This is part three of my event sourcing series. In the first two posts, I showed you approaches that rely solely on projections. Now is the time to introduce read models to support all your query needs. And maybe, solve some performance issues as well. Of course, I’ll discuss the downsides of read models, too.
To finish this post, I’ll do a deep dive into a code example.
This is the second post in my series on event sourcing. Last time, we saw a simple implementation based only on projections. While simple, it can only be queried by the event stream ID. In this post, we look at an approach that allows for more lookup criteria while still avoiding a read model.
This is the first post in a series about event sourcing. I’ll start with a very simple event sourcing implementation that is often good enough. Most of our event streams are implemented in this simple approach. In the following posts, the concepts will be extended to match additional requirements. I’ll touch on read models, consistency, long event streams, archiving, compensation, event skipping, lifetimes, and bi-temporal event sourcing. Every post will explain the concepts and our...
We started using event sourcing over ten years ago. One of the hardest lessons was that there is a kind of events that is not obvious at first but have a big impact on your system design. I call this kind secondary domain events.
In this post, I’ll explain what secondary domain events are, and how they impact the design.
Link to the talk on YouTube:
Link to the talk on YouTube:
Bi-temporal event sourcing combines storing data as a sequence of events, which tell what has happened with the data, and the data has two associated points of time, one when the data entered the system and one when the data takes effect. This post is about our 8+ years of experience with bi-temporal event sourcing, along with code samples showing how to achieve this. Feel free to skip the code blocks and just read the conceptual parts. But you’ll miss the beauty of F# 😂 This post is part...