38 lines
2.0 KiB
Python
38 lines
2.0 KiB
Python
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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_node1717180425106 = glueContext.create_dynamic_frame.from_catalog(database="datalake_raw_878695318857_ek_1201695", table_name="crawler_stockdata", transformation_ctx="AWSGlueDataCatalog_node1717180425106")
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# Script generated for node SQL Query
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SqlQuery3637 = '''
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select year(cast(transaction_ts as timestamp)) as year,
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month(cast(transaction_ts as timestamp)) as month,
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day(cast(transaction_ts as timestamp)) as day,
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hour(cast(transaction_ts as timestamp)) as hour,
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transaction_ts, symbol, price, amount, dollar_amount, type
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from myDataSource;
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'''
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SQLQuery_node1717180577964 = sparkSqlQuery(glueContext, query = SqlQuery3637, mapping = {"myDataSource":AWSGlueDataCatalog_node1717180425106}, transformation_ctx = "SQLQuery_node1717180577964")
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# Script generated for node AWS Glue Data Catalog
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AWSGlueDataCatalog_node1717180955274 = glueContext.write_dynamic_frame.from_catalog(frame=SQLQuery_node1717180577964, database="datalake_processed_878695318857_ek_1201695", table_name="processed_stockdata", additional_options={"enableUpdateCatalog": True, "updateBehavior": "UPDATE_IN_DATABASE", "partitionKeys": ["symbol", "year", "month", "day", "hour"]}, transformation_ctx="AWSGlueDataCatalog_node1717180955274")
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job.commit()
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