tmdgusya Rob Pike's 5 Rules of Programming
The Rules
- You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places. Don't guess — prove it.
- Measure. Don't tune for speed until you've measured. Even then, don't unless one part of the code overwhelms the rest.
- Fancy algorithms are slow when n is small, and n is usually small. Big-O doesn't matter when constants dominate. Use Rule 2 first.
- Fancy algorithms are buggier than simple ones. Use simple algorithms and simple data structures.
- Data dominates. Choose the right data structures and the algorithms become self-evident. "Write stupid code that uses smart objects."
How to Apply
Before Any Optimization
Stop and ask these questions in order:
- "Have I measured?" — If no, measure first. Any optimization without measurement data is premature. Use whatever profiling tool is natural for the project's language and ecosystem.
- "Does one part overwhelm the rest?" — If no single area dominates, there is nothing worth optimizing. Small improvements spread across many areas rarely matter.
- "What's n?" — If n is small (and it usually is), the simple O(n²) approach likely beats the clever O(n log n) one due to constants, cache behavior, and implementation complexity.
- "Is this a data structure problem?" — Before changing the algorithm, consider whether a different data structure makes the problem trivial. The right structure often eliminates the need for a clever algorithm entirely.
- "Is the added complexity worth it?" — Simple code that is 10% slower is almost always preferable to clever code that is fragile and hard to maintain.
Anti-Patterns to Block
When you catch yourself or the user doing any of these, STOP and redirect:
| Impulse | Rule violated | Response |
|---|---|---|
| "This loop looks slow, let me optimize it" | Rule 1 | Have you profiled? The bottleneck may be elsewhere entirely. |
| "Let me add a cache here" | Rule 2 | Measure first. Does this path actually dominate runtime? |
| "Let me use a B-tree / trie / skip list" | Rule 3 | What's n? If small, a sorted slice + binary search wins. |
| "Let me implement a custom allocator" | Rule 4 | Start simple. Measure. Only get fancy if data forces you. |
| "The algorithm is O(n²), needs fixing" | Rule 3 | What's n? O(n²) with n=100 is 10μs. Measure first. |
| "Let me parallelize this" | Rule 2 | Is this actually CPU-bound? Measure. Often it's I/O. |
When Optimization IS Justified
Proceed with optimization only when ALL of these are true:
- You have measurement data showing a specific bottleneck
- That bottleneck dominates overall runtime (not just 5-10% of it)
- The proposed fix is the simplest change that addresses the measured problem
- You will re-measure after the change to confirm improvement