🔄 Definition — Consistent hashing is a distributed hashing technique used to distribute data across multiple nodes in a network, minimizing the need for data redistribution when nodes are added or removed.
🔗 Hash Ring — It uses a virtual ring structure where both nodes and data are assigned positions based on hash values. Data is stored on the node that appears next in a clockwise direction on the ring.
⚖️ Load Balancing — This method helps achieve load balancing by ensuring that only a small portion of data needs to be moved when the system changes, thus maintaining system stability.
🛠️ Applications — Consistent hashing is widely used in distributed systems like distributed hash tables, caching systems, and databases to improve scalability and fault tolerance.
📉 Traditional Hashing Issues — Unlike traditional hashing, consistent hashing reduces the overhead of rehashing and data movement, which is crucial for systems that frequently scale up or down.
How Consistent Hashing Works
🔄 Hash Function — A hash function is used to map both nodes and data to positions on a virtual ring, ensuring a uniform distribution.
🔗 Node Assignment — Nodes are assigned positions on the ring based on their hash values, and data is stored on the nearest node in a clockwise direction.
🔄 Data Movement — When a node is added or removed, only a small portion of data needs to be reassigned, minimizing disruption.
🔁 Key Replication — To ensure data availability, keys can be replicated across multiple nodes, providing redundancy in case of node failure.
🔄 Load Balancing — Consistent hashing helps distribute the load evenly across nodes, preventing any single node from becoming a bottleneck.
Advantages and Disadvantages
✅ Scalability — Consistent hashing allows systems to scale easily by adding or removing nodes with minimal data movement.
✅ Fault Tolerance — The technique provides resilience against node failures by redistributing data to other nodes.
❌ Complexity — Implementing consistent hashing can be more complex than traditional hashing methods.
❌ Uneven Load — If not properly managed, some nodes may still end up with more data than others, leading to hotspots.
✅ Minimal Rehashing — Only a small fraction of keys need to be rehashed when the system changes, reducing overhead.
Real-World Applications
🌐 DynamoDB — Amazon's DynamoDB uses consistent hashing to manage data distribution across its nodes.
🌐 Akamai — This company uses consistent hashing for its web caching solutions, ensuring efficient data retrieval.
🌐 BitTorrent — Utilizes consistent hashing in its peer-to-peer networks to distribute data among peers.
🌐 URL Shorteners — Consistent hashing helps in distributing shortened URLs across multiple servers.
🌐 Distributed Caching — Systems like Memcached use consistent hashing to distribute cache data across multiple servers.
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