Python/Pyspark 迭代代碼（用於 AWS Glue ETL 作業）

Question

我正在使用 AWS Glue，如果不使用迭代，您將無法讀取/寫入多個動態幀。 我在下面編寫了這段代碼，但在兩件事上苦苦掙扎：

1. “tableName”，即過濾后的表列表是否正確（我要迭代的所有表都以 client_historical_* 開頭）。
2. 我被困在如何使用下面的映射動態填充 Redshift 表名。

紅移映射：

client_historical_ks --> table_01_a
client_historical_kg --> table_01_b
client_historical_kt --> table_01_c
client_historical_kf --> table_01_d

代碼：

client = boto3.client('glue',region_name='us-east-1')

databaseName = 'incomingdata'
tables = client.get_tables(DatabaseName = databaseName)
tableList = tables['TableList']

for table in tableList:
    start_prefix = client_historical_
    tableName = list(filter(lambda x: x.startswith(start_prefix), table['Name']))
    datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "incomingdata", table_name = tableName, transformation_ctx = "datasource0")
    datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = datasource0, catalog_connection = "Redshift", connection_options = {"dbtable": "nameoftablehere", "database": "metadata"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink4")

Answer 1

您可以創建一個映射字典，然后執行您的代碼您還可以過濾循環之外的表，然后僅在所需的表上循環。

mapping = {'client_historical_ks': 'table_01_a',
'client_historical_kg': 'table_01_b',
'client_historical_kt': 'table_01_c',
'client_historical_kf': 'table_01_d'}

client = boto3.client('glue',region_name='us-east-1')

databaseName = 'incomingdata'
tables = client.get_tables(DatabaseName = databaseName)
tableList = tables['TableList']
start_prefix = 'client_historical_'
tableNames = list(filter(lambda x: x.startswith(start_prefix), table['Name']))

for table in tableNames:
    target_table = mapping.get(table)
    datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "incomingdata", table_name = table, transformation_ctx = "datasource0")
    datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = datasource0, catalog_connection = "Redshift", connection_options = {"dbtable": target_table, "database": "metadata"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink4")