What are the trade-offs in consistency models?

What are the trade-offs in consistency models?

Consistency models define how data is synchronized across distributed systems. The main trade-offs involve consistency, availability, and partition tolerance, often referred to as the CAP theorem.

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: Consistency, Availability, and Partition Tolerance. This means if a network partition occurs, you must choose between maintaining consistency or availability.

What are some examples of consistency models?

Examples include strong consistency, eventual consistency, and causal consistency. Each model has different implications for how data is accessed and updated.

How do these models affect system performance?

The choice of consistency model can significantly impact performance. Strong consistency often leads to higher latency due to synchronization, while eventual consistency can improve performance but may lead to stale reads.