MyVector Magic: Elevating MySQL with AI Search

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. ------------------------------------------------------------- Episode Transcript: 00:00.000 --> 00:25.000 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. 00:25.000 --> 00:32.000 Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started. 00:32.000 --> 00:37.000 Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I'm LeFred. 00:37.000 --> 00:38.000 And I'm Scott Stroz. 00:38.000 --> 00:47.000 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. 00:47.000 --> 00:55.000 Bringing over 30 years of experience in open source relational databases with deep expertise in MySQL, of course, and ClickHouse. 00:55.000 --> 01:08.000 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. 01:08.000 --> 01:21.000 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. 01:21.000 --> 01:23.000 Thank you very much, everyone. 01:23.000 --> 01:34.000 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. 01:34.000 --> 01:43.000 And you actually created or wrote a plugin for MySQL Community that kind of helped with that, MyVector. 01:43.000 --> 01:48.000 Can you give us an overview of what MyVector is and what problem it's meant to solve? 01:48.000 --> 01:50.000 Sure. Thank you very much for the question. 01:50.000 --> 02:00.000 And I'm very happy that this year of AI and HeatWave, everything that actually contributes to this technology because it's fairly new. 02:00.000 --> 02:06.000 It's been developing for many years, as we already know, but now it's in our hands. 02:06.000 --> 02:16.000 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. 02:16.000 --> 02:20.000 But we could also use it in a business-wise database. 02:20.000 --> 02:29.000 So one correction I want to make is I am a contributor to MyVector plugin, not to author. 02:29.000 --> 02:34.000 The author is Shankar Iyer, and he's a developer for databases for many years. 02:34.000 --> 02:40.000 He's got a lot of experience where I've actually been presenting and supporting this project. 02:40.000 --> 02:49.000 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. 02:49.000 --> 02:55.000 This is basically a very, in simple terms, what it does. 02:55.000 --> 03:00.000 And this has been in development for some time. 03:00.000 --> 03:14.000 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. 03:14.000 --> 03:24.000 Adding approximate nearest neighbor n-search directly in SQL within MySQL database was kind of needed. 03:24.000 --> 03:29.000 And there has been similar implementations with MySQL. 03:29.000 --> 03:33.000 But MyVector is the open source version of that as a plugin. 03:33.000 --> 03:39.000 So just to wrap up that answer is MyVector column type for embedding storage. 03:39.000 --> 03:41.000 And there's a MyVector. 03:41.000 --> 03:46.000 There's a bunch of functions that MyVector distance for the similarity competition. 03:46.000 --> 03:50.000 Of course, it uses HNSW-based index algorithm, which is very popular. 03:50.000 --> 03:52.000 There's a white paper around it. 03:52.000 --> 04:01.000 It's not a rocket science or just something that was invented for MyVector that is known science. 04:01.000 --> 04:06.000 And basically, it provides an SQL native interface within MySQL. 04:06.000 --> 04:08.000 Hope that answers that question. 04:08.000 --> 04:10.000 Thank you very much, Alkin, yeah. 04:10.000 --> 04:22.000 It answers everything and very happy that you also, let's say, talk about the author that we already met also in Belgium recently. 04:22.000 --> 04:31.000 So I would like to ask you, so why is it important to have this similarity search indexes in MySQL then? 04:31.000 --> 04:40.000 Yeah. So again, going back to the AI-driven application, semantic search, product ...