- Observability: other versions:
- Get started
- What is Elastic Observability?
- What’s new in 8.17
- Quickstart: Monitor hosts with Elastic Agent
- Quickstart: Monitor your Kubernetes cluster with Elastic Agent
- Quickstart: Monitor hosts with OpenTelemetry
- Quickstart: Unified Kubernetes Observability with Elastic Distributions of OpenTelemetry (EDOT)
- Quickstart: Collect data with AWS Firehose
- Add data from Splunk
- Applications and services
- Application performance monitoring (APM)
- Get started
- Learn about data types
- Collect application data
- View and analyze data
- Act on data
- Use APM securely
- Manage storage
- Configure APM Server
- Monitor APM Server
- APM APIs
- Troubleshooting
- Upgrade
- Release notes
- Known issues
- Synthetic monitoring
- Get started
- Scripting browser monitors
- Configure lightweight monitors
- Manage monitors
- Work with params and secrets
- Analyze monitor data
- Monitor resources on private networks
- Use the CLI
- Configure projects
- Multi-factor Authentication
- Configure Synthetics settings
- Grant users access to secured resources
- Manage data retention
- Use Synthetics with traffic filters
- Migrate from the Elastic Synthetics integration
- Scale and architect a deployment
- Synthetics support matrix
- Synthetics Encryption and Security
- Troubleshooting
- Real user monitoring
- Uptime monitoring (deprecated)
- Tutorial: Monitor a Java application
- Application performance monitoring (APM)
- CI/CD
- Cloud
- Infrastructure and hosts
- Logs
- Troubleshooting
- Incident management
- Data set quality
- Observability AI Assistant
- Reference
Inspect uptime duration anomalies
editInspect uptime duration anomalies
editEach monitor location is modeled, and when a monitor runs for an unusual amount of time, at a particular time, an anomaly is recorded and highlighted on the Monitor duration chart.
Enable uptime duration anomaly detection
editCreate a machine learning job to detect anomalous monitor duration rates automatically.
- To access this page, go to Observability > Uptime > Monitors, and then click a monitor to view its the details.
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In the Monitor duration panel, click Enable anomaly detection.
If anomaly detection is already enabled, click Anomaly detection and select to view duration anomalies directly in the Machine Learning app, enable an anomaly rule, or disable the anomaly detection.
- You are prompted to create a response duration anomaly rule for the machine learning job which will carry out the analysis, and you can configure which severity level to create the rule for.
When an anomaly is detected, the duration is displayed on the Monitor duration chart, along with the duration times. The colors represent the criticality of the anomaly: red (critical) and yellow (minor).
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