This post is inspired by the content from Brandon Rhodes on The Composition Over Inheritance Principle, which I found particularly intriguing. My aim in this post is to review and rewrite the code examples from my perspective.
Motivation
The debate between inheritance and composition is a longstanding one. While both approaches offer ways to create useful abstractions, they come with their unique challenges and benefits. The distinction, however, sometimes introduces complexities rather than simplifying our design.
A prime example of this complexity is what I refer to as the "2 x 2 problem." This issue arises with inheritance when we encounter scenarios requiring multiple functionalities or behaviors, each of which can have multiple extensions.
Consider the case of a logging system, as illustrated in the referenced article. A logger typically needs to manage two key aspects: filtering and output destination. But what happens when we require multiple filtering methods and various output destinations? The conventional inheritance-based implementation of a Logger class, as we will see, may not be the most maintainable solution:
class Logger:
def __init__(self):
self.content = []
def log(self, message):
self.content.append(message)
return message
class LevelFilteredLogger(Logger):
def __init__(self, level):
self.level = level
super().__init__()
def log(self, message):
if self.level in message:
return super().log(message)
class LengthFilteredLogger(Logger):
def __init__(self, length_limit):
self.length_limit = length_limit
super().__init__()
def log(self, message):
if len(message) < self.length_limit:
return super().log(message)
class STDOutLogger(Logger):
def log(self, message):
print(message)
return super().log(message)
class FileLogger(Logger):
def __init__(self, filename):
self.filename = filename
super().__init__()
def log(self, message):
with open(self.filename, 'a') as f:
f.write(message + '\n')
return super().log(message)
The challenge arises when attempting to create combinations from these two dimensions. A LevelFilteredFileLogger example demonstrates this complexity:
class LevelFilteredFileLogger(LevelFilteredLogger):
def __init__(self, level, filename):
self.filename = filename
super().__init__(level)
def log(self, message):
msg = super().log(message)
if msg:
with open(self.filename, 'a') as f:
f.write(msg + '\n')
return msg
if __name__ == '__main__':
logger = LevelFilteredFileLogger('ERROR', 'log.txt')
logger.log('DEBUG: debug message')
logger.log('INFO: info message')
logger.log('ERROR: error message')
Creating more combinations quickly becomes cumbersome without resorting to multiple inheritance. Additionally, this method of combining functionalities may sometimes fail to maintain the intended behavior.
A solution to this problem will be discussed later on. But first, let us explore a related issue and a possible approach through the adapter pattern.
Adapter Pattern
The adapter pattern proves particularly useful in scenarios where the existing abstraction cannot be modified, often encountered in legacy systems. Here, the original developer may have defined specific "filtering" and "output" functionalities. Subsequently, a requirement emerges to introduce a new functionality, necessitating a combination of this new feature with the existing ones. To illustrate this challenge more effectively, consider the following adjusted code example:
class FileHandler:
def __init__(self, filename):
self.filename = filename
def write(self, message):
with open(self.filename, 'a') as f:
f.write(message + '\n')
class FileLogger:
def __init__(self, file_handler):
self.file_handler = file_handler
def log(self, message):
self.file_handler.write(message)
class LevelFilteredLogger(FileLogger):
def __init__(self, file_handler, level):
self.level = level
super().__init__(file_handler)
def log(self, message):
if self.level in message:
super().log(message)
This scenario depicts a development path where initially, the focus was on supporting file-based logging, followed by the introduction of filtering capabilities. This approach might seem adequate initially. However, the complexity resurfaces with the need to incorporate another filtering method.
To address this using the adapter pattern, the strategy involves:
- First, create a
STDOutHandler dedicated to outputting logs to the standard output.
- Subsequently, a
STDOutAdapter is developed to bridge the STDOutHandler with the FileHandler interface expected by the FileLogger. This is crucial because, at this juncture, the FileLogger lacks the capability to interact with a STDOutHandler, and modifications to the FileLogger are undesirable.
- The adapter effectively facilitates the substitution of
FileHandler with any alternative that conforms to the required interface.
- By employing the
STDOutAdapter, it becomes feasible to integrate it within the LevelFilteredLogger.
- Here, the
STDOutAdapter is created to comply with the FileHandler interface, encapsulating a STDOutHandler. It delegates the execution of the write function to the STDOutHandler.
The code illustrating the implementation of these additional classes is as follows:
class STDOutHandler:
def log(self, message):
print(message)
class STDOutAdapter(FileHandler):
def __init__(self):
self.STDOutLogger = STDOutHandler()
super().__init__(None)
def write(self, message):
self.STDOutLogger.log(message)
class LengthFilteredLogger(FileLogger):
def __init__(self, file_handler, length_limit):
self.length_limit = length_limit
super().__init__(file_handler)
def log(self, message):
if len(message) < self.length_limit:
super().log(message)
if __name__ == '__main__':
file_handler = FileHandler('log.txt')
file_logger = FileLogger(file_handler)
file_logger.log('Hello World')
std_out_handler = STDOutAdapter()
std_out_logger = FileLogger(std_out_handler)
std_out_logger.log('Hello World')
level_filtered_file_logger = LevelFilteredLogger(std_out_handler, 'INFO')
level_filtered_file_logger.log('INFO: Hello World')
level_filtered_file_logger.log('DEBUG: Hello World')
length_filtered_std_logger = LengthFilteredLogger(std_out_handler, 10)
length_filtered_std_logger.log('Hello World')
length_filtered_std_logger.log('Hello')
This approach presents a viable solution when modifications to the existing codebase are not an option. By leveraging the adapter pattern, we've effectively minimized the necessity for multiple classes, enabling the straightforward replacement of one handler with another.
Bridge Pattern
Building on the previous discussion, the bridge pattern also emerges as an effective strategy to address the outlined challenges. This pattern comes into play when there are no constraints against modifying the original source code. Unlike the adapter pattern, which necessitates conforming to an existing, possibly ill-suited interface due to unanticipated extensions, the bridge pattern advocates for establishing necessary abstractions upfront.
In contrast to the somewhat cumbersome adaptation required to fit the FileHandler interface in the adapter pattern scenario, the bridge pattern would have us define a Handler class at the outset. This foundational class would then serve as a base for both STDOutHandler and FileHandler, each implementing the Handler class's interface methods. This design allows for the straightforward addition of new handler types, ensuring the system's extensibility and maintainability.
import abc
class Handler:
@abc.abstractmethod
def write(self, message):
pass
class STDOutHandler:
def write(self, message):
print(message)
class FileHandler:
def __init__(self, filename):
self.filename = filename
def write(self, message):
with open(self.filename, 'a') as f:
f.write(message + '\n')
With the bridge pattern's application, the Logger is designed to accommodate any Handler, thereby significantly simplifying the process of extending logging destinations. The structure of the Logger class, adhering to this pattern, would be as follows:
class Logger:
def __init__(self, handler):
self.handler = handler
def log(self, message):
self.handler.write(message)
The LevelFilteredLogger and LengthFilteredLogger would look like this:
class LevelFilteredLogger(Logger):
def __init__(self, handler, level):
self.level = level
super().__init__(handler)
def log(self, message):
if self.level in message:
super().log(message)
class LengthFilteredLogger(Logger):
def __init__(self, handler, length_limit):
self.length_limit = length_limit
super().__init__(handler)
def log(self, message):
if len(message) < self.length_limit:
super().log(message)
if __name__ == '__main__':
file_handler = FileHandler('log.txt')
file_logger = Logger(file_handler)
file_logger.log('Hello World')
std_out_handler = STDOutHandler()
std_out_logger = Logger(std_out_handler)
std_out_logger.log('Hello World')
level_filtered_std_logger = LevelFilteredLogger(std_out_handler, 'INFO')
level_filtered_std_logger.log('INFO: Hello World')
level_filtered_std_logger.log('DEBUG: Hello World')
length_filtered_file_logger = LengthFilteredLogger(file_handler, 10)
length_filtered_file_logger.log('Hello World')
length_filtered_file_logger.log('Hello')
Decorator Pattern
The decorator pattern also offers a unique perspective on addressing this issue. It encourages the definition of classes that maintain identical signatures, enabling their encapsulation within one another to facilitate stackable behavior. In the context of loggers, this approach begins with the establishment of destination-based logging classes:
class STDOutLogger:
def log(self, message):
print(message)
class FileLogger:
def __init__(self, filename):
self.filename = filename
def log(self, message):
with open(self.filename, 'a') as f:
f.write(message + '\n')
Then, we define the filtering-based logging classes to contain destination-based loggers:
class LevelFilteredLogger:
def __init__(self, logger, level):
self.logger = logger
self.level = level
def log(self, message):
if self.level in message:
self.logger.log(message)
class LengthFilteredLogger:
def __init__(self, logger, length_limit):
self.logger = logger
self.length_limit = length_limit
def log(self, message):
if len(message) < self.length_limit:
self.logger.log(message)
The strength of the decorator pattern is evident in its flexibility to combine functionalities such as LevelFiltering and LengthFiltering. This can be achieved by instantiating a LevelFilteredLogger that encapsulates an instance of LengthFiltering, thus avoiding the necessity to define a new class for this combined functionality.
if __name__ == '__main__':
file_logger = FileLogger('log.txt')
file_logger.log('Hello World')
std_out_logger = STDOutLogger()
std_out_logger.log('Hello World')
level_filtered_std_logger = LevelFilteredLogger(std_out_logger, 'INFO')
level_filtered_std_logger.log('INFO: Hello World')
level_filtered_std_logger.log('DEBUG: Hello World')
length_filtered_file_logger = LengthFilteredLogger(file_logger, 10)
length_filtered_file_logger.log('Hello World')
length_filtered_file_logger.log('Hello')
length_and_level_filtered_logger = LengthFilteredLogger(level_filtered_std_logger, 10)
length_and_level_filtered_logger.log('INFO: Hello World Again')
length_and_level_filtered_logger.log('DEBUG: Hello World')
length_and_level_filtered_logger.log('INFO: HI')
A Combination of Patterns
To synthesize the various strategies discussed, a comprehensive solution might be structured as follows:
import abc
class Filter:
@abc.abstractmethod
def should_keep(self, message):
pass
class Handler:
@abc.abstractmethod
def write(self, message):
pass
class Logger:
def __init__(self, filters: list[Filter], handlers: list[Handler]):
self.filters = filters
self.handlers = handlers
def log(self, message):
if all(f.should_keep(message) for f in self.filters):
for h in self.handlers:
h.write(message)
class LevelFilter:
def __init__(self, level):
self.level = level
def should_keep(self, message):
return self.level in message
class LengthFilter:
def __init__(self, length_limit):
self.length_limit = length_limit
def should_keep(self, message):
return len(message) < self.length_limit
class FileHandler:
def __init__(self, filename):
self.filename = filename
def write(self, message):
with open(self.filename, 'a') as f:
f.write(message + '\n')
class STDOutHandler:
def write(self, message):
print(message)
if __name__ == '__main__':
file_handler = FileHandler('log.txt')
length_file_logger = Logger([LengthFilter(10)], [file_handler])
length_file_logger.log('Hello World')
std_out_handler = STDOutHandler()
level_std_out_logger = Logger([LevelFilter('INFO')], [std_out_handler])
level_std_out_logger.log('INFO: Hello World')
length_and_level_logger = Logger([LengthFilter(10), LevelFilter('INFO')], [file_handler, std_out_handler])
length_and_level_logger.log('INFO: Hello World')
Conclusion
The utility of design patterns becomes significantly clearer when contextualized with specific problems and examples. Each pattern offers a distinct approach to resolution, with certain strategies proving more effective under specific circumstances, particularly in relation to constraints like the feasibility of altering the original source code or the initial design. In summary, several key principles underlie these patterns:
- Aggregation provides a broader range of possibilities compared to inheritance.
- The establishment of abstractions (interfaces) facilitates easier extension of the codebase.
- While indirection can offer valuable flexibility, it also has the potential to introduce complexity and confusion.
References