Easy Complexity Question 63 of 224

What are the time and space complexities of binary search?

DSA interview set · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: ARRAY IN MEMORY

01234

Index starts at 0. Scan once for max — O(n).

Simple meaning

On a sorted array, each step halves the search range, so time is O(log n) and extra space is O(1) iterative or O(log n) recursive.

WHY — Complexity instead of guessing?

Why interviewers care about Complexity:

Complexity questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to DSA work.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

End with when you use it and one common pitfall.

STEPS — What happens step by step?

Before you speak the answer, walk the interviewer through these steps:

1
On a sorted array,
each step halves the search range, so time is O(log n) and extra space is O(1) iterative or O(log n) recursive.
2
It does not apply
to unsorted data unless you sort first, which dominates at O(n log n).
3
Off-by-one bounds are the
usual implementation risk.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“It does not apply to unsorted data unless you sort first, which dominates at O(n”

Break into beats

Itdoesnotapplytounsorted

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

On a sorted array, each step halves the search range, so time is O(log n) and extra space is O(1) iterative or O(log n) recursive. It does not apply to unsorted data unless you sort first, which dominates at O(n log n).