Evaluate the impact of consistency model choices on availability and associated RU cost

The choice of consistency models has a significant impact on the availability and associated Request Units (RU) cost when designing and implementing native applications using Microsoft Azure Cosmos DB. Azure Cosmos DB provides five consistency levels to choose from: Strong, Bounded staleness, Session, Consistent prefix, and Eventual.

Consistency models determine the order in which updates are reflected across replicas in a distributed database system. The choice of consistency model strikes a balance between data consistency and availability. Let’s evaluate the impact of these choices on availability and RU cost.

1. Strong Consistency:

• Strong consistency guarantees that the most recent write is always returned, and subsequent reads are guaranteed to see that write.
• Achieving strong consistency requires synchronously replicating writes across all replicas.
• This consistency level offers the highest data consistency, but it comes with decreased availability and increased RU cost.
• It is suitable for scenarios where data consistency takes precedence over availability.

2. Bounded staleness:

• Bounded staleness allows you to specify a maximum lag (in terms of time or version) for reads from replicas.
• This consistency level offers configurable consistency with an upper bound on staleness.
• It provides a balance between consistency and availability.
• The RU cost depends on the staleness window. A smaller window increases RU cost due to increased replication.
• It is suitable for scenarios where you can tolerate a certain degree of stale data but still need stronger consistency guarantees.

3. Session Consistency:

• Session consistency ensures strong consistency within a session and may provide read-your-writes guarantees.
• It is achieved by routing all requests from the same session to the same replica.
• This consistency level offers good availability with low RU cost compared to strong consistency.
• The RU cost is primarily driven by the number of write operations.
• It is suitable for scenarios where data consistency within a user session is important.

4. Consistent Prefix:

• Consistent prefix guarantees that reads never see out-of-order writes.
• It offers a monotonic read guarantee, but the reads might not see the latest writes.
• This consistency level provides good availability and lower RU cost compared to strong consistency.
• The RU cost increases with higher write rates or large reads.
• It is suitable for scenarios where maintaining read order is important, but the latest values are not critical.

5. Eventual Consistency:

• Eventual consistency offers the highest availability with the lowest RU cost.
• It provides no ordering guarantees, and replicas may eventually converge.
• The reads may see stale or conflicting data for a short duration.
• This consistency level is suitable for scenarios where eventual convergence is acceptable, and real-time consistency is not crucial.

When designing and implementing native applications using Azure Cosmos DB, it is crucial to carefully evaluate the consistency requirements of your application. Consider the trade-offs between consistency, availability, and RU cost. Test and measure the performance of your application under different consistency models to determine the optimal choice.

Here’s an example of how to configure consistency level in C# using the Cosmos DB .NET SDK:

using Microsoft.Azure.Cosmos;

// Create a new instance of CosmosClient with desired ConsistencyLevel
CosmosClient cosmosClient = new CosmosClient(“connectionString”);
CosmosContainer container = cosmosClient.GetContainer(“databaseId”, “containerId”);

// Set the desired ConsistencyLevel for the container
ContainerProperties containerProperties = container.ReadContainerAsync().Result.Resource;
containerProperties.DefaultConsistencyLevel = ConsistencyLevel.Strong;
ContainerResponse response = container.ReplaceContainerAsync(containerProperties).Result;

In the above code snippet, we create a new instance of `CosmosClient` and set the desired `ConsistencyLevel` by modifying the `DefaultConsistencyLevel` property of the container. You can replace `ConsistencyLevel.Strong` with other consistency options based on your requirements.

Remember, consistency models impact both availability and RU cost. Understanding these trade-offs is essential to design and implement native applications effectively using Microsoft Azure Cosmos DB.