Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
The right way to abstract
Start by implementing the problem in the concrete, not in the abstract. Don't try to guess what interfaces you will need. Discover them.
Only after implementing the problem in the concrete, go on to decoupling. Ask yourself: "What do I need to decouple to make it better, to allow it to deal with change as the data or business changes?".
Leaving the code in the concrete is also an option. Don't force abstractions when they are not necessary. Prefer duplication over the wrong abstraction because duplication is cheaper to deal with.
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Wrong abstractions
If you find yourself passing parameters and adding conditional paths through shared code, the abstraction is wrong. It's no longer right for the codebase.
Wrong abstractions are no longer common abstractions; they are series of conditions mixed with vaguely related ideas. T...
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DRY is not a silver bullet
"Don’t repeat yourself" (DRY) is usually good advice, but you have to get the abstractions right. Otherwise they quickly become unmaintainable.
Here's how it usually plays out:
- You write a cool abstraction
- Time passes
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Fix wrong abstractions
In the long run, it's better to fix wrong abstractions ASAP than to add more code and make everything more complicated.
Your goal is to remove the abstraction and conditionals, and to reduce each caller to only the code it needs. Here's how:
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