What is Consistent Hashing Used For?
Last Updated :
06 Mar, 2024
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:
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.
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.
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
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