Final_Project_Cassandra_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I. ETL Pipeline for Pre-Processing the Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLEASE RUN THE FOLLOWING CODE FOR PRE-PROCESSING THE FILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Python packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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\n",
    "from prettytable import PrettyTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating list of filepaths to process original event csv data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/workspace\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": {},
   "source": [
    "#### Processing the files to create the data file csv that will be used for Apache Casssandra tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6821\n"
     ]
    }
   ],
   "source": [
    "# check 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": {},
   "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": {},
   "source": [
    "## Begin writing your Apache Cassandra code in the cells below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This should make a connection to a Cassandra instance your local machine \n",
    "# (127.0.0.1)\n",
    "\n",
    "from cassandra.cluster import Cluster\n",
    "cluster = Cluster()\n",
    "\n",
    "# To establish connection and begin executing queries, need a session\n",
    "session = cluster.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Keyspace\n",
    "try:\n",
    "    session.execute(\"\"\"\n",
    "    CREATE KEYSPACE IF NOT EXISTS events \n",
    "    WITH REPLICATION = \n",
    "    { 'class' : 'SimpleStrategy', 'replication_factor' : 1 }\"\"\"\n",
    ")\n",
    "\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set KEYSPACE to the keyspace specified above\n",
    "try:\n",
    "    session.set_keyspace('events')\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "The table **song_sessions**, the **item_session** as PARTITION KEY and **sessionid** as a COMPOSITE PARTITION KEY to filter the result by these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"CREATE TABLE IF NOT EXISTS song_sessions \"\n",
    "query = query + \"(sessionid int, item_session int, artist text, song_title text, song_length float, PRIMARY KEY (sessionid, item_session))\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "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_sessions (sessionid, item_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])))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do a SELECT to verify that the data have been inserted into each table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+---------------------------------+--------------------+\n",
      "|   Artist  |               Song              |       Length       |\n",
      "+-----------+---------------------------------+--------------------+\n",
      "| Faithless | Music Matters (Mark Knight Dub) | 495.30731201171875 |\n",
      "+-----------+---------------------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "query = \"select artist, song_title, song_length from song_sessions where item_session = 4 and sessionid = 338\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "song_sessions = PrettyTable(['Artist', 'Song', 'Length'])\n",
    "for row in rows:\n",
    "    song_sessions.add_row([row.artist, row.song_title, row.song_length])\n",
    "\n",
    "print(song_sessions) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COPY AND REPEAT THE ABOVE THREE CELLS FOR EACH OF THE THREE QUESTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table **song_playlist_session**, the **sessionid** and **userid** column was used as a COMPOSITE PARTITION KEY to filter the result by these columns and It use a CLUSTERING COLUMN **item_session** to sort the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"CREATE TABLE IF NOT EXISTS song_playlist_session \"\n",
    "query = query + \"(sessionid int, userid int, item_session int, artist text, first_name text, last_name text, song_title text, PRIMARY KEY ((sessionid, userid), item_session))\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "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_playlist_session (sessionid, userid, item_session, artist, song_title, first_name, last_name) \"\n",
    "        query = query + \"values(%s, %s, %s, %s, %s, %s, %s)\"\n",
    "        session.execute(query, (int(line[8]), int(line[10]), int(line[3]), line[0], line[9], line[1], line[4] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+------------------------------------------------------+-------------+\n",
      "|       Artist      |                         Song                         |     User    |\n",
      "+-------------------+------------------------------------------------------+-------------+\n",
      "|  Down To The Bone |                  Keep On Keepin' On                  | Sylvie Cruz |\n",
      "|    Three Drives   |                     Greece 2000                      | Sylvie Cruz |\n",
      "| Sebastien Tellier |                      Kilometer                       | Sylvie Cruz |\n",
      "|   Lonnie Gordon   | Catch You Baby (Steve Pitron & Max Sanna Radio Edit) | Sylvie Cruz |\n",
      "+-------------------+------------------------------------------------------+-------------+\n"
     ]
    }
   ],
   "source": [
    "query = \"select artist, song_title, first_name, last_name from song_playlist_session where sessionid = 182 and userid = 10 order by item_session\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "song_artist_sessions = PrettyTable(['Artist', 'Song', 'User'])\n",
    "for row in rows:\n",
    "    song_artist_sessions.add_row([row.artist, row.song_title, f\"{row.first_name} {row.last_name}\"])\n",
    "print(song_artist_sessions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table **song_who_listened**, the **song_title** as a COMPOSITE PARTITION KEY to filter the result by this column and It use a CLUSTERING COLUMN **userid** to sort the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"CREATE TABLE IF NOT EXISTS song_who_listened \"\n",
    "query = query + \"(song_title text, userid int, first_name text, last_name text, PRIMARY KEY ((song_title), userid))\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "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_who_listened (song_title, userid, first_name, last_name) \"\n",
    "        query = query + \"values(%s, %s, %s, %s)\"\n",
    "        session.execute(query, (line[9], int(line[10]), line[1], line[4] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|       User       |\n",
      "+------------------+\n",
      "| Jacqueline Lynch |\n",
      "|   Tegan Levine   |\n",
      "|   Sara Johnson   |\n",
      "+------------------+\n"
     ]
    }
   ],
   "source": [
    "query = \"select first_name, last_name from song_who_listened where song_title='All Hands Against His Own'\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "song_who_listened = PrettyTable(['User'])\n",
    "for row in rows:\n",
    "    song_who_listened.add_row([f\"{row.first_name} {row.last_name}\"])\n",
    "\n",
    "print(song_who_listened)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the tables before closing out the sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"drop table if exists song_sessions\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"drop table if exists song_artist_sessions\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"drop table if exists song_who_listened\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close the session and cluster connection¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.shutdown()\n",
    "cluster.shutdown()"
   ]
  }
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