存在一些解决Apache Flink Python Table API UDF依赖问题的方法。下面是一种可能的解决方法,包含了代码示例。
$ virtualenv pyflink_env
$ source pyflink_env/bin/activate
(pyflink_env) $ pip install apache-flink
from pyflink.table import EnvironmentSettings, StreamTableEnvironment
# 创建TableEnvironment
settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
table_env = StreamTableEnvironment.create(environment_settings=settings)
# 注册UDF函数
table_env.create_temporary_function("my_udf", my_udf_function)
# 查询数据
table_env.execute_sql("""
CREATE TABLE source_table (
`id` INT,
`name` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'input_topic',
...
)
""")
table_env.execute_sql("""
CREATE TABLE result_table (
`id` INT,
`name` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'output_topic',
...
)
""")
table_env.from_path('source_table').select("id, my_udf(name)").insert_into('result_table')
# 执行任务
table_env.execute("UDF Example")
(pyflink_env) $ pip install apache-flink pyflink
(pyflink_env) $ zip -r my_udf_job.zip my_udf_job.py pyflink_env/lib/python3.7/site-packages
(pyflink_env) $ flink run -py my_udf_job.zip
这样,您就可以在Apache Flink Python Table API中使用自定义的UDF函数,并解决了依赖问题。