We integrated the Docker PaaS with the DBaaS Time Series, to allow you to graph your application metrics about your containers in a Grafana dashboard. We automatically send metrics for:
- CPU usage
- memory usage
- network I/O
- disk I/O
- out of memory kill count (coming soon!)
Configure your token
From the RunAbove manager, join the DBaaS Time Series lab and generate a new credentials pair (read/write). Next, inject the write credentials into your applications using
Metrics are aggregated, averaged and pushed every minute.
Analyse my data
Once your applications metrics are sent to the DBaaS Time Series, you can create some graphs to easily watch how your applications behave, and adapt your stack according to your needs. Available metrics for graphs are:
Filtering using tags
On each metrics, you can filter on the following tags:
- frameworkID: your marathon ID (useful if you have multiple stack logging metrics with the same credentials)
- appID: your application ID (useful if you want to graph multiple applications instead of the full stack)
- taskID: your task ID (identifying a single container)
The CPU data are not dependent of the host capacity, but on the limit you are setting in your application. For example, if you create an application with 2 cpus, and if it consumes only one, your CPU usage will be 50%
Be careful though, if you create an application with a cpu limit less than one, let's say 0.5, the application could be able to consume one cpu if needed resources are available. Your CPU usage will be 200%.
To interpret your data in Grafana, you have multiple ways. We have two interesting use cases:
- you have a few apps, or a graph per app: you should filter your CPU usage metrics using the flag
taskID: *and a
- you have a lot of apps to be displayed in a single graph: you should filter your CPU usage metrics using the flag
appID: *and an
As for the CPU data, the usage and limit increase depending on your instances count. We recommend to use task ID tags to make the data more readable.
Networking and disk data
These data are cumulative. We recommend to use rate graphs for these values, so you will be able to see usage variation. Be careful, you can sometime have negative values. This usually happens on a scale down because of the rate graph: collected data are cumulative, and when a container is deleted, they are removed from the sum. So, the rate is interpreting this as a negative value.