Prioritizing rows in a union

I just remembered a pretty common data challenge the other day. Suppose you have a number of tables, all with similar information in them. You want to union their contents, but you need to prioritize them, so you want to choose all the rows from table A, then rows from table B that are not included in A, then rows from C that are not included in A or B, and so on.

This is a pretty common use case in data cleansing or data warehousing applications. There are a few different ways to go about this, some more obvious than others.

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Have you tried sp_ctrl3?

I frequently need to look up object definitions when I’m developing or query tuning. You could use Object Explorer in SSMS, but that takes a lot of time and clicking. Then there’s the Alt+F1 shortcut, which will trigger the sp_help stored procedure. That however, comes with a lot of annoying built-in limitations, so a few years ago I started building and maintaining a “better Alt+F1” of sorts.

I decided to call it “Ctrl+3“. But I suppose you could assign it to any keyboard shortcut you want.

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A visual representation of SQL Server Agent jobs

If all you have is a hammer, everything will eventually start looking like a nail. This is generally known as Maslow’s hammer and refers to the fact that you use the tools you know to solve any problem, regardless if that’s what the problem actually needs. With that said, I frequently need a way to visualize the load distribution of scheduled jobs over a day or week, but I could never be bothered to set up a web server, learn a procedural programming language or build custom visualizations in PowerBI.

So here’s how to do that without leaving Management Studio.

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Human-readable ranges of integers or dates

This is a real-world problem that I came across the other day. In a reporting scenario, I wanted to output a number of values in an easy, human-readable way for a report. But just making a long, comma-separated string of numbers doesn’t really make it very readable. This is particularly true when there are hundreds of values.

So here’s a powerful pattern to solve that task.

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Comparing nullable columns

Do you ever compare the values of a lot of columns in two tables? Sure you do. Like, for instance, in a cross update, when you need to figure out which rows you should actually update. But it gets worse if the columns are nullable. The fact that any value could potentially be NULL vastly complicates the comparison and might wreak havoc not only on your code but also on your query performance.

But there’s hope.

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