Consistent Hashing: The Complete Developer Guide
This article explains the concept of consistent hashing, a hashing technique that maps data to nodes in a way that minimizes the number of nodes that need to be updated when the system changes. It covers how consistent hashing works under the hood and its real-world use cases, such as distributed caching.
Why it matters
Consistent hashing is a fundamental technique for building scalable, fault-tolerant distributed systems that can efficiently handle changes to the underlying infrastructure.
Key Points
- 1Consistent hashing maps data to nodes in a consistent, load-balanced, and scalable way
- 2It uses a combination of hash functions and a ring data structure to assign keys to nodes
- 3Consistent hashing allows efficient updates when nodes are added or removed from the system
- 4Key applications include distributed caching systems like Memcached and Redis
Details
Consistent hashing is a hashing technique that maps a set of keys (e.g., data) to a set of nodes (e.g., servers) in a way that satisfies three key properties: consistency (the same key always maps to the same node), load balancing (keys are distributed evenly across nodes), and scalability (the system can handle changes to the set of nodes without requiring a complete rebalancing of the keys). It works by using a hash function to map both keys and nodes to a circular 'ring' data structure, with each node responsible for a range of hash values on the ring. When a new node is added, the existing nodes' ranges are updated to accommodate the new node, minimizing the number of keys that need to be moved. This makes consistent hashing well-suited for distributed caching systems, where nodes can be added or removed without requiring a full cache rebuild.
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