On Well-Designed Event Models

I propose that events composing a well-designed event model should be of a form:

Where:

• E_Ai^x→y is the event communicating transition of aggregate/entity A_i  from business state S^x to business state S^y

• A_i is the i-th instance of aggregate or entity A consisting of attributes {α, β... ω} 

• State transition (S^x → S^y) is accompanied by changes in a subset of attributes of A_i {α, β... μ} to new values {α', β'... μ'}, these new values (if required, with previous values {α, β... μ}) are included in the data payload of the event E_Ai^x→y{α', β'... μ'}

This approach is in contrast with a trivial event model where events are simply attribute-level changes, e.g. instead of emitting E_Ai^x→y{α', β'... μ'}, emit the following series:

Ergo, developing a business state-centric event model requires starting with business state transition diagram for entity/aggregate A:

The benefit of business state-centric event model is to make explicit the state transitions (which typically involve changes to more than one attribute), instead of those having to be inferred/reconstructed by the consumer of attribute level events E^α_Ai {α'} = A_i {α'}.

CQRS – The Sweet Spot

Experience teaches all of us that in technology there’s no panacea. Every tool has its uses and, conversely, can be misused. To really understand a piece of technology one needs to understand its design intent, what problem its creators were trying to solve, and what trade-offs they selected.

It is a truism that every engineering decision involves trade-offs – selecting certain strengths at the price of accepting certain limitations (not just in software/systems engineering, but in all engineering disciplines) at a given level of investment. Thus, every instrument has a sweet spot for its application.

CQRS is an interesting example to consider. It builds on the strengths of a more vanilla version of EDA (event-driven architecture) – uncoupling domains from each other – and adds asymmetric read/write models and event sourcing.

By enabling updates at highly granular, attribute level, coupled with arbitrarily denormalized aggregate reads, CQRS creates opportunities for independently scaling the read and write/update stores – much more so than adding read replicas to traditional, symmetric-model domain implementations.

These strengths come at a certain price of additional complexity – managing highly granular even models, ensuring interoperability with non-CQRS-based domains, dealing with compacting of logs in event sourcing, etc.

It would seem that domains that would benefit most from this approach are those where multiple actors are attempting simultaneous updates to varying attributes of the same aggregate – i.e. domains demanding high-concurrency writes.