- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Advanced concepts
- API quick reference
- Examples
- Tutorial: Getting started with anomaly detection
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Performing population analysis
- Altering data in your datafeed with runtime fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Mapping anomalies by location
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Appendix E: Metricbeat anomaly detection configurations
editAppendix E: Metricbeat anomaly detection configurations
editThese anomaly detection job wizards appear in Kibana if you use the Metricbeat system module to monitor your servers. For more details, see the datafeed and job definitions in GitHub.
Metricbeat system
editDetect anomalies in Metricbeat System data (ECS).
These configurations are only available if data exists that matches the recognizer query specified in the manifest file.
| Name | Description | Job | Datafeed |
|---|---|---|---|
high_mean_cpu_iowait_ecs |
Detect unusual increases in cpu time spent in iowait (ECS) |
|
|
max_disk_utilization_ecs |
Detect unusual increases in disk utilization (ECS) |
|
|
metricbeat_outages_ecs |
Detect unusual decreases in metricbeat documents (ECS) |
|
|
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