OOP and Delegation: “Wrapper” Objects

Beside inheritance and composition, object-oriented programmers often also talk about something called delegation, which usually implies controller objects that embed other objects to which they pass off operation requests. The controllers can take care of administrative activities, such as keeping track of accesses and so on. In Python, delegation is often implemented with the __getattr__ method hook; because it intercepts accesses to nonexistent attributes, a wrapper class (sometimes called a proxy class) can use __getattr__ to route arbitrary accesses to a wrapped object. The wrapper class retains the interface of the wrapped object and may add additional operations of its own.

Consider the file trace.py, for instance:

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class wrapper:
    def __init__(self, object):
        self.wrapped = object                    # Save object
    def __getattr__(self, attrname):
        print('Trace:', attrname)                # Trace fetch
        return getattr(self.wrapped, attrname)   # Delegate fetch

Recall from Chapter 29 that __getattr__ gets the attribute name as a string. This code makes use of the getattr built-in function to fetch an attribute from the wrapped object by name string—getattr(X,N) is like X.N, except that N is an expression that evaluates to a string at runtime, not a variable. In fact, getattr(X,N) is similar to X.__dict__[N], but the former also performs an inheritance search, like X.N, while the latter does not (see Namespace Dictionaries for more on the __dict__ attribute).

You can use the approach of this module’s wrapper class to manage access to any object with attributes—lists, dictionaries, and even classes and instances. Here, the wrapper class simply prints a trace message on each attribute access and delegates the attribute request to the embedded wrapped object:

>>> from trace import wrapper
>>> x = wrapper([1,2,3])                         # Wrap a list
>>> x.append(4)                                  # Delegate to list method
Trace: append
>>> x.wrapped                                    # Print my member
[1, 2, 3, 4]

>>> x = wrapper({"a": 1, "b": 2})                # Wrap a dictionary
>>> list(x.keys())                               # Delegate to dictionary method
Trace: keys
['a', 'b']

The net effect is to augment the entire interface of the wrapped object, with additional code in the wrapper class. We can use this to log our method calls, route method calls to extra or custom logic, and so on.

We’ll revive the notions of wrapped objects and delegated operations as one way to extend built-in types in Chapter 31. If you are interested in the delegation design pattern, also watch for the discussions in Chapters 31 and 38 of function decorators, a strongly related concept designed to augment a specific function or method call rather than the entire interface of an object, and class decorators, which serve as a way to automatically add such delegation-based wrappers to all instances of a class.

Note

Version skew note: In Python 2.6, operator overloading methods run by built-in operations are routed through generic attribute interception methods like __getattr__. Printing a wrapped object directly, for example, calls this method for __repr__ or __str__, which then passes the call on to the wrapped object. In Python 3.0, this no longer happens: printing does not trigger __getattr__, and a default display is used instead. In 3.0, new-style classes look up operator overloading methods in classes and skip the normal instance lookup entirely. We’ll return to this issue in Chapter 37, in the context of managed attributes; for now, keep in mind that you may need to redefine operator overloading methods in wrapper classes (either by hand, by tools, or by superclasses) if you want them to be intercepted in 3.0.