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etl.py
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etl.py
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import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *
def process_song_file(cur, filepath):
"""
Function to perform ETL on song dataset.
Inserts records in songs table.
Args:
cur (refcursor): Cursor to execute database queries
filepath (string): Name of song file
Returns:
no value
"""
# 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)
def process_log_file(cur, filepath):
"""
Function to perform ETL on log dataset.
Inserts records in time, users, and songplays tables.
Args:
cur (refcursor): Cursor to execute database queries
filepath (string): Name of log file
Returns:
no value
"""
# 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 = [
t,
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_of_year',
'month',
'year',
'weekday')
time_df = pd.DataFrame(dict(zip(column_labels, time_data)))
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.iterrows():
cur.execute(user_table_insert, row)
# convert data in ts column to timestamp
df['ts'] = pd.to_datetime(df['ts'],unit='ms')
# 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
songplay_data = [
row.ts,
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):
"""
Function to process the file from filepath directory with func function.
Inserts records in time, users, and songplays tables.
Args:
cur (refcursor): Cursor to execute database queries
conn (object): Database connection object
filepath (string): Name of file to perform ETL
func (func): Fuction to perform ETL
Returns:
no value
"""
# 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():
"""
Function to establish connection to Sparkify database and run ETL pipeline.
Args:
no value
Returns:
no value
"""
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()