Blog

Mert Özen's blog posts on software development, backend, frontend, and DevOps.

Series

What Big-O Doesn't Tell You: Constants, Cache and Measuring with BenchmarkDotNet

Big-O tells you about growth, but real time is decided by constants, the cache, allocations and the input distribution. An O(n²) insertion sort twice as fast as LINQ on 10 books, LinkedList 3.8x slower, Dictionary falling to O(n²) with a bad GetHashCode, and estimating complexity by measuring with the doubling experiment.

8 Nov 2026 12 min 4

Computer Science Fundamentals PART 01

What Is Big-O Notation: Growth Rate, O, Ω, Θ and Worst Case

Big-O describes an algorithm's cost not in seconds but by how its step count grows as the input size n grows. The difference between O, Ω and Θ, choosing n correctly, best/average/worst case, and the real growth of List, HashSet and binary search from 10 to 1,000,000 elements measured with BenchmarkDotNet.

8 Nov 2026 11 min 4