// Jest Snapshot v1, https://goo.gl/fbAQLP exports[` spec No sample detectors created renders component 1`] = `

Anomaly detection

The anomaly detection plugin automatically detects anomalies in your data in near real-time using the Random Cut Forest (RCF) algorithm. Learn more (opens in a new tab or window)

How it works


1. Define your detector
Select a data source, set the detector interval, and specify a window delay. Learn more (opens in a new tab or window)
2. Configure your detector
Choose the fields in your index that you want to check for anomalies. You may also set a category field to see a granular view of anomalies within each entity. Learn more (opens in a new tab or window)
3. Preview your detector
After configuring your model, preview your results with sample data to fine-tune your settings. Learn more (opens in a new tab or window)
4. View results
Run your detector to observe results in real-time. You can also enable historical analysis to view anomalies in your data history. Learn more (opens in a new tab or window)

Start with a sample detector to learn about anomaly detection

New to anomaly detection? Get a better understanding of how it works by creating a detector with one of the sample datasets.

Monitor HTTP responses


Detect high numbers of error response codes in an index containing HTTP response data.

Monitor eCommerce orders


Detect any unusual increase or decrease of orders in an index containing online order data.

Monitor host health


Detect increases in CPU and memory utilization in an index containing various health metrics from a host.

`; exports[` spec Some detectors created renders component with non-sample detector 1`] = `

Anomaly detection

The anomaly detection plugin automatically detects anomalies in your data in near real-time using the Random Cut Forest (RCF) algorithm. Learn more (opens in a new tab or window)

How it works


1. Define your detector
Select a data source, set the detector interval, and specify a window delay. Learn more (opens in a new tab or window)
2. Configure your detector
Choose the fields in your index that you want to check for anomalies. You may also set a category field to see a granular view of anomalies within each entity. Learn more (opens in a new tab or window)
3. Preview your detector
After configuring your model, preview your results with sample data to fine-tune your settings. Learn more (opens in a new tab or window)
4. View results
Run your detector to observe results in real-time. You can also enable historical analysis to view anomalies in your data history. Learn more (opens in a new tab or window)

Start with a sample detector to learn about anomaly detection

New to anomaly detection? Get a better understanding of how it works by creating a detector with one of the sample datasets.

Monitor HTTP responses


Detect high numbers of error response codes in an index containing HTTP response data.

Monitor eCommerce orders


Detect any unusual increase or decrease of orders in an index containing online order data.

Monitor host health


Detect increases in CPU and memory utilization in an index containing various health metrics from a host.

`; exports[` spec Some detectors created renders component with sample detector 1`] = `

Anomaly detection

The anomaly detection plugin automatically detects anomalies in your data in near real-time using the Random Cut Forest (RCF) algorithm. Learn more (opens in a new tab or window)

How it works


1. Define your detector
Select a data source, set the detector interval, and specify a window delay. Learn more (opens in a new tab or window)
2. Configure your detector
Choose the fields in your index that you want to check for anomalies. You may also set a category field to see a granular view of anomalies within each entity. Learn more (opens in a new tab or window)
3. Preview your detector
After configuring your model, preview your results with sample data to fine-tune your settings. Learn more (opens in a new tab or window)
4. View results
Run your detector to observe results in real-time. You can also enable historical analysis to view anomalies in your data history. Learn more (opens in a new tab or window)

Start with a sample detector to learn about anomaly detection

New to anomaly detection? Get a better understanding of how it works by creating a detector with one of the sample datasets.

Monitor HTTP responses


Detect high numbers of error response codes in an index containing HTTP response data.

INSTALLED

Monitor eCommerce orders


Detect any unusual increase or decrease of orders in an index containing online order data.

Monitor host health


Detect increases in CPU and memory utilization in an index containing various health metrics from a host.

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