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Update parking_cycle_hangars_denormalisation.py #1441

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merged 3 commits into from
Oct 3, 2023
Merged

Update parking_cycle_hangars_denormalisation.py #1441

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60374dd
Update parking_cycle_hangars_denormalisation.py
LBHSBALLEY Sep 29, 2023
703d27d
Update parking_cycle_hangars_denormalisation.py
LBHSBALLEY Sep 29, 2023
2f6e96f
Update parking_cycle_hangars_denormalisation.py
LBHSBALLEY Sep 29, 2023
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25 changes: 12 additions & 13 deletions scripts/jobs/parking/parking_cycle_hangars_denormalisation.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
Expand All @@ -9,11 +8,11 @@
from scripts.helpers.helpers import get_glue_env_var, create_pushdown_predicate_for_max_date_partition_value
environment = get_glue_env_var("environment")

def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
def spark_sql_query(glue_context, 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)
return DynamicFrame.fromDF(result, glue_context, transformation_ctx)

SqlQuery0 = '''
WITH HangarTypes as (
Expand Down Expand Up @@ -110,15 +109,15 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
glue_context = GlueContext(sc)
spark = glue_context.spark_session
job = Job(glue_context)
job.init(args['JOB_NAME'], args)
## @type: DataSource
## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_licence_party", transformation_ctx = "DataSource0"]
## @return: DataSource0
## @inputs: []
DataSource0 = glueContext.create_dynamic_frame.from_catalog(
DataSource0 = glue_context.create_dynamic_frame.from_catalog(
database = "dataplatform-" + environment + "-liberator-raw-zone",
table_name = "liberator_licence_party",
transformation_ctx = "DataSource0",
Expand All @@ -128,7 +127,7 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_allocations", transformation_ctx = "DataSource3"]
## @return: DataSource3
## @inputs: []
DataSource3 = glueContext.create_dynamic_frame.from_catalog(
DataSource3 = glue_context.create_dynamic_frame.from_catalog(
database = "dataplatform-" + environment + "-liberator-raw-zone",
table_name = "liberator_hangar_allocations",
push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_allocations", 'import_date'),
Expand All @@ -137,7 +136,7 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_types", transformation_ctx = "DataSource2"]
## @return: DataSource2
## @inputs: []
DataSource2 = glueContext.create_dynamic_frame.from_catalog(
DataSource2 = glue_context.create_dynamic_frame.from_catalog(
database = "dataplatform-" + environment + "-liberator-raw-zone",
table_name = "liberator_hangar_types",
push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_types", 'import_date'),
Expand All @@ -146,21 +145,21 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_details", transformation_ctx = "DataSource1"]
## @return: DataSource1
## @inputs: []
DataSource1 = glueContext.create_dynamic_frame.from_catalog(
DataSource1 = glue_context.create_dynamic_frame.from_catalog(
database = "dataplatform-" + environment + "-liberator-raw-zone",
table_name = "liberator_hangar_details",
push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_details", 'import_date')
push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_details", 'import_date'),
transformation_ctx = "DataSource1")
## @type: SqlCode
## @args: [sqlAliases = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, sqlName = SqlQuery0, transformation_ctx = "Transform0"]
## @return: Transform0
## @inputs: [dfc = DataSource1,DataSource2,DataSource3,DataSource0]
Transform0 = sparkSqlQuery(glueContext, query = SqlQuery0, mapping = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, transformation_ctx = "Transform0")
Transform0 = spark_sql_query(glue_context, query = SqlQuery0, mapping = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, transformation_ctx = "Transform0")
## @type: DataSink
## @args: [connection_type = "s3", catalog_database_name = "dataplatform-" + environment + "-liberator-refined-zone", format = "glueparquet", connection_options = {"path": "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", "partitionKeys": ["import_year" ,"import_month" ,"import_day" ,"import_date"], "enableUpdateCatalog":true, "updateBehavior":"UPDATE_IN_DATABASE"}, catalog_table_name = "parking_cycle_hangars_denormalisation", transformation_ctx = "DataSink0"]
## @return: DataSink0
## @inputs: [frame = Transform0]
DataSink0 = glueContext.getSink(path = "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", connection_type = "s3", updateBehavior = "UPDATE_IN_DATABASE", partitionKeys = ["import_year","import_month","import_day","import_date"], enableUpdateCatalog = True, transformation_ctx = "DataSink0")
DataSink0 = glue_context.getSink(path = "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", connection_type = "s3", updateBehavior = "UPDATE_IN_DATABASE", partitionKeys = ["import_year","import_month","import_day","import_date"], enableUpdateCatalog = True, transformation_ctx = "DataSink0")
DataSink0.setCatalogInfo(catalogDatabase = "dataplatform-" + environment + "-liberator-refined-zone",catalogTableName = "parking_cycle_hangars_denormalisation")
DataSink0.setFormat("glueparquet")
DataSink0.writeFrame(Transform0)
Expand Down