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ANDI GUTMANS: Hi, everyone.
I'm Andi, and I'm so excited to share with you today what's new
and what's next for our Google Cloud databases
in this GenAI era.
Now, with the Next '23, we laid out
a vision to infuse GenAI into all of our databases
to make it easy for you to build enterprise apps.
And also, deliver the most impactful AI systems
for practitioners building, migrating, and modernizing
their data-driven applications.
We've made tremendous progress.
So let me tell you what we've been up to.
In February, we expanded our vector support beyond postgres
to also include MySQL, Redis, and Spanner.
And today, we're excited to also announce
vector support for Firestore.
Today, we're announcing a new pgvector compatible index,
the ScaNN index, which is based on Google's state of the art
approximate nearest neighbor search indexing and search
algorithms.
With AlloyDB's new scan index, index
build times will be up to eight times faster.
It also uses three to four times less memory.
Think about what you can do with all that extra memory.
And we have measured up to four times faster query performance.
Today, we're announcing AlloyDB model endpoint management,
which makes it even easier to call remote, VertexAI,
and third party models such as Anthropic, HuggingFace, and even
OpenAI.
We're introducing a technology preview of natural language
support in AlloyDB.
Now, this new capability improves
accuracy versus do-it-yourself SQL generation
by building semantic knowledge about the data set in order
to better translate questions.
And because natural inputs--
natural language inputs to applications
are often imprecise, AlloyDB also
introduces a mechanism to interactively clarify
user intent.
Today, we're also announcing a new security model in AlloyDB.
Developers can now set up a new kind of database view--
we call it the parameterized secure view--
to help defend against prompt injection attacks.
With this, we now have both flexible, secure, and accurate
natural language support in AlloyDB.
SANDY GHAI: Let's take a look and find a seat on one
of these flights.
OK.
Are there any good seats, let's say, on flight 41?
Now, by the way, this is totally something
I would ask a human booking agent,
but I think we can all recognize it's a terrible question.
A good seat isn't a precise term,
and even the best ML to SQL models
can struggle to translate this.
So instead of just arbitrarily generating the query,
the app asks what I meant.
This is what Andi referred to earlier as intent clarification.
OK, I want a window seat in premium economy.
And by the way, just to go back there,
AlloyDB is actually helping with that intent clarification.
It's using its knowledge of the data set to propose a follow-up
question.
And OK, we have some options.
Let's book it.
ANDI GUTMANS: Recently, we open-sourced
LongChain integrations for all of our Google Cloud databases.
With these integrations, developers
can build increasingly sophisticated RAG applications.
How do these integrations with databases
really empower developers on their GenAI journeys?
HARRISON CHASE: There are so many different ways.
So I think the obvious one is Retrieval Augmented Generation,
or RAG.
And so having some sort of vector index, the like of which
you just talked about, is crucial for building
a lot of these applications that need more data than the language
model it was trained on.
And this idea of connecting language models to data
is core to LongChain from the start.
And so those types of databases and those types of integrations
are obviously one of the main parts.
But I think it's also a little bit underappreciated
how many other ways you need databases
to build these applications.
So going out in a few different directions,
the main UX for GenAI applications is chat,
and you need a way to store all that chat data
and use it in future conversations.
And then maybe also do analytics over it.
So having really solid integrations with databases
that support these chat messages is crucial.
And we've got a bunch of those.
You can use databases as caches.
So caching is really nice for LLM applications.
It speeds up the speed and also keeps down
the cost when working.
And so basically, you can look for exact or similar questions
and similar prompts and return answers.
Whether this be at runtime or in testing,
we've found that during evaluation is a really good time
to have that cache.
And then a third thing that I'd just highlight
is the text to SQL, as shown earlier,
is just a massive use case.
And so having a really good database
integrations there enables a whole set of other applications.
ANITA KIBUNGUCHY-GRANT: Today, we're
excited to usher in a new era for database professionals
with Gemini in Databases.
Gemini in Databases delivers AI-powered assistance
that simplifies all aspects of the database journey,
helping teams focus on what matters most.
Gemini in Databases helps developers, operators,
and database administrators build applications faster using
natural language.
They can also manage, optimize, and govern
an entire fleet of databases with a single pane of glass.
And finally, accelerate migrations with AI-assisted code
conversions.
So let's look at each in more detail.
Gemini brings a SQL generation, summarization and fix-it
capabilities to Database Studio.
GABE WEISS: Our sales team is trying
to set up a dashboard to track inventory and have
asked for my help.
I can take what they've asked for directly into Database
Studio and ask in plain English, list inventory
where stock exceeds 50% of average daily sales.
Now, Gemini is taking into account
the schema and our electronics database
and suggesting SQL to list all of our excess inventory.
But now, with our Database Studio,
I can easily filter and see at a glance all of my instances
that aren't failover protected, which
is the signal that I get when things aren't set up
for high availability.
I can click on the alert here in the dashboard.
And down below, I get a very nice filtered list
of all of my instances that aren't protected.
Now I can click on the issue bubble in my list
and I get a very clear description
of what the problem is, quick links to edit my configuration,
or go straight to the resource.
And then how to fix it.
And in this case, enable high availability.
ANDI GUTMANS: Last year, we launched the Cloud SQL
Enterprise Plus edition for MySQL and Postgres,
and we've continued to innovate on Enterprise Plus since launch.
Maintenance and instance scale-up
happen now within less than two seconds,
and typically one second.
And we've added enhanced disaster recovery capabilities.
Now, to best support Redis workloads,
we launched Memorystore for Redis Cluster.
And today, we're excited to announce
some new, much requested features.
This includes AOF and RDB-based persistence and new node shapes
for maximum flexibility.
Today, we're excited to announce Bigtable Data Boost.
A breakthrough technology, Data Boost
delivers high performance, workload isolated,
on demand processing of transactional data.
You can now execute analytical queries, ETL jobs,
and train machine learning models directly
on your transactional data.
HARI RAMAMURTHY: As the world's largest home improvement
retailer, The Home Depot is a familiar name to many.
We use the entire portfolio of Google Cloud data products
to power our operations and deliver the best possible
experience for our customers.
Spanner has become an essential tool here at The Home Depot.
Benefiting from Spanner's five nines of time,
we have the ability to scale up or scale down
to match the workload profiles that
vary based on the time of the day,
saving us money and ensuring we are always
ready for the peak demand.
We use Bigtable across a number of different use cases,
across recommendations, personalization,
inventory aggregation, as well as in our catalog.
Now, certain workloads require eventual consistency.
These systems handle massive spikes in activity,
sometimes over 500,000 database operations per second.
Now, this elasticity of Bigtable has been monumental.
ANDI GUTMANS: Thank you so much for joining today.
Have a great Next.
ANITA KIBUNGUCHY-GRANT: Have a great Next.
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