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etl.py
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etl.py
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import configparser
from datetime import datetime
import os
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf, col, monotonically_increasing_id
from pyspark.sql.functions import year, month, dayofmonth, hour, dayofweek, weekofyear, date_format
from pyspark.sql.types import *
config = configparser.ConfigParser()
config.read('dl.cfg')
os.environ["AWS_ACCESS_KEY_ID"]= config['AWS']['AWS_ACCESS_KEY_ID']
os.environ["AWS_SECRET_ACCESS_KEY"]= config['AWS']['AWS_SECRET_ACCESS_KEY']
def create_spark_session():
"""
Descriptil: Create sparksession
"""
spark = SparkSession \
.builder \
.config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:2.7.0") \
.getOrCreate()
return spark
def process_song_data(spark, input_data, output_data):
"""
Description: User spark to perform ETL operations on the song data,
extract fields for the songs and artists tables then save to parquet file on s3
parameters:
- spark: spark session
- input_data: path to input data (from s3)
- output_data : path to write output parquet data (to s3)
"""
# get filepath to song data file
song_data = input_data + "song_data/*/*/*/*.json"
# define schema for song data
song_schema = StructType([
StructField('num_songs', IntegerType()),
StructField('artist_id', StringType()),
StructField('artist_latitude', DoubleType()),
StructField('artist_longitude', DoubleType()),
StructField('artist_location', StringType()),
StructField('artist_name', StringType()),
StructField('song_id', StringType()),
StructField('title', StringType()),
StructField('duration', DoubleType()),
StructField('year', IntegerType())
])
# read song data file
df = spark.read.schema(song_schema).json(song_data)
# extract columns to create songs table
songs_table = df.select('song_id', 'title', 'artist_id', 'year', 'duration').dropDuplicates()
# write songs table to parquet files partitioned by year and artist
songs_table.write.partitionBy('year', 'artist_id').parquet(os.path.join(output_data,'parquet/songs'), 'overwrite')
# extract columns to create artists table
artists_table = df.selectExpr('artist_id', 'artist_name as name', 'artist_location as location', 'artist_latitude as lattitude','artist_longitude as longitude').dropDuplicates()
# write artists table to parquet files
artists_table.write.parquet(os.path.join(output_data, 'parquet/artists'), 'overwrite')
def process_log_data(spark, input_data, output_data):
"""
Description: User spark to perform ETL operations on the log data,
extract fields for the users, time and songplays tables and then save to parquet file on s3
parameters:
- spark: spark session
- input_data: path to input data (from s3)
- output_data : path to write output parquet data (to s3)
"""
# get filepath to log data file
log_data = input_data + "log_data/*/*/*.json"
# define schema for logs data
song_schema = StructType([
StructField('artist', StringType()),
StructField('auth', StringType()),
StructField('firstName', StringType()),
StructField('gender', StringType()),
StructField('itemInSession', IntegerType()),
StructField('lastName', StringType()),
StructField('length', DoubleType()),
StructField('level', StringType()),
StructField('location', StringType()),
StructField('method', StringType()),
StructField('page', StringType()),
StructField('registration', DoubleType()),
StructField('sessionId', IntegerType()),
StructField('song', StringType()),
StructField('status', IntegerType()),
StructField('ts', LongType()),
StructField('userAgent', StringType()),
StructField('userId', StringType())
])
# read log data file
df = spark.read.json(log_data)
# filter by actions for song plays
df = df.filter(df.page == 'NextSong')
# extract columns for users table
users_table = df.selectExpr('userId as user_id','firstName as first_name','lastName as last_name', 'gender', 'level').dropDuplicates()
# write users table to parquet files
users_table.write.parquet(os.path.join(output_data, 'parquet/users'), 'overwrite')
# create timestamp column from original timestamp column
get_timestamp = udf(lambda x: int(int(x)/1000))
df = df.withColumn('timestamp', get_timestamp('ts'), )
# create datetime column from original timestamp column
get_datetime = udf(lambda x: str(datetime.fromtimestamp(int(x)/1000)))
df = df.withColumn('datetime', get_datetime('ts'), )
# extract columns to create time table
time_table = df.selectExpr("datetime as start_time")\
.withColumn('hour', hour('start_time'))\
.withColumn('day', dayofmonth('start_time'))\
.withColumn('week', weekofyear('start_time'))\
.withColumn('month', month('start_time'))\
.withColumn('year', year('start_time'))\
.withColumn('weekday', dayofweek('start_time')).dropDuplicates()
# write time table to parquet files partitioned by year and month
time_table.write.partitionBy('year', 'month').parquet(os.path.join(output_data, 'parquet/time'), 'overwrite')
# read in song data to use for songplays table
song_data = input_data + "song_data/*/*/*/*.json"
song_df = spark.read.json(song_data)
song_df.createOrReplaceTempView("songs_table")
df.createOrReplaceTempView('logs_table')
# extract columns from joined song and log datasets to create songplays table
songplays_table = songplays_table = spark.sql('''
SELECT DISTINCT
l.datetime AS start_time,
l.userId AS user_id,
l.level AS level,
s.song_id AS song_id,
s.artist_id AS artist_id,
l.sessionId AS session_id,
l.location AS location,
l.userAgent AS user_agent,
EXTRACT(month FROM l.datetime) AS month,
EXTRACT(year FROM l.datetime) AS year
FROM logs_table AS l
JOIN songs_table AS s ON s.title=l.song AND s.artist_name=l.artist
''')
# write songplays table to parquet files partitioned by year and month
songplays_table = songplays_table.withColumn('songplay_id', monotonically_increasing_id())
songplays_table.write.partitionBy('year', 'month').parquet(os.path.join(output_data,'parquet/songplays'), 'overwrite')
def main():
"""
Description: Entery function, create spark session and pass along side input_data, output_data to
process_song_data and process_log_data functions to perform ETL
"""
spark = create_spark_session()
input_data = "s3a://udacity-dend/"
output_data = "s3a://au-sparkify-datalake/"
# input_data = "data/"
# output_data = "data/"
#process_song_data(spark, input_data, output_data)
process_log_data(spark, input_data, output_data)
if __name__ == "__main__":
main()