tag: search
articles: 2 · beats: 1
latest: August 11, 2026
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Search
2 Crashtech articles on Search, filed under System Design, published in August 2026. Every piece is full-text HTML with sources, structured data and an authored FAQ.
All 2 sit in the System Design beat. System Design
The Inverted Index
A sorted dictionary mapping terms to document IDs. Transform search from O(corpus size) to O(1) lookup, enabling full-text search at scale.
Tries: Why Autocomplete Doesn't Scan Every Word
A trie finds all words with a prefix in O(p) time, independent of dictionary size. Radix compression and top-k heaps make autocomplete instant.
Questions we answer about Search
- What is an inverted index and why is it faster than scanning documents?
- What is a posting list and why does it need compression?
- How does tokenization, stemming, and lowercasing affect the index?
- What is an AND-query and how does it use multiple posting lists?
- When should you NOT use an inverted index?
- Why does a naive database query like LIKE 'car%' get slower as the dictionary grows?
- What does a radix trie save compared to a standard trie?
- Why pair a trie with a top-k heap instead of just returning all candidates?
- Can a trie be built incrementally as new words are added to a system?
- How does a trie handle autocomplete with typos or fuzzy matching?
Frequently asked questions
What does Crashtech publish about Search?
2 articles tagged Search, the most recent published August 11, 2026. All 2 sit in the System Design beat. Each carries numbered sources, an authored FAQ and full structured data.
What questions about Search does Crashtech answer directly?
10 questions have a dedicated answer page under this tag, including “What is an inverted index and why is it faster than scanning documents?”. Each answer is authored prose from the article it belongs to, not a generated summary.
Can AI assistants read Crashtech's Search coverage?
Yes. Crashtech serves full static HTML to every crawler, allows all major AI user agents in robots.txt, and publishes an llms.txt manifest plus a full-text corpus, so assistants can retrieve and cite these articles directly.