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Category: T-SQL

Rounding to Intervals in Various Languages

Published 2019-08-19 by Kevin Feasel

Dave Mason doesn’t like rounding to intervals very much in T-SQL:

If I had to choose any of these options for production, I’d probably go with Query 1, just because I think it would make the most sense to any other developer that might encounter it (including future me, who probably wouldn’t remember writing the code). But I’m not really thrilled with any of the options. The one thing T-SQL has going for it though, is the relative ease for truncating the TIME off of a DATETIME by casting to DATE and back to DATETIME. I couldn’t find anything like this in DAX.

Dave also shows how to do this in DAX and Powershell.

Another alternative that Dave doesn’t mention is to invert the problem: if you have a fixed set of intervals you care about (e.g., 15-minute, 30-minute, hour, 4-hour, etc.), you can create a time table. This is like a date table but contains times of the day where you’ve precalculated the intervals. Then you join to the time table and have your results right there. If you do go this route, I’d try to keep the grain of the time table as shallow as possible, maybe using DATETIME2(0) instead of DATETIME2(7).

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Pitfalls with Window Functions

Published 2019-08-14 by Kevin Feasel

Itzik Ben-Gan takes us through two issues you might run into when using window functions:

There are two common pitfalls involving window functions, both of which are the result of counterintuitive implicit defaults that are imposed by the SQL standard. One pitfall has to do with calculations of running totals where you get a window frame with the implicit RANGE option. Another pitfall is somewhat related, but has more severe consequences, involving an implicit frame definition for the FIRST_VALUE and LAST_VALUE functions.

There’s a lot going on in these two examples, so read on.

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Spaces in CHAR Columns

Published 2019-08-12 by Kevin Feasel

John McCormack wants to store a single space in a CHAR(1) column:

I was asked by a colleague why his where clause wasn’t being selective when filtering on a space value. The column was a char(1) data type. To understand the curious case of the space in char(1), we need to understand how the char data type works and also a bit more about the need for it in this scenario.

The ANSI standard makes sense, but it is something you have to keep in mind in cases like this.

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Procedure Parameters: Optional and Required

Published 2019-07-30 by Kevin Feasel

Kenneth Fisher takes us through procedure parameters:

If you are executing a stored procedure with a bunch of parameters it can be a bit of a pain if you have to pass a value in for each of them. Fortunately, it’s pretty easy to make some parameters required and others optional. You simply give them a default value.

Kenneth also points out that functions don’t behave this way, and shows how to handle parameters where you don’t want to accept NULL under any circumstances. This is useful when NULL is just a placeholder for “I don’t really want to use this parameter” but the application doesn’t know how to avoid sending the parameter in the first place.

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Getting the Largest Value Per Group

Published 2019-07-25 by Kevin Feasel

Erik Darling has a series on getting the highest value using CROSS APPLY. Part 1 covers the no-index route:

Let’s say you wanna get the highest thing. That’s easy enough as a concept.

Now let’s say you need to get the highest thing per user. That’s also easy enough to visualize.

There are a bunch of different ways to choose from to write it.

Part 2 covers the yes-index route:

In this round, row number had a tougher time than other ways to express the logic.

It just goes to show you, not every query is created equal in the eyes of the optimizer.

I don’t think I’m spoiling too much by saying that you really want a good index in place when using CROSS APPLY in this manner.

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Median Calculation with T-SQL

Published 2019-07-24 by Kevin Feasel

Nisarg Upadhyay shows three ways to calculate the median in T-SQL:

To calculate the median of any dataset, we first need to arrange all values from the dataset in a specific order. After arranging the data, we must determine the middle value of the specified dataset. If the dataset contains an odd number of values, than the middle value of the entire dataset will be considered as a median. Following is the example:

Median (M) = value of ((X + 1)/2) th item. (x is the number of values in the dataset)

Honestly, CLR’s probably the best approach here if you want a fast calculation for a reasonably large number of rows. Using ML Services and R/Python is another alternative, though the launchpad spinup time will probably make it slower than CLR.

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Simple Query Zen

Published 2019-07-23 by Kevin Feasel

Erik Darling wants you to simplify your life queries:

See, when a query is big and complicated to you, there’s a pretty good chance you’re gonna get a big and complicated query plan, because it’s big and complicated to the optimizer, too.

This isn’t to say the optimizer is dumb or bad or ugly; it’s just that there’s only so long it’s willing to spend coming up with a plan.

Remember, cheap plan fast. Not perfect, not great, maybe good enough.

It’s a good operating philosophy: if you have a query which has gone off the rails, one of the best things you can do is try to turn the query into several small steps. It’s possible to reduce complexity that way…though you may also gain complexity in the process if you do it wrong.

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