Glossary · Phase 11 · Professional practice
Consistent hashing
Placing servers and keys on a ring so that adding or removing a server moves only a small share of keys.
Try it now
Chains multiply, so every dependency costs you. Copies fail only if they all fail at once.
Learn it properly
System design fundamentals · Requirements, rough numbers, the standard parts and where systems break. About 40 minutes.
Other terms from the same lesson
- back-of-the-envelope estimate: Rough math, such as requests per second or storage per year, done before building to see which problems are real.
- CAP theorem: When the network between copies breaks, a system must choose: stay consistent and refuse some requests, or stay available and risk stale answers.
- eventual consistency: Copies of data may briefly disagree after a write, but come to agree soon after.
- non-functional requirement: How well a system must work rather than what it does: latency, scale, availability, consistency, cost and security.
- read replica: A copy of a database that serves reads, slightly behind the primary that takes the writes.
- sharding: Splitting data across several databases by a key, such as user id, so each holds only part of it.
- single point of failure: A part the whole system needs that has no backup, so its failure takes everything down.
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