A Fine Slice Of SQL Server
Erik Darling reminds us that even good things can go bad:
Look, I really like EXISTS and NOT EXISTS. I do. They solve a lot of problems.
This post isn’t a criticism of them at all, nor do I want you to stop using them. I would encourage you to use them more, probably.
But there’s some stuff you need to be aware of when you use them, whether it’s in control-flow logic, or in queries.
But do read on to see a specific type of issue you can run into with a left semi join.
Comments closedItzik Ben-Gan continues a series on table expressions:
This is the thirteenth and last installment in a series about table expressions. This month I continue the discussion I started last month about inline table-valued functions (iTVFs).
Last month I explained that when SQL Server inlines iTVFs that are queried with constants as inputs, it applies parameter embedding optimization by default. Parameter embedding means that SQL Server replaces parameter references in the query with the literal constant values from the current execution, and then the code with the constants gets optimized. This process enables simplifications that can result in more optimal query plans. This month I elaborate on the topic, covering specific cases for such simplifications such as constant folding and dynamic filtering and ordering. If you need a refresher on parameter embedding optimization, go over last month’s article as well as Paul White’s excellent article Parameter Sniffing, Embedding, and the RECOMPILE Options.
This was a really good series. If you haven’t seen the entries, set aside some time and check it out.
Comments closedErik Darling answers the long-standing question “Who is active?” with “You is active!”:
– New parameter,
@get_memory_info, that exposes memory grant information, both in two top-level scalar columns and a new XML-basedmemory_infocolumn.– Better handling of the newer CX* parallelism wait types that have been added post-2016
– A top-level implicit_transaction identifier, available in
@get_transaction_info = 1mode– Added
context_infoandoriginal_login_nametoadditional_infocollection– A number of small bug fixes
– Transition code to use spaces rather than tabs
Spaces rather than tabs? SQL should have tabs! But functional programming languages are great and they use spaces! I’m so conflicted!
Comments closedI was a bit surprised by this, because I thought the whole point of these new functions was to avoid errors like this.
This was a surprising result, for sure. Apparently the function returns an error when a cast is explicitly not permitted. But like Erik writes, that seems to go against the spirit of the function.
Comments closedKevin Wilkie deals with not-so-datey dates:
Because of the “not-so-dateness” of the data, you cannot convert non-date items into a date, at least not with the CONVERT function.
“Not-so-datey” is, of course, a technical term. Read on to see what you can use as an alternative to CONVERT().
Aaron Bertrand splits and cares about sort order:
Last year, I wrote about replacing all your CLR or custom string splitting functions with native calls to
STRING_SPLIT. As I work on a project migrating several Microsoft SQL Server instances to Linux, I am encountering one of the roadblocks I mentioned last time: the need to provide an element in the output to indicate the order of the elements in the input string. This meansSTRING_SPLITin its current form is out, because 1) it offers no such column; and, 2) the results are not guaranteed to be returned in any specific order. Are there other ways to achieve this functionality at scale and without CLR?
As Koen mentions in the comments, you can now get STRING_SPLIT with a sort parameter, but Aaron’s response is also valid: not everybody will have access to that today, so it still makes sense to understand the options.
Comments closedRonen Ariely has mixed feelings on updates to the STRING_SPLIT function:
The main issue with this function, is that it returns a SET of rows with no specific order.
As you must know by now, a TABLE is a SET of rows (Rowstore table which is the more common in SQL Server) or columns (Columnstore table). The rows in the table are not stored in specific order (even if using clustered index, the rows can physically be stored in different locations on the disk, not necessarily maintained continuously one after the other. In addition, the server might read the rows in parallel and not necessarily in the order of the index. As a result, The order in which rows are returned in a result set are not guaranteed unless an ORDER BY clause is specified.
And this is the main issue with the STRING_SPLIT… until today
Read on to see how this update makes STRING_SPLIT() much better, and also how it could be even better still.
Daniel Hutmacher has fun with implicit conversion:
Implicit conversions in SQL Server follow a specific, predictable order, called data type precedence. This means that if you compare or add/concatenate two values, a and b, with different data types, you can predict which one will be implicitly converted to the data type of the other one in order to be able to complete the operation.
I stumbled on an interesting exception to this rule the other day.
Click through for an example of date searching with LIKE.
Erik Darling summarizes the main man:
I’d like to start this post off by thanking my co-blogger Joe Obbish for being lazy and not blogging about this when he first ran into it three years ago.
Now that we’re through with pleasantries, let’s talk turkey.
Over in this post, by Arvind Shyamsundar, which I’m sure Microsoft doesn’t consider official documentation since it lacks a GUID in the URL, there’s a list of… things about parallel inserts.
Read on for the summary of limitations.
Comments closedWhile working with some poorly performing code in T-SQL that used a PIVOT operator, I wondered if there was a more efficient way to get a result set of pivoted data. It may have been a fool’s errand, but I still wanted to try. It dawned on me that I could use the STRING_AGG() function to build a delimited list of pivoted column names and values. From there, I’d have to “shred” the delimited data to rows and columns. But how?
Read on to see how.
Comments closed