Introduction to Consistent Hashing

In the world of distributed systems, one of the common challenges is how to evenly distribute data across multiple servers or nodes while maintaining efficiency when nodes are added or removed. This is where Consistent Hashing comes into play.

In this post, we’ll explore what consistent hashing is, how it works, and how it helps solve common issues in distributed systems like load balancing and minimizing re-distribution of data.

What is Consistent Hashing?

Consistent hashing is a technique used to distribute data across a changing number of nodes (servers) in a way that minimizes the movement of data when nodes are added or removed. Unlike traditional hashing methods, which might require a complete reshuffling of data, consistent hashing allows for only a small fraction of data to be relocated when changes occur.

Key Features of Consistent Hashing:

• Minimal Data Movement: Only a small subset of data is moved when nodes are added or removed.
• Scalability: Consistent hashing works well even when nodes frequently join or leave.
• Fault Tolerance: If a node goes down, only the data mapped to that node is affected.

How Does Consistent Hashing Work?

Let’s break it down step by step.

1. Hashing the Nodes: Each server or node is assigned a point on a circular "hash ring" using a hash function. The hash function takes the node identifier (such as an IP address or hostname) and maps it to a position on the ring.

2. Hashing the Keys: Similarly, each data item (key) is mapped to the same ring using the same hash function. The key is then assigned to the nearest node in the clockwise direction on the ring.

3. Handling Node Additions or Removals: When a new node is added or removed, only the keys that are closest to that node will be affected. The rest of the keys remain unchanged. This is a huge advantage because it reduces the amount of data that needs to be moved.

Example:

• Imagine we have a ring with 3 nodes: Node A, Node B, and Node C.
• Data items (keys) like Key1, Key2, and Key3 are mapped to points on the ring.
• When Node B is removed, only the data items that were mapped to Node B will need to be reassigned, and the rest remain as they are.

Virtual Nodes (or "Virtual Buckets")

One challenge with consistent hashing is that it may lead to uneven distribution of keys. This can happen because a small number of nodes might be positioned very close to each other on the ring, leading to an imbalance.

To solve this, virtual nodes are used. A virtual node is a logical representation of a physical node on the ring. By assigning each physical node multiple virtual nodes (randomly spread across the ring), we can achieve a more uniform distribution of data across the nodes.

Advantages of Consistent Hashing

• Minimal Rebalancing: When a node is added or removed, only a small portion of the data needs to be moved to a new node.
• Scalability: The system can grow or shrink as needed, without excessive data movement.
• Fault Tolerance: If a node fails, the data can be redistributed efficiently, without significant impact on the rest of the system.

Common Use Cases

• Distributed Caches: Consistent hashing is widely used in caching systems like Memcached and Redis, where the goal is to distribute cache keys across multiple servers while minimizing the rehashing of data when servers are added or removed.
• Distributed Databases: Systems like Cassandra and DynamoDB use consistent hashing to distribute data across nodes in a distributed database system.
• Load Balancers: Load balancers use consistent hashing to distribute traffic evenly across multiple servers, minimizing the chances of hot spots and ensuring smooth scaling.

Conclusion

Consistent hashing is a powerful and efficient way to manage the distribution of data across a distributed system. By reducing the movement of data when nodes are added or removed, it helps ensure scalability, fault tolerance, and minimal disruption.

Whether you're building a distributed cache or a fault-tolerant database, understanding consistent hashing is crucial for designing scalable and resilient systems.

Thanks for reading!