What are the differences between consistency models in distributed systems?

What are the differences between consistency models in distributed systems?

Consistency models define how data is synchronized across distributed systems. The main types include strong consistency, eventual consistency, and causal consistency. Strong consistency ensures that all nodes see the same data at the same time, while eventual consistency allows for temporary discrepancies. Causal consistency maintains the order of operations based on their cause.

Can you explain strong consistency in more detail?

Strong consistency guarantees that once a write is acknowledged, all subsequent reads will reflect that write. This means that all nodes in the system will have the same view of the data immediately after the write operation, which is crucial for applications requiring real-time data accuracy.

What are the trade-offs of using eventual consistency?

Eventual consistency offers higher availability and partition tolerance, but it sacrifices immediate accuracy. This means that while the system can continue to operate even if some nodes are down, there may be a delay before all nodes reflect the most recent updates.

How does causal consistency differ from strong consistency?

Causal consistency allows operations that are causally related to be seen in the same order by all nodes, but it does not require all operations to be seen immediately. In contrast, strong consistency requires all operations to be seen in the exact order they were applied, regardless of their causal relationships.