Decorator is a very expressive language feature in python. It helps developers to write cleaner, modular code that is easier to extend and maintain. It also helps implement
AOP and
decorator pattern in python.
Decorator syntaxTo declare a decorator function, we can define a function that takes a another function as argument and returns a function. The usage of decorator is very simple, just add a line begins with '@' symbol and the name of the decorator before the function to be decorated.
1 def decorator(func):
2 def new_func():
3 print "decorator message"
4 func()
5 return new_func
6
7 @decorator
8 def foo():
9 print "hello world"
10
11 foo()
The effect is when we call foo function, besides print "hello world" message, the message "decorator message" is also printed. That is, the decorator function extends foo function's behavior.
The code above is equivalent to:
1 def decorator(func):
2 def new_func():
3 print "decorator message"
4 func()
5 return new_func
6
7 def foo():
8 print "hello world"
9
10 foo = decorator(foo)
11 foo()
Decorators can also accept arguments, as long as it returns a decorator function that takes a function as argument.
1 def decorator_with_arg(arg):
2 def decorator(func):
3 def new_func():
4 print arg
5 func()
6 return new_func
7 return decorator
8
9 @decorator_with_arg("arg for decorator")
10 def foo():
11 print "hello world"
12
13 foo()
The example above is equivalent to :
1 def decorator_with_arg(arg):
2 def decorator(func):
3 def new_func():
4 print arg
5 func()
6 return new_func
7 return decorator
8
9 def foo():
10 print "hello world"
11
12 foo = decorator_with_arg("arg for decorator")(foo)
13 foo()
The decorator_with_arg function creates a
closure, so that the arg argument can still be used after the decorator_with_arg returned. Since it's possible for a decorator to accept arguments, the decorator's behavior can changed based on the argument passed in. So it's possible to write more flexible code.
A more practical exampleHere is a more practical example. We create a tracable decorator which is a debugging utility. It will keep records of the number of times that a function decorated with it is invoked.
1 trace_log = {}
2
3 def tracable(func):
4 def decorated_func(*arg, **kwarg):
5 if not trace_log.has_key(func.__name__):
6 trace_log[func.__name__] = 1
7 else:
8 trace_log[func.__name__] += 1
9 func(*arg, **kwarg)
10
11 return decorated_func
12
13 def print_trace_log():
14 for key, value in trace_log.items():
15 print "%s called %d times"%(key, value)
16
17 @tracable
18 def foo1():
19 print "foo1"
20
21 @tracable
22 def foo2(arg, kwd="keyword arg"):
23 print "foo2"
24 print arg
25 print kwd
26
27 @tracable
28 def foo3():
29 print "foo3"
30
31 foo1()
32 foo1()
33 foo1()
34 foo2(12)
35 foo2(13)
36
37 print_trace_log()
If we run the code, it will prints that foo1 function is called three times and foo2 function is called twice.
As the example showed, the code for implementing tracing is separated from business logic code contained within foo1 and foo2. The maintainability of the program is much higher than if the code for tracing is mixed with business logic code.
SummaryWe got several benefits from decorator.
First, it helps achieve a better separation of business logic code and auxiliary code.
Second, it helps finding out where the auxiliary is used because decorator employs a very special syntax.
Third, we can extend or change our business logic without having to change existing code. New code can be implemented as a decorator.
References:Charming Python: Decorators make magic easyDecorators for Functions and MethodsPythonDecorators
