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1070 points dondraper36 | 1 comments | | HN request time: 0.213s | source
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codingwagie ◴[] No.45069135[source]
I think this works in simple domains. After working in big tech for a while, I am still shocked by the required complexity. Even the simplest business problem may take a year to solve, and constantly break due to the astounding number of edge cases and scale.

Anyone proclaiming simplicity just hasnt worked at scale. Even rewrites that have a decade old code base to be inspired from, often fail due to the sheer amount of things to consider.

A classic, Chesterton's Fence:

"There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, “I don’t see the use of this; let us clear it away.” To which the more intelligent type of reformer will do well to answer: “If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.”"

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sodapopcan ◴[] No.45070127[source]
This is the classic misunderstanding where software engineers can't seem to communicate well with each other.

We can even just look at the title here: Do the simplest thing POSSIBLE.

You can't escape complexity when a problem is complex. You could certainly still complicate it even more than necessary, though. Nowhere in this article is it saying you can avoid complexity altogether, but that many of us tend to over-complicate problems for no good reason.

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lll-o-lll ◴[] No.45070394[source]
> We can even just look at the title here: Do the simplest thing POSSIBLE.

I think the nuance here is that “the simplest thing possible” is not always the “best solution”. As an example, it is possible to solve very many business or operational problems with a simple service sitting in front of a database. At scale, you can continue to operate, but the amount of man-hours going into keeping the lights on can grow exponentially. Is the simplest thing possible still the DB?

Complexity is more than just the code or the infrastructure; it needs to run the entire gamut of the solution. That includes looking at the incidental complexity that goes into scaling, operating, maintaining, and migrating (if a temporary ‘too simple but fast to get going’ stack was chosen).

Measure twice, cut once. Understand what you are trying to build, and work out a way to get there in stages that provide business value at each step. Easier said than done.

Edit: Replies seem to be getting hung up over the “DB” reference. This is meant to be a hypothetical where the reader infers a scenario of a technology that “can solve all problems, but is not necessarily the best solution”. Substitute for “writing files to the file system” if you prefer.

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1. mattlutze ◴[] No.45072898[source]
No pop psychology maxim is universally true. However in your example we're presented with the outdated understanding of "tech debt."

> As an example, it is possible to solve very many business or operational problems with a simple service sitting in front of a database.

If this is the simplest approach within the problem space or business's constraints, and meets the understood needs, it may indeed be the right choice.

> At scale, you can continue to operate, but the amount of man-hours going into keeping the lights on can grow exponentially. Is the simplest thing possible still the DB?

No problem in a dynamic human system can be solved statically and left alone. If the demands on a solution grows, and the problem space or business's needs changes, then the solution should be reassessed and the new conditions solved for.

Think of it alternatively as resource-constrained work allocation, or agile problem solving. If we don't have enough labor available (and we rarely do) to solve everything "best," then we need to draw a line. Decades of practice now have shown that it's a crap shoot to guess at the shape of levels of complexity down the road.

Best case you spend time that could go into something else valuable today, to solve a problem for a year from now; worst case you get the assumptions wrong and fail to solve that second "today" problem as well as still needing to spend future time on refactoring.