使用 Glue 将数据从 RDS 移动到 S3
Moving data from RDS to S3 using Glue
我在 Amazon Arora Postgres 中有一个 table。我需要将 table 以 csv 格式移动到 S3 存储桶。我在 AWS glue 中创建了以下 pyspark 代码。而不是在 S3 存储桶中存储为 csv 文件。在 S3 存储桶中创建了多个文件，如 运行-XXX-part1。有没有办法将 rds table 导出到 S3 中的 csv 文件。
代码：
导入系统
来自 awsglue.transforms 导入 *
来自 awsglue.utils 导入 getResolvedOptions
来自 pyspark.context 导入 SparkContext
来自 awsglue.context 导入 GlueContext
从 awsglue.job 导入作业
## @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 = "test1", table_name = "testdb_public_reports3", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
## @type: ApplyMapping
## @args: [mapping = [("orderapprovedby", "string", "orderapprovedby", "string"), ("lname", "string", "lname", "string"), ("unitofmeasurement", "string", "unitofmeasurement", "string"), ("orderrequesteddtm", "timestamp", "orderrequesteddtm", "timestamp"), ("orderdeliverydtm", "timestamp", "orderdeliverydtm", "timestamp"), ("allowedqty", "decimal(10,2)", "allowedqty", "decimal(10,2)"), ("addressid", "int", "addressid", "int"), ("fname", "string", "fname", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("mname", "string", "mname", "string"), ("lname", "string", "lname", "string"), ("designation", "string", "designation", "string"), ("joiningtime", "timestamp", "joiningtime", "timestamp"), ("leavingtime", "timestamp", "orderdeliverydtm", "leavingtime"),("fname", "string", "fname", "string")], transformation_ctx = "applymapping1")
## @type: DataSink
## @args: [connection_type = "s3", connection_options = {"path": "s3://deloitte-homefront-poc/PROCESSED"}, 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://path"}, format = "csv", transformation_ctx = "datasink2")
job.commit()
仅使用 glue 和 pyspark 来导出数据不是一个好的选择。您可以按照 aws 提供的分步指南进行操作
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/postgresql-s3-export.html
您仍然想使用 Glue 并想要单个输出文件
#replace
datasink2 = glueContext.write_dynamic_frame.from_options(frame = applymapping1, connection_type = "s3", connection_options = {"path": "s3://path"}, format = "csv", transformation_ctx = "datasink2")
#with
df=applymapping1.toDF()
df.repartition(1).write.csv(path)
我在 Amazon Arora Postgres 中有一个 table。我需要将 table 以 csv 格式移动到 S3 存储桶。我在 AWS glue 中创建了以下 pyspark 代码。而不是在 S3 存储桶中存储为 csv 文件。在 S3 存储桶中创建了多个文件，如 运行-XXX-part1。有没有办法将 rds table 导出到 S3 中的 csv 文件。 代码： 导入系统 来自 awsglue.transforms 导入 * 来自 awsglue.utils 导入 getResolvedOptions 来自 pyspark.context 导入 SparkContext 来自 awsglue.context 导入 GlueContext 从 awsglue.job 导入作业
## @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 = "test1", table_name = "testdb_public_reports3", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
## @type: ApplyMapping
## @args: [mapping = [("orderapprovedby", "string", "orderapprovedby", "string"), ("lname", "string", "lname", "string"), ("unitofmeasurement", "string", "unitofmeasurement", "string"), ("orderrequesteddtm", "timestamp", "orderrequesteddtm", "timestamp"), ("orderdeliverydtm", "timestamp", "orderdeliverydtm", "timestamp"), ("allowedqty", "decimal(10,2)", "allowedqty", "decimal(10,2)"), ("addressid", "int", "addressid", "int"), ("fname", "string", "fname", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("mname", "string", "mname", "string"), ("lname", "string", "lname", "string"), ("designation", "string", "designation", "string"), ("joiningtime", "timestamp", "joiningtime", "timestamp"), ("leavingtime", "timestamp", "orderdeliverydtm", "leavingtime"),("fname", "string", "fname", "string")], transformation_ctx = "applymapping1")
## @type: DataSink
## @args: [connection_type = "s3", connection_options = {"path": "s3://deloitte-homefront-poc/PROCESSED"}, 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://path"}, format = "csv", transformation_ctx = "datasink2")
job.commit()
仅使用 glue 和 pyspark 来导出数据不是一个好的选择。您可以按照 aws 提供的分步指南进行操作 https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/postgresql-s3-export.html
您仍然想使用 Glue 并想要单个输出文件
#replace
datasink2 = glueContext.write_dynamic_frame.from_options(frame = applymapping1, connection_type = "s3", connection_options = {"path": "s3://path"}, format = "csv", transformation_ctx = "datasink2")
#with
df=applymapping1.toDF()
df.repartition(1).write.csv(path)