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我正在尝试在 AWS Glue 中创建 ETL 作业。用例如下：当在运行 ETL 作业后在一个源表中添加一列时，当我们尝试重新运行 etl 作业时，etl 作业失败，说找不到列（在目标表中）

如何启用 ETL 在目标表中创建该列。因为 ETL 已经有权在表不存在时创建表。

例子：

源表：

Table X: column_1, column_2

Table Y: column_1, column_3, column_4

ETL 作业配置为加入它们两者，导致

Table_XY: column_1, column_2, column_3, column_4

在此之前，它可以完美运行。

现在，如果表 Y 得到如下修改

Table Y: column_1, column_3, column_4, **column_5**

我重新运行爬虫（检测源列）

然后我重新运行 ETL 作业，它失败并显示以下错误消息

在架构中找不到列“column_5”

我该如何解决这个问题？

用胶水脚本更新：

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

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
## @type: DataSource
## @args: [database = "db_source", table_name = "sourc_table_x", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "db_source", table_name = "sourc_table_x", transformation_ctx = "datasource0")

## @type: DataSource
## @args: [database = "db_source", table_name = "sourc_table_y", redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasource1"]
## @return: datasource1
## @inputs: []
datasource1 = glueContext.create_dynamic_frame.from_catalog(database = "db_source", table_name = "sourc_table_y", redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasource1")

## @type: Join
## @args: [keys1 = ['column_1'], keys2 = ['column_1']]
## @return: join2
## @inputs: [frame1 = datasource0, frame2 = datasource1]
join2 = Join.apply(frame1 = datasource0, frame2 = datasource1, keys1 = ['column_1'], keys2 = ['column_1'], transformation_ctx = "join2")

## @type: ResolveChoice
## @args: [choice = "make_cols", transformation_ctx = "resolvechoice2"]
## @return: resolvechoice2
## @inputs: [frame = join2]
resolvechoice2 = ResolveChoice.apply(frame = join2, choice = "make_cols", transformation_ctx = "resolvechoice2")

## @type: DropNullFields
## @args: [transformation_ctx = "dropnullfields3"]
## @return: dropnullfields3
## @inputs: [frame = resolvechoice2]
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")

## @type: DataSink
## @args: [catalog_connection = "my-db-connection", connection_options = {"dbtable": "target_table_xy", "database": "db_target"}, transformation_ctx = "datasink4"]
## @return: datasink4
## @inputs: [frame = dropnullfields3]
datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = dropnullfields3, catalog_connection = "my-db-connection", connection_options = {"dbtable": "target_table_xy", "database": "db_target"}, transformation_ctx = "datasink4")
job.commit()
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