A Fine Slice Of SQL Server
Louis Davidson listened to some advice:
A few weeks ago, I wrote a post on using temporary stored procedures in SQL Server. Kevin Feasel of Curated SQL had this reply Using Temporary Stored Procedures to Output Common Messages. I had heard of FORMATMESSAGE before, but I had completely coupled this in my mind with formatting SQL Server error messages. (Which turns out to be a great use of this tech)
Click through to see how it works and some additional testing with RAISERROR().
Leave a CommentHowever, SQL Server 2025 does bring some great options for doing fuzzy string matches, making custom Data Quality options even richer. I’ve spoken about this at some user groups recently (including tomorrow, remotely for TriPASS, and in a few weeks in Melbourne and Sydney for Difinity), and in that session I go much deeper into how I see data matching going. I’ll also write more about these methods in future posts, but it’ll take a few posts, covering quite a few sub-topics.
If you want to see that session, our user group (the Triangle Area SQL Server Users Group) is hosting it Wednesday morning Australia time, or this evening US Eastern Standard Time.
Leave a CommentLouis Davidson shows a neat use for temporary stored procedures:
On another connection (on another computer for that matter), I am right no doing some pretty long loads of some test data. The script is comprised of 6 queries, and they each may take 10 minutes (not completely sure, this is my first run of the scripts). And of course, I want to get some feedback on these queries to know how long they are taking.
One common way to do this is to put a
One addition I’d make to Louis’s post is to make use of the FORMATMESSAGE() functionality that SQL Server 2016 introduced. This use case is right in its wheelhouse.
SET @message = FORMATMESSAGE(N'%s%s%s%s',
@Message,
CASE
WHEN @AddTimeToMessageFlag = 1 THEN CONCAT(N' : Message Time - ', SYSDATETIME())
ELSE N''
END,
CASE
WHEN @AddSpidToMessageFlag = 1 THEN CONCAT(N' : ProcessId - ', @@spid)
ELSE N''
END,
CASE
WHEN @AddOriginalLoginToOutputFlag = 1 THEN CONCAT(N' : LoggedInUserId - ', original_login())
ELSE N''
END);
FORMATMESSAGE() provides a moderate benefit to readability versus a lengthy CONCAT(). And if you always wanted to emit all fields versus the optional setup that Louis has in place, FORMATMESSAGE() makes the result even clearer to understand.
SET @message = FORMATMESSAGE(N'%s : Message Time - %s : ProcessId - %i : LoggedInuserId - %s',
@Message,
CAST(SYSDATETIME() AS NVARCHAR(100)),
@@spid,
original_login());
Do you ever worry about how you declare
NUMERICorDECIMALdata types in SQL?
Do you sometimes “add a bit more” precision—just to be safe?
Have you considered how that small decision could actually change your arithmetic results?I ran into this recently when comparing data between two environments that should have produced identical results. One calculated field was slightly off — and the culprit turned out to be a difference in numeric data type declarations.
Read on to see what happened. The differences weren’t massive, but if you were expecting an exact match, seeing a difference, even at the 7th or 8th spot after the decimal, could be jarring.
Leave a CommentAndy Yun is speaking my language:
One thing that’s always driven me crazy is when I have large numbers in my resultsets and the lack of commas for readability. For a lot of different things I do, the more commas a number has, the more attention I want to give to a given value.
Andy shows examples of formatting to two and zero spots after the decimal, respectively. In a talk I give on analyzing business data with T-SQL, I also demonstrate how to show currency-based results:
FORMAT(SUM(o.Quantity * sih.LastCostPrice), N'$0,###.##') AS TotalCost
This starts each cost record with a dollar sign, ensures you have commas in the thousands spots, and have a two-digit decimal value. That would return back a result like $31,409,113.00, which is a lot easier to read than 31409113.
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.
Comments closedHugo 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).
Comments closedAndy 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.
Comments closedLouis 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.
Comments closedBrendan Tierney gets comparing:
When comparing text strings we have a number of functions on Oracle Database to help us. These include SOUNDEX, PHONIC_ENCODE and FUZZY_MATCH. Let’s have a look at what each of these can do.
These are some classic word comparison techniques, but they work pretty well in specific circumstances.
Comments closed