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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
Units and descriptions: Understand any metric
2016-02-29 · via Datadog | The Monitor blog

“What does this metric actually measure?”

“Are these latency numbers in milliseconds or microseconds?”

When you’re diagnosing an issue in production, you don’t want to spend time answering questions like these to decipher your metrics.

To eliminate ambiguity and help you make sense of your systems as quickly as possible, we’ve rolled out a comprehensive metadata catalog covering standard metrics collected by our supported integrations. This catalog supplies measurement units and a brief description for approximately 3,000 metrics, which surface automatically on your graphs and dashboards.

Redis dashboard with units

You may have already noticed metric metadata appearing on your dashboards. Units are displayed automatically on timeseries graphs, query value widgets, and toplists, as shown in the screenshot of a Redis dashboard above.

On timeseries graphs, just move your cursor over any graph to see the relevant units. The raw data is automatically converted to easily readable display units (fractions of a second to ms, millions of bytes per second to MiB/s, etc.).

Postgres commits, with units

Units are also displayed at the bottom of timeboard graphs, and metric descriptions are available by selecting “Metrics Info” from the gear dropdown.

Timeboard metric metadata

You can find a complete list of collected metrics, their units, and their descriptions under the new “Metrics” tab for integrations in the Datadog app. The same per-integration breakdown is available in the docs as well.

DynamoDB metric metadata

If you’re already a Datadog customer, measurement units and metric descriptions are now available for all your standard integrations.

If you don’t yet have a Datadog account, you can get a full-featured 14-day trial here.