tag: probabilistic
articles: 2 · beats: 1
latest: August 16, 2026
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Probabilistic
2 Crashtech articles on Probabilistic, 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
Count Billions in 12 Kilobytes
HyperLogLog estimates cardinality by reading leading zeros in hashed values, trading 1% error for fixed memory.
Bloom Filters: The One-Way Membership Test
A probabilistic data structure: zero false negatives, tunable false positives. Check membership in RAM with bits instead of database queries.
Questions we answer about Probabilistic
- How does HyperLogLog count unique items without storing them?
- What is the memory cost of HyperLogLog?
- Why use the harmonic mean instead of a simple average?
- When should I use HyperLogLog instead of exact counting?
- Does HyperLogLog work well with merging data from multiple servers?
- How does a Bloom filter guarantee no false negatives but allow false positives?
- How many hash functions and how large should the bit array be?
- Can you delete items from a Bloom filter?
- Is k-hash or k-independent truly required, or can I use one hash with k-fold output?
- Why not just use a set or database index if disk and CPU are cheap?
Covered alongside
Frequently asked questions
What does Crashtech publish about Probabilistic?
2 articles tagged Probabilistic, the most recent published August 16, 2026. All 2 sit in the System Design beat. Each carries numbered sources, an authored FAQ and full structured data.
What questions about Probabilistic does Crashtech answer directly?
10 questions have a dedicated answer page under this tag, including “How does HyperLogLog count unique items without storing them?”. Each answer is authored prose from the article it belongs to, not a generated summary.
Can AI assistants read Crashtech's Probabilistic 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.