The Fast Versus Clean Tradeoff
March 17, 2019
Frustrated by that one person on your dev team who always takes twice as long to finish a feature as the the rest of the team? They’ll write tests, clear documentation, and refactor everything as they go, only to see their work scraped two weeks later when priorities change. Are you the architect on your project who meticulously thinks through proper abstractions, service boundaries, and class diagrams, only to see other engineers writing hacky code that breaks abstraction and constantly adds tech debt that slows you down?
These two approaches - let’s call them The Quick and Dirty Method and The Slow and Clean Method - are two forces that are constantly at odds with each other in any software project; the yin and yang of feature development, if you will.
As a young engineer, I lived almost exclusively in the Slow and Clean realm. Only after a few years, missed deadlines, and some difficult conversations did I learn that there is, in fact, a place for both methods. In the rest of this post, we’ll discuss the qualities and tradeoffs of both approaches and answer the question, “which one should I pick for my current work?”.
The Quick and Dirty Method is generally characterized by doing whatever it takes to get the code to work, then moving on to the next thing. This may include:
- Breaking abstraction boundaries or using private APIs
- Writing code that isn’t easily testable
- Knowingly adding to technical debt
- Using a deprecated API or legacy tool that has little current support
This is often known as “hackathon code”. It is perfectly adapted to that particular situation, when you have a limited amount of time to work and you are aiming for the shortest possible path between “I want this feature” and “it’s working in production”.
The Quick and Dirty Method is great for getting your work out quickly and moving on to the next thing. Every moment you spend on a feature has the opportunity cost of not allowing you to be working on the next thing. In a strict agile, or dev shop environment, saving this time could be the difference between succeeding at your sprint and failing, letting your customers down as a result. It could also be the critical difference between finding product market fit and running out of funding for your startup.
However, this comes at a cost: you’re borrowing technical capital from your future self. In other words, you’re adding tech debt that will eventually have to be paid. Perhaps feature A works now and you can get it done in one week, but when you want to add feature B on top of it, it takes a week and a half since you’re forced to work around the hacks you’re relying on. This debt will eventually begin to compound and really slow you down. Your debts must eventually be paid.
Unless, perhaps, this is code you know is temporary, and will be deleted or rewritten in a few weeks. In that case, you’re borrowing at 0% interest.
The Slow and Clean Method is marked by an air of perfectionism or polish. This polish tends to include:
- Refactoring existing code
- Extending an established pattern or creating a new one if your current work does not fit the current one
- Writing extensive tests and documentation
- Preparing and reviewing a design document
- Adding robustness to your new feature such as error handling, edge cases, and backwards compatibility
Code written with the Slow and Clean Method tends to be more like code you see in computer science literature. Your paradigms and abstractions exhaustively cover your program’s set of use cases. The code is colloquially known as “clean”.
The structure of a given piece of code is not only affected by the set of features it supports, but also the order in which those features were written. The set of assumptions you have when a program supports one or two use cases is vastly different than the set of assumptions you have when it supports dozens of cases. As a result, as a program develops it accrues “cruft” from previous assumptions that are now known to be outdated. Code written with the Slow and Clean Method tends to be closer to what code would look like if all the information was available at the outset, through things like consistent refactoring and well thought out abstraction.
As a result, code like this tends to be more robust, less buggy, and more enjoyable for develops to work with. It allows them to move fast when adding new features that fit the established patterns and it is easier for new developers to ramp up on.
While these are all good qualities, code like this takes a certain level of experience to write; it takes years of experience only a more senior engineer or domain specialist possesses. As a result, it is generally more expensive in terms of dollars spent. It is also more expensive in another sense: time. Refactoring and doing various design iterations takes a significant amount of time. This time is a luxury that many startups simply don’t have.
The answer is, unfortunately, that it depends. It depends on what stage your program or product is at in its development cycle and what type of constraints your environment imposes on your developers.
For example, the Quick and Dirty Method clearly prevails if your product still has not achieved product market fit. Your development team is building features, throwing them against the wall (in an educated and user-informed fashion) to see what sticks. You go into this knowing that a significant amount of the code you write this week will probably be gone one or two months down the line. In this environment, it doesn’t make sense to spend a ton of time and money on senior engineers and fancy abstractions. Fast and dirty is more than sufficient to quickly build an MVP and test your hypothesis. If it doesn’t work out, you didn’t spend a bunch of money on something that ends up getting deleted.
On the other hand, Slow and Clean is a no-brainer for a large company’s infrastructure project or any mission-critical system. If all ten thousand servers at your company are going to depend on this system for the next five years, it’s well worth putting extra thought and time into a solid design and extensible abstractions so that when a rat chews through a wire at the datacenter or the intern pushes a bad config change everything doesn’t come crashing down. If there’s also a large number of developers working on the codebase, it may also pay off to have a clear way to extend various aspects.
Most projects will lie somewhere in between these two extremes. The reality is that there isn’t a stark binary between these two methods, but rather a smooth spectrum. You can do certain parts of a feature Fast and Dirty and other aspects Clean and Slowly. Thus, things get muddy. A good rule of thumb I have found is that the earlier in your project’s development cycle you are, the more you will want to lean towards Fast and Dirty. As your project scales and matures, you’ll start to move closer to Slow and Clean.
As with many engineering choices, there is no one right answer. Rather, there are sets of tradeoffs that you will have to choose from under the conditions reality imposes on you. Being a great engineer means being thoughtful and experienced enough to be as aware of these tradeoffs as possible.
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