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Consistent Hashing Explained

  • 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.

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