Tackling Complexity in CQRS

Tackling Complexity in CQRS

The CQRS pattern can do wonders: it can maximize scalability, performance, security, and even “beat” the CAP theorem. Nonetheless, CQRS has acquired a controversial name because of the complexity it introduces. For instance, in his article on CQRS, Martin Fowler argues that the pattern should be applied sparingly and even cautiously:

  • “… for most systems CQRS adds risky complexity”
  • “… you should be very cautious about using CQRS”
  • “So while CQRS is a pattern that’s good to have in the toolbox, beware that it is difficult to use well and you can easily chop off important bits if you mishandle it.”

From my point of view, the CQRS-induced complexity is largely accidental, and thus can be avoided. To illustrate my point, I want to discuss the goal of CQRS, and then analyze 3 common sources of accidental complexity in CQRS-based systems.

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Finding Proper Scopes for Unit Tests

In my previous rant post on TDD I’ve argued that the majority of the problems many experience doing TDD are caused by testing in too narrow scopes - using classes as units of testability, instead of functional use cases. However, widening the scope of the test too much is just another extreme. So how one finds the sweet spot? In this post I’d like to share the heuristic that I use.

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Tackling Complexity in the Heart of DDD

Tackling Complexity in the Heart of Domain-Driven Design

Let’s do a little experiment: try to explain the gist of Domain-Driven Design to someone who has no clue about it. This, especially doing it succinctly, is not easy. Heck, I struggle with it myself. Bounded contexts, entities, domain events, value objects, domains, aggregates, repositories… where do you even start?

To find the order in the apparent chaos, I want to analyze the DDD methodology from a rather unusual perspective — by applying Domain-Driven Design to Domain-Driven Design itself. After all, this methodology is intended to deal with complex domains, isn’t it?

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