Amazon web services 使用AWS胶水创建分区数据并保存到s3中
我有上面的脚本,我不明白为什么不工作,或者它是否是正确的方式 有人能回顾一下,让我知道我做错了什么吗Amazon web services 使用AWS胶水创建分区数据并保存到s3中,amazon-web-services,apache-spark,amazon-s3,aws-glue,Amazon Web Services,Apache Spark,Amazon S3,Aws Glue,我有上面的脚本,我不明白为什么不工作,或者它是否是正确的方式 有人能回顾一下,让我知道我做错了什么吗 这里的目标是每天运行此作业,并将此表按上述方式分区,并将其保存在s3 json或parquet中。在操作列时,您引用的数据帧错误 applymapping1.select*实际上应该是df.select* import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from py
这里的目标是每天运行此作业,并将此表按上述方式分区,并将其保存在s3 json或parquet中。在操作列时,您引用的数据帧错误 applymapping1.select*实际上应该是df.select*
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
from pyspark.sql.functions import col,year,month,dayofmonth,to_date,from_unixtime
## @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)
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "db_name", table_name = "table_name", transformation_ctx = "datasource0")
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("dateregistered", "timestamp", "dateregistered", "timestamp"), ("id", "int", "id", "int")], transformation_ctx = "applymapping1")
df = applymapping1.toDF()
repartitioned_with_new_columns_df = applymapping1.select("*")
.withColumn("date_col", to_date(from_unixtime(col("dateRegistered"))))
.withColumn("year", year(col("date_col")))
.withColumn("month", month(col("date_col")))
.withColumn("day", dayofmonth(col("date_col")))
.drop(col("date_col"))
#.repartition(1)
dyf = DynamicFrame.fromDF(repartitioned_with_new_columns_df, glueContext, "enriched")
datasink = glueContext.write_dynamic_frame.from_options(
frame = dyf,
connection_type = "s3",
connection_options = {
"path": "bucket-path",
"partitionKeys": ["year", "month", "day"]
},
format = "json",
transformation_ctx = "datasink")
job.commit()