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Inside MySQL: Sakila Speaks

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The Inside MySQL, Sakila Speaks podcast is dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL product updates, and inciteful interviews with members of the MySQL Community. Sit back and enjoy as your hosts, Fred Descamps and Scott Stroz, bring you the latest updates on your favorite open-source database.

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Recent Episodes

September 18, 2025

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 recommendation, question and answering, anomaly detection, etc. 04:40.000 --> 04:43.000 These really require a similarity searches. 04:43.000 --> 04:47.000 Have we done similarity searches in the past? Yes, we have. 04:47.000 --> 04:52.000 If you remember, this is a long, long time ago, but those technologies are still in effect. 04:52.000 --> 05:03.000 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. 05:03.000 --> 05:10.000 I used to work for an e-commerce site and users would search for a product. 05:10.000 --> 05:15.000 And then we would also display the similar products. 05:15.000 --> 05:23.000 And in order to do that in MySQL, we had to use external services like, like I said, some search. 05:23.000 --> 05:25.000 So it is very important. 05:25.000 --> 05:29.000 But with the AI-driven application, it's not important anymore. 05:29.000 --> 05:30.000 It's a must have. 05:30.000 --> 05:35.000 Basically, you don't need to run a separate vector database. 05:35.000 --> 05:45.000 And basically, if the data is already in MySQL, you could use this technology using, you know, similarity search functionalities. 05:45.000 --> 05:49.000 Back at FOSDEM, you gave a presentation about MyVector. 05:49.000 --> 05:55.000 And over the weekend at FOSDEM, there were a lot of other sessions about vector and indexes. 05:55.000 --> 06:01.000 Has MyVector made any significant changes since you last talked about it in public? 06:01.000 --> 06:07.000 Yes, there was another public talk after FOSDEM that was a vector search conference. 06:07.000 --> 06:14.000 And we've had a bunch of talks about vector searches, vector technologies, which was around this open source databases, including MySQL. 06:14.000 --> 06:19.000 There were, I think, four or five MySQL talks around the vector search. 06:19.000 --> 06:33.000 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. 06:33.000 --> 06:43.000 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. 06:43.000 --> 06:51.000 And you have to compile and everyone is not very competent enough or have enough time to compile MySQL. 06:51.000 --> 07:02.000 So we built images for 8.0 and 8.4 and 9x versions for easy testing. 07:02.000 --> 07:12.000 And there were some improvements on performance and index stability, of course, and so that's about it. 07:12.000 --> 07:18.000 Maybe it doesn't sound a lot, but this is a lot of work, basically, considering it's an open source project. 07:18.000 --> 07:21.000 Yeah, thank you. I can imagine it's a lot of work. 07:21.000 --> 07:31.000 So let's go now in the more technical, let's dig a bit in technical and a bit deeper there. 07:31.000 --> 07:41.000 So you said earlier that MyVector is using this HNSW, which is a hierarchical navigable small world indexes, right? 07:41.000 --> 07:48.000 Why was this type chosen over other or over alternatives? 07:48.000 --> 07:55.000 And do you know if or you yourself have tried alternatives or not? 07:55.000 --> 07:59.000 We would like to know a bit more about why that choice. 07:59.000 --> 08:01.000 That's a great question, actually. 08:01.000 --> 08:11.000 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. 08:11.000 --> 08:21.000 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. 08:21.000 --> 08:26.000 We were talking about this, you know, the n-search and everything else. 08:26.000 --> 08:33.000 This is basically, I thought it was more like a de facto standard of the n-search. 08:33.000 --> 08:44.000 And it turned out to be that way because a lot of the other open source databases or implementations were circling around HNSW. 08:44.000 --> 08:49.000 And that's not to say that there are not other options out there. 08:49.000 --> 09:00.000 But usually when technologies like this launched, you don't go and reinvent the wheel, but basically build upon an existing technology. 09:00.000 --> 09:09.000 Since HNSW was widely available in terms of a knowledge wise, it was chosen HNSW. 09:09.000 --> 09:13.000 And, you know, it has high accuracy. 09:13.000 --> 09:16.000 It's a, it's got support for dynamic inserts and leads. 09:16.000 --> 09:19.000 And, and it has an efficient memory usage. 09:19.000 --> 09:21.000 These are the top three things that I know about it. 09:21.000 --> 09:31.000 But, you know, you know, from the other open source databases, like I said, the benchmarking were all circling around this. 09:31.000 --> 09:41.000 And if you were to use a different indexing, it would be very difficult to compare apple to apple from a different indexing perspective. 09:41.000 --> 09:52.000 So, I think, again, I'm, I'm not saying there are no other and methods there are, but they might be less accurate. 09:52.000 --> 09:54.000 They may have different, options. 09:54.000 --> 10:04.000 but if you want to kind of, play something in the market that everybody knows, it would be better off, using the known, methodologies. 10:04.000 --> 10:11.000 So you've given me something I need to look up so I know what I'm going to be doing over the weekend, which HNSW. 10:11.000 --> 10:18.000 so does the data that we use need to be trained before it can be indexed? 10:18.000 --> 10:20.000 No, there's no training. 10:20.000 --> 10:23.000 Basically it's the, it's the, embeddings. 10:23.000 --> 10:30.000 The, the, the difference between the training and the embeddings is you just need to generate the embeddings. 10:30.000 --> 10:55.000 And that's where, that's where an additional step, like if your data is already in the database and you, you want to use this, vector, search technology using HNSW indexing for the n-search, you need to generate the embeddings, whether externally or internally with, with, with a service or, something like that. 10:55.000 --> 11:18.000 We, I know that there are some, the, the other types of index that are maybe less popular, that, index the embeddings, sometime they also need to, to have some training before, but, yeah, this one doesn't, which is, which is good because every time you want to, to add the data, whatever, it's quite complicated if you want to train it. 11:18.000 --> 11:19.000 Right. 11:19.000 --> 11:20.000 Yeah. 11:20.000 --> 11:25.000 Basically it's, it's, it's generate the embedding, insert in the MySQL and build the HNSW index. 11:25.000 --> 11:38.420 So, as you are discussing about, this, this index, what, what I'm, curious because, I also try, I try and check, different type of flow indexes to, to understand what they do and what it is. 11:38.420 --> 11:46.160 but, I would like to know what's the size of this index compared to the actual size of the data, right? 11:46.440 --> 11:59.440 Because I know, and maybe it's the case, on your implementation that, the full representation of the, of the, of the vector is stored on the index on some of them or most of them. 11:59.440 --> 12:09.620 So I would like to know, if you have made some check there and, if, if the size is compared, right, to the, the, the full embeddings and the index. 12:09.920 --> 12:16.940 Just to recap that, the, the, the full vector is stored inside the index structure on a fast axis. 12:17.540 --> 12:22.560 So, so there's, there's no reference in back or anything like that. 12:22.560 --> 12:46.380 It's in the, this, we were talking about this, the size of the, index is that depends on the, vector dimension dimensions, a number of vectors that we're storing and, and then, and some of the parameters that, you know, per node, that, that index, but, we did some, some sizing and testing around it as yes. 12:46.380 --> 12:54.160 The accuracy increases when the dimensions are high as we know, and the size, size gets, gets higher. 12:54.440 --> 13:04.800 So, we're looking into this also, if there is any, any option to optimize that or, use some compression technology to, for this index. 13:05.160 --> 13:16.360 And, that's, something, is, is kind of, important to know that because this is not in, you know, DB, this is basically in the file system. 13:16.380 --> 13:19.080 And it needs to be, you know, placed correctly. 13:19.360 --> 13:32.200 You, you mentioned before how the, how MyVector is available and I know it's available as a plugin and just to clarify for our listeners, do you also provide the binary packages or do we still need to compile it, from the source? 13:32.980 --> 13:33.480 Yes. 13:33.480 --> 13:46.860 As I mentioned earlier, since FOSDEM the, the, the latest, release of, MyVector included, the, binary releases or, x86. 13:47.480 --> 13:53.840 And, and, that will, that is published in the GitHub page and, as open source. 13:54.100 --> 13:57.220 so you no longer need to compile it from the source. 13:57.220 --> 14:02.360 If you want to test it out, just to plug in, my, um, 14:02.360 --> 14:16.980 I have launched a new blog technical blog page and started blogging and, I will blog about this so, so that, the listeners and, and the readers can actually, have a link available in the, in the blog. 14:16.980 --> 14:28.440 So they can go in and test it out and Docker images were available, but, binary releases also added, recently, for the, for the test. 14:28.440 --> 14:30.380 Awesome. Thank you, Alkin. 14:30.720 --> 14:42.720 So last question, do you know if there are already, some, companies or, users, using MyVector in production or not yet? 14:43.260 --> 14:45.500 There are some POCs going on. 14:45.860 --> 14:47.700 And as you know, this is open source. 14:47.700 --> 14:58.360 there, there were some interest after force them, you know, we, people reached out, who were actually doing, this type of research and analysis. 14:58.360 --> 15:03.920 It was on the existing MySQL databases and, we provided help and information. 15:04.380 --> 15:05.540 They might have taken it. 15:05.600 --> 15:06.580 They might have forked it. 15:06.640 --> 15:08.980 They might have embedded into their existing implementation. 15:09.580 --> 15:16.500 we don't know, but, as far as I know, there, there are a few POCs are going, they're testing with the existing data. 15:16.500 --> 15:29.520 So, as you know, just to add up since FOSDEM, there has been one shift happened in this type of technology, which is the MCP servers. 15:29.520 --> 15:45.640 So, that is one thing that I wanted to add over here with that shift, generating embeddings and, and actually having an MCP server that will actually add context to the n-search. 15:45.640 --> 15:53.620 Like a chat bot implementation made it, made things a little bit more, not only useful, but also interesting. 15:54.320 --> 16:01.380 So say you have, you know, support tickets or some, some data that's actually related to your, you know, internal customers. 16:01.580 --> 16:03.600 You could add MCP server. 16:03.800 --> 16:06.160 There are some public MCP servers. 16:06.320 --> 16:09.780 There are some open source and MCP server implementations. 16:10.080 --> 16:12.300 And, and we're also looking into that. 16:12.560 --> 16:15.560 And, I want to mention over here on my last. 16:15.640 --> 16:24.200 talk during the vector search conference, I have actually, presented a MyVector with an MCP server demo. 16:24.200 --> 16:27.880 and, and that recording is should be available. 16:27.880 --> 16:30.900 So for that, and, and, and actually it works. 16:30.900 --> 16:44.520 this is pure MySQL open source, pure MyVector open source and pure MCP server open source with the, you know, clinical trials data, like the, one of the public data data sets that I've used. 16:44.520 --> 16:52.300 you can actually ask questions and it'll answer, and then you can continue the chat using this, this, very given technology. 16:52.300 --> 16:54.060 That's awesome. 16:54.060 --> 17:00.060 I've actually been playing around with, writing my own MCP servers and having them interact with MySQL. 17:00.060 --> 17:14.880 And I think MCP is going to be, is going to wind up being pretty, being pretty big because it does give that, domain specific context that the LLMs can actually use to generate content or answers or whatever. 17:14.880 --> 17:18.120 So I, I'm, I'm interested to play around with that. 17:19.000 --> 17:19.520 Absolutely. 17:19.740 --> 17:20.120 Absolutely. 17:20.120 --> 17:23.100 This is very interesting development, very recent. 17:23.740 --> 17:28.440 a lot of people are experimenting right now with the MCP servers. 17:29.160 --> 17:44.300 MCP servers, are going to be, I think the salt and pepper of, or, or sauce of, of this technology, you know, having the vectors and beddings and the, you know, n-search, HNSW index and the MCP server. 17:44.300 --> 17:47.220 So they, it's going to complete the puzzle in my opinion. 17:47.600 --> 17:50.920 And, and also, like I said, I've already given a demo. 17:51.060 --> 17:53.840 We are also looking into this, this technology. 17:54.120 --> 17:59.220 and, of course there's, that is also still under development. 17:59.740 --> 18:08.980 if you're opening up for a public MCP, then it's actually your, your security and compliance is, is now outside again. 18:08.980 --> 18:16.200 We want to do, if you want to do everything internally, if you want to do everything in your own database, with your own security and compliance. 18:16.700 --> 18:20.660 So that's a game changer to have something, available for yourself. 18:21.260 --> 18:21.660 Excellent. 18:21.840 --> 18:23.140 So thank you very much, Alkin. 18:23.660 --> 18:25.060 thank you for your time. 18:25.460 --> 18:27.740 We know you are on your boat right now, sailing. 18:27.960 --> 18:28.900 So that's awesome. 18:29.180 --> 18:29.580 Thank you. 18:29.640 --> 18:30.420 Thanks a lot, guys. 18:30.600 --> 18:31.120 Thank you, Alkin. 18:31.180 --> 18:33.120 And thank you for all your contributions to the community. 18:33.120 --> 18:36.880 That's a wrap on this episode of Inside MySQL: Sakila Speaks. 18:37.040 --> 18:38.280 Thanks for hanging out with us. 18:38.560 --> 18:42.320 If you enjoyed listening, please click subscribe to get all the latest episodes. 18:42.600 --> 18:45.540 We would also love your reviews and ratings on your podcast app. 18:45.860 --> 18:50.080 Be sure to join us for the next episode of Inside MySQL: Sakila Speaks.

September 4, 2025

Homegrown Intelligence: AI Features for On-Prem MySQL Enterprise

leFred and Scott sit down with Gaurav Chadha to explore MySQL AI, a new solution that brings advanced AI features available in HeatWave to organizations running MySQL Enterprise Edition on-premises. Discover how MySQL AI bridges the gap between cloud innovation and on-premise infrastructure, making transformative AI capabilities more accessible, secure, and efficient for teams that rely on MySQL Enterprise Edition wherever their databases reside. -------------------------------------------------------------- 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 Welcome back to another episode of Inside MySQL: Sakila Speaks. Hi, I'm LeFred. 00:37.000 --> 00:38.000 And I'm Scott Stroz. 00:38.000 --> 00:41.000 Today, we are thrilled to have Guarav Chadha joining us. 00:41.000 --> 00:51.000 Guarav is a Senior Development Manager leading development of MySQL HeatWave Lakehouse with a keen interest in systems, machine learning and computer architecture. 00:51.000 --> 01:10.000 Guarav brings a multifaceted expertise to database technology. Following the completion of his PhD from the University of Michigan, Ann Arbor, Guarav started at Oracle Labs in 2016, working on a research project which eventually graduated into MySQL HeatWave. 01:10.000 --> 01:16.000 But today we will talk with him about MySQL and AI on premise. Welcome Guarav. 01:16.000 --> 01:17.000 Thanks, Fred. Hi, Scott. 01:17.000 --> 01:18.000 Hi, Guarav. How are you? 01:18.000 --> 01:19.000 Doing good. 01:19.000 --> 01:32.000 So we're going to dive right in. And AI, we see AI is taking over the world. It's being touted for the solution to everything. 01:32.000 --> 01:41.000 How do you see AI transforming traditional on-premise database environments, especially in enterprise setups? 01:41.000 --> 01:54.000 Yes, Scott. So, I completely agree. AI is a transformational technology, and it has the potential to improve everything that we see around us. 01:54.000 --> 02:07.000 So, with regards to traditional on-premise database environments, especially in enterprise setups, I see multiple categories here. So, AI is a technology and a toolset. 02:07.000 --> 02:32.000 And like many other operators in databases, it can help with more and different data analysis. So, think of AI as a new set of SQL operators, which can tease out or analyze data and derive insights that are hard to do it with other operators, with other analysis tools. 02:32.000 --> 02:45.000 And hard for folks to call up. And hard for folks to code up. And that's where I think AI enhances it very easily enters into the database environments. 02:45.000 --> 02:56.000 What I mean by that is examples are recommendation systems, anomaly detection, so on and so forth. 02:56.000 --> 03:02.000 The other category is what I would say user assistance. 03:02.000 --> 03:15.000 So, not everyone is a SQL expert. And we want database technology and databases to be accessible to more people who may or may not come from a traditional database background. 03:15.000 --> 03:22.000 And SQL is a very powerful language and where it can be daunting to start with. 03:22.000 --> 03:35.000 So, again, this is a general category where maybe folks who are not very familiar with a specific programming language like SQL could write things out in just plain natural text. 03:35.000 --> 03:42.000 And AI tools could translate this into a programmatic interface or programmatic language or SQL directly. 03:42.000 --> 03:50.000 And that's another facet where I think AI can make database systems more approachable to a larger category of folks. 03:50.000 --> 03:57.000 It can also give you more user friendly responses, like instead of saying, oh, here's the error code, something went wrong. 03:57.000 --> 04:00.000 It can give you more information, more user friendly responses. 04:00.000 --> 04:06.000 So those are some examples of where I would say the second category, user assistance. 04:06.000 --> 04:12.000 The third category of where AI could help is database management. 04:12.000 --> 04:21.000 So databases are systems of record, the sources of truth and have a very high bar of staying up and being available. 04:21.000 --> 04:30.000 AI can help schedule maintenance at the right time where maybe the workload is low. 04:30.000 --> 04:35.000 They can predict things that might get slow. 04:35.000 --> 04:43.000 We have a whole area called predictive maintenance and make databases more highly available, more easily approachable. 04:43.000 --> 04:44.000 Thank you. 04:44.000 --> 04:46.000 This sounds very interesting. 04:46.000 --> 04:50.000 And because we are talking about MySQL on-prem, right? 04:50.000 --> 05:04.000 So from these categories, what features could we expect then one day to see in MySQL enterprise with AI or for AI? 05:04.000 --> 05:07.000 So what can you tell us about that? 05:07.000 --> 05:16.000 So for MySQL AI, we are bringing a whole host of AI features to on-premise MySQL deployments. 05:16.000 --> 05:22.000 And we will lean heavily with this first version on the first category, which is data analysis. 05:22.000 --> 05:25.000 How can AI help with data analysis? 05:25.000 --> 05:34.000 And within this, I would focus on, I would say, a few subcategories. 05:34.000 --> 05:48.000 The first is, with AI and generative AI specifically, it has brought the industry a new tool set to search through and understand documents. 05:48.000 --> 05:58.000 And not just structured data or relational data, just plain documents, which is true for a lot of enterprise companies. 05:58.000 --> 06:09.000 Companies have years and years worth of information stored in documents, in PDF documents, in HTML documents, and not really put into a database necessarily. 06:09.000 --> 06:12.000 And this has always been hard to search. 06:12.000 --> 06:23.000 It has been very manual, it has been very hard to bring to a database and perform a very fast and meaningful search. 06:23.000 --> 06:39.000 With generative AI and what we call vector store and vector search, you can search through unstructured data like documents, semantically, instead of just through keywords. 06:39.000 --> 06:42.000 You can search them by meaning. 06:42.000 --> 06:47.000 That's a very powerful technology that we are bringing to MySQL AI. 06:47.000 --> 07:02.000 So if users have documents in their file systems, they can ingest them into the database, and we will automatically create what we call a vector store out of it, which prepares the data in these documents to be searched semantically. 07:02.000 --> 07:13.000 Obviously, in order to this, we are adding a new operator, which does this semantic search, we call this vector distance. 07:13.000 --> 07:31.000 Additionally, I spoke about data analysis tools like recommendation systems, like anomaly detection, and these operators also being brought to MySQL AI, where you can plug them into your logs, or you can plug them into other metrics. 07:31.000 --> 07:40.000 And figure out when things can go wrong, or any other domain that is useful. 07:40.000 --> 07:46.000 An example of a domain for anomaly detection would be financial fraud, credit card fraud. 07:46.000 --> 07:49.000 So it's very useful in those scenarios. 07:49.000 --> 07:56.000 And the last category I would say among data analysis is generative AI. 07:56.000 --> 08:15.000 We're bringing LLMs to MySQL AI, and the power of LLMs really is they can generate new data and new user-friendly text from just bullet points, for instance. 08:15.000 --> 08:22.000 So not just analyzing data, but generating new data is possible through LLMs. 08:22.000 --> 08:29.000 So that I would say covers the first category. 08:29.000 --> 08:31.000 This is all data analysis. 08:31.000 --> 08:34.000 Among the second category, which is user assistance. 08:34.000 --> 08:41.000 User assistance is by bringing LLMs to on-premise MySQL AI deployments. 08:41.000 --> 08:50.000 It gives the user freedom to build more user-friendly applications or make the existing applications more user-friendly. 08:50.000 --> 08:53.000 And this is what we will start with, with version one of MySQL AI. 08:53.000 --> 09:04.000 So are there any specific features in MySQL Enterprise, like Firewall or Enterprise Audit, that support AI-enabled applications? 09:04.000 --> 09:09.000 So as we discussed, AI is an incredibly powerful set of tools and technologies. 09:09.000 --> 09:15.000 And this is our first salvo in enabling our customers to build and augment applications using AI. 09:15.000 --> 09:28.000 So we're bringing a whole tool set, we're bringing faster data analysis, more meaningful and different kinds of data analysis to help users build and augment existing applications. 09:28.000 --> 09:34.000 The door is certainly open to bringing AI to the ecosystem of products, as you mentioned, around the MySQL server. 09:34.000 --> 09:39.000 But with this first version, we are building these right into the MySQL server. 09:39.000 --> 09:54.000 With this MySQL AI, like you call it, right, is it compatible with or will it be compatible with all the architecture solutions that we also provide on-premise, 09:54.000 --> 10:01.000 like such as the InnoDB cluster, the cluster set, replica set, you know, for HA, for disaster recovery? 10:01.000 --> 10:08.000 If somebody goes in that direction, will he be able to keep deploying his MySQL the same way? 10:08.000 --> 10:15.000 So the AI feature set, the tool sets, are built right into the MySQL server. 10:15.000 --> 10:23.000 So all the architecture solutions which are deploying MySQL AI instances benefit from these features. 10:23.000 --> 10:33.000 Okay, so just to be clear, so we're not going to have two distinct products where one's MySQL EE and another one is MySQL AI. 10:33.000 --> 10:35.000 They'll all be together in the same product? 10:35.000 --> 10:41.000 So MySQL AI will be a distinct offering. 10:41.000 --> 10:48.000 It will have everything that Enterprise Edition has, plus the additional AI features we spoke about. 10:48.000 --> 10:51.000 And this is all about user choice. 10:51.000 --> 10:57.000 Customers can continue using MySQL EE if that is what they prefer. 10:57.000 --> 11:10.000 They can switch to MySQL AI and or buy new MySQL AI deployments to try out these new AI tools and get them familiar, get themselves familiar with it. 11:10.000 --> 11:17.000 The MySQL AI will have everything that Enterprise Edition has, plus the AI features. 11:17.000 --> 11:32.000 So it opens the door for users and customers to build their applications worry-free, as they've always built with MySQL EE, because the entire feature set of EE will be present in MySQL AI. 11:32.000 --> 11:37.000 But additionally, they can build more newer things with AI. 11:37.000 --> 11:43.000 In the previous episode, so the other speakers or guests, right? 11:43.000 --> 11:49.000 They extol the virtues of the cloud for AI, our cloud. 11:49.000 --> 11:52.000 Everything was nice and fast and it's good. 11:52.000 --> 12:06.000 And I would like to ask you if there are a performance trade-off between then deploying AI solutions in the cloud using MySQL HeatWave versus on-prem with this new MySQL AI. 12:06.000 --> 12:21.000 So with MySQL AI, we have brought the AI technology, we have built and deployed in the cloud to our EE customers, to on-premise environments. 12:21.000 --> 12:24.000 Cloud has some unquestionable advantages. 12:24.000 --> 12:28.000 Cloud has the benefit of scale-out, which can bring higher performance. 12:28.000 --> 12:37.000 It has GPUs, which can execute LLMs faster or larger LLMs, higher quality LLMs. 12:37.000 --> 12:48.000 So with MySQL AI, we have brought the AI technology that we built and deployed in the cloud to our on-premise customers and our Enterprise Edition customers. 12:48.000 --> 12:57.000 There are a very large, very large set of customers who are on-premise for a variety of reasons. 12:57.000 --> 13:02.000 And we want to serve them where they are. 13:02.000 --> 13:07.000 What I do want to point out is that cloud has some unquestionable advantages. 13:07.000 --> 13:14.000 It has the benefit of scaling out with more and more hardware, which can give you high performance. 13:14.000 --> 13:22.000 And like HeatWave, all these AI features are built to scale out with more resources. 13:22.000 --> 13:31.000 Cloud also has GPUs, which bring more performance for LLMs and can execute larger and higher quality LLMs. 13:31.000 --> 13:47.000 Cloud also has our HeatWave analytics engine, which provides faster analytics performance, allowing users to build combined applications with analytics, OLTP, AI. 13:47.000 --> 13:58.000 What is very important to note, all AI features we bring into MySQL AI are 100% API compatible with HeatWave in the cloud. 13:58.000 --> 14:02.000 So users can build their applications on MySQL AI. 14:02.000 --> 14:15.000 And should they feel the need for higher performance or an expanded feature set, and they want to move to the cloud, the applications will work without modifications. 14:15.000 --> 14:19.000 And we have tried very hard to make it 100% API compatible. 14:19.000 --> 14:40.000 So with MySQL AI, we have optimized inference of open-width LLMs on CPUs, right from one core all the way to 192 cores, using proprietary weight caching and quantization techniques. 14:40.000 --> 14:49.000 And this allows us to, the users to deploy MySQL AI on a range of computer infrastructure, depending on their need. 14:49.000 --> 14:51.000 It can be very small MySQL node. 14:51.000 --> 14:53.000 It can be a very beefy MySQL node. 14:53.000 --> 15:13.000 And as they improve the hardware, the performance, the latency, the quality, the concurrency we deliver increases, providing incentive to users to deploy on larger and larger hardware. 15:13.000 --> 15:25.000 Are we targeting, like, is MySQL AI targeted towards existing MySQL EE customers who are looking for more features? 15:25.000 --> 15:35.000 Or are we kind of targeting other potential customers and luring them in with AI to get them into the MySQL ecosystem? 15:35.000 --> 15:50.000 Both. We are bringing more features to MySQL EE, and we hope that more and more users, more classes of users, more classes of applications, find their home in MySQL. 15:50.000 --> 16:00.000 And absolutely, we want to allow our existing on-premise customers to be able to bring more of their workloads into MySQL. 16:00.000 --> 16:06.000 And for a number of reasons, many customers want to be on-premise. 16:06.000 --> 16:10.000 They might require deployments in the edge devices. 16:10.000 --> 16:13.000 They might require deployments in air-gapped environments for data security. 16:13.000 --> 16:21.000 So we want to enable these existing customers to bring more workloads. 16:21.000 --> 16:29.000 Of course, there are other customers who may not have ever looked at MySQL EE because they have requirements to deploy AI or to deploy generative AI. 16:29.000 --> 16:41.000 And we want to obviously give them this tool set and this enhanced feature set to bring their primary, secondary, tertiary workloads to MySQL EE. 16:41.000 --> 16:46.000 That's very nice to bring this to our on-prem customer. 16:46.000 --> 17:05.000 So correct me if I'm wrong, but to what I understood is that we modified LLMs to work on normal CPU for performance, and so we can run it. 17:05.000 --> 17:20.000 We don't need to have GPUs, but you also said that, yeah, if we really want to use a very large LLM, then it's better to use it to use the cloud than on-prem. 17:20.000 --> 17:33.000 But I wanted to also ask you, because LLMs that are evolving and not at the same speed of the MySQL releases. 17:33.000 --> 17:35.000 I have two questions in this one. 17:35.000 --> 17:42.000 It's like, oh, if there are new LLMs, will it be the possibility to the user to use it directly in GenAI? 17:42.000 --> 17:49.000 Or will it be updated at every new release of MySQL? 17:49.000 --> 18:01.000 Good question. So there's absolutely no exaggeration to state that a new LLM seemingly is released every month with its unique set of characteristics and benefits. 18:01.000 --> 18:20.000 So we will definitely bring new LLMs, which enhance the performance of all quality of results, as and when we feel that is useful to add to the existing set of LLMs we offer with future MySQL releases. 18:20.000 --> 18:31.000 So we will have we have taken the approach of building in LLMs in the package that we ship to our customers. 18:31.000 --> 18:37.000 There are many reasons for this. We have optimized these LLMs to run on CPUs. 18:37.000 --> 18:54.000 So we are able to run larger LLMs faster on CPUs, allowing our users to seamlessly use these LLMs without extra hardware or call outs to other services. 18:54.000 --> 19:02.000 So we will continue doing that with new LLMs as new ones prove to be useful for our users. 19:02.000 --> 19:14.000 What you say is that, OK, when we're going to release a new version of MySQL, if there are new LLMs that were interesting to update, they will be updated at that time, right? 19:14.000 --> 19:19.000 Build the LLMs into the package the users download and deploy. 19:19.000 --> 19:24.000 Users do not need to bring their own LLMs. 19:24.000 --> 19:51.000 Gurav, thank you for joining us today. It really was interesting for me, I can say personally, to learn about some of the advancements that are coming in MySQL AI and how it's going to be integrating with our EE version to allow people who need on premise or prefer on premise installations of MySQL to actually harness some of the AI power that we offer in HeatWave. 19:51.000 --> 19:54.000 Thank you, Scott. Thank you, Fred. 19:54.000 --> 19:55.000 Thank you very much. Bye bye. 19:55.000 --> 20:00.000 That's a wrap on this episode of Inside MySQL: Sakila Speaks. Thanks for hanging out with us. 20:00.000 --> 20:04.000 If you enjoyed listening, please click subscribe to get all the latest episodes. 20:04.000 --> 20:07.000 We would also love your reviews and ratings on your podcast app. 20:07.000 --> 20:12.000 Be sure to join us for the next episode of Inside MySQL: Sakila Speaks.

August 21, 2025

Let HeatWave Drive: The AutoPilot Advantage

In this episode, leFred and Scott are joined by Onur Korcerber to explore the many features of HeatWave AutoPilot. Learn how AutoPilot’s intelligent automation helps manage MySQL instances with ease, optimizes performance, and reduces operational costs. Onur shares practical insights and real-world examples showing how customers can streamline their database operations with HeatWave AutoPilot. ------------------------------------------------------------- Episode Transcript: 00:00:00:00 - 00:00:31:20 Welcome to Inside MySQL: Sakila Speaks. A podcast dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL project updates and insightful interviews with members of the MySQL community. Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started! 00:00:31:22 - 00:01:03:00 Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I am leFred and I'm Scott Stroz, joining us today is Onur Kocberber. Onur is currently a director of Development at Oracle, leading efforts on MySQL HeatWave, specifically working on the AutoPilot. Based in Oracle's Zurich office, Onur focuses in advanced research and development to improve cloud database performance through interpretable machine learning techniques. 00:01:03:02 - 00:01:24:16 He plays a key role in the ongoing growth of HeatWave, including work on new offering like the HeatWave Lakehouse and HeatWave GenAI service. Welcome, Onur. Thanks. Thanks leFred, thanks Scott. Great to be here. So Onur, can you tell us a bit about your journey? What led you to Oracle and specifically to the MySQL HeatWave team? All right. 00:01:24:16 - 00:01:53:10 So I, I was a grad student at EPFL Lausanne in Switzerland, and, I was doing research specific doing database, accelerators, both for, with hardware and software. And, at the time, I knew that Oracle Labs had a very exciting project about, building basically hardware, software, core design, database machines. And once I graduated, I knew that there were really good set of people. 00:01:53:10 - 00:02:21:18 And that's, how I joined. So I came to basically Zurich, to to the Oracle Labs branch. And then eventually, maybe fast forward ten years, we have, HeatWave database service, but, what we see includes MySQL and other things I will discuss today. That is fantastic. So, Onur, this entire season has been dedicated to, everything AI. 00:02:21:18 - 00:02:47:07 What AI offerings that HeatWave has and some of our listeners, I would guess maybe many of our listeners probably aren't too familiar with, HeatWave AutoPilot. Can you give us a high altitude overview of what AutoPilot is and, what problems that might be resolved? So the database systems today are all cloud databases, right? And, these are many services. 00:02:47:07 - 00:03:21:04 And the onus is on us, in terms of managing these systems. So the customers are expecting basically a full, full fledged, automated service with no, let's say rough edges. And that's where, AutoPilot, comes into play. And when we started the project, when, MySQL HeatWave was becoming a cloud service, we, also started the AutoPilot project, and, we basically targeted four different, let's say, problem domains. 00:03:21:04 - 00:03:53:04 So these are, setting up the system, data, basically loading the data or data management query execution and then failure handling. And, for each of these, categories, we basically looked at what, how we could, improve customer experience as well as customer performance. And at the same time, we put the machine learning, as one of our, basically main objectives because, this is a very old topic, right? 00:03:53:04 - 00:04:18:12 This is this is not a new topic like database management on automatic database, admins and DBAs and such. So that's why we took all the, academic research, plus the realities all today, which is the cloud services. And then, we looked at these four different pillars and then fast forward to today, we have like a double digit numbers in the AutoPilot suite. 00:04:18:14 - 00:04:55:12 Wonderful. And that's awesome. So and why then, this HeatWave AutoPilot is a game changer for users. Right. So, one of the things that we were seeing in the early days of our services that customers would sometimes put together, let's say, scripts or rules or let's say, some sort of, business practices, right? And in AutoPilot, we are taking all of those, especially what you're observing or what you're anticipating, right, that, the customers will have problems with. 00:04:55:16 - 00:05:18:07 And then we are offering them out-of-the-box ready to use for the for the customers. Some of those are fully automated, like, let's say, for or planned improvements. These are like these are happening completely transparent to the use it and some of the features that are a bit more about, the cost optimization of the service or performance optimizations are provided as an advisor. 00:05:18:08 - 00:05:43:03 So essentially we are constantly watching what the customer might, let's say, what would the cost of problems that the customers might have? And we are offering it out of the box included in the, in the service. And that is something, we see when we look at our competitors, we see that, some of the problems that we are solving are just seen as kind of still left as rough, rough edges. 00:05:43:05 - 00:06:02:08 And that's why it is really important. And at the core of it, we have a lot of machine learning models. These models are automatically up to...updated as we also update the version of the service. Therefore customers don't have to worry anything about, basically those, those, those problems that they are running into. Great. 00:06:02:08 - 00:06:31:10 Thank you. So, and when I follow what you just said, then, it seems that, these AutoPilot feature can save OCI customers some money, right? Right. So for certain cases, absolutely. For example, let's take auto provisioning. This is the feature that, the, made available almost at the same time when the, with the GA and, since our GA, this has been used, very actively. 00:06:31:10 - 00:06:54:02 And in this feature, for example, we say this is the number of nodes, that's, a customer should provision for accelerating their, analytical queries with HeatWave. And the great thing here is that, they don't have to overprovision their cluster or they don't, they don't need to under provision their cluster and then run into all sorts of possible issues. 00:06:54:04 - 00:07:13:07 So then one, one part of it is that they have the optimal cost, right? So they, they pay or they provision what they, what they should. And at the same time they also say, save time by just not having to, worry about it. And then similarly, for example, we have an auto load and unload feature. 00:07:13:07 - 00:07:40:05 So if you see there is some let's say there is going to be some benefit from from customer workload, we would automatically load or unload tables. And again, this would either give you a performance boost, which again translates into some sort of cost saving, or at the same time we would just, unload the unnecessary tables so that the customer wouldn't have to, let's say, increase their resource consumption, because they don't they don't have to. 00:07:40:07 - 00:08:15:12 And then we have a bunch of other like, similar features actually, that that will do. For example, there's auto compression that already gives you better price performance, but by default. Right. So that's definitely, every the most of the optimizations we do is translating into some sort of cost saving for the customers. That's awesome. I find that actually pretty, interesting that we offer ways to make sure the customer is basically streamlining their process, and then they're not overpaying for resources because some people might spin up a huge instance when they don't, in fact, need it. 00:08:15:14 - 00:08:39:07 So what are some features of AutoPilot that can help make storing and retrieving data a little bit more efficient? So I mean, let me give you an OLTP example. Of course auto indexing is is one of them. Right. So indexing, is definitely one of the holy grail problems in computer science, I would say. And we have a feature, that basically recommend secondary indexes. 00:08:39:07 - 00:09:04:23 So that's I see people ... people who are familiar with the MySQL know that how important indexes are. So we actually have an index advisor and that's, pretty effective. We see this today with customers as well. And that's just working really well. And having the right indexes is definitely making the, data retrieval, extremely efficient. 00:09:05:01 - 00:09:26:21 And if I were to give you an example from the analytical site, we, we have adaptive query execution. So we are basically over time, the improve the, the the query plan. Right. So this is also making, everything, a lot more efficient. And if I were to give maybe an example from the Lakehouse side. 00:09:26:21 - 00:09:57:14 So this is another, basically feature where we deal with semi-structured data. We do we, we automatically ingest, the unstructured files by understanding the, the, the schema. And, this way we can represent the unstructured data in the right format, which could translate into a better, let's say, space, usage guide so that you don't have to maybe pick a larger type than anticipated, than what the customer anticipated. 00:09:57:16 - 00:10:32:13 So and all these things, are they sometimes they look small, but these are the real problems because, especially when it comes to whether it's indexes or whether it is query plans or whether it is unstructured data, in all these instances, we are dealing with hundreds, if not thousands of either queries or tables and such. And and for a particular user, maybe dealing with 1 or 2 is easy, but dealing with thousands, I think every DBA would know or every user would know that it's, it's it's a tedious process with a lot of gotchas. 00:10:32:13 - 00:10:59:09 And corner cases will be basically take all these things into account in our AutoPilot suite. And then we update our, learnings and our optimizations as the versions go. But thank you. Yeah. Nice. Good answer. So what do you think are, the biggest misconception that the developers have about, machine learning driven, database optimization, right. 00:10:59:11 - 00:11:19:18 Because, yeah, there is the old DBA. Is that the they should know everything. And then the also thwy run the reports and sometimes people say, yeah, is it good or not I don't know. So do you know that, do you have an answer for this. Yes. So this is one of my favorite topics. Yeah. 00:11:19:19 - 00:11:52:18 So this is, something that we, we we have an internal discussion going on. Right. So I am also receiving a lot of requests from other teams or, people who are, and like, very, let's say, excited or ambitious about, applying like, machine learning to, to their domain, to their problems, like, one of the things that I keep seeing is that so there is, basically systems for ML and ML for systems that I, this is, I think, a very good, way of describing. 00:11:52:20 - 00:12:22:20 So we are at the end of the day, we are building computer systems and we should use ML for optimizing our computer systems. So and most of the time what I see is that like people who start, like basically trying to people who start trying to, apply ML, they put ML is a first object, whereas it should be actually not the first objective, it should be first the systems, how we build a system, a computer system. 00:12:22:22 - 00:12:46:22 And then we need to understand what is the hole, right, in our problem space that we can fill with machine learning. So most of the people who go and collect, let's say a data set and a the draw, let's say a regressor or a classifier on that data set. They say that it or it works well in the test, but it doesn't work in the in the real like a production like. 00:12:47:00 - 00:13:09:20 And to me this is the missing a systems inside. So we basically have to have a systems inside. So I will give you a very specific example. For HeatWave for when we are loading data into HeatWave, we can control InnoDB parallel thread and you know, parallel, like a thread count is a known lock that, anybody would know, let's say, how to tune. 00:13:09:22 - 00:13:32:01 But when it comes to the HeatWave load, it is, basically like the trigger behavior is changing, right? So basically we need to understand why the thread break here. The idea is changing. So that means that we have to collect the data in a way that we exercise the parts that HeatWave would exercise now. So if people were basically just saying, oh, like machine learning is going to solve everything for us, right? 00:13:32:01 - 00:13:54:09 Then we start with the data set. It will work. Basically, first the system inside, then the machine learning. You will be highly, highly effective. So that is why I think the second part is a while. Sure that what we put a lot of focus on is interpretability or explainability. So we try to fail our models first before the customers fail them. 00:13:54:10 - 00:14:23:06 alright and with that, we this is we just ship models, right. So this is, this is actually very, very important because otherwise it's some people might say, oh, you know, let's just fancy like a version of rule based tuning or it only works in certain cases. Right? So, basically to summarize, there is a lot of technical debt in machine learning models, but using the systems inside you can get good system engineer. 00:14:23:06 - 00:14:42:02 I think it's it's the real, secret sauce of shipping machine learning models that are effective at production. And then it's a long topic that after you ship it, you have to monitor them. You have to make sure that you are not regressing. There are not too heavy concepts and such. So yeah, what I would change that ML is machine learning is a tool. 00:14:42:04 - 00:15:12:07 And like any tool, it has its own drawbacks. And as long as we are aware of them, we can, these can provide really strong systems, that take advantage of ML. But again, systems first, ML second. That is my philosophy. I think that's actually a pretty good philosophy. So I know you might not be able to tell us everything, but are there any up any upcoming features that you're particularly excited about that you can actually talk about? 00:15:12:09 - 00:15:39:17 All right, so, well, I think this is not a surprise about generative AI. Right. So generative AI is, now the, the, the hottest, topic that, we are dealing with and, we, are working actively on generative AI based AutoPilot features, let's say. And one important difference there is that, when I say systems first, right, systems is about numerical data, right? 00:15:39:17 - 00:16:01:09 We deal with numbers that are coming in, let's say cache misses, buffer pool heat ratios, read write ratios right or performance and like all these numbers that they are essentially a time series that are just flowing in. And machine learning is like traditional machine learning is is very good at it or sometimes categorical, like, the data set, right? 00:16:01:11 - 00:16:23:22 When it happens, like should, it should have been this way or another. Right. Those are easy. But what is generative AI now bringing is, being able to deal with completely unstructured data. So what is unstructured data is text. For example the text. Then what text means means generating SQL code or text means dealing with logs, right. 00:16:23:22 - 00:16:53:15 Or log files or or automatically, thinking into, let's say like, your own, let's say diagnosis. That goes to data insight. So those are the areas, let's say that, that we are working on. Excellent. So because you're talking about, AI, GenAI, the resources is something, we hear, more and more in the database, area. 00:16:53:16 - 00:17:21:12 So, because HeatWave AutoPilot brings us, already a lot of, intelligence, automation but, what about the the natural language to a SQL. So the NL to SQL, do you think, this will be also something that, will come, for us and, and do you see it as a serious productivity tool for the analyst and the developers? 00:17:21:14 - 00:17:52:18 Are there still, orders, to making it available and, enough, for a production use? Right. That's a very good question. Okay. This is definitely a very hot topic for for everyone in the industry, I believe. And, so, yeah. So what is really happening in the NL to SQL domain is, as the large language models are getting larger, we are seeing a very big improvement in the accuracy of these tools. 00:17:52:20 - 00:18:36:02 And of course, what I mean, my accuracy of the known benchmarks. Right. And also what is really... Another interesting trend that is happening is that, you know, you look at traditional, like benchmarks, like for OLTP, OLAP, you know, TPC-C, TPC-H, TPD-DS right? They've been around for a very long time. What is interesting about, this NL to SQL benchmarks is that the more that they're out, it's, it takes maybe a year or less than a year to, to to get really good scores, you know, like, people are conquering is benchmarks and they're very fast, pace so it is true that it was not ready, but now we are seeing 00:18:36:02 - 00:18:58:06 signs that it is actually, they are getting very, very good at it. And and of course, it really depends on your, your complexity, how many SQL constructs you have. Right. And how complex, it could become because you certain SQL is just pages. Right. So that that means you need an assistant. But certain SQL is just, you know, you just want to learn something about your database. 00:18:58:08 - 00:19:17:02 Maybe you're a business executive or maybe you're a data analyst who is not very well versed in SQL. I can tell good for for for those type of use cases where you're a business analyst or whether you're a data analyst, I think that the tools are definitely there. So more complicated queries where even humans don't write in one go, right? 00:19:17:02 - 00:19:41:15 Like its an NL to query it it's pages right there. It's a great system, but it is performing maybe similar to the coding assistance that that we have todat. But yeah, it's it's definitely in a corner like, it like is is basically something that everybody is looking at actively and so, so we are so that's, I think you will hear, 00:19:41:15 - 00:20:04:08 Hopefully, some more cool news about it, soon.. That's awesome. That's actually something I'm really interested in. Just because, like you said, allowing people to query data without actually having the SQL knowledge is, is kind of intriguing. So one last question. As you, as would the three of us should know. You know, here at Oracle, we tend to eat our own dog food. 00:20:04:10 - 00:20:33:12 And it helped improve MySQL in some areas like high availability where we tuned group replication. Do you have a similar experience with the AI related tools that you could, talk about? Oh, yes. That's a very good, question. And this is an active topic that is, basically, happening right now, within the MySQL Org. So, yeah, I'm working on two different projects. 00:20:33:17 - 00:20:59:02 One is, we are using generative AI, our own generative AI service. I think they've generative AI service to generate, HeatWave release notes. So if you go to mysql.com today and if you look for HeatWave release nodes, you will see that there is actually banner up there that says these nodes are generated with the assistant off assistance of, HeatWave generative AI service. 00:20:59:04 - 00:21:20:12 And this is a system that we built in, again, purely running on our, software. And, it's working. Our technical writers love it. And we have actually working on several other improvements that we we are trying to write more, with that. And it is going to come. So there's something of the, quote unquote drinking our own champagne, right. 00:21:20:14 - 00:21:50:14 And, with that also reflected back right to the, to our generative AI team. So the AutoPilot team initially basically said, okay, like, these are the things that you should improve because so if you're running into these problems, our customers will also run into these problems. Right. So that's that. Is there one one one good. Also way of let's say, improving our product and another one, it's, something we call Ask MySQL Expert in short Ask ME. 00:21:50:16 - 00:22:17:17 So we are we is to build a general, question and answer machine that is able to, aanswer questions like the how to questions or the troubleshooting questions internally, like within the within the MySQL org and some of it's actually we, we demoed in some of the keynote speeches that that we gave and the also recently released a version of this as a sample app to our customers. 00:22:17:19 - 00:22:40:05 So basically, one part is that it's a question and answer machine that we are using internally. And, we, we got really good feedback. There are, certain cases where, especially junior, engineers, they learn a lot because they need to onboard, faster, than let's say, you know, basically compared to the compared to the past. 00:22:40:05 - 00:23:08:11 So that is why I think because there's a lot of knowledge to, to carry up to the you to use this tool. And at the same time we release this version to our cust... to our customers. But this one is something that they could bring their own data to build their own. Asked me let's it right? Of course, in this case they could call the tool something different because the case is something they could just, extend so they can just open their own data and, and build this, question and answer, let's say, machine. 00:23:08:13 - 00:23:33:19 And IT will, of course, we are evolving these tools forward so that eventually some of it could be also be part of our, our service. So and, yeah, these are actually two specific examples where we are using generative AI actively. There is a lot more, the only I think, you know, limitation in this case is like, is for us to catch up with this technology, right? 00:23:33:19 - 00:24:00:14 Because the Geratvie AI space is moving really fast and identifying what is working and what is not working and what is useful today. Our what is just forward looking. It is maybe let's say 10% or of of of of my time, like just so that we are not always also working on, very big, let's say, blue sky ideas as opposed to kind of making people's lives easier today. 00:24:00:16 - 00:24:28:03 Thank you very much. So yeah, I saw during the MySQL summit the example of the, of the app, with, the knowledge base, but for example, for the HeatWave release note that I wasn't even aware of. And, after this recording, I will watch it immediately, just for fun. So, to wrap up, I want to I have something in mind that I want to ask you, if you agree with, but, with the MySQL HeatWave service. 00:24:28:03 - 00:24:52:10 Right. We have AI that, so you can build your AI application or, AI anything with the vector search. You you have, you know, HeatWave that will help for you doing AI, but it seems with the AutoPilot and stuff we have also AI that helps, MySQL HeatWave users to improve your experience. Right. 00:24:52:12 - 00:25:28:18 So that's a very good distinction. And I have to admit that sometimes I am also not, let's say making, like, drawing that, like, you're absolutely right. So some of these applications we built are for customers and they could just go and extend it, or they can take inspiration from that and build something else. Right. So that's we're demonstrating our own, technology that, when it comes to AutoPilot, it is essentially another way of, let's say distributing this application, but it's all under control and it is out of our service that it's building to our service. 00:25:28:20 - 00:25:50:19 And you're absolutely right. In both cases, we are using similar, ideas or similar technologies. And, one of them, again, we are giving it to the people so that they could go and extend it the way they like and the others is is our under control. We see how people are using it and we are improving it as as we go along. 00:25:50:19 - 00:26:11:04 That's why there's always a new, let's say, auto feature. I mean, coming out and then sometimes the lines are actually pretty blurry, like, okay, I don't want to make things complicated, but one thing is that we are also working on some intersection where let's say that you have an application that, let's say, uses AutoPilot in a way that let's say a user would interact, but. 00:26:11:04 - 00:26:33:21 Right. So those these are interesting boundaries that we are always, looking. But from the product communication or maybe from the road network perspective, we, we don't really talk too much about this kind of stuff because it's just, maybe it's a little bit more like an intellectual exercise for us to see the limits of our technologies. Thank you very much, Onur, for, taking the time to talk with us. 00:26:33:23 - 00:26:53:10 I thank you, thanks for having me on this. Thank you. Onur. That's a wrap on this episode of inside my exclusive Killer Speaks. Thanks for hanging out with us. If you enjoyed listening, please click subscribe to get all the latest episodes. We would also love your reviews and ratings on your podcast app. Be sure to join us for the next episode of Inside Mysql: 00:26:53:10 - 00:27:05:18 Sakila Speaks.

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What is Inside MySQL: Sakila Speaks?: The Inside MySQL, Sakila Speaks podcast is dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL product updates, and inciteful interviews with members of the MySQL Community. Sit back and enjoy as your hosts, Fred Descamps and Scott Stroz, bring you the latest updates on your favorite open-source database.
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