etl.py

import configparser
from datetime import datetime
import os
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf, col
from pyspark.sql.functions import year, month, dayofmonth, hour, weekofyear, date_format 
from pyspark.sql.functions import monotonically_increasing_id


config = configparser.ConfigParser()
config.read('dl.cfg')

os.environ['AWS_ACCESS_KEY_ID']=config['AWS_KEYS']['AWS_ACCESS_KEY_ID']
os.environ['AWS_SECRET_ACCESS_KEY']=config['AWS_KEYS']['AWS_SECRET_ACCESS_KEY']


def create_spark_session():
    """
    function for creating 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):
    """
    function for processing song data
    
    parameters:
    1) spark: spark session that has been created 
    2) input_data: path to the s3 bucket with song_data
    3) output_data: path where the output data will be written
    """
    
    # get filepath to song data file
    song_data = input_data + 'song_data/*/*/*/*.json'

    # read song data file
    df = spark.read.json(song_data)

    # extract columns to create songs table
    songs_table = df.select('song_id', 'title', 'artist_id',
                            'year', 'duration') \
                    .dropDuplicates()
    songs_table.createOrReplaceTempView('songs')

    # 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/songs.parquet'), 'overwrite')

    # extract columns to create artists table
    artists_table = df.select('artist_id', 'artist_name', 'artist_location',
                              'artist_latitude', 'artist_longitude') \
                      .withColumnRenamed('artist_name', 'name') \
                      .withColumnRenamed('artist_location', 'location') \
                      .withColumnRenamed('artist_latitude', 'latitude') \
                      .withColumnRenamed('artist_longitude', 'longitude') \
                      .dropDuplicates()
    artists_table.createOrReplaceTempView('artists')

    # write artists table to parquet files
    artists_table.write.parquet(os.path.join(output_data, 'artists/artists.parquet'), 'overwrite')


def process_log_data(spark, input_data, output_data):
    """
    function for processing log data
    
    parameters:
    1) spark: spark session that has been created 
    2) input_data: path to the s3 bucket with log_data
    3) output_data: path where the output data will be written
    """
    
   # get filepath to log data file
    log_data = input_data + 'log_data/*.json'

    # read log data file
    df = spark.read.json(log_data)

    # filter by actions for song plays
    actions_df = df.filter(df.page == 'NextSong') \
                   .select('ts', 'userId', 'level', 'song', 'artist',
                           'sessionId', 'location', 'userAgent')

    # extract columns for users table    
    users_table = df.select('userId', 'firstName', 'lastName',
                            'gender', 'level').dropDuplicates()
    users_table.createOrReplaceTempView('users')
    # write users table to parquet files
    users_table.write.parquet(os.path.join(output_data, 'users/users.parquet'), 'overwrite')

    # create timestamp column from original timestamp column
    get_timestamp = udf(lambda x: str(int(int(x)/1000)))
    actions_df = actions_df.withColumn('timestamp', get_timestamp(actions_df.ts))
    
    # create datetime column from original timestamp column
    get_datetime = udf(lambda x: str(datetime.fromtimestamp(int(x) / 1000)))
    actions_df = actions_df.withColumn('datetime', get_datetime(actions_df.ts))
    
    # extract columns to create time table
    time_table = actions_df.select('datetime') \
                           .withColumn('start_time', actions_df.datetime) \
                           .withColumn('hour', hour('datetime')) \
                           .withColumn('day', dayofmonth('datetime')) \
                           .withColumn('week', weekofyear('datetime')) \
                           .withColumn('month', month('datetime')) \
                           .withColumn('year', year('datetime')) \
                           .withColumn('weekday', dayofweek('datetime')) \
                           .dropDuplicates()
    
    # write time table to parquet files partitioned by year and month
    time_table.write.partitionBy('year', 'month') \
                    .parquet(os.path.join(output_data,
                                          'time/time.parquet'), 'overwrite')

    # read in song data to use for songplays table
    song_df = spark.read.json(input_data + 'song_data/*/*/*/*.json')

    # extract columns from joined song and log datasets to create songplays table
    actions_df = actions_df.alias('log_df')
    song_df = song_df.alias('song_df')
    joined_df = actions_df.join(song_df, col('log_df.artist') == col(
        'song_df.artist_name'), 'inner')
    songplays_table = joined_df.select(
        col('log_df.datetime').alias('start_time'),
        col('log_df.userId').alias('user_id'),
        col('log_df.level').alias('level'),
        col('song_df.song_id').alias('song_id'),
        col('song_df.artist_id').alias('artist_id'),
        col('log_df.sessionId').alias('session_id'),
        col('log_df.location').alias('location'), 
        col('log_df.userAgent').alias('user_agent'),
        year('log_df.datetime').alias('year'),
        month('log_df.datetime').alias('month')) \
        .withColumn('songplay_id', monotonically_increasing_id())

    songplays_table.createOrReplaceTempView('songplays')
    # write songplays table to parquet files partitioned by year and month
    time_table = time_table.alias('timetable')

    songplays_table.write.partitionBy(
        'year', 'month').parquet(os.path.join(output_data,
                                 'songplays/songplays.parquet'),
                                 'overwrite')


def main():
    """
    function that runs whole process.
    - creating spark session
    - reading song and log data from s3 bucket
    - transforming data to the tables
    - writing output data to the s3
    """
    
    spark = create_spark_session()
    input_data = "s3a://udacity-dend/"
    output_data = "s3a://kpilarski-udacity_datalake/"
    
    process_song_data(spark, input_data, output_data)    
    process_log_data(spark, input_data, output_data)


if __name__ == "__main__":
    main()