Name and define some Consistency Models/Levels in Azure Cosmos DB
Azure Cosmos DB offers several consistency models/levels that allow you to define the trade-off between consistency, availability, and performance for your data operations. Here are the commonly used consistency models in Azure Cosmos DB:
- Strong Consistency: Strong consistency ensures that a read operation always returns the most recent committed version of the data. With strong consistency, all replicas across all regions are immediately updated with the latest changes, guaranteeing linearizability. However, strong consistency can have an impact on availability and latency, as it may require waiting for replication and synchronization across all replicas.
- Bounded Staleness Consistency: Bounded staleness consistency allows you to specify a time duration or a number of versions to limit the staleness of data that can be read. You can define a lag value or a maximum allowed replication delay to ensure that reads do not go beyond a specific level of staleness. This consistency level balances between strong consistency and higher availability or lower latency.
- Session Consistency: Session consistency provides consistency within a session or a single client context. Within a session, all read operations see the latest committed version of the data. This consistency model allows for high read and write availability, as it does not require synchronization across all replicas. However, it may not guarantee the immediate visibility of changes made by other sessions.
- Consistent Prefix Consistency: Consistent prefix consistency guarantees that reads never see out-of-order writes. It ensures that the order of writes is preserved within a single partition. However, it may not provide the same level of ordering guarantees across multiple partitions. This consistency level is suitable for scenarios where maintaining strict global ordering is not necessary.
- Eventual Consistency: Eventual consistency allows for the highest read and write availability by relaxing consistency guarantees. It guarantees that all replicas will eventually converge and become consistent. However, during convergence, there may be a temporary inconsistency or divergence in data versions across replicas.
These consistency models provide different trade-offs between consistency, availability, and performance. The choice of consistency model depends on the requirements and priorities of your application, considering factors such as data consistency needs, read and write latency, and the impact on availability and scalability.