<reponame>gbh198/Relational-Data-Modelling-with-PostgreSQL<gh_stars>0
import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *


def process_song_file(cur, filepath):
    """  
        Description: This function processes song files from song_data directory to create two tables: songs and artists.
        
        Parameterized with 2 arguments:
        1. cur : Cursor object allowing to run Postgres command.
        2. filepath : Path to the location of directory.
        
    """
    
    # open song file
    df = pd.DataFrame(pd.read_json(filepath, lines=True, orient='columns'))

    # insert song record
    song_data = (df.values[0][7], df.values[0][8], df.values[0][0], df.values[0][9], df.values[0][5])
    cur.execute(song_table_insert, song_data)
    
    # insert artist record
    artist_data = (df.values[0][0], df.values[0][4], df.values[0][2], df.values[0][1], df.values[0][3])
    cur.execute(artist_table_insert, artist_data)


def process_log_file(cur, filepath):
    """  
        Description: This function processes files from log_data directory to create three tables: time, users & songplays
        
        Parameterized with 2 arguments:
        1. cur : Cursor object allowing to run Postgres command.
        2. filepath : Path to the location of directory.
        
    """
    
    # open log file
    df = pd.DataFrame(pd.read_json(filepath, lines=True, orient='columns'))

    # filter by NextSong action
    df = df[df['page']=='NextSong']

    # convert timestamp column to datetime
    t = pd.to_datetime(df['ts'], unit='ms')
    
    # insert time data records
    time_data = list(zip(t.dt.strftime('%Y-%m-%d %I:%M:%S'), t.dt.hour, t.dt.day, t.dt.week, t.dt.month, t.dt.year, t.dt.weekday))
    column_labels = ('start_time', 'hour', 'day', 'week', 'month', 'year', 'weekday')
    time_df = pd.DataFrame(time_data, columns=column_labels)

    for i, row in time_df.iterrows():
        cur.execute(time_table_insert, list(row))
        # commit is not done here, process-data function will do it once for all.

    # load user table
    user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']]

    # insert user records
    for i, row in user_df.iterrows():
        cur.execute(user_table_insert, row)

    # insert songplay records
    for index, row in df.iterrows():
        
        # get songid and artistid from song and artist tables
        cur.execute(song_select, (row.song, row.artist, row.length))
        results = cur.fetchone()
        
        if results:
            songid, artistid = results
        else:
            songid, artistid = None, None

        # insert songplay record
        start_time = pd.to_datetime(row.ts, unit='ms').strftime('%Y-%m-%d %I:%M:%S')
        songplay_data = (start_time, row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent)    
        cur.execute(songplay_table_insert, songplay_data)


def process_data(cur, conn, filepath, func):
    """  
        Description: This function appends all available JSON files in a given directory (song_data or log_data) to a list 
                     for being processed by a given function (process_song_file or process_log_file).
                     
        
        Parameterized with 4 arguments:
        1. cur : Cursor object allowing to run Postgres command.
        2. conn : Connection object to DB.
        3. filepath : Path to the location of directory.
        4. func : Functions for processing data  

    """
        
    # get all files matching extension from directory
    all_files = []
    for root, dirs, files in os.walk(filepath):
        files = glob.glob(os.path.join(root,'*.json'))
        for f in files :
            all_files.append(os.path.abspath(f))

    # get total number of files found
    num_files = len(all_files)
    print('{} files found in {}'.format(num_files, filepath))

    # iterate over files and process
    for i, datafile in enumerate(all_files, 1):
        func(cur, datafile)
        conn.commit()
        print('{}/{} files processed.'.format(i, num_files))


def main():
    """
    Description: 
    1. Connecting to DB by creating connection object and allowing cursor objet to run commands.
    2. Processing logdata and songdata with pre-defined functions.
    3. Closing DB connection. 
        
    """
    
    # connect to DB
    print("Connecting to DB")
    conn = psycopg2.connect("host=127.0.0.1 dbname=sparkifydb user=student password=<PASSWORD>")
    cur = conn.cursor()

    # process data
    print("Starting data processing....")
    process_data(cur, conn, filepath='data/song_data', func=process_song_file)
    process_data(cur, conn, filepath='data/log_data', func=process_log_file)
    print("Data processing is done")
    
    # close connection
    conn.close()


if __name__ == "__main__":
    main()