What are the differences between eventual consistency and strong consistency?

Can you explain the differences between eventual consistency and strong consistency?

Sure! Eventual consistency means that, given enough time, all updates will propagate through the system, and all nodes will eventually reflect the same state. Strong consistency, on the other hand, ensures that any read operation will return the most recent write for a given piece of data.

What are some use cases for each consistency model?

Eventual consistency is often used in systems like social media feeds or caching, where immediate consistency isn't critical. Strong consistency is crucial in banking systems or any application where accurate and up-to-date information is essential.

What are the trade-offs between using one over the other?

The trade-off for eventual consistency is higher availability and performance, but at the cost of temporary inconsistencies. Strong consistency provides accuracy but can lead to reduced availability and performance due to the need for coordination among nodes.

Can you give an example of a database that uses each model?

Certainly! Amazon DynamoDB is an example of a database that supports eventual consistency, while Google Spanner is known for providing strong consistency.