updated requests and responses

_monitoring-plugins/ad/index.md

@@ -79,7 +79,7 @@ To get the entity model size of a detector, use the [profile detector API]({{sit
 This formula provides a good starting point, but make sure to test with a representative workload.
 {: .note }
 
-For example, for a cluster with 3 data nodes, each with 8G of JVM heap size, a maximum memory percentage of 10% (default), and the entity model size of the detector as 1MB: the total number of unique entities supported is (8.096 * 10^9 * 0.1 / 1M ) * 3 = 2429.
+For example, for a cluster with three data nodes, each with 8 GB of JVM heap size, a maximum memory percentage of 10% (default), and the entity model size of the detector as 1MB: the total number of unique entities supported is (8.096 * 10^9 * 0.1 / 1 MB ) * 3 = 2429.
 
 If you set the total number of unique entities higher than this number that you calculate (in this case: 2429), the anomaly detector makes its best effort to model the extra entities. The detector prioritizes entities that occur more often and are more recent.
 

_monitoring-plugins/ad/settings.md

@@ -40,7 +40,7 @@ Setting | Default | Description
 `plugins.anomaly_detection.batch_task_piece_size` | 1,000 | The date range for a historical task is split into smaller pieces and the anomaly detection plugin runs the task piece by piece. Each piece contains 1,000 detection intervals by default. For example, if detector interval is 1 minute and one piece is 1,000 minutes, the feature data is queried every 1,000 minutes. You can change this setting from 1 to 10,000.
 `plugins.anomaly_detection.batch_task_piece_interval_seconds` | 5 | Add a time interval between two pieces of the same historical analysis task. This interval prevents the task from consuming too much of the available resources and starving other operations like search and bulk index. You can change this setting from 1 to 600 seconds.
 `plugins.anomaly_detection.max_top_entities_for_historical_analysis` | 1,000 | The maximum number of top entities that you run for a high cardinality detector historical analysis. The range is from 1 to 10,000.
-`plugins.anomaly_detection.max_running_entities_per_detector_for_historical_analysis` | 10 | The number of entity tasks that you can run in parallel for a single high cardinality detector. The task slots available on your cluster also impact how many entities run in parallel. If a cluster has 3 data nodes, each data node has 10 task slots by default. Say you already have two high cardinality detectors and each of them run 10 entities. If you start a single-flow detector that takes 1 task slot, the number of task slots available is 10 * 3 - 10 * 2 - 1 = 9. if you now start a new high cardinality detector, the detector can only run 9 entities in parallel and not 10. You can tune this value from 1 to 1,000 based on your cluster's capability. If you set a higher value, the anomaly detection plugin runs historical analysis faster but also consumes more resources.
+`plugins.anomaly_detection.max_running_entities_per_detector_for_historical_analysis` | 10 | The number of entity tasks that you can run in parallel for a single high cardinality detector. The task slots available on your cluster also impact how many entities run in parallel. If a cluster has 3 data nodes, each data node has 10 task slots by default. Say you already have two high cardinality detectors and each of them run 10 entities. If you start a single-flow detector that takes 1 task slot, the number of task slots available is 10 * 3 - 10 * 2 - 1 = 9. If you now start a new high cardinality detector, the detector can only run 9 entities in parallel and not 10. You can tune this value from 1 to 1,000 based on your cluster's capability. If you set a higher value, the anomaly detection plugin runs historical analysis faster but also consumes more resources.
 `plugins.anomaly_detection.max_cached_deleted_tasks` | 1,000 | You can rerun historical analysis for a single detector as many times as you like. The anomaly detection plugin only keeps a limited number of old tasks, by default 1 old task. If you run historical analysis three times for a detector, the oldest task is deleted. Because historical analysis generates a number of anomaly results in a short span of time, it's necessary to clean up anomaly results for a deleted task. With this field, you can configure how many deleted tasks you can cache at most. The plugin cleans up a task's results when it's deleted. If the plugin fails to do this cleanup, it adds the task's results into a cache and an hourly cron job performs the cleanup. You can use this setting to limit how many old tasks are put into cache to avoid a DDoS attack. After an hour, if still you find an old task result in the cache, use the [delete detector results API]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/api/#delete-detector-results) to delete the task result manually. You can tune this setting from 1 to 10,000.
 `plugins.anomaly_detection.delete_anomaly_result_when_delete_detector` | False | Whether the anomaly detection plugin deletes the anomaly result when you delete a detector. If you want to save some disk space, especially if you've high cardinality detectors generating a lot of results, set this field to true. Alternatively, you can use the [delete detector results API]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/api/#delete-detector-results) to manually delete the results.
 `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.