Consistent hashing and traditional (modular) hashing are two different hashing techniques, each with distinct characteristics and use cases, particularly in distributed systems and load balancing.

Traditional Hashing

• Basic Concept: In traditional hashing, a hash function maps keys to a fixed number of buckets or slots. For example, using the modulo operator to distribute data across a fixed array of buckets.
• Primary Use: Commonly used in hash tables in programming to quickly retrieve data using keys.
• Pros:
  • Simplicity: Easy to implement and understand.
  • Efficiency: Provides constant-time complexity for lookups, insertions, and deletions in an ideal scenario.
• Cons:
  • Handling Resizing: When the hash table needs to be resized (due to too many elements or too few), rehashing all keys is necessary, which can be resource-intensive.
  • Load Imbalance: Can lead to an uneven distribution of data, causing load imbalance.

Consistent Hashing

• Basic Concept: Consistent hashing distributes keys across a hash ring or hash space. The hash function maps both keys and servers (or nodes) onto this ring. Each key is assigned to the first server that appears in the clockwise direction on the ring.
• Primary Use: Widely used in distributed caching systems and load balancing (e.g., in distributed databases like DynamoDB or caching systems like Memcached).
• Pros:
  • Minimal Rehashing: When a server/node is added or removed, only a small fraction of keys needs to be remapped, leading to minimal disruption.
  • Load Distribution: Offers better load distribution, especially in dynamic environments where nodes frequently join and leave.
• Cons:
  • Complexity: More complex to implement compared to traditional hashing.
  • Non-uniform Distribution: Without careful implementation, it can lead to a non-uniform distribution of data across nodes.

Key Differences

• Rehashing Process: Traditional hashing requires extensive rehashing when resizing, while consistent hashing minimizes rehashing when nodes are added or removed.
• Data Distribution: Consistent hashing provides a more stable data distribution in a dynamic environment, whereas traditional hashing can lead to load imbalance.
• Use Cases: Traditional hashing is suitable for static or standalone systems, whereas consistent hashing is designed for distributed environments where the set of nodes can change dynamically.

Conclusion

Consistent hashing is particularly advantageous in distributed systems for its ability to minimize rehashing and maintain a balanced load, even as nodes are added or removed. In contrast, traditional hashing is more suited for situations where the hash table's size remains constant or changes infrequently. Understanding the specific requirements of the system is crucial in choosing the appropriate hashing technique.