What is the Binary Search pattern?

The Binary Search pattern is a systematic approach to solving a class of coding interview problems. By recognizing the Binary Search structure, you can solve problems in optimal time complexity using proven techniques.

When should I use the Binary Search pattern?

Use the Binary Search pattern when the problem structure matches its recognition signals — typically when brute force would be O(n²) or worse, and the pattern reduces complexity to O(log n).

What is the time complexity of Binary Search?

The Binary Search pattern typically runs in O(log n) time and uses O(1) space. The exact complexity depends on the specific sub-variant being applied.

Is Binary Search important for coding interviews?

Yes — the Binary Search pattern is frequently tested at FAANG companies and competitive engineering interviews. Mastering this pattern gives you a reliable framework to solve many problem variations quickly.

How do I practice the Binary Search pattern?

Start by studying the pattern's mental model and recognition signals on DSAPatterns, then practice the curated LeetCode problems below in order from Easy to Hard. Aim for pattern recognition before memorizing solutions.

← back to patterns

pattern / MEDIUM

Binary Search.

Divide and conquer on sorted data

complexity

time

O(log n)

space

O(1)

practice questions

browse and solve all 10 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Maintain a search space [low, high]. At each step, check the middle. Based on the condition, eliminate half the search space.

Binary Search finds a target in a sorted array in O(log n) time by repeatedly dividing the search interval in half. The pattern extends far beyond simple search.

when to use ✓

01Find element in sorted array
02Find first/last occurrence
03Search in rotated sorted array
04Find square root, peak element
05Minimum eating speed, splitting arrays

when not ✗

01Unsorted data without transformation
02Non-monotonic conditions

04'

recognition signals

Sorted array or rotated sorted array

Find minimum/maximum satisfying condition

Monotonic function or property

Search space can be split in half

Classic binary searchFind first/last occurrenceSearch in rotated arrayBinary search on answerFind peak element

05'

templates

</>Beginner Template

1# Beginner: Fixed-size sliding window
2def max_sum_k(nums, k):
3    n = len(nums)
4    if n < k:
5        return 0
6    window_sum = sum(nums[:k])
7    max_sum = window_sum
8    for i in range(k, n):
9        window_sum += nums[i] - nums[i - k]
10        max_sum = max(max_sum, window_sum)
11    return max_sum