What are the trade-offs of consistency in distributed systems?

What are the main trade-offs of consistency in distributed systems?

In distributed systems, the main trade-offs of consistency involve balancing availability and partition tolerance, often referred to as the CAP theorem. You can achieve strong consistency, but it may lead to reduced availability during network partitions.

Can you explain the CAP theorem in more detail?

Sure! The CAP theorem states that in a distributed data store, you can only guarantee two out of the following three properties at the same time: Consistency, Availability, and Partition Tolerance. This means if a network partition occurs, you must choose between maintaining consistency or availability.

What are some real-world examples of systems that prioritize availability over consistency?

Examples include systems like Amazon DynamoDB and Cassandra, which prioritize availability and partition tolerance, allowing for eventual consistency rather than strong consistency.

How does eventual consistency work in practice?

Eventual consistency means that, while updates may not be immediately visible to all nodes, the system guarantees that if no new updates are made, all replicas will eventually converge to the same value.