Monitoring Kubernetes with Datadog
This is the last post in a 4-part series about Kubernetes monitoring. Part 1 discusses how Kubernetes changes your monitoring strategies, Part 2 explores Kubernetes metrics and events you should monitor, Part 3 covers the different ways to collect that data, and this post details how to monitor Kubernetes performance with Datadog.
If you’ve read Part 3 on collecting metrics, you already know that properly monitoring your Dockerized infrastructure orchestrated with Kubernetes requires a tool capable of:
- Ingesting metrics from all the different layers of your infrastructure, even if your clusters are distributed across multiple data centers or cloud providers
- Aggregating metrics around Kubernetes labels for better context
- Tracking your running applications via Autodiscovery as they move across hosts
- All the advanced graphing and alerting features you need for production-ready infrastructure
Datadog offers all the essential functionalities for monitoring Kubernetes. Our Kubernetes and Docker integrations have been designed to tackle the considerable challenges of monitoring orchestrated containers, as explained in Part 1.
This post will show you how to set up Datadog to automatically collect the key metrics discussed in Part 2 of this series.
Full observability for your containerized infrastructure
Easily monitor each layer
After reading the previous parts of this series, you know that it’s essential to monitor the different components of your Kubernetes-orchestrated infrastructure. Datadog integrates with all of them to provide you with a complete picture of cluster health and performance:
- Datadog’s Kubernetes integration aggregates metrics, events and labels from Kubernetes
- The Docker integration natively collects all the container metrics you need for better accuracy when monitoring Kubernetes
- No matter where your Kubernetes clusters are running–AWS, Google Cloud Platform, or Azure– you can monitor the underlying hosts of your Kubernetes clusters with Datadog
- With 300 + integrations and full support for custom metrics, Datadog allows you to monitor all the applications running on your Kubernetes clusters
Thanks to Datadog’s Autodiscovery feature, you can continuously monitor your Dockerized applications without interruption even as they expand, contract, and shift across containers and hosts.
Autodiscovery continuously listens to Docker events. Whenever a container is created or started, the Agent identifies which application is running in the new container, loads your custom monitoring configuration for that application, and starts collecting and reporting metrics. Whenever a container is stopped or destroyed, the Agent understands that too.
Datadog’s Autodiscovery applies Kubernetes labels to application metrics so you can keep monitoring them based on labels, as you do for Kubernetes and Docker data.
Collect, visualize, and alert on Kubernetes metrics in minutes with Datadog.
Unleash Datadog for Kubernetes monitoring
First, you need to get the Datadog Agent running on your Kubernetes nodes.
Install the Datadog Agent
The Datadog Agent is open source software that collects and reports metrics from each of your nodes, so you can view and monitor your entire infrastructure in one place. Installing the Agent usually only takes a few commands.
When monitoring Kubernetes, it’s recommended to run the Agent in a container. We have created a Docker image with both the Docker and the Kubernetes integrations enabled.
Thanks to Kubernetes, you can take advantage of DaemonSets to automatically deploy the Datadog Agent on all your nodes (or on specific nodes by using nodeSelectors). You just need to create a manifest
.yaml file, pasting in the text you’ll find within the Datadog Agent installation page.
Then simply deploy the DaemonSet with the command
kubectl create -f /path/to/the/manifest/.yaml
Now that the Agent is running on nodes across your Kubernetes cluster, the next step is to configure it.
Configure the Agent
The Datadog Agent can be configured by editing the conf.yaml file in the conf.d directory. It is necessary so our Kubernetes check can collect metrics from cAdvisor which is running in the Kubelet.
First, override the instances. In the case of a standalone cAdvisor instance, use:
instances: host: localhost port: 4194 method: http
Also add the kubelet port number, such as:
Then if you want the Agent to collect events from the Kubernetes API, you have to set the
collect_events: True on only one Agent across the entire Kubernetes cluster. Other Agents should have this parameter set to False in order to avoid duplicate events. You also have to specify the namespace from which events will be collected (if not specified, the default namespace will be used):
Then you can control if the metrics should be aggregated per container image (
use_histogram: True) or per container (
Finally you can add custom tags using:
init_config: tags: - optional_tag1 - optional_tag2
You can also define a whitelist of patterns to collect raw metrics. For example:
enabled_rates: - cpu.* - network.* enabled_gauges: - filesystem.*
For additional information, you can refer to the Datadog Agent Docker container documentation.
Check that the Agent is running
You can make sure the Datadog Agent is running by executing
kubectl get daemonset
If the agent is correctly deployed, the output should have this form:
NAME DESIRED CURRENT NODE-SELECTOR AGE dd-agent 3 3 <none> 11h
The number of desired and current pods should be equal to the number of running nodes in your Kubernetes cluster (you can check this number by running
kubectl get nodes).
Dive into the metrics!
Once the Agent is configured on your Kubernetes nodes, you will have access to our default Kubernetes screenboard among your list of available dashboards. It displays the most important metrics presented in Part 2 and should be a great starting point for monitoring Kubernetes clusters.
You can also clone the template dashboard and customize it depending on your needs. You can for example add metrics from your containerized applications to be able to easily correlate them with Kubernetes and Docker metrics.
As you build out your dashboards, don’t forget to orient your Kubernetes monitoring around labels even when working with Docker or application metrics.
Use all the power of Datadog
Datadog offers all the advanced functionalities you need for monitoring Kubernetes, including flexible alerting, outlier and anomaly detection, dynamic aggregation using labels and tags, and correlation of metrics and events between systems.
Start monitoring your Kubernetes clusters
In this post, we’ve walked through how to use Datadog to collect, visualize, and alert on metrics from your infrastructure orchestrated by Kubernetes. If you’ve followed along with your Datadog account, you should now have greater visibility into the health and performance of your clusters and be better prepared to address potential issues.
If you don’t yet have a Datadog account, you can start monitoring Kubernetes clusters with a free trial.