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.

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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 qu