Inside MySQL: Sakila Speaks

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 --

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

In this episode, leFred and Scott are joined by Onur Kocberber 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

In this episode, leFred and Scott welcome Jayant Sharma and Sanjay Jinturkar to the Sakila Studio for an insightful conversation on machine learning and generative AI within HeatWave. Discover how these cutting-edge technologies are integrated, what makes HeatWave unique, and how organizations can leverage its capabilities to unlock new possibilities in data and AI. Tune in for practical insights, real-world use cases, and a closer look at the future of analytics. ------------------------------------------------------------ Episode Transcript: 00:00:00:00 - 00:00:32:01 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! 00:00:32:03 - 00:00:54:17 Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I am leFred and I'm Scott Stroz. Today for the second episode of season three dedicated on AI. I am pleased to welcome Sanjay Jinturkar. Sorry if I pronounce it badly. No, you did it right. Hi there. Thank you. So Sanjay is the senior director at Oracle based in New Jersey. 00:00:54:19 - 00:01:21:13 He leads product development for it with AutoML and GenAI with a strong focus on integrating these technologies directly into each HeatWave database. And Sanjay has been instrumental in enhancing HeatWave's machine learning and GenAI tool sets, enabling use case like predictive maintenance, fraud detection and intelligent dicument and Q&A. And also we have a second guest today. 00:01:21:13 - 00:01:48:21 It's a Jayant Sharma. Hi, Jayant. Hello. So Jayant Sharma is senior director of product management at Oracle. He has over 20 years of experience in databases, spatial analytics and application development. He's currently focused on the product strategy and design of the Heatwave MySQL managed services offering. Hey Fred. Thank you, both of you for joining us today. So I'm going to dive right in with the question for Jayant. 00:01:48:23 - 00:02:12:14 Why did Oracle decide to integrate machine learning in generative AI capabilities directly into HeatWave? Thank you Scott, first for this opportunity. And yes, we have to start with first, you know, talking about MySQL, right? MySQL is the world's most popular open source database. And what do all of these customers, the thousands of customers that they have, do with it? 00:02:12:16 - 00:02:47:05 They manage a business process. They manage their enterprise, right? Their focus is on what they want to do, why they want to do it, and not so much the how. That's what MySQL makes it easier. And Heatwave is a managed service on MySQL. Okay, so as folks are modernizing their applications, taking advantage of new technology, they want to be able to use new workloads, new analytics, and modernize their business processes, make it more efficient, make it more effective. 00:02:47:07 - 00:03:09:17 In order to do that, they want to do things such as machine learning and use the benefits of generative AI. However, what they want to focus on, as we said, is what they want, why they want to do it and not the how. So they don't want to have to think about. I have all of this data that's potentially a goldmine. 00:03:09:19 - 00:03:40:07 How do I extract nuggets from it, and how do I safely move it and transfer in between the best of breed tools? I want to be able to do things where they are. I want to bring the capabilities, these new capabilities to my data. I don't want to take my data to where those capabilities are exposed, right? That is why we made it possible to do machine learning and GenAI where your gold mine is, where your data is in MySQL in Heatwave. 00:03:40:09 - 00:04:06:07 Awesome. Thank you. So, I would like to ask you to Sanjay, then. How Do the the, machine learning engine in the HeatWave, offer differ from, using external machine learning pipelines with the with the data we have in the database? It differs in a couple of weeks, specifically how the models are built, who builds them and where they are built. 00:04:06:09 - 00:04:46:09 So our pipeline, we provide, automated pipeline, which can take your data in MySQL database or Lakehouse, and then automatically generate the model for you. So it does the, usual tasks of pre-processing, hyperparameter optimization, and, data cleansing, etc. automatically so that the user doesn't have to do that. We would even go ahead and do, explanations for you in certain use cases, given that this is automated, a big side effect of that is users don't need to be experts in machine learning. 00:04:46:11 - 00:05:16:08 What they need to focus on is their business problem, and how that business problem maps onto one of the features that we provide. From there onwards, the pipeline takes over and generates the models for it. And the third piece is that all of this work is done within HeatWave. We don't take the data going back to what Jayant was say, saying, we have got machine learning and generative AI to where the data resides, not the other way around. 00:05:16:10 - 00:05:47:20 So we are building the models inside Heatwave whereby the data is not taken out and thereby it is more secure and the user does not have to worry about data leakage or track where all they have taken the data and how many times they have done it. So these are the three key ways in which we differ. If you use one of the third party solutions, they will end up asking you to do this on your own or asking you to take the data out of the database and build it on your machine, so on and so forth. 00:05:47:22 - 00:06:21:06 But we have made it automated, easy to use and very secure to do so. So Sanjay, we're going to stay with you to, to keep talking about AutoML in HeatWave. So what are some of the key features of AutoML and how does it simplify model training and deployment for users? Fantastic question. You know, as I said in my in the previous, conversation, we are hitting the common tasks that are associated with model training and deployment. 00:06:21:08 - 00:06:46:03 So let's take training here. Typically when the user has to train a model, they are going to take their data. They will clean it up, do some pre-processing. Then they will figure out which particular algorithm they should be using. Tune those algorithms in doing the hyperparameter tuning, so on and so forth. All of these are individual tasks. 00:06:46:05 - 00:07:12:15 Our goal is to have the user focus on their business problem and take away the engineering piece of it, take away the technology piece of it, and do it automatically for them. So we have this pipeline which does this, all of it, all of it automatically in a single pass. So it will do pre-processing. It's going to figure out, the appropriate algorithm to use during model building. 00:07:12:17 - 00:07:39:05 It will figure out what are the best set of hyperparameters and what their values should be, during the training process and give you the, the model. So that's one part the second part is we provide an ability to deploy these models via REST interfaces. So once the model is trained they can deploy this. 00:07:39:07 - 00:08:09:09 And thirdly from time to time the users data is going to drift. Or what I mean by that is the train model. The data on which it was trained no longer reflects the reality. And in that case, you have to retrain the model. So we provide tools to measure that drift. And if it goes beyond a certain threshold, then you can go ahead and retrain your model automatically. 00:08:09:11 - 00:08:53:01 So these are a couple of ways in which we have simplified the model training and the deployment for users. Thank you. Thank you very much for this, detailed, answer. And now... So as we discussed about, you know, the, the data not leaving, to a third party, product. But I would like to, to ask, to, Jayant, if, if there were some performance improvement that, users have seen by doing this, ML natively in HeatWave, instead of removing the data, to external platforms. Certainly, Fred. 00:08:53:03 - 00:09:24:01 So there are two aspects to this. There's, there are efficiencies that, result and there are performance improvement because of the way AutoML is implemented and how it works in HeatWave. Let's start with the efficiency first. The first thing as Sanjay was talking about right, is that we've automated the pipeline. You have to only focus on what is your business problem and how that maps to a particular task in machine learning. 00:09:24:01 - 00:09:47:04 So for example, do I want to predict something. And therefore use regression, do I want to identify or label something and therefore use classification. And AutoML will figure out which particular algorithm. There are multiple ways in which you may do regression, for example, which particular one applies or is best suited for the task at hand. Right. 00:09:47:04 - 00:10:15:06 So efficiency there is AutoML handles it in a single pass, not the normal process requires you to have an iterative do things multiple times. Try it on multiple algorithms or different ways of solving the same problem, and then evaluate which one does it best. AutoML does this in a single pass by. Very smart ways of sampling your data and running quick tests to identify the best approach. 00:10:15:08 - 00:10:35:15 So that's the efficiency. The second when it does this, why is it so fast? It's so fast because it uses it the full capability of the underlying infrastructure, which is the HeatWave nodes. Right. The number of heat wave nodes you've got the size of these HeatWave nodes. It does these things in parallel and fully utilizes the infrastructure. 00:10:35:17 - 00:11:02:22 So what is the benefit of that? You can do things a) faster and b) potentially cheaper, which gives you the luxury of trying multiple what if

Kick off Season 3 of Inside MySQL: Sakila Speaks as leFred and Scott welcome Matt Quinn for an engaging introduction to the world of Artificial Intelligence. In this episode, we step back from the database and explore what AI really is, how it's shaping society and technology, and why it matters to anyone in tech today. Whether you're just curious about AI or eager to understand its key concepts, join us as we break down the basics and set the stage for a season of discovery. ------------------------------------------------------------ Episode Transcript: 00:00:00:00 - 00:00:31:22 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! 00:00:32:00 - 00:00:58:22 Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I am leFred and I'm Scott Stroz. Join us today. It's Matt Quinn, vice president and head of AI at Orracle. Matt leads how Oracle Cloud Infrastructure's AI services are adopted by customers in EMEA. Matt brings deep expertise in enterprise software strategy and a passion for making AI both powerful and its adoption practical. 00:00:59:00 - 00:01:21:03 Today he is here to help us unpack what GenAI really means for the organizations we work for and buy from, and what it means for developers, data professionals, and MySQL users everywhere. Matt, welcome to Inside MySQL: Sakila Speaks. It's great to have you with us to kick off season three of our podcast. Thank you very much, Fred,  Scott, great to be with you. 00:01:21:08 - 00:01:43:21 Looking forward to, to an interesting conversation and getting us going for season three. Awesome. Matt, thanks for being here with us. So right off the bat, when most people hear the term AI, they probably think of chat bots. But that's just one form of AI. Can you help provide us with like a high overview of the different types of AI that exist? 00:01:43:23 - 00:02:15:10 Absolutely. And I think AI and itself is a broad church, right? There's a number of different, kinds of AI. The term actually dates back to the 1950s as a concept for you know, machine thinking. It's had a couple of false dawns over the time when compute and data to train. I wasn't really quite ready for this, but as we got into the 90s and the early noughties, as compute power grew, as storage grew, a confluence of internet accessibility, lots of data becoming available, and then we time fed forward. 00:02:15:12 - 00:02:33:12 We found that organizations could do the fundamentals of what we know of AI today things like machine learning. So learning a trend and a pattern, looking at what happened in the past and do a statistical regression on that to predict some future outcome based on what happened in the past. And we use examples of this today without even knowing it. 00:02:33:12 - 00:02:52:11 You know, is this email that's coming into my email system, is this spam or not spam? Those kind to classifier types of AI have been prevalent for the last ten, 15, 20 years, and we're moving forward to where AI has this more kind of human interaction. It's surfacing and it's suddenly popped into the zeitgeist, for for conversation. 00:02:52:15 - 00:03:14:03 So it has multiple facets. We have machine learning trained something to do, something very specific, show it, something that it's seen before and enable it to predict the future based on what it's learned. But we're starting to see this wave of generative AI do more advanced, more nuanced, more humanlike things, and I think that's a really powerful kind of inflection point that we've seen in the last two, three years. 00:03:14:05 - 00:03:39:02 Thank you. So because in your first, answer, you said you said about the 70s and 90s, but why is I having such a huge moment right now? So what changed since that time? I think that the real inflection point is the the kind of conversational nature of it. You can speak human to it, and it can speak human back to you. 00:03:39:04 - 00:04:01:13 If I think about how compute evolved, you know, it used to be I had to type cryptic commands on the green screen in order to be able to use a computer, which meant the audience of people who could use computer to do something was very limited. In the 80s is the GUI. The graphical user interface kind of emerged suddenly it was a keyboard in a mouse, and the population of people who could interact with the computer was much broader. 00:04:01:15 - 00:04:19:02 Mobile did the same for us, but you still had to learn things. You had to take the human to interact in a way that made sense to the computer. With generative AI, I think what's happened in the last 2 or 3 years is actually the computer is coming to meet the human. Suddenly it's able to interact with us in our language. 00:04:19:07 - 00:04:37:19 I can have a conversation with it. I can ask a question in natural language. Now I might need to engineer my prompts to get the right kind of outcomes to guide it. Actually, the computer understands what I say. It can meet my language and understand that interact with me in a very human way. And I think that's caught the imagination of people. 00:04:37:19 - 00:04:59:18 They've suddenly had this 'aha' moment and that then has gone from, you know, an academic or data or IT kind of problem. It's broken out of it and gone into the board to say, well, actually, what does this mean? How will this work? And as people start to imagine what it could do beyond, you know, asking a question about, you know, what recipe do I have? 00:04:59:18 - 00:05:20:13 Or how can I find an answer to a question I could historic could use a search engine for, but save me some heavy lifting organizations to look at it and say, oh, hang on a minute. What manual processes in my organization...What low value repetitive tasks are happening in my organization that this might help me change? So suddenly AI has gone from being an IT conversation to being a business conversation. 00:05:20:13 - 00:05:48:15 It's it's got the opportunity. It's got the ear of the board. And suddenly that's just pivoted the demand and the interest in AI I think in the last couple of years. That is quite insightful. So because I has become the big thing in the world and everybody is talking about AI, there's got to be some, some common myths or misconceptions about AI out there that you've heard give us one or a couple that you've you've heard that you need to clear up and be like, that's not actually the case. 00:05:48:17 - 00:06:11:19 So there's a couple of things that I think, reoccurring in the conversations I have with customers, with, with engineers, with particularly people outside of IT. And one of those is around privacy. And I think that the challenge that we have with AI is the first services that really burst this into the public domain. There's kind of ChatGPT services. 00:06:11:19 - 00:06:31:04 There's first, opportunity where you could just go to a website, sign up for free, try something for free, engage with it and have a human like conversation. But that spread like wildfire, like 100 million users in a crazy amount of time. The interesting thing there is that free service, and I always like the phrase if something's free, you are the product. 00:06:31:06 - 00:06:54:21 That's those kinds of public sites where it's, you know, it's a consumer-grade service. There's no charge. The huge costs sitting underneath those models, like running the infrastructure, running the applications, having train the models. So the reality is in that environment, the value exchange it was happening is the prompts that I give that free service are available to be used to retrain the model to extend it, to make the product better. 00:06:54:23 - 00:07:24:01 So you're giving access to the data that you provide through a prompt to the service provider that is running that service. That's the value exchange. Now that's created this perception in people's minds that AI isn't private or safe or secure. And I think the reality is, when you do this in an enterprise context, you can absolutely run those models in a ring fenced way, the same way you'd run a database platform where it's isolated. 00:07:24:07 - 00:07:41:09 It's not sending data back to the model provider, it's secure and it's yours. And that enables you to do things. Bring your private data combined with the intelligence that the model has been trained on with public data. And that's what builds builds a system. But it doesn't have to be a system where you're losing control of that data. 00:07:41:13 - 00:08:02:22 So I think there's a lot of FUD around that fear, uncertainty and doubt. And it's up to us as technologists to help dispel the myths and separate where that might be happening in certain domains. That free service is public services. Maybe that is happening, but in an enterprise it scenario, you absolutely can put the security and privacy guardrails around it to meet the kind of enterprise controls that you'd expect. 00:08:03:00 - 00:08:36:10 Whilst reaping the benefits of the AI productivity gains, that you could have. So I think that that to me is the big one. Awesome. Thank you. So, because you said that, you you talked about AI, in industries, and how it's used. And I really like the analogy with the database. So for us, with MySQL, we really enjoy, the databases, could you, paint a picture of how AI is being used across the industries, or is it just specific, or can we use it, in different ways? 00:08:36:10 - 00:08:58:07 And, now it's a great question. I think, like most technological innovations, the thing that is most disruptive about AI is it has an opportunity to be a general purpose technology. And so if I think about things like the internet and electricity before it, electricity is a ge