tag: compression
articles: 2 · beats: 1
latest: August 13, 2026
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Compression
2 Crashtech articles on Compression, 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
Columnar Storage: Why Column Stores Beat Row Stores for Analytics
Columnar storage reads only needed columns, skipping the rest. Dictionary encoding shrinks data 50–100×. Analytics queries go from minutes to milliseconds.
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.
Questions we answer about Compression
- Why does reading a single column in a row store require reading the whole table?
- What is dictionary encoding and how much does it compress?
- When should I use row storage instead of columnar?
- Does columnar storage require special hardware or complex setup?
- Why is vectorized execution mentioned alongside columnar storage?
- 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?
Frequently asked questions
What does Crashtech publish about Compression?
2 articles tagged Compression, the most recent published August 13, 2026. All 2 sit in the System Design beat. Each carries numbered sources, an authored FAQ and full structured data.
What questions about Compression does Crashtech answer directly?
10 questions have a dedicated answer page under this tag, including “Why does reading a single column in a row store require reading the whole table?”. Each answer is authored prose from the article it belongs to, not a generated summary.
Can AI assistants read Crashtech's Compression 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.