Consistent hashing is a technique used for distributing data evenly across servers, ensuring minimal data movement when servers are added or removed.
In consistent hashing, servers are positioned in a circle, and each data item is assigned to the nearest server in a clockwise direction on the circle.
This method helps in maintaining a balanced distribution of data, reducing the need for extensive data relocation when the system scales.
It ensures that the addition or removal of a server affects only a small portion of the data, unlike traditional hashing methods where most data needs to be redistributed.
Virtual nodes are introduced to enhance load distribution by mapping servers to multiple virtual positions on the circle.
Implementations such as Redis, Cassandra, and DynamoDB employ consistent hashing to manage data distribution efficiently.
The demonstration includes an example of a JavaScript code implementation of consistent hashing for educational purposes.
The code showcases key functions like hash calculation, adding/removing servers, finding server for a key, and maintaining balanced data distribution.
For real-world implementation, optimizations like faster hashing algorithms, increased virtual nodes, and data persistence are recommended.
Consistent hashing provides a scalable solution for maintaining data consistency and efficient load balancing in distributed systems.