Binary Search Visualizer
3 min read
Step through binary search with color-coded pointers and animated playback
Setup
Playback Controls
800ms
Low
Mid
High
Found
Eliminated
Array Visualization
Step
0
Comparisons
0
Search Space
--
Eliminated
0
Max Steps (log2 n)
--
Step-by-Step Log
Initialize search to begin logging...
Binary Search Algorithm
function binarySearch(arr, target):
low = 0
high = arr.length - 1
while low <= high:
mid = floor((low + high) / 2)
if arr[mid] == target:
return mid // Found!
else if arr[mid] < target:
low = mid + 1 // Search right half
else:
high = mid - 1 // Search left half
return -1 // Not found
Time Complexity: O(log n)
Space Complexity: O(1)
low = 0
high = arr.length - 1
while low <= high:
mid = floor((low + high) / 2)
if arr[mid] == target:
return mid // Found!
else if arr[mid] < target:
low = mid + 1 // Search right half
else:
high = mid - 1 // Search left half
return -1 // Not found
Time Complexity: O(log n)
Space Complexity: O(1)
Search Space Reduction
Learn Binary Search
About This Tool
Created by Michael Lip, this binary search visualizer provides an interactive way to understand one of computer science's most fundamental search algorithms. Watch the search space halve with each step, and see exactly how the low, mid, and high pointers converge on the target.
This visualizer was designed, built, and tested by human developers at Zovo.one. All implementations follow the canonical binary search algorithm as described in CLRS and Knuth's TAOCP. No generative AI was used in the creation of this tool.
Browser Compatibility
Tested on Chrome 134+, Firefox 128+, Safari 18+, Edge 134+. Uses modern CSS features and ES2020 JavaScript.
Technology Stack
| Component | Technology | Version |
|---|---|---|
| Frontend | Vanilla HTML/CSS/JS | ES2020+ |
| Animation | CSS Transitions + setInterval | CSS3 |
| Fonts | Google Fonts (Inter) | Variable |
| Charts | QuickChart.io API | v1 |
Testing & Validation
- Tested with arrays of size 1 to 1000 elements
- Edge cases: target at start, end, middle, and not present
- Validated comparison counts against theoretical O(log n) bound
- Cross-tested with Python bisect module results