在研究Flink的“用户自定义方法”（UserDefinedFunction）时，我们看到存在如下几种类型的装饰器：

1. UDF：User Defined Scalar Function
2. UDTF：User Defined Table Function
3. UDAF：User Defined Aggregate Function
4. UDTAF：User Defined Table Aggregate Function

在很多案例中，我们看到udf、udtf和udaf几个装饰器修饰function

@udf(result_type=DataTypes.BIGINT())
def add(i, j):return i + j@udtf(result_types=[DataTypes.BIGINT(), DataTypes.BIGINT()])
def range_emit(s, e):for i in range(e):yield s, i@udaf(result_type=DataTypes.FLOAT(), func_type="pandas")
def mean_udaf(v):return v.mean()

但是没有见到udtaf修饰function的案例，比如

# 错误的
@udtaf(result_type=DataTypes.ROW([DataTypes.FIELD("word", DataTypes.STRING()) , DataTypes.FIELD("count", DataTypes.BIGINT())]), accumulator_type=DataTypes.ROW([DataTypes.FIELD("word", DataTypes.STRING())]), func_type="general")
def lower(line):yield Row('a', 1)

这是因为这儿存在一个悖论

udtaf要求func_type必须是general

def udtaf(f: Union[Callable, TableAggregateFunction, Type] = None,input_types: Union[List[DataType], DataType, str, List[str]] = None,result_type: Union[DataType, str] = None,accumulator_type: Union[DataType, str] = None,deterministic: bool = None, name: str = None,func_type: str = 'general') -> Union[UserDefinedAggregateFunctionWrapper, Callable]:"""Helper method for creating a user-defined table aggregate function.:param f: user-defined table aggregate function.:param input_types: optional, the input data types.:param result_type: the result data type.:param accumulator_type: optional, the accumulator data type.:param deterministic: the determinism of the function's results. True if and only if a call tothis function is guaranteed to always return the same result given thesame parameters. (default True):param name: the function name.:param func_type: the type of the python function, available value: general(default: general):return: UserDefinedAggregateFunctionWrapper or function... versionadded:: 1.13.0"""if func_type != 'general':raise ValueError("The func_type must be 'general', got %s."% func_type)if f is None:return functools.partial(_create_udtaf, input_types=input_types, result_type=result_type,accumulator_type=accumulator_type, func_type=func_type,deterministic=deterministic, name=name)else:return _create_udtaf(f, input_types, result_type, accumulator_type, func_type,deterministic, name)

如果func_type不是’general’，则会抛出错误，所以func_type="pandas"是不可以的。
udtaf修饰方法后的返回类型是UserDefinedAggregateFunctionWrapper。

def _create_udtaf(f, input_types, result_type, accumulator_type, func_type, deterministic, name):return UserDefinedAggregateFunctionWrapper(f, input_types, result_type, accumulator_type, func_type, deterministic, name, True)

delegate function要求非func_type必须是pandas

Table API下只有这些方法接受udtaf修饰function返回的UserDefinedAggregateFunctionWrapper。

• def aggregate(self, func: Union[Expression, UserDefinedAggregateFunctionWrapper]) -> ‘AggregatedTable’
• def flat_aggregate(self, func: Union[Expression, UserDefinedAggregateFunctionWrapper]) -> ‘FlatAggregateTable’

这些方法的在底层会调用被修饰的UserDefinedFunctionWrapper。

    def aggregate(self, func: Union[Expression, UserDefinedAggregateFunctionWrapper]) \-> 'AggregatedTable':"""Performs a global aggregate operation with an aggregate function. You have to close theaggregate with a select statement... versionadded:: 1.13.0"""if isinstance(func, Expression):return AggregatedTable(self._j_table.aggregate(func._j_expr), self._t_env)else:func._set_takes_row_as_input()if hasattr(func, "_alias_names"):alias_names = getattr(func, "_alias_names")func = func(with_columns(col("*"))).alias(*alias_names)else:func = func(with_columns(col("*")))return AggregatedTable(self._j_table.aggregate(func._j_expr), self._t_env)

进而会调用到_java_user_defined_function。由于udtaf修饰的方法不是UserDefinedFunction对象，而是一个function，所以它会通过_create_delegate_function创建新的func 。

class UserDefinedFunctionWrapper(object):
……def _java_user_defined_function(self):……if not isinstance(self._func, UserDefinedFunction):func = self._create_delegate_function()……

而_create_delegate_function则要求udtaf中的function的func_type必须是pandas

    def _create_delegate_function(self) -> UserDefinedFunction:assert self._func_type == 'pandas'return DelegatingPandasAggregateFunction(self._func)

这就和之前udtaf中要求func_type必须是general相背。
所以我们没看到udtaf修饰function的案例。