/
etl.py
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/
etl.py
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import configparser
import os
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType as Dbl, IntegerType as Int, \
StringType as Str, StructField as Fld, StructType as R
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():
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):
# get filepath to song data file
song_data = os.path.join(input_data, 'song_data/*/*/*/*.json')
# read song data file
songSchema = R(
[
Fld('num_songs', Int()),
Fld('artist_id', Str()),
Fld('artist_lattitude', Dbl()),
Fld('artist_longitude', Dbl()),
Fld('artist_location', Str()),
Fld('artist_name', Str()),
Fld('song_id', Str()),
Fld('title', Str()),
Fld('duration', Dbl()),
Fld('year', Int())
]
)
df = spark.read.json(song_data, schema=songSchema)
df.createOrReplaceTempView('songData')
# extract columns to create songs table
songs_table = df.select('song_id', 'title', 'artist_id', 'year', 'duration').\
dropDuplicates(['song_id'])
# write songs table to parquet files partitioned by year and artist
songs_table.write.partitionBy('year', 'artist_id').parquet(os.path.join(output_data, 'songs'))
# extract columns to create artists table
artists_table = df.select('artist_id', 'artist_name', 'artist_location', 'artist_lattitude', 'artist_longitude')
artists_table.withColumnRenamed('artist_name', 'name')
artists_table.withColumnRenamed('artist_location', 'location')
artists_table.withColumnRenamed('artist_lattitude', 'lattitude')
artists_table.withColumnRenamed('artist_longitude', 'longitude')
# write artists table to parquet files
artists_table.write.parquet(os.path.join(output_data, 'artists'))
def process_log_data(spark, input_data, output_data):
# get filepath to log data file
log_data = os.path.join(input_data, 'log-data/*.json')
logSchema = R(
[
Fld('aritst', Str()),
Fld('auth', Str()),
Fld('firstName', Str()),
Fld('gender', Str()),
Fld('itemInSession', Str()),
Fld('lastName', Str()),
Fld('length', Dbl()),
Fld('level', Str()),
Fld('location', Str()),
Fld('method', Str()),
Fld('page', Str()),
Fld('registration', Dbl()),
Fld('sessionId', Str()),
Fld('song', Str()),
Fld('status', Int()),
Fld('ts', Int()),
Fld('userAgent', Str()),
Fld('userId', Int())
]
)
# read log data file
df = spark.read.json(log_data, schema=logSchema)
df.createTempView('logData')
# filter by actions for song plays.parquet
df = df.where('page' == 'NextSong')
# extract columns for users table
users_table = df.select('user_id', 'first_name', 'last_name', 'gender', 'level').\
dropDuplicates(['user_id'])
# write users table to parquet files
users_table.write.parquet(os.path.join(output_data, 'users'))
# create timestamp column from original timestamp column
get_timestamp = udf(lambda x: F.to_timestamp(x))
df = df.withColumn('timestamp', get_timestamp('ts'))
# create datetime column from original timestamp column
get_datetime = udf(lambda x: F.to_date(x))
df = df.withColumn('date', get_datetime('ts'))
# extract columns to create time table
time_table = df.select('timestamp', 'hour(timestamp)',
'day(timestamp)', 'week(timestamp)',
'month(timestamp)', 'weekeday(timestamp)')
time_table.withColumnRenamed('timestamp', 'start_time')
time_table.withColumnRenamed('hour(timestamp)', 'hour')
time_table.withColumnRenamed('day(timestamp)', 'day')
time_table.withColumnRenamed('week(timestamp)', 'week')
time_table.withColumnRenamed('month(timestamp)', 'month')
time_table.withColumnRenamed('weekday(timestamp', 'weekday')
# write time table to parquet files partitioned by year and month
time_table.write.parquet('users')
# read in song data to use for songplays table
song_df = spark.read.parquet(os.path.join(output_data, 'songs'))
# extract columns from joined song and log datasets to create songplays table
songplays_table = df.join(song_df, df.song == song_df.title). \
select('timestamp', 'user_id', 'level', 'song_id',
'artist_id', 'session_id', 'location', 'user_agent')
songplays_table.withColumnRenamed('timestamp', 'start_time')
# write songplays table to parquet files partitioned by year and month
songplays_table.write.paritionBy('year', 'month(start_time').parquet(os.path.join(output_data, 'songplays'))
songplays_table.withColumn('songplay_id', F.monotonicallyIncreasingId())
def main():
spark = create_spark_session()
input_data = "s3a://udacity-dend/"
output_data = "s3a://udacityoutput/"
process_song_data(spark, input_data, output_data)
process_log_data(spark, input_data, output_data)
if __name__ == "__main__":
main()