Computer Science5 min read
Big-O in five minutes
How to judge whether an algorithm will survive real data.
Big-O describes growth, not speed. It answers: if the input gets 10× bigger, how much more work happens?
O(1) is constant — a dictionary lookup. O(log n) halves the problem each step, like binary search. O(n) touches everything once. O(n²) compares every pair, which collapses fast past a few thousand items.
When code feels slow, don't micro-optimise first. Find the loop inside a loop and remove a whole order of growth.
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