Oracle Ace Alkin Tezuysal joins leFred and Scott to introduce the MyVector plugin for MySQL Community Edition, bringing powerful vector search capabilities to your favorite open-source database. Learn how MyVector enables advanced AI and similarity search features, why this matters for modern applications, and how the MySQL community can easily get started.

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Episode Transcript:

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Welcome to Inside MySQL: Sakila Speaks, a podcast dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL product updates and insightful interviews with members of the MySQL community.

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Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started.

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Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I'm LeFred.

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And I'm Scott Stroz.

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Joining us today is Alkin Tezuysal. We know each other for a long time already and Alkin serves as Director of Services at Altinity Inc.

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Bringing over 30 years of experience in open source relational databases with deep expertise in MySQL, of course, and ClickHouse.

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He co-authored key references works including MySQL Cookbook 4th edition that came in 2022 and Database Design and Modeling with Postgres and MySQL in 2024.

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Alkin, you have been honored as MySQL Rockstar in 2023. And since this year, you are also an Oracle Ace Pro for MySQL. Congratulations and welcome to Inside MySQL: Sakila Speaks.

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Thank you very much, everyone.

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We're glad you're here. Alkin, as you may not know, this season of the podcast is dedicated to all things AI as it relates to MySQL and HeatWave.

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And you actually created or wrote a plugin for MySQL Community that kind of helped with that, MyVector.

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Can you give us an overview of what MyVector is and what problem it's meant to solve?

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Sure. Thank you very much for the question.

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And I'm very happy that this year of AI and HeatWave, everything that actually contributes to this technology because it's fairly new.

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It's been developing for many years, as we already know, but now it's in our hands.

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We can use it. We can definitely use it on our day-to-day activities, whether it's troubleshooting your dishwasher or your washing machine.

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But we could also use it in a business-wise database.

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So one correction I want to make is I am a contributor to MyVector plugin, not to author.

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The author is Shankar Iyer, and he's a developer for databases for many years.

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He's got a lot of experience where I've actually been presenting and supporting this project.

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And that's the small correction. Other than that, MyVector is a native plugin for MySQL that adds support for storing and searching high dimensional vectors.

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This is basically a very, in simple terms, what it does.

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And this has been in development for some time.

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And as we have seen other, you know, databases, other open source databases also went into this with the, you know, launching of AI to our, you know, end users.

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Adding approximate nearest neighbor n-search directly in SQL within MySQL database was kind of needed.

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And there has been similar implementations with MySQL.

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But MyVector is the open source version of that as a plugin.

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So just to wrap up that answer is MyVector column type for embedding storage.

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And there's a MyVector.

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There's a bunch of functions that MyVector distance for the similarity competition.

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Of course, it uses HNSW-based index algorithm, which is very popular.

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There's a white paper around it.

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It's not a rocket science or just something that was invented for MyVector that is known science.

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And basically, it provides an SQL native interface within MySQL.

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Hope that answers that question.

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Thank you very much, Alkin, yeah.

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It answers everything and very happy that you also, let's say, talk about the author that we already met also in Belgium recently.

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So I would like to ask you, so why is it important to have this similarity search indexes in MySQL then?

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Yeah. So again, going back to the AI-driven application, semantic search, product recommendation, question and answering, anomaly detection, etc.

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These really require a similarity searches.

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Have we done similarity searches in the past? Yes, we have.

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If you remember, this is a long, long time ago, but those technologies are still in effect.

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And we had search indexes like the Solr, this Phoenix, if you recall those, where we used to have a replica, generate index and search for it.

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I used to work for an e-commerce site and users would search for a product.

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And then we would also display the similar products.

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And in order to do that in MySQL, we had to use external services like, like I said, some search.

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So it is very important.

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But with the AI-driven application, it's not important anymore.

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It's a must have.

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Basically, you don't need to run a separate vector database.

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And basically, if the data is already in MySQL, you could use this technology using, you know, similarity search functionalities.

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Back at FOSDEM, you gave a presentation about MyVector.

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And over the weekend at FOSDEM, there were a lot of other sessions about vector and indexes.

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Has MyVector made any significant changes since you last talked about it in public?

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Yes, there was another public talk after FOSDEM that was a vector search conference.

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And we've had a bunch of talks about vector searches, vector technologies, which was around this open source databases, including MySQL.

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There were, I think, four or five MySQL talks around the vector search.

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From the development side, yes, there's one important improvement that was made that was the necessary support for binary distributions other than the Docker images.

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So we worked on those and built, you know, three different versions of MySQL binary distributions for testing, because it's more like a DIY.

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And you have to compile and everyone is not very competent enough or have enough time to compile MySQL.

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So we built images for 8.0 and 8.4 and 9x versions for easy testing.

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And there were some improvements on performance and index stability, of course, and so that's about it.

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Maybe it doesn't sound a lot, but this is a lot of work, basically, considering it's an open source project.

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Yeah, thank you. I can imagine it's a lot of work.

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So let's go now in the more technical, let's dig a bit in technical and a bit deeper there.

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So you said earlier that MyVector is using this HNSW, which is a hierarchical navigable small world indexes, right?

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Why was this type chosen over other or over alternatives?

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And do you know if or you yourself have tried alternatives or not?

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We would like to know a bit more about why that choice.

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That's a great question, actually.

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And when we first all heard or started knowing about this HNSW, hierarchical navigable small word for the n-search, like approximate nearest neighbor search.

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That was, it sounded like when I did my research and started reading about it, I think we met with you in London last year.

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We were talking about this, you know, the n-search and everything else.

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This is basically, I thought it was more like a de facto standard of the n-search.

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And it turned out to be that way because a lot of the other open source databases or implementatio