We're talking with Andy Mendelsohn, who is the executive vice president responsible for
the database at Oracle.
I'm Michael Krigsman.
I'm an industry analyst and the host of CXOTalk.
Andy, thank you so much for taking time to be with us today.
Andy, you have been at Oracle for many years and so give us some historical context of
where the autonomous database fits into this long Oracle history.
Oracle, as a company, goes way back to, actually, the late '70s or early '80s.
We started out as a software company focusing on what was then a new area, relational database
management.
If you think about the history of technology, there are very few technology companies from
that era that are still left.
I think one of the remarkable things about Oracle and the Oracle database technology
we build is how we've been very good at making sure this technology is the market-leading
database technology, as we go from one generation to the next.
In the '80s, it was the mainframes.
In the '90s, the Internet came along.
Now we're moving to cloud.
What we've done really well at Oracle is we've navigated these changes from one generation
to the next.
That's what autonomous database is all about.
We're now navigating this change from the Internet era to the cloud era, from the software
side, and making sure, again, we have the best database technology for the next generation,
which is what we embody into this whole autonomous database concept.
What has been the key, if you can put your finger on something that has enabled you to
remain relevant to such a profound degree through these changes in technology over such
a long time?
The big thing we had done was, we figured out how to make the Oracle database scale
out for all sorts of workloads, especially transaction processing workloads.
To this day, no other relational database vendor has ever figured out how to do that.
We have this technology called Real Application Clusters, which is very unique.
None of the other vendors were able to copy it, and so it gave us this huge differentiator
as we went to the internet era.
As we're going now into the cloud era, what we think customers want is they want to eliminate
all the friction around running Oracle databases, relational databases, all the friction around
developing applications around databases.
What customers really want now is they want to focus on their business.
They want to focus on, "I'm a bank.
I want to focus on running my bank better.
I want to understand how to better market to my customers."
They don't want to run IT anymore, and so the big thing we're working on is eliminating
all the human labor around using provisioning, running, operating, building applications
around the Oracle database and just let people do what they want.
They want to run their business.
They want to leverage the value out of all that data they have.
At a high level, what we are doing is we are creating a self-driving database.
Everybody sort of understands what a self-driving car is.
You go into the car, you plug in the address, and the car is supposed to take you there.
We want to do the same thing.
We want our customers or our developers to be able to just say, "Okay, the data engineers
say this is the data we want to model.
Create a set of tables for storing that information in the database."
Then, after that, you just let your organization loose on it.
Your developers can now just build their applications against that data.
They can load up the data.
They can run some SQL queries.
We will do everything for them.
We will provision the infrastructure for them under that database.
We will make it highly available for them.
We'll give them an SLA if they want.
We will make it fully secure, implementing all of the best practices for them.
We take over all the tedious efforts that they have in their traditional organization
for provisioning databases, for upgrading databases, for patching databases, for tuning
the databases.
Andy, to place this into greater context, explain how this is different from traditional
databases.
At the foundation, with an autonomous database, we are using a very optimized infrastructure
for running the Oracle database that's based on our Exadata technology.
That is now a very mature platform and, in our cloud data centers our in cloud at a customer
where we run the customer datacenter, we base our infrastructure on this Exadata technology,
which is an optimized platform of hardware, servers, storage, networking technologies,
and the database software that gives customers a very high performance, highly reliable,
highly secure platform for running your Oracle database.
That's the foundation, a very mature infrastructure technology that we developed originally for
on-prem and has now become sort of cloud-native technology where you can provision the equivalent
of an Exadata box with the push of a button.
Then we're implementing best practices around how you make database secure, how you make
them highly available.
That's sort of the database technology core.
In the database space, we implement.
We are leveraging an autonomous database.
A lot of this differentiated technology we've built over the years.
Beyond the Exadata technology, we have the rack clustering technology I mentioned earlier
that we built many years ago for doing scale out of Oracle databases, so we have a very
powerful, shareable infrastructure for the database.
On top of that, we're using our multitenant technology.
Multitenant means that this powerful infrastructure can be easily shared among modeable businesses.
We create the equivalent of a virtual database that each customer gets when they ask to provision
a database, an autonomous database.
We don't give them the whole infrastructure.
Essentially, consider it like a virtual database out of this large infrastructure.
Beyond that, we're taking care of the self-tuning of the customer's workload.
At a high level, we're looking at two different kinds of workloads.
Customers are doing analytics with their databases or they're doing more like run your business
applications on the database, more operational workloads like running a business application
like Fusion applications to do your accounting, e-commerce work, supply chain management,
manufacturing, et cetera.
We have this common technology stack and we optimize it for these two kinds of workloads.
There's an autonomous database for analytics.
We call that autonomous data warehouse.
There are autonomous databases for more operational use cases.
We call that autonomous transaction processing.
Then we have our self-tuning technology that goes into each of these two cases to give
customers automatic, high performing systems.
What were the kind of challenges or technical hurdles that you had to overcome to build
the autonomous database?
If you think about what we're doing, our goal is to eliminate all the human labor that our
customers either on the operations side or their developers or analysts side had to do
around the database.
This is a huge exercise in artificial intelligence.
At each of the layers of the stack, we had to go through and figure out where is the
human labor still present.
We had to build an appropriate algorithm of some sort to automate what the humans do in
that layer of the stack.
Exadata, I mentioned earlier, is a very mature platform we've been working on for many years
since 2008.
We've already pretty much automated all of the security and availability areas, and performance
and scalability areas in that technology stack.
We also have lots of technology that actually observed the hardware, predicts failures are
going to happen, alerts people to sort of know that this hardware device is going to
fail and we should replace it before it fails.
The new things we're working on there are understanding, when something fails, diagnosing
what is wrong and then fixing it.
This is one of the main things people do around databases and protection IT shops.
We are working on building algorithms that basically track all of the unusual, exceptional
events going on in the database.
We're tracking all the telemetry, all the statistics around performance related statistics
coming out of the database.
We're building machine learning algorithms to observe all this data and try to predict
failures.
If there is a failure, try to repair automatically from the failure.
These are very difficult problems to solve and our engineers are very excited about working
on them.
Can you identify some particular aspect of the technology that was particularly difficult?
One of the big technology, key technologies in these databases are things called query
optimizers.
Query optimizers have been around since the beginning of relational database technology
in the '80s.
These pieces of technology were early expert systems that were designed to look at customer
SQL statements and figure out the best way of executing them for the highest performance,
et cetera.
These algorithms, these optimizers are dependent on knowing the statistical distributions of
the data in the database.
As you load lots of data, the distributions change.
For many years, one of the big problems customers had is, the query optimizer would choose the
wrong way of executing a query because it didn't have up-to-date statistics on what
the data looks like in a database.
One of the key things we did with autonomous databases is we said, "Okay.
We're just going to go to real-time statistics," and what that means is that whenever a customer
loads some data or they update some data, we're making sure all the statistical information
that we use in the query optimizer is up-to-date with the actual data in the database.
That makes the query optimizer a whole lot better at generating the right execution plans
versus the way things used to work where the customers were responsible for periodically
updating statistics when they sort of felt like it.
That was one of the key things we did to make this all work really well.
Beyond that, one of the other things we've done is we've built an expert system around
the query optimizer that really allows us to understand, be the super DBA.
We can understand exactly what the customer workload is.
We can do experimentation to figure out, "Oh, the workload has changed.
Maybe we need a new index.
Let's go off on the side, do some experiments, figure out what the best new index to use
is for these queries.
Put them into production."
We're doing this all online without any customers knowing we're doing this.
The next time the customer runs a SQL statement that maybe can benefit from this new index,
we will go use it.
We know what the runtime for that query used to be.
With the new index, of course, it should be faster and 99.99% of the time it will be.
But if occasionally it's actually slower, we notice that, too, and we will pull that
new plan out of production.
This is one of the big benefits of an autonomous database versus what customers have today
is we never have performance regression.
From the business standpoint, is the primary benefit this labor efficiency and the fact
that there are all of these database tasks that do not require labor anymore?
Yes.
If you're a CIO, you care about making sure the business is running well, at low risk
and low cost.
This autonomous database does this for them.
They get this very high-performance technology that can run in a cloud.
On a cloud, you pay per use, and so to the extent [that] we have much better performance
than our competitors, we actually have lower costs.
There's a really radical change going on here where we now, in a cloud service, can offer
data management that's the best in the world at the lowest cost.
For a CIO, it's also really low risk.
All these applications that have been written over the year, all the packaged off-the-shelf
applications he's using, almost all of them use Oracle databases.
We give them a very comfortable, low-risk way to go on a journey to the cloud.
The CTO types and developers, they love this because now they can do their job without
any other organization in their way slowing them down.
They can go straight to the cloud.
They can provision databases.
That's all they need to do.
We take care of all the upgrades for them.
We take care of the patching for them.
It's a really nice world.
It sounds like you're stripping out the low-value tasks and leaving the higher value activities.
Yeah, that's exactly right.
The way the business will look at this is that these people who they used to think of
as DBAs are going to be much more valuable.
They're data engineers now.
They're data architectures.
They're helping my analysts.
Yeah, exactly.
They're going to move to a higher value role in the business.
Andy, any thoughts on the general implications of these types of autonomous systems on organizations
and on the future of work?
Yeah, so autonomous systems are not just about a database.
I'd mentioned at the beginning there are autonomous cars.
I think it's all about empowering people.
Today, if you look at a person, they all have this phone they're carrying around that's
doing all kinds of things that people used to have to do by themselves with 20 other
devices or no devices at all.
They had to do it by writing things down on a piece of paper.
I think autonomous databases and autonomous systems, in general, are all about making
humans much more empowered, much more powerful by giving us really powerful tools to do our
jobs much more productively and do things we could never have done before in the past.
I think there's a bright future for autonomous systems.
You just have to view them as systems that are helping empower people as opposed to systems
that are replacing people.
It's just really making people much more powerful and much more productive.
As we finish up, any thoughts or advice for CIOs who are looking at this and saying, "Yeah,
I need to embrace this new world.
I'm not sure how to do it"?
Yeah, I think everybody is recognizing now that cloud is the next generation of computing
technology and it's inevitable.
Whether it happens in your organization in the next 2 years, the next 10 years, or 20
years, you have to start plotting out your roadmap, how you're going to get from where
you are today to cloud.
One of the things that I see a lot of enterprise customers doing is they've invested a lot
in building out applications over the last 25 years that are running in their data centers.
They're not going to throw those things out, and they're not going to just lift and shift
them magically to a cloud tomorrow.
What they would like to do is transition, in an orderly way from where they are today,
to a future where maybe they have no data center.
Andy Mendelsohn of Oracle, thank you very, very much for taking time with us today.
Okay.
You're welcome.