For example: The above example shows three Deployments on our cluster. In this post, we’ve gone over how to use Datadog to gain deep visibility into your EKS cluster and the applications running on it. AWS provides documentation on deploying Prometheus to your cluster, which allows you to start visualizing your cluster’s control plane metrics in graphs like the one shown below. Once you’ve signed in to Kubernetes Dashboard, the main page will show you the overall status of your cluster: Here you can view much of the same information about the objects in your cluster as we saw from queries through kubectl. There are several steps needed to prepare your cluster for the Agent. Build a framework for monitoring dynamic infrastructure and applications. While it is possible to deploy the Datadog Agent without the Cluster Agent, using the Cluster Agent is recommended as it offers several benefits, particularly for large-scale EKS clusters: You can read more about the Datadog Cluster Agent here. Datadog can automatically collect logs for Docker, many AWS services, and other technologies you may be running on your EKS cluster. For example, selecting Pods in the sidebar shows an overview of pod metadata as well as resource usage information—if you have deployed Heapster—similar to what kubectl top pods would return. Source Markdown for this post is available on GitHub. Uncomment that line so that your manifest has the following: You can now instrument your applications to send custom metrics on port 8125 of the node they are running on. The terminal will state Ready! How AWS Fargate works. You should also be able to quickly drill down into specific sets of containers by using tags to sort and filter by pod, deployment, service, and more. As we explore these monitoring interfaces, we will also note when either Heapster or Metrics Server is required to access all the available metrics. Kubernetes on AWS. This is where logs can come in handy. If you don’t already have a Datadog account but want to follow along and start monitoring your EKS cluster, sign up for a free trial. You can also view logs from a specific pod by clicking the icon to the far right in the pod’s row. It organizes and provides visualizations of the information about your cluster that you can access through the command line, including cluster state and resource metrics. Each node’s kubelet uses cAdvisor to aggregate resource usage data from the containers running on that node. So, monitoring metrics emitted by Kubernetes can give you a fuller view of resource usage and activity. Datadog Reporting in GitHub Actions. We are facing issues while. Datadog provides a number of powerful alerts so that you can detect possible issues before they cause serious problems for your infrastructure and its users, all without needing to constantly monitor your cluster. In addition to the metrics that you get through Datadog’s integrations, you can send custom metrics from your applications running on your EKS cluster to Datadog using the DogStatsD protocol. Note again that, like kubectl describe, this information is different from what’s returned by something like kubectl top, which reports that node or pod’s actual CPU or memory usage. For Kubernetes, it’s recommended to run the Agent as a container in your cluster. Refer to the dedicated Kubernetes documentation to deploy the Agent in your Kubernetes cluster. Datadog’s AWS integration pulls in CloudWatch metrics and events so that you can visualize and alert on them from a central platform, even if you don’t install the Datadog Agent on your nodes. EKS uses AWS IAM for user authentication and access to the cluster, but it relies on Kubernetes role-based access control (RBAC) to authorize calls by those users to the Kubernetes API. For example, you can filter and view your resources by Kubernetes Deployment (kube_deployment) or Service (kube_service), or by Docker image (image_name). Up to Kubernetes version 1.13, this service was Heapster. Likewise, if you have Datadog APM enabled, the service tag lets you pivot seamlessly from logs to application-level metrics and request traces from the same service, for more detailed troubleshooting. After you install the service, Datadog will be able to aggregate these metrics along with other resource and application data. “This is huge for us. We can see various metadata for the node, including labels and annotations; condition checks reporting things like whether the node is out of disk space; overall and allocatable resource capacity, and a breakdown of memory and CPU requests and limits by pod. The disk check is included in the Datadog Agent package, so you don’t need to install anything else on your server.. Configuration. The Datadog Agent is open source software that collects and forwards metrics, logs, and traces from each of your nodes and the containers running on them. For example, you can view EC2 instance CPU utilization for your worker nodes. For example, logs from our Redis containers will be tagged source:redis and service:redis. To do this, create a new role in the AWS IAM Console and attach a policy that has the required permissions to query the CloudWatch API for metrics. Each line displays the total amount of CPU (in cores, or in this case m for millicores) and memory (in MiB) that the node is using, and the percentage of the node’s allocatable capacity that number represents. This image is hosted on the Docker Hub i… Launch it here. And you can use AWS CloudWatch to visualize, monitor, and alert on metrics emitted by the AWS services that power your EKS cluster. We can do this with the DATADOG_TRACE_AGENT_HOSTNAME environment variable, which tells the Datadog tracer in your instrumented application which host to send traces to. Datadog’s Watchdog automatically detects anomalies in your application performance metrics without any manual configuration, surfacing abnormal behavior in services across your infrastructure. Before turning to the Agent, however, make sure that you’ve deployed kube-state-metrics Recall that kube-state-metrics is an add-on service that generates cluster state metrics and exposes them to the Metrics API. First, create the Cluster Agent’s RBAC file, cluster-agent-rbac.yaml: Next, create the node-based Agent’s RBAC file, datadog-rbac.yaml: The next step is to ensure that the Cluster Agent and node-based Agents can securely communicate with each other. You can also provide custom values by including the following Kubernetes annotation in the manifest for the service you are deploying to your cluster: For example, let’s say our application uses a service, redis-cache. You can find steps for deploying kube-state-metrics here. For example, if we have an EC2 worker node called ip-123-456-789-101.us-west-2.compute.internal, we would view it with: There is a lot of information included in the return output. To deploy the Cluster Agent, create a manifest, datadog-cluster-agent.yaml, which creates the Datadog Cluster Agent Deployment and Service, links them to the Cluster Agent service account we deployed above, and points to the newly created secret: Make sure to insert your Datadog API key as indicated in the manifest above. Then create the secret: You can confirm that the secret was created with the following: Now that we have a secret in Kubernetes, we can include it in our Cluster Agent and node-based Agent manifests so that they can securely communicate with each other. This service generates cluster state metrics from the state information from the core API servers, and exposes them through the Metrics API endpoint so that a monitoring service can access them. However, these methods do have some drawbacks. Datadog, le principal prestataire de services de surveillance à l'échelle du cloud. » Get Datadog API credentials. Therefore, to test my code during development, I needed to have an agent server running somewhere. trying to integrate coredns with Datadog to collect the Coredns Metrics. Source: Datadog . From version 1.8, Heapster has been replaced by Metrics Server (a pared down, lightweight version of Heapster). To get even more insight into your cluster, you can also have Datadog collect process-level data from your containers, as well as logs, request traces, and custom metrics from the applications on your cluster. This includes control plane metrics and information that is stored in the etcd data stores about the state of the objects deployed to your cluster, such as the number and condition of those objects, resource requests and limits, etc. Future releases of EKS will likely require you to use Metrics Server instead of Heapster to collect monitoring data from your cluster. Questions, corrections, additions, etc.? Set up Datadog metrics, events, and traces collection. This exporter can process application traces along with a batch processor to be set up with a timeout of 10 seconds. The host map gives you a high-level view of your nodes. The query result is used to validate the canary based on the specified threshold range. You can configure your triggered alarms to send a notification to the appropriate team, or even initiate actions like starting or rebooting an EC2 instance. Note that EKS currently runs Kubernetes versions 1.10 or 1.11, so both services are supported. On this page. It lets you automatically scale your pods using any metric that is collected by Datadog. This will return logs for a previous instance of the specified pod or container, which can be useful for viewing logs of a crashed pod: Kubernetes Dashboard is a web-based UI for administering and monitoring your cluster. In this post, we’ll look at the key Fargate metrics you should monitor in addition to the Amazon ECS and EKS metrics you’re already collecting. But first, we’ll describe how the Fargate serverless container platform works. Datadog’s Kubernetes, Docker, and AWS integrations let you collect, visualize, and monitor all of these metrics and more. So, for both the Cluster Agent and the node-based Agents, we’ll need to set up a service account, a ClusterRole with the necessary RBAC permissions, and then a ClusterRoleBinding that links them so that the service account can use those permissions. Datadog: New Relic: 4/5: 5/5: 2. So far, we have covered how to use Datadog to monitor Kubernetes and Docker. CloudWatch provides a number of prebuilt dashboards for individual services, and a cross-service dashboard that shows select metrics from across your services. Datadog Exporter Overview. You can find the logo assets on our press page. For our EKS cluster, we want to make sure to collect at least EC2 metrics. Datadog includes customizable, out-of-the-box dashboards for many AWS services, and you can easily create your own dashboards to focus on the metrics that are most important to your organization. This visualizes similar data available from kubectl describe : In this case we see the requests and limits of CPU and memory for that node, and what percentage of the node’s allocatable capacity those requests and limits represent. For example, you may label certain pods related to a specific application and then filter down in Datadog to visualize the infrastructure for that application. Source Markdown for this post is available on GitHub. Datadog APM includes support for auto-instrumenting applications; consult the documentation for supported languages and details on how to get started. Resources are identified via various CloudWatch dimensions, which act as tags. But troubleshooting a problem may require more detailed information. Ease of Use. (You can also use nodeSelectors to install it only on a specified subset of nodes.). Amazon Elastic Kubernetes Service (EKS) est un service Kubernetes géré qui permet d’automatiser certains aspects du déploiement et de la maintenance de n’importe quel environnement Kubernetes standard. In Parts 1 and 2 of this series, we saw that key EKS metrics come from several sources, and can be broken down into the following main types: In this post, we’ll explore how Datadog’s integrations with Kubernetes, Docker, and AWS will let you track the full range of EKS metrics, as well as logs and performance data from your cluster and applications. In addition to threshold alerts tied to specific metrics, you can also create machine-learning-driven alerts. We also see that these pods reflect the most recent desired state for those pods (UP-TO-DATE) and are available. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics. As a result, there are multiple ways to collect them. Note that the Datadog Cluster Agent is configured as a Deployment and Service, rather than as a DaemonSet, because we’re not installing it on every node. See our documentation for more details on using Live Process Monitoring. We also see how many pods the node can hold and how many pods are currently running. Traditional platforms. Likewise, we can see that the nginx and redis Deployments request one pod each and that both of them currently have one pod running. Datadog will automatically pull in tags from your AWS account, Docker containers, and Kubernetes cluster. Once deployed onto your cluster, Heapster or Metrics Server will expose core resource usage metrics through the Metrics API, making them available to services like the Horizontal Pod Autoscaler; certain internal monitoring tools; and dedicated monitoring services. Collect metrics related to disk usage and IO. Please let us know. For example, the --tail flag lets you restrict the output to a set number of the most recent log messages: Another useful flag is --previous. In this case, the template variable, %%host%%, will auto-detect the host IP. This can be particularly useful to see a breakdown of the resource requests and limits of all of the pods on a specific node. Enter your AWS account ID and the name of the role you created in the previous step. You can collect metrics from CloudWatch with: In each case, you’ll need to configure secure access to the CloudWatch API. In your node-based Datadog Agent manifest, you can add custom host-level tags with the environment variable DD_TAGS followed by key:value pairs separated by spaces. Datadog includes a number of checks based on Kubernetes indicators, such as node status, which you can also use to define alerts. Enabling Datadog’s AWS integrations lets you pull in CloudWatch metrics and events across your AWS services. In this section, we will look at the following methods that you can use to monitor Kubernetes cluster state and resource metrics: Essentially, these are ways of interacting with the Kubernetes API servers' RESTful interface to manage and view information about the cluster. These features are not configured by default, but you can easily enable them by adding a few more configurations to your Datadog Agent manifest (not the Cluster Agent manifest if you are using the Cluster Agent). Hello, We have coredns configured as a default eks-componemt for us using the kube-dns service in kube-system namespace . The kubectl get