A sample chapter is above average in quality, but might be the best chapter of the book. Style is good and important points are properly emphasized, but still there are some problematic statements, for example:. The Bourne, Korn, and Bash Shells all have this feature. The C Shell and tcsh don't. For more about shells, see Chapter 6. For now, simply type the following command to find out which shell you have: 3.
I also strongly doubt that "A file handle is an abstract representation of a file. The reason I cannot call Mr. Lasser's book a "life-saver" is because I would not have perished from the Earth without it. Indeed, I probably would have figured almost all of the stuff in this book out, given six or seven years. But you gotta ask yourself, "at what cost?
This book is not for Dummies. But while you may pretend to enjoy a rugged hike through the steeper parts of the learning curve, Mr. Lasser's book is like strapping on a jet-pack. The book is conversational, sometimes funny though it helps if you spend a lot of your time in front of computers , and extremely direct. If you are just curious about what this Unix thing might be good for, read the book slowly, learn a lot, and gain a solid foundation for becoming the captain of your computing destiny.
If you have something you need to get done, read it quickly, learn-- well, a lot, and get where you're going in a hurry. 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.
It is the specifications that should be generous, not their interpretation. 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 ].
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.
We cover these techniques in Chapter9. Prototyping first may help keep you from investing far too much time for marginal gains.
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 adesign 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. 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. Prototyping is important for system design as well as optimization — it is much easier to judge whether a prototype does what you want than it is to read a long specification. He wouldn't issue long specifications; he'd lash together some combination of shell scripts and awk code that did roughly what was needed, tell the customers to send him some clerks for a few days, and then have the customers come in and look at their clerks using the prototype and tell him whether or not they liked it.
His estimates tended to be accurate, but he lost out in the culture to managers who believed that requirements writers should be in control of everything. Using prototyping to learn which features you don't have to implement helps optimization for performance; you don't have to optimize what you don't write. The most powerful optimization tool in existence may be the delete key. We'll go into a bit more depth about related ideas in Chapter Even the best software tools tend to be limited by the imaginations of their designers.
Nobody is smart enough to optimize for everything, nor to anticipate all the uses to which their software might be put. Designing rigid, closed software that won't talk to the rest of the world is an unhealthy form of arrogance. It embraces multiple languages, open extensible systems, and customization hooks everywhere.
Never assume you have the final answer. Therefore, leave room for your data formats and code to grow; otherwise, you will often find that you are locked into unwise early choices because you cannot change them while maintaining backward compatibility. When you design protocols or file formats, make them sufficiently self-describing to be extensible.
Always, always either include a version number, or compose the format from self-contained, self-describing clauses in such a way that new clauses can be readily added and old ones dropped without confusing format-reading code. Unix experience tells us that the marginal extra overhead of making data layouts self-describing is paid back a thousandfold by the ability to evolve them forward without breaking things. When you design code, organize it so future developers will be able to plug new functions into the architecture without having to scrap and rebuild the architecture.
This rule is not a license to add features you don't yet need; it's advice to write your code so that adding features later when you do need them is easy. You owe this grace to people who will use and maintain your code after you. You'll be there in the future too, maintaining code you may have half forgotten under the press of more recent projects.
When you design for the future, the sanity you save may be your own. A tribute page is maintained by the Postel Center for Experimental Networking.
Knuth himself attributes the remark to C. Basics of the Unix Philosophy Prev Chapter1. Philosophy Next. Basics of the Unix Philosophy. There is no Rule 6. When in doubt, use brute force. Rule of Modularity: Write simple parts connected by clean interfaces. Rule of Clarity: Clarity is better than cleverness. Rule of Composition: Design programs to be connected to other programs. Rule of Separation: Separate policy from mechanism; separate interfaces from engines. Rule of Simplicity: Design for simplicity; add complexity only where you must.
Rule of Transparency: Design for visibility to make inspection and debuggingeasier.
Rule of Robustness: Robustness is the child of transparency and simplicity. Rule of Representation: Fold knowledge into data so program logic can be stupid and robust.
Rule of Least Surprise: In interface design, always do the least surprising thing. Rule of Silence: When a program has nothing surprising to say, it should say nothing. Rule of Repair: When you must fail, fail noisily and as soon as possible. Rule of Economy: Programmer time is expensive; conserve it in preference to machine time.