This insanely cool new performance-related update is one of nicest features in SQL Server 2019, and certainly one I’ve been looking forward to for a long time.
If you’ve done any work around performance tuning and user-defined scalar functions, I’m pretty sure you’ll love this.
Continue readingWe need to talk about the nullable columns in your database. Specifically, because of how NULL values are compared, they can dramatically affect how some lookup operations perform.
Everyone has a script, a hack or a checklist they can’t function without. In this edition of T-SQL Tuesday, Bert Wagner challenged us to write about our favorite scripts. This is my take.
I’m trying a new type of blog post, and if it works out, I would be happy to post more of the same going forward. The format is a real-world troubleshooting mystery, and I’ll clue you in to the details along the way.
How quickly can you crack it?
Dynamic data masking is a neat new feature in recent SQL Server versions that allows you to protect sensitive information from non-privileged users by masking it. But using a brute-force guessing attack, even a non-privileged user can guess the contents of a masked column. And if you’re on SQL Server 2014 or earlier, you won’t have the option of using data masking at all.
Read on to see how you can bypass dynamic data masking, and for an alternative approach that uses SQL Server column-level security instead.
Scalar functions can be a real headache when you’re performance tuning. For one, they don’t parallelize. In fact, if you use a scalar function in a computed column, it will prevent any query that uses that table from going parallel – even if you don’t reference that column at all!
SQL Server Management Studio allows you to view effective permissions on an object, but it’s limited in a few important respects. To work around some of those limitations, I’ve built a stored procedure to display all the defined and effective permissions across an entire SQL Server database.
Just for laughs, we’re going to take a look at a slightly-too-complex yet elegant solution to a seemingly simple task: creating a list of 100 fictional company names for a demo database.
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.
With good naming and datatyping conventions, an automated script can help you with the process of creating foreign key constraints across your database, or actually, suggest table relations where you’ve forgotten to implement them.
