In the fourth part of my event sourcing series, we’ll take a look at why long event streams – streams with lots of events – are a problem and what options there are to deal with this problem. Why read models are not always enough to solve performance issues due to long event streams. And of course, I’ll discuss the involved trade-offs.
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...
One of the great features of .NET is that you can mix its programming languages (C#, F#, VB.NET) in a single solution and that assemblies written in one language can call assemblies written in other languages. This is great, especially when starting with F#. A team does not need to switch everything at once, but can keep using the existing C# code and use Interop to call C# from F# or vice versa.In this article, we take a look at the Interop story in 2026. Spoilers: it works great.
In our product, I sometimes encounter the problem of dynamic IN-lists in SQL. First, they appear harmless, but soon they lead to an unnecessary amount of query plans. In this blog post I share how to circumvent those issues with TVP. A clean, performant and reusable way to solve this problem!
Not having to annotate types for every argument and return value in F# is a big relief. In this blog post, I share some tips and tricks to reduce the need for type annotations.
Yesterday evening, I gave a workshop titled “To test, or not to test” at the Software Crafters Zürich Meetup. In the workshop, we gathered reasons to write tests: being confident that the code works, being confident that regressions can be prevented, helping to drive the implementation, and having documentation of the system. Interestingly, when I prepared the workshop, I forgot about the documentation aspect of the tests. Here is why and why it matters.
Welcome to our team! Today is your first day, which means it’s the day you’ll release your first feature. You’ll see everything needed to design, implement, and release a feature in our system. We’ll touch on F# language features, our TDD style, and some architecture topics.
This blog post is part of the F# Advent Calendar 2024 (thanks, Sergey, for the organisation).
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...