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.
Typically, the advice on fill factor is “if it ain’t broke, don’t fix it”. But occasionally, you’ll find a database or even a server with a crazy default setting that just fills your disk and buffer pool without any real benefit. Here’s a nifty script to rebuild any tables and indexes to a fill factor of 100%.
We 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.
Regular readers of my blog will know that I occasionally share some useful scripts on my Downloads page. And even though I update some of those scripts regularly when new versions of SQL Server come out, or if I run into a bug feature, there really hasn’t been a practical way for readers to subscribe to those updates or to contribute with good ideas.
I recently attended the annual PASS Summit conference in Seattle, and as part of my personal goal to try to learn new (and scary) things, I took a precon on working with Git.
So as of now, a bunch of downloads are available on GitHub (which is, really, a much better place to host scripts than a shared Dropbox link). You can download them as usual, and if you want, you can add your improvements and send me a pull request. I know I’ve received a ton of good ideas and suggestions over the years, but more often than not, I haven’t had the proper environment to test those changes in, or I just haven’t had the time to dig into my old code.
But now you can:
I like that there is a “Compare” function right out-of-the-box in Visual Studio, and even though many regular developers will choose to download a third-party application for the job, it’s perfectly fine for me.
Two problems: First off, I couldn’t find a straightforward way to open “compare” in the Visual Studio IDE without right-clicking an existing item in a source control repository. And second, wouldn’t it be cool if we could put a shortcut to it on the Windows “Send to” context menu?
I’m the type of developer that invents wheels. Yes, every wheel I design is unique in its own way, and hand-crafted for a specific purpose. And so it has also been with calendar dimensions (typically when I do data warehousing work).
This got me thinking – why not design the mother of all calendar dimensions? One that includes every conceivable calendar and property that I and others could use and re-use. One that could save me a ton of coding, and lessen the burden of having to validate it each and every time?
And that’s how I got started designing my one calendar script to rule the all.
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.
It’s not entirely uncommon to want to group by a computed expression in an aggregation query. The trouble is, whenever you group by a computed expression, SQL Server considers the ordering of the data to be lost, and this will turn your buttery-smooth Stream Aggregate operation into a Hash Match (aggregate) or create a corrective Sort operation, both of which are blocking.
Is there anything we can do about this? Yes, sometimes, like when those computed expressions are YEAR() and MONTH(), there is. But you should probably get your nerd on for this one.
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?
Download and print this nifty little PDF with all of the INNER, LEFT, RIGHT, FULL and CROSS JOINs visualized! It’ll look great on your office wall or cubicle. Your coworkers and your interior decorator will love you for it.
How it works: For each join example, there are two tables, the left and the right table, shown as two columns. For the sake of simplicity, these tables are called “a” and “b” respectively in the code.
You’ll notice that the sheet uses a kind of pseudo-code when it comes to table names and column names.