Processing Continuously Coming Data  Using File Connector and in Mule 4-Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](insert_2_mysql_continous_data.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[YouTube Video Tutorial](https://youtu.be/Mz2KxpnPfMI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a database a table in MySQL\n",
    "\n",
    "CREATE DATABASE mulesoft;\n",
    "\n",
    "USE mulesoft;\n",
    "\n",
    "CREATE TABLE Persons (\n",
    "    PersonID int,\n",
    "    \n",
    "    LastName varchar(255),\n",
    "    \n",
    "    FirstName varchar(255),\n",
    "    \n",
    "    Address varchar(255),\n",
    "    \n",
    "    City varchar(255)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### SQL query string\n",
    "\n",
    "INSERT INTO Persons (PersonID,LastName, FirstName, Address, City)\n",
    "\n",
    "VALUES (:PersonID, :LastName, :FirstName, :Address, :City)\n",
    "\n",
    "\n",
    "### Input parameters\n",
    "\n",
    "{\"PersonID\":payload.PersonID,\n",
    "\n",
    " \"LastName\":payload.LastName,\n",
    " \n",
    " \"FirstName\":payload.FirstName,\n",
    " \n",
    " \"Address\": payload.Address,\n",
    " \n",
    " \"City\":payload.City}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import string\n",
    "import time\n",
    "from datetime import datetime\n",
    "import pymysql\n",
    "def generate_random(size):\n",
    "       return ''.join(random.choice(string.ascii_letters) for x in range(size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>PersonID</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80352</td>\n",
       "      <td>ZibI</td>\n",
       "      <td>xpwo</td>\n",
       "      <td>fIyw</td>\n",
       "      <td>fEiY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110364</td>\n",
       "      <td>tZxA</td>\n",
       "      <td>UoUa</td>\n",
       "      <td>rDAS</td>\n",
       "      <td>PgPb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>142463</td>\n",
       "      <td>YEJx</td>\n",
       "      <td>eOkD</td>\n",
       "      <td>CAoi</td>\n",
       "      <td>xXgp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>137163</td>\n",
       "      <td>sqBr</td>\n",
       "      <td>McQh</td>\n",
       "      <td>iwtq</td>\n",
       "      <td>nKwA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>161287</td>\n",
       "      <td>enTn</td>\n",
       "      <td>GEPi</td>\n",
       "      <td>Btsy</td>\n",
       "      <td>ZJmw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20635</td>\n",
       "      <td>zjHh</td>\n",
       "      <td>Duwj</td>\n",
       "      <td>qRcw</td>\n",
       "      <td>yrWg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>156193</td>\n",
       "      <td>uSIi</td>\n",
       "      <td>offK</td>\n",
       "      <td>VvUA</td>\n",
       "      <td>TbMj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>160445</td>\n",
       "      <td>whlI</td>\n",
       "      <td>LUlB</td>\n",
       "      <td>vKVu</td>\n",
       "      <td>uTPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39257</td>\n",
       "      <td>KIaM</td>\n",
       "      <td>LEkI</td>\n",
       "      <td>MIsi</td>\n",
       "      <td>dMyp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22778</td>\n",
       "      <td>OJYo</td>\n",
       "      <td>xBxi</td>\n",
       "      <td>JOYQ</td>\n",
       "      <td>bLNF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PersonID LastName FirstName Address  City\n",
       "0     80352     ZibI      xpwo    fIyw  fEiY\n",
       "1    110364     tZxA      UoUa    rDAS  PgPb\n",
       "2    142463     YEJx      eOkD    CAoi  xXgp\n",
       "3    137163     sqBr      McQh    iwtq  nKwA\n",
       "4    161287     enTn      GEPi    Btsy  ZJmw\n",
       "5     20635     zjHh      Duwj    qRcw  yrWg\n",
       "6    156193     uSIi      offK    VvUA  TbMj\n",
       "7    160445     whlI      LUlB    vKVu  uTPN\n",
       "8     39257     KIaM      LEkI    MIsi  dMyp\n",
       "9     22778     OJYo      xBxi    JOYQ  bLNF"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_rows = 10\n",
    "num_characters = 4\n",
    "PersonID =  np.random.randint(low = 0, high = 200000, size=10)\n",
    "LastName =  [generate_random(num_characters) for x in range(num_rows)]\n",
    "FirstName = [generate_random(num_characters) for x in range(num_rows)]\n",
    "Address =   [generate_random(num_characters) for x in range(num_rows)]\n",
    "City =     [generate_random(num_characters) for x in range(num_rows)]\n",
    "df = pd.DataFrame({'PersonID':PersonID, 'LastName':LastName, 'FirstName':FirstName, 'Address':Address, 'City':City})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.random.randint(low = 0, high = 100, size = 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuously genetate and save csv file and png plot evey second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows = 10\n",
    "num_characters = 4\n",
    "while True:\n",
    "    PersonID =  np.random.randint(200000, size=10)\n",
    "    LastName =  [generate_random(num_characters) for x in range(num_rows)]\n",
    "    FirstName = [generate_random(num_characters) for x in range(num_rows)]\n",
    "    Address =   [generate_random(num_characters) for x in range(num_rows)]\n",
    "    City =     [generate_random(num_characters) for x in range(num_rows)]\n",
    "    df = pd.DataFrame({'PersonID':PersonID, 'LastName':LastName, 'FirstName':FirstName, 'Address':Address, 'City':City})\n",
    "    file_name = 'data/' + str(datetime.now()).replace(' ', '_').replace(\":\", '_')[:19]\n",
    "    df.to_csv(file_name + '.csv', index = False)\n",
    "    plt.plot(np.random.randint(low = 0, high = 100, size = 20))\n",
    "    plt.savefig(file_name + 'png')\n",
    "    time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data is being inserted into the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  1370\n",
      "Number of rows:  1378\n",
      "Number of rows:  1380\n",
      "Number of rows:  1390\n",
      "Number of rows:  1390\n",
      "Number of rows:  1400\n",
      "Number of rows:  1407\n",
      "Number of rows:  1410\n"
     ]
    }
   ],
   "source": [
    "host = \"localhost\"\n",
    "port = 3306\n",
    "dbname = \"mulesoft\"\n",
    "user = \"root\"\n",
    "password = \"a1b2c3d4!\"\n",
    "\n",
    "\n",
    "while True:\n",
    "\n",
    "    conn = pymysql.connect(host, user = user,port = port,\n",
    "                               passwd = password, db = dbname)\n",
    "    df = pd.read_sql('select current_timestamp() as time, count(*) AS count from Persons;', con = conn)\n",
    "    conn.close()\n",
    "    print('Number of rows: ', df['count'].tolist()[0])\n",
    "    time.sleep(3)"
   ]
  }
 ],
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