- Open Source
To do their jobs properly, developers need to know how their apps are behaving inside their containers. This capability is captured under the category of 'observability'
To monitor container-based apps properly you need to have direct and deep visibility into the underlying container platform. Containers can crash and be rescheduled in seconds, often meaning failures could go unnoticed by end users, but this doesn’t mean there isn’t a problem.
Through its close integration with the underlying container platforms, Portainer is able to help users not only identify issues in the application deployment but also identify issues in the container platform itself and provide a live visualization of what’s running where and how
Portainer is able to display your application logs, either at an individual container/pod level, or via an aggregated service/application view. Logs remain visible for the life of the container and are presented to the user via the Portainer GUI. Portainer even allows the logs to be saved locally to allow for in depth forensic analysis.
Application logs can be easily redirected to an external logging solution, such as ELK or Syslog.
Portainer also includes a cluster visualizer, allowing the user to quickly see which components of their applications are running on which physical hosts. This feature can be used to validate that load balancing is working as expected or that any placement constraints have been honoured.
Portainer displays any application rescheduling events in a clear and concise way, so the application administrator can quickly see if their application has been crashing and restarting/recovering, and the frequency of this occurring.
And finally, Portainer includes the ability to display an interactive console for every container/pod running in the environment, this is a great troubleshooting tool when you need to know exactly what is going on, and be able to triage directly in the running application environment.
Portainer displays the real-time performance of all applications running on the cluster through a dashboard. It incorporates a live steam of CPU/RAM/Disk/Network stats for each container/pod in the stack.