cadvisor VS Grafana

cAdvisor

https://github.com/google/cadvisor exports Prometheus metrics, but also offers a simple web-ui for container metrics.

version: '3' volumes: prometheus-data: driver: local grafana-data: driver: local services: prometheus: image: prom/prometheus:latest container_name: prometheus ports: - "9090:9090" volumes: - /etc/prometheus:/etc/prometheus - prometheus-data:/prometheus restart: unless-stopped command: - "--config.file=/etc/prometheus/prometheus.yml" grafana: image: grafana/grafana:latest container_name: grafana ports: - "3000:3000" volumes: - grafana-data:/var/lib/grafana restart: unless-stopped node_exporter: image: quay.io/prometheus/node-exporter:latest container_name: node_exporter command: - '--path.rootfs=/host' pid: host restart: unless-stopped volumes: - '/:/host:ro,rslave' cadvisor: # TODO: latest tag is not updated, check latest release https://github.com/google/cadvisor/releases image: gcr.io/cadvisor/cadvisor-arm:v0.47.0 container_name: cadvisor ports: - "8080:8080" network_mode: host volumes: - /:/rootfs:ro - /var/run:/var/run:ro - /sys:/sys:ro - /var/lib/docker/:/var/lib/docker:ro - /dev/disk/:/dev/disk:ro privileged: true restart: unless-stopped depends_on: - redis redis: image: redis:latest container_name: redis ports: - "6379:6379"

For CPU and memory metrics, you can use cAdvisor to collect container level data.

Perhaps https://github.com/google/cadvisor + prometheus (or influx or whatever else) + grafana?

We could have used a much more focussed tool like Prometheus or Cadvisor to gather system stats, but that is not the main objective of this article.

Result: Interestingly, the GitHub Issue that talks about cAdvisor and Docker Desktop for Mac for running cAdvisor is still open and not fixed.

If you're running things under systemd, you can enable process accounting and use cAdvisor.

Check this thread if you’re having difficulties, but it might run out of the box these days: https://github.com/google/cadvisor/issues/1846

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.