On Microservice Architecture and 18 wheelers

time to read 5 min | 808 words

imageI talked a bit about microservices architecture in the past few weeks, but I think that there is a common theme to those posts that is missed in the details.

A microservices architecture, just like Domain Driven Design or Event Source and CQRS are architectural patterns that are meant to manage complexity. In the realms of operations, Kubernetes is another good example of a tool that is meant to manage complexity.

I feel that this is a part that is all too often getting lost. The law of leaky abstractions means that you can’t really reduce complexity, you can only manage it. This means that tools and architectures that are meant to deal with complexity are themselves complex, by necessity. The problem is when you try to take a solution that was successfully applied to solve a complex problem, and  apply that to something that isn’t of equal complexity.

Keep the following formula in mind:

Solution Complexity = Architecture Complexity + ( Problem Complexity / Architecture Factor )

Let’s try to solve this formula for a couple of projects. One would be managing a little league soccer website and the other would be the standard online shop. Here are the results

Cost / Benefit of Architecture

Little League

Online Shop

Architecture Complexity



Problem Complexity



Architecture Factor



Solution Complexity



By the way, the numbers are arbitrary, I’m trying to show a point, and showing it with numbers make it easier to get the point across. The formula is real, though, based on my experience.

The idea behind the formula and the table above is simple. Every architecture you make can be ranked along two axes. One is the architectural complexity and the second is the architecture factor. The architectural complexity is a fixed (usually) number that ranks how complex it is to use the architecture. The architectural factor is how much this architecture help you deal with the overall problem complexity.

You can see above that applying the same architecture for two different problem can result in very different results. The overall solution complexity for the little league website is less than the online shop, as expected. But you can also see that there are huge fixed costs here that drive the overall complexity far higher.

Using a different architecture, which will have a much smaller architectural factor, but also much lower fixed complexity, will allow you to deliver a solution that has much lower complexity (and get it faster, with less bugs, etc).

Choosing a microservice architecture implies that you are going to have a net benefit here. The additional complexity of using microservices is offset by the fact that the architectural factor is going to reduce your overall complexity. Otherwise, it just doesn’t make sense.

An 18 wheelers is a great thing to have, if you need to ship a whole bunch of stuff. It is the Wrong Tool For The Job if you need to commute to work.

In most cases, people select the architecture that sounds right for their project, mostly because they focus on the architectural factor. Without taking into account the fixed complexity cost. When they run into that, they either re-evaluate or strive forward regardless. Let’s assume that you run into a project where they chose the microservice architecture, and then they realize that some parts of it are complex, so they cut some corners. I’m thinking about something like what is shown here. Let’s analyze what you end up with?

Architecture Complexity – 10, Architecture Factor – 1, Problem Complexity – 8 = Overall Complexity = 18

And that is for the good case where your architectural factor isn’t actually below 1, which I would argue is actually going to be in the kind of architecture that these kind of solutions reach. A Distributed Monolith has an architecture complexity of 10 and a factor of 0.75. So trying to solve a problem that has a complexity of 8 here will result in overall complexity of 20.6.

I don’t actually have real numbers to evaluate different architectures and solution complexities. That would probably require rigorous study, but empirical evidence can give good off the cuff numbers for most of the common architectures. I’m going to leave it up to the comments, if someone want to take this challenge.

Keep this in mind when you are choosing your architecture, for both green fields and brown fields projects. That can save you a lot of trouble.