Rob Pike, 是AT&T Bell Lab前Member of Technical Staff ，现在google研究操作系统，Unix先驱，UTF-8的设计人，The Unix Programming Environment 和 The Practice of Programming 的作者之一。 在《 Notes on C Programming 》中从另一个稍微不同的角度表述了 Unix 的哲学（或者说是程序局部优化6原则）:
1. 你无法断定程序会在什么地方耗费运行时间。瓶颈经常出现在想不到的地方，所以别急于胡乱找个地方改代码，除非你已经证实那儿就是瓶颈所在。
2. 估量。在你没对代码进行估量，特别是没找到最耗时的那部分之前，别去优化速度。
3. 花哨的算法在 n 很小时通常很慢，而 n 通常很小。花哨算法的常数复杂度很大。除非你确定 n 总是很大，否则不要用花哨算法（即使 n 很大，也优先考虑原则 2 ）。比如，解决常见问题时，最简单的树——二叉树（binary tree）,总是比那些复杂的树（AVL树，伸展树（splay tree）和红黑树、B-树（B-tree）,多叉树（trie））来的高校。
4. 花哨的算法比简单算法更容易出 bug 、更难实现。尽量使用简单的算法配合简单的数据结构。
只要掌握了数据结构中的四大法宝，就可以包打天下，他们是：array 、linked list 、hash table、binary tree 。这四大法宝可不是各自为战的，灵活结合才能游刃有余。比如，一个用hash table组织的symbol table，其中是一个个由字符型array构成的linked list。
5. 以数据为中心。如果已经选择了正确的数据结构并且把一切都组织得井井有条，正确的算法也就不言自明。编程的核心是数据结构，而不是算法。
6. 没有原则 6 。
Ken Thompson —— Unix 最初版本的设计者和实现者，禅宗偈语般地对 Pike 的原则4 作了强调：
拿不准就穷举
附：E文原文（节选自《Notes on C Programming 》Rob Pike， February 21, 1989 ）
E文全文地址：http://www.lysator.liu.se/c/pikestyle.html
Most programs are too complicated - that is, more complex than they need to be to solve their problems efficiently. Why? Mostly it's because of bad design, but I will skip that issue here because it's a big one. But programs are often complicated at the microscopic level, and that is something I can address here.
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't, unless one part of the code overwhelms the rest.
Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.) For example, binary trees are always faster than splay trees for workaday problems.
Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
The following data structures are a complete list for almost all practical programs:
array
linked list
hash table
binary tree
Of course, you must also be prepared to collect these into compound data structures. For instance, a symbol table might be implemented as a hash table containing linked lists of arrays of characters.
Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be selfevident. Data structures, not algorithms, are central to programming. (See Brooks p. 102.)
Rule 6. There is no Rule 6.