Time vs space complexityConstant, linear, logarithmic, quadratic growthWorst vs average case
Big-O Notation: Measuring the Cost of Code
1 min readLearnJul 17, 2026
Key Takeaway for Students
Big-O describes how an algorithm's running time or memory grows as the input gets larger, ignoring hardware and constants. Spotting O(n), O(log n) and O(n^2) patterns helps you predict which code stays fast at scale.
Reading in Plain English mode — toggle below to switch
Big-O describes how an algorithm's running time or memory grows as the input gets larger, ignoring hardware and constants. Spotting O(n), O(log n) and O(n^2) patterns helps you predict which code stays fast at scale.