Working with detectors
In Amazon Lookout for Metrics, a detector is a resource that monitors a dataset and identifies anomalies (data that falls outside of the expected range). Detectors use machine learning (ML) to find patterns in business data, and to distinguish between expected variations in data and legitimate anomalies. A detector can monitor a dataset that contains metrics data that you manage in Amazon Simple Storage Service (Amazon S3), live data from another service such as Amazon CloudWatch, or events from a database. When new data points fall outside of the expected range, the detector records the anomaly and sends an alert.
A dataset is a collection of timestamped data points that can each have multiple metrics and attributes. You choose one of the metrics to be the measure, which is the primary metric that the detector monitors for anomalies.
You can also configure up to 10 additional attributes as dimensions. Dimensions are additional information that the detector uses to segment anomalies, filter the dataset, and identify contributing factors.
For example, you can choose a field named availability for a measure. If you don't choose a
dimension, the detector monitors availability across all records. If you choose a field named country
for a dimension, then the detector monitors availability in each country as a separate metric:
availability in Canada, availability in Italy, and so on.
Alternately, you can create a filter on country that includes only the countries that you are interested in monitoring. In this case, availability is monitored only in the countries that are included in the filter.
Detectors primarily work against live data. A detector analyzes new data periodically to find anomalies in measure values. The interval at which it analyzes data can be between 5 minutes and 1 day. To allow time for the datasource to collect all data before analysis starts, you also configure a delay on the dataset. At the end of an interval, the detector waits for the duration of the delay before analyzing data.
When you create a detector, you can also provide historical data. If you provide historical data, the detector uses it to learn patterns and relationships between fields in your data. If not, the detector spends several intervals learning on live data.
Every time it runs, the detector analyzes all of the data generated during the interval, identifies anomalous data points, and assigns a severity score to each. If the severity of an anomaly exceeds a threshold, the detector sends an alert. You can configure alerts to send a notification to an Amazon Simple Notification Service (Amazon SNS) topic, or to invoke an AWS Lambda function. If you get too many or too few results, you can change the threshold that triggers the alert.
Topics
