![]() ![]() You can see DataDog has been deployed with 3 main components: When helm command finished, you will see some things similar as: ➜ ~ kubectl get pods -n monitoring -no-headers | grep datadog datadog-2tspw 1/1 Running 0 2m datadog-6bd328d579-tj8lp 1/1 Running 0 2m33s datadog-cluster-agent-54hv37f8fb-cgp2f 1/1 Running 0 2m23s datadog-kube-state-metrics-56gre5ft89-lx8e9 1/1 Running 0 1m56s datadog-pvltl 1/1 Running 0 2m55s datadog-qtlh9 1/1 Running 0 3m12s ➜ ~ kubectl get svc -no-headers | grep datadog datadog ClusterIP 172.20.6.24 8125/UDP 2m datadog-cluster-agent ClusterIP 172.20.10.15 5005/TCP 2m datadog-cluster-agent-metrics-api ClusterIP 172.20.12.27 443/TCP 2m datadog-kube-state-metrics ClusterIP 172.20.24.17 8080/TCP 2m ➜ ~ kubectl get deployments -no-headers | grep datadog datadog 1/1 1 1 3m datadog-cluster-agent 1/1 1 1 3m datadog-kube-state-metrics 1/1 1 1 3m ➜ ~ kubectl get daemonsets -no-headers | grep datadog datadog 3 3 3 3 3 3m I will share more examples later in the Dashboards and Monitors sections.ĭeploy DataDog using helm: ➜ ~ helm install -name datadog -namespace monitoring -f datadog-k8s-values.yaml -set datadog.apiKey= -set datadog.appKey= -version=1.39.9 stable/datadog ![]() Labels or tags are very important when you design and set up a monitoring system for your core infrastructure, which will help you easily organize, group, filter or focus your data to troubleshoot and understand your environment. I’m using the EKS cluster for this tutorial, so you can see the tags defined as cloud:aws and distribution:eks, and naming my cluster as the following convention -xxx, which is dev-eks-datadog-001. I have generated sample values.yaml for DataDog deployment as follows: Since version 2.x.x, DataDog Helm Chart has refactored and moved to its official repo, but the concepts are the same. You can visit to learn how to set up.įor some reason, in this blog, I will use DataDog Helm Chart from a stable repository and version 1.39.9. Administrator permission on a running AWS EKS cluster. ![]()
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