AWS Приклеить строку задания ETL к отметке времени для Athena - PullRequest
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AWS Приклеить строку задания ETL к отметке времени для Athena

0 голосов
/ 12 июля 2020

1000x CSV-файлов, находящихся в S3. Я создал задание ETL, чтобы преобразовать их в Parquet для Афины. Когда они у меня есть в Афине, я использую следующий запрос для преобразования метки времени в допустимый формат для Афины. 

SELECT table.col0, 
       Coalesce(
         try(date_parse(table.col0, '%m/%d/%Y %H:%i:%s')),
         try(date_parse(table.col0, '%m/%d/%Y %H:%i'))
       ) as DateConvertedToTimestamp, col1, col2, col3, col4 FROM "database"."table"

Как я могу определить задание преобразования метки времени в AWS Glue ETL? Ниже приведены журналы текущего задания ETL 

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 = "miso", table_name = "lmp_rt_5min", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "miso", table_name = "lmp_rt_5min", transformation_ctx = "datasource0")
## @type: ApplyMapping
## @args: [mapping = [("col0", "string", "col0", "string"), ("col1", "string", "col1", "string"), ("col2", "double", "col2", "float"), ("col3", "double", "col3", "float"), ("col4", "double", "col4", "float")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("col0", "string", "col0", "string"), ("col1", "string", "col1", "string"), ("col2", "double", "col2", "float"), ("col3", "double", "col3", "float"), ("col4", "double", "col4", "float")], 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://commercialanalytics/MISO/LMP_RT_5min/Parquet"}, 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://commercialanalytics/MISO/LMP_RT_5min/Parquet"}, format = "parquet", transformation_ctx = "datasink4")
job.commit()

Заранее благодарим.

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