Stay with me. This is not clickbait!
I made you a script so you can create your own SQL Server database using the public IMDB dataset. All you need is a SQL Server 2022, Powershell 7, and a little time.
Stay with me. This is not clickbait!
I made you a script so you can create your own SQL Server database using the public IMDB dataset. All you need is a SQL Server 2022, Powershell 7, and a little time.
In case you missed it, I designed a solution last year to collect session feedback for my annual Data Saturday Stockholm event, and I made this solution publicly available to anyone who runs a similar event. The setup is really simple – you just plug in a Sessionize API key, it collects all the necessary details on your sessions and speakers, and you’re off to the races. If you want to set it up in your own environment, that’s fine too – it’s all open source.
As of a few days ago, there’s also a handy reporting functionality, so you can send out feedback report links to all your speakers.
Today’s post is not about SQL Server, but if you’re working in Azure from a dynamic IP address, you might still find it useful.
Recently, my home ISP has started changing my public IP address. This causes me some headache because I have a couple of Azure Network Security Group rules (think of them as firewall rules) that specifically allow my home IP access to all of my Azure resources. When my home IP changes, those rules have to be updated accordingly.
So I made a PowerShell-based solution to automatically maintain them.
R2 is Cloudflare’s own implementation of AWS S3 storage, with some big benefits – one of them being no egress fees, which is great if you want to publish or distribute a lot of data (like I did with this demo database). In this post, I thought I’d briefly document how to set up R2, and how to use it to back up and restore your SQL Server databases.
As part of spending waaaaaay to much time trying to solve the 2023 Advent of Code challenges, I came across multiple instances where I had to dust off some old math that I hadn’t paid attention to since I went to school back in the 90ies.
So for my own convenience, and yours, I’ve built functions for some common math that you might perhaps encounter at some point. I found this whole experience to be a great way to familiarize myself with a lot of the new functionality in SQL Server 2022, including GENERATE_SERIES(), LEAST(), GREATEST() and more. The Github repo contains a SQL Server 2019 version where I’ve built drop-in versions of the 2022 functions, but they probably won’t perform as well as the built-in stuff.
If there are any other functions you’d like to add, feel free to add a comment to this post, or, by all means, a pull request to the repo.
You can get into a situation where you have two tables with values associated with date ranges. What’s worse, those date ranges don’t necessarily have to align, which can make joining them a seemingly complex task, but it is surprisingly simple when you learn how to think of overlapping date ranges, along with this relatively simple T-SQL join pattern.
A question from a client got me thinking. Relational databases (at least the ones I know and love) can’t really index for queries that use LIKE queries for a substring of a column value. If you want to search for strings beginning with a given string, a regular rowstore index will have you covered. If you’re looking for entire words or sentences, a full text index might be a good call. But because of the very way indexes work, you’ll never get great performance searching for just arbitrary parts of a string.
So today I’ll put on my lab coat and do a little rocket surgery, just to prove to the world that it can be done.
Professional driver on closed roads, always wear protection. Your mileage may vary.
This blog post started as a “what if” contemplation in my head: Suppose you have a reasonably large table with a clustered index and a number of non-clustered indexes. If your WHERE clause filters by multiple columns covered by those non-clustered indexes, could it potentially be faster to rewrite that WHERE clause to use those non-clustered indexes?
The answer might surprise you.
A client asked me about SQL Server collations, and if they should consider the new UTF8 collations (new since SQL Server 2019). I tried to hide my blank stare of ignorance, and promised them I’d look it up and get back to them.
Not gonna lie, I think UTF and Unicode can be pretty confusing at times, so I did some googling and some testing, and here’s what I found.
An underrated, and perhaps less well-known T-SQL function is QUOTENAME(). If you haven’t heard of it, chances are that it could do wonders for your dynamic SQL scripts.
To quickly recap quoting, consider the following script:
SELECT N'DROP PROCEDURE '+OBJECT_SCHEMA_NAME([object_id])+N'.'+[name]+N';'
FROM sys.procedures
WHERE [name] LIKE N'%test';
What happens if one of your object names contains a space, a quote, an apostrophe, a square bracket, etc? You’ll end up with a syntax error, or even worse, a SQL injection attack (pretty elaborate, but still quite possible). To solve for this, we quote the object names. In SQL Server, you can surround schema and object names with double quotes (if you’ve set QUOTED_IDENTIFIER) or square brackets.
Simple, right?
SELECT N'DROP PROCEDURE ['+OBJECT_SCHEMA_NAME([object_id])+N'].['+[name]+N'];'
FROM sys.procedures
WHERE [name] LIKE N'%test';
But just adding a [ before and a ] after won’t work if your evil user as embedded square brackets or a semicolon in the object name. What if your object name is “Testing [quoting]; test”?