>>SHAY BANON: Elastic Cloud is our SaaS-hosted Elasticsearch service. You go to Elastic Cloud
and we will deploy an Elasticsearch server for you and manage it for you. We’ve also
introduced Elastic Cloud enterprise, we have seen quite a lot of organizations that want
to go and manage more than one cluster, they want to be able to manage hundreds of clusters,
tens of clusters, all of these aspects of being able to go and orchestrate an Elasticsearch
deployment within your org. Many reasons to why you’d want to use it.
The challenge that we've had, both for our SaaS-service, and our customers of Elastic
Cloud, is that you are slightly limited with what you can do. You basically had what
we call one slider. This one slide basically controlled how much memory and how much storage
you would want to have for an Elasticsearch deployment. And as you see here, and what
we've done, actually a deployment of a cluster or deployment of our technology is more advanced
than that. What about machine learning? What about different types of data nodes: warm
data nodes or hot data nodes when it comes to logging use cases? So we wanted you to
have more control over the various aspects of a cluster when you go and deploy it on
Elastic Cloud and configure it. This allows us, for example, to be very aggressive
when it comes to logging use cases, and old data can then have ratios of one gigabyte
of memory to 80 or 100 gigabytes of storage, versus recent data that has ratio of one to
20, or 1 to 24, because your want the results to come back faster.
So we've worked over the past year in introducing these feature and I would like to welcome
Andrew on stage to demo it. Andrew? (Applause).
>>ANDREW MOLDOVAN: OK. Thanks, Shay. Hello, everybody.
So, many of you will be familiar with this page already, it is our current Elastic Cloud
create cluster page. And, as Shay was mentioning, you can create an Elasticsearch cluster on
here that has a pre-set, pre-defined ram-to-disk ratio. And it always goes through the same
I/O optimized hardware, and every node has all the rules. So, you can't create dedicated
master or dedicated machine learning, for example.
So, while we do use the best instance types that AWS and GCP have to offer, a lot of you
have asked for more flexibility here. Maybe you have a logging use case, maybe you want
to use memory or CPU optimized hardware, or maybe you just want to run machine learning
and APM on hardware that fits. So we have been listening and we have worked on the newest
feature that will allow you to manage each part of the Elastic Stack individually and
to allocate it to specific hardware. So, my demo will walk through the logging
use case and specifically the hot-warm architecture. So, hot-warm is a well-known pattern in
Elasticsearch where you have the newer indices go to more expensive and faster hardware,
and you have the older indices that can be moved to what we call warm nodes, which are
cheaper usually, and a bit slower, but they’re better at storage. So, I will pick hot-warm
and click configure. As you can see, I’m able to already manage each part of the Stack
right here. I have the masters. I’ve got data hot. I’ve got data warm, machine learning,
and Kibana right now. APM and Logstash are going to be added in the future.
So, I can go ahead and decide that I want 1 gig of RAM for masters is fine, but I’ll
put it to three zones, and if you saw that, the architecture diagram has actually updated.
So, I will pick 4 gigabytes of RAM for my data hot, put that into two zones, and I will
do the same for my data warm, just for now, in two zones.
And as you can also see, each of these instance configurations is actually going to specific
hardware. So, the master is going to an R4, the data
hot is going to an I3, and D2 and M4, and so different instance types.
This is on AWS, but it certainly could be on GCP or anything else. The hardware that
we are going to be putting for master, or data hot, or for the different instances are
what we’ve decided are the optimized hardware for that. So, machine learning needs a bit
more RAM than most, but one zone is fine for now. Kibana I’ll just leave alone. I’ll
go ahead and click create. And so this is the activity page where you can actually watch
this deployment coming online. The deployment has a number of different steps. I won’t
actually wait for everything but I will mention one part here is that with the hot-warm architecture,
you will also be able to manage the indices in the cloud, so you can tell our index creator,
I want to move indices every week from the hot hardware, to the warm hardware, or whatever
you want. So, I’ve already created a deployment that
looks exactly like this one. So I will just go over to that. It will take a minute and
a half, or two minutes to come online. This is the deployment overview page, and you can
see that we have all of our nice instances here. So we've got -- in the different zones
that I picked -- I have data warm, master, data hot, master, data warm, machine learning,
and so on. What's really cool is I can show you that
—well, I can also filter, which is kind of fun — we can show you the allocator,
the host, that is actually where this instance is living. And I can tell you, or I can show
you, that is, what is the instance type of that. So we said that the master was supposed
to be running on an R4, so I will open up this host, and we're going to scroll to the
bottom here, and there is the R4 instance type on that host.
And I can do the same for data hot, for example, so this data hot is supposed to be on some
kind of an I3 tagged host, and there is the I3 tag as well.
So this is a very short,very simple demo, but I think it is extremely powerful example
of what we can now do in cloud, you can manage the pieces and throw them to specific hardware,
you have a lot more power in your cloud deployment than ever before. All of these changes are
coming to Elastic Cloud enterprises and to Elastic Cloud. To be able to manage all this
fun UI, we’ve actually re-vamped the user console entirely. So it will not look like
this, it will actually look like this. It is both Elastic Cloud enterprise and Elastic
Cloud that will use the same code base, and you will get a lot of other fun usability
improvements in there. I think that's it. Thank you very much.
(Applause). >>SHAY BANON: Thank you very much, Andrew.