cAdvisor

tags: ITRI, cAdvisor, K8s

Ref: Community of 8bitmen > What is cAdvisor? How Does it Work? Explained…

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1. What is cAdvisor?

It is container advisor. 從名字顯而易見：

• It provides resource usage, performance characteristics & related information about the containers running on the cloud.
• It is an open-source tool & runs as a daemon process in the background collecting, processing & aggregating useful DevOps information.
  • native support for Docker
  • enables us to track historical resource usage with histograms & stuff

Where	Description
In Linux	Where the official cAdvisor releases are built It has a small image size
In K8s	cAdvisor is integrated into the Kubelet binary It is pretty intelligent to auto-discover all the containers running in the machine & collect CPU, memory, file system & network usage statistics. It also provides a comprehensive overall machine usage by analyzing the root container.

簡而言之，cAdvisor 要能夠協助拿到 metadata 並藉由其它可視化工具顯示出來。抽象化來看 cAdvisor 想要解決的問題是：

Now, what is a Kubelet? I’ve talked about it in the cAdvisor Kubernetes section up ahead in the article. So, stay tuned.

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1-1. How Do I Access the Container Data Collected by cAdvisor?

cAdvisor 所收集的 data can be viewed with the help of:

• web-based UI which it exposes at its port.
• via a REST API

1-2. cAdvisor 可以收集怎麼樣的 data 呢？

• Container Information, typically
  • container name
  • list of sub-containers
  • container-spec
  • detailed usage statistics of the container for:
    • the last N seconds,
    • a histogram of resource usage, etc.
• Machine Information
  • the number of schedulable logical core CPUs,
  • memory capacity in bytes,
  • maximum supported CPU frequency,
  • available file systems, network devices,
  • the machine topology – nodes, cores, threads etc.

1-3. The Author's Experrience with cAdvisor

The author worked on a massive e-Comm project where we used Grafana, Prometheus & cAdvisor to set up a dashboard monitoring system.

• The dashboards were used to study the server instances, their uptime, exceptions, errors their contextual scenarios etc.

1-4. Prometheus + Grafana using dashboard to show the data

• All the data was displayed on custom Grafana dashboards.
• Queries were fired from the dashboard which hit Prometheus, which was plugged-in to Grafana as a data source.

Container information was streamed into Prometheus from cAdvisor.

The below diagram shows the data flow between these open source tools, kind of a Grafana, Prometheus, cAdvisor based dashboard monitoring architectural flow.

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2. How to Run cAdvisor With Docker?

[Ref] Quick Start: Running cAdvisor in a Docker Container

[shows] A single cAdvisor can be run to monitor the whole machine.

Also, the tool is a static Go binary with no external dependencies. We can run it standalone if we wish to. The runtime behaviour can be controlled via a series of flags.

cAdvisor also performs some housekeeping periodically. With these flags, we can control how & when the tool performs the task.

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3. Running cAdvisor With Prometheus

[Ref] Services and resources monitoring with Prometheus and Grafana running on Docker

就是一個數據分析 (data analytics) 和監控 (monitoring) 的組合。

• cAdvisor → Prometheus 兩者各自做了什麼事？
  • cAdvisor exposes container statistics as metrics
  • Prometheus needs to config Job to connect with cAdvisor.

Prometheus web-based UI

It is used to get a more comprehensive view of things.

Other examples to practice

• Monitoring Docker Services with Prometheus
• MONITORING DOCKER CONTAINER METRICS USING CADVISOR

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4. Running cAdvisor With Kubernetes by Heapster

Kubernetes uses a project known as Heapster.

• Heapster acts as a base monitoring platform. 也像其他的應用程式一樣在 k8s 上運行。
• Heapster runs in Kubernetes pods & pulls data from Kubelets.
  • 它的 pod 能發現同一 cluster 下的所有 nodes.
• It collects cluster-wide monitoring & event data.
  1. 然后從每一个 node 的 kubelet 中提取 metrics (一種度量標準)
  2. 並通過 pod 和 label 將其聚合起来
  3. 最後將其存儲到存儲系统或是監控服務中

• Heapster 的 API： Heapster Metric Model
• Heapster 中的監控數據：Metrics (with Labels + InfluxDB)

[Ref] k8s 监控部署

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5. What is Kubelet?

A Kubelet kind of manages things on the cluster.

• It manages the pods & the containers on a machine.
• It is responsible for fetching individual container usage statistics from cAdvisor.
  • The collected data is exposed via REST API
  • The entire data is saved in a data store such as InfluxDB & streamed to Grafana for visualization.

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