The idea is simply to write comparative tests into the code; you have the "proven but unsupported" production method and then, at its side, a very "safe" method that performs exactly the same calculation, on a small subset of the data.
Why wouldn't you use the safe method every time? It seems to me that if you have a method that works and is safe, why would you not use that? Maybe I'm missing the point completely.
Considering the subject matter in the article, I suspect the article is about "proven but unsupported" things such as the "Quirky Update" methods. The problem is the definition of what is "safe" and what is not and it varies from one person to another. If the definition of "safe" is "something that is guaranteed to work forever", then almost all code will fail such an expectation due to deprecation of features in favor of supposedly better features.
Why would someone take a chance on something that could change in the future? Heh... ask a FoxPro user or a survivor of SQL Server 2000 sp4. ;-)
is pronounced ree-bar and is a Modenism for R
First step towards the paradigm shift of writing Set Based code: Stop thinking about what you want to do to a row... think, instead, of what you want to do to a column.
Although they tell us that they want it real bad, our primary goal is to ensure that we dont actually give it to them that way.
Although change is inevitable, change for the better is not.
Just because you can do something in PowerShell, doesnt mean you should. Helpful Links:
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