Kube-proxy is a network proxy that runs on each node in your cluster, implementing part of the Kubernetes Service.
The prerequisites for monitoring Kube-proxy with Netdata are to have Kube-proxy and Netdata installed on your system.
Netdata auto discovers hundreds of services, and for those it doesn’t turning on manual discovery is a one line configuration. For more information on configuring Netdata for Kube-proxy monitoring please read the collector documentation.
You should now see the Kube-proxy section on the Overview tab in Netdata Cloud already populated with charts about all the metrics you care about.
Netdata has a public demo space (no login required) where you can explore different monitoring use-cases and get a feel for Netdata.
The rate at which the Kubernetes proxy rules are synced across the cluster. It is important to monitor this metric to ensure that the Kubernetes proxy is working correctly and that service requests are being routed correctly. Issues that can arise from inadequate syncing of proxy rules include service requests being routed to the wrong node or not being routed at all.
This metric measures the latency of the Kubernetes proxy rule syncing process. A high latency indicates that the process is taking longer than expected and may be an indication of an underlying issue. By monitoring this metric, we can identify potential bottlenecks and take corrective action.
This metric measures the rate of HTTP requests made by the Kubernetes REST client, broken down by HTTP status code. This metric can give us insight into the health of our services, as well as what requests are successful and which are not. Monitoring this metric can help to identify issues with the services, such as problems with availability or performance.
This metric measures the rate of HTTP requests made by the Kubernetes REST client, broken down by HTTP method. This metric can give us insight into the types of requests being made, and how successful they are. Monitoring this metric can help to identify issues with the services, such as requests not being handled correctly or requests taking too long to complete.
This metric measures the duration of HTTP requests. It is important to monitor this metric to ensure that requests are being handled in a timely manner. High latency requests can indicate an underlying issue, such as slow queries or a lack of resources. Monitoring this metric can help to identify potential bottlenecks and take corrective action.