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Datadog | The Monitor blog

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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Monitor Varnish using Datadog
2015-07-28 · via Datadog | The Monitor blog

This post is part 3 of a 3-part series on how to best monitor Varnish. Part 1 explores the key metrics available in Varnish, and Part 2 is about collecting those metrics on an ad-hoc basis.

In order to implement ongoing, meaningful monitoring, you will need a dedicated system that allows you to store all relevant Varnish metrics, visualize them, and correlate them with the rest of your infrastructure. You also need to be alerted when anomalies occur. In this post, we’ll show you how to start monitoring Varnish with Datadog.

Varnish cache Datadog dashboard

Integrating Datadog and Varnish

Verify that Varnish and varnishstat are working

Before you begin, run this command to verify that Varnish is running properly:

varnishstat -1 && echo -e "VarnishStat - OK" || \ || echo -e "VarnishStat - ERROR"

Make sure the output displays “Varnishstat - OK”:

Varnish running check

Install the Datadog Agent

The Datadog Agent is open-source software that collects and reports metrics from your different hosts so you can view, monitor and correlate them on the Datadog platform. Installing the Agent usually requires just a single command. Installation instructions for different systems are available here.

As soon as the Datadog Agent is up and running, you should see your host reporting metrics in your Datadog account.

Varnish host reporting to Datadog

Configure the Agent

Next you will need to create a Varnish configuration file for the Agent. You can find the location of the Agent configuration directory for your OS here. In that directory you will find a sample Varnish config file called conf.yaml.example. Copy this file to varnish.yaml, then edit it to include the path to the varnishstat binary, and an optional list of tags that will be applied to every collected metric:

init_config:

instances:

- varnishstat: /usr/bin/varnishstat

tags:

- instance:production

Save and close the file.

Restart the Agent

Next restart the Agent to load your new configuration. The restart command varies somewhat by platform; see the specific commands for your platform here.

Verify the configuration settings

To check that Datadog and Varnish are properly integrated, execute the Datadog info command. The command for each platform is available here.

If the configuration is correct, you will see a section like this in the info output:

Checks

======

[...]

varnish

-----

- instance #0 [OK]

- Collected 8 metrics & 0 events

Turn on the integration

Finally, click the Varnish “Install Integration” button inside your Datadog account. The button is located under the Configuration tab in the Varnish integration settings.

Install Varnish integration with Datadog

Metrics!

Once the Agent begins reporting Varnish metrics, you will see a Varnish dashboard among your list of available dashboards in Datadog.

The basic Varnish dashboard displays the key metrics highlighted in our introduction to Varnish monitoring.

Varnish dashboard on Datadog

You can easily create a more comprehensive dashboard to monitor your entire web stack by adding additional graphs and metrics from outside systems. For example, you might want to graph Varnish metrics alongside metrics from your Apache web servers, or alongside host-level metrics such as network traffic. To start building a custom dashboard, clone the default Varnish dashboard by clicking on the gear on the upper right of the dashboard and selecting “Clone Dash”.

Clone Varnish dashboard

Alerting on Varnish metrics

Once Datadog is capturing and visualizing your metrics, you will likely want to set up some alerts to be automatically notified of potential issues.

Datadog can monitor individual hosts, containers, services, processes—or virtually any combination thereof. For instance, you can monitor all of your Varnish hosts, or all hosts in a certain availability zone, or a single key metric being reported by all hosts corresponding to a specific tag.

Below we’ll walk through a representative example: an alert on Varnish’s dropped connections.

Monitor Varnish’s dropped client connections

Datadog alerts can be threshold-based (alert when the metric exceeds a set value) or change-based (alert when the metric changes by a certain amount). In this case, we’ll take the first approach since we want to be alerted whenever the metric’s value is nonzero.

The sess_dropped metric counts client connections Varnish had to drop. There are several possible causes for dropped connections detailed in part 1, but regardless this metric should always be equal to 0.

  1. Create a new metric monitor. Select “New Monitor” from the “Monitors” dropdown in Datadog. Select “Metric” as monitor type.

    Create Datadog alert
  2. Define your metric monitor. We want to know when the number of dropped client connections per second exceeds a certain value. So we define the metric of interest to be the sum of varnish.sess_dropped.

    Monitor sess_dropped
  3. Set metric alert conditions. Since we want to alert on a fixed threshold, rather than on a change, we select “Threshold Alert.” We’ll set the monitor to alert us whenever Varnish starts dropping client connections. Here we alert whenever the metric has surpassed the threshold of zero at least once during the past minute. You should decide whether “greater than zero” is the right threshold for your organization, or whether some greater number of dropped connections is preferable to paging an engineer.

    Set alert conditions
  4. Customize the notification to notify your team. In this case we will post a notification in the ops team’s chat room and page the engineer on call. In the “Say what’s happening” section we name the monitor and add a short message that will accompany the notification to suggest a first step for investigation. We @mention the Slack channel that we use for ops and use @pagerduty to route the alert to PagerDuty.

    Say what's happening
  5. Save the integration monitor. Click the “Save” button at the bottom of the page. You’re now monitoring a key Varnish work metric, and your on-call engineer will be paged anytime Varnish drops client connections.

Conclusion

In this post we’ve walked you through integrating Varnish with Datadog to visualize your key metrics and notify the right team whenever your web infrastructure shows signs of trouble.

If you’ve followed along using your own Datadog account, you should now have improved visibility into what’s happening in your web environment, as well as the ability to create automated alerts tailored to your infrastructure, your usage patterns, and the metrics that are most valuable to your organization.

If you don’t yet have a Datadog account, you can sign up for a free trial and start monitoring your infrastructure, your applications, and your services today.


Source Markdown for this post is available on GitHub. Questions, corrections, additions, etc.? Please let us know.