add two new settings for Anomaly Detection 2.5 (#2450)

* add two new settings for Anomaly Detection

Signed-off-by: Amit Galitzky <amgalitz@amazon.com>

* fixed setting mistake

Signed-off-by: Amit Galitzky <amgalitz@amazon.com>

* Update _monitoring-plugins/ad/settings.md

* Update _monitoring-plugins/ad/settings.md

* Update _monitoring-plugins/ad/settings.md

Co-authored-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

Signed-off-by: Amit Galitzky <amgalitz@amazon.com>
Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com>
Co-authored-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
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Amit Galitzky 2023-01-23 11:03:14 -08:00 committed by GitHub
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@ -45,3 +45,5 @@ plugins.anomaly_detection.delete_anomaly_result_when_delete_detector | False | W
plugins.anomaly_detection.dedicated_cache_size | 10 | If the real-time analysis of a high cardinality detector starts successfully, the anomaly detection plugin guarantees keeping 10 (dynamically adjustable via this setting) entities' models in memory per node. If the number of entities exceeds this limit, the plugin puts the extra entities' models in a memory space shared by all detectors. The actual number of entities varies based on the memory that you've available and the frequencies of the entities. If you'd like the plugin to guarantee keeping more entities' models in memory and if you're cluster has sufficient memory, you can increase this setting value.
plugins.anomaly_detection.max_concurrent_preview | 2 | The maximum number of concurrent previews. You can use this setting to limit resource usage.
plugins.anomaly_detection.model_max_size_percent | 0.1 | The upper bound of the memory percentage for a model.
plugins.anomaly_detection.door_keeper_in_cache.enabled | False | When set to `true`, OpenSearch places a bloom filter in front of an inactive entity cache to filter out items that are not likely to appear more than once.
plugins.anomaly_detection.hcad_cold_start_interpolation.enabled | False | When set to `true`, enables interpolation in high-cardinality anomaly detection (HCAD) cold start.