>>SHAY BANON: So we've introduced Canvas last year as an effort that we have around being
able to create another tool, along side dashboards, that allow you to go and try to express yourself
on top of data that exists in Elasticsearch in a more free-form manner.
And when you try to do that, you also want to have a language that is easier to use,
and easier to explore, when it comes to exploring data in Elasticsearch.
We love our APIs, we love the Query DSL, but obviously SQL, as an example, is a very approachable
language that you can go and use.
And we've also talked about SQL as an engine on top of Elasticsearch last year.
We've been working really hard last year to try to bring these two features to fruition.
In a nutshell, it means taking these dashboards and moving it to something like this, which
is more expressive, same data, it tends to be the same data, but more expressive, something
you would be proud to put, not necessarily only on an operational NOC, but at your CEO
office.
And in order to show you where we are at when it comes to Canvas and SQL, I would like
to welcome Rashid on stage.
(Applause).
>>RASHID KHAN: Thank you.
So like Shay said, last year, I got the privilege to introduce you to Canvas, and
this year, I get to show you how Canvas works and the nitty-gritty of how it operates tomorrow.
Today, I only have 5 minutes.
So instead of showing you how it works, I want to show you what we've got coming up.
And like Shay said, we’ve started to integrate it with SQL, which means we can do neat stuff,
like create charts and graphs with one-liners, using nothing but SQL.
If you are interested in what is on the screen right now, this is actually from a
slot car track that we have set up at Elastic{ON} that we are streaming the data into Elasticsearch
from.
So we are going to look at a little bit of the data from that.
Instead of looking at this page, I want to move on to the next page.
So I'm going to go ahead and disable my auto refresh here and pop over to this page that
is much more boring.
And all we've got now is a blue dot in the middle there.
I will click on this and open up my little SQL editor here.
Right now, we are not selecting much at all right now, but because this is SQL, we
can do things like this.
So we can do, say, select driver as Y, Y referring to the Y axis, and we will do a group by driver.
All right.
There we go.
We have a list of drivers, not at all in alphabetical order but, of course, this is
Elasticsearch.
So we can still sort.
So let's order by driver.
All right.
So we’ve got everybody in alphabetical order now.
But let’s add something more useful instead of just a static row of dots here.
Let’s say average time as X, and also let's do max of speed, so the highest speed
that I attain on my way around the track, as size.
Okay.
Let's try that.
Okay, cool.
So now, I've got this list of how fast everybody is making it around the track on the X axis,
and then how fast they're going, what their max speed was, as the size of the dot.
And we're seeing some pretty obvious trends here, the faster you go, chances are, the
faster you are going to make it around the track.
But one of the things that I really love about SQL is that, well, a table's a table.
So whether you are doing aggregations of -- right now, we are doing that under the covers, we
are using average, max, and the group by is just running a terms aggregation right now.
But again in SQL, a table is just a table.
We don't have to do any of that, we can really get an idea of where the majority of races
are coming in, instead of just looking at rollups, we can look at raw documents.
So let’s change some stuff up here, we will get rid of the group by order, and we
will get rid of the driver all together.
So instead, let's do -- let’s do speed as Y, and we will leave time across the X
axis, and, oh, let's leave speed as size still anyway.
So no more group bys, there we go.
Okay, cool.
And now we've got a whole bunch of documents spread out across, and we can see that, for
the most part, there is a fat middle to this where a lot of people come in there, like,
right around here.
But there's also that really awesome, big dot way up there in the corner, it was the
rare intersection, where somebody managed to go around the track incredibly quickly,
but also hit a very, very high speed.
Although, technically, there was somebody
that managed to do it in a smoother way, that is, they didn't hit as high of a speed, but
they also did not have to brake as suddenly, they maintained a better exit speed than the
one with the highest speed, you need to fit the nice balance, and that is what we shoot
for at Elastic, a balance between operating really smoothly and also being really, really
fast.
So, good luck at the slot car track, I really hope you end up being the dot in the upper
left there.
All right.
Back to you, Shay.
>>SHAY BANON: Thank you very much, Rashid