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. Setup. 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. You may also need to grant additional permissions to access data from any AWS services you want to monitor. How AWS Fargate works. So far, we have covered how to use Datadog to monitor Kubernetes and Docker. Datadog APM traces individual requests as they propagate across your nodes, containers, and services. Build a framework for monitoring dynamic infrastructure and applications. Deploying HPAs can help your cluster automatically respond to dynamic workloads by spinning up new pods, for example, to add resource capacity or to distribute requests. 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. Launch it here. The best way to do this is by creating a Kubernetes secret. A dedicated monitoring service gives you a more complete picture of your EKS cluster’s health and performance. Example monitoring implementation with Datadog. Datadog will then enable its Redis monitoring check (redisdb) and query port 6379 of that container’s local host IP for metrics. You can also get additional context by looking at the other tags from different sources that Datadog has automatically applied to the container. » Get Datadog API credentials. First, enable tracing in the Datadog Agent by adding the following environment variable to your Datadog Agent manifest: Then, uncomment the hostPort for the Trace Agent so that your manifest includes: Next, we have to ensure that our application containers send traces only to the Datadog Agent instance running on the same node. In the above example, we see that the pod fan-6c948597c6-7hhtp has a CPU request of one core, and that this represents 50 percent of the node’s capacity, which is two cores. Once installed, you can use kubectl, the standard Kubernetes command line tool, to perform spot checks on the status and resource utilization of the objects running on your cluster. You can query CloudWatch metrics and statuses of your AWS resources using available commands. For our EKS cluster, we want to make sure to collect at least EC2 metrics. You can use tags to easily filter, search, and drill down to see the exact data you need. When we deploy Redis to our cluster, we can tell Datadog to ingest Redis logs from pods running that service using the following annotation: This tells Datadog’s Autodiscovery to look for containers identified by redis and tag logs coming from them with source:redis and service:redis-cache. It provides additional security because only one Agent needs the permissions required to access the API server. Gaining a better understanding of performance metrics is the best way to get a quick read of infrastructure health. Likewise, for the node summary at the bottom, we see that the total CPU requests of all pods on the node is 1,660 millicores, or 1.66 cores, which is 83 percent of the total available capacity. But as we discussed in Part 1, that’s only part of the EKS story; you will also want to monitor the performance and health of the various infrastructure components in your cluster that are provisioned from AWS services, such as EBS volumes, ELB load balancers, and others. ). This tutorial relies on the Katacoda scenario embedded below. The AWS CloudWatch web console lets users visualize and alert on metrics from most AWS services. Once the Datadog Agent has been deployed to your cluster, you should be able to see information about your EKS infrastructure flowing into Datadog. Once you deploy the Agent, you will have immediate access to the full range of Kubernetes cluster state and resource metrics discussed in Part 1. Autoscale your EKS cluster with Datadog metrics. # This is required by the agent to query the Kubelet API. The disk check is included in the Datadog Agent package, so you don’t need to install anything else on your server.. Configuration. Source Markdown for this post is available on GitHub. Now that the Agent has been deployed to your cluster, you should see information from your EKS infrastructure automatically flowing into Datadog. We will cover monitoring services in more detail below, but note that a monitoring agent on your nodes can also directly collect metrics from the node, separately from the core metrics pipeline. So, let’s say we want the Datadog Agent to automatically detect whenever a container is running Redis, and configure a check to start collecting Redis metrics from that container. 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. As a result, there are multiple ways to collect them. And you can use AWS CloudWatch to visualize, monitor, and alert on metrics emitted by the AWS services that power your EKS cluster. In particular, you can use the CloudWatch get-metric-statistics command to request metrics for a specific service. This is where logs can come in handy. Traditional platforms. Datadog, le principal prestataire de services de surveillance à l'échelle du cloud. You can then use Datadog to visualize the full lifespan of these requests, end to end. 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.