data-transport/notebooks/mongodb.ipynb

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 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Writing to mongodb\n",
    "\n",
    "Insure mongodb is actually installed on the system, The cell below creates a dataframe that will be stored within mongodb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Writing to mongodb database\n",
    "#\n",
    "import transport\n",
    "from transport import providers\n",
    "import pandas as pd\n",
    "_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
    "mgw = transport.factory.instance(provider=providers.MONGODB,db='demo',collection='friends',context='write')\n",
    "mgw.write(_data)\n",
    "print (transport.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading from mongodb\n",
    "\n",
    "The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using **db.runCommand**\n",
    "\n",
    "- Basic read of the designated collection **find=\\<collection>**\n",
    "- Executing an aggregate pipeline against a collection **aggreate=\\<collection>**\n",
    "\n",
    "**NOTE**\n",
    "\n",
    "It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           name  age\n",
      "0    James Bond   55\n",
      "1  Steve Rogers  150\n",
      "--------- STATISTICS ------------\n",
      "   _id  _counts  _mean\n",
      "0    0        2  102.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import transport\n",
    "from transport import providers\n",
    "mgr = transport.instance(provider=providers.MONGODB,db='foo',collection='friends')\n",
    "_df = mgr.read()\n",
    "PIPELINE = [{\"$group\":{\"_id\":0,\"_counts\":{\"$sum\":1}, \"_mean\":{\"$avg\":\"$age\"}}}]\n",
    "_sdf = mgr.read(aggregate='friends',pipeline=PIPELINE)\n",
    "print (_df)\n",
    "print ('--------- STATISTICS ------------')\n",
    "print (_sdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    "The auth_file is intended to be **JSON** formatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'host': 'klingon.io',\n",
       " 'port': 27017,\n",
       " 'username': 'me',\n",
       " 'password': 'foobar',\n",
       " 'db': 'foo',\n",
       " 'collection': 'friends',\n",
       " 'authSource': '<authdb>',\n",
       " 'mechamism': '<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"host\":\"klingon.io\",\"port\":27017,\"username\":\"me\",\"password\":\"foobar\",\"db\":\"foo\",\"collection\":\"friends\",\n",
    "    \"authSource\":\"<authdb>\",\"mechamism\":\"<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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