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我有 dynamo db 表，我正在使用胶水作业将 dynamo db 数据发送到 s3。每当运行胶水作业以将新数据更新到 s3 时，它也会附加旧数据。它应该覆盖下面的旧 data.Job 脚本

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 = "abc", table_name = "xyz", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "abc", table_name = "xyz", transformation_ctx = "datasource0")
## @type: ApplyMapping
## @args: [mapping = [("address", "string", "address", "string"), ("name", "string", "name", "string"), ("company", "string", "company", "string"), ("id", "string", "id", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("address", "string", "address", "string"), ("name", "string", "name", "string"), ("company", "string", "company", "string"), ("id", "string", "id", "string")], transformation_ctx = "applymapping1")
## @type: ResolveChoice
## @args: [choice = "make_struct", transformation_ctx = "resolvechoice2"]
## @return: resolvechoice2
## @inputs: [frame = applymapping1]
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_struct", 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: [connection_type = "s3", connection_options = {"path": "s3://xyztable"}, format = "parquet", transformation_ctx = "datasink4"]
## @return: datasink4
## @inputs: [frame = dropnullfields3]
datasink4 = glueContext.write_dynamic_frame.from_options(frame = dropnullfields3, connection_type = "s3", connection_options = {"path": "s3://xyztable"}, format = "parquet", transformation_ctx = "datasink4")
job.commit()
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2 回答 2

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用这个替换你的倒数第二行

df = dropnullfields3.toDF()

df.write.mode('overwrite').parquet('s3://xyzPath')

并且它会在您每次运行 tbe 作业时替换文件夹，因为胶水库目前不支持模式，所以我们在这里使用 pyspark 库。

于 2020-05-26T03:46:06.273 回答
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如果您尝试覆盖 s3 中的数据，DynamicFrame 目前无法更改为保存模式，但您可以更改并使用此处toDF()共享的方法

于 2020-05-23T23:14:45.157 回答