Node memory monitor3/18/2023 Minimum amount of a given resource required for containers to run (should be summed over a node) Total memory capacity of your cluster’s nodes Total CPU capacity of your cluster’s nodes Percentage of allocated CPU currently in use Monitoring system resources helps ensure that your clusters and applications remain healthy. To understand how the number of running pods impacts resource usage (CPU, memory, etc.) in your cluster, you should correlate this metric with the resource metrics described in the next section. Keeping an eye on the number of pods currently running (by node or replica set, for example) will give you an overview of the evolution of your dynamic infrastructure. You should make sure the number of available pods always matches the desired number of pods outside of expected deployment transition phases. Number of pods currently existing but not available Kube_deployment_status_replicas_unavailable Kube_deployment_status_replicas_available Number of pods desired when the deployment started to specify a cap on the number (or percentage) of extra pods that can be created beyond the desired pods. to make sure you always have at least a certain number (or percentage) of pods available throughout the process. Note that readiness checks can be a better solution in some cases to make sure your pods are healthy before they receive requests (see section about health checks below).ĭuring a rolling update, you can also specify in the PodSpec. spec.minReadySeconds, which will temporarily prevent your pods from becoming available. You can specify a delay in your PodSpec using. They need some time to start so you want to leave them unavailable during that initiation time and not have them handle any incoming requests. Let’s say you have a Jenkins cluster where slaves are pods in Kubernetes. Indeed for some types of deployments, you might want to enforce a waiting period before making them available. NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE But current pods are not necessarily available immediately for their intended use. Then it deploys the needed pods the newly created pods are up and counted as current. In order to make sure Kubernetes does its job properly, you want to be able to check the health of pod deployments.ĭuring a deployment rollout, Kubernetes first determines the number of desired pods required to run your application(s). You’ll want to be sure that pods are healthy and correctly deployed, and that resource utilization is optimized. Since Kubernetes plays a central role in your infrastructure, it has to be closely monitored. Now that we’ve made this clear, let’s dig into the metrics you should monitor. Even when you are using Docker metrics, however, you should still aggregate them using the labels from Kubernetes. Throughout this post, we’ll highlight the metrics that you should monitor. That’s why you should really consider tracking metrics from your containers instead of from Kubernetes. This can lead to inaccuracies due to mismatched sampling intervals, especially for metrics where sampling is crucial to the value of the metric, such as counts of CPU time. And one of Heapster’s limitations is that it collects Kubernetes metrics at a different frequency (aka “housekeeping interval”) than cAdvisor, which makes the overall metric collection frequency for metrics reported by Heapster tricky to evaluate. As mentioned above, Kubernetes relies on Heapster to report metrics instead of the cgroup file directly. It’s important to understand that metrics reported by your container engine (Docker or rkt) can have different values than the equivalent metrics from Kubernetes. Part 3 of this series, which describes the different solutions to collect Kubernetes metrics, will give you more details on how Heapster works and how to configure it for that purpose. On each node, cAdvisor collects data about running containers that Heapster then queries through the kubelet of the node. We cannot talk about Kubernetes metrics without introducing Heapster: it is for now the go-to source for basic resource utilization metrics and events from your Kubernetes clusters. Where metrics come from Heapster: Kubernetes’ own metrics collector This part of the series digs into the different metrics you should monitor. But if you use the proper tools, know which metrics to track, and know how to interpret performance data, you will have good visibility into your containerized infrastructure and its orchestration. Part 1 discusses how Kubernetes changes your monitoring strategies, this post breaks down the key metrics to monitor, Part 3 covers the different ways to collect that data, and Part 4 details how to monitor Kubernetes performance with Datadog.Īs explained in Part 1, using Kubernetes for container orchestration requires a rethinking of your monitoring strategy. This post is Part 2 of a 4-part series about Kubernetes monitoring. Monitoring Kubernetes performance metrics
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