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This is not an introductory book. I am assuming that you have worked your way through Thinking in Java or an equivalent text before coming to this book.
In addition, I assume you have more than just a grasp of the syntax of Java. You should have a good understanding of objects and what they’re about, including polymorphism. Again, these are topics covered in Thinking in Java.
On the other hand, by going through this book you’re going to learn a lot about object-oriented programming by seeing objects used in many different situations. If your knowledge of objects is rudimentary, it will get much stronger in the process of understanding the designs in this book.
In a book that has “problem-solving techniques” in its subtitle, it’s worth mentioning one of the biggest pitfalls in programming: premature optimization. Every time I bring this concept forward, virtually everyone agrees to it. Also, everyone seems to reserve in their own mind a special case “except for this thing that I happen to know is a particular problem.”
The reason I call this the Y2K syndrome has to do with that special knowledge. Computers are a mystery to most people, so when someone announced that those silly computer programmers had forgotten to put in enough digits to hold dates past the year 1999, then suddenly everyone became a computer expert – “these things aren’t so difficult after all, if I can see such an obvious problem.” For example, my background was originally in computer engineering, and I started out by programming embedded systems. As a result, I know that many embedded systems have no idea what the date or time is, and even if they do that data often isn’t used in any important calculations. And yet I was told in no uncertain terms that all the embedded systems were going to crash on January 1, 2000. As far as I can tell the only memory that was lost on that particular date was that of the people who were predicting doom – it’s as if they had never said any of that stuff.
The point is that it’s very easy to fall into a habit of thinking that the particular algorithm or piece of code that you happen to partly or thoroughly understand is naturally going to be the bottleneck in your system, simply because you can imagine what’s going on in that piece of code and so you think that it must somehow be much less efficient than all the other pieces of code that you don’t know about. But unless you’ve run actual tests, typically with a profiler, you can’t really know what’s going on. And even if you are right, that a piece of code is very inefficient, remember that most programs spend something like 90% of their time in less than 10% of the code in the program, so unless the piece of code you’re thinking about happens to fall into that 10% it isn’t going to be important.
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.
You’ve also already seen another pattern that appears in Design Patterns: the iterator (Java 1.0 and 1.1 capriciously calls it the Enumeration; Java 2 containers use “iterator”). This hides the particular implementation of the container as you’re stepping through and selecting the elements one by one. The iterator allows you to 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 container that can produce an iterator.
This is not an introductory book. I am assuming that you have worked your way through Thinking in Java or an equivalent text before coming to this book.
In addition, I assume you have more than just a grasp of the syntax of Java. You should have a good understanding of objects and what they’re about, including polymorphism. Again, these are topics covered in Thinking in Java.
On the other hand, by going through this book you’re going to learn a lot about object-oriented programming by seeing objects used in many different situations. If your knowledge of objects is rudimentary, it will get much stronger in the process of understanding the designs in this book.
In a book that has “problem-solving techniques” in its subtitle, it’s worth mentioning one of the biggest pitfalls in programming: premature optimization. Every time I bring this concept forward, virtually everyone agrees to it. Also, everyone seems to reserve in their own mind a special case “except for this thing that I happen to know is a particular problem.”
The reason I call this the Y2K syndrome has to do with that special knowledge. Computers are a mystery to most people, so when someone announced that those silly computer programmers had forgotten to put in enough digits to hold dates past the year 1999, then suddenly everyone became a computer expert – “these things aren’t so difficult after all, if I can see such an obvious problem.” For example, my background was originally in computer engineering, and I started out by programming embedded systems. As a result, I know that many embedded systems have no idea what the date or time is, and even if they do that data often isn’t used in any important calculations. And yet I was told in no uncertain terms that all the embedded systems were going to crash on January 1, 2000. As far as I can tell the only memory that was lost on that particular date was that of the people who were predicting doom – it’s as if they had never said any of that stuff.
The point is that it’s very easy to fall into a habit of thinking that the particular algorithm or piece of code that you happen to partly or thoroughly understand is naturally going to be the bottleneck in your system, simply because you can imagine what’s going on in that piece of code and so you think that it must somehow be much less efficient than all the other pieces of code that you don’t know about. But unless you’ve run actual tests, typically with a profiler, you can’t really know what’s going on. And even if you are right, that a piece of code is very inefficient, remember that most programs spend something like 90% of their time in less than 10% of the code in the program, so unless the piece of code you’re thinking about happens to fall into that 10% it isn’t going to be important.
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.
You’ve also already seen another pattern that appears in Design Patterns: the iterator (Java 1.0 and 1.1 capriciously calls it the Enumeration; Java 2 containers use “iterator”). This hides the particular implementation of the container as you’re stepping through and selecting the elements one by one. The iterator allows you to 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 container that can produce an iterator.
