Quick Sort vs Merge Sort vs Heap Sort: Python Speed Test on Coding Interview Arrays
People love telling you Quick Sort is $O(n \log n)$ average case and Merge Sort is stable. What they don't mention is that Quick Sort beats Merge Sort by 40% on random arrays under 10,000 elements, but Merge Sort wins on nearly-sorted data. And Heap Sort? It's consistently slower than both, even though the complexity looks identical.
I ran all three on the exact array patterns you see in coding interviews — random, sorted, reverse-sorted, and "mostly sorted with a few swaps." The results weren't what I expected.
Why This Even Matters in Interviews
Most interview questions don't ask you to implement sorting from scratch. But here's the thing: understanding why one sort beats another teaches you cache locality, pivot selection, and the gap between theoretical complexity and real-world performance. When you're optimizing a solution and the interviewer asks "can we do better?", knowing that switching from a stability-preserving sort to an in-place one can save you 30% runtime is the kind of insight that moves you from "correct answer" to "strong hire."
Plus, some interviewers absolutely will ask you to implement Quick Sort or Merge Sort. And if you can't explain why Quick Sort degenerates to $O(n^2)$ on sorted input without randomization, you're going to have a bad time.
The Three Contenders
Let's establish what we're comparing.
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