Kubernetes monitoring is the practice of observing and measuring the health and performance of a Kubernetes cluster and its components.
The main goal is to ensure the availability, reliability, and scalability of the cluster and the applications running on it.
Key metrics to measure include those related to clusters, nodes, pods, deployments, services, containers, and applications.
Best practices for monitoring K8S involve examining details in granular level data; gathering historical system data beyond metrics; understanding control plane monitoring; creating an instrumentation strategy with alerting; using a platform to run containers on physical/virtual machine clusters; utilizing Prometheus; selecting comprehensive Kubernetes monitoring tools.
Popular Kubernetes monitoring tools include Prometheus, Grafana, Fluentd, LOGIQ, and ELK Stack.
Kubernetes monitoring is crucial to ensure the health and performance of a Kubernetes cluster and its components, including nodes, pods, containers, and applications running on the cluster. Monitoring Kubernetes can be challenging due to the numerous variables involved. Therefore, it is necessary to collect and analyze various metrics such as CPU usage, memory consumption, network traffic, and other performance indicators to ensure that the cluster is running optimally and to detect and troubleshoot any issues that may arise.
The article lists the key Kubernetes Monitoring metrics to measure, including cluster metrics, node metrics, pod metrics, deployment metrics, and container metrics.
Additionally, it outlines best practices for Kubernetes monitoring, such as maintaining uniform monitoring throughout the cluster, using appropriate tools, and ensuring efficient data collection.