Get AI-Ready With Erik: Embedding Freshness

Summary

In this video, I delve into the topic of keeping embeddings fresh in SQL Server databases, a critical aspect often overlooked due to its complexity and lack of straightforward solutions from Microsoft. I explore why regenerating all embeddings can be impractical and discuss more efficient methods like using queue tables and triggers to handle incremental updates. By walking through the setup process and providing examples, including how to create a computed column (despite SQL Server’s current limitations), I aim to equip you with practical strategies to avoid embedding drift and ensure your data remains relevant and accurate. Whether you’re dealing with user profiles, content management systems, or any application that relies on embeddings, this video offers valuable insights into maintaining the integrity of your data.

Chapters

• *00:00:00* – Introduction
• *00:00:31* – Embedding Freshness Options
• *00:01:09* – Microsoft’s Approach Critique
• *00:02:14* – Table Creation Challenges
• *00:03:06* – Queue Table Setup
• *00:04:03* – Trigger Implementation
• *00:05:30* – Embedding Process Details
• *00:08:12* – Preventing Embedding Drift

Full Transcript

Erik Darling here with Darling Data and more AI embedding goodness for you fine folks out there in the world. We’re going to talk in this video about embedding freshness or keeping embeddings fresh in SQL Server because right now there is not a terribly good story with doing that. So embeddings of course get stale if the source text is edited, new records are added, or records are deleted. Your options are to regenerate all of them, which can be slow and expensive, or you could use some facility inside of SQL Server to track changes and update incrementally, which is probably the smarter thing to do. We are not going to do the stupid thing because the whole point of this course is to help you avoid the stupid thing, right? Don’t do the dumb thing. If you can take anything away from all of this, don’t do the dumb thing. Let Microsoft be your cautionary tale. Don’t do the dumb thing.

So again, coming back to our dear friends at Microsoft, you would think that they would make this easy, but no. Apparently they didn’t want to make this easy, right? And I don’t know. I kind of don’t blame them on this one, but it would be nice if this were a little more convenient. So like if I wanted to create a table and I wanted to say, hey, I want a computed column. You can already see this squiggle.

Now, be fair here. All right. SSMS puts red squiggles under a lot of stuff. It’s completely valid. Like, just has no idea what’s going on most of the time and is lost. This red squiggle is, this is a legitimate red squiggle, right? This is there, right? There for a reason this time, at least. But we cannot create this table and the error messages are rather amusing.

It will tell you that the database model doesn’t exist. Make sure the name is entered correctly. I think I get this one because my, my SQL instance is case sensitive, but it’s still, it’s still funny. Right?

That might make you freak out. Uh, but we’ve got incorrect syntax near the keyword use. So, uh, we can not create a computed column that will just generate this stuff for us. SQL service is like, nah, no.

And who knows? Maybe we’ll get this someday, right? Maybe this is a preview feature too. We don’t know, right? We, who could, who could, who could foresee the future when there’s no communication between the PMs who manage things and, and the end users who care about things, right? Why, why would we ever want to know these things?

But, uh, what you would generally want to do is in this, you know, again, uh, something that is very database, right? Something that is very sort of natural to, um, you know, most people who have had to manage databases is to create a queue table, which we could do here. And we can do that.

And we can set this up in a way that will give us, uh, something to work off of in which we can tell what, what action we need to take on a row and sort of just process through this queue table and do our updates. I’m going to give you an example trigger for, uh, for inserts. Um, in real life, you would also want to create other triggers to handle updates and deletes, right?

You would not want to put everything into one trigger because life gets real weird and complicated when you do that, right? All that sort of checking the inserted and deleted tables, uh, make separate triggers. What’s wrong with you?

But, uh, you would do something like this where you would do the normal things, the, the, the, the standard, the canonical trigger header, right? You bail out if row count big is zero. Uh, you make sure that no other sessions have interfered with important settings, set options, uh, at the, at the session level.

And then you would insert into your queue table, this stuff, right? These, this information from, um, when stuff got inserted into, uh, the, the, the post table, right? So like data goes into the post table.

We put that row into the queue table and then the queue table would process, right? Then we’d have like something else that would process, um, all the other stuff. From here, you would probably want, you know, a cursor, uh, like a, like a separate, you would want to store procedure that like works off the queue table.

And you would want to like loop over it, right? Cause again, like generating embeddings is not free, right? It’s an external call.

It’s outside the database. SQL Server does not generate embeddings on its own. You have to call out to a separate service. It could be a local service on your VM, uh, with a mix of Olama and something that helps SQL Server talk to Olama. Uh, cause Olama only has HTTP, uh, as a, um, as a protocol does not have HTTPS that SQL Server requires.

So you need something that sits between, um, you know, Olama and SQL Server to sort of deal with the HTTP, HTTPS stuff. Um, and, uh, you know, again, it’s not free, right? It’s not happening within the SQL engine.

It’s outside SQL. And if you were to use open AI for this and you had to make an internet call, God bless. You know, I used to make fun of people. Cause like, you know, like I’ve, I’ve worked with lots of like, you know, clients who have had to install software made by people who hate them.

Right? Like I made, I made a little joke in, I think the last video about like, I’m a software developer who loves databases. Like you would have the people like make, you know, like they have a trigger on a table that would use XP command shell to call like curl.

And, or like call like an executable that would go do something on the internet. And you’re like, Oh, of course it sucks. So what’s the matter with your face?

Like why, why would you do that? But here would you, you would, if you just had a store procedure, right? Like, you know, again, like, you know, row at a time or batch process this stuff, you would want to find any pending items. Um, if it’s a delete, then, you know, it’s easy enough to just delete a row.

Um, like I only had an insert trigger up there, but again, you would want, you know, update and delete triggers to handle other modifications to the table. And if merge fires, God help you. Um, and then you would want to delete and reinsert any rows that were marked as an update by the trigger.

And then if it was an insert, then you would just stick the new row in. Um, you know, you could also use last activity date to find change posts, to find any posts that were altered. Uh, you wouldn’t need triggers for that, but you would like, you would have to pull the table constantly for stuff.

Um, the trade-offs there are of course on the pro pro side, like, you know, triggers can be, especially under concurrency triggers can be tough to, you know, get right. Um, it’s a bit of a simpler setup, but, um, you know, then you like the, like the cons there are, you know, it’s, it’s possible that you might miss some changes. Um, you know, if you like paralyzing the process, like if you want to have like multiple workers, I’ll looking at the queue table for stuff that can get complicated.

So, you know, a lot of locking hints and other things go into, um, go into making that work, you know, as flawlessly as possible. Uh, but then you also have like the overhead of, you know, pulling the table. And then, you know, when you find work to do going and doing work, but the basic idea behind either of them is that you, you know, find posts that without embeddings, uh, that need them.

Uh, you would find modified posts that need embeddings and re-embed them. And if you delete a row, then, you know, it doesn’t matter cause that doesn’t go into the polling table. Cause there’s nothing to do.

It’s just gone from the table. But that’s like sort of like, you know, some, like some ideas around how to keep embeddings fresh and up to date in your database. Because one thing that you don’t want is like embedding drift, right?

You don’t want like, you know, um, like say like our example in the last video was the about me thing in the users table, right? Let’s say someone’s like, I’m a software developer who loves databases one day. And then the next day they’re like, um, like I’m a hot yoga instructor, right?

Like, like, but your embedding would still say, I’m a software developer who loves databases. And so like the, like the headhunters from the stack overflow job board would be like, let me get you this job. And like, sorry, I’m just all hot yoga.

I’m just all hot yoga now, right? You want some hot yoga? I got you. But this, and they’re like, but the embedding said you, you were a software developer who loves databases. And everyone’s just walks away sad and confused and no one actually does hot yoga.

And well, what can you do? Anyway, that’s enough here. Thank you for watching.

I hope you enjoyed yourselves. I hope you learned something and I will see you in tomorrow’s video. The final video of the week, our Friday, our gal Friday. And, uh, we’ll, we’ll, we’ll, we’ll do that, I guess.

Thank you for watching.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Generating Embeddings

Summary

In this video, I delve into the intricacies of generating embeddings in SQL Server 2025, Azure SQL Database, and Azure Managed Instance using T-SQL’s AI generate embeddings function. However, I caution viewers against performing these operations directly within SQL Server due to potential inefficiencies and external dependency issues. The demonstration involves creating a `users_to_embed` table with an ID column and a vector data type embedding column, illustrating the process of generating embeddings for user profiles that include their “About Me” descriptions. While this method works, it highlights the limitations and challenges of using SQL Server for such tasks, suggesting that Python might be a more suitable environment for handling these operations efficiently.

Chapters

• *00:00:00* – Introduction to AI Generate Embeddings in SQL Server
• *00:03:15* – Generating Embeddings for User Profiles
• *00:07:24* – Finding Similar Users Using Vector Distance
• *00:10:10* – Best Practices and Considerations
• *00:11:43* – Conclusion and Next Steps

Full Transcript

Erik Darling here with Darling Data. And in today’s video, we’re going to talk a bit more about generating embeddings in SQL Server 2025, Azure SQL Database, and Azure Managed Instance. To do so, we are going to continue to invoke our T-SQL friend AI generate embeddings. But this time, what I want to do is kind of show you why you might just want to stick with Python on this. Do it outside of SQL Server for the love of God. So, I’m actually not going to drop in, recreate this table because I’ve already got it loaded up and you will, in short order, see why I did not decide to do this all from scratch.

We’d be wasting some time. So, this would be our sort of demonstration table, right? We would have all the things that we would need to be AI ready, like yours truly.

We would have an ID column, which is an integer, right? Because if we wanted to be forward thinking about someday having a vector index on this table, if they ever become not horrible, if they ever have to ever finish that damn thing, instead of like the Homer Simpson car features that we have now. So, we would want to have that up there, but who knows if that restriction will be relaxed when the vector indexes stop being horrible, right?

Like, maybe we’ll be able to use a big int. Like, hmm, you got fabric. Anyway, this is the table we would use and we would have our wonderful embedding column here, which is a vector data type that accepts a 1024 exactly dimension vector and is currently using the float32 model of storage rather than the float16 preview model of storage.

Now, in real life, you know, if I were running through this, I would want to stick maybe about 50 users into the table who have an about me column, which is a reasonable length, right? Because we’re going to be using the about me column for this one, right? For various reasons.

So, like, let’s say, you know, like, there’s lots of stuff in the Stack Overflow database that one could wish to embed, right? We’ve talked about sort of like the post table where you have titles and then you have bodies and, you know, stuff like tags, you know, you probably wouldn’t want to have, you probably wouldn’t want to do that. But then, like, you know, in the comments table, there’s the comment text column and, oh, screw comments.

But then, like, in the users table, you know, like, location, probably not, website, probably not. But about me would actually make a pretty reasonable embedding because what you could do is take the things that someone has written about themselves, right? The kind view that someone has taken on their Stack Overflow profile.

And it’s like, I am the best coder in the world. And you could search for people who might be good at, I don’t know, answering a question or maybe like even, you know, that Stack Overflow job board, which saved the company, thankfully. You know, hey, thank God we have that job board.

And, you know, you could even find maybe people who would be good matches for a particular job if you were to embed their About Me profile and, you know, do some semantic searching on it. Because you could do some similarity search between things in the job posting and what people are out there in the world. Right?

It’s like everything except the salary. But we could stick a bunch of users in here and we could, you know, just to keep, like, the embedding stuff reasonable. We would say that, you know, we want people who have an About Me that is somewhere between, like, one hundred and two thousand characters.

Beyond that, like, what the hell are you rambling on about? It’s like, let me just, like, paste, like, book verses in there. Out of your mind.

So we’ve got this users to embed table. And this users to embed table. And I realize that this sounds a little, you know, after dark. But trust me, it’s really just vector stuff.

We’ve got 50 users in here that we wish to embed. And, of course, the sort of, you know, like yesterday, the way that we generate embeddings in T-SQL is by calling the AI generate embeddings function, passing in a string that we wish to vectorize.

Right? In this case, I am a software developer who loves databases. I’ve never met one. Right?

They all seem to hate databases. It sounds like the passion. It’s like, oh, what can I do to this thing today and then complain about later? Right? Like, JSON. Vector stuff.

Yeah. Anyway. If we run this, we will get back our embedding JSON. Which, again, starting at line two and scrolling on down. All these numbers apparently mean that you’re a software developer who loves databases.

And so we get 1,024 of these numbers back. It’s fantastic news for us because our column is a vector 1,024. Isn’t that, isn’t it amazing the way this stuff just lines right up in a demo?

Right? How could it be better? But, so if we wanted to update just one user, right? Just one.

Let’s just show you that it works. Uh, we could set their embedding column to, uh, pass in a column. Right? We could say, we want to generate an embedding for the about me column. And they’ve got put into the user, demo user embeddings table, uh, just for the first user.

Right? So just, just one of them. And we run this. Right? And it’s, again, not the slowest thing in the world, uh, for, for one row.

But trust me, doing, doing the rest of them, uh, you know, you have like, like, I think most people who use SQL Server are pretty like comfortable with the idea that once modifications go past a certain like rows row count, you’ll probably want to batch things up.

You’ll probably want to think about that a little bit earlier if you are generating embeddings with T-SQL and, uh, calling this. This, because remember like, like, like what this thing calls out to is not inside of SQL server.

SQL Server does not generate embeddings. Right? SQL Server does not have any facility within it to generate an embedding with. Okay.

Uh, this is still an external model. You still have to call outside of the database, have something do something, then send it back to the database.

And then you get to do the update. Right? So I’m, I’m pretty lucky here in that, you know, um, like this is a VM and all of the processing that I’m doing on here takes place in the VM.

Right? But like, you know, if you look over here, you know, like we still have to call out to Olama. Olama has to do something and send it back.

Olama has to talk to this other thing because Obama, not Obama, Olama only allows, uh, only as right now only has HTTP requests and the external endpoints require HTTPS. So you need like a separate thing that has HTTPS that can talk to HTTP.

And so like, like right now there’s like steps to take. Right? And if you were using like open AI, you’re talking about like an internet call, right? So you’re talking about like reaching out to the internet.

So like, like probably want to think real carefully about like, like how many rows at a time you’re going to count on for this. Because, um, if I were to update all 50 of these rows at once, it would be much slower.

I’m just, you know, just not patient enough for that. But if we look at what we like, you know, we updated the table with, you know, we’ll see all the stuff today. Um, you know, like, like looking at the embedding, isn’t all that interesting.

So just that like, that’s the number of bytes that ended up in the table, uh, for the embedding. That’s what a thousand and 24 bytes, uh, thousand and 24 vectors, um, you know, measures out to bite wise. Uh, so just, just happened to be for John Skeet.

Hey, John Skeet. How you doing? But then, uh, you know, if we wanted to find similar users or maybe, you know, we’re, we’re a headhunter and we’re like, uh, I got this stack overflow job board, you know, I’m going to go find some nerds.

Uh, you could do this. Right. And you could, you could write a query. That’s like, I want to find a database performance tuning expert and you could do all this stuff. Right.

Right. And you can even call AI generate. I don’t recommend it. Don’t particularly recommend it. Right. Granted, like when you, like, uh, I don’t particularly recommend it. Um, you could even do this where you could generate an embedding directly in here.

Right. You could say, I want to like, rather than, you know, like, like, like pre, um, like, you know, and, and vectorizing this string and turning it into an embedding. You can turn it into an embedding on the fly with AI generate embeddings right in the, uh, like right in the query.

And then you can just say, well, I want to find the vector distance between, you know, the embedding that’s in the table and the string that I’m sending in. Right. You can do this.

Right. You’re, you’re allowed to, but think, think hard about doing things like this. Like this is maybe not how you want to do things, but we can run this query and we can get back, you know, for our, our very small 50 row table. Uh, we can get back some, some people.

Um, I don’t know you, I, I, I’ve talked to this guy before, GBN. Um, he’s, he’s, he’s a hoot. Um, the rest of these people, I don’t know.

Um, so, um, I, I couldn’t tell you if they’re actually database experts, but yeah. Yeah. Yeah. Yeah.

He is, he is actually a satanic sysadmin, I guess. Um, SQL Server developer Moe. All right. Well, you know, um, just choosing the, the first hundred characters of someone’s about me is maybe not always a winner. But anyway, uh, just a little bit about how you can, um, you know, use AI generate embeddings, uh, inside SQL Server.

Again, SQL Server does not generate embeddings on its own. These are all external calls. So think very, very carefully about, uh, how and where you use them in your queries.

Um, you know, if you need to generate a lot of embeddings, I would still probably want to do that external to SQL Server and then put the data in rather than have SQL Server call out generate embeddings and bring it back in. Um, in the context of a query, you know, smallish tables, you can, you can do this on the fly if you want. But, you know, some testing that I’ve done on larger, um, larger queries was, uh, even though this is just like a scalar value, it’s still not a, not a good time.

Anyway, thank you for watching. I hope you enjoyed yourselves. I hope you learned something and I will see you over in tomorrow’s video.

As always, as usual, where we will talk about, well, we’re gonna, we’re gonna, we got two more unnamed tabs up here of things to talk about. So we’ll, we’ll go and do that. Anyway, thank you for watching.

Goodbye. Thank you for watching. Goodbye.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Creating External Models

Summary

In this video, I delve into creating and using external models to generate embeddings directly within SQL Server 2025, Azure SQL Database, and Azure Managed Instance. While there’s minimal coverage in the full course “Get AI Ready with Erik,” which you can access using a special coupon code found in the video description, I provide deeper insights on how to integrate Python scripts for more efficient embedding generation outside of SQL Server. However, leveraging native SQL Server capabilities is incredibly convenient, especially when setting up your environment properly. I cover essential steps like enabling the External REST Endpoint and creating an external model using OLAMA or another provider. By walking through these processes, you can generate embeddings directly in T-SQL, which integrates seamlessly with transactions and triggers, making deployment simpler and more integrated than external methods.

Chapters

• *00:00:00* – Introduction
• *00:01:27* – External Models in SQL Server 2025
• *00:03:06* – Benefits of Internal AI Embeddings
• *00:04:22* – SP Configure Setting Overview
• *00:05:37* – Creating an External Model
• *00:08:17* – Generating Embeddings with T-SQL

Full Transcript

Erik Darling here with Darling Data, and we are going to talk about creating and using external models to generate embeddings in SQL Server 2025, Azure SQL Database, and Azure Managed Instance also. But there’s a good reason for that. And it is something that I am glad we are able to do, but it takes a little bit more work than you might be into. So, up until now, at least in these videos, there’s been a minimal coverage in the full course, right? The full course, Get AI Ready with Erik, which you can use this wonderful coupon code for, and this coupon code exists down in the video description. There’s a bit more coverage on, you know, including, including like Python scripts and, you know, all the other stuff you need in order to generate embeddings externally and then get them into SQL Server, which is, which is, to be honest, is much more efficient, right? Because there’s all sorts of things you can do external to SQL Server that are a lot faster than the things you can do inside of SQL Server. However, doing things inside of SQL Server has many conveniences, so long as you are able to prep your environment to accommodate such conveniences. So, SQL Server 2025 can generate embeddings natively. We have two new functions, like, well, you know, let’s create external model, which allows us to register a provider with which we can call these things. And we have the AI generate embeddings function, which allows us to generate vectors natively in T-SQL.

Cool. Wait, both of those things sound just dandy, don’t they? So, there are some benefits here. Like, you don’t have to do any Pythoning, which, you know, honestly, if you’re doing this in SQL Server, like, you probably just have Cloud Code write you some Python to do the other stuff, right? Like, you don’t actually have to know Python. You know, you might, you’re going to have to, like, you know, spend some time making sure the robots get stuff right, but, you know, like, the robots are good at stuff like Python, C Sharp, things like that, because, like, those languages are a lot easier for robots to deal with. SQL, they’re terrible with. There’s a lot more, or rather, there’s a lot more debugging with SQL than there is with other stuff. And they can fix the other stuff. Like, if you’re like, hey, robot, run this script, and, like, the script has, like, hits an error, they’d be like, oh, well, I can fix that.

With SQL, you’re like, hit an error, and they’re like, dog. You’re on your own. But you can do some stuff with AI generating beddings, right? Like, works in triggers, works in store procedures. It’s integrated with transactions. And the deployment is a bit simpler. In the full course, I teach you how to do all this. There are some external dependencies that you will need to do this stuff. I think, like, the biggest thing is that OLAMA is only, like, set up to do HTTP requests, not HTTPS. And this stuff requires HTTPS. So you need another program to sort of sit between, like, SQL servers calls in OLAMA to, like, get past the HTTPS requirement there.

In the full course, I teach you how to do all that. In this one, in these short videos, you don’t get the full thing because I still need people to buy the course so I can support the banality of my existence. So we’ve got the new SP configure setting called External REST Endpoint. And this will tell us whether it is enabled or not. We have, of course, enabled this because we’ve done this all before. If you need to change that, you can use, well, this command here. I’m not going to type it for you, but you can do it.

And then you will also need to create an external model. I am not going to drop and recreate mine because I like mine just fine. But this is generally what it will look like. If you were doing this just locally, it’ll look like this. If you’re using, like, OpenAI, there’s other stuff to it. But, you know, I don’t want, like, stop giving Sam Altman money.

Like, my sometimes friend Andy Pablo was posting pictures on Twitter about, like, going to the OpenAI offices. And for a company that lost $9 billion or something last year, they, they, like, if, I could lose $9 billion, too. Like, they bet on that. But don’t use OpenAI. Stop giving that man money.

But this is generally what it will look like if we wanted to do something locally. For the purpose of this course, I’m using this model. So that’s like, you don’t, you won’t use this. You’ll do something else. I’m using OLAMA to do all the processing. And I have a little local webby thing set up to do all this for me.

All right. So this is the, this is the location of the, where I send stuff to get a response back that’s a vector form, that’s a vector data type or an embedding, rather. You can see what models you have registered, right? If you run a fancy query like this, right, and you will see that I, me, I already did all this stuff. So I got all this stuff in there. Man, I did that a while ago, huh? I guess so.

But the end result, once you have done all of this stuff, is that you can generate embeddings in SQL Server. Now, if we go over here, and you can just ignore that, because I had to do something else stupid. We’ve got some other stuff running over here, right? And if we run this query, I got to select the whole thing, I suppose.

If we run this, and we go look at these, well, we should, you know, normally you see a little bit more action in here. You know, that was, that was kind of boring. I apologize. But we get back a string, right? So we said, hey, SQL Server, I want you to generate some AI embeddings for me.

And I want to know what the embedding for how do I optimize SQL Server performance would look like. And I want you to use my, the model that I set up with create external model. That’s this thing up here. And now I get back this, right?

And this is apparently the secret to the universe, is a long list of floats. Look at all these magnificent floaty beasts, these creatures of just fanciful existence. We get back 1024, well, yeah, because they don’t start on line one up here, right?

They start on line two. So because the model that I chose, like, what I tell it to do is generate a 1024 vector embedding. So that’s what I get back.

That’s why we have a, it goes down to 1025 here, but the actual numbers up here start on line two. So that’s what we get back. And this is what we can use to put into our column, columns in SQL Server where we have embeddings, right? And I define those as vector, right now, float32, because that’s what’s generally available.

Float16 is still a preview feature. But I have my vector column set up as float32 with 1024 dimension capacity, right? Or, like, exactitude or something, because I can’t put in less and I can’t put in more.

So this is the string that I get back, and it’s all set up to do the thing that I want it to do. And from there, we can do all sorts of other fun stuff. But additional fun stuff is going to have to wait until tomorrow, right?

So this is a basic overview of how we generate an embedding directly with T-SQL. Thank you for watching. I hope you enjoyed yourselves.

I hope you learned something. And I will see you in tomorrow’s video where we will continue on this path. For those of you who are getting sick of this path, I don’t have that many more videos on the subject. left to release, so your time is almost done.

All right. Thank you for watching.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

SQL Server Performance Office Hours Episode 51

To ask your questions, head over here.

Summary

In this video, I delve into a series of technical questions and challenges faced by the SQL community. Erik Darling from Darling Data shares insights on computed columns, index usage, memory grants, and blocking issues—explaining why certain optimizations might not always yield expected results. The discussion covers topics like parameter sniffing, query plan stability with Query Store, and the nuances of CPU utilization after implementing dynamic SQL solutions. I also touch upon my recent endeavors in automating content creation for YouTube videos and WordPress blog posts using local LLMs, highlighting how these tools are transforming content production processes. Additionally, I announce upcoming speaking engagements at various data conferences across North America and Europe, offering attendees free access to advanced T-SQL training materials.

Chapters

• *00:00:00* – Introduction
• *00:03:45* – Parameter Sniffing Solutions
• *00:10:37* – Blocking Analysis
• *00:18:52* – Episode Wrap-Up and Future Plans

Full Transcript

Erik Darling here with Darling Data, and I am in the midst of a complete psychological collapse. Windows update ruined my entire life, and then I think some sort of Streamlabs update just put the nail in the coffin. Windows update over the weekend rebooted like five things that I had important stuff going on with, and then I go to open Streamlabs this morning to record, and it’s like, you’re not logged in, you can’t record. And I’m like, I just want to save this to my computer. And they’re like, no, you have to log in to something. So I did the path of least resistance. I was like, I’ll just log in via YouTube. And it turns out that like, all my old, like, like, when I tried to be a streamer influencer and like, I don’t know, like vape and wear a cool hat and have neon lights, all those things were there, but my current recording setup wasn’t there. And so I am slowly trying to rebuild the disaster of this morning. So if anything looks weird, sounds weird, smells weird. That’s why. I don’t know. Maybe it’ll be better now. Who knows, right? Maybe it’s just like, maybe there’s too much stuff going on. Anyway, it is Monday. So we are going to office hours, we are going to have a great time. And I’m going to answer five user community, community user submitted questions. I think as long as I count correctly. And down in the video description, I’ve been having the robots slowly work through things. And I built this whole pipeline, streamline process to download my YouTube, my entire YouTube catalog. And use local LLMs to create summaries, chapters and transcripts. So you should start seeing the summaries and chapters hit the YouTube videos, there’s some hiccups there with the YouTube API rate limiting me. But then all the WordPress blog posts should have transcripts in them. And then moving forward, you’ll see that in all of the new videos, which includes this one. So that’s been fun. But down below the brand new robot created and robot quality summaries and chapters, you will find all sorts of helpful links with which you can hire me in various forms and fashions.

Yeah, you can you can you can you can consult with me, I’ve redone the training and consulting website with all sorts of spicy new offerings to appeal to a broader variety, broader spectrum of people. I’ve prettified all the training stuff. So it’s easier to sort of get to everything you want. And of course, you can do all the usual things where you you know, if you want to become a, you know, helpful member and if there’s questions, there’s questions, you can do that. And as always, please do like subscribe, tell a friend, tell your best friend in the world. Tell a family member, maybe just tell someone who you want to see someone new, you know, hey, are you also having a complete psychological breakdown because of Windows update and stream labs? Get in here. And I feel like this, this guy could really help you through. So with that out of the way, I will be leaving and this one is also starting. Data to Nashville in March, data Saturday, Chicago in March, SQL in SQL day in Poland, and data Saturday in Croatia in May and June, respectively. I will be teaching advanced T SQL pre cons at all of them. Attendees to these get free access to the entire course material. So if you’re in the area, you should get in my area and learn some T SQL and skip work that day. And I’ll say you were there. I know I’ll validate your parking. But until then, until the database marshmallows have defrosted and thawed themselves. Does defawing mean freezing? Right? Because thawing would be Yeah. Anyway, these these poor poor things look at them. Well, we’re all muddling through, aren’t we? Anyway, let’s answer some questions over here. Because that’s what we came to do. I created a computed column with a specific formula. That’s nice. I prefer I prefer computed columns made with breast milk. But you know, formulas cool too. To match a common where clause pattern added an index on it. You’re checking some boxes here, friend. Good for you. And the statistics show good histogram data. Well, is the first time for everything. But the optimizer still expands the formula and does a scan instead of seeking on my computed column index. I’m not using any functions that would prevent matching. What causes the optimizer to ignore a computed column that perfectly matches the expression? Well, the only way to, you know, you know, guarantee that your computer, your computer, your computed column will get used is to reference the computed column directly and not rely on expression matching. Our beloved cost based SQL Server optimizer expands all any expressions. Any expressions that it finds that is computed columns, index views, stuff like that. So, you know, you might be in a situation where perhaps the index you created on the computed column was a little bit too narrow for the remainder of your query. Perhaps SQL Server is stuck with a choice between, you know, a nonclustered index thing and a key lookup versus a clustered index scan.

And perhaps that’s why your computed column is not being used. That would be my first guess. But, you know, generally, if we want SQL Server to use something, we ought to be explicit about it. You know, if you like, you know, it’s nice when expression matching works, but, you know, like you usually do that when there’s a third party vendor app that you have no control over. If you’re in control of the queries, you ought to just use the computed column directly because you know what’s best there. It’s sort of like with index views. You know, it’s like if you get mad at the optimizer for not matching an index view or for expanding an index view that you do reference directly, put a no expand hint on it.

I think I know what I’m doing over here. But yeah, I think one thing that you should certainly do is run the query with an index hint telling it which index to use and just see what the execution plan looks like. Because often that will tell you why SQL Server decided not to use the query plan that you were hoping it would. Usually it will be a cost-based decision, right? You might be able to figure something out there.

Let’s see. This query. Oh, this query, huh? This query spills about two gigs to 10 dB. Wow, that’s a lot of spilling. But the estimated memory grant looks reasonable. Well, you know, there’s the memory grant that you think you need and then there’s the memory grant that you actually need, right? It’s like retirement. Like, ah, I can retire on this much money. And then you retire on that much money. You’re like, wait a minute. I can’t do anything.

Why would SQL Server underestimate memory like this? Variety of reasons. And when does memory grant feedback fail to fix it? Well, you might actually be getting memory grant feedback and you might have a parameter sensitivity situation. Now, imagine that you run your query the first time and let’s just say it gets like a 3 gig memory grant, right? And everything goes fine.

And then the query runs like 10 times after that. But it’s for a really small amount of data now, right? Because it was a big amount of data. 3 gig memory grant. Yeah, we got it. Now it’s like run like 10 times for a small amount of data. And memory grant feedback might have adjusted your memory grant way down. And then if you ran it again for the big one, your memory grant had been adjusted down so far.

And now SQL Server’s like, wait a minute. I needed those two gigs. I forgot. Ah. Like, you know, it’s like on your way to the airport and you’re like, I left my wallet at home. It might have been trying to fix something else and then broken that.

But, you know, sometimes, you know, the memory grant that you asked for, you know, sometimes it can be shared, sometimes not. So it’s possible that, you know, you like the memory grant that you got in entirety was was reasonable. But then the memory, the memory fraction that one of your other memory consuming query plan operators got was was not very good.

So there are a number of things to look at in there. You know, I think that primarily if it is a parameter sensitivity situation, you should do as much indexing or query rewriting as you can to alleviate the need for memory consuming operators where possible. Specifically, sorts are usually most prone to this sort of issue.

Of course, various hashes might spill as well, but sorts usually cause quite a bit of ruckus. So that, you know, as far as like why SQL Server would underestimate memory, you know, all the typical underestimation scenarios apply here. You know, without seeing more of the query or understanding more about, you know, what’s going on with it.

It would be an exhausting time trying to list it all, but, you know, local variable misestimates, parameter sensitivity, you know, big plan, little plan. Oh, this one’s for three rows. This one’s for three billion rows.

You know, the table variables often cause misestimates. All sorts of things might be kicking in. So, I don’t know. There’s always recompile hints, right? Always recompile hints.

Query store shows a stable plan and low average duration. Oh, average, you say. But users still report random slowness.

What does query store not tell us that could help explain this? Well, it’s maybe not what query store doesn’t tell you. And even the query store GUI is not guilty of this.

But, my friend, you’re telling me about low averages, but have you looked at mins and maxes? Because average is nice, but mins and maxes can show you outliers in data that averages tend to lose. So, if you look at the average and it’s like, let’s say, 100 milliseconds.

And you look at the min and it’s like 10 milliseconds. But then you look at the max and it’s like 15 seconds. Well, you’ve got a bit of investigating to do.

This, again, could be a parameter sensitivity situation. This could be a blocking situation. If you look at the max CPU and the max duration, and the max CPU is still pretty low, but the max duration is still pretty high, then there’s something else going on, right?

There’s something else amok with this query, right? It could be getting blocked. It could be waiting on other resources. Sometimes there’s all sorts of things that you must investigate and uncover. And, as always, my rates are reasonable.

So, if you’re out there listening, boy, boy, boy, boy, can I investigate. I fixed parameter sniffing with Dynamic SQL. High five.

You and me, you and me, drinks anytime you want. But now CPU is higher overall. Did I actually improve performance or just change failure mode? I don’t know.

How high is CPU? Like, what’s higher, right? Is it, like, did it go from, like, 5% to 7%? Did it go from 5% to 15%?

In either case. So, here’s the thing with CPU being higher overall. Up to a point, that doesn’t tend to bother me. There’s a lot of people in the world who will brag, who will put on, like, a top hat and wear, like, fancy ribbons and tell you that their SQL Server CPU was constantly at 10%.

And to them, I say, why are you overpaying Microsoft? Why is that good? You out of your mind?

Like, how many cores do you have? You know, it’s like, what, $2,000 to $7,000 a core? You’re only using 10% of that? 90% of your money to Microsoft is wasted.

So, you know, parameter sniffing is one of those things where, you know, it can go in the direction of, you know, you have, like, a tiny little serial plan. And it blows up when it has to process a lot of rows. And you can also have a situation where, like, you know, you get, like, a big parallel, like, hash join crazy plan.

And all of a sudden, you start running out of server resources. Like, you might start waiting on memory via resource semaphore. Or you might start waiting on CPU via thread pool.

So, there are two ways that can go. But, you know, you know, I think that when, you know, I think you’ve balanced things a bit, right? Because you are probably getting, like, easy little serial plans where appropriate.

And you are probably getting larger parallel plans where appropriate. And things are just sort of balanced out a bit. So, you have, like, queries that are processing more data using more CPUs, right?

They’re going parallel. They are using DOP CPUs and doing things. And you have your smaller single-threaded queries off doing their thing. But most likely, you have improved the situation overall for the people who now get parallel plans and use more CPU and push CPU higher because they are probably no longer waiting a very, very long time for a very, very slow serial execution plan to finish.

So, I think that you have done a good thing. I think that you have done a smart and reasonable thing. And I think that you should probably not worry about, unless CPU, unless by, like, CPU is higher overall, you mean, like, now CPU is constantly at, like, 95%, you’ve probably done a smart thing.

All right. SP, who is active? I know that guy.

He owes me money. He owes short blocking chains, but users still experience big delays. How can blocking be minimal, but user latency still be high? That’s a dense one.

It sets the mind wandering in several different directions. You know, blocking is not the only thing that causes delays. If you want to continue to be database focused, you know, like, you know, the, so, like, I don’t know, like, to me, I’m thinking, like, short blocking chain, like, like, is it like 20 queries pile up for, I don’t know, like, 10 milliseconds, 20 milliseconds, three seconds?

I don’t know, like, I don’t know, I’m just having a hard time, like, like, like, okay, like, SB, who is active shows short blocking chains, but, like, man, there’s so many other things that can slow a query down, right? Like, like, like, like, like, if you stay in the database, you know, those short blocking chains might not be the things that users complain about, right? There’s all sorts of other things that might be slow.

And if you, and if you, once you get outside the database, you know, like, if you start thinking about your app servers, and, you know, what they do when they receive data, and all sorts of other stuff, you know, what you want to do is, you know, SB, who is active, is great at showing you what’s happening in the moment. But perhaps you need to broaden your view a little bit, perhaps take a step back from just what’s happening right now, and look at the server and a little bit more overall, you know, if your server has been up for a reasonable amount of time, and by that, I mean, like, Goldilocks zone amount of time, like, if it’s been up for like 100 or so hours, looking at aggregated wait stats can be very, very useful. If, you know, if, you know, you are, been up for 3000 hours, and the wait stats picture becomes a bit muddy, you could, you could try, you know, persisting wait stats and other things off to, you know, tables with a little timestamp in there and, you know, figuring out sort of, you know, like, like, you know, like every five, 10 minutes or so, like, you know, like how wait stats are changing.

And you could probably, you know, figure out a lot more from that than just running SP who is active quickly in the moment. If you’ve got query store on there, you can always look in there for, you know, you can use my store, not the GUI, because the GUI is an atrocity. GUI is something that you would design if you didn’t like someone.

You can use my store procedure, SP quickie store, you can find, you know, queries with, that are slow, that happen during periods of time when users are typically complaining about things. That’s sort of what I’d go for there. You might, you might even want to look at, you know, capturing the block process and or XML deadlock report and see how much action there is in there.

Because you might, you might be surprised that, you know, the, the short blocking change that you see with SP who is active might just be part of the picture. They might not be the whole picture. There might be a whole lot more going on in your server that, that you could, you could delve into and that you could use to complete your analysis a little bit, a little bit more thoughtfully.

Anyway, that’s probably good there. Uh, that wraps up the 51st episode of Office Hours. Um, as promised, I said absolutely nothing and made no fanfare about episode 50 because it’s not that big a deal.

Like episode 100, maybe. I’ll wear like a hat or something, like a party hat. Maybe I’ll, maybe I’ll, maybe I’ll live stream episode 100 because I have all this stuff now I can do.

Anyway, that’s cool. Thank you for watching. I hope you enjoyed yourselves. I hope you learned something. And I will see you in tomorrow’s video where we will, we will talk more about, um, our, our new AI overlords and SQL Server. Anyway, thank you and goodbye.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Vector Index Preview Feature Limitations

Summary

In this video, I delve into the limitations of vector index preview in SQL Server 2025, sharing insights from my course “Get AI Ready with Erik.” I highlight key constraints such as read-only tables, restrictions on inserts and updates, and the necessity for an integer primary key. Additionally, I discuss practical strategies for managing data across multiple tables to accommodate these limitations, emphasizing the importance of planning ahead to ensure smooth operations when using vector indexes in your database environment.

Topics

`SQL Server 2025`, `Vector Indexes`, `Preview Feature`, `Limitations`, `AI in SQL Server`, `Data Modification`, `Read-Only Tables`, `Partitioning`, `Replication`, `Integer Primary Key`, `Clustered Index`, `Embeddings`, `Azure SQL Database`, `Stale Vector Index`, `Disabling Vector Indexes`, `Table Management`, `Vector Search`, `Vector Distance`, `Roadmap`, `SQL Server 2025 Features`

Chapters

• *00:00:00* – Introduction
• *00:00:30* – Vector Index Preview Limitations
• *00:01:00* – No Inserts, Updates or Deletes Allowed
• *00:01:30* – Table Read Only
• *00:02:00* – Partitioning Not Supported
• *00:02:46* – Replication to Subscribers Not Supported
• *00:03:08* – Integer Primary Key Required
• *00:03:48* – Azure SQL Database Regions
• *00:04:15* – Disabling Vector Indexes
• *00:04:49* – Managing Data with Vector Indexes
• *00:05:22* – Combining Searches
• *00:07:17* – Stone Age of Vector Indexes
• *00:08:33* – Microsoft’s Priorities

Full Transcript

Erik Darling here with Darling Data. Here to try to continue to educate you about AI and the vector stuff in SQL Server 2025. This is all, you know, small snippets of material from my course, Get AI Ready with Erik. You can buy that for a hundred bucks off with the coupon code up here. And when you do, you support me doing menial things in life, paying rent, cable bills, cell phone bills, nothing fun, right? No joy, no joy, just AI. But we’re going to talk about vector index preview feature limitations in this one. And because they are significant, and it’s hard to imagine, like, it’s hard to imagine releasing them in the current state as generally available. Like, these are just, this is just the stuff that we know about. This is not all the stuff behind the scenes that, you know, probably still needs tidying up, fixing all the other stuff. But just to sort of zoom in on some other things. When you create a vector index on a table, there are no inserts, updates, or deletes allowed. This is probably the number one reason that vector indexes are preview only. You know, like, we didn’t have the preview feature switch when columnstore indexes came out and like, you know, they were like, oh, these make the table read only two. And we didn’t have that. So if we did, maybe that would have been something. But if you try to do anything, you will get this funny error data modification statement failed, because table post embeddings has a vector index on it. So no inserts, updates or deletes from you. So just from the Microsoft docs, you know, obviously, tables read only, so that stinks. You can’t, like you can’t partition tables at vector indexes on. I don’t know if I care so much about this, right? Like, partitioning is a data management feature that everyone seems to confuse for a performance feature. And yeah, I just I just don’t care. You know, if it honestly, if that kept happening, and people were just stopped asking questions about partitioning their tables, I’d be I’d be thrilled. Another one that I kind of don’t care about no replication to subscribers, replication. This is like capital R replication, not like availability group, like, you know, log shipping, mirroring, stuff. This is like, you know, merge or transactional replication, you can’t replicate vector indexes to subscribers. Again, if this gets people away from replication, all in favor of it, right? Yeah, what a nightmare.

An integer primary key is required. So this is this is like for us like a single column. So like I think this one is probably the funniest one to me. Because Microsoft is almost like self limiting you in this way. Like, like, like, like, you can’t use like a big int, right? Like anyone who’s table like tables like are they’re like, wow, this table is going to get big, we need a big int for this. Microsoft’s almost like 2 billion rule limit, pass this, I can’t make any promises, right? Like that’s, that’s, that’s a funny one, right? So it has to be an integer has to that has to have a cluster primary key on just the integer. You can’t have any composite keys. You can’t like, you know, have your cluster primary key on a GUID or something like that.

Or something like that has to be the integer. That’s an amusing one. I don’t know the story behind that. But you know, maybe maybe they’re trying to save you from yourself. And of course, if you drop the index and you insert data into the table, you have to rebuild it if you want to start using it again, that one’s fairly straightforward. Azure SQL database, at least in some regions, where the vector, the disk and vector indexes have rolled out to they, as of this, as of this date, not all Azure regions support this.

They do have a setting called allow stale vector index that allows rights, but the index will be stale until you rebuild it. This is not available in SQL Server 2025. You can’t currently disable a vector index, right? You have to drop it, right? If you try to disable the vector index, you will get this error that says one or more of the specified alter index options is unsupported for a vector index. So no disabling vector indexes. So if you have a system where you need to add data regularly, where like, you know, to a table, you’ll probably want to keep embeddings in a separate table from like, you know, your transactional stuff where people need to do things so that you may continue to make money off them.

You know, you might even set it up. So like, you have two tables. One of them is for, you know, like rows that you are actively, you know, like have like an active vector index on them. And another table that gets like new records that you have to like, you know, that you haven’t put into the vector search table yet, because you have to sort of like batch that, right? You would have to set up a process where, you know, like it’s some cadence that makes sense to you, you would have to drop the vector index.

You know, like either, you know, you’ve already either generate embeddings for new records or already have them like in like a second table in like put the new put the like newer embeddings into the vector into the table that you want to have the vector index on and then rebuild the vector index. Then you could, you know, you could combine searches like, like one, like, you know, like, so like a union all query where one of them does the vector search, the other one does the vector distance search on the unindexed data. So the downside for that is if you are like, if you have like sort of like hot vector data, you know, like new records wouldn’t be searchable with vector search until you, until you like batch them into the new table, you would have to continue to use like the vector distance to do that.

But the general pattern that you would want to follow is combine them. Right. So almost like, you know, like some people like some people will pretty commonly have like, like, like, like, not really like partitioned with capital P partitioned, but they’ll have like sort of like an archival table that has like much older stuff in it. And they’ll have a newer table for like newer data, maybe even like three tables where it’s like, you know, you have like archived and then like, you know, like lukewarm data and then like hot data.

And you’ll just like union all those together with like constraints on each so that like if there’s a date based search, SQL Server knows which one to go to. But you could you could do something sort of similar with your like, you know, not archived, but like indexed vector data and your new vector data where you just sort of union all two things. And just like, well, this I can vector search this I need to vector distance because I don’t have an index on that yet.

And I can’t use vector search without a vector index. And I can’t just put the data directly into the table with the vector index on it because the vector index makes a table read only. You would just have to sort of find a way to combine both of those, which, you know, right, right now is not a great story.

And that’s why vector indexes are in preview. They’re not, you know, they’re not in the generally available category because they’re sort of a nightmare, right? Like it makes a lot of processes not fun to use.

There’s a lot of feature interoperability that just isn’t great with stuff. So, like, I get it. I get why Microsoft made the choice.

But, you know, you know, vector indexes will probably be nice someday. But for now, like we’re looking at like the stone age of vector indexes in SQL Server. And, you know, I think what’s what’s really depressing about it, you know, from like someone who cares about the database product quite a bit is there’s like no roadmap.

There’s no timeline. There’s no like, you know, it’s just sort of like, oh, we’ll get to it when we get to it. Like, stay away.

It’s, you know, you can’t get an answer from anyone on anything. And, you know, you can’t help but feel that Microsoft’s priorities in other areas are taken away from what what was trotted out by all sorts of folks at the company to be like the flagship feature of SQL Server 2025. Whenever everything that we saw, like the word SQL Server was barely in like the marketing header.

It was like it’s like like, yeah, it’s like SQL Server 2025. But it was like fabric, AI, ground to cloud to whatever. And you’re like, OK, well, where’s where’s the database?

And then didn’t we? Isn’t that what we care about? Isn’t that what we’re here for? So I don’t know. It would be cool if, you know, we had people were a little bit more transparent about when these things might stop sucking. But for now, we must embrace the suck for the suck has embraced the warm embrace of suckiness is what we have.

Anyway, that’s probably enough here. Thank you for watching. I hope you enjoyed yourselves.

I hope you learned something and I will see you. Oh, I don’t know when I when I see you. Who knows? Maybe I’ll just quit. Maybe that’s enough of this. All right.

Thank you for watching.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Vector Index Intricacies

Summary

In this video, I delve into the fascinating world of vector indexes in SQL Server, a topic that might seem a bit dry but is incredibly powerful for certain types of data analysis and search operations. Vector indexes are fundamentally different from traditional B-tree indexes; they create a graph where each vector (a series of floating point numbers) becomes a node, and edges connect similar vectors. This unique structure allows for faster searches by navigating the graph rather than scanning every single vector, making it particularly useful for tasks like content similarity search. I walk you through how vector search works using SQL Server’s preview function, demonstrating its efficiency compared to exact vector distance calculations. Along the way, we explore concepts like recall and see firsthand the trade-offs between exact and approximate searches. To give you a practical example, I run a demo comparing the results of an exact search with those from vector search on a sample dataset, highlighting both their similarities and differences. If you’re curious about diving deeper into AI and SQL Server 2025, be sure to check out my course “Get AI Ready with Erik,” where you can learn more advanced techniques and get hands-on experience.

Topics

`SQL Server`, `Vector Indexes`, `B-Tree Indexes`, `Columnstore Indexes`, `Graph Search`, `Greedy Search Algorithm`, `Vector Distance`, `Recall (Information Retrieval)`, `AI in SQL Server`, `Post Embeddings`, `Docker Container Networking`, `Exact Search`, `Approximate Search`, `Vector Search Function`, `SQL Server 2025`

Chapters

• *00:00:00* – Introduction
• *00:00:31* – Vector Indexes vs Columnstore
• *00:01:02* – Graph-Based Structure of Vector Indexes
• *00:01:50* – Searching the Graph
• *00:02:19* – Greedy Search Algorithm
• *00:03:08* – Life Metaphor for Search Strategy
• *00:03:47* – Vector Search Function Overview
• *00:04:32* – Efficiency of Vector Search
• *00:05:12* – Non-Sargable Predicate Comparison
• *00:06:03* – Recall in Approximate Searches
• *00:07:28* – Critique of Microsoft’s Efforts
• *00:08:45* – Exact vs Vector Search Example

Full Transcript

Erik Darling here with Darling Data, here to talk to you about some boring stuff about vector indexes, because they’re not like regular indexes, right? It’s not like a B-tree index at all. It’s a completely different structure, which is, you know, why, I believe, much in the way that, you know, columnstore, well, actually, no, I’m lying. columnstore is much closer to a normal index than a vector index is, but the only reason that I bring that up is obviously because when Microsoft first released columnstore indexes, they struggled mightily with the columnstore index, making the table that it was created on read-only. We’re going to talk more about this other stuff, but currently, vector indexes do that, but vector indexes are not like B-tree indexes in that the way that, data is searched and sort of, you know, written out is a lot different, right? It’s not a B-tree where you have pages just sort of linking to each other and you can seek and, you know, do all this other stuff within it. It’s a completely different sort of structure, which is also probably why Microsoft is struggling so mightily with getting the creation of them to be fast, because…

Anyway, let’s get on with things here. Disk and indexes build a graph, basically, where each vector, right, each vector that you have, those are that, you know, series of floating point numbers, is a node in the graph, and then edges connect sort of similar vectors. So you can, you can sort of seek from, like, you know, like, within that, or like, seek around that graph, but it’s not, it’s not really the same thing.

Whenever you search, you navigate the graph instead of scanning everything. So like that, like, like using the vector search function, which is in preview, you know, like, you can, you can seek within that rather than like, you know, like, like a vector distance, you have to basically like scan everything, measure the distance and then, like spit out whatever, you know, like the distance there and like any filtering that you apply or any ordering that you do as a result of that sort of runtime calculation. It’s not stored anywhere.

But vector, vector search uses, or vector indexes use sort of a greedy search, which is a problem solving strategy that sort of, like, I guess like the premise of it is, like, if you make the best local choice, every time you connect, like similar vectors, like if you make the best local choice at each step, then the hope is that it will lead to the best sort of like global solution. So it’s sort of like, if you do everything right in life, I mean, look, you’re still going to die, but maybe some good stuff will happen to you along the way.

You know, more, more likely, you know, you’re going to watch like crappy musicians get rich and famous and like terrible actors make millions and millions of dollars and you’re just going to, you know, work your butt off and have to watch training videos about AI and SQL Server. But the search algorithm that gets used is called the greedy search, right? I’m always searching for new ways to be greedier because I hear that the greedier you are, the more money you make.

So I’m always just trying to figure out how can I be greedier? So far, it hasn’t worked, right? But you start at an entry point and you look at like, like you get to a node and you’re like, well, like, like this is like, like, like fan out and look at, look at all the neighbors of that node.

And then you move to whatever neighbor is the closest to your query, right? So like, if you have like, like an 0.5 here, you’d be like, well, what are the closest to this? Like 0.6, 0.7, 0.8, 0.9, you’re like, ah, 0.6 is the closest I’ll go to you.

And then you kind of repeat until you don’t find a closer neighbor. At which point you might backtrack and try alternative paths to see if you turn something, if you find something better, find a better path through the graph. And then you return the best candidates that showed up in there.

What it’s faster because you don’t examine every single vector, right? Like when you use vector search, like the function vector search, SQL Server, like is able to sort of like look at a vector and figure out what is closest to it and like move to that rather than just like, you know, like running like the vector distance on everything, figuring out what that distance is and then going on with it. You can think of like the vector searching as always being like a non-sargable predicate where like if you were to say like date diff, like one column, like date different days between two columns and like is greater than four.

SQL Server doesn’t know any of that ahead of time. It has to run that function for every row that you want to compare, like figure out what the difference in days between two date columns is. And then it can figure out like if it meets that, if those rows meet that predicate, you can’t do it.

Like it doesn’t know any of that ahead of time unless you create a, create a computed column and do all the other stuff. So vector search is faster because the graph will guide the search to other relevant things that might, that are similar to it. Search time with vector distance, like I showed you in another video, gets slower as your data gets bigger because you have more things to compare and figure out the distance between.

In the AI world, there’s a concept of recall. Recall. And recall is the, like what fraction of true neighbors did an approximate search.

Fine. Because vector search is an approximate search, whereas vector distance is like an act, like an exact search. So you can think of that as sort of like if an exact search, like went through all of the neighbors and it found like ABCDEFGHIJ.

Like a, like a disc and, like a vector index search would, might find like ABCDEFGHXY. Recall is like how much of the sort of approximate search, like matches what an exact search would find. Uh, so in, in that, that case up above where only like the last two are different, IJ versus XY, the recall would be 80%.

Um, Microsoft research reports 95% plus recall on billion point benchmarks using their disc ANN indexes. Which, you know, some, I mean, it’s, it’s good, right? 95% plus.

Great. You know, it’s just, you, you wish that they were generally available. You wish they didn’t make the tables you create them on read only. You would, might even wish that creating them didn’t take the gargantuan effort that it does. You might even say, I don’t know, maybe, maybe put the fabric down and dedicate some engineers to this thing that seems important.

I don’t know. Right? Stop, stop fussing about with these gag gifts to the world.

Like, no one needs fabric. We have Databricks, we have Snowflake, we have other things that already do this job. Right? Showing up late to the party with your pants off.

Anyway, uh, let’s give ourselves a single query vector. And that single query vector is going to represent the search phrase, Docker container networking. Right?

So this is the vector that we care about. Uh, using exact search, right? With this vector distance function. We’re going to find the top 20 rows, uh, that, um, that have the, the closest distance, right? So low, again, lower number better.

We’re gonna find the top 20 closest matches to, uh, to, uh, Docker, Docker container networking in the post embeddings table. And then we’re going to use vector search down here. And I’m going to talk more about vector search, but we’re going to use vector search here to, um, see how close or see how much we get on that.

Cause from this one, right, we’ve got the top 20 by exact search here. We’re going to get the top 20. That’s this thing here.

Top n equals 20. Uh, so we’re going to get the top 20 rows from this, uh, that come out of this function. Right? So this is only going to return 20 rows. And since we’re dumping it into a temp table, we don’t need to worry about that one. This one, we were saying, give us the top 20 ordered by like, which ones are the closest neighbors.

So using vector search, we can do sort of the same thing, right? Where we hit the post embeddings table. We look at the embedding, uh, we tell, or rather we tell it which column to use here.

Uh, for some reason you can’t alias this thing. Um, we’re going to, we’re going to say, we want it to be similar to the vector embedding that we found before using the cosine metric and give us the top 20 rows from that. And if we look at, uh, what came back from those, what we’re going to see is of course, the exact, well, I mean, I say of course, but I say of course, because I’ve done this demo before, but, um, you know, both of these things found 20 rows, right?

But the only 16 of those rows overlapped, meaning that, you know, the, like there, there is a difference in the search results between the exact search and what vector search found. If you want to find out what that difference is, I would highly recommend you buy my course, Get AI Ready with Erik, which if you use this coupon, we’ll buy you a hundred, a hundred dollars off. The, the, the price of admission that, that link is down in the video description.

You can click on this fully assembled pre pre-made link for you and you can, and you can buy it and you can, you can learn all sorts of additional things about AI and SQL Server 2025. All right. Thank you for watching.

I hope you enjoyed yourselves. I hope you learned something and I will see you in tomorrow’s video where we will do, oh, I don’t know, something equally vector-y and search-y. All right. Goodbye.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

What Do You Call A Join That Isn’t A Semi Join?

There’s a gap in SQL Server database terminology that’s been bugging me as long as I’ve been teaching people about databases.

You’ve got semi joins, and you’ve got anti-semi joins.

Everyone (loosely, very loosely, everyone) knows what those do: Find a match, or confirm there isn’t one. Lemon-squeezey.

The engine doesn’t need to fully articulate the relationship if you have a one-to-many or many-to-many relationship between two tables.

Proof of existence (or non-existence) is enough. Short-circuit, move on. Once and one time only, to biblically know me, in the back of my go-cart.

Then you’ve got INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN. Further, there’s SQL Server’s APPLY, and the ANSI-friendly LATERAL join.

These are different from the semi-variety.

They don’t just check for a match, they enumerate every single matching row combination (and preserve non-matching, varying with the degree of left/right/full outer-ness, of course).

For the extra-pedantic reader, I’m aiming for somewhere in the “punch” and “children” region of the taxonomy mnemonic, here.

• One-to-many? You get every matching row from the many side, hooray!
• Many-to-many? This is where you start reaching for DISTINCT and wondering what happened to your data model life.

So what do you call that category?

Not “full join”!

That’s a specific join type and using it as a category name is asking for a bad, confusing time. Like being a teenager, complete with a room full of adults being puzzled by your very presence in the world.

What I’ve come to discover in some research: Nobody ever really settled on a term.

The database theory world assumed inner/outer joins were the default, named the exception (semi joins), and called it a day. It’s sort of like if weekdays were just called days and weekends were called end-days (speaking of end-days, does anyone know when we can look forward to those?).

Brainstorming Gone Bad

That’s not good enough for me, so I started thinking through options.

• Fanout Join
• Enumerating Join
• Multiplicative Join
• Exhaustive Join (four ideas in, I was exhausted, clearly)

They all capture something useful, but they’re all longer and less clear than “semi join.”

Two or three syllables too many, usually. Syllables are also exhausting.

Worse, they’re clunky. #@^%ing clunky, Jimmy.

“Semi” works because it’s a snappy Latin prefix that means exactly what it says, and leaves plenty of room for immaturity amongst friends.

You need something equally snappy that means the opposite.

Tight, snappy, punchy, catchy. That’s what we’re known for here at Darling Data. Hello, sp_HumanEventsBlockViewer.

The problem is there’s no natural single-word English antonym for “semi” that prefixes onto “join” without sounding weird.

Yes, I consulted a thesaurus, that’s also where I got “antonym” from. Thanks for noticing.

Already overloaded by academia:

• Whole Join
• Complete Join

Embarrassing:

• All Join

These sound like breakfast cereals for people having trouble getting one out the door.

This is unfortunate and undesirable.

At Darling Data, we try to avoid unfortunate and undesirable, that’s why we don’t work with MySQL.

Back To Latin 101

If “semi” came from Latin, then the answer probably lives there too.

Of course I had to look these all up, which was a tremendous mental strain. You’re welcome.

• omni — all, every
• pleni — full
• toti — whole
• holo (Greek) — whole

There’s a clear winner: Omni.

• Same syllable count as semi (confirmed by clapping)
• A prefix (again, loosely) people already know
• Semantically accurate (enough for me, anyway)

You’re finding all matches, not just proof of life. Or death. Or afterlife.

And! It doesn’t conflict with any existing SQL Server database terminology.

Semi join and Omni join

Those belong together.

• Same Latin family
• Definite antonyms
• You can put them on a slide and the audience gets the contrast

The database field never really bothered to name the category, because it was always the default.

But the default still needs a name when you’re explaining optimizer behavior, execution plan operators, or why your query just multiplied itself into sweet, sweet oblivion.

Enumerating joins, multiplying joins, set-based join, Cartesian-derived join, and result joins have all been used somewhat randomly in various database literature and documentation.

But those are unfortunate and undesirable too. Clunky, Jimmy.

Omni join!

You heard it here first.

Spread the word, far and wide. Like a monorail.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Vector Indexes Just Not There Yet

Summary

In this video, I discuss vector indexes in SQL Server 2025, focusing on their current state as a preview feature. I walk through the process of inserting data into a table to prepare for creating a vector index and explain why these indexes are not yet fully production-ready. I highlight key limitations such as read-only tables, lack of multi-column support, and the absence of inline index definitions, emphasizing that while they offer some functionality, there is still much work to be done before vector indexes can be considered mature features in SQL Server.

Topics

`SQL Server 2025`, `Vector Indexes`, `Vector Data Type`, `Vector Distance`, `Vector Norm`, `Vector Normalize`, `AI Generate Embeddings`, `AI Generate Chunks`, `Preview Features`, `Read-Only Table`, `Clustered Primary Key`, `Compressible Columnstore Index`, `Vector Index Quantization`, `System Databases`, `Inline Index Definition`, `Multi Column Vector Index`, `Multi Key Vector Index`, `Enterprise Database Product`, `SQL Server 2025 Update`, `Erik Darling`, `Darling Data`, `Get AI Ready with Erik Course`

Chapters

• *00:00:00* – Introduction
• *00:00:28* – Embeddings Progress
• *00:01:13* – Why Vector Indexes Aren’t There Yet
• *00:02:04* – SQL Server 2025 Preview Features
• *00:03:11* – Creating a Vector Index
• *00:04:26* – Query to Create Vector Index
• *00:06:47* – After Creating a Vector Index
• *00:10:30* – Conclusion

Full Transcript

Erik Darling here with Darling Data, and we are just waiting for the last, oh, I don’t know, thousand or so records to go into this table to make it an even 10,000 rows so that I can show you some stuff about vector indexes. You can see maybe from, let me, well, you know, let’s go in here and let’s zoom in a little bit. We have local zoomit running in here. So, I was putting, like, 8,000 rows into the Taylor, and it’s gonna, it’s gonna take a little while, right, because I had 2,000 rows in there, but I wanted to get it up to 10,000 rows. So, you know, I’m just waiting on that to finish up. We’re almost done. Yeah, it’s 7,500 out of 8,000 embeddings, and, oh, I don’t know, let’s see.

Did this finally stop moving? Yeah, it did. All right, so our local olama has finished creating embeddings. Some input tokens were not marked as outputs. Okay, that’s great. Well, anyway, nice to know. Let’s talk a little bit about why vector indexes are just kind of not there yet.

This is all part of my Get AI Ready with Erik course. You can buy it with the link up there that is also handily down in the video description below. And the nice tidally added coupon code will get you 100 full US American dollars off. So right now in SQL Server 2025, the vector things that you have generally available are, of course, the vector data type, which currently only allows for a float 32 in GA.

You have three vector centric functions, vector distance, vector norm, and vector normalize. I go over all those in more detail in the full course material. I’m not going to, I can’t, I can’t do everything here. Give it all away for free. What’s the point?

You can create external models. So you can do things. Well, not like I just showed you. Well, actually you could do that. I’m going to show you just not in this video, but you can use like functions within SQL Server, like AI generate embeddings and AI generate chunks to sort of do the job that the Python script I was just showing you would do. So SQL Server 2025 has a database scope configuration with the ability to allow you to use preview features, which you have to explicitly opt into.

So you don’t accidentally use a preview feature in production and then be like, oh, I didn’t know. Whoops. If you want to turn on preview features, if you just don’t care, like I don’t care about this local VM. So screw it, just do whatever I want with it. You can do that by saying, by altering the database scope configuration to set preview features equals on.

Preview features has not quite made it to blue text in SSMS 22 point, whatever this is yet. I don’t know. Is this thing telling me there’s an update? What is this saying? Oh, there is. 22 point, 2.1 is now available. Well, we’ll get to that later.

Maybe, maybe preview features will turn blue in that one. We don’t know. But creating a vector index requires a single column integer primary key of the clustered variety. And of course, a vector column vector data type column on which to create it.

Our post embeddings table meets all of those qualifications. And as long as everything goes right, we should have exactly 10,000 rows in there. Now we clearly don’t have a vector index currently on the table because if we did all of those inserts that we were just doing would have failed.

All right. So we can run this and it’ll just say it wasn’t there. So we did not do anything. But like there are two, I mean, the two main problems with vector indexes. One, they make the table that you create them on read only in full, not just the vector column, the entire table read only.

No, no writes whatsoever. Insert, update, delete, merge out. All right. No. I mean, obviously no partition switching because you can’t partition this stuff, but all the things in the world. So what I want to show you is what happens when you create a vector index in SQL Server by running this.

All right. And like I’m not going to go through all the code because it’s a pretty hellacious mess. But, you know, this thing took about 10 seconds for about 10,000 rows. You know, fairly long time for an index create.

I believe it is sped up with cumulative update one a bit for 2025. But this is the this is the query that currently builds a vector index. And there’s a lot of stuff going on in here. Right.

Like this is not a normal index create. And it is kind of funny that like when you when you like turn on execution plans, normally when you create an index, you get an execution plan back. Doing it for creating a vector index, you don’t get an execution plan back for it.

But you do get this handy warning when you create a vector index that the join order has been enforced because a local join hint is used. So, you know, real, real professional there. And that’s that’s always a good sign.

But this is the code that currently does it right where we’re doing all this wacky stuff. And this is, you know, not how a normal index gets created. Microsoft right now has to do all of this stuff in order to build a vector index, which is why it’s so slow and why this is a preview feature.

And this is not, you know, production ready for anyone, because if you like to tell someone that, like, you know, like when you create an index in your database, it runs a series of like, you know, I don’t know. I don’t know if internally this is from a store procedure. This is just ad hoc SQL that runs.

But if you were to tell someone this is what you’re doing, like a serious database person, I think there’s a pretty good chance you would get like laughed at pretty hard. You know, cool workaround. But this isn’t this is not what you expect from an enterprise database product.

Not not at all. Yeah. So anyway, this is why vector index is preview only.

And once you create a vector index, there are some other some other things in there, too. Right. Once you create a vector index, you can see a bit about it by using.

I mean, it’s kind of also kind of we have to use sys.internal tables and join that off to some other stuff to find like information about it. But like like this is looking at the post embeddings table. This is the full table that we have, which is like one point something million rows.

I think it’s a you know, it’s a pretty big index. And it’s not like you can compress this thing. I mean, you can create a columnstore index, which is compressible. Like you would still have like the base table or like if it was a non clustered columnstore.

So like you could there is some compression that like kind of works on this, but not not like page or row compression. Right. So this is the index that you get back.

There’s also this other funny row called vector index quantization table, which apparently would be like a compressed vector index thing. I don’t have the full details on that, but rather anything more than what I see there. But it’s not in use yet.

There’s also sort of a funny thing where like you can’t turn on preview features and system databases. So like when I when I when I was first reading about this and I was like vector indexes are read only. Well, that’s kind of whack.

But we could we could like maybe put them into a temp table, put like some stuff into a temp table and then create the vector index on like a smaller set of things and then search like in that. But since you can’t turn on preview features and system databases, you can’t create a temp table with a vector index on it. Which I don’t know, I mean, you know, you’re messing with my work around here.

You’re messing with like the like the perf tuner stuff in here. I don’t I don’t like I don’t particularly care for that. There’s also some other funny like interoperability things where like like most index things you can like create in line with the table definition.

You can not do that with vector indexes. Vector indexes don’t allow you the inline index definition thing. You get one vector index per column, meaning that if you for some reason wanted to like measure like if some reason like you wanted like, you know, like like Euclidean or dot product or you wanted to mix cosine with the other two, you couldn’t index the same vector column two ways, which is weird. And you also can’t have multi column vector indexes, you know, like the vector index like no includes, which is, you know, not awesome for people who might want to like, you know, hit their vector column and, you know, select other columns, things like that.

But, you know, there’s also like like like like no multi key vector indexes. So there’s kind of a lot of stuff about the vector implementation right now that feels really half baked. It feels like this was like rushed out the door, like there was a lot of pressure to get, you know, some movement on this.

And, you know, it just shows us really unpolished, you know, I’m not going to say unprofessional because screw it. Look at me. But, you know, it’s just like really unpolished as as a as a feature at the moment. And again, like we’re not we’re not getting like at least I’m not getting any like feedback about like roadmaps or timeline for when any of this stuff will get worked out.

So, you know, I can’t tell you any of that. Maybe maybe some MVP out there can can tell you because, you know, they’ll they’ll they’ll they’ll kiss the right end of someone and get some details. But that ain’t me, babe. All right. Anyway, vector indexes, maybe someday.

Right now, you know, no, no, no, no buenos to be had. All right. Thank you for watching. Thank you for watching.

Going Further

If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.

Get AI-Ready With Erik: Vector Search Function, Nice Someday

Summary

In this video, I delve into the fascinating world of vector search functions in SQL Server, specifically focusing on how they can transform your search queries by making them more efficient and accurate. I break down the intricacies of using the vector search function, which returns a single column called distance, and explain its unusual syntax and functionality. By walking through a practical example with a full post embeddings table, I demonstrate how to use this function effectively while highlighting its quirks, such as the odd behavior when aliasing tables and the necessity for a top number of rows to be specified. Additionally, I compare vector search against vector distance, discussing their performance differences and providing insights into optimizing your queries for better results.

Topics

`SQL Server`, `vector search function`, `table-valued function`, `distance column`, `cosine metric`, `vector index`, `query plan`, `full post embeddings table`, `cross-apply`, `embedding tables`, `foreign key`, `column store indexes`, `vector distance function`, `filtered searches`, `SQL Server preview feature`, `exact search`, `course recommendation`

Chapters

• *00:00:00* – Introduction
• *00:01:30* – Vector Search Function Basics
• *00:04:07* – Query Plan and Performance Comparison
• *00:05:31* – Filters After Vector Search
• *00:06:21* – Top Number Considerations

Full Transcript

Erik Darling here with Darling Data, and continuing on with more vector-y nonsense, I’m going to talk about why the vector search function will someday be able to make your search queries suck a little bit less. So the vector search function is a table-valued function that returns one column called distance. The inside of the function is very, very strange, at least syntactically to me, because you can’t just use like position, like at least I haven’t been able to get it to work with like positional stuff. But you have a table that equals, you know, whatever table you want to hit. You can alias that table here and then use columns from the table in here outside of the vector search function, you know, cross-apply-ish. Then we have the column in this table, right, that has our, that is a vector data type that has our vector index on it. We have the original vector that we want to compare and find things that are, that are similar to it similar to it. And then we tell it which metric to use, which, you know, because we’re doing all similarity search, we want cosine. And then we have to give it a top number of rows to pull out. I’m going to talk a little bit more about this at the end of the video, but this is, this is an odd, this is an odd duck to me. So this is like sort of the basic breakdown of it. Now, what we’re going to do is just search a little bit and just kind of look at a query plan for one, using the vector search function against our, our full post embeddings table, just to find 10 rows, right?

So like, obviously this, you know, stuff is all in there. It’s kind of weird. Is it like, you can’t alias this, right? And if you say like PE dot embedding, uh, it’s like, nah, I don’t know you, right? Like what? It’s kind of, it’s a little bit odd that that’s a, that’s a thing there, but you know, what can you do? Oh, green screen. Never knew you cared. So, uh, we run this query. We will see our vector index in use. And you can see that you, you, you, you, you can actually seek into a vector index, which is, is going to be great. Um, what’s kind of weird is that you will, you always get a one row guess out of the vector index, which is maybe not great. Like I’ve, I’ve messed around with this and it’s always one row and you get this new vector index seek, uh, icon, which looks a little bit like someone sitting down doing something lewd, but I don’t know. That’s maybe that’s just my Rorschach test, but, uh, the only column that you get back is the added VS dot star. And this is aliased as VS. The only column you get back is this distance column from there. If you want other columns, you have to either, they have to either be in the table.

You store your post and bet your, where your embeddings are, or like you have to join off to another table. So for example, if we wanted to get information from the actual post table, uh, for like rows that the po like matched with the post embeddings table, we could join like the, again, the syntax is very odd. You can join like the table that you reference in here outside over here. I mean, it’s kind of like cross supply ish, but you know, it’s just like an odd thing to see. So now we can get like base table data out and also, you know, search for similar embeddings at the same time. So it does take a little bit more there again, like, like, like to start your, you’re going to want, not because of the vector index thing, but to start, you’re probably going to want to keep your embeddings in a different table, uh, with like a foreign key that like, you know, like maintains the primary key from whatever table your text data lives in.

Cause you don’t, you’re not, you’re not really don’t want to blow it out your main table with all the embedding stuff. It’s pretty big right now. And there’s not really like a great compression story for it, especially if it’s like all in like, you know, some historical legacy base table, um, like new tables, you could create columnstore indexes on, but like old ones, it’s not good. So let’s just do a brief comparison here between, um, vector distance and vector search. All right. So we’ll run these two queries and we’ll get some timing from them because timing is important, right? So the vector search, uh, just for a small table takes about a second right here. And the vector search, uh, is about 16 milliseconds. And of course, you know, that’s, that’s exactly what sort of, that’s sort of exactly what you would expect because being able to seek into an index, even if it’s a vector index, it takes a long time to create only ever gives you a one row estimate and, um, you know, makes your table read only.

At least it still gives you a faster query. Whereas with, with vector distance, vector distance, you don’t get that. What’s interesting. I think about this query plan is like, I expected to see like a filter operator way later in the query plan for the, for the vector distance. But this one, it shows up at the nested loops join, right? Like this is where we do our filtering.

Cause I, I, I had stuck the vector index in a where clause up here. Uh, there we go. All right. So it was just like where vector distance is less than 0.2. And, and like, just, just to have a number in there. And I was like, ah, where’s the filter operator, but no, there it is sitting at the nested loops join, like a big weirdo.

So, I mean, I guess that’s, that’s, that’s better than, you know, a lot of other things. It might, uh, result in the late filter in SQL Server, but you know, still, still, still a little funny looking. Um, so one thing to understand about the vector search function though, is that any filters, um, that you might have in like the outer part of the query will apply after vector search does its thing.

Not before, not during, after. Inside vector search, you have to specify the top end number of similar vectors that you want it to return first. And then your where clause might filter those results out.

So if your filter eliminates like a lot of the results that the top end similarity thing finds, you might get way, way fewer rows than you expected back, which might, might be fine for some, some searches, right? That might be totally okay, right? You might be like, give me the top 10, then I got to weed them out a little bit, or give me the top 100, then I got to weed them out a little bit.

Um, but sometimes if you are expecting to like, you know, get back a top 10 or a top five or anything like that, you might need to put a much larger top number in here in order to get those rows out. Right. In order to get that number out.

And it’s, it’s really hard to sort of predict that ahead of time. So you might have to do a lot more work to like, you know, like say I want the top 10. Like I say, I want like the top 5,000 or 10,000 here and then apply whatever search stuff and then still give you this back. Like, I don’t know.

It’s, it’s, it’s a real weird situation in there. But, uh, if you’re using filtered searches, use a larger top end to account for filtering. Um, you know, again, you want to create separate embedding tables by, uh, you know, like by category so that, you know, you can, you know, sort of manage them separately and differently. Or if, you know, you’re, you’re not going to go the vector index route.

If you’re not like in, you know, just like get down with the preview feature sickness, uh, you know, right now you can just use a vector distance function. You can get exact search, but which is, you know, and still, you can still filter on that, but you know, it’s just, it’s just slower because there’s no, like it can’t take advantage of an index. So, anyway, that’s about a good here.

Thank you for watching. I hope you enjoyed yourselves. I hope you learned something. I hope you will buy my wonderful course, Get AI Ready with Erik, and I will see you next time. All right.

Goodbye.

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