Question

我有一个Glue ETL脚本，它正在处理分区的Athena表并将其输出到CSV。该表分为两个条件，单元和站点。当Glue作业运行时，它将为单位分区和站点分区的每种组合创建一个不同的CSV文件。相反，我只想要一个包含所有分区的输出文件，类似于雅典娜表的结构

我略带“ datasource0.toDF（）。repartition（1）”，但是我不确定它如何与AWS提供的脚本交互。我已经完成了镶木表的制作，但是此脚本的结构不同

请注意以下脚本，我已删除了大多数标记映射

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 = "formatted-test-2018-2019", table_name = "sitedata", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "formatted-test-2018-2019", table_name = "sitedata", transformation_ctx = "datasource0")
datasource0.toDF().repartition(1)
## @type: ApplyMapping
## @args: [mapping = [("time", "string", "time", "string"), ("unit", "string", "unit", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("time", "string", "time", "string"), ("`b58.18li2501.pv`", "double", ("site", "string", "site", "string"), ("unit", "string", "unit", "string")], transformation_ctx = "applymapping1")
## @type: DataSink
## @args: [connection_type = "s3", connection_options = {"path": "s3://20182019testdata/ParsedCSV-Data"}, format = "csv", transformation_ctx = "datasink2"]
## @return: datasink2
## @inputs: [frame = applymapping1]
datasink2 = glueContext.write_dynamic_frame.from_options(frame = applymapping1, connection_type = "s3", connection_options = {"path": "s3://20182019testdata/ParsedCSV-Data"}, format = "csv", transformation_ctx = "datasink2").repartition(1)
job.commit()

我想修改以上脚本，以仅输出一个包含分区列的CSV文件。我该怎么办？

Answer 1

您需要先重新分区，然后再编写DynamicFrame。

repartitioned1 = applymapping1.repartition(1)
datasink2 = glueContext.write_dynamic_frame.from_options(frame = repartitioned1, connection_type = "s3", connection_options = {"path": "s3://20182019testdata/ParsedCSV-Data"}, format = "csv", transformation_ctx = "datasink2")

关于在输出文件中包括分区列，我认为这是不可能的。解决方法是，您可以将一列复制到其他名称的新列中。

df = applymapping1.toDF
repartitioned_with_new_column_df = df.withColumn("_column1", df["column1"]).repartition(1)
dyf = DynamicFrame.fromDF(repartitioned_with_new_column_df, glueContext, "enriched")
datasink2 = glueContext.write_dynamic_frame.from_options(frame = dyf, connection_type = "s3", connection_options = {"path": "s3://20182019testdata/ParsedCSV-Data", , "partitionKeys": ["_column1"]}, format = "csv", transformation_ctx = "datasink2")

Answer 2

作为aws support。您可以使用.coalesce(1)。像这样的东西：

dynamic_Frame=applymapping1.coalesce(1)
datasink2 = glueContext.write_dynamic_frame.from_options(frame = dynamic_Frame, connection_type = "s3", connection_options =

它适用于我的情况。

AWS Glue，输出一个带有分区的文件

2 个答案: