Beside inheritance and composition, object-oriented programmers often speak of 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 logging or validating accesses, adding extra steps to interface components, or monitoring active instances.

In a sense, delegation is a special form of composition, with a single embedded object managed by a wrapper (sometimes called a proxy) class that retains most or all of the embedded object’s interface. The notion of proxies sometimes applies to other mechanisms too, such as function calls; in delegation, we’re concerned with proxies for all of an object’s behavior, including method calls and other operations.

This concept was introduced by example in Chapter 28, and in Python is often implemented with the __getattr__ method hook we studied in Chapter 30. Because this operator overloading method intercepts accesses to nonexistent attributes, a wrapper class can use __getattr__ to route arbitrary accesses to a wrapped object. Because this method allows attribute requests to be routed generically, the wrapper class retains the interface of the wrapped object and may add additional operations of its own.

By way of review, consider the file trace.py (which runs the same in 2.X and 3.X):

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 30 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 Chapter 22 and Chapter 29 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, adapt a class to a new interface, and so on.

We’ll revive the notions of wrapped objects and delegated operations as one way to extend built-in types in the next chapter. If you are interested in the delegation design pattern, also watch for the discussions in Chapter 32 and Chapter 39 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.