38 lines
1.9 KiB
Python
38 lines
1.9 KiB
Python
import sys
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from awsglue.transforms import *
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from awsglue.utils import getResolvedOptions
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from pyspark.context import SparkContext
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from awsglue.context import GlueContext
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from awsglue.job import Job
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from awsglue import DynamicFrame
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def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
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for alias, frame in mapping.items():
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frame.toDF().createOrReplaceTempView(alias)
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result = spark.sql(query)
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return DynamicFrame.fromDF(result, glueContext, transformation_ctx)
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args = getResolvedOptions(sys.argv, ['JOB_NAME'])
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sc = SparkContext()
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glueContext = GlueContext(sc)
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spark = glueContext.spark_session
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job = Job(glueContext)
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job.init(args['JOB_NAME'], args)
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# Script generated for node AWS Glue Data Catalog
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AWSGlueDataCatalog_node1717182213254 = glueContext.create_dynamic_frame.from_catalog(database="datalake_processed_878695318857_ek_1201695", table_name="processed_stockdata", transformation_ctx="AWSGlueDataCatalog_node1717182213254")
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# Script generated for node SQL Query
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SqlQuery3688 = '''
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select ROUND(SUM(amount), 2) as total_volume,
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ROUND(SUM(dollar_amount), 2) as total_dollars,
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COUNT(*) as total_cnt_of_transactions,
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type, symbol, year, month, day
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from datalake_processed_878695318857_ek_1201695.processed_stockdata
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group by symbol, year, month, day, type;
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'''
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SQLQuery_node1717182234219 = sparkSqlQuery(glueContext, query = SqlQuery3688, mapping = {"myDataSource2":AWSGlueDataCatalog_node1717182213254}, transformation_ctx = "SQLQuery_node1717182234219")
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# Script generated for node AWS Glue Data Catalog
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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")
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job.commit() |