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 import DynamicFrame

def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
    for alias, frame in mapping.items():
        frame.toDF().createOrReplaceTempView(alias)
    result = spark.sql(query)
    return DynamicFrame.fromDF(result, glueContext, transformation_ctx)
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Script generated for node AWS Glue Data Catalog
AWSGlueDataCatalog_node1717182213254 = glueContext.create_dynamic_frame.from_catalog(database="datalake_processed_878695318857_ek_1201695", table_name="processed_stockdata", transformation_ctx="AWSGlueDataCatalog_node1717182213254")

# Script generated for node SQL Query
SqlQuery3688 = '''
select ROUND(SUM(amount), 2) as total_volume,
ROUND(SUM(dollar_amount), 2) as total_dollars,
COUNT(*) as total_cnt_of_transactions,
type, symbol, year, month, day
from datalake_processed_878695318857_ek_1201695.processed_stockdata
group by symbol, year, month, day, type;
'''
SQLQuery_node1717182234219 = sparkSqlQuery(glueContext, query = SqlQuery3688, mapping = {"myDataSource2":AWSGlueDataCatalog_node1717182213254}, transformation_ctx = "SQLQuery_node1717182234219")

# Script generated for node AWS Glue Data Catalog
AWSGlueDataCatalog_node1717182241035 = glueContext.write_dynamic_frame.from_catalog(frame=SQLQuery_node1717182234219, database="datalake_processed_878695318857_ek_1201695", table_name="agg_stockdata", additional_options={"enableUpdateCatalog": True, "updateBehavior": "UPDATE_IN_DATABASE", "partitionKeys": ["symbol", "year", "month", "day"]}, transformation_ctx="AWSGlueDataCatalog_node1717182241035")

job.commit()