Kubewatch

Kubewatch addresses the operational challenge of monitoring and notifying teams about resource changes in Kubernetes clusters, which can be cumbersome and prone to oversight. The tool's architecture is built around three main components: the controller, config, and handlers.

The controller is the core component that initializes using a config object, either from a .kubewatch.yaml file or command line arguments. It utilizes SharedIndexInformers from kubernetes/client-go to listen for and watch resource changes, updating this information with the Kubernetes API Server. When events related to subscribed resources are detected, they are pushed to a rate-limiting queue to manage event handling efficiently. The controller then passes these events to the appropriate handlers after applying necessary filters.

The config object contains the kubewatch configuration, including handlers and filters, which are used to create a new client. This configuration can be managed through commands like kubewatch config add and kubewatch resource add, allowing for precise control over what resources are watched and how notifications are handled.

Kubewatch supports multiple notification channels, including Slack, Hipchat, Mattermost, MS Teams, Flock, and SMTP. Each handler implements the Handler interface to manage how events are processed and notifications are sent. For example, the Slack handler sends notifications to a specified Slack channel based on the configuration provided.

Operationally, kubewatch can be deployed in various ways, such as using Helm, kubectl, or Docker. When using Helm, you can set up the configuration and resources to be watched directly in the Helm installation command. With kubectl, you can create a ConfigMap to hold the kubewatch configuration and deploy it as a Pod or Deployment. Docker deployment involves mounting the configuration file and Kubernetes config to run the container interactively.

Key operational considerations include ensuring the correct configuration of notification channels and resources to be watched, as well as managing the performance impact of event handling at scale. The tool's use of rate-limiting queues helps in managing event volumes, but large clusters may still require careful tuning to avoid performance degradation. Additionally, the choice of deployment method (Helm, kubectl, or Docker) will depend on the existing infrastructure and operational preferences of the team.