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Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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How to monitor Oracle’s Kubernetes Engine with Datadog
Mallory Mooney · 2019-09-10 · via Datadog | The Monitor blog

Oracle’s Container Engine for Kubernetes (OKE) is a service that helps you deploy, manage, and scale Kubernetes clusters in the cloud. With OKE, organizations can build dynamic containerized applications by incorporating Kubernetes with services running on their Oracle Cloud Infrastructure.

We’ve partnered with Oracle so that you can use the Datadog Agent to get comprehensive visibility into your Kubernetes clusters on Oracle Cloud Infrastructure. Once you’ve enabled our Kubernetes integration, you can visualize your OKE container infrastructure, monitor live processes, and track key metrics from all of your pods and containers in one place.

With Datadog’s Live Container view, you can monitor your entire cluster in real time.
live container view
With Datadog’s Live Container view, you can monitor your entire cluster in real time.

How Oracle’s Container Engine for Kubernetes works

Oracle Cloud Infrastructure Container Engine for Kubernetes (OKE) provides a CLI and Console (browser-based interface) for creating and managing Kubernetes clusters. You can set up OKE to automatically provision and launch Kubernetes clusters based on a custom configuration or through a “quick cluster” option in the Console.

When OKE launches a cluster, it creates control plane and worker nodes in a node pool along with all of the network resources needed for that cluster, including a Virtual Cloud Network. You can view more details about the cluster and nodes in the OKE Console, as seen in the example below.

view of a cluster created with the oracle kubernetes engine

Since OKE is a managed service, you can easily modify your cluster and download your cluster’s kubeconfig file in order to perform additional management tasks with kubectl, including deploying the Datadog Agent.

Monitor your OKE clusters with Datadog

Monitoring Kubernetes is crucial to understanding the health of your dynamic, distributed environment. Once you deploy the Datadog Agent on your OKE cluster, you can track the load on your clusters, pods, and individual nodes to get better insights into how to provision and deploy your resources. In addition to monitoring your nodes, pods, and containers, the Agent can also collect and report metrics from the services running in your cluster, so that you can:

Deploying the Agent as a DaemonSet via our Helm chart is the most straightforward (and recommended) method, since it ensures that the Agent will run as a pod on every node within your cluster and that each new node automatically has the Agent installed. You can also configure the Agent to collect process data, traces, and logs by adding a few extra lines to a Helm values file.

Explore a high-level view of your OKE clusters

Datadog includes several built-in Kubernetes dashboards so you can monitor your Kubernetes Controller Manager and Scheduler as well as get a high-level view of your pods and kubelets. These dashboards automatically track key Kubernetes events and metrics, including:

  • the number of running pods per node

  • the most CPU intensive pods

  • the number of running containers

You can clone any built-in dashboard and modify it to monitor the data that’s most important to you.

custom kubernetes dashboard

Monitor containers and processes in real time

With Datadog’s Live Container view, you can get detailed insights into your containers’ resource consumption, logs, and health in real time. Regardless of the size of your Kubernetes deployments on Oracle Cloud Infrastructure, you can quickly drill down to inspect any container (or a group of containers) when you need to troubleshoot an issue.

Datadog organizes your data with tags, including metadata that the Agent automatically extracts from Kubernetes (e.g., pod_name, container_id, docker_image, kube_deployment). These tags help you search, filter, and group all of the containers in your OKE cluster in Datadog. The example below shows all containers running from a single deployment, grouped by host.

docker containers filtered by kubernetes deployment and grouped by host

If you select a specific container, you can view its resource metrics (at two-second resolution), running processes, and logs. From there, you can pivot to a host dashboard as well as other related logs and processes collected by the Agent. This provides you with more context as you monitor your systems and debug issues.

If one of your containers begins consuming too many resources, you can dig into its processes to determine which one is causing the problem through Datadog’s Live Process view. For example, you can track all running processes for a single OKE node.

list of processes for a single kubernetes node

This view enables you to easily visualize resource consumption (e.g., total CPU, RSS memory) for processes within a single container, using any of the tags that are automatically pulled from Kubernetes.

Autodiscover containerized services

As your infrastructure grows, tracking its many moving pieces becomes more difficult as containers are created (or destroyed) and move across nodes. The Datadog Agent’s Autodiscovery feature helps you stay on top of the containerized services running in your dynamic environment. Once enabled, the Datadog Agent will automatically collect and report on data from the services running on the containers in your OKE cluster (e.g., Cassandra, Redis, PostgreSQL), even as they move across your infrastructure. And, if a container shuts down or is destroyed, the Agent will disable checks for those containers. Autodiscovery helps you monitor only the resources you need so you can be confident in knowing that the data you see in Datadog is the latest.

Know what’s going on with your OKE clusters

With Datadog, you can get deep visibility into all of the Kubernetes clusters running on your Oracle Cloud Infrastructure, alongside more than 1,000 other technologies running in your environment. If you already use Datadog, check out our Kubernetes documentation to learn more about how you can seamlessly monitor your OKE clusters alongside the rest of your infrastructure. Otherwise, sign up for a free trial of Datadog to get started.