Consistent Hashing: A Guide to Scaling Systems

🔑Consistent hashing evenly distributes load among servers to prevent overloaded or underutilized servers.

📊By using consistent hashing, systems can easily add or remove servers without major disruptions.

🔄When a server is added/removed, only a small fraction of data needs to be readjusted, minimizing the impact on the system.

💡Consistent hashing provides fault tolerance by replicating data across multiple servers.

🌐Consistent hashing can be used in distributed caching systems, content delivery networks, and peer-to-peer networks.

How does consistent hashing prevent overloaded servers?

—Consistent hashing ensures an even distribution of data among servers, preventing any single server from becoming overloaded.

Can consistent hashing handle server additions or removals?

—Yes, consistent hashing allows for seamless addition or removal of servers with minimal data readjustment.

What is the benefit of fault tolerance in consistent hashing?

—Fault tolerance in consistent hashing is achieved by replicating data across multiple servers, ensuring data availability even if a server fails.

Where can consistent hashing be applied?

—Consistent hashing is commonly used in distributed caching systems, content delivery networks, and peer-to-peer networks.

How does consistent hashing handle data readjustment?

—When a server is added or removed, only a small fraction of data needs to be readjusted, minimizing the impact on the system.

00:00Consistent hashing is a method used for scaling systems by evenly distributing load among servers.

09:36Adding or removing servers in a consistent hashing system only requires adjusting a fraction of the data, minimizing disruptions.

12:43Consistent hashing offers fault tolerance by replicating data across multiple servers.