Read_Mongo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import json\n",
    "from datetime import timezone, datetime\n",
    "import string\n",
    "import uuid\n",
    "from uuid import UUID\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_generator(size=6, chars=string.ascii_uppercase):\n",
    "    return ''.join(random.choice(chars) for x in range(size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "client = MongoClient('localhost', 27017)\n",
    "db=client.mongoengine_test\n",
    "collection=db.post\n",
    "\n",
    "documents_number = collection.count() * 200000\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "itr=0\n",
    "MAX_ITR=10\n",
    "\n",
    "read_readings={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of docs 2511000\n"
     ]
    }
   ],
   "source": [
    "startf = datetime.now();\n",
    "while itr<MAX_ITR: \n",
    "    start = datetime.now();\n",
    "    for index in range(documents_number):\n",
    "        try:\n",
    "            vmid1=\"vm\"+random_generator(2,'1234')\n",
    "            #calculate the time for the insert\n",
    "            \n",
    "            collection.find({\"vmid\":vmid1})\n",
    "            timec=int((datetime.now() - start).total_seconds())\n",
    "            if timec==1:\n",
    "                #print (timec, 'secs for',index)\n",
    "                j=j+1\n",
    "                read_readings[j]=index\n",
    "                itr=itr+1\n",
    "                break\n",
    "                   \n",
    "        except:\n",
    "            print ('Unexpected error:', sys.exc_info()[0], ', for index ', index)\n",
    "            raise\n",
    "count1=collection.count()\n",
    "print (\"total number of docs\", count1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7efc85483c50>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc8546c4a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "producer_df = pd.DataFrame.from_dict(read_readings, orient='index').rename(columns={0: 'write_per_second'})\n",
    "producer_df.sort_index(inplace=True)\n",
    "producer_df\n",
    "producer_df.plot(kind='bar', subplots=True, figsize=(10, 10), title=\"Write Performance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}