Project_NoSQL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Part I. ETL Pipeline for Pre-Processing the Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## PLEASE RUN THE FOLLOWING CODE FOR PRE-PROCESSING THE FILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Import Python packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Import Python packages \n",
    "import pandas as pd\n",
    "import cassandra\n",
    "import re\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import json\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Creating list of filepaths to process original event csv data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/workspace\n",
      "['/home/workspace/event_data/2018-11-05-events.csv', '/home/workspace/event_data/2018-11-23-events.csv', '/home/workspace/event_data/2018-11-22-events.csv', '/home/workspace/event_data/2018-11-21-events.csv', '/home/workspace/event_data/2018-11-03-events.csv', '/home/workspace/event_data/2018-11-28-events.csv', '/home/workspace/event_data/2018-11-01-events.csv', '/home/workspace/event_data/2018-11-19-events.csv', '/home/workspace/event_data/2018-11-11-events.csv', '/home/workspace/event_data/2018-11-17-events.csv', '/home/workspace/event_data/2018-11-15-events.csv', '/home/workspace/event_data/2018-11-29-events.csv', '/home/workspace/event_data/2018-11-14-events.csv', '/home/workspace/event_data/2018-11-06-events.csv', '/home/workspace/event_data/2018-11-12-events.csv', '/home/workspace/event_data/2018-11-30-events.csv', '/home/workspace/event_data/2018-11-25-events.csv', '/home/workspace/event_data/2018-11-13-events.csv', '/home/workspace/event_data/2018-11-27-events.csv', '/home/workspace/event_data/2018-11-04-events.csv', '/home/workspace/event_data/2018-11-16-events.csv', '/home/workspace/event_data/2018-11-02-events.csv', '/home/workspace/event_data/2018-11-20-events.csv', '/home/workspace/event_data/2018-11-18-events.csv', '/home/workspace/event_data/2018-11-09-events.csv', '/home/workspace/event_data/2018-11-10-events.csv', '/home/workspace/event_data/2018-11-08-events.csv', '/home/workspace/event_data/2018-11-26-events.csv', '/home/workspace/event_data/2018-11-07-events.csv', '/home/workspace/event_data/2018-11-24-events.csv']\n"
     ]
    }
   ],
   "source": [
    "# checking your current working directory\n",
    "print(os.getcwd())\n",
    "\n",
    "# Get your current folder and subfolder event data\n",
    "filepath = os.getcwd() + '/event_data'\n",
    "\n",
    "# Create a for loop to create a list of files and collect each filepath\n",
    "for root, dirs, files in os.walk(filepath):\n",
    "    \n",
    "# join the file path and roots with the subdirectories using glob\n",
    "    file_path_list = glob.glob(os.path.join(root,'*'))\n",
    "    print(file_path_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Processing the files to create the data file csv that will be used for Apache Casssandra tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8056\n"
     ]
    }
   ],
   "source": [
    "# initiating an empty list of rows that will be generated from each file\n",
    "full_data_rows_list = [] \n",
    "    \n",
    "# for every filepath in the file path list \n",
    "for f in file_path_list:\n",
    "\n",
    "# reading csv file \n",
    "    with open(f, 'r', encoding = 'utf8', newline='') as csvfile: \n",
    "        # creating a csv reader object \n",
    "        csvreader = csv.reader(csvfile) \n",
    "        next(csvreader)\n",
    "        \n",
    " # extracting each data row one by one and append it        \n",
    "        for line in csvreader:\n",
    "            #print(line)\n",
    "            full_data_rows_list.append(line) \n",
    "            \n",
    "# uncomment the code below if you would like to get total number of rows \n",
    "print(len(full_data_rows_list))\n",
    "# uncomment the code below if you would like to check to see what the list of event data rows will look like\n",
    "#print(full_data_rows_list)\n",
    "\n",
    "# creating a smaller event data csv file called event_datafile_full csv that will be used to insert data into the \\\n",
    "# Apache Cassandra tables\n",
    "csv.register_dialect('myDialect', quoting=csv.QUOTE_ALL, skipinitialspace=True)\n",
    "\n",
    "with open('event_datafile_new.csv', 'w', encoding = 'utf8', newline='') as f:\n",
    "    writer = csv.writer(f, dialect='myDialect')\n",
    "    writer.writerow(['artist','firstName','gender','itemInSession','lastName','length',\\\n",
    "                'level','location','sessionId','song','userId'])\n",
    "    for row in full_data_rows_list:\n",
    "        if (row[0] == ''):\n",
    "            continue\n",
    "        writer.writerow((row[0], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[12], row[13], row[16]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6821\n"
     ]
    }
   ],
   "source": [
    "# checking the number of rows in your csv file\n",
    "with open('event_datafile_new.csv', 'r', encoding = 'utf8') as f:\n",
    "    print(sum(1 for line in f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Part II. Complete the Apache Cassandra coding portion of your project. \n",
    "\n",
    "## Now you are ready to work with the CSV file titled <font color=red>event_datafile_new.csv</font>, located within the Workspace directory.  The event_datafile_new.csv contains the following columns: \n",
    "- artist \n",
    "- firstName of user\n",
    "- gender of user\n",
    "- item number in session\n",
    "- last name of user\n",
    "- length of the song\n",
    "- level (paid or free song)\n",
    "- location of the user\n",
    "- sessionId\n",
    "- song title\n",
    "- userId\n",
    "\n",
    "The image below is a screenshot of what the denormalized data should appear like in the <font color=red>**event_datafile_new.csv**</font> after the code above is run:<br>\n",
    "\n",
    "<img src=\"images/image_event_datafile_new.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Begin writing your Apache Cassandra code in the cells below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Creating a Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This will make a connection to a Cassandra instance your local machine \n",
    "# (127.0.0.1)\n",
    "\n",
    "from cassandra.cluster import Cluster\n",
    "try:\n",
    "    cluster = Cluster(['127.0.0.1'])\n",
    "# To establish connection and begin executing queries, need a session\n",
    "    session = cluster.connect()\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Create Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Creating a Keyspace \n",
    "try:\n",
    "    session.execute(\"\"\"\n",
    "    CREATE KEYSPACE IF NOT EXISTS sparkify_proj\n",
    "    WITH REPLICATION = \n",
    "    { 'class' : 'SimpleStrategy', 'replication_factor' : 1 }\"\"\"\n",
    ")\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Set Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "connected to sparkify_proj:\n"
     ]
    }
   ],
   "source": [
    "# Setting KEYSPACE to the keyspace specified above\n",
    "try:\n",
    "    session.set_keyspace('sparkify_proj')\n",
    "    print('connected to sparkify_proj:')\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Now we need to create tables to run the following queries. Remember, with Apache Cassandra you model the database tables on the queries you want to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Create queries to ask the following three questions of the data\n",
    "\n",
    "### 1. Give me the artist, song title and song's length in the music app history that was heard during  sessionId = 338, and itemInSession  = 4\n",
    "\n",
    "\n",
    "### 2. Give me only the following: name of artist, song (sorted by itemInSession) and user (first and last name) for userid = 10, sessionid = 182\n",
    "    \n",
    "\n",
    "### 3. Give me every user name (first and last) in my music app history who listened to the song 'All Hands Against His Own'\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Creating a table \"song_session\" with primary_key of session_id and item_in_session, in order to query the first question\n",
    "\n",
    "create_table_query_1 = \"CREATE TABLE IF NOT EXISTS song_session\"\n",
    "create_table_query_1 = create_table_query_1 + (\"\"\" (session_id INT, item_in_session INT, artist TEXT, song_title TEXT,\n",
    "                       song_length FLOAT, PRIMARY KEY (session_id, item_in_session)\n",
    "                        )\"\"\")\n",
    "try:\n",
    "    session.execute(create_table_query_1)\n",
    "except Exception as e:\n",
    "    print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Inserting data in table: song_session\n",
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f)\n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "        query = \"INSERT INTO song_session(session_id, item_in_session, artist, song_title, song_length)\"\n",
    "        query = query + \"VALUES(%s, %s, %s, %s, %s)\"\n",
    "        session.execute(query, (int(line[8]), int(line[3]), line[0], line[9], float(line[5])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Do a SELECT to verify that the data have been inserted into each table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artist: Faithless \n",
      "Song Title: Music Matters (Mark Knight Dub) \n",
      "Song Length: 495.30731201171875\n"
     ]
    }
   ],
   "source": [
    "## This query selects the artist, song and length with a given session_id: 338 and item_in_session: 4\n",
    "select_query_1 = (\"\"\"select artist, song_title, song_length\n",
    "                     from song_session where session_id = %s and item_in_Session = %s\n",
    "                 \"\"\")\n",
    "try:\n",
    "    rows = session.execute(select_query_1,(338,4))\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print(\"Artist: \"+row.artist, \"\\nSong Title: \"+row.song_title, \"\\nSong Length: \"+str(row.song_length))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### COPY AND REPEAT THE ABOVE THREE CELLS FOR EACH OF THE THREE QUESTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Creating a table with primary_key: user_id, session_id and clustering_column: item_in_session \\\n",
    "# in order to query the second question\n",
    "\n",
    "create_table_query_2 = \" CREATE TABLE IF NOT EXISTS user_session\"\n",
    "create_table_query_2 = create_table_query_2 + (\"\"\" (user_id INT, session_id INT, item_in_session INT, artist_name TEXT, song_title TEXT,\n",
    "                       user_first_name TEXT, user_last_name TEXT, PRIMARY KEY (user_id, session_id, item_in_session)\n",
    "                        )\"\"\")\n",
    "\n",
    "try:\n",
    "    session.execute(create_table_query_2)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## inserting data in table: user_session\n",
    "\n",
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f)\n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "        query = \"INSERT INTO user_session(user_id, session_id, item_in_session, artist_name, song_title, user_first_name, user_last_name)\"\n",
    "        query = query + \"VALUES(%s, %s, %s, %s, %s, %s, %s)\"\n",
    "        session.execute(query, (int(line[10]), int(line[8]), int(line[3]), line[0], line[9], line[1], line[4]))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artist: Down To The Bone \n",
      "Song Title: Keep On Keepin' On \n",
      "First Name: Sylvie \n",
      "Last Name:Cruz\n",
      "Artist: Three Drives \n",
      "Song Title: Greece 2000 \n",
      "First Name: Sylvie \n",
      "Last Name:Cruz\n",
      "Artist: Sebastien Tellier \n",
      "Song Title: Kilometer \n",
      "First Name: Sylvie \n",
      "Last Name:Cruz\n",
      "Artist: Lonnie Gordon \n",
      "Song Title: Catch You Baby (Steve Pitron & Max Sanna Radio Edit) \n",
      "First Name: Sylvie \n",
      "Last Name:Cruz\n"
     ]
    }
   ],
   "source": [
    "## This query selects the artist, song, user_first_name and user_last_name for userid = 10, sessionid = 182.\n",
    "\n",
    "select_query_2 = (\"\"\"select artist_name, song_title, user_first_name, user_last_name\n",
    "                       from user_session where user_id = %s and session_id = %s ;\n",
    "                    \"\"\") \n",
    "try:\n",
    "    rows = session.execute(select_query_2,(10,182)) #pass the parameters\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print(\"Artist: \"+row.artist_name, \"\\nSong Title: \"+row.song_title, \"\\nFirst Name: \"+str(row.user_first_name), \\\n",
    "          \"\\nLast Name:\"+str(row.user_last_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## creating a table \"user_ong_list\" with primary_key: song_title and clustering_column: user_id \\\n",
    "## in order to query third question\n",
    "\n",
    "create_table_query_3 = \" CREATE TABLE IF NOT EXISTS user_song_list\"\n",
    "create_table_query_3 = create_table_query_3 + (\"\"\" (user_id INT, song_title TEXT, user_first_name TEXT,\n",
    "                           user_last_name TEXT, PRIMARY KEY (song_title, user_id)\n",
    "                        )\"\"\")\n",
    "\n",
    "try:\n",
    "    session.execute(create_table_query_1)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "##inserting data in table: user_song_list\n",
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f)\n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "        query = \"INSERT INTO user_song_list(user_id, song_title, user_first_name, user_last_name)\"\n",
    "        query = query + \"VALUES(%s, %s, %s, %s)\"\n",
    "        session.execute(query, (int(line[10]), line[9], line[1], line[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Name:Jacqueline \n",
      "Last Name:Lynch\n",
      "First Name:Tegan \n",
      "Last Name:Levine\n",
      "First Name:Sara \n",
      "Last Name:Johnson\n"
     ]
    }
   ],
   "source": [
    "## This Query select the user name (first and last) who listened to the song 'All Hands Against His Own'\n",
    "\n",
    "select_query_3 = (\"\"\"select user_first_name, user_last_name from user_song_list \n",
    "                        where song_title = %s\n",
    "                    \"\"\")\n",
    "try:\n",
    "    rows = session.execute(select_query_3,('All Hands Against His Own',))\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print(\"First Name:\"+str(row.user_first_name),\"\\nLast Name:\"+str(row.user_last_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Drop the tables before closing out the sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cassandra.cluster.ResultSet at 0x7fd6a9099390>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Dropping the tables before closing out the sessions\n",
    "session.execute(\"DROP TABLE IF EXISTS song_session\")\n",
    "session.execute(\"DROP TABLE IF EXISTS user_session\")\n",
    "session.execute(\"DROP TABLE IF EXISTS user_song_list\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Close the session and cluster connection¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "session.shutdown()\n",
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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