CQRS CQRS was introduced by Greg Young; his explanation in 2010 CQRS is simply the creation of two objects where there was previously only one. The separation occurs based upon whether the methods are a command or a query (the same definition that is used by Meyer in Command and Query Separation, a command is … Read more
Disclaimer I am Greg Young (if you cant read my name :)) I am going to answer this question though I believe it will likely get deleted anyways. This question alone for me is a bit odd, but the answers are fairly bizarre. I won’t take the time to answer each reply individually but will … Read more
Event Sourcing is optional and in most cases complicates things more than it helps if introduced too early. Especially when transitioning from a legacy architecture and even more when the team has no experience with CQRS. Most of the advantages being attributed to ES can be obtained by storing your events in a simple Event … Read more
Here is my take on this. 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 … Read more
If you validate the username using the read model before you send the command, we are talking about a race condition window of a couple of hundred milliseconds where a real race condition can happen, which in my system is not handled. It is just too unlikely to happen compared to the cost of dealing … Read more
The event store should not need to know about the specific fields or properties of events. Otherwise every modification of your model would result in having to migrate your database (just as in good old-fashioned state-based persistence). Therefore I wouldn’t recommend option 1 and 2 at all. Below is the schema as used in Ncqrs. … Read more
I am one of the original authors of Kafka. Kafka will work very well as a log for event sourcing. It is fault-tolerant, scales to enormous data sizes, and has a built in partitioning model. We use it for several use cases of this form at LinkedIn. For example our open source stream processing system, … Read more