Docker Swarm & Container Overview

It modifies a little bit the original dashboard to adapt the graphs to fit better with a Docker swarm cluster which is running cAdvisor and Node Exporter on each node.

To use it you need:

• A Docker swarm mode cluster.
• Launch some services. Swarm will automatically propagate some labels that are used by the dashboard.
• If you don’t launch the services using the “docker stack deploy XXX” command there’s another label that you’ll need to launch per service (–container-label com.docker.stack.namespace=XXX) to identify those services by its intended usage.
• You’ll need to launch cAdvisor and node-exporter as global service in the cluster, and use the same network for them and the Prometheus instance.
• Node exporter can export extra metrics, in this case I found useful to export the hostname, to allow the node metrics to be split by it. An example of this can be found in the repo: https://github.com/bvis/docker-node-exporter
• Prometheus needs to be launched with the auto-discovery configuration based on DNS.
• It uses Elasticsearch to search errors in the logs generated by logstash
• It uses Elasticsearch, in another index, to check for alerts fired and resolved
• It assumes that in your cluster you are using a proxy for public traffic. You can decide from your services list which one is your proxy. I find useful to have split the traffic of this service, because it basically can distort the traffic metrics. This configuration should work:

Copy

global:
  scrape_interval:     30s
  evaluation_interval: 30s

  labels:
      cluster: swarm
      replica: "1"

scrape_configs:
  - job_name: 'cadvisor'
    dns_sd_configs:
    - names:
      - 'tasks.cadvisor'
      type: 'A'
      port: 8080

  - job_name: 'node-exporter'
    dns_sd_configs:
    - names:
      - 'tasks.node-exporter'
      type: 'A'
      port: 9100

Based on Docker Host & Container Overviewby by uschtwill https://grafana.net/dashboards/395.

More info about the usage of this dashboard can be found in this repo

If you liked it, you may buy me a coffee.