tag: databases
articles: 2 · beats: 1
latest: August 17, 2026
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Databases
2 Crashtech articles on Databases, 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
LSM-Trees vs B-Trees: Why Cassandra Chose Sequential Writes
B-trees seek random disk positions. LSM-trees buffer in memory and flush sequentially, converting random I/O into sequential writes for millions of ops/sec.
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.
Questions we answer about Databases
- What is the fundamental difference between LSM-trees and B-trees?
- Why does sequential disk I/O matter for write performance?
- What happens during the LSM compaction process?
- What is read amplification and how do Bloom filters fix it?
- When should I choose LSM-trees over B-trees?
- 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?
Frequently asked questions
What does Crashtech publish about Databases?
2 articles tagged Databases, the most recent published August 17, 2026. All 2 sit in the System Design beat. Each carries numbered sources, an authored FAQ and full structured data.
What questions about Databases does Crashtech answer directly?
10 questions have a dedicated answer page under this tag, including “What is the fundamental difference between LSM-trees and B-trees?”. Each answer is authored prose from the article it belongs to, not a generated summary.
Can AI assistants read Crashtech's Databases 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.