Part I of this series can be found here.

My code didn’t improve just because I was giving up on OOP principles. I was simultaneously adopting functional principles in their place, whether I realized it at first or not. I found several benefits in doing so:


Consider a unit test for a pure function. You determine the appropriate range of input values, you pass the values in to the function, you check that the return values match what you expect. Nothing could be simpler.

Now consider testing a method that does the same job, but as an instance method. You’re not just considering the input and output values of a function with a simple unit test anymore. You either have to go through every step of the method and make sure you’re not touching any variables you’re not supposed to, or change the unit test to check every variable that is in scope (i.e. at least every method on that object), if you’re being comprehensive. And if you take a shortcut and don’t test every in-scope variable, anyone can add a line to your method that updates another variable, and your tests will never complain. Methods that perform external IO, such as reading values from a database to get their relevant state, are even worse — you have to insert and compare the appropriate values in the database out of band of the actual test, not to mention making sure the database is available in the same environment as the unit tests in the first place.

When software is designed in such a way that side effects are minimized, test suites are easy to write and maintain. When side effects occur everywhere in code, automated testing is impractical at best and impossible at worst.

Limited coupling of systems

When I take the idea of eliminating side effects to their logical conclusion, I end up with a lot of data objects that don’t do anything except hold state, a lot of pure functions that produce output values from input parameters, and a mere handful of methods that set state on the data objects based on the aggregate results from the pure functions. This, not coincidentally, works extremely well with loosely coupled and distributed systems architectures. With modern web services communicating between systems that were often developed independently, it’s usually not even possible to send instance methods along with the data. It just doesn’t make sense to transfer anything except state. So why bundle methods and state together internally if you will have to split them up again when dealing with the outside world? Simplify it and keep it decoupled everywhere.

Decoupling internally means that the line between ‘internal’ code and ‘external’ code is malleable, as well. This leads me to another benefit of eliminating side effects:

Scalability (scaling out)

If all you’re doing is passing state from one internal pure function to another internal pure function, what’s stopping you from moving one of those functions to another computer altogether? Maybe your software has a huge bottleneck in the processing that occurs in just one function that gets repeatedly applied to different inputs. In this case, it may be reasonable to farm that function out to a dedicated server, or a whole load balanced/distributed server farm, by simply dropping in a service boundary.

Concurrency (scaling up)

One of the keys to maximizing how well your software scales up on multi-CPU systems is minimizing shared state. When methods have side effects, the developer must perform rigorous manual analysis to ensure that all the state touched by those methods is threadsafe. Further, just because it’s threadsafe doesn’t mean it will perform well; as the number of threads increases, lock contention for that state can increase dramatically.

When a method is totally pure, on the other hand, you have a guarantee that that method, at least, will scale linearly with your hardware and the number of threads. In other words, as long as your hardware is up to snuff, running 8 threads of your method should be 8 times faster; you simply don’t have to worry about it. On top of that, pure methods are inherently threadsafe; no side effects means no shared state in the method, so threads won’t interfere with each other. While you probably won’t be able to avoid shared state completely, keeping as many functions pure as possible means that the few places that you do maintain shared state will be well-known, well-understood, and much easier to analyze for thread safety and performance.

For all these reasons, I found adopting a functional style to be a huge win. However, not all is wine and roses for C# developers who have learned to love functional programming…

(to be continued in part III)