Understanding Consistency Levels in Azure Cosmos DB
Azure Cosmos DB is a globally distributed, multi-model database service. When you have data replicated across multiple regions, you need to understand how reads and writes are handled to ensure your application behaves as expected. This is where consistency levels come into play.
Azure Cosmos DB offers five well-defined consistency levels that allow you to balance strong consistency with availability and latency. The choice of consistency level has a direct impact on read latency, write availability, and how quickly changes are propagated across all regions.
Key Takeaway: Choosing the right consistency level is crucial for optimizing your application's performance and user experience in a distributed environment. Most applications can start with a strong quorum or bounded staleness consistency level.
The Five Consistency Levels
The following table summarizes the five consistency levels provided by Azure Cosmos DB, from strongest to weakest:
| Consistency Level | Read Cost | Write Cost | Availability | Latency | Data Staleness | Notes |
|---|---|---|---|---|---|---|
| Strong | 1 RU/op | 1 RU/op | High | Lowest | None | Guarantees that a read returns the most up-to-date data, even across regions. |
| Bounded Staleness | 1 RU/op | 1 RU/op | High | Low | At most 'k' versions of an item or 't' time interval | Reads are guaranteed to be at most 'k' versions or 't' time interval behind the writes. |
| Session | 1 RU/op | 1 RU/op | High | Low | At most one version of an item | Guarantees that within a single client session, reads will reflect writes performed in the same session. |
| Consistent Prefix | 1 RU/op | 1 RU/op | High | Low | Any prefix of the history | Reads return a prefix of the history of writes, but not necessarily the latest write. |
| Eventual | 1 RU/op | 1 RU/op | Highest | Highest | Potentially stale | Reads may return stale data, but all reads will eventually converge to the latest state. |
Understanding the Trade-offs
When designing your application, consider the following trade-offs:
- Consistency vs. Availability: Stronger consistency levels offer greater data guarantees but may reduce availability during network partitions.
- Consistency vs. Latency: Stronger consistency often leads to higher read latency as the system needs to coordinate across more replicas.
- Cost: All consistency levels have the same RU costs for read and write operations. The difference lies in the performance and availability characteristics.
Visualizing Consistency
The following diagram illustrates how data propagates across regions for different consistency levels. (Note: This is a conceptual placeholder.)
Choosing the Right Consistency Level
Here are some common scenarios and recommended consistency levels:
- Shopping Cart/User Profiles: Session consistency is often sufficient, as users primarily interact with their own data.
- Leaderboards/Analytics: Eventual or Consistent Prefix might be acceptable if a slight delay in seeing the absolute latest data is tolerable.
- Financial Transactions/Inventory Management: Strong consistency is typically required to prevent data anomalies like double-spending or incorrect inventory counts.
- Global Applications with High Read/Write Throughput: Bounded Staleness offers a good balance between strong consistency and low latency/high availability.
You can configure the default consistency level for your Azure Cosmos DB account at the account level. You can also override the consistency level per request.
Important: Azure Cosmos DB guarantees that all operations within a single logical partition will be applied in the order they are written, regardless of the consistency level chosen for the account.
Default Consistency Level
If you don't explicitly set a consistency level, Azure Cosmos DB defaults to the Session consistency level. This provides a good balance for most applications.
Conclusion
Understanding and selecting the appropriate consistency level for your Azure Cosmos DB account is a critical step in designing highly available, performant, and correct distributed applications. Evaluate your application's requirements for data freshness, availability, and latency to make the optimal choice.