Wanted to capture this insight from Rob Pike (circa 1989) in full, but it didn’t feel like my current weekly link blog was quite right for the enormity of this handful of rules.

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 self-evident. Data structures, not algorithms, are central to programming. (See The Mythical Man-Month: Essays on Software Engineering by F. P. Brooks, page 102.)

Rule 6

There is no Rule 6.

Just fantastic :)

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