It's fantastic,” said Salomon, "The engineering team even started sharing metrics on monitors around the office. For information that may change often in a containerized environment, like host IPs and container ports, it’s helpful to use template variables so that the Agent can dynamically detect and communicate this information. Instead, data about the cluster state is maintained in key-value format in the etcd data stores. 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. The host map gives you a high-level view of your nodes. Some actions provide the ability to report custom datadog metrics from a workflow, however there weren’t any actions that automatically collected, formatted and reported development or developer velocity metrics to Datadog. Datadog needs read-only access to your AWS account in order to query CloudWatch metrics. If a metric is not submitted from one of the more than 400 Datadog integrations it’s considered a custom metric.Custom metrics help you track your application KPIs: number of visitors, average customer basket size, request latency, or performance distribution for a custom algorithm. Custom Metrics Overview. Note that EKS currently runs Kubernetes versions 1.10 or 1.11, so both services are supported. Datadog provides monitoring and insights into infrastructure and application performance across your entire stack. Note that the following instructions are tailored to monitoring Amazon EKS pods running on EC2 instances. 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. In other words, it aggregates and exposes metrics, logs, and events from AWS resources. See AWS’s documentation for detailed steps on deploying Dashboard and its supporting services, creating the necessary eks-admin service account, and then accessing Dashboard. Hello, We have coredns configured as a default eks-componemt for us using the kube-dns service in kube-system namespace . This includes an InfluxDB timeseries database, which is used to store the metric information for persistence. (Kubernetes Dashboard will retain and display resource usage metrics for the past 15 minutes.). 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 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. The Metrics tab of the web console lets you select individual AWS services and then view metrics for specific resources within that service. In this post, we will go over methods for accessing these categories of metrics, broken down by where they are generated: Finally, we’ll look at how a dedicated monitoring service can aggregate metrics from all sources and provide more complete visibility into your cluster. Essentially, it is a graphical wrapper for the same functions that kubectl can serve; you can use Kubernetes Dashboard to deploy and manage applications, monitor your Kubernetes objects, and more. For example: The above example shows three Deployments on our cluster. Three commands that are particularly useful for monitoring are: You can also view logs from individual pods using the kubectl logs command, which is useful for troubleshooting problems. For example, you can use the AutoScalingGroupName dimension to view CPU utilization for the EC2 instances that are part of a specific Auto Scaling group. Using a MetricTemplate custom resource, you configure Flagger to connect to a metric provider and run a query that returns a float64 value. If you select a span, you can view system metrics as well as relevant logs from the host that executed that span of work, scoped to the same timeframe. But first, we’ll describe how the Fargate serverless container platform works. Datadog will also import AWS event information for certain services. 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. Incident Management is now generally available! Please let us know. This repository contains the Agent Integrations that Datadog officially develops and supports. It reduces overall load on the Kubernetes API by using a single Cluster Agent as a proxy for querying cluster-level metrics. You may also need to grant additional permissions to access data from any AWS services you want to monitor. The Disk check is enabled by default, and the Agent collects metrics on all local partitions. Try less than 8 minutes easy! You can read more about how to use Datadog’s alerts in our documentation. Datadog updates the map every few seconds to reflect changes, such as containers being launched or terminated. These involve providing the appropriate permissions to the Cluster Agent and to the node-based Agents so each can access the information it needs. trying to integrate coredns with Datadog to collect the Coredns Metrics. Datadog will automatically pull in tags from your AWS account, Docker containers, and Kubernetes cluster. Datadog Cluster Agent | Custom & External Metrics Provider Introduction. Datadog alerts integrate with notification services like PagerDuty and Slack, letting you easily notify the right teams. In the final post of this series, we will cover how to use Datadog to monitor your entire EKS cluster—from the AWS components it relies on, to the state of its deployments, to the applications running on it—from a unified platform. 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. 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. For example, you can see if replicas for a Deployment are not launching properly, or if your nodes have little remaining resource capacity. https://www.datadoghq.com/blog/collecting-eks-cluster-metrics You can also get additional context by looking at the other tags from different sources that Datadog has automatically applied to the container. In part this is because CloudWatch gathers metrics from AWS services through a hypervisor rather than reading directly from each EC2 instance. Working with integrations is easy, the main page of … The Katacoda scenario has Terraform 0.13, the helm CLI, a running Kubernetes cluster, and the Terraform files required for this tutorial. As such, it is one of the easiest ways to collect metrics for the AWS services that your EKS cluster uses. 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. Datadog’s Agent will automatically collect metrics from your nodes and containers. You can also create custom dashboards to correlate metrics that are most important to you. Objects can be pods and their constituent containers or the various types of pod controllers, such as Deployments. To add a new integration, please see the Integrations Extras repository and the accompanying documentation. It also tells Datadog which log processing pipeline to use to properly parse key attributes from your logs, such as the timestamp and the severity. From version 1.8, Heapster has been replaced by Metrics Server (a pared down, lightweight version of Heapster). # This is required by the agent to query the Kubelet API. In the screenshot below, we’ve deployed an HPA that will monitor requests per second to pods running NGINX across our cluster, averaged by pod. Resources are identified via various CloudWatch dimensions, which act as tags. 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. (In this case, datapoints are aggregated at five-minute intervals, so this metric would have to be above the threshold for two datapoints within a 15-minute period.). After you install the service, Datadog will be able to aggregate these metrics along with other resource and application data. Support policies. In general, though, New Relic is considered a better "jack-of-all-trades" APM solution. This integration also includes support for Autodiscovery, so the Datadog Agent can immediately detect applications running in your cluster and collect monitoring data from them. 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. For our EKS cluster, we want to make sure to collect at least EC2 metrics. Viewing these alongside Kubernetes events can give you a better picture of what is going on with your cluster’s infrastructure. These annotations all begin with the following format: The container identifier tells Datadog what to look for in the names of new containers. Datadog also automatically pulls in any host tags from your EC2 instances (both those attached by AWS and any custom tags), so you can view your nodes by availability zone or by EC2 instance type. Kubernetes on VMware Tanzu Kubernetes Grid Integrated Edition . Autodiscovery is active by default. 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. Future releases of EKS will likely require you to use Metrics Server instead of Heapster to collect monitoring data from your cluster. 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. The permissions required to access CloudWatch are different from those attached to the EKS service role needed to administer your EKS cluster—see the AWS documentation for more information. However, these methods do have some drawbacks. 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. Logs can be invaluable for troubleshooting problems, identifying errors, and giving you greater insight into the behavior of your infrastructure and applications. The Agent will also begin reporting additional system-level metrics from your nodes and containers. Amazon Elastic Kubernetes Service (EKS) is a managed Kubernetes service that automates certain aspects of deployment and maintenance for any standard Kubernetes environment. As an example, below we’re setting a threshold alert that monitors a Kubernetes metric, CPU requests, measured per node. 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. Datadog’s Kubernetes, Docker, and AWS integrations let you collect, visualize, and monitor all of these metrics and more. Autoscale your EKS cluster with Datadog metrics. Questions, corrections, additions, etc.? This means that you can set alerts not just on the EKS cluster itself but also on the applications and services running on it. If so, the Agent then automatically configures and runs the appropriate check. Once the Datadog Agent has been deployed to your cluster, you should be able to see information about your EKS infrastructure flowing into Datadog. You can find steps for deploying Heapster or Metrics Server on GitHub. Figure 3 – Live Container view displays high-granularity metrics from all the containers running in your environment. How easy is it to get started with Datadog? 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. The Datadog Cluster Agent can act as an External Metrics Provider, meaning that if you are using the Cluster Agent to monitor your EKS infrastructure, you can deploy an HPA that will autoscale your pods based on any metric collected by Datadog. While you can get Kubernetes pod-level information, it’s difficult to get resource metrics on a container level. Before diving into specific ways of accessing and viewing Kubernetes metrics, it’s useful to understand how the different types of metrics are exposed or generated, because that can affect how you view them. These alerts can apply to any of the metrics, logs, or APM data that Datadog collects. The disk check is included in the Datadog Agent package, so you don’t need to install anything else on your server.. Configuration. Datadog APM provides you with deep insight into your application’s performance-from automatically generated dashboards monitoring key metrics Datadog tracing * How to integrate:- There are several steps needed to prepare your cluster for the Agent. Azure Kubernetes Service (AKS) Kubernetes on GKE. Kubernetes on AWS EKS. This tutorial relies on the Katacoda scenario embedded below. The Datadog Cluster Agent runs on a single node and serves as a proxy between the API servers and the rest of the node-based Agents in your cluster. Now, we’ll go over how to use Datadog to get full visibility into your EKS cluster and the applications and services running on it. You can find steps for deploying kube-state-metrics here. If you created an Auto Scaling policy for your worker node group, for example to scale up your node fleet to maintain a specified average CPU utilization, AWS will automatically add that policy as a CloudWatch alarm to alert you if the policy is triggered. You can also sort your containers by resource usage to quickly surface resource-heavy containers. The source sets the context for the log, letting you pivot from metrics to related logs. when all of the dependencies launch. Next, while Kubernetes Dashboard will display cluster state metrics by default, in order to view resource usage metrics from the Metrics API, you must make sure that you have already deployed Heapster. See below for more information on this. 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. In this case, it’s because the Deployment configuration specifies that pods running in this Deployment must be healthy for 90 seconds before they will be made available, and the Deployment was launched 17 seconds ago. The terminal will state Ready! Autoscale your EKS cluster with Datadog metrics. As of version 1.10, Kubernetes also … 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. Now that the Agent has been deployed to your cluster, you should see information from your EKS infrastructure automatically flowing into Datadog. Gaining a better understanding of performance metrics is the best way to get a quick read of infrastructure health. This output shows the four worker nodes in our EKS cluster. Then, create a file, dca-secret.yaml, with the following: Replace with the string from the previous step. It also makes it possible to configure Kubernetes’s Horizontal Pod Autoscaling to use any metric that Datadog collects (more on this below). 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. That is, because it is a Kubernetes cluster hosted on and using AWS services, the important metrics to monitor come from a variety of sources. In Part 1 of this series, we looked at key metrics for tracking the performance and health of your EKS cluster. We also see that these pods reflect the most recent desired state for those pods (UP-TO-DATE) and are available. Many third-party monitoring products and services use these to access, for example, the CloudWatch API and aggregate metrics automatically. The query result is used to validate the canary based on the specified threshold range. We are facing issues while. This exporter can process application traces along with a batch processor to be set up with a timeout of 10 seconds. 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. Actively monitoring your AWS resources all the time isn’t really feasible, so CloudWatch lets you set alarms that will trigger when a specific metric from an AWS service exceeds or falls below a set threshold. For an EKS cluster, which is a potentially very dynamic environment, setting proper alarms can make you aware of problems sooner. 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. Recall that these EKS metrics fall into three general categories: Kubernetes cluster state metrics, resource metrics (at the node and container level), and AWS service metrics. Datadog includes a number of checks based on Kubernetes indicators, such as node status, which you can also use to define alerts. You can also view logs from a specific pod by clicking the icon to the far right in the pod’s row. In order to enable log collection from your containers, add the following environment variables: Then, add the following to volumeMounts and volumes: With log collection enabled, you should start seeing logs flowing into the Log Explorer. Questions, corrections, additions, etc.? Similarly, Datadog’s Live Container view gives you real-time insight into the status and performance of your containers, updated every two seconds. 5 ms 10 ms 15 ms 20 ms 25 ms 30 ms 35 ms 40 ms 45 ms raw_query raw_query.catalog 50 ms 55 ms context_query sp... query 4.60 ms fetch 60 ms ".1 ms 39.0 ms 29.6 ms pylons. Note that Kubernetes Dashboard does not currently support Metrics Server. For example, forecasting tracks metric trends in order to predict and reveal possible future problems. Out of the box, Kubernetes's Horizontal Pod Autoscaler (HPA) can autoscale a controller's replica count based on a targeted level of CPU utilization averaged across that controller's pods. 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. Datadog automatically collects any tags that you add in AWS as well as metadata about each AWS component. You can use tags to easily filter, search, and drill down to see the exact data you need. Datadog APM includes support for auto-instrumenting applications; consult the documentation for supported languages and details on how to get started. This can be particularly useful to see a breakdown of the resource requests and limits of all of the pods on a specific node. Using tags, you can set different alerts that are targeted to specific resources. A dedicated monitoring service gives you a more complete picture of your EKS cluster’s health and performance. Datadog Reporting in GitHub Actions. See Datadog’s documentation for detailed instructions on this process. For Kubernetes, it’s recommended to run the Agent as a container in your cluster. # Has to be the same as the one exposed in the DCA. So far, we have covered two primary tools for monitoring Kubernetes metrics: Kubernetes Dashboard and the kubectl command line tool. For example, you might want to be alerted if the CPU load on your instances rises above a certain point, perhaps due to a surge in usage or maybe because of a problem with the pods running on the nodes. Datadog APM traces individual requests as they propagate across your nodes, containers, and services. 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. 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. If you don’t yet have a Datadog account, you can sign up for a 14-day free trial and start monitoring your EKS clusters today. You can find the logo assets on our press page. Nor will these tools or services let you monitor the applications or technologies running on your cluster. But if you are running an EKS cluster, Kubernetes metrics are likely only part of the story because you are using AWS services to provision components of your infrastructure. 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. It can centralize all of these sources of data into the same platform, and it can use an agent to access resource metrics directly from the node and its kubelet, without requiring you to install Metrics Server or Heapster. Under “Limit metric collection,” check off the AWS services you want to monitor with Datadog. 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. 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. Note that the output includes the percent of total available capacity that each resource request or limit represents. It provides additional security because only one Agent needs the permissions required to access the API server. “This is huge for us. 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. (Note that this cannot apply to a DaemonSet, as a DaemonSet automatically launches a pod on each available node.) See our documentation for more details on using Live Process Monitoring. It lets you automatically scale your pods using any metric that is collected by Datadog. The best way to do this is by creating a Kubernetes secret. Also, CloudWatch won’t provide any insight into pod- or container-level metrics, nor does it include disk space utilization information. This is also where kube-state-metrics becomes useful. Datadog will then enable its Redis monitoring check (redisdb) and query port 6379 of that container’s local host IP for metrics. This includes, for example, overall resource utilization metrics for your EC2 instances, disk I/O metrics for your persistent EBS volumes, latency and throughput metrics for your load balancers, and others. But troubleshooting a problem may require more detailed information. Example monitoring implementation with Datadog. Get metrics and logs from kubernetes service in real time to: Visualize and monitor kubernetes states; Be notified about kubernetes failovers and events. You can see AWS’s developer documentation for more information on supported languages and platforms for developing a custom solution.
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