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Category: T-SQL

EXISTS Is Self-Contained

Published 2017-06-30 by Kevin Feasel

Shane O’Neill ponders an existential problem:

So, drinking my first (of many) coffee of the day, I asked him what was wrong with it.

I have two tables. 1 with values 1,2,3 & the other with values 1,2,3,4,5. When I use delete exists, it should just delete 1,2,3 but table1 is always empty.

Hmmm, not an unreasonable assumption I suppose so I asked him for his code.

Read on for Shane’s explanation, though he doesn’t like the verbosity.  My version is, what happens in EXISTS stays in EXISTS.  It just returns a signal to the outer query saying yea or nay and the outer query does its thing accordingly.  In this case, if you want to tie results back to the delete operation, use IN (the ANSI standard way) or JOIN (typically my preferred way, given that IN can get dicey with more complex criteria).

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New T-SQL Functions

Published 2017-06-28 by Kevin Feasel

Dennes Torres shows off four T-SQL functions releasing in SQL Server 2017:

Translate

Translate does the work of several replace functions, simplifying some queries.

The function is called ‘Translate’ because its main objective: transform one kind of information in another by doing a bunch of replaces.

For example: GeoJson and WKT are two different formats for coordinates. In GeoJson a coordinate is represented using the format ‘[137.4, 72.3]’ while in WKT a point is represented using the format ‘(137.4 72.3)’.

We would need several ‘Replace’s to transform GeoJson format in WKT format and the reverse. The ‘Translate’ function can do this easily.

I knew the first three, but Translate sneaked right past me.

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Ditching IsNumeric()

Published 2017-06-26 by Kevin Feasel

Phil Factor on the IsNumeric() function:

IsNumeric() is actually answering the question ‘Can this string be converted or ‘cast’ to a numeric datatype by SQL Server?’ It is pretty good at getting this answer right but it doesn’t tell you which datatype. It also isn’t interested in such things as overflow.

This problem of IsNumeric() was solved in SQL Server 2012. There was no way of morphing IsNumeric() intro something more valuable, so Try_Cast(), Try_Parse(), and Try_Convert() were introduced. The introduction of these system functions really solve the problem, unless you are still on earlier versions than SQL Server 2012. I’ll show how to do the same thing in previous versions of SQL Server, and demonstrates one or two tricks you can do with these functions.

I’ll stick with the post-2012 version, please.

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Building Random Number Ranges

Published 2017-06-26 by Kevin Feasel

David Fowler shows how to generate a random number for each record in a result set:

Hmmmmm…. It looks like we’ve got the same number for every person, that wasn’t what we wanted.  And that’s my issue with RAND(), it’ll give you a different random number every time it runs but if run as part of a query it’ll always return the same number for every row returned.

So what else can we do?  We’ll there is something that gives us a ‘random’ value for every row in the query and that’s our good old friend NEWID().  Let’s try the same query but this time we’ll swap RAND() with NEWID().

One major use case for this is sampling data sets for model training and testing:  if you pull from a range of 1-10, you could perhaps train against 1-5, cross-validate against 6-7, and test against 8-10.  Doing this instead of TOP X% reduces the likelihood of sampling bias.

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Calculating Relative Risk In T-SQL

Published 2017-06-20 by Kevin Feasel

Mala Mahadevan explains how to calculate relative risk using T-SQL:

In this post we will explore a common statistical term – Relative Risk, otherwise called Risk Factor. Relative Risk is a term that is important to understand when you are doing comparative studies of two groups that are different in some specific way. The most common usage of this is in drug testing – with one group that has been exposed to medication and one group that has not. Or , in comparison of two different medications with two groups with each exposed to a different one.

Read on for an example of a statistical formula calculation which might actually be easier in T-SQL than R.

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Cochran-Mantel-Haenszel Test

Published 2017-06-12 by Kevin Feasel

Mala Mahadevan explains the Cochran-Mantel-Haenszel test, with two parts up so far.  First, her data set:

Below is the script to create the table and dataset I used. This is just test data and not copied from anywhere.

Second, an introduction to the test itself and solutions in R and T-SQL:

This test is an extension of the Chi Square test I blogged of earlier. This is applied when we have to compare two groups over several levels and comparison may involve a third variable.
Let us consider a cohort study as an example – we have two medications A and B to treat asthma. We test them on a randomly selected batch of 200 people. Half of them receive drug A and half of them receive drug B. Some of them in either half develop asthma and some have it under control. The data set I have used can be found here. The summarized results are as below.

This series is not yet complete, so stay tuned.

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The REVERSE Function

Published 2017-06-08 by Kevin Feasel

John Morehouse explains what the REVERSE function is and why it can be useful:

Recently I had a conversation with a colleague that wasn’t aware of the REVERSE function in SQL Server.  I had sent them a code snippet and they inquired as to what the purpose of the function was.  Essentially, this function that reverses any string value.

For example, the phrase:

The brown dog jumped over the lazy fox

reversed looks like this

xof yzal eht revo depmuj god nworb ehT

Read on to see a good example of when you might use REVERSE.

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Check Those Aliases

Published 2017-06-02 by Kevin Feasel

Erik Darling warns you about accidentally using the wrong alias in a query:

People will often tell you to clearly alias your tables, and they’re right. It will make them more readable and understandable to whomever has to read your code next, puzzling over the 52 self joins and WHERE clause that starts off with 1 = 2. It can also help solve odd performance problems.

Take this query, for instance.

This isn’t just for subqueries; even simple joins can go haywire when you accidentally use the wrong alias and both tables happen to have the same column name.

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Building Newlines In SQL Scripts

Published 2017-06-01 by Kevin Feasel

Shane O’Neill engages in wacky newline misadventures:

GO is a special little guy. It’s not exactly T-SQL. It’s a way of telling the SQL Server Management Studio (SSMS) to send everything before it, from the beginning of the script or the preceding GO, to the SQL Server instance.

If you read the documents, the main point to take away is…

A Transact-SQL statement cannot occupy the same line as a GO command. However, the line can contain comments.

The problem turns out to be a little trickier than you’d first imagine.  Also, after reading this post, I think it’s lunchtime…

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Fisher’s Exact Test

Published 2017-05-25 by Kevin Feasel

Mala Mahadevan explains Fisher’s Exact Test and provides examples in T-SQL and R:

The decision rule in two sample tests of hypothesis depends on three factors :
1 Whether the test is upper, lower or two tailed (meaning the comparison is greater, lesser or both sides of gender and speaker count)
2 The level of significance or degree of accuracy needed,
3 The form of test statistic.
Our test here is to just find out if gender and speaker count are related so it is a two tailed test. The level of significance we can use is the most commonly used 95% which is also the default in R for Fischer’s Test. The form of the test statistic is P value. So our decision rule would be that gender and speaker category are related if P value is less than 0.05.

Click through for the R code followed by a code sample which should explain why you don’t want to do it in T-SQL.

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