要将简单的JSON解析为CSV格式,您可以使用AWS的Athena和Glue服务。以下是解决方案的步骤和代码示例:
CREATE DATABASE IF NOT EXISTS mydatabase;
USE mydatabase;
CREATE EXTERNAL TABLE IF NOT EXISTS mytable (
col1 string,
col2 int,
col3 string
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1'
) LOCATION 's3://mybucket/myfolder/'
TBLPROPERTIES ('has_encrypted_data'='false');
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
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 = "mydatabase", table_name = "mytable")
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("col1", "string", "col1", "string"), ("col2", "int", "col2", "int"), ("col3", "string", "col3", "string")], 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_catalog(frame = dropnullfields3, database = "mydatabase", table_name = "mytable_csv", transformation_ctx = "datasink4")
job.commit()
运行ETL脚本:将ETL脚本上传到AWS Glue,并运行它以将JSON数据转换为CSV格式并加载到Athena中。
查询数据:在Athena中查询转换后的CSV数据。
SELECT * FROM mydatabase.mytable_csv;
以上是使用Athena和Glue将简单的JSON解析为CSV格式的解决方案和代码示例。您可以根据您的具体需求和JSON结构进行适当的调整。