Erik Darling answers an office hours question in detail. The question comes down to why a filter on ROW_NUMBER() where the row number is equal to 1 could differ from the same query where row number is less than or equal to 1. Knowing that ROW_NUMBER() starts at 1 and can never be anything other than a natural number, you’d think that SQL Server would treat these exactly the same. But Erik shows an example where the two can differ, and the answer was a good one. I will admit that my pre-video guess was wrong but once he showed the execution plans, things clicked. And, like Erik mentions, this is why it’s so important to dig into the execution plan, because the answers are typically in there somewhere.
Day: October 22, 2025
Gilbert Quevauvilliers grabs a query:
Load testing is essential when working with Microsoft Fabric capacity. With limited resources, deploying a Power BI report without testing can lead to performance issues, downtime, and frustrated users. In this series, I’ll show you how to automate load testing using Fabric Notebooks, making the process faster, easier, and repeatable.
Inspired by Phil Seamark’s approach, this method eliminates manual complexity and allows you to capture real user queries for accurate testing.
Read on for the first part, in which Gilbert uses the Performance Analyzer to capture query details.
Leave a CommentHugo Kornelis prefers surrogate keys:
I am currently most known for my performance tuning and execution plan work. But when I started working with database, I actually came from a background of data modelling, database design, and normalization. And that has never fully left me. In fact, I have in the past two years created a whole series of YouTube videos about database design and normalization. And a much longer time ago, I recorded a Pluralsight course on this topic that is still available for viewing.
One of the very basics of schema design for a relational database is to store atomic values in every column. One column, one value, no more, no less. That automatically rules out all repeating groups. So a single column to list all my email addresses? Sorry. You are doing it wrong. And you will pay the price when you try to protect the integrity of your data. Or even just report on it.
I wanted to copy this second paragraph because CJ Date, in his book Database Design and Relational Theory (2nd edition), issues a mea culpa around repeating groups, stating that it’s best to ignore his prior arguments on the topic. Though in Date’s case, he specifies a repeating group as something like { Name, Email1, Email2, Email3, … } rather than a delimited list.
But even composite items can be in 1st normal form. For example, a US telephone number has a country code (+1), an area code, an exchange, and a four-digit number, followed potentially by an extension. The name “Bob” is an array of characters, and each array of characters is a composite of bits forming 1-4 bytes depending on collation and other details. At the end of the day, first normal form is about the shape of the tuple (a heading exists with a known set of names and data types; all tuples follow the same header; no duplicate tuples are allowed; attribute and tuple order does not matter for operations; and all attributes are regular inasmuch as they have names, data types, are not hidden, etc.).
At the end of the day, what Hugo is saying is good practice: if you have a business need to identify segments of an attribute separately, then it makes sense for each segment to be an attribute on its own. But because there is no solid mathematical property that explains exactly what an attribute is, and because database normalization is ultimately a series of mathematical formulations, we cannot use normalization as the reason to keep or separate the contents of an attribute. Thankfully, there is more to database design than normalization alone (and thankfully, database normalization itself is such a robust field that provides good advice that people should follow).
Leave a CommentCourtney Woolum splits a string:
If you’ve escaped string parsing thus far, count thyself lucky. I’ve seen some numbing scripts from pre-SQL Server 2016 when STRING_SPLIT was introduced. I think the fact this month’s T-SQL Tuesday is entirely on string parsing says enough about how many ways there are to paint this particular cat.
In the post, Courtney mentions learning early on to avoid using the APPLY operator. I want to have harsh words with whoever taught her that. Purposefully avoiding the APPLY operator artificially hamstrings your ability to write effective T-SQL code.
Leave a CommentLouis Davidson goes looking for list items:
From the title of “Favorite String Parsing”, I will say 100% it is using SQL Server 2025’s addition of Regular Expressions. Previously, parsing text in SQL Server was one of my least favorite things to do. Regular expressions will make it just a bit nicer, because it has a lot more power than
SUBSTRING,LEFT,RIGHT, andCHARINDEX/PATINDEX. All generally “good enough” functions for a lot of the things you need to do, but often woefully inadequate for parsing large amounts of text.
Yeah, T-SQL RegEx is definitely a nicer approach, though HTML doesn’t have to follow the consistency rules of XML due to browsers being very forgiving in their interpretation of the language, so it’s easy to get tangled up trying to parse websites.
Leave a CommentAndy Brownsword digs into a nice capability around using regular expressions:
Now that we’re on the cusp of adoption within SQL Server, it’ll be a valuable tool there too. However, after trying it out last week, one omission stood out – one of my favourite features for string parsing: Named Groups.
Where a usual expression can be used to match a string, Named Groups can also be used to extract details from the string. Using an example from Steve’s invitation where a PO number
20260720321433begins with a year/month and then a number, this could be split with named groups:
Andy mentions the community displeasure for CLR and that displeasure annoys me to no end. I think 90% of the hysteria around CLR in SQL Server was a misunderstanding in terms and unwillingness to learn other programming languages. If you ever catch me in person, I’ll rant about it at length.
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