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All about consistent hashing

by · October 23, 2024

Consistent Hashing is a technique used primarily in distributed systems to efficiently distribute data across multiple nodes. It addresses the challenges of load balancing and data distribution, particularly when nodes are added or removed from the system. This technique minimizes the amount of data that needs to be redistributed when changes occur.

Key Concepts of Consistent Hashing

  1. Hashing: Consistent hashing uses a hash function to map both the data (keys) and the nodes (servers) to a fixed-size hash space (usually a circular or ring structure). This allows for a uniform distribution of keys across the nodes.
  2. Circular Space: The hash space is visualized as a circle (or ring) where:
    • Each node is assigned a position on the circle based on its hash value.
    • Each key is also assigned a position on the same circle.
  3. Node Responsibility: In this model, each key is assigned to the first node encountered when moving clockwise around the circle from the key’s position. This means that each node is responsible for a segment of the hash space.
  4. Dynamic Scaling: One of the primary benefits of consistent hashing is that when nodes are added or removed, only a small number of keys need to be redistributed. This minimizes the impact on the overall system.

Detailed Example of Consistent Hashing

Let’s illustrate consistent hashing with a step-by-step example:

Step 1: Set Up the Environment

Assume we have a hash space that can range from 0 to 255 (for simplicity), and we have three nodes:

  • Node A
  • Node B
  • Node C

Step 2: Assign Hash Values to Nodes

Using a hash function, we calculate the hash values for each node. Suppose the hash values (based on a hypothetical hash function) are as follows:

  • Node A: Hash value = 50
  • Node B: Hash value = 150
  • Node C: Hash value = 200

We place these nodes on the circular hash space:

yamlCopy code   0
   |
   |
50 | A
   |
   |
   |    B
150 | 
   |
   |
   |           C
200 | 
   |
   |
   |
   255

Step 3: Assign Keys to Nodes

Now, let’s say we have several keys to hash:

  • Key 1
  • Key 2
  • Key 3

Assuming the hash values for these keys are:

  • Key 1: Hash value = 30
  • Key 2: Hash value = 120
  • Key 3: Hash value = 220

Using the consistent hashing principle:

  • Key 1 (30): It falls between 0 and 50, so it is assigned to Node A.
  • Key 2 (120): It falls between 50 and 150, so it is assigned to Node B.
  • Key 3 (220): It falls between 200 and 255, so it is assigned to Node C.

The assignments look like this:

mathematicaCopy code   0
   |
   |
50 | A (Key 1)
   |
   |
   |    B (Key 2)
150 | 
   |
   |
   |           C (Key 3)
200 | 
   |
   |
   |
   255

Step 4: Adding a New Node

Let’s add a new node, Node D. Suppose it has a hash value of 100.

mathematicaCopy code   0
   |
   |
50 | A (Key 1)
   |
   |
   |    B (Key 2)
150 | D
   |
   |
   |           C (Key 3)
200 | 
   |
   |
   |
   255

Reassigning Keys:

  • Key 1 (30): Still assigned to Node A.
  • Key 2 (120): Previously assigned to Node B, but now it falls between 100 and 150. It will be reassigned to Node D.
  • Key 3 (220): Still assigned to Node C.

Only Key 2 has been reassigned, demonstrating the efficiency of consistent hashing when adding nodes.

Step 5: Removing a Node

Now, let’s remove Node B.

mathematicaCopy code   0
   |
   |
50 | A (Key 1)
   |
   |
   |    
150 | D (Key 2)
   |
   |
   |           C (Key 3)
200 | 
   |
   |
   |
   255

Reassigning Keys:

  • Key 1 (30): Still assigned to Node A.
  • Key 2: Now falls between 50 and 100, so it remains with Node D.
  • Key 3 (220): Still assigned to Node C.

In this case, no keys need to be reassigned, illustrating the minimal impact of removing a node.

Advantages of Consistent Hashing

  1. Scalability: Nodes can be added or removed with minimal impact on the overall data distribution.
  2. Load Balancing: With proper hash functions, data is evenly distributed across nodes.
  3. Reduced Data Movement: Only a small subset of keys needs to be moved when nodes change.

Use Cases

  • Distributed Caching: In systems like Memcached or Redis, consistent hashing is used to distribute cached data across multiple servers.
  • Distributed Databases: Databases like Cassandra and DynamoDB utilize consistent hashing to partition data across nodes.
  • Microservices: In microservices architecture, consistent hashing can help route requests to specific service instances based on the request parameters.

Conclusion

Consistent hashing is a powerful technique for data distribution in distributed systems. Its ability to handle dynamic node changes with minimal disruption makes it an essential concept in modern computing, especially for applications that require high availability and scalability.

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