Decorator Benchmarks¶

The wrapt module ensures that your decorators will work in all situations. The implementation therefore does not take the shortcuts that people usually take with decorators of using function closures. Instead it implements the wrappers as a class, which also acts as a descriptor. Ensuring correctness though does come at an additional cost in runtime overhead. The following attempts to quantify what that overhead is and compare it to other solutions typically used.

Results were collected under MacOS X Mountain Lion on a 2012 model MacBook Pro, running with Python 2.7.

Undecorated Calls¶

These tests provide a baseline for comparing decorated functions against a normal undecorated function call.

Test Code:

def function1():
    pass

class Class(object):

    def function1(self):
        pass

    @classmethod
    def function1cm(cls):
        pass

    @staticmethod
    def function1sm():
        pass

Test Results:

$ python -m timeit -s 'import benchmarks' 'benchmarks.function1()'
10000000 loops, best of 3: 0.132 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function1()'
10000000 loops, best of 3: 0.143 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function1cm()'
1000000 loops, best of 3: 0.217 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function1cm()'
10000000 loops, best of 3: 0.159 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function1sm()'
1000000 loops, best of 3: 0.199 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function1sm()'
10000000 loops, best of 3: 0.13 usec per loop

Note that differences between calling the class and static methods via the class vs the instance are possibly more to do with needing to traverse the dotted path.

Function Closures¶

These tests provide results for decorated functions where the decorators are implemented using function closures.

Test Code:

def wrapper2(func):
    def _wrapper2(*args, **kwargs):
        return func(*args, **kwargs)
    return _wrapper2

@wrapper2
def function2():
    pass

class Class(object):

    @wrapper2
    def function2(self):
        pass

    @classmethod
    @wrapper2
    def function2cmi(cls):
        pass

    @staticmethod
    @wrapper2
    def function2smi():
        pass

Test Results:

$ python -m timeit -s 'import benchmarks' 'benchmarks.function2()'
1000000 loops, best of 3: 0.326 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function2()'
1000000 loops, best of 3: 0.382 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function2cmi()'
1000000 loops, best of 3: 0.46 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function2cmi()'
1000000 loops, best of 3: 0.384 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function2smi()'
1000000 loops, best of 3: 0.389 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function2smi()'
1000000 loops, best of 3: 0.319 usec per loop

Note that decorators implemented as function closures cannot be added around staticmethod and classmethod decorators and must be added inside of those decorators.

wrapt.decorator¶

These tests provides results for decorated functions where the decorators are implemented using the wrapt module. Separate results are provided for when using the C extension and when using the pure Python implementation.

Test Code:

@wrapt.decorator
def wrapper3(wrapped, instance, args, kwargs):
    return wrapped(*args, **kwargs)

@wrapper3
def function3():
    pass

class Class(object):

    @wrapper3
    def function3(self):
        pass

    @wrapper3
    @classmethod
    def function3cmo(cls):
        pass

    @classmethod
    @wrapper3
    def function3cmi(cls):
        pass

    @wrapper3
    @staticmethod
    def function3smo():
        pass

    @staticmethod
    @wrapper3
    def function3smi():
        pass

Test Results (C Extension):

$ python -m timeit -s 'import benchmarks' 'benchmarks.function3()'
1000000 loops, best of 3: 0.382 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3()'
1000000 loops, best of 3: 0.836 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3cmo()'
1000000 loops, best of 3: 1.11 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3cmo()'
1000000 loops, best of 3: 1.06 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3cmi()'
1000000 loops, best of 3: 0.535 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3cmi()'
1000000 loops, best of 3: 0.455 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3smo()'
1000000 loops, best of 3: 1.37 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3smo()'
1000000 loops, best of 3: 1.31 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3smi()'
1000000 loops, best of 3: 0.453 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3smi()'
1000000 loops, best of 3: 0.378 usec per loop

Note that results for where the decorator is inside that of the classmethod decorator is quite a bit less than that where it is outside. This due to a potential bug in Python whereby it doesn’t apply the descriptor protocol to what the classmethod decorator wraps. Instead it is executing a straight function call, which has less overhead.

Test Results (Pure Python):

$ python -m timeit -s 'import benchmarks' 'benchmarks.function3()'
1000000 loops, best of 3: 0.771 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3()'
100000 loops, best of 3: 6.67 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3cmo()'
100000 loops, best of 3: 6.89 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3cmo()'
100000 loops, best of 3: 6.77 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3cmi()'
1000000 loops, best of 3: 0.911 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3cmi()'
1000000 loops, best of 3: 0.863 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3smo()'
100000 loops, best of 3: 7.26 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3smo()'
100000 loops, best of 3: 7.17 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function3smi()'
1000000 loops, best of 3: 0.835 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function3smi()'
1000000 loops, best of 3: 0.774 usec per loop

Note that results for where the decorator is inside that of the classmethod decorator is quite a bit less than that where it is outside. This due to a potential bug in Python whereby it doesn’t apply the descriptor protocol to what the classmethod decorator wraps. Instead it is executing a straight function call, which has less overhead.

decorator.decorator¶

These tests provides results for decorated functions where the decorators are implemented using the decorator module available from PyPi.

Test Code:

@decorator.decorator
def wrapper4(wrapped, *args, **kwargs):
    return wrapped(*args, **kwargs)

@wrapper4
def function4():
    pass

class Class(object):

    @wrapper4
    def function4(self):
        pass

    @classmethod
    @wrapper4
    def function4cmi(cls):
        pass

    @staticmethod
    @wrapper4
    def function4smi():
        pass

Test Results:

$ python -m timeit -s 'import benchmarks' 'benchmarks.function4()'
1000000 loops, best of 3: 0.465 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function4()'
1000000 loops, best of 3: 0.537 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function4cmi()'
1000000 loops, best of 3: 0.606 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function4cmi()'
1000000 loops, best of 3: 0.533 usec per loop

$ python -m timeit -s 'import benchmarks' 'benchmarks.Class.function4smi()'
1000000 loops, best of 3: 0.532 usec per loop

$ python -m timeit -s 'import benchmarks; c=benchmarks.Class()' 'c.function4smi()'
1000000 loops, best of 3: 0.456 usec per loop

Note that decorators implemented using the decorator module cannot be added around staticmethod and classmethod decorators and must be added inside of those decorators.