As Brian Kernighan once observed, “Controlling complexity is the essence of computer programming” [Kernighan-Plauger]. Debugging dominates development time, and getting a working system out the door is usually less a result of brilliant design than it is of managing not to trip over your own feet too many times.

Assemblers, compilers, flowcharting, procedural programming, structured programming, “artificial intelligence”, fourth-generation languages, object orientation, and software-development methodologies without number have been touted and sold as a cure for this problem. All have failed as cures, if only because they ‘succeeded’ by escalating the normal level of program complexity to the point where (once again) human brains could barely cope. As Fred Brooks famously observed [Brooks], there is no silver bullet.

The only way to write complex software that won't fall on its face is to hold its global complexity down — to build it out of simple parts connected by well-defined interfaces, so that most problems are local and you can have some hope of upgrading a part without breaking the whole.

Because maintenance is so important and so expensive, write programs as if the most important communication they do is not to the computer that executes them but to the human beings who will read and maintain the source code in the future (including yourself).

In the Unix tradition, the implications of this advice go beyond just commenting your code. Good Unix practice also embraces choosing your algorithms and implementations for future maintainability. Buying a small increase in performance with a large increase in the complexity and obscurity of your technique is a bad trade — not merely because complex code is more likely to harbor bugs, but also because complex code will be harder to read for future maintainers.

Code that is graceful and clear, on the other hand, is less likely to break — and more likely to be instantly comprehended by the next person to have to change it. This is important, especially when that next person might be yourself some years down the road.

It's hard to avoid programming overcomplicated monoliths if none of your programs can talk to each other.

Unix tradition strongly encourages writing programs that read and write simple, textual, stream-oriented, device-independent formats. Under classic Unix, as many programs as possible are written as simple filters, which take a simple text stream on input and process it into another simple text stream on output.

Despite popular mythology, this practice is favored not because Unix programmers hate graphical user interfaces. It's because if you don't write programs that accept and emit simple text streams, it's much more difficult to hook the programs together.

Text streams are to Unix tools as messages are to objects in an object-oriented setting. The simplicity of the text-stream interface enforces the encapsulation of the tools. More elaborate forms of inter-process communication, such as remote procedure calls, show a tendency to involve programs with each others' internals too much.

To make programs composable, make them independent. A program on one end of a text stream should care as little as possible about the program on the other end. It should be made easy to replace one end with a completely different implementation without disturbing the other.

GUIs can be a very good thing. Complex binary data formats are sometimes unavoidable by any reasonable means. But before writing a GUI, it's wise to ask if the tricky interactive parts of your program can be segregated into one piece and the workhorse algorithms into another, with a simple command stream or application protocol connecting the two. Before devising a tricky binary format to pass data around, it's worth experimenting to see if you can make a simple textual format work and accept a little parsing overhead in return for being able to hack the data stream with general-purpose tools.

When a serialized, protocol-like interface is not natural for the application, proper Unix design is to at least organize as many of the application primitives as possible into a library with a well-defined API. This opens up the possibility that the application can be called by linkage, or that multiple interfaces can be glued on it for different tasks.

(We discuss these issues in detail in Chapter 7.)

In our discussion of what Unix gets wrong, we observed that the designers of X made a basic decision to implement “mechanism, not policy”—to make X a generic graphics engine and leave decisions about user-interface style to toolkits and other levels of the system. We justified this by pointing out that policy and mechanism tend to mutate on different timescales, with policy changing much faster than mechanism. Fashions in the look and feel of GUI toolkits may come and go, but raster operations and compositing are forever.

Thus, hardwiring policy and mechanism together has two bad effects: It makes policy rigid and harder to change in response to user requirements, and it means that trying to change policy has a strong tendency to destabilize the mechanisms.

On the other hand, by separating the two we make it possible to experiment with new policy without breaking mechanisms. We also make it much easier to write good tests for the mechanism (policy, because it ages so quickly, often does not justify the investment).

This design rule has wide application outside the GUI context. In general, it implies that we should look for ways to separate interfaces from engines.

One way to effect that separation is, for example, to write your application as a library of C service routines that are driven by an embedded scripting language, with the application flow of control written in the scripting language rather than C. A classic example of this pattern is the Emacs editor, which uses an embedded Lisp interpreter to control editing primitives written in C. We discuss this style of design in Chapter 11.

Another way is to separate your application into cooperating front-end and back-end processes communicating through a specialized application protocol over sockets; we discuss this kind of design in Chapter 5 and Chapter 7. The front end implements policy; the back end, mechanism. The global complexity of the pair will often be far lower than that of a single-process monolith implementing the same functions, reducing your vulnerability to bugs and lowering life-cycle costs.

Many pressures tend to make programs more complicated (and therefore more expensive and buggy). One such pressure is technical machismo. Programmers are bright people who are (often justly) proud of their ability to handle complexity and juggle abstractions. Often they compete with their peers to see who can build the most intricate and beautiful complexities. Just as often, their ability to design outstrips their ability to implement and debug, and the result is expensive failure.

Even more often (at least in the commercial software world) excessive complexity comes from project requirements that are based on the marketing fad of the month rather than the reality of what customers want or software can actually deliver. Many a good design has been smothered under marketing's pile of “checklist features” — features that, often, no customer will ever use. And a vicious circle operates; the competition thinks it has to compete with chrome by adding more chrome. Pretty soon, massive bloat is the industry standard and everyone is using huge, buggy programs not even their developers can love.

Either way, everybody loses in the end.

The only way to avoid these traps is to encourage a software culture that knows that small is beautiful, that actively resists bloat and complexity: an engineering tradition that puts a high value on simple solutions, that looks for ways to break program systems up into small cooperating pieces, and that reflexively fights attempts to gussy up programs with a lot of chrome (or, even worse, to design programs around the chrome).

That would be a culture a lot like Unix's.

‘Big’ here has the sense both of large in volume of code and of internal complexity. Allowing programs to get large hurts maintainability. Because people are reluctant to throw away the visible product of lots of work, large programs invite overinvestment in approaches that are failed or suboptimal.

(We'll examine the issue of the right size of software in more detail in Chapter 13.)

Because debugging often occupies three-quarters or more of development time, work done early to ease debugging can be a very good investment. A particularly effective way to ease debugging is to design for transparency and discoverability.

A software system is transparent when you can look at it and immediately understand what it is doing and how. It is discoverable when it has facilities for monitoring and display of internal state so that your program not only functions well but can be seen to function well.

Designing for these qualities will have implications throughout a project. At minimum, it implies that debugging options should not be minimal afterthoughts. Rather, they should be designed in from the beginning — from the point of view that the program should be able to both demonstrate its own correctness and communicate to future developers the original developer's mental model of the problem it solves.

For a program to demonstrate its own correctness, it needs to be using input and output formats sufficiently simple so that the proper relationship between valid input and correct output is easy to check.

The objective of designing for transparency and discoverability should also encourage simple interfaces that can easily be manipulated by other programs — in particular, test and monitoring harnesses and debugging scripts.

Software is said to be robust when it performs well under unexpected conditions which stress the designer's assumptions, as well as under normal conditions.

Most software is fragile and buggy because most programs are too complicated for a human brain to understand all at once. When you can't reason correctly about the guts of a program, you can't be sure it's correct, and you can't fix it if it's broken.

It follows that the way to make robust programs is to make their internals easy for human beings to reason about. There are two main ways to do that: transparency and simplicity.

One very important tactic for being robust under odd inputs is to avoid having special cases in your code. Bugs often lurk in the code for handling special cases, and in the interactions among parts of the code intended to handle different special cases.

We observed above that software is transparent when you can look at it and immediately see what is going on. It is simple when what is going on is uncomplicated enough for a human brain to reason about all the potential cases without strain. The more your programs have both of these qualities, the more robust they will be.

Modularity (simple parts, clean interfaces) is a way to organize programs to make them simpler. There are other ways to fight for simplicity. Here's another one.

Even the simplest procedural logic is hard for humans to verify, but quite complex data structures are fairly easy to model and reason about. To see this, compare the expressiveness and explanatory power of a diagram of (say) a fifty-node pointer tree with a flowchart of a fifty-line program. Or, compare an array initializer expressing a conversion table with an equivalent switch statement. The difference in transparency and clarity is dramatic. See Rob Pike's Rule 5.

Data is more tractable than program logic. It follows that where you see a choice between complexity in data structures and complexity in code, choose the former. More: in evolving a design, you should actively seek ways to shift complexity from code to data.

The Unix community did not originate this insight, but a lot of Unix code displays its influence. The C language's facility at manipulating pointers, in particular, has encouraged the use of dynamically-modified reference structures at all levels of coding from the kernel upward. Simple pointer chases in such structures frequently do duties that implementations in other languages would instead have to embody in more elaborate procedures.

(We also cover these techniques in Chapter 9.)

(This is also widely known as the Principle of Least Astonishment.)

The easiest programs to use are those that demand the least new learning from the user — or, to put it another way, the easiest programs to use are those that most effectively connect to the user's pre-existing knowledge.

Therefore, avoid gratuitous novelty and excessive cleverness in interface design. If you're writing a calculator program, ‘+’ should always mean addition! When designing an interface, model it on the interfaces of functionally similar or analogous programs with which your users are likely to be familiar.

Pay attention to your expected audience. They may be end users, they may be other programmers, or they may be system administrators. What is least surprising can differ among these groups.

Pay attention to tradition. The Unix world has rather well-developed conventions about things like the format of configuration and run-control files, command-line switches, and the like. These traditions exist for a good reason: to tame the learning curve. Learn and use them.

(We'll cover many of these traditions in Chapter 5 and Chapter 10.)

One of Unix's oldest and most persistent design rules is that when a program has nothing interesting or surprising to say, it should shut up. Well-behaved Unix programs do their jobs unobtrusively, with a minimum of fuss and bother. Silence is golden.

This “silence is golden” rule evolved originally because Unix predates video displays. On the slow printing terminals of 1969, each line of unnecessary output was a serious drain on the user's time. That constraint is gone, but excellent reasons for terseness remain.

Well-designed programs treat the user's attention and concentration as a precious and limited resource, only to be claimed when necessary.

(We'll discuss the Rule of Silence and the reasons for it in more detail at the end of Chapter 11.)

Software should be transparent in the way that it fails, as well as in normal operation. It's best when software can cope with unexpected conditions by adapting to them, but the worst kinds of bugs are those in which the repair doesn't succeed and the problem quietly causes corruption that doesn't show up until much later.

Therefore, write your software to cope with incorrect inputs and its own execution errors as gracefully as possible. But when it cannot, make it fail in a way that makes diagnosis of the problem as easy as possible.

Consider also Postel's Prescription:^[10] “Be liberal in what you accept, and conservative in what you send”. Postel was speaking of network service programs, but the underlying idea is more general. Well-designed programs cooperate with other programs by making as much sense as they can from ill-formed inputs; they either fail noisily or pass strictly clean and correct data to the next program in the chain.

However, heed also this warning:

McIlroy adjures us to design for generosity rather than compensating for inadequate standards with permissive implementations. Otherwise, as he rightly points out, it's all too easy to end up in tag soup.

In the early minicomputer days of Unix, this was still a fairly radical idea (machines were a great deal slower and more expensive then). Nowadays, with every development shop and most users (apart from the few modeling nuclear explosions or doing 3D movie animation) awash in cheap machine cycles, it may seem too obvious to need saying.

Somehow, though, practice doesn't seem to have quite caught up with reality. If we took this maxim really seriously throughout software development, most applications would be written in higher-level languages like Perl, Tcl, Python, Java, Lisp and even shell — languages that ease the programmer's burden by doing their own memory management (see [Ravenbrook]).

And indeed this is happening within the Unix world, though outside it most applications shops still seem stuck with the old-school Unix strategy of coding in C (or C++). Later in this book we'll discuss this strategy and its tradeoffs in detail.

One other obvious way to conserve programmer time is to teach machines how to do more of the low-level work of programming. This leads to...

Human beings are notoriously bad at sweating the details. Accordingly, any kind of hand-hacking of programs is a rich source of delays and errors. The simpler and more abstracted your program specification can be, the more likely it is that the human designer will have gotten it right. Generated code (at every level) is almost always cheaper and more reliable than hand-hacked.

We all know this is true (it's why we have compilers and interpreters, after all) but we often don't think about the implications. High-level-language code that's repetitive and mind-numbing for humans to write is just as productive a target for a code generator as machine code. It pays to use code generators when they can raise the level of abstraction — that is, when the specification language for the generator is simpler than the generated code, and the code doesn't have to be hand-hacked afterwards.

In the Unix tradition, code generators are heavily used to automate error-prone detail work. Parser/lexer generators are the classic examples; makefile generators and GUI interface builders are newer ones.

(We cover these techniques in Chapter 9.)

The most basic argument for prototyping first is Kernighan & Plauger's; “90% of the functionality delivered now is better than 100% of it delivered never”. Prototyping first may help keep you from investing far too much time for marginal gains.

For slightly different reasons, Donald Knuth (author of The Art Of Computer Programming, one of the field's few true classics) popularized the observation that “Premature optimization is the root of all evil”.^[11] And he was right.

Rushing to optimize before the bottlenecks are known may be the only error to have ruined more designs than feature creep. From tortured code to incomprehensible data layouts, the results of obsessing about speed or memory or disk usage at the expense of transparency and simplicity are everywhere. They spawn innumerable bugs and cost millions of man-hours — often, just to get marginal gains in the use of some resource much less expensive than debugging time.

Disturbingly often, premature local optimization actually hinders global optimization (and hence reduces overall performance). A prematurely optimized portion of a design frequently interferes with changes that would have much higher payoffs across the whole design, so you end up with both inferior performance and excessively complex code.

In the Unix world there is a long-established and very explicit tradition (exemplified by Rob Pike's comments above and Ken Thompson's maxim about brute force) that says: Prototype, then polish. Get it working before you optimize it. Or: Make it work first, then make it work fast. ‘Extreme programming' guru Kent Beck, operating in a different culture, has usefully amplified this to: “Make it run, then make it right, then make it fast”.

The thrust of all these quotes is the same: get your design right with an un-optimized, slow, memory-intensive implementation before you try to tune. Then, tune systematically, looking for the places where you can buy big performance wins with the smallest possible increases in local complexity.