Category: T-SQL

Daniel Hutmacher shows how to calculate the number of weekdays between two dates:

It has to be said, if performance is important (and it should be), you’ll know that user-defined scalar T-SQL functions can kill your query. So you might want to implement the above in the form of a CROSS APPLY instead

Here’s where I advocate for two tables on every instance:  a tally table and a date table.  The date table should look like a date dimension, including every potentially-interesting piece of information about a date—including if it’s a weekday.  It might not perform quite as fast as Daniel’s solution (I’d have to test to know for sure), but it’s definitely easier.   If you can’t get a viable date table on your instances for whatever reason, Daniel’s solution does work and does not require any additional objects.

Daniel Hutmacher shows a few methods for calculating Fibonacci sequences in T-SQL:

There’s a really nice mathematical way that I found on Stack Overflow, using the golden ratio (from Wikipedia). And it’s actually set-based. I don’t have the requisite mathematical skills to evaluate the correctness or precision. Note that it returns afloat value result, the upside of which is that you can calculate much higher values. The downside is the loss of precision that comes with float.

This is a bit of a brain teaser but if you learn the techniques, they can definitely come in handy in the future.  I like the third solution because it’s a reminder that writing code is just as much about understanding the domain as it is understanding the syntax.

Kennie Nybo Pontoppidan looks at how NULL values complicate arithmetic operations:

If NULL is used in an in-row expression, the full expression will be NULL. This makes sense for most arithmetic operations, because NULL means unknown.

Here is an example with NULL plus/minus ONE:

There’s a consistency to NULL operations, but it’s sometimes a weird consistency.

Daniel Hutmacher looks at ways to get windows of distinct elements from a table:

Probably the most common distinct aggregate is COUNT(DISTINCT). In some respects, it’s similar to the windowed function DENSE_RANK(). DENSE_RANK(), as opposed to ROW_NUMBER(), will only increment the row counter when the ordering column(s) actually change from one row to the next, meaning that we can use DENSE_RANK() as a form of windowed distinct count

This is a very interesting approach to the problem.  Read the whole thing.

James Anderson reminds you to check those WHERE clauses:

We have said that a NULL value for s.DateOfSale is not in the range we are interested in. This means the rows with NULLs in the s.DateOfSale column (our employees yet to make a sale) will be filtered out. It will also filter out employees with sales in months other than March. We have converted the LEFT JOIN into an INNER JOIN.

James’s fix is to move the filter to the join clause, which eliminates the implicit inner join.  When I see a condition like this in a code review, the first question on my mind is whether the correct fix is James’s fix or whether the developer really meant to do an inner join.  There’s a potential performance gain from using an inner join over a left outer join (due to being able to drive from either table and thus having a larger number of potential execution plans) if it turns out you really do want to filter all rows and not just making the join criterion more specific.