Excellent.
So we have this we have telegraph running on the influx based server, and it's connecting to three
different S&P Demons one local one on Mega-fauna server, which is 10 one three three zero three and
one of my Moscow 10 one three three zero four.
The Telegraph is requesting data from each of the SMP days, using UDP on four one six one.
So I've created firewall rules on each of those servers to allow the IP address of this influx DB server,
because that's where Telegraph is running to connect to those SMP demons and request.
Okay, so that works.
So just to prove that I'm on my influx DB server, I can run an SNP walk command.
Do S&P walk Virgin to public on my local computer and I'll get a response.
I can also run that for the cafe on a server there 10 one three three zero three.
And that gets a response.
Also, the same for 10 one three three zero four.
There's a response now each of the S&P Demons on those servers was configured to return.
Our kids starting at that prefix, that's much more data than would be returned if I just had those
two configured, because that also includes that and that.
Plus a lot more.
And also, one telegraph configuration is hauling each of those three S&P diamonds because you can also
verify that in influx DB by going to the Explore tab in your influx DB select interface.
Search for source and I can see my three service.
Excellent set now means I can import a pre-built dashboard, so on my documentation import and Typekit
dashboard for influx DB and telegraph this year this fall, Jason here.
Copy that that's copied to Clipboard.
Go into Safana dashboards, manage import, paste that into their and press load.
It's called snappy interfaces.
Import.
OK, now I'm going to set that down to last five minutes.
OK, so we have some things already set up for us.
So up here.
Influx DB mice will bafana it found those automatically that's using a dashboard variable.
We'll look into that in future videos.
There is a table here.
Easy to edit.
You can use these queries as reference if you want to create your own dashboards from different influx
DB data sources.
The important thing here is this map line here for just delete that.
Then the headings don't look so right.
So if I just put that back.
Then the column headings look much better.
These are the ones I'm looking for in Octopussy for each device.
If I press a to edit that, it's not one I'm using map again down here.
So if I just delete that, then all the series names are quite long and hard to read.
So I'm using the map function down here to make the series names much smaller.
It's also using what's called a derivative non-negative, and that is making the graph shows differences
between each timestamp.
Support took that away and zoomed out six hours, maybe 12 hours.
It's just forever increasing, so that's what the non-negative derivative is doing, showing us the
difference.
Can I put that back to five minutes apply.
Then you can see that I'm just looking at different interface properties on these crops up time.
So each of my different services have a different up time.
I'm doing mathematical equation on the result.
They're using the map function.
So I took that away shows that this is incorrect.
That's better.
So use those queries and the queries from the other influx DB dashboard as well that we imported earlier.
So that was influx DB system there as reference.
Note, though, that influx DB too, since it uses the flux query language is much more complicated
than the old influx que el query language.
If you look on dashboards on Gravano, you'll notice there aren't very many dashboards for influx DB
to.
I think a lot of people struggle with influx DB to be aware of that.
It's not easy.
You might have to find a specialized course in influx DB to if you want to continue with it.
And also another thing too.
I found it quite fragile.
If you do queries for six hours or more, the influx DB server starts to slow down very, very quickly.
Not always, but occasionally.
Sometimes you have to go onto the server and restart the service, so keep your influx DB queries shorter
time spans while you're setting them up or experimenting.
So it's not that influx DB one wasn't such a fancy system didn't come with an in-built fancy user interface
like this, and the crude language was much simpler.
But as you can see, it's come a long way.
For example, data here got a lot of choices.
But just be aware that influx, Debbie, is quite complicated in itself.
So you may need to do a specialized course in it anyway, so there's plenty of information for you to
use as a reference if you continue with the influx, Debbie.
So excellent.