etl.py

import os
import glob
import psycopg2
import pandas as pd
import numpy as np
from sql_queries import *


# cur parameter: actual connection to DATABASE
# filepath parameter: JSON file path
# this function receive a connection and json path parameters.
# read the information using pandas and insert into database.
def process_song_file(cur, filepath):
    # open song file
    df = pd.read_json(filepath, lines=True)

    # insert song record
    song_data = df[['song_id', 'title', 'artist_id', 'year',
                    'duration']].values[0].tolist()
    cur.execute(song_table_insert, song_data)

    # insert artist record
    artist_data = df[['artist_id', 'artist_name', 'artist_location',
                      'artist_latitude', 'artist_longitude']].values[0].tolist()
    cur.execute(artist_table_insert, artist_data)

# this function receive a connection and json path parameters.
# read the information using pandas and insert into database.


def process_log_file(cur, filepath):
    # open log file
    df = pd.read_json(filepath, lines=True)

    # 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 = np.transpose(np.array([df['ts'].values, t.dt.hour.values, t.dt.day.values, t.dt.week.values,
                                       t.dt.month.values, t.dt.year.values, t.dt.weekday.values]))
    column_labels = ('ts', 'hour', 'day', 'week', 'month', 'year', 'weekday')
    time_df = pd.DataFrame(data=time_data, columns=column_labels)

    # iterate into every row and create a insert statement for every row
    for i, row in time_df.iterrows():
        cur.execute(time_table_insert, list(row))

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

    # insert user records
    for i, row in user_df.drop_duplicates().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.encode('utf-8'),
                                  row.artist.encode('utf-8'), row.length))

        # retrieve the information by tuples
        results = cur.fetchone()
        # asing song_id and artist_id into variables
        songid, artistid = results if results else (None, None)

        # insert songplay record
        songplay_data = (row.ts, row.userId, row.level, songid, artistid,
                         row.sessionId, row.location, row.userAgent)
        cur.execute(songplay_table_insert, songplay_data)

# iterate into every directory and retrieve json files to process data


def process_data(cur, conn, filepath, func):
    # 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():
    conn = psycopg2.connect("host=127.0.0.1 dbname=sparkifydb user=student password=student")
    cur = conn.cursor()

    process_data(cur, conn, filepath='data/song_data', func=process_song_file)
    process_data(cur, conn, filepath='data/log_data', func=process_log_file)

    conn.close()


if __name__ == "__main__":
    main()