Consistent Hashing
Imagine you have 12 Redis servers and you're distributing keys using
hash(key) % N. Everything works fine until you add one more server.When you go from 12 to 13 servers, around 92% of your keys can suddenly be mapped to different servers. That means a huge portion of your cache becomes useless at exactly the moment you're trying to scale.
This is the problem consistent hashing solves. Instead of mapping keys directly based on the number of servers, consistent hashing places both servers and keys on a hash ring. When a server is added or removed, only a small portion of the keys need to move, while most of the existing mappings remain unchanged.
It's a relatively simple algorithm, but it's one of those concepts that completely changes how you think about scaling distributed systems.
If you're learning system design or backend engineering, definitely understand this one.
Read the full article 👉 [LINK]
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