Time Complexity & Big-O Notation Explained Simply
The article explains time complexity and Big-O notation, which measure how the number of operations increases with input size. It highlights four key Big-O notations: O(1), O(n), O(log n), and O(n²), providing examples for each. The piece emphasizes the importance of analyzing time complexity, especially in programming interviews.
- ▪Time complexity measures how the number of operations grows as input size grows.
- ▪The four key Big-O notations are O(1), O(n), O(log n), and O(n²).
- ▪Analyzing time complexity is crucial before and after optimizing code.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/bitveen/time-complexity-big-o-notation-explained-simply-9jn |
| Publication time | Sun, 17 May 2026 02:30:00 +0000 |
| Retrieval time | 2026-05-17T02:40:19.130Z |
| Last seen | 2026-05-17T02:40:19.130Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | v5q8ROHnMJuv |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3929499) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ankit Maheshwari Posted on May 17 • Originally published at bitveen.com Time Complexity & Big-O Notation Explained Simply #dsa #programming #beginners #computerscience Handling 10 inputs is easy. Handling 10 lakh inputs is where real skill shows. That's what time complexity measures. ⚡ What is Time Complexity? It measures how the number of operations grows as input size grows — not seconds, not milliseconds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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