Early on in the CQRS/ES days, I saw a lot of questions on modeling problems with event sourcing. We try to choose a single modeling strategy for the entire application, and it doesn’t work. Meaning the functionality as a whole is eventually consistent. Also, they most likely won’t mean anything and won’t have any domain value at all.In theory and I find it to be a good rule of thumb is to have your commands and events reflecting the intent of the user staying true to DDD. This difficulty by itself makes event sourcing unsuitable for most applications and only relevant for very isolated and specific use cases.That’s when CQRS comes in. But that belied the truth – no app I’ve worked with is JUST a DDD app, or JUST a CRUD app, or JUST an event sourcing app. Specifically, trying to fit every square modeling problem into the round hole of event sourcing. Martin Fowler has a very interesting article about it.Greg Young identified quite well how CQRS and event sourcing shares a symbiotic relationship. Simple mediator implementation in .NET. We shouldn’t just pick transaction script for every handler just because it’s easy.
We still need that “Refactor” step in TDD, so it’s important to think about our model.Listen to the code in the handler – if you’ve chosen a bad approach, refactor! Otherwise, I don’t reuse.Since I’ve built isolation between individual requests and responses, I can choose different patterns based on each request:Each request handler can determine the appropriate strategy based on *that request*, isolated from decisions in other handlers. Since each entity is represented by a stream of events there is no way to reliably query the data. While if every functionality affects the same event-sourced component will rapidly become an event-sourced monolith. Event sourcing is a particularly efficient write model since it works basically as an append log where new information is always added enabling minimal locking. If there’s too much going on in the handler, push it down into the domain.
MediatR. As promised in my previous post, in this article I examine practical aspects related to DDD and, in particular to CQRS and Event Sourcing patterns. Event Stream - is a representation of the entity in event sourcing. If it’s better served with a different model altogether, refactor that direction. Processing each event in the stream will produce the latest state of that entity. While the system settles on a value, it can return stale or inconsistent data, a period that is known as the inconsistency window.By definition that queue between the write and read model can fill up, the system can have an unforeseen peak of usage and can take more than it is expected to process. A strategy should be prepared upfront and considered on the system design.One of the most important design decisions and one of the hardest things to get right is how granular the events should be. On the other hand, since the read model is completely independent allows the freedom to choose the most adequate technology to optimize for queries. It’s a set of events that happened for the entity with the exact id. Specifically, trying to fit every square modeling problem into the round hole of event sourcing. However, the events in an event store are immutable and can’t be deleted, to undo an action means sending the command with the opposite action. A lot has been written about how to handle event schema changes, there are several articles on how to handle this. The main goal of my experiment is to implement an aggregate according to the Event Sourcing paradigm, and to create a separate read model to feed the pages of a Web application. Illustrating this on a simple example, if a given AddressStreetChanged event is published it clearly shows the intent of the user by changing the street of the address but how many of your listeners will need that information without for example the door number? A senior developer and a software architect take a deep dive into what those getting started with CQRS and event sourcing should know. They are built in a way that the action the user needs to make is intuitive and is incorporated on the interaction the user makes with the software, guiding the user through the process. It seems the best of both worlds. Eventual consistency is the notion that a system will eventually converge on a value if no more updates are done to a given entity. No architecture prevents bad design. Both have dire consequences, the first you will have to worry about disk space, the extra effort of building that internal state, and keeping it synchronized. But how they got there is immaterial. The advantages are obvious and they share a very peculiar symbiosis with the current tech state of the art, making them very relevant. As long as it is applied to the right use case… Otherwise, that meteor will come down and hit you in the face, and those tears will be of despair instead of happiness.Deep diving into CQRS with event sourcing you will find that a queue separates the write and the read model. Our final picture looks a bit more reasonable:Instead of forcing ourselves to rely on a single pattern across the entire application, we choose the right approach for the context.One last note – it’s easy in this sort of system to devolve into ugly handlers:Driving all our requests through a single mediator pinch point doesn’t mean we absolve ourselves of the responsibility of thinking about our modeling approach. I use the MediatR. ... As in the example above, we have done both CQRS and Event Sourcing only according to the desire to create a user.
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