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pyspark_code.py
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pyspark_code.py
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import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.dynamicframe import DynamicFrame
## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
## @type: DataSource
## @args: [database = "db_youtube_raw", table_name = "raw_statistics", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
predicate_pushdown = "region in ('ca','gb','us')"
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "db_youtube_raw", table_name = "raw_statistics", transformation_ctx = "datasource0", push_down_predicate = predicate_pushdown)
## @type: ApplyMapping
## @args: [mapping = [("video_id", "string", "video_id", "string"), ("trending_date", "string", "trending_date", "string"), ("title", "string", "title", "string"), ("channel_title", "string", "channel_title", "string"), ("category_id", "long", "category_id", "long"), ("publish_time", "string", "publish_time", "string"), ("tags", "string", "tags", "string"), ("views", "long", "views", "long"), ("likes", "long", "likes", "long"), ("dislikes", "long", "dislikes", "long"), ("comment_count", "long", "comment_count", "long"), ("thumbnail_link", "string", "thumbnail_link", "string"), ("comments_disabled", "boolean", "comments_disabled", "boolean"), ("ratings_disabled", "boolean", "ratings_disabled", "boolean"), ("video_error_or_removed", "boolean", "video_error_or_removed", "boolean"), ("description", "string", "description", "string"), ("region", "string", "region", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("video_id", "string", "video_id", "string"), ("trending_date", "string", "trending_date", "string"), ("title", "string", "title", "string"), ("channel_title", "string", "channel_title", "string"), ("category_id", "long", "category_id", "long"), ("publish_time", "string", "publish_time", "string"), ("tags", "string", "tags", "string"), ("views", "long", "views", "long"), ("likes", "long", "likes", "long"), ("dislikes", "long", "dislikes", "long"), ("comment_count", "long", "comment_count", "long"), ("thumbnail_link", "string", "thumbnail_link", "string"), ("comments_disabled", "boolean", "comments_disabled", "boolean"), ("ratings_disabled", "boolean", "ratings_disabled", "boolean"), ("video_error_or_removed", "boolean", "video_error_or_removed", "boolean"), ("description", "string", "description", "string"), ("region", "string", "region", "string")], transformation_ctx = "applymapping1")
## @type: ResolveChoice
## @args: [choice = "make_struct", transformation_ctx = "resolvechoice2"]
## @return: resolvechoice2
## @inputs: [frame = applymapping1]
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_struct", transformation_ctx = "resolvechoice2")
## @type: DropNullFields
## @args: [transformation_ctx = "dropnullfields3"]
## @return: dropnullfields3
## @inputs: [frame = resolvechoice2]
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")
## @type: DataSink
## @args: [connection_type = "s3", connection_options = {"path": "s3://bigdata-on-youtube-cleansed-euwest1-14317621-dev/youtube/raw_statistics/"}, format = "parquet", transformation_ctx = "datasink4"]
## @return: datasink4
## @inputs: [frame = dropnullfields3]
#MAKE SURE YOU COPY ONLY WHAT IS NEEDED
datasink1 = dropnullfields3.toDF().coalesce(1)
df_final_output = DynamicFrame.fromDF(datasink1, glueContext, "df_final_output")
datasink4 = glueContext.write_dynamic_frame.from_options(frame = df_final_output, connection_type = "s3", connection_options = {"path": "s3://de-on-youtube-cleansed-useast1-dev/youtube/raw_statistics/", "partitionKeys": ["region"]}, format = "parquet", transformation_ctx = "datasink4")
job.commit()