What are the disadvantages of using Event sourcing and CQRS?

Here is my take on this.

1. CQRS + ES can make things a lot simpler in complex software systems by having rich domain objects, simple data models, history tracking, more visibility into concurrency problems, scalability and much more. It does require a different way thinking about the systems so it could be difficult to find qualified developers. But CQRS makes it simpler to separate responsibilities across developers. For example, a junior developer can work purely with the read side without having to touch business logic.

2. Copies of data will require more disk space for sure. But storage is relatively cheap these days. It may require the IT support team to do more backups and planning how to restore the system in a case in things go wrong. However, server virtualization these days makes it a more streamlined workflow. Also, it is much easier to create redundancy in the system without a monolithic database.

3. I do not consider higher memory usage a problem. Business object hydration should be done on demand. Objects should not keep references to events that have already been persisted. And event hydration should happen only when persisting data. On the read side you do not have Entity -> DTO -> ViewModel conversions that usually happened in tiered systems, and you would not have any kind of object change tracking that full featured ORMs usually do. Most systems perform significantly more reads than writes.

4. Longer boot up time can be a slight problem if you are using multiple heterogeneous databases due to initialization of various data contexts. However, if you are using something simple like ADO .NET to interact with the event store and a micro-ORM for the read side, the system will "cold start" faster than any full featured ORM. The important thing here is not to over-complicate how you access the data. That is actually a problem CQRS is supposed to solve. And as I said before, the read side should be modeled for the views and not have any overhead of re-mapping data.

5. Two-phase commit can work well for systems that do not need to scale for thousands of users in my experience. You would need to choose databases that would work well with the distributed transaction coordinator. PostgreSQL can work well for read and write separate models, for example. If the system needs to scale for a high number of concurrent users, it would have to be designed with eventual consistency in mind. There are cases where you would have aggregate roots or context boundaries that do not use CQRS to avoid eventual consistency. It makes sense for non-collaborative parts of the domain.

6. You can query events in serialized a format like JSON or XML, if you choose the right database for the event store. And that should be only done for purposes of analytics. Nothing inside the system should query event store by anything other than the aggregate root id and the event type. That data would be indexed and live outside the serialized event.

Just to comment on point 5. I've been told that Facebook does use ES with Eventual Consistency, which is why you can sometimes see a post disappear and reappear after you've posted it.

Usually the read-model your browser is accessing is located 'close' to you, but after you make a post the SPA switches over to a read-model that is close to your write-model. The close proximity between the write-model (events) and the read-model mean you get to see your own post.

However, 15 minutes later your SPA switches back to the first, closer, read-model. If the event containing your post hasn't yet propagated to that read-model you'll see your own post disappear only to reappear sometime later.

I know it's been almost 3 years since this question was asked, but still this article may be useful for someone. Key points are

• Scaling with snapshots
• Visibility of data
• Schema changing
• Dealing with complex domains
• Need to explain it to most new team members

Event sourcing and CQRS is great because it gets rids developers being stuck with one pre-modeled database which the developer has to work with for the lifetime of the application unless there is a big data migration project.

This is a big misconception. The relational databases were invented exactly for the evolution of the model (thanks to simple two-dimensional tables as opposed to pre-defined hierarchical structures). With views and procedures ensuring the encapsulation of data access, the logical and physical model can evolve independently. This is also why SQL defines DDL and DML in the same language. Some RDBMS also allow all those evolutions to be versioned and deployed online (continuous delivery) as Oracle Edition Based Redefinition.

Big data structures are predefined and can be read only with the code developed for this structure. Ok when consumed immediately but you will have hard time to read it 10 years later without the exact version, and language compiler or interpreter.