使用AWS Glue Job在Redshift中导入数据时添加时间戳列 [英] Adding timestamp column in importing data in redshift using AWS Glue Job

问题描述

我想知道在由AWS Glue Job加载时是否可以在表中添加时间戳列.

I would like to know if it is possible to add a timestamp column in a table when it is loaded by an AWS Glue Job.

第一种情况:

A列|栏B |时间戳

Column A | Column B| TimeStamp

A | 2 | 2018-06-03 23:59:00.0

A|2|2018-06-03 23:59:00.0

当抓取工具"更新数据目录中的表并再次运行作业时，该表将在表中添加带有新时间戳记的新数据.

When a Crawler updates the table in the data catalog and run the job again, the table will add the new data in the table with a new time stamp..

A列|栏B |时间戳

Column A | Column B| TimeStamp

A | 4 | 2018-06-04 05:01:31.0

A|4|2018-06-04 05:01:31.0

B | 8 | 2018-06-04 06:02:31.0

B|8|2018-06-04 06:02:31.0

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: [TempDir, JOB_NAME]
args = getResolvedOptions(sys.argv, ['TempDir','JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "sampledb", table_name = "abs", transformation_ctx = "datasource0")
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("ColumnA", "char", "ColumnA", "char"), ("ColumnB", "char", "ColumnB", "char")], transformation_ctx = "applymapping1")
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_cols", transformation_ctx = "resolvechoice2")
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")
datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = dropnullfields3, catalog_connection = "TESTDB", connection_options = {"dbtable": "TABLEA", "database": "anasightprd01"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink4")

推荐答案

将DynamicFrame转换为spark的DataFrame，添加具有当前时间戳的新列，然后在写入之前将其转换回DynamicFrame.

Convert DynamicFrame to spark's DataFrame, add a new column with current timestamp and then convert it back to DynamicFrame before writing.

import org.apache.spark.sql.functions._

...

val timestampedDf = dropnullfields3.toDF().withColumn("TimeStamp", current_timestamp())
val timestamped4 = DynamicFrame(timestampedDf, glueContext)

这是您的Python代码的外观:

Here how your Python code should look like:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext, DynamicFrame
from awsglue.job import Job
from pyspark.sql.functions import current_timestamp

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

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "sampledb", table_name = "abs", transformation_ctx = "datasource0")
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("ColumnA", "char", "ColumnA", "char"), ("ColumnB", "char", "ColumnB", "char")], transformation_ctx = "applymapping1")
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_cols", transformation_ctx = "resolvechoice2")
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")
# add TimeStamp column
timestampedDf = dropnullfields3.toDF().withColumn("TimeStamp", current_timestamp())
timestamped4 = DynamicFrame.fromDF(timestampedDf, glueContext, "timestampedDf")
datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = timestamped4, catalog_connection = "TESTDB", connection_options = {"dbtable": "TABLEA", "database": "anasightprd01"}, redshift_tmp_dir = args["TempDir"], transformation_ctx = "datasink4")

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