Interview prep and everyday performance reasoning both come back to the same question: what’s the actual complexity of this operation? This cheatsheet keeps the answers to that question in one filterable table.
What Is the Big-O Complexity Cheatsheet?
Big-O Complexity Cheatsheet is one of the tools in WebdevToolbox’s References category. It lists average and worst-case time complexity, plus space complexity, for the data structure operations and algorithms developers look up most.
How to Use It
- Open the tool at /tools/big-o-cheatsheet.
- Scan the Data Structures table for common operations (access, search, insert/delete).
- Scan the Algorithms table for sorting, searching, and graph traversal complexity.
- Use the filter box to jump straight to a specific structure or algorithm by name.
Key Features
- Per-operation granularity — Array access (O(1)) and Array search (O(n)) are listed separately, since lumping “Array” into one complexity would be misleading.
- Color-coded complexity — green for O(1), blue for logarithmic, amber/red for higher complexity, so you can spot expensive operations at a glance.
- Live filter — type a keyword to instantly narrow both tables.
- Average vs. worst case shown separately — see where an algorithm like quicksort’s practical speed and theoretical worst case diverge.
When to Use the Big-O Complexity Cheatsheet
- Interview preparation — quickly refresh complexity facts before a technical interview.
- Choosing a data structure — compare hash map vs. balanced tree lookup costs before committing to one.
- Code review discussions — back up a performance concern with the actual Big-O classification.
Try It Now
Open Big-O Complexity Cheatsheet directly from anywhere on the site using ⌘K and typing the tool name.
Part of the References collection on WebdevToolbox — free, browser-based developer tools with no login required.