Write_Mongo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import pymongo\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_generator(size=6, chars=string.ascii_uppercase):\n",
    "    return ''.join(random.choice(chars) for x in range(size))\n",
    "def random_hexgenerator():\n",
    "    hexN=\"0x\"+uuid.uuid4().hex\n",
    "    return hexN[:18]\n",
    "def timestamp_gen():\n",
    "    ts=int(datetime.now(tz=timezone.utc).timestamp() * 1000)\n",
    "    return ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain a mongo connection\n",
    "from pymongo import MongoClient\n",
    "client = MongoClient('localhost', 27017)\n",
    "\n",
    "# obtain a handle to the random database\n",
    "db=client.mongoengine_test\n",
    "collection=db.post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents_number = collection.count() * 200000\n",
    "batch_number = 5 * 1000;\n",
    "job_id = '1'\n",
    "j=0\n",
    "itr=0\n",
    "MAX_ITR=10\n",
    "total_docs=0\n",
    "job_name = 'Job#' + job_id\n",
    "write_readings={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:34: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job#1  inserted  143965  in  10.305141 s\n"
     ]
    }
   ],
   "source": [
    "batch_documents = [i for i in range(batch_number)];\n",
    "\n",
    "startf = datetime.now();\n",
    "while itr<MAX_ITR: \n",
    "    start = datetime.now();\n",
    "    for index in range(documents_number):\n",
    "        try:\n",
    "            ts = timestamp_gen()\n",
    "            value = random.random()\n",
    "            syscall_nr=random.randint(1,101)\n",
    "            syscall_name=random_generator(6,\"abcdefghijklmnopqrstuvwxyz\")\n",
    "            dtb=random_hexgenerator()\n",
    "            rsp=random_hexgenerator()\n",
    "            rip=random_hexgenerator()\n",
    "            pid=random.randint(1,10000)\n",
    "            vmid=\"vm\"+random_generator(2,'1234')\n",
    "            logtype=random_generator(3,'xyz')\n",
    "            document = {\n",
    "                'value' : value,\n",
    "                'syscall_nr':syscall_nr,\n",
    "                'syscall_name':syscall_name,\n",
    "                'dtb':dtb,\n",
    "                'rsp':rsp,\n",
    "                'rip':rip,\n",
    "                'pid':pid,\n",
    "                'vmid':vmid,\n",
    "                'logtype':logtype,\n",
    "                'ts':ts \n",
    "                }\n",
    "            #creating array/batch of documents to insert \n",
    "            batch_documents[index % batch_number] = document\n",
    "            \n",
    "            if (index + 1) % batch_number == 0:\n",
    "                collection.insert(batch_documents)\n",
    "                index += 1;\n",
    "            \n",
    "            #calculate the time for the insert\n",
    "            timec=int((datetime.now() - start).total_seconds())\n",
    "            \n",
    "            #if index % 4000 == 0: \n",
    "               # print (' inserted ', index, ' documents.')\n",
    "\n",
    "            if timec==1:\n",
    "                #print (timec, 'secs for',index)\n",
    "                j=j+1\n",
    "                write_readings[j]=index\n",
    "                itr=itr+1\n",
    "                total_docs=total_docs + index\n",
    "                break\n",
    "                   \n",
    "        except:\n",
    "            print ('Unexpected error:', sys.exc_info()[0], ', for index ', index)\n",
    "            raise\n",
    "print (job_name, ' inserted ',total_docs, ' in ', (datetime.now() - startf).total_seconds(), 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fea2c26aef0>], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea2d7fc6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "producer_df = pd.DataFrame.from_dict(write_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
}