In distributed databases, consistency models define how data is shared and kept synchronized across different nodes. The main types of consistency models include strong consistency, eventual consistency, causal consistency, and sequential consistency. Each model has its own rules and guarantees about how updates are applied and perceived by different parts of the system, impacting how developers design applications to handle data.
Strong consistency ensures that any read operation returns the most recent write for a given data item. This means that once a write is acknowledged, all subsequent reads will see that write, regardless of the node being accessed. This model is crucial for scenarios like banking systems, where accurate and up-to-date data is a must. An example of a distributed database that provides strong consistency is Google Spanner; it uses a two-phase commit protocol to ensure that all transactions are applied in a consistent order.
On the other hand, eventual consistency allows for some delays in data synchronization, meaning that updates made on one node may not be immediately reflected on others. However, the model guarantees that if no new updates are made, eventually all nodes will converge to the same value. This approach is beneficial for applications like social media platforms, where immediate consistency is less critical than availability. Amazon DynamoDB is a well-known example that employs eventual consistency, enabling it to deliver high availability and performance while tolerating temporary discrepancies in data.