Debunking Common CQRS Myths

CQRS (Command Query Responsibility Segregation) is an architectural pattern that focuses on separating the handling of commands (write operations) from queries (read operations). While simple in concept, numerous myths and misunderstandings surrounding its use can lead to unnecessary complexity, especially in terms of system architecture and user interface design. Let’s address these common myths, layer by layer, and clarify what CQRS really involves.

Myth 1: CQRS and Event Sourcing Are the Same Thing

One of the biggest misunderstandings is that CQRS always involves event sourcing, or vice versa. While these two concepts are often used together, they are not dependent on each other.

Event sourcing focuses on storing every state change as an event and rebuilding the state by replaying these events. This can simplify building read models over time in event-driven systems. However, you can implement CQRS without using event sourcing at all. Likewise, you can use event sourcing without applying CQRS. The two are distinct architectural patterns that, although complementary, serve different purposes.

Event sourcing is generally more useful when working within bounded contexts and dealing with systems where historical state tracking is important. However, the overhead of managing event sourcing can be significant, and it shouldn’t be applied unless there is a clear need for it.

Myth 2: CQRS Requires an Eventually Consistent Read Store

Another common misconception is that CQRS mandates the use of an eventually consistent read store, where the results of a command (write operation) take some time to reflect in the query (read side). This is not a requirement.

Immediate consistency is entirely possible in a CQRS setup, where the read model is updated as soon as the command succeeds, all within the same transaction. In fact, in many existing systems, transitioning from an immediate to an eventual consistency model can add unnecessary complexity and confuse users who expect instant updates. It’s often easier and more effective to start with immediate consistency and gradually shift to eventual consistency where it is genuinely needed, rather than forcing it upfront.

Transitioning to eventual consistency should be a gradual process, especially when user experience and expectations are at stake. For example, if users expect instant updates after they submit a request, suddenly shifting to eventual consistency could frustrate them unless the underlying business processes also change to accommodate this.

Myth 3: CQRS Requires a Message Bus, Queues, or Asynchronous Messaging

A lot of people mistakenly believe that implementing CQRS means you need to use message buses or asynchronous messaging systems like NServiceBus. This isn’t the case.

While asynchronous messaging systems can be useful for handling eventual consistency in more complex scenarios, there’s nothing in CQRS that explicitly requires this. You can very well implement CQRS without any form of messaging infrastructure. Whether to use queues or a message bus depends entirely on the consistency requirements and scalability needs of your system.

The takeaway here is to avoid unnecessary complexity at the start. Don’t introduce queues or a bus until you know you need eventual consistency, or have proven that your system benefits from asynchronous processing. Immediate consistency with simpler infrastructure might be sufficient for many use cases.

Myth 4: Commands Are Always Fire and Forget

Another common myth is that commands in CQRS are inherently fire-and-forget, meaning that after a command is issued, there’s no need for feedback to the user. In practice, this is rarely the case.

Most business operations require at least a basic level of confirmation. Users need to know if their request was successfully received and accepted. While the actual fulfillment of the command can happen asynchronously, the acceptance of the request should typically be handled synchronously. This can be as simple as providing an acknowledgment message that the system has registered the request.

In scenarios where fulfillment takes time (e.g., processing payments or large data operations), you’ll likely need to introduce processes like sagas or workflows to handle long-running tasks and provide updates to the user over time. Fire-and-forget is generally too simplistic for real-world business needs, where feedback and request correlation are critical.

 

Myth 5: Read Models Must Be Eventually Consistent

Many assume that the read models in CQRS must always be eventually consistent, where the results of write operations don’t immediately reflect in the read view. This assumption is misguided.

Read models only need to be eventually consistent when the business requirements demand it. For many systems, immediate consistency is a perfectly valid approach, especially when users expect real-time feedback. Before deciding on eventual consistency, you should carefully assess whether delayed updates will affect the user experience and how your system can handle failures and delays.

Switching to eventual consistency means introducing a whole new set of challenges, like handling failed updates to the read model, or figuring out how to manage the user experience when data isn’t immediately available. You need to ensure that your system can gracefully handle these scenarios, or else you’ll likely encounter more support issues than before.

Myth 6: CQRS Solves Consistency and Concurrency Issues

There’s a false belief that CQRS automatically fixes issues related to data consistency and concurrency. This couldn’t be further from the truth.

In fact, if you try to handle all commands in a strictly serialized manner to avoid concurrency issues, you might end up with performance bottlenecks. CQRS doesn’t eliminate concurrency problems; it simply shifts them. On the query side, you also have to deal with potential out-of-order events, duplicate events, or event failures. Denormalizing read models to handle such situations is possible, but it still requires careful design.

CQRS won’t let you escape these challenges, and it doesn’t automatically lead to scalable systems. You still need to address concurrency and consistency in both the command and query sides of the architecture.

Myth 7: CQRS Is Easy to Implement

Despite its conceptual simplicity, CQRS is far from easy to implement in practice. The separation of concerns between commands and queries may seem straightforward, but many implementations fail because of a lack of understanding of the business domain.

CQRS doesn’t replace the need for a deep understanding of business requirements. It might help in organizing and fulfilling those needs more effectively, but it doesn’t guarantee success. You can still build the wrong system with CQRS if you don’t fully grasp what the business truly needs.

Replacing legacy systems with a CQRS architecture also comes with significant risk. A complete rewrite is always dangerous, and the mere presence of CQRS doesn’t mitigate those risks. You’ll need to think through these transitions carefully, keeping business priorities in focus.

 

Myth 8: CQRS Requires Separate Databases

One myth that needs to be dispelled is the idea that CQRS requires separate databases for handling commands and queries. This is not true.

CQRS does not require the use of separate databases. What it mandates is separate object models for handling commands and queries, but these models can reside within the same database. You can split the models based on their responsibilities without having to create two separate databases.

That said, using separate databases can be beneficial for performance or scalability reasons, but it’s entirely optional. The core of CQRS is about separating the responsibilities, not necessarily the physical data storage.

Myth 9: CQRS Always Requires Separate Models for Reads and Writes

While CQRS enables you to create separate models for reading and writing, it does not always require this approach. In simpler systems or early-stage implementations, you might still use a shared model for both reads and writes, gradually transitioning to separate models if the business demands it.

The power of CQRS lies in its flexibility. It allows you to optimize each side (command and query) independently, but it does not impose rigid rules about how that optimization must happen.

The Takeaway

CQRS is a flexible and powerful architecture for separating the concerns of commands and queries, but it doesn’t come with the rigid requirements that many assume. It doesn’t mandate eventual consistency, separate databases, or event sourcing, and it doesn’t solve concurrency issues by itself. Above all, CQRS should be applied with a clear understanding of the business’s needs, and its complexity should only be introduced as required. Keep your implementation simple and build complexity only where necessary to truly meet the goals of your system.