# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy of this
 # software and associated documentation files (the "Software"), to deal in the Software
 # without restriction, including without limitation the rights to use, copy, modify,
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 # permit persons to whom the Software is furnished to do so.
 #
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
 # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
 # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
 # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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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

args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Step 0 - Get Source Data
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "[DATABASE_NAME]", table_name = "[TABLE_NAME]", transformation_ctx = "datasource0")

# Step 1 - Apply Column Mapping
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [[COLUMN_MAPPINGS]], transformation_ctx = "applymapping1")

# Step 2 - Convert to DataFrame and Repartition
repartition2 = applymapping1.toDF().repartition(1)

# Step 3 - Convert to DynamicFrame
convert3 = DynamicFrame.fromDF(repartition2, glueContext, "nested")

# Step 4 - Save Output Data
datasink4 = glueContext.write_dynamic_frame.from_options(frame = convert3, connection_type = "s3", connection_options = {"path": "[OUTPUT_PATH]"}, format = "csv", transformation_ctx = "datasink2")

# Step 5 - Run Job
job.commit()