, memory=42.00 GB>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(memory_limit='14GB', processes=True)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.4 s, sys: 778 ms, total: 17.2 s\n",
      "Wall time: 15.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "filepath = HDFS_HOME + \"/gdelt_500MB.snappy.parq\"\n",
    "df = dd.read_parquet(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 810 ms, sys: 131 ms, total: 941 ms\n",
      "Wall time: 17.2 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>date</th>\n",
       "      <th>event_date</th>\n",
       "      <th>event_code</th>\n",
       "      <th>event_base_code</th>\n",
       "      <th>event_root_code</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>geo_type</th>\n",
       "      <th>country_code</th>\n",
       "      <th>adm1_code</th>\n",
       "      <th>source_url</th>\n",
       "      <th>netloc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-03</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>NI</td>\n",
       "      <td>NI</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-02-03</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>NI</td>\n",
       "      <td>NI</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-02-03</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>23.0036</td>\n",
       "      <td>106.3590</td>\n",
       "      <td>4</td>\n",
       "      <td>CH</td>\n",
       "      <td>CH16</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-02-03</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>19</td>\n",
       "      <td>32.7831</td>\n",
       "      <td>-96.8067</td>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>USTX</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-02-03</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>31.7444</td>\n",
       "      <td>34.6809</td>\n",
       "      <td>4</td>\n",
       "      <td>IS</td>\n",
       "      <td>IS01</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  event_id        date  event_date  event_code  event_base_code  \\\n",
       "0        1  2013-02-03  1979-01-01          30               30   \n",
       "1        2  2013-02-03  1979-01-01         100              100   \n",
       "2        3  2013-02-03  1979-01-01          43               43   \n",
       "3        4  2013-02-03  1979-01-01         190              190   \n",
       "4        5  2013-02-03  1979-01-01          46               46   \n",
       "\n",
       "   event_root_code      lat       lon  geo_type country_code adm1_code  \\\n",
       "0                3  10.0000    8.0000         1           NI        NI   \n",
       "1               10  10.0000    8.0000         1           NI        NI   \n",
       "2                4  23.0036  106.3590         4           CH      CH16   \n",
       "3               19  32.7831  -96.8067         3           US      USTX   \n",
       "4                4  31.7444   34.6809         4           IS      IS01   \n",
       "\n",
       "  source_url netloc  \n",
       "0       None   None  \n",
       "1       None   None  \n",
       "2       None   None  \n",
       "3       None   None  \n",
       "4       None   None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "event_id                   object\n",
       "date               datetime64[ns]\n",
       "event_date         datetime64[ns]\n",
       "event_code                float64\n",
       "event_base_code           float64\n",
       "event_root_code           float64\n",
       "lat                       float64\n",
       "lon                       float64\n",
       "geo_type                  float64\n",
       "country_code               object\n",
       "adm1_code                  object\n",
       "source_url                 object\n",
       "netloc                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 52.7 s, sys: 6.65 s, total: 59.3 s\n",
      "Wall time: 10min 23s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "640389681"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df.shape[0].compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 50.8 s, sys: 4.72 s, total: 55.5 s\n",
      "Wall time: 8min 31s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "640389681"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.5 s, sys: 2.21 s, total: 19.7 s\n",
      "Wall time: 2min 9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "640389681"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "len(df['event_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Common `event_root_code`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.78 s, sys: 379 ms, total: 5.16 s\n",
      "Wall time: 13 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4     161810583\n",
       "1      91186748\n",
       "5      50343855\n",
       "3      47064431\n",
       "2      45385155\n",
       "19     41597232\n",
       "11     40943344\n",
       "17     30890694\n",
       "8      23330016\n",
       "7      21233148\n",
       "12     16838770\n",
       "6      14513080\n",
       "9      12507727\n",
       "18     10146874\n",
       "13      9420267\n",
       "10      8488917\n",
       "14      6083109\n",
       "16      6006285\n",
       "15      2331574\n",
       "20       267872\n",
       "Name: event_root_code, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df['event_root_code'].value_counts().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Common `netloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.47 s, sys: 907 ms, total: 9.38 s\n",
      "Wall time: 52.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "www.dailymail.co.uk            3961657\n",
       "                               3603963\n",
       "www.msn.com                    3374323\n",
       "allafrica.com                  3185057\n",
       "www.business-standard.com      1854742\n",
       "www.washingtonpost.com         1795871\n",
       "www.theguardian.com            1619331\n",
       "www.thenews.com.pk             1609585\n",
       "timesofindia.indiatimes.com    1564965\n",
       "www.reuters.com                1317667\n",
       "news.yahoo.com                 1270725\n",
       "www.thehindu.com               1221602\n",
       "www.sfgate.com                 1217931\n",
       "www.4-traders.com              1195832\n",
       "www.yahoo.com                  1166510\n",
       "www.nytimes.com                1084120\n",
       "indianexpress.com              1066858\n",
       "www.dawn.com                   1055886\n",
       "sputniknews.com                1052059\n",
       "www.cbc.ca                     1021724\n",
       "Name: netloc, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df['netloc'].value_counts().compute().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of NaN per Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 55.2 s, sys: 6.16 s, total: 1min 1s\n",
      "Wall time: 9min 5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "event_id                   0\n",
       "date                       0\n",
       "event_date                 0\n",
       "event_code                 0\n",
       "event_base_code            0\n",
       "event_root_code            0\n",
       "lat                        0\n",
       "lon                        0\n",
       "geo_type                   0\n",
       "country_code          126031\n",
       "adm1_code             126031\n",
       "source_url         207800680\n",
       "netloc             207800680\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df.isna().sum().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.57 s, sys: 696 ms, total: 7.26 s\n",
      "Wall time: 31.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.date(1979, 1, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df['event_date'].min().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.14 s, sys: 594 ms, total: 6.73 s\n",
      "Wall time: 31.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.date(2013, 2, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df['date'].min().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full GDELT Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 47s, sys: 12.9 s, total: 2min\n",
      "Wall time: 1min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_headers = pd.read_excel('data/CSV.header.fieldids.xlsx')\n",
    "columns = df_headers.columns.values\n",
    "FILEPATH = \"processed_data/gdelt.snappy.parq/\"\n",
    "df = dd.read_parquet(FILEPATH, sep='\\t', names=columns, dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11min 37s, sys: 45.7 s, total: 12min 22s\n",
      "Wall time: 53min 6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GLOBALEVENTID                    0\n",
       "SQLDATE                          0\n",
       "MonthYear                        2\n",
       "Year                             2\n",
       "FractionDate                     0\n",
       "Actor1Code                59289286\n",
       "Actor1Name                59288453\n",
       "Actor1CountryCode        269443372\n",
       "Actor1KnownGroupCode     624457810\n",
       "Actor1EthnicCode         630318058\n",
       "Actor1Religion1Code      624180455\n",
       "Actor1Religion2Code      631803830\n",
       "Actor1Type1Code          360460814\n",
       "Actor1Type2Code          615220456\n",
       "Actor1Type3Code          633649374\n",
       "Actor2Code               163731995\n",
       "Actor2Name               166884120\n",
       "Actor2CountryCode        338490456\n",
       "Actor2KnownGroupCode     625795389\n",
       "Actor2EthnicCode         630940177\n",
       "Actor2Religion1Code      624563644\n",
       "Actor2Religion2Code      632084016\n",
       "Actor2Type1Code          412705482\n",
       "Actor2Type2Code          620190161\n",
       "Actor2Type3Code          633867663\n",
       "IsRootEvent                      0\n",
       "EventCode                        3\n",
       "EventBaseCode                    3\n",
       "EventRootCode                  242\n",
       "QuadClass                        0\n",
       "GoldsteinScale                2688\n",
       "NumMentions                      0\n",
       "NumSources                       0\n",
       "NumArticles                      0\n",
       "AvgTone                          0\n",
       "Actor1Geo_Type            23239615\n",
       "Actor1Geo_FullName        73736093\n",
       "Actor1Geo_CountryCode     72963145\n",
       "Actor1Geo_ADM1Code        72963145\n",
       "Actor1Geo_Lat             48915957\n",
       "Actor1Geo_Long            48872547\n",
       "Actor1Geo_FeatureID       48769864\n",
       "Actor2Geo_Type            16698073\n",
       "Actor2Geo_FullName       178079212\n",
       "Actor2Geo_CountryCode    177472195\n",
       "Actor2Geo_ADM1Code       177472195\n",
       "Actor2Geo_Lat            121391311\n",
       "Actor2Geo_Long           121358194\n",
       "Actor2Geo_FeatureID      121289821\n",
       "ActionGeo_Type             5108233\n",
       "ActionGeo_FullName        16305038\n",
       "ActionGeo_CountryCode     15390643\n",
       "ActionGeo_ADM1Code        15390643\n",
       "ActionGeo_Lat             10334518\n",
       "ActionGeo_Long            10280820\n",
       "ActionGeo_FeatureID       18100037\n",
       "DATEADDED                   190740\n",
       "SOURCEURL                212908953\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_na = df.isna().sum().compute()\n",
    "df_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 50s, sys: 8.27 s, total: 1min 58s\n",
      "Wall time: 5min 39s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "num_rows = len(df['GLOBALEVENTID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_na / num_rows).sort_values(ascending=True).plot(kind='barh', figsize=(10, 12));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gis",
   "language": "python",
   "name": "gis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}