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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 - 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Introducing Live Process monitoring in Datadog
Michael Gerstenhaber · 2017-12-07 · via Datadog | The Monitor blog

In order to add greater depth and detail to your full-stack monitoring, Datadog is introducing a new way to explore, inspect, and monitor every process across your distributed infrastructure. That’s every PID, on every host, in one place.

We love tools like htop for real-time process monitoring, but SSH-ing into a server to run command line diagnostics quickly becomes unwieldy when your infrastructure comprises hundreds or thousands of hosts. Building on our recent release of Live Containers, our new Live Process view enables open-ended debugging and inventory management for massive, distributed systems, while preserving the features we depend on in our trusty system tools.

With Live Processes, you can query all your running processes, filter and group them using tags, and drill in to see process-level system metrics at two-second granularity.

Going deeper

Live Process view in Datadog

We already help monitor your infrastructure and applications with our more than 1,000 [integrations], which faithfully collect work and resource metrics from your systems. But often, after drilling down, you find that some system resource is saturated on a host. You can bounce the host and move on, but in order to prevent the issue from happening again, you need a deeper level of understanding and visibility.

By monitoring that host at the process level, you can see why the host is resource-constrained, and which piece of software is causing the issue. This visibility is especially important when a particular process goes haywire, starving other processes of resources and bringing down hosts or entire distributed services. Without complete visibility at the process level, identifying the culprit that triggered the chain reaction is nearly impossible.

Seeing the forest and the trees

The problem with monitoring every process is one of cardinality. We found that an average host runs about 100 processes, with significant variance depending on the software running. Postgres, for instance, can easily spawn thousands of workers on a single host.

Add to this the fact that familiar tools for process monitoring collect metrics frequently, and for good reason: processes move fast. By providing metrics for each PID every two seconds, Datadog’s Live Process view gives you the resolution necessary to understand spikes in CPU that could be causing problems hidden by aggregating over longer periods.

Because we provide a full accounting of every process, we also built intelligent aggregation and filtering to help you efficiently explore the hundreds of thousands or millions of processes that may be running across your cloud deployments or data centers. Using tags collected from cloud providers like AWS or provisioning systems like Chef, Puppet, or Ansible, you can pivot and slice every process tree across your deployment. Tags provide valuable context for your process metrics and enable you to navigate seamlessly between different views of your infrastructure and applications.

Exploring your processes

Process queries search all the metadata related to each process, including all arguments and flags. In the example above, we have filtered to Redis processes that were passed port 6383.

Next, we have pivoted the table by “role,” a tag from Chef, to see whether there are resource issues that are localized to some part of the system. We can then drill into any problematic scopes immediately or filter down to reduce noise and narrow the investigation.

Visualizing process trees

Process tree on a single host

Alternatively, if you identify a host that is having issues, you can dive right in to see a more traditional htop view for that host. Here, it can be useful to visualize the process tree, which can help spot orphan processes. Summary graphs provide local context and can help you understand the effects one process has on others across the system.

Peering into containers

Inspecting the processes running inside a Docker container

Processes are also what containers actually “contain.” With the launch of Live Processes, the complementary Live Container view has been enriched to show you the process tree within each container. Understanding what is going on under the hood of your containers can help you more effectively manage and tune your container fleet.

When a containerized process starts to act abnormally, its behavior is often hidden in the top-line stats of the container. Alternately, when container metrics do indicate a problem, it is not always clear where the issue originates. This problem is particularly acute during container migrations, where existing applications are often ported directly to containers, and a single container might still run dozens of processes. But in our experience, even containers built for a microservices architecture can spawn a deeper process tree than expected. Now you can immediately dive into process-level details to determine what is happening inside your containers.

Inventory management

Full-text search enables you to identify different versions of the same service

Running reports against your process trees allows you to understand what software is being run, and where. This can help you manage and audit expensive licenses and understand their importance to your organization.

Additionally, many programs embed version information, either in their working directory or as arguments passed in at execution. Our fuzzy search will match any of these fields and allow you to surface, say, which versions of Postgres are being run. In some cases, running a mixed environment can lead to lost data due to version incompatibility issues. Now, based on the user field or the team tag, you can identify the engineers and teams running each version and check with them to make sure they are aware of the version mismatch.

Even when software packages do not expose version information so explicitly, sorting by “Start time” can help provide insight into the version of code being run. This is especially helpful during development, when changes are made and tested continuously.

Get started!

To get started, update your Agent to version 5.16 or higher. For standard installs, add the following line to /etc/dd-agent/datadog.conf:

process_agent_enabled: true

For Docker and Kubernetes daemonset installs, set the env variable DD_PROCESS_AGENT_ENABLED to true and mount /etc/passwd as a read-only volume into your container.

For full details, see the documentation here. And if you don’t yet have a Datadog account, you can sign up for a 14-day free trial to get complete visibility into every process, host, service, and container.