在 AWS Glue 中删除具有空值的行的问题

Question

当前，AWS Glue 作业读取 S3 集合并将其写入 AWS Redshift 时遇到问题，其中我们有一个包含 null 值的列。

这项工作应该相当简单，并且大部分代码都是由 Glue 接口自动生成的，但由于 Redshift 中没有空列，这些列在我们的数据集中有时为空，我们无法完成这项工作。

代码的精简版如下所示，代码是Python，环境是PySpark。

datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "db_1", table_name = "table_1", transformation_ctx = "datasource0")

resolvedDDF = datasource0.resolveChoice(specs = [
  ('price_current','cast:double'),
  ('price_discount','cast:double'),
])

applymapping = ApplyMapping.apply(frame = resolvedDDF, mappings = [
  ("id", "string", "id", "string"), 
  ("status", "string", "status", "string"), 
  ("price_current", "double", "price_current", "double"), 
  ("price_discount", "double", "price_discount", "double"), 
  ("created_at", "string", "created_at", "string"), 
  ("updated_at", "string", "updated_at", "string"), 
], transformation_ctx = "applymapping")

droppedDF = applymapping.toDF().dropna(subset=('created_at', 'price_current'))

newDynamicDF = DynamicFrame.fromDF(droppedDF, glueContext, "newframe")

dropnullfields = DropNullFields.apply(frame = newDynamicDF, transformation_ctx = "dropnullfields")

datasink = glueContext.write_dynamic_frame.from_jdbc_conf(frame = dropnullfields, catalog_connection = "RedshiftDataStaging", connection_options = {"dbtable": "dbtable_1", "database": "database_1"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink")

我们对 Redshift 中的 price_current 和 created_at 表有一个非空约束，并且由于我们系统中的一些早期错误，一些记录在没有所需数据的情况下到达了 S3 存储桶。我们只想删除这些行，因为它们只占要处理的整体数据的一小部分。

尽管有 dropna 代码，我们仍然从 Redshift 收到以下错误。

Error (code 1213) while loading data into Redshift: "Missing data for not-null field"
Table name: "PUBLIC".table_1
Column name: created_at
Column type: timestampt(0)
Raw field value: @NULL@

Answer 1

如果您不想删除默认值，可以传递它们

df= dropnullfields.toDF()

df = df.na.fill({'price_current': 0.0, 'created_at': ' '})

dyf = DynamicFrame.fromDF(df,'glue_context_1')

datasink = glueContext.write_dynamic_frame.from_jdbc_conf(frame = dyf, catalog_connection = "RedshiftDataStaging", connection_options = {"dbtable": "dbtable_1", "database": "database_1"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink")

如果您想删除 ，请使用以下代码代替 df.na.fill

df = df.na.drop(subset=["price_current", "created_at"])