As an application developer or data architect, understanding the best use-cases for the different consistency models enables you to effectively design and implement native applications that meet your specific data access requirements.
Azure Cosmos DB offers five consistency models: strong, bounded staleness, session, consistent prefix, and eventual.
To discern the best use cases for these models, let’s take a deeper look at each one along with some practical applications.
1. Strong Consistency Model
Strong consistency ensures linearizability, which means reads are guaranteed to return the most recent write. Consequently, in a globally distributed network, there might be some latency during the data access due to this synchronization. This consistency model would be best suited in a scenario where the priority is absolute consistency, such as a financial application where the accurate reflection of real-time transactions is crucial.
2. Bounded Staleness Consistency Model
Unlike the instantaneous synchrony in strong consistency, bounded staleness guarantees that reads may lag behind writes by a certain amount of time. This means the data across the distributed network is eventually consistent within a predetermined time lag. This level of consistency would be appropriate in a collaborative application where a slight delay in syncing data changes across the system is acceptable.
3. Session Consistency Model
Session consistency-often used in user-centric scenarios-provides consistency within a single session, meaning a user always receives the latest data changes they made. For instance, in an eCommerce application, a particular user’s shopping cart must reflect any additions made while maintaining different states for different users.
4. Consistent Prefix Consistency Model
Consistent Prefix guarantees that reads will never see out of order writes. So, if the sequence of data writes were A, B, C, a client’s read will always return A, AB, or ABC, but never AC, BA, etc. This can be ideal in a blog platform where it’s important for users to see the posts in the order they were published.
5. Eventual Consistency Model
In Eventual consistency, there’s a possibility of lag between the reads and writes, but in time, they’ll synchronize without any specific ordering. This scenario would be most beneficial for a social media platform where users keep posting content and immediate consistency isn’t a priority.
Each consistency model in Azure Cosmos DB comes with its trade-offs between the degrees of consistency, availability, and latency, depicted by the CAP theorem, and they’re all offered without compromising on system performance, reliability, or scalability.
Make sure to evaluate considerations like read and write latencies, availability requirements, data volume, and the specific application’s nature when deciding the most optimal consistency model to use. Remember, Azure Cosmos DB’s multiple consistency models enable you to make this decision per application, rather than imposing a one-size-fits-all consistency model, thus giving you the power to make the best design and implementation decisions.
In conclusion, different consistency models have distinct use cases based on the type and nature of the data operations needed in an application. A successful development strategy in devising native applications in Azure Cosmos DB requires a clear understanding and astute application of these principles.
Practice Test
True/False: Eventual consistency can be an acceptable consistency model when your application needs continuous availability.
- Answer: True
Explanation: Eventual consistency is ideal when the application prioritizes availability and partition tolerance over consistency.
Which consistency model ensures linearizability and total global order guaranteeing reads see the latest writes?
- a) Eventual Consistency
- b) Session Consistency
- c) Bounded staleness consistency
- d) Strict Consistency
- Answer: d) Strict Consistency
Explanation: Strict Consistency offers a linearizable consistency model that ensures reads see latest writes from all regions in a total global order.
You are designing an application where high reliability is more important than immediate consistency. Which consistency model should you use?
- a) Strict Consistency
- b) Eventual Consistency
- c) Session Consistency
- d) Consistent Prefix Consistency
- Answer: b) Eventual Consistency
Explanation: Eventual consistency is ideal for applications that prioritize reliability and availability over immediate consistency.
True/False: Session consistency guarantees linearizability within a single session.
- Answer: True
Explanation: Session consistency guarantees monotonic reads, monotonic writes, and read your writes consistency within a single session.
Multiselect: Which consistency models start replicating writes to the second region only after the write is durably committed in the first region?
- a) Strict Consistency
- b) Session Consistency
- c) Bounded Staleness Consistency
- d) Eventual Consistency
- Answer: a) Strict Consistency, c) Bounded Staleness Consistency
Explanation: Both Strict and Bounded Staleness Consistency models ensure replication to a second region only after a write is securely committed in the first region.
True/False: Bounded staleness consistency model provides guarantees of eventual consistency but within defined lag limits.
- Answer: True
Explanation: Bounded staleness guarantees consistency within a certain bound or lag.
Single Select: Which model guarantees consistency within a session but allows for eventual consistency across different sessions?
- a) Session Consistency
- b) Bounded Staleness Consistency
- c) Strict Consistency
- d) Eventual Consistency
- Answer: a) Session Consistency
Explanation: Session consistency provides strong consistency within a session but enables eventual consistency across different sessions.
True/False: Consistent prefix consistency model guarantees out of order reads.
- Answer: False
Explanation: Consistent prefix guarantees that reads never see out-of-order writes.
Eventual consistency is ideal in which use cases?
- a) Large scale social media applications
- b) Banking transaction system
- c) Real-time multiplayer games
- d) Air ticket reservation system
- Answer: a) Large scale social media applications
Explanation: Eventual consistency is used in scenarios like social media apps where high availability is prioritized over immediate consistency.
True/False: Bounded staleness consistency model is useful when you need a balance between lower latency and consistency.
- Answer: True
Explanation: Bounded staleness provides a balance between read latency and consistency by guaranteeing a specific lag between reads and writes.
Interview Questions
What is the use case for Eventual consistency model in the context of Microsoft Azure Cosmos DB?
The use case for the Eventual consistency model is when the application prioritizes high availability and partitions tolerance over consistency. Applications that are okay with the potential of reading stale data, like a content delivery network or a cache, can opt for Eventual consistency.
Which consistency model is appropriate for mobile applications where the availability and latency are the most crucial factors?
Eventual consistency is the most appropriate model for such applications. This is because it provides the lowest latency and the highest availability, meeting the needs for a mobile application.
In what scenario would you use the Bounded staleness consistency model when using Azure Cosmos DB?
Bounded staleness is used when your application needs a certain level of freshness or staleness guarantee, but can still tolerate a degree of inconsistency. It’s used often when you need a compromise between strong consistency and high availability.
Can you share a use case for Session consistency model in Azure Cosmos DB?
Session consistency maintains consistency within a single session, making it appropriate for scenarios like a shopping cart in an e-commerce application, where a user’s interactions need to be consistent within their session, but not necessarily immediately consistent with all other users.
Can you provide a use case for Strong consistency with Azure Cosmos DB?
Strong consistency is used when it’s essential that everyone sees the same data at the same time. Financial applications or mission-critical line-of-business applications are examples of where strong consistency would be used, as it’s important for all reads to reflect the most recent writes.
In which case would you leverage the consistent prefix consistency model in Cosmos DB?
The consistent prefix model guarantees that reads never see out-of-order writes. This model is will be useful in systems like message queues, where the order of operations is vital.
When dealing with multi-region database applications in Azure Cosmos DB, which consistency level helps in ensuring trade-off between consistency, availability, and performance?
Bounded staleness consistency level helps to maintain a good trade-off between consistency, availability, and performance in multi-region database applications.
Are there any scenarios where the use of multiple consistency models within the same application is appropriate?
Yes, an application might use strong consistency for critical data that require synchronous reads and writes, and eventual consistency for non-critical data that can tolerate stale reads.
What type of application would benefit from using an eventual consistency model in Cosmos DB?
Applications which are more focused on read scalability and are acceptable with the trade-off of data updates being asynchronously propagated, such as a news feed or a comments section in an application, would benefit from an eventual consistency model.
What consistency level in Azure Cosmos DB is suitable for applications requiring linearizability and global order guarantee?
For applications that require linearizability and a global order guarantee, the strong consistency level in Azure Cosmos DB is suitable.
When would a session-level consistency model be most appropriate in Cosmos DB?
A session-level consistency model would be most appropriate in situations where it’s important to maintain consistency within a single user session, but concurrently running sessions do not need to immediately see each other’s changes. Examples of this could include social media apps or online games.
Which consistency model in Azure Cosmos DB can be useful in collaborative applications such as shared calendar or multi-player games?
The bounded staleness consistency model can be useful in such scenarios. It guarantees a lag in propogation of updates, which can help applications maintain a certain level of predictability even as changes are being made rapidly.
When dealing with IoT solutions that require updated asset state over geographically distant regions, which consistency model can be relevant?
Strong consistency model would be relevant in such scenarios to ensure all regions consistently reflect the current and most updated state of IoT assets.
Which consistency model could be useful when the application needs to ensure the order of data updates in Cosmos DB?
The consistent prefix consistency model guarantees that reads never see out-of-order writes, useful when the order of data updates is important in the application.
What type of consistency would be beneficial for reference data which are not updated frequently, but are read often and need to reflect the most current state when changes occur in Cosmos DB?
Strong consistency would be beneficial for such cases as this model ensures all reads see the most recent write.
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