The steady march of complexity

It’s not a new idea that complexity is the Achilles’ heel of large software projects, but it’s such an important point that I’m going to give it my semi-annual drumbeat

It’s not intuitive just how bad this problem gets as projects trend bigger, especially in corporate environments where there’s a low bar on quality (short-term shipping is favored over long-term sustainability). I’d go so far as to say that after some point the majority of every engineer’s time is being attritted to complexity — working around it is what most people are doing most of the time.

As a recent example from my own life: We have a data deletion facility that allows a user to delete the entirety of the test data in their account. It works by rotating through a series of model types, querying a user’s account for each one, and deleting all the objects that are found. I was making a change yesterday to add a couple new model types to the process after a recent change had made them user visible.

It went a little off the beaten path because the new model types were of an ephemeral sort; they’re still stored the same as any other, but historically weren’t deleted by convention. It should still have been straightforward, but upon digging in, I found that the team that owned their base type had created a series of save hooks that didn’t support the entirety of the save interface. It was a simplification that was strictly incorrect, but one that was possible because the models were being deployed in a limited sense.

This still should have been okay because I was just running a deletion instead of a save, but elsewhere in the codebase a different team had installed data redaction save/delete hooks that had the (probably unintended) side effect of converting all delete operations into save operations with a special op: :delete directive to flag it as a deletion to the underlying machinery. This of course is part of the extended slightly-less-common save API which the limited API of the first save hooks didn’t support.

It was still tractable, but required open-heart surgery deep in the plumbing. A 15 minute project turned into four hours. Four hours of lost time and productivity during which no progress is made on macro projects. Theoretically, I shouldn’t even be working on this sort of thing, but generally these sorts of minor product bugs don’t get fixed otherwise.

And it’s not an outlier — accidental difficulty is the norm for most things anyone tries to do, and over time only becomes more normal. The teams putting in the problematic components above were nominally doing the right thing at the time, but every new feature that breaks well outside the bounds of its area of responsibility becomes deadweight on every future change. These features are almost always strictly additive, and introduction of new ones accelerates as engineering teams grow.

Taming complexity is a hugely important, hugely unsolved problem. Again, corporate software tends to be particularly bad, but even well-designed projects still have the problem, albeit to a lesser extent 1. The solutions aren’t new or novel — more modularity, more encapsulation, smaller APIs between modules — but as a profession we need to develop better instincts in these areas, and better frameworks to force the issue.

1 e.g. Postgres: try adding a new feature to the B-tree implementation, and you may be amazed by the vast amount of context you need to ingest before even being able to get started.

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The steady march of complexity

Published
October 3, 2019

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