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24 April 2015

Response to "The Long-Term Problem With Dynamically Typed Languages"

I enjoyed this post quite a bit: "The Long-Term Problem With Dynamically Typed Languages". I think he's got some great points. I especially like the analogy with the "broken windows effect". It's interesting to hear about someone's experience using a software system or practice for a long time.


The best data point I have on this personally is my current project/team. The codebase is over 500KLOC now. The majority of it is in Python, followed by JS. I’ve been working on it since the beginning—over 4 years. We’ve built components and then extended them way beyond their original design goals. There’s a lot of technical debt. Some of it we’ve paid down through refactoring. Other parts we’ve rewritten. Mostly we live with it.

As time has gone on, we’ve gained a better understanding of the problem domain. The architecture of the software system we want is very different than what we have or what we started with. Now we’re spending our time figuring out how to get from where we are to where we want to be without having to rewrite everything from scratch.

I agree we have the lava layers problem the author describes, with multiple APIs to do the same thing. But I’m not sure if we would spend our time unifying them if we had some kind of miraculous tooling afforded by static types.

Our time is better spent reevaluating our architecture and enabling new use-cases. For example, one change we’ve been working towards reduces the turn-around time for a particular data analysis pipeline from 30 minutes to 1 millisecond (6 orders of magnitude). Now our product will be able to do a whole bunch of cool stuff that was impossible before. It took a lot of prototyping to get here. I don’t think static types would have helped.

My team’s biggest problem has always been answering the question: “How do we stay in business?” We've optimized for existence. We’ve had to adapt our system to enable product changes that make our users happy. Maybe once your product definition is so stable, like Google Search or Facebook Timeline, you can focus on a codebase that scales to 10,000 engineers and 10+ years of longevity. I haven't worked on such a project in my career. For me the requirements are always changing.

(Originally from my comment here)
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