The Standard Template Everyone Uses is Only Half the Story
Most coding interview prep teaches you one binary search pattern — the while left <= right loop with midpoint calculation. But six LeetCode problems later, I found three distinct variants, each with different loop conditions, pointer updates, and termination logic. The performance gap? Negligible in runtime, but massive in bug surface area.
This isn't about which variant is "faster." All three run in $O(\log n)$ time. The real question is: which one survives the stress of a 45-minute interview when you're second-guessing every left = mid vs left = mid + 1 decision?
I benchmarked all three variants on six classic problems: standard search, first/last occurrence, search insert position, peak element, rotated array search, and finding minimum in rotated array. Here's what broke, what held up, and which pattern I'd actually use under pressure.
Three Variants, Same Algorithm, Different Failure Modes
Binary search collapses a sorted search space by half each iteration. The recurrence relation is $T(n) = T(n/2) + O(1)$, which solves to $O(\log n)$. But the implementation details — how you move pointers, when you return, what your loop invariant is — create three distinct patterns.
Variant 1: Classic left <= right with Early Return
This is the textbook version. Loop while left <= right, compute mid = (left + right) // 2, return immediately when you find the target.
python
---
*Continue reading the full article on [TildAlice](https://tildalice.io/binary-search-variants-leetcode-benchmark/)*
Top comments (0)