The Pattern Concept¶
“Design patterns help you learn from others’ successes instead of your own failures .”
Probably the most important step forward in object-oriented design is the “design patterns” movement, chronicled in Design Patterns (ibid) . That book shows 23 different solutions to particular classes of problems. In this book, the basic concepts of design patterns will be introduced along with examples. This should whet your appetite to read Design Patterns by Gamma, et. al., a source of what has now become an essential, almost mandatory, vocabulary for OOP programmers.
The latter part of this book contains an example of the design evolution process, starting with an initial solution and moving through the logic and process of evolving the solution to more appropriate designs. The program shown (a trash sorting simulation) has evolved over time, and you can look at that evolution as a prototype for the way your own design can start as an adequate solution to a particular problem and evolve into a flexible approach to a class of problems.
What is a Pattern?¶
Initially, you can think of a pattern as an especially clever and insightful way of solving a particular class of problems. That is, it looks like a lot of people have worked out all the angles of a problem and have come up with the most general, flexible solution for it. The problem could be one you have seen and solved before, but your solution probably didn’t have the kind of completeness you’ll see embodied in a pattern.
Although they’re called “design patterns,” they really aren’t tied to the realm of design. A pattern seems to stand apart from the traditional way of thinking about analysis, design, and implementation. Instead, a pattern embodies a complete idea within a program, and thus it can sometimes appear at the analysis phase or high-level design phase. This is interesting because a pattern has a direct implementation in code and so you might not expect it to show up before low-level design or implementation (and in fact you might not realize that you need a particular pattern until you get to those phases).
The basic concept of a pattern can also be seen as the basic concept of program design: adding a layer of abstraction. Whenever you abstract something you’re isolating particular details, and one of the most compelling motivations behind this is to separate things that change from things that stay the same. Another way to put this is that once you find some part of your program that’s likely to change for one reason or another, you’ll want to keep those changes from propagating other changes throughout your code. Not only does this make the code much cheaper to maintain, but it also turns out that it is usually simpler to understand (which results in lowered costs).
Often, the most difficult part of developing an elegant and cheap-to-maintain design is in discovering what I call “the vector of change.” (Here, “vector” refers to the maximum gradient and not a container class.) This means finding the most important thing that changes in your system, or put another way, discovering where your greatest cost is. Once you discover the vector of change, you have the focal point around which to structure your design.
So the goal of design patterns is to isolate changes in your code. If you look at it this way, you’ve been seeing some design patterns already in this book. For example, inheritance can be thought of as a design pattern (albeit one implemented by the compiler). It allows you to express differences in behavior (that’s the thing that changes) in objects that all have the same interface (that’s what stays the same). Composition can also be considered a pattern, since it allows you to change-dynamically or statically-the objects that implement your class, and thus the way that class works.
Another pattern that appears in Design Patterns is the iterator, which has been implicitly available in for loops from the beginning of the language, and was introduced as an explicit feature in Python 2.2. An iterator allows you to hide the particular implementation of the container as you’re stepping through and selecting the elements one by one. Thus, you can write generic code that performs an operation on all of the elements in a sequence without regard to the way that sequence is built. Thus your generic code can be used with any object that can produce an iterator.
The Design Patterns book discusses 23 different patterns, classified under three purposes (all of which revolve around the particular aspect that can vary). The three purposes are:
- Creational: how an object can be created. This often involves isolating the details of object creation so your code isn’t dependent on what types of objects there are and thus doesn’t have to be changed when you add a new type of object. The aforementioned Singleton is classified as a creational pattern, and later in this book you’ll see examples of Factory Method and Prototype.
- Structural: designing objects to satisfy particular project constraints. These work with the way objects are connected with other objects to ensure that changes in the system don’t require changes to those connections.
- Behavioral: objects that handle particular types of actions within a program. These encapsulate processes that you want to perform, such as interpreting a language, fulfilling a request, moving through a sequence (as in an iterator), or implementing an algorithm. This book contains examples of the Observer and the Visitor patterns.
The Design Patterns book has a section on each of its 23 patterns along with one or more examples for each, typically in C++ but sometimes in Smalltalk. (You’ll find that this doesn’t matter too much since you can easily translate the concepts from either language into Python.) This book will not repeat all the patterns shown in Design Patterns since that book stands on its own and should be studied separately. Instead, this book will give some examples that should provide you with a decent feel for what patterns are about and why they are so important.
After years of looking at these things, it began to occur to me that the patterns themselves use basic principles of organization, other than (and more fundamental than) those described in Design Patterns. These principles are based on the structure of the implementations, which is where I have seen great similarities between patterns (more than those expressed in Design Patterns). Although we generally try to avoid implementation in favor of interface, I have found that it’s often easier to think about, and especially to learn about, the patterns in terms of these structural principles. This book will attempt to present the patterns based on their structure instead of the categories presented in Design Patterns.
One of the events that’s occurred with the rise of design patterns is what could be thought of as the “pollution” of the term - people have begun to use the term to mean just about anything synonymous with “good.” After some pondering, I’ve come up with a sort of hierarchy describing a succession of different types of categories:
- Idiom: how we write code in a particular language to do this particular type of thing. This could be something as common as the way that you code the process of stepping through an array in C (and not running off the end).
- Specific Design: the solution that we came up with to solve this particular problem. This might be a clever design, but it makes no attempt to be general.
- Standard Design: a way to solve this kind of problem. A design that has become more general, typically through reuse.
- Design Pattern: how to solve an entire class of similar problem. This usually only appears after applying a standard design a number of times, and then seeing a common pattern throughout these applications.
I feel this helps put things in perspective, and to show where something might fit. However, it doesn’t say that one is better than another. It doesn’t make sense to try to take every problem solution and generalize it to a design pattern - it’s not a good use of your time, and you can’t force the discovery of patterns that way; they tend to be subtle and appear over time.
One could also argue for the inclusion of Analysis Pattern and Architectural Pattern in this taxonomy.
One of the struggles that I’ve had with design patterns is their classification - I’ve often found the GoF approach to be too obscure, and not always very helpful. Certainly, the Creational patterns are fairly straightforward: how are you going to create your objects? This is a question you normally need to ask, and the name brings you right to that group of patterns. But I find Structural and Behavioral to be far less useful distinctions. I have not been able to look at a problem and say “clearly, you need a structural pattern here,” so that classification doesn’t lead me to a solution (I’ll readily admit that I may be missing something here).
I’ve labored for awhile with this problem, first noting that the underlying structure of some of the GoF patterns are similar to each other, and trying to develop relationships based on that similarity. While this was an interesting experiment, I don’t think it produced much of use in the end because the point is to solve problems, so a helpful approach will look at the problem to solve and try to find relationships between the problem and potential solutions.
To that end, I’ve begun to try to collect basic design structures, and to try to see if there’s a way to relate those structures to the various design patterns that appear in well thought-out systems. Currently, I’m just trying to make a list, but eventually I hope to make steps towards connecting these structures with patterns (or I may come up with a different approach altogether - this is still in its formative stages).
Here  is the present list of candidates, only some of which will make it to the final list. Feel free to suggest others, or possibly relationships with patterns.
- Encapsulation: self containment and embodying a model of usage
- Variation in behavior
- Mirror: “the ability to keep a parallel universe(s) in step with the
- golden world”
- Shadow: “follows your movement and does something different in a different
- medium” (May be a variation on Proxy).
When I put out a call for ideas in my newsletter , a number of suggestions came back which turned out to be very useful, but different than the above classification, and I realized that a list of design principles is at least as important as design structures, but for a different reason: these allow you to ask questions about your proposed design, to apply tests for quality.
- Principle of least astonishment (don’t be astonishing).
- Make common things easy, and rare things possible
- Consistency. One thing has become very clear to me, especially because of Python: the more random rules you pile onto the programmer, rules that have nothing to do with solving the problem at hand, the slower the programmer can produce. And this does not appear to be a linear factor, but an exponential one.
- Law of Demeter: a.k.a. “Don’t talk to strangers.” An object should only reference itself, its attributes, and the arguments of its methods. This may also be a way to say “minimize coupling.”
- Independence or Orthogonality. Express independent ideas independently. This complements Separation, Encapsulation and Variation, and is part of the Low-Coupling-High-Cohesion message.
- Managed Coupling. Simply stating that we should have “low coupling” in a design is usually too vague - coupling happens, and the important issue is to acknowledge it and control it, to say “coupling can cause problems” and to compensate for those problems with a well-considered design or pattern.
- Subtraction: a design is finished when you cannot take anything else away .
- Simplicity before generality . (A variation of Occam’s Razor, which says “the simplest solution is the best”). A common problem we find in frameworks is that they are designed to be general purpose without reference to actual systems. This leads to a dizzying array of options that are often unused, misused or just not useful. However, most developers work on specific systems, and the quest for generality does not always serve them well. The best route to generality is through understanding well-defined specific examples. So, this principle acts as the tie breaker between otherwise equally viable design alternatives. Of course, it is entirely possible that the simpler solution is the more general one.
- Reflexivity (my suggested term). One abstraction per class, one class per abstraction. Might also be called Isomorphism.
- Once and once only: Avoid duplication of logic and structure where the duplication is not accidental, ie where both pieces of code express the same intent for the same reason.
In the process of brainstorming this idea, I hope to come up with a small handful of fundamental ideas that can be held in your head while you analyze a problem. However, other ideas that come from this list may end up being useful as a checklist while walking through and analyzing your design.
Alex Martelli’s Video Lectures on Design Patterns in Python: http://www.catonmat.net/blog/learning-python-design-patterns-through-video-lectures/
|||From Mark Johnson.|
|||But be warned: the examples are in C++.|
|||This list includes suggestions by Kevlin Henney, David Scott, and others.|
|||A free email publication. See www.BruceEckel.com to subscribe.|
|||This idea is generally attributed to Antoine de St. Exupery from The Little Prince: “La perfection est atteinte non quand il ne reste rien à ajouter, mais quand il ne reste rien à enlever,” or: “perfection is reached not when there’s nothing left to add, but when there’s nothing left to remove”.|
|||From an email from Kevlin Henney.|