Consistent Hashing is a technique used in distributed systems to efficiently distribute and balance data across multiple nodes or servers. It is primarily used for:
1. Load Balancing
In load balancing, consistent hashing helps distribute incoming requests or data across multiple servers in a balanced manner.
- Each server is assigned a range of hash values, and incoming requests or data keys are hashed to determine which server should handle them.
- By using consistent hashing, the system can add or remove servers without significantly affecting the distribution of requests or data, as only a portion of the keys or requests need to be remapped to different servers.
2. Caching
In caching systems, consistent hashing is used to determine which cache node should store or retrieve data.
- Each data key is hashed to determine which cache node should be responsible for storing or retrieving the data.
- Consistent hashing ensures that each piece of data is stored in a consistent location, reducing cache misses and improving cache efficiency.
3. Partitioning
Consistent hashing is used to partition data across multiple nodes in a distributed database or storage system.
- Each node is responsible for a range of hash values, and data keys are hashed to determine which node should store or retrieve the data.
- By using consistent hashing, the system can scale horizontally by adding new nodes without the need to rehash all the data, as only a portion of the data needs to be remapped to the new nodes.
4. Resilience
Consistent hashing provides resilience against node failures in distributed systems.
- When a node fails or is removed from the system, only a portion of the data needs to be remapped to other nodes, reducing the impact of node failures on the overall system.
- Consistent hashing ensures that the distribution of data remains balanced even in the presence of node failures, maintaining system performance and availability.
Overall, consistent hashing is a versatile technique used in distributed systems to improve load balancing, caching efficiency, partitioning, and resilience, making it a fundamental building block for scalable and reliable distributed systems