以下是一个使用AWS Glue的Python代码示例,用于从S3读取分区数据并将分区作为DynamicFrame的列添加:
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from awsglue.context import GlueContext
from pyspark.context import SparkContext
# 创建GlueContext
sc = SparkContext()
glueContext = GlueContext(sc)
# 获取传递给脚本的参数
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
# 创建DynamicFrame
datasource = glueContext.create_dynamic_frame.from_catalog(database = "your_database_name",
table_name = "your_table_name",
transformation_ctx = "datasource")
# 从S3读取分区数据
partitioned_data = glueContext.create_dynamic_frame.from_options(connection_type = "s3",
connection_options = {"paths": ["s3://your_bucket_name/your_partitioned_data_path/"]},
format = "your_data_format",
format_options = {"your_format_options": "your_value"},
transformation_ctx = "partitioned_data")
# 将分区作为DynamicFrame的列添加
joined_data = Join.apply(datasource, partitioned_data, 'your_join_key', 'your_join_type')
# 打印DynamicFrame的内容
joined_data.show()
# 将DynamicFrame保存回S3
glueContext.write_dynamic_frame.from_options(frame = joined_data,
connection_type = "s3",
connection_options = {"path": "s3://your_bucket_name/your_output_path/"},
format = "your_output_format",
format_options = {"your_format_options": "your_value"})
# 作业结束
sc.stop()
请根据你的实际情况修改示例中的"your_database_name"、"your_table_name"、"your_bucket_name"、"your_partitioned_data_path"、"your_data_format"、"your_format_options"、"your_join_key"、"your_join_type"、"your_output_path"、"your_output_format"和"your_format_options"等参数。