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Consistent Hashing

What is Consistent Hashing?

Consistent hashing is a technique used in distributed systems for load balancing and data partitioning. It provides a way to distribute data or requests among a cluster of nodes in a way that minimizes the need for remapping when nodes are added or removed.

Why is Consistent Hashing Useful?

Consistent hashing solves two main problems in distributed systems:

  1. Load Balancing: It allows for an even distribution of data or requests across nodes in a cluster, ensuring no single node gets overloaded.

  2. Partition Tolerance: When a node is added or removed from the cluster, only a small fraction of data or requests need to be remapped, ensuring minimal disruption to the system.

How Does Consistent Hashing Work?

  1. Hash Function: A hash function is used to map data or requests to points on a circular hash ring. The hash function should provide a uniform distribution of values.

  2. Hash Ring: The hash ring is a circular space where each node in the cluster is assigned a position on the ring using the hash function.

  3. Data/Request Mapping: To map a data item or request to a node, its key is hashed using the same hash function, and the resulting value is mapped to the nearest node position on the ring in a clockwise direction.

  4. Node Addition/Removal: When a new node is added to the cluster, it is assigned a position on the ring. Only the data or requests that fall between the new node and the next node in a clockwise direction need to be remapped. Similarly, when a node is removed, only the data or requests that were previously mapped to that node need to be remapped to other nodes.

Advantages of Consistent Hashing

  • Incremental Scalability: Nodes can be added or removed without disrupting the entire system, ensuring scalability.
  • Even Load Distribution: Data or requests are distributed evenly across nodes, preventing hotspots and ensuring better performance.
  • Fault Tolerance: If a node fails, its data or requests are automatically remapped to other nodes, providing fault tolerance.

Disadvantages of Consistent Hashing

  • Non-uniform Load Distribution: In some cases, data or requests may not be distributed evenly across nodes due to the hash function's behavior or the distribution of keys.
  • Remapping Overhead: While consistent hashing minimizes remapping, there is still some overhead involved when nodes are added or removed.
  • Hot Spot Prevention: Additional techniques like virtual nodes may be required to prevent hotspots when the distribution of keys is skewed.

Use Cases for Consistent Hashing

Consistent hashing is widely used in distributed systems, including:

  • Distributed Caching: Memcached, Redis
  • Distributed Storage: Cassandra, Amazon DynamoDB
  • Load Balancing: HAProxy, Nginx
  • Peer-to-Peer Networks: BitTorrent
  • Content Delivery Networks (CDNs): Akamai, Cloudflare