WEBVTT 00:01.050 --> 00:04.140 Hello and welcome to this lecture. In this lecture 00:04.170 --> 00:10.370 we talk about Monitoring a Kubernetes cluster. 00:10.410 --> 00:16.890 So how do you monitor resource consumption on Kubernetes? Or more importantly what would you like 00:16.890 --> 00:18.300 to monitor? 00:18.300 --> 00:23.330 I’d like to know Node level metrics such as the number of nodes in the cluster, 00:23.460 --> 00:31.500 how many of them are healthy as well as performance metrics such as CPU. Memory, network and disk utilization. 00:32.160 --> 00:38.670 As well as POD level metrics such as the number of PODs, and performance metrics of each POD such 00:38.670 --> 00:41.940 as the CPU and Memory consumption on them. 00:41.940 --> 00:48.600 So we need a solution that will monitor these metrics store them and provide analytics around this data 00:49.830 --> 00:56.040 As of this recording, Kubernetes does not come with a full featured built-in monitoring solution. 00:56.040 --> 01:03.510 However, there are a number of open-source solutions available today, such as the Metrics-Server, Prometheus, 01:04.020 --> 01:09.450 Elastic Stack, and proprietary solutions like Datadog and Dynatrace. 01:12.580 --> 01:18.870 Heapster was one of the original projects that enabled monitoring and analysis features for kubernetes 01:18.870 --> 01:25.640 You will see a lot of reference online when you look for reference architectures on monitoring Kubernetes. 01:25.780 --> 01:33.580 However, Heapster is now Deprecated and a slimmed down version was formed known as the Metrics Server. 01:33.580 --> 01:40.310 You can have one metrics server per kubernetes cluster the metric server retrieves metrics from each 01:40.310 --> 01:46.100 of the kubernetes nodes and pods, aggregates them and stores them in memory. 01:46.100 --> 01:52.340 Note that the metric server is only an in memory monitoring solution and does not store the metrics 01:52.400 --> 01:57.480 on the desk and as a result you cannot see historical performance data. 01:57.650 --> 02:04.760 For that you must rely on one of the advanced monitoring solutions we talked about earlier in this lecture. 02:04.760 --> 02:10.050 So how are the metrics generated for the PODs on these nodes? Kubernetes 02:10.060 --> 02:17.040 runs an agent on each node known as the kubelet, which is responsible for receiving instructions 02:17.040 --> 02:22.430 from the kubernetes API master server and running PODs on the nodes. 02:22.650 --> 02:30.750 The kubelet also contains a subcomponent known as as cAdvisor or Container Advisor. cAdvisor is 02:30.750 --> 02:37.140 responsible for retrieving performance metrics from pods, and exposing them through the kubelet API 02:37.200 --> 02:44.480 to make the metrics available for the Metrics Server. If you are using minikube for your local cluster, 02:44.840 --> 02:46.850 run the command minikube 02:47.000 --> 02:54.770 addons enable metrics-server. For all other environments deploy the metrics server by cloning the 02:54.770 --> 03:00.950 metrics-server deployment files from the github repository. And then deploying the required components 03:01.310 --> 03:08.810 using the kubectl create command. This command deploys a set of pods, services and roles to enable 03:08.810 --> 03:14.150 metrics server to poll for performance metrics from the nodes in the cluster. 03:14.150 --> 03:22.190 Once deployed, give the metrics-server some time to collect and process data. Once processed, cluster performance 03:22.190 --> 03:27.700 can be viewed by running the command kubectl top node. 03:27.770 --> 03:34.850 This provides the CPU and Memory consumption of each of the nodes. As you can see 8% of the CPU 03:34.850 --> 03:40.480 on my master node is consumed, which is about 166 milli cores. 03:40.820 --> 03:48.730 Use the kubectl top pod command to view performance metrics of pods in kubernetes. 03:48.890 --> 03:50.420 That's it for this lecture. 03:50.420 --> 03:55.740 Head over to the coding exercises section and practice viewing performance metrics on the kubernetes 03:55.740 --> 03:57.120 cluster. 03:57.170 --> 03:57.660 Thank you.