Consistency level in Azure cosmos db

 Consistency level in Azure cosmos db

Azure Cosmos DB offers five well-defined consistency levels to provide developers with the flexibility to choose the desired trade-off between consistency, availability, and latency for their applications. Here are the available consistency levels in Azure Cosmos DB:

 1. Strong Consistency:

   - Description: Guarantees linearizability, meaning that reads are guaranteed to return the most recent committed version of the data across all replicas. It ensures the highest consistency but might have higher latency and lower availability compared to other levels.

   - Use Case: Critical applications where strong consistency is required, such as financial or legal applications.

 2. Bounded staleness:

   - Description: Allows you to specify a maximum lag between reads and writes. You can set the maximum staleness in terms of time or number of versions (operations). This level provides a balance between consistency, availability, and latency.

   - Use Case: Scenarios where you can tolerate a slight delay in consistency, such as social media feeds or news applications.

 3. Session Consistency:

   - Description: Guarantees monotonic reads and writes within a single client session. Once a client reads a particular version of the data, it will never see a version of the data that's older than that in subsequent reads. This level provides consistency within a user session.

   - Use Case: User-specific data scenarios where consistency within a user session is important, like in online shopping carts.

 4. Consistent Prefix:

   - Description: Guarantees that reads never see out-of-order writes. Within a single partition key, reads are guaranteed to see writes in the order they were committed. This level provides consistency per partition key.

   - Use Case: Applications that require ordered operations within a partition key, such as time-series data or event logging.

 5. Eventual Consistency:

   - Description: Provides the weakest consistency level. Guarantees that, given enough time and lack of further updates, all replicas will converge to the same data. There is no strict guarantee about the order in which updates are applied to different replicas.

   - Use Case: Scenarios where eventual consistency is acceptable, such as content management systems or applications dealing with non-critical data.

6. Custom Consistency:

   - Description: Allows developers to define custom consistency levels based on specific requirements. This flexibility enables fine-tuning consistency for unique application needs.

   - Use Case: Applications with highly specific or unique consistency requirements not met by the predefined consistency levels.

When selecting a consistency level, it's essential to understand the requirements of your application, as well as the implications on latency, availability, and throughput. Each consistency level offers a different balance between these factors, allowing you to tailor the database behavior to match your application's needs.