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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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Monitor CoreDNS with Datadog
David M. Lentz · 2019-05-30 · via Datadog | The Monitor blog

CoreDNS is a DNS server that can also provide service discovery for microservice-based applications. It’s the default DNS server in Kubernetes, providing name resolution and service discovery for the services operating in the cluster. CoreDNS is easily customizable, so you can define how it should act on each request beyond simply executing a DNS lookup. Datadog is pleased to announce that with our new integration, you can now monitor CoreDNS metrics to better understand the activity in your Kubernetes clusters.

Look inside the cluster

Along with Kubernetes metrics you’re already monitoring, CoreDNS metrics can help you see what’s happening inside your cluster. A simple request rate metric like coredns.request_count will show you how busy CoreDNS is, and you can look deeper to understand how requests are being resolved.

The screenshot below illustrates two metrics you can monitor to see how CoreDNS is responding to requests. The graph on the left shows the percentage of requests resolved from the CoreDNS cache. If this is low, even while overall traffic is high, you should consider raising the TTL value in the CoreDNS cache plugin configuration to keep records cached longer. The graph on the right shows the breakdown of requests forwarded by CoreDNS to two different upstream DNS servers. In this example, the forward plugin sends all requests to either 8.8.8.8 or 9.9.9.9.

One graph shows the percentage of requests served from the CoreDNS cache. Another graph shows the count of requests forwarded to an upstream server in each of the last sixty minutes.
One graph shows the percentage of requests served from the CoreDNS cache. Another graph shows the count of requests forwarded to an upstream server in each of the last sixty minutes.

If any of your configured upstream servers are missing from this graph, they may not be responding. In this case, you can revise the forward section of your Corefile, using health_check and expire values to tune the performance of the plugin.

Monitor DNS latency

You can also use Datadog to monitor your CoreDNS latency. If CoreDNS is slow to resolve requests, your users could encounter poor performance even if your backend services are all responding quickly. In a case like this, the coredns.request_duration.seconds.sum metric can show you how much DNS latency is contributing to overall, user-facing response time.

The latency curve shown on this graph is calculated by dividing the total request duration by the number of samples taken.
A graph shows how long CoreDNS took to respond to requests in the last fifteen minutes.
The latency curve shown on this graph is calculated by dividing the total request duration by the number of samples taken.

Keep an eye on errors

When CoreDNS encounters an error, it returns an rcode—a standard DNS error code. Errors like NXDomain and FormErr can reveal a problem with the requests CoreDNS is receiving, while a ServFail error could indicate an issue with the function of the CoreDNS server itself. When you monitor the coredns.response_code_count metric, you can reveal trends in your error rates and create alerts to automatically notify you if error rates cross an acceptable threshold. This metric includes a tag for each rcode present, and you can use those tags to create a graph that shows the count of each rcode value. You can then see at a glance how many errors of each type have been reported over a given time interval.

A bar chart show the count of the different rcodes in each of the last fifteen minutes.

Monitor resource usage

Resource usage metrics show you how your CoreDNS server is using its available infrastructure resources. To spot resource constraints, you can collect metrics showing how CoreDNS is consuming host resources like memory and processor time, and OS resources like file descriptors.

A timeseries graph shows an area representing data about open file descriptors, and one representing data about CPU usage.
A timeseries graph shows an area representing data about open file descriptors, and one representing data about CPU usage.

If you see CoreDNS using more and more resources on your host, you can scale out and load balance your DNS across multiple CoreDNS instances. If your graphs show you’re running out of file descriptors, you may need to modify your server configuration to increase the available limit. (See man limits.conf for information about how to do this on a Linux host.) By setting an alert on these metrics, you can receive an automated notification in time to make the necessary changes before your cluster’s performance is affected.

Watch your plugins

You can customize your CoreDNS server by adding any number of plugins—functions that process each DNS query and extend the functionality of CoreDNS. You can choose from a collection of plugins built into CoreDNS, external plugins contributed by the community, and custom plugins you write yourself. Some plugins emit metrics, so you can monitor the particular function they provide. (By convention, a plugin’s README.md file will include a section describing its metrics.) When you create your own plugins, you can build in telemetry that is useful to you. See the example plugin from the CoreDNS project for plugin code that emits a simple metric.

To configure the CoreDNS integration to collect plugin metrics, add those metrics to the list in the conf.d/coredns.d/conf.yaml file in the Agent’s configuration path. Once you’re collecting metrics from your CoreDNS plugins, you can monitor them in your Datadog dashboards and alerts alongside your traffic, error, and resource metrics for a detailed view of your DNS activity.

Resolve to increase visibility

You can use Datadog to monitor your Kubernetes clusters in EKS, AKS, GKE, or on self-managed infrastructure, and now you can add CoreDNS monitoring to learn more about the performance of your clusters. To start monitoring CoreDNS, first enable the prometheus plugin. This will expose Prometheus metrics at the endpoint defined in the plugin’s configuration. When you’re seeing Prometheus metrics at that URL, you’re ready to configure the integration to send metrics to your Datadog account. See our integration documentation for details. If you’re not already using Datadog, sign up for a free 14-day trial and start monitoring CoreDNS and more than1,000 other technologies in one place.