On Wednesday, March 8, 2017 5:54:46 PM CET Heiko W.Rupp wrote:
Hi,
I know I brought that up in the past ... (and I am sure Juca will easily
find where :-)
In the good old datacenter, one deployed an application server with
application, added it to monitoring and it hummed there happily. The
server sometimes got rebooted for OS updates or other things, but the
app-server always stayed the same and the monitoring system knew all its
pets by their name and the admins happily did this:
http://www.starwars-union.de/bilder/news/20110401_sunset.jpg
Nowadays in orchestration system the situation more looks like this
http://www.animationsfilme.ch/wp-content/uploads/2013/07/DespicableMe_01.jpg
where containers come and go and an application in container once it has
died is not re-started but a new container with its own ID is started.
Of course we can identify applications with labels so that I don't need
to know the container id. So I can gather and display metrics for those
and all is fine.
But: all those containers will create new
- metric ids
- inventory entries
- ???
The question is now: how long do we want/need to keep them?
Isn't the question rather "how do we associate them with the
application(s)"?
Because if we want to track e.g. CPU load generated by an application in a
container - isn't that something users would want to look at history of? IMHO,
using the ephemeral "container id" as (part of) metric ids is a wrong thing to
do, because really the user isn't interested in the container itself, but the
applications that are running in it and their consumption of container's
resources.
Hawkular-metrics has a TTL for the datapoints, but I think metric
definitions are not evicted.
Similar a container being killed can't easily tell inventory that its
entry can go away.
For inventory-new we could use the expiration feature of
Hawkular-metrics for datapoints, where e.g. the agent would regularly
sync data and thus refresh the last-seen time to keep an entry "alive".
Also for the pure metrics - how much of historic data do we want/need.
And if we would e.g. aggregate those for long(er) term storage I think
we could perhaps actually aggregate over all individual time series over
many parallel pods and aggregate them into one for the entire
application.
Heiko
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Lukas Krejci