hequn8128 commented on a change in pull request #11342: [FLINK-16483][python]
Add Python building blocks to make sure the basic functionality of vectorized
Python UDF could work
URL: https://github.com/apache/flink/pull/11342#discussion_r389532863
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File path: flink-python/pyflink/table/udf.py
##########
@@ -297,15 +308,17 @@ def _get_python_env():
return
gateway.jvm.org.apache.flink.table.functions.python.PythonEnv(exec_type)
-def _create_udf(f, input_types, result_type, deterministic, name):
- return UserDefinedScalarFunctionWrapper(f, input_types, result_type,
deterministic, name)
+def _create_udf(f, input_types, result_type, udf_type, deterministic, name):
+ return UserDefinedScalarFunctionWrapper(
+ f, input_types, result_type, udf_type, deterministic, name)
def _create_udtf(f, input_types, result_types, deterministic, name):
return UserDefinedTableFunctionWrapper(f, input_types, result_types,
deterministic, name)
-def udf(f=None, input_types=None, result_type=None, deterministic=None,
name=None):
+def udf(f=None, input_types=None, result_type=None, deterministic=None,
name=None,
+ udf_type="general"):
Review comment:
How about adding a `PythonFunctionKind` class which contains `GENERAL` and
`PANDAS`? It is consistent with Java and also would be easier for users to add
the parameter.
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