Infrastructure monitoring is an integral part of infrastructure management. It is an IT manager’s first line of defense against surprise downtime. Severe issues can inject considerable downtime to live infrastructure, sometimes causing heavy loss of money and material.
Source: opensource.com
Uber’s software architectures consists of thousands of microservices that empower teams to iterate quickly and support our company’s global growth. These microservices support a variety of solutions, such as mobile applications, internal and infrastructure services, and products along with complex configurations that affect these products at city and sub-city levels. To maintain our growth and architecture, Uber’s Observability team built a robust, scalable metrics and alerting pipeline responsible for detecting, mitigating, and notifying engineers of issues with their services as soon as they occur.
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M3, Uber’s open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics. To facilitate the growth of Uber’s global operations, we need to be able to quickly store and access billions of metrics on our back-end systems at any given time. As part of our robust and scalable metrics infrastructure, we built M3, a metrics platform that has been in use at Uber for several years now.
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This post explores the current state of metrics and monitoring in Kubernetes by walking through the gradual thought process that I experienced when learning this topic. Kubernetes needs some metrics for it’s basic out-of-the-box functionality, like autoscaling and scheduling. This is regardless of any monitoring solution you may want for the purpose of troubleshooting and alerting.
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