47 KiB
47 KiB
| layout | title | parent | nav_order |
|---|---|---|---|
| default | Anomaly detection API | Anomaly detection | 1 |
Anomaly detection API
Use these anomaly detection operations to programmatically create and manage detectors.
Table of contents
- TOC {:toc}
Create anomaly detector
Introduced 1.0 {: .label .label-purple }
Creates an anomaly detector.
This command creates a detector named http_requests that finds anomalies based on the sum and average number of failed HTTP requests:
Request
POST _plugins/_anomaly_detection/detectors
{
"name": "test-detector",
"description": "Test detector",
"time_field": "timestamp",
"indices": [
"order*"
],
"feature_attributes": [
{
"feature_name": "total_order",
"feature_enabled": true,
"aggregation_query": {
"total_order": {
"sum": {
"field": "value"
}
}
}
}
],
"filter_query": {
"bool": {
"filter": [
{
"exists": {
"field": "value",
"boost": 1
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
}
}
Sample response
{
"_id": "m4ccEnIBTXsGi3mvMt9p",
"_version": 1,
"_seq_no": 3,
"_primary_term": 1,
"anomaly_detector": {
"name": "test-detector",
"description": "Test detector",
"time_field": "timestamp",
"indices": [
"order*"
],
"filter_query": {
"bool": {
"filter": [
{
"exists": {
"field": "value",
"boost": 1
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "mYccEnIBTXsGi3mvMd8_",
"feature_name": "total_order",
"feature_enabled": true,
"aggregation_query": {
"total_order": {
"sum": {
"field": "value"
}
}
}
}
]
}
}
To set a category field for high cardinality:
Request
POST _plugins/_anomaly_detection/detectors
{
"name": "Host OK Rate Detector",
"description": "ok rate",
"time_field": "@timestamp",
"indices": [
"host-cloudwatch"
],
"category_field": [
"host"
],
"feature_attributes": [
{
"feature_name": "latency_max",
"feature_enabled": true,
"aggregation_query": {
"latency_max": {
"max": {
"field": "latency"
}
}
}
}
],
"window_delay": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "MINUTES"
}
}
}
Sample response
{
"_id": "4CIGoHUBTpMGN-4KzBQg",
"_version": 1,
"_seq_no": 0,
"anomaly_detector": {
"name": "Host OK Rate Detector",
"description": "ok rate",
"time_field": "@timestamp",
"indices": [
"server-metrics"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"shingle_size": 1,
"schema_version": 2,
"feature_attributes": [
{
"feature_id": "0Kld3HUBhpHMyt2e_UHn",
"feature_name": "latency_max",
"feature_enabled": true,
"aggregation_query": {
"latency_max": {
"max": {
"field": "latency"
}
}
}
}
],
"last_update_time": 1604707601438,
"category_field": [
"host"
]
},
"_primary_term": 1
}
To create a historical detector:
Request
POST _plugins/_anomaly_detection/detectors
{
"name": "test1",
"description": "test historical detector",
"time_field": "timestamp",
"indices": [
"host-cloudwatch"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"feature_attributes": [
{
"feature_name": "F1",
"feature_enabled": true,
"aggregation_query": {
"f_1": {
"sum": {
"field": "value"
}
}
}
}
],
"detection_date_range": {
"start_time": 1577840401000,
"end_time": 1606121925000
}
}
You can specify the following options.
| Options | Description | Type | Required |
|---|---|---|---|
name |
The name of the detector. | string |
Yes |
description |
A description of the detector. | string |
Yes |
time_field |
The name of the time field. | string |
Yes |
indices |
A list of indices to use as the data source. | list |
Yes |
feature_attributes |
Specify a feature_name, set the enabled parameter to true, and specify an aggregation query. |
list |
Yes |
filter_query |
Provide an optional filter query for your feature. | object |
No |
detection_interval |
The time interval for your anomaly detector. | object |
Yes |
window_delay |
Add extra processing time for data collection. | object |
No |
category_field |
Categorizes or slices data with a dimension. Similar to GROUP BY in SQL. |
list |
No |
detection_date_range |
Specify the start time and end time for a historical detector. | object |
No |
Preview detector
Introduced 1.0 {: .label .label-purple }
Passes a date range to the anomaly detector to return any anomalies within that date range.
Request
POST _plugins/_anomaly_detection/detectors/<detectorId>/_preview
{
"period_start": 1588838250000,
"period_end": 1589443050000
}
Sample response
{
"anomaly_result": [
...
{
"detector_id": "m4ccEnIBTXsGi3mvMt9p",
"data_start_time": 1588843020000,
"data_end_time": 1588843620000,
"feature_data": [
{
"feature_id": "xxokEnIBcpeWMD987A1X",
"feature_name": "total_order",
"data": 489.9929131106
}
],
"anomaly_grade": 0,
"confidence": 0.99
}
...
],
"anomaly_detector": {
"name": "test-detector",
"description": "Test detector",
"time_field": "timestamp",
"indices": [
"order*"
],
"filter_query": {
"bool": {
"filter": [
{
"exists": {
"field": "value",
"boost": 1
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "MINUTES"
}
},
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "xxokEnIBcpeWMD987A1X",
"feature_name": "total_order",
"feature_enabled": true,
"aggregation_query": {
"total_order": {
"sum": {
"field": "value"
}
}
}
}
],
"last_update_time": 1589442309241
}
}
If you specify a category field, each result is associated with an entity:
Sample response
{
"anomaly_result": [
{
"detector_id": "4CIGoHUBTpMGN-4KzBQg",
"data_start_time": 1604277960000,
"data_end_time": 1604278020000,
"schema_version": 0,
"anomaly_grade": 0,
"confidence": 0.99
}
],
"entity": [
{
"name": "host",
"value": "i-00f28ec1eb8997686"
}
]
},
{
"detector_id": "4CIGoHUBTpMGN-4KzBQg",
"data_start_time": 1604278020000,
"data_end_time": 1604278080000,
"schema_version": 0,
"feature_data": [
{
"feature_id": "0Kld3HUBhpHMyt2e_UHn",
"feature_name": "latency_max",
"data": -17
}
],
"anomaly_grade": 0,
"confidence": 0.99,
"entity": [
{
"name": "host",
"value": "i-00f28ec1eb8997686"
}
]
}
...
Start detector job
Introduced 1.0 {: .label .label-purple }
Starts a real-time or historical anomaly detector job.
Request
POST _plugins/_anomaly_detection/detectors/<detectorId>/_start
Sample response
{
"_id" : "m4ccEnIBTXsGi3mvMt9p",
"_version" : 1,
"_seq_no" : 6,
"_primary_term" : 1
}
Stop detector job
Introduced 1.0 {: .label .label-purple }
Stops a real-time or historical anomaly detector job.
Request
POST _plugins/_anomaly_detection/detectors/<detectorId>/_stop
Sample response
Stopped detector: m4ccEnIBTXsGi3mvMt9p
Search detector result
Introduced 1.0 {: .label .label-purple }
Returns all results for a search query.
Request
GET _plugins/_anomaly_detection/detectors/results/_search
POST _plugins/_anomaly_detection/detectors/results/_search
{
"query": {
"bool": {
"must": {
"range": {
"anomaly_score": {
"gte": 0.6,
"lte": 1
}
}
}
}
}
}
Sample response
{
"took": 9,
"timed_out": false,
"_shards": {
"total": 25,
"successful": 25,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": ".opendistro-anomaly-results-history-2020.04.30-1",
"_type": "_doc",
"_id": "_KBrzXEBbpoKkFM5mStm",
"_version": 1,
"_seq_no": 58,
"_primary_term": 1,
"_score": 1,
"_source": {
"detector_id": "2KDozHEBbpoKkFM58yr6",
"anomaly_score": 0.8995068350366767,
"execution_start_time": 1588289313114,
"data_end_time": 1588289313114,
"confidence": 0.84214852704501,
"data_start_time": 1588289253114,
"feature_data": [
{
"feature_id": "X0fpzHEB5NGZmIRkXKcy",
"feature_name": "total_error",
"data": 20
}
],
"execution_end_time": 1588289313126,
"anomaly_grade": 0
}
},
{
"_index": ".opendistro-anomaly-results-history-2020.04.30-1",
"_type": "_doc",
"_id": "EqB1zXEBbpoKkFM5qyyE",
"_version": 1,
"_seq_no": 61,
"_primary_term": 1,
"_score": 1,
"_source": {
"detector_id": "2KDozHEBbpoKkFM58yr6",
"anomaly_score": 0.7086834513354907,
"execution_start_time": 1588289973113,
"data_end_time": 1588289973113,
"confidence": 0.42162017029510446,
"data_start_time": 1588289913113,
"feature_data": [
{
"feature_id": "X0fpzHEB5NGZmIRkXKcy",
"feature_name": "memory_usage",
"data": 20.0347333108
}
],
"execution_end_time": 1588289973124,
"anomaly_grade": 0
}
}
]
}
}
In high cardinality detectors, the result contains entity information.
To see an ordered set of anomaly records for an entity with an anomaly within a certain time range for a specific feature value:
Request
POST _plugins/_anomaly_detection/detectors/results/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"detector_id": "4CIGoHUBTpMGN-4KzBQg"
}
},
{
"range": {
"anomaly_grade": {
"gt": 0
}
}
},
{
"nested": {
"path": "entity",
"query": {
"bool": {
"must": [
{
"term": {
"entity.value": "i-00f28ec1eb8997685"
}
}
]
}
}
}
}
]
}
},
"size": 8,
"sort": [
{
"execution_end_time": {
"order": "desc"
}
}
],
"track_total_hits": true
}
Sample response
{
"took": 443,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 7,
"relation": "eq"
},
"max_score": null,
"hits": [
{
"_index": ".opendistro-anomaly-results-history-2020.11.07-1",
"_type": "_doc",
"_id": "BiItoHUBTpMGN-4KARY5",
"_version": 1,
"_seq_no": 206,
"_primary_term": 1,
"_score": null,
"_source": {
"detector_id": "4CIGoHUBTpMGN-4KzBQg",
"schema_version": 2,
"anomaly_score": 2.462550517055763,
"execution_start_time": 1604710105400,
"data_end_time": 1604710094516,
"confidence": 0.8246254862573076,
"data_start_time": 1604710034516,
"feature_data": [
{
"feature_id": "0Kld3HUBhpHMyt2e_UHn",
"feature_name": "latency_max",
"data": 3526
}
],
"execution_end_time": 1604710105401,
"anomaly_grade": 0.08045977011494891,
"entity": [
{
"name": "host",
"value": "i-00f28ec1eb8997685"
}
]
},
"sort": [
1604710105401
]
},
{
"_index": ".opendistro-anomaly-results-history-2020.11.07-1",
"_type": "_doc",
"_id": "wiImoHUBTpMGN-4KlhXs",
"_version": 1,
"_seq_no": 156,
"_primary_term": 1,
"_score": null,
"_source": {
"detector_id": "4CIGoHUBTpMGN-4KzBQg",
"schema_version": 2,
"anomaly_score": 4.892453213261217,
"execution_start_time": 1604709684971,
"data_end_time": 1604709674522,
"confidence": 0.8313735633713821,
"data_start_time": 1604709614522,
"feature_data": [
{
"feature_id": "0Kld3HUBhpHMyt2e_UHn",
"feature_name": "latency_max",
"data": 5709
}
],
"execution_end_time": 1604709684971,
"anomaly_grade": 0.06542056074767538,
"entity": [
{
"name": "host",
"value": "i-00f28ec1eb8997685"
}
]
},
"sort": [
1604709684971
]
},
{
"_index": ".opendistro-anomaly-results-history-2020.11.07-1",
"_type": "_doc",
"_id": "ZiIcoHUBTpMGN-4KhhVA",
"_version": 1,
"_seq_no": 79,
"_primary_term": 1,
"_score": null,
"_source": {
"detector_id": "4CIGoHUBTpMGN-4KzBQg",
"schema_version": 2,
"anomaly_score": 3.187717536855158,
"execution_start_time": 1604709025343,
"data_end_time": 1604709014520,
"confidence": 0.8301116064308817,
"data_start_time": 1604708954520,
"feature_data": [
{
"feature_id": "0Kld3HUBhpHMyt2e_UHn",
"feature_name": "latency_max",
"data": 441
}
],
"execution_end_time": 1604709025344,
"anomaly_grade": 0.040767386091133916,
"entity": [
{
"name": "host",
"value": "i-00f28ec1eb8997685"
}
]
},
"sort": [
1604709025344
]
}
]
}
}
In historical detectors, specify the detector_id.
To get the latest task:
Request
GET _plugins/_anomaly_detection/detectors/<detector_id>?task=true
To query the anomaly results with task_id:
Request
GET _plugins/_anomaly_detection/detectors/results/_search
{
"query": {
"term": {
"task_id": {
"value": "NnlV9HUBQxqfQ7vBJNzy"
}
}
}
}
Sample response
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 2.1366,
"hits": [
{
"_index": ".opendistro-anomaly-detection-state",
"_type": "_doc",
"_id": "CoM8WncBtt2qvI-LZO7_",
"_version": 8,
"_seq_no": 1351,
"_primary_term": 3,
"_score": 2.1366,
"_source": {
"detector_id": "dZc8WncBgO2zoQoFWVBA",
"worker_node": "dk6-HuKQRMKm2fi8TSDHsg",
"task_progress": 0.09486946,
"last_update_time": 1612126667008,
"execution_start_time": 1612126643455,
"state": "RUNNING",
"coordinating_node": "gs213KqjS4q7H4Bmn_ZuLA",
"current_piece": 1583503800000,
"task_type": "HISTORICAL",
"started_by": "admin",
"init_progress": 1,
"is_latest": true,
"detector": {
"description": "test",
"ui_metadata": {
"features": {
"F1": {
"aggregationBy": "sum",
"aggregationOf": "value",
"featureType": "simple_aggs"
}
}
},
"detection_date_range": {
"start_time": 1580504240308,
"end_time": 1612126640308
},
"feature_attributes": [
{
"feature_id": "dJc8WncBgO2zoQoFWVAt",
"feature_enabled": true,
"feature_name": "F1",
"aggregation_query": {
"f_1": {
"sum": {
"field": "value"
}
}
}
}
],
"schema_version": 0,
"time_field": "timestamp",
"last_update_time": 1612126640448,
"indices": [
"nab_art_daily_jumpsdown"
],
"window_delay": {
"period": {
"unit": "Minutes",
"interval": 1
}
},
"detection_interval": {
"period": {
"unit": "Minutes",
"interval": 10
}
},
"name": "test-historical-detector",
"filter_query": {
"match_all": {
"boost": 1
}
},
"shingle_size": 8,
"user": {
"backend_roles": [
"admin"
],
"custom_attribute_names": [],
"roles": [
"all_access",
"own_index"
],
"name": "admin",
"user_requested_tenant": "__user__"
},
"detector_type": "HISTORICAL_SINGLE_ENTITY"
},
"user": {
"backend_roles": [
"admin"
],
"custom_attribute_names": [],
"roles": [
"all_access",
"own_index"
],
"name": "admin",
"user_requested_tenant": "__user__"
}
}
}
]
}
}
Delete detector
Introduced 1.0 {: .label .label-purple }
Deletes a detector based on the detector_id.
To delete a detector, you need to first stop the detector.
Request
DELETE _plugins/_anomaly_detection/detectors/<detectorId>
Sample response
{
"_index" : ".opendistro-anomaly-detectors",
"_type" : "_doc",
"_id" : "m4ccEnIBTXsGi3mvMt9p",
"_version" : 2,
"result" : "deleted",
"forced_refresh" : true,
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"_seq_no" : 6,
"_primary_term" : 1
}
Update detector
Introduced 1.0 {: .label .label-purple }
Updates a detector with any changes, including the description or adding or removing of features. To update a detector, you need to first stop the detector.
Request
PUT _plugins/_anomaly_detection/detectors/<detectorId>
{
"name": "test-detector",
"description": "Test detector",
"time_field": "timestamp",
"indices": [
"order*"
],
"feature_attributes": [
{
"feature_name": "total_order",
"feature_enabled": true,
"aggregation_query": {
"total_order": {
"sum": {
"field": "value"
}
}
}
}
],
"filter_query": {
"bool": {
"filter": [
{
"exists": {
"field": "value",
"boost": 1
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "MINUTES"
}
}
}
Sample response
{
"_id" : "m4ccEnIBTXsGi3mvMt9p",
"_version" : 2,
"_seq_no" : 4,
"_primary_term" : 1,
"anomaly_detector" : {
"name" : "test-detector",
"description" : "Test detector",
"time_field" : "timestamp",
"indices" : [
"order*"
],
"filter_query" : {
"bool" : {
"filter" : [
{
"exists" : {
"field" : "value",
"boost" : 1.0
}
}
],
"adjust_pure_negative" : true,
"boost" : 1.0
}
},
"detection_interval" : {
"period" : {
"interval" : 10,
"unit" : "Minutes"
}
},
"window_delay" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"schema_version" : 0,
"feature_attributes" : [
{
"feature_id" : "xxokEnIBcpeWMD987A1X",
"feature_name" : "total_order",
"feature_enabled" : true,
"aggregation_query" : {
"total_order" : {
"sum" : {
"field" : "value"
}
}
}
}
]
}
}
To update a historical detector:
Request
PUT _plugins/_anomaly_detection/detectors/<detectorId>
{
"name": "test1",
"description": "test historical detector",
"time_field": "timestamp",
"indices": [
"nab_art_daily_jumpsdown"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"feature_attributes": [
{
"feature_name": "F1",
"feature_enabled": true,
"aggregation_query": {
"f_1": {
"sum": {
"field": "value"
}
}
}
}
],
"detection_date_range": {
"start_time": 1577840401000,
"end_time": 1606121925000
}
}
Get detector
Introduced 1.0 {: .label .label-purple }
Returns all information about a detector based on the detector_id.
Request
GET _plugins/_anomaly_detection/detectors/<detectorId>
Sample response
{
"_id" : "m4ccEnIBTXsGi3mvMt9p",
"_version" : 1,
"_primary_term" : 1,
"_seq_no" : 3,
"anomaly_detector" : {
"name" : "test-detector",
"description" : "Test detector",
"time_field" : "timestamp",
"indices" : [
"order*"
],
"filter_query" : {
"bool" : {
"filter" : [
{
"exists" : {
"field" : "value",
"boost" : 1.0
}
}
],
"adjust_pure_negative" : true,
"boost" : 1.0
}
},
"detection_interval" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"window_delay" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"schema_version" : 0,
"feature_attributes" : [
{
"feature_id" : "mYccEnIBTXsGi3mvMd8_",
"feature_name" : "total_order",
"feature_enabled" : true,
"aggregation_query" : {
"total_order" : {
"sum" : {
"field" : "value"
}
}
}
}
],
"last_update_time" : 1589441737319
}
}
Use job=true to get anomaly detection job information.
Request
GET _plugins/_anomaly_detection/detectors/<detectorId>?job=true
Sample response
{
"_id" : "m4ccEnIBTXsGi3mvMt9p",
"_version" : 1,
"_primary_term" : 1,
"_seq_no" : 3,
"anomaly_detector" : {
"name" : "test-detector",
"description" : "Test detector",
"time_field" : "timestamp",
"indices" : [
"order*"
],
"filter_query" : {
"bool" : {
"filter" : [
{
"exists" : {
"field" : "value",
"boost" : 1.0
}
}
],
"adjust_pure_negative" : true,
"boost" : 1.0
}
},
"detection_interval" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"window_delay" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"schema_version" : 0,
"feature_attributes" : [
{
"feature_id" : "mYccEnIBTXsGi3mvMd8_",
"feature_name" : "total_order",
"feature_enabled" : true,
"aggregation_query" : {
"total_order" : {
"sum" : {
"field" : "value"
}
}
}
}
],
"last_update_time" : 1589441737319
},
"anomaly_detector_job" : {
"name" : "m4ccEnIBTXsGi3mvMt9p",
"schedule" : {
"interval" : {
"start_time" : 1589442051271,
"period" : 1,
"unit" : "Minutes"
}
},
"window_delay" : {
"period" : {
"interval" : 1,
"unit" : "Minutes"
}
},
"enabled" : true,
"enabled_time" : 1589442051271,
"last_update_time" : 1589442051271,
"lock_duration_seconds" : 60
}
}
Use task=true to get historical detector task information.
Request
GET _plugins/_anomaly_detection/detectors/<detectorId>?task=true
Sample response
{
"_id": "BwzKQXcB89DLS7G9rg7Y",
"_version": 1,
"_primary_term": 2,
"_seq_no": 10,
"anomaly_detector": {
"name": "test-ylwu1",
"description": "test",
"time_field": "timestamp",
"indices": [
"nab*"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 10,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"shingle_size": 8,
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "BgzKQXcB89DLS7G9rg7G",
"feature_name": "F1",
"feature_enabled": true,
"aggregation_query": {
"f_1": {
"sum": {
"field": "value"
}
}
}
}
],
"ui_metadata": {
"features": {
"F1": {
"aggregationBy": "sum",
"aggregationOf": "value",
"featureType": "simple_aggs"
}
}
},
"last_update_time": 1611716538071,
"user": {
"name": "admin",
"backend_roles": [
"admin"
],
"roles": [
"all_access",
"own_index"
],
"custom_attribute_names": [],
"user_requested_tenant": "__user__"
},
"detector_type": "HISTORICAL_SINGLE_ENTITY",
"detection_date_range": {
"start_time": 1580094137997,
"end_time": 1611716537997
}
},
"anomaly_detection_task": {
"task_id": "sgxaRXcB89DLS7G9RfIO",
"last_update_time": 1611776648699,
"started_by": "admin",
"state": "FINISHED",
"detector_id": "BwzKQXcB89DLS7G9rg7Y",
"task_progress": 1,
"init_progress": 1,
"current_piece": 1611716400000,
"execution_start_time": 1611776279822,
"execution_end_time": 1611776648679,
"is_latest": true,
"task_type": "HISTORICAL",
"coordinating_node": "gs213KqjS4q7H4Bmn_ZuLA",
"worker_node": "PgfR3JhbT7yJMx7bwQ6E3w",
"detector": {
"name": "test-ylwu1",
"description": "test",
"time_field": "timestamp",
"indices": [
"nab*"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 10,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"shingle_size": 8,
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "BgzKQXcB89DLS7G9rg7G",
"feature_name": "F1",
"feature_enabled": true,
"aggregation_query": {
"f_1": {
"sum": {
"field": "value"
}
}
}
}
],
"ui_metadata": {
"features": {
"F1": {
"aggregationBy": "sum",
"aggregationOf": "value",
"featureType": "simple_aggs"
}
}
},
"last_update_time": 1611716538071,
"user": {
"name": "admin",
"backend_roles": [
"admin"
],
"roles": [
"all_access",
"own_index"
],
"custom_attribute_names": [],
"user_requested_tenant": "__user__"
},
"detector_type": "HISTORICAL_SINGLE_ENTITY",
"detection_date_range": {
"start_time": 1580094137997,
"end_time": 1611716537997
}
},
"user": {
"name": "admin",
"backend_roles": [
"admin"
],
"roles": [
"all_access",
"own_index"
],
"custom_attribute_names": [],
"user_requested_tenant": "__user__"
}
}
}
Search detector
Introduced 1.0 {: .label .label-purple }
Returns all anomaly detectors for a search query.
Request
GET _plugins/_anomaly_detection/detectors/_search
POST _plugins/_anomaly_detection/detectors/_search
Sample Input:
{
"query": {
"match": {
"name": "test-detector"
}
}
}
Sample response
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 994,
"relation": "eq"
},
"max_score": 3.5410638,
"hits": [
{
"_index": ".opendistro-anomaly-detectors",
"_type": "_doc",
"_id": "m4ccEnIBTXsGi3mvMt9p",
"_version": 2,
"_seq_no": 221,
"_primary_term": 1,
"_score": 3.5410638,
"_source": {
"name": "test-detector",
"description": "Test detector",
"time_field": "timestamp",
"indices": [
"order*"
],
"filter_query": {
"bool": {
"filter": [
{
"exists": {
"field": "value",
"boost": 1
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "MINUTES"
}
},
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "xxokEnIBcpeWMD987A1X",
"feature_name": "total_order",
"feature_enabled": true,
"aggregation_query": {
"total_order": {
"sum": {
"field": "value"
}
}
}
}
],
"last_update_time": 1589442309241
}
}
]
}
}
Get detector stats
Introduced 1.0 {: .label .label-purple }
Provides information about how the plugin is performing.
Request
GET _plugins/_anomaly_detection/stats
GET _plugins/_anomaly_detection/<nodeId>/stats
GET _plugins/_anomaly_detection/<nodeId>/stats/<stat>
GET _plugins/_anomaly_detection/stats/<stat>
Sample response
{
"_nodes" : {
"total" : 3,
"successful" : 3,
"failed" : 0
},
"cluster_name" : "multi-node-run",
"anomaly_detectors_index_status" : "green",
"detector_count" : 1,
"models_checkpoint_index_status" : "green",
"anomaly_results_index_status" : "green",
"nodes" : {
"IgWDUfzFRzW0FWAXM5FGJw" : {
"ad_execute_request_count" : 8,
"ad_execute_failure_count" : 7,
"models" : [
{
"detector_id" : "m4ccEnIBTXsGi3mvMt9p",
"model_type" : "rcf",
"model_id" : "m4ccEnIBTXsGi3mvMt9p_model_rcf_0"
},
{
"detector_id" : "m4ccEnIBTXsGi3mvMt9p",
"model_type" : "threshold",
"model_id" : "m4ccEnIBTXsGi3mvMt9p_model_threshold"
}
]
},
"y7YUQWukQEWOYbfdEq13hQ" : {
"ad_execute_request_count" : 0,
"ad_execute_failure_count" : 0,
"models" : [ ]
},
"cDcGNsPoRAyRMlPP1m-vZw" : {
"ad_execute_request_count" : 0,
"ad_execute_failure_count" : 0,
"models" : [
{
"detector_id" : "m4ccEnIBTXsGi3mvMt9p",
"model_type" : "rcf",
"model_id" : "m4ccEnIBTXsGi3mvMt9p_model_rcf_2"
},
{
"detector_id" : "m4ccEnIBTXsGi3mvMt9p",
"model_type" : "rcf",
"model_id" : "m4ccEnIBTXsGi3mvMt9p_model_rcf_1"
}
]
}
}
}
Historical detectors contain additional fields:
Sample response
{
"anomaly_detectors_index_status": "yellow",
"anomaly_detection_state_status": "yellow",
"historical_detector_count": 3,
"detector_count": 7,
"anomaly_detection_job_index_status": "yellow",
"models_checkpoint_index_status": "yellow",
"anomaly_results_index_status": "yellow",
"nodes": {
"Mz9HDZnuQwSCw0UiisxwWg": {
"ad_execute_request_count": 0,
"models": [],
"ad_canceled_batch_task_count": 2,
"ad_hc_execute_request_count": 0,
"ad_hc_execute_failure_count": 0,
"ad_execute_failure_count": 0,
"ad_batch_task_failure_count": 0,
"ad_executing_batch_task_count": 1,
"ad_total_batch_task_count": 8
}
}
}
Create monitor
Introduced 1.0 {: .label .label-purple }
Create a monitor to set up alerts for the detector.
Request
POST _plugins/_alerting/monitors
{
"type": "monitor",
"name": "test-monitor",
"enabled": true,
"schedule": {
"period": {
"interval": 20,
"unit": "MINUTES"
}
},
"inputs": [
{
"search": {
"indices": [
".opendistro-anomaly-results*"
],
"query": {
"size": 1,
"query": {
"bool": {
"filter": [
{
"range": {
"data_end_time": {
"from": "{{period_end}}||-20m",
"to": "{{period_end}}",
"include_lower": true,
"include_upper": true,
"boost": 1
}
}
},
{
"term": {
"detector_id": {
"value": "m4ccEnIBTXsGi3mvMt9p",
"boost": 1
}
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"sort": [
{
"anomaly_grade": {
"order": "desc"
}
},
{
"confidence": {
"order": "desc"
}
}
],
"aggregations": {
"max_anomaly_grade": {
"max": {
"field": "anomaly_grade"
}
}
}
}
}
}
],
"triggers": [
{
"name": "test-trigger",
"severity": "1",
"condition": {
"script": {
"source": "return ctx.results[0].aggregations.max_anomaly_grade.value != null && ctx.results[0].aggregations.max_anomaly_grade.value > 0.7 && ctx.results[0].hits.hits[0]._source.confidence > 0.7",
"lang": "painless"
}
},
"actions": [
{
"name": "test-action",
"destination_id": "ld7912sBlQ5JUWWFThoW",
"message_template": {
"source": "This is my message body."
},
"throttle_enabled": false,
"subject_template": {
"source": "TheSubject"
}
}
]
}
]
}
Sample response
{
"_id": "OClTEnIBmSf7y6LP11Jz",
"_version": 1,
"_seq_no": 10,
"_primary_term": 1,
"monitor": {
"type": "monitor",
"schema_version": 1,
"name": "test-monitor",
"enabled": true,
"enabled_time": 1589445384043,
"schedule": {
"period": {
"interval": 20,
"unit": "MINUTES"
}
},
"inputs": [
{
"search": {
"indices": [
".opendistro-anomaly-results*"
],
"query": {
"size": 1,
"query": {
"bool": {
"filter": [
{
"range": {
"data_end_time": {
"from": "{{period_end}}||-20m",
"to": "{{period_end}}",
"include_lower": true,
"include_upper": true,
"boost": 1
}
}
},
{
"term": {
"detector_id": {
"value": "m4ccEnIBTXsGi3mvMt9p",
"boost": 1
}
}
}
],
"adjust_pure_negative": true,
"boost": 1
}
},
"sort": [
{
"anomaly_grade": {
"order": "desc"
}
},
{
"confidence": {
"order": "desc"
}
}
],
"aggregations": {
"max_anomaly_grade": {
"max": {
"field": "anomaly_grade"
}
}
}
}
}
}
],
"triggers": [
{
"id": "NilTEnIBmSf7y6LP11Jr",
"name": "test-trigger",
"severity": "1",
"condition": {
"script": {
"source": "return ctx.results[0].aggregations.max_anomaly_grade.value != null && ctx.results[0].aggregations.max_anomaly_grade.value > 0.7 && ctx.results[0].hits.hits[0]._source.confidence > 0.7",
"lang": "painless"
}
},
"actions": [
{
"id": "NylTEnIBmSf7y6LP11Jr",
"name": "test-action",
"destination_id": "ld7912sBlQ5JUWWFThoW",
"message_template": {
"source": "This is my message body.",
"lang": "mustache"
},
"throttle_enabled": false,
"subject_template": {
"source": "TheSubject",
"lang": "mustache"
}
}
]
}
],
"last_update_time": 1589445384043
}
}
Profile detector
Introduced 1.0 {: .label .label-purple }
Returns information related to the current state of the detector and memory usage, including current errors and shingle size, to help troubleshoot the detector.
This command helps locate logs by identifying the nodes that run the anomaly detector job for each detector.
It also helps track the initialization percentage, the required shingles, and the estimated time left.
Request
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/<type>
GET /_plugins/_anomaly_detection/detectors/<detectorId>/_profile/<type1>,<type2>
Sample Responses
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile
{
"state":"DISABLED",
"error":"Stopped detector: AD models memory usage exceeds our limit."
}
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true&pretty
{
"state": "RUNNING",
"models": [
{
"model_id": "cneh7HEBHPICjJIdXdrR_model_rcf_2",
"model_size_in_bytes": 4456448,
"node_id": "VS29z70PSzOdHiEw4SoV9Q"
},
{
"model_id": "cneh7HEBHPICjJIdXdrR_model_rcf_1",
"model_size_in_bytes": 4456448,
"node_id": "VS29z70PSzOdHiEw4SoV9Q"
},
{
"model_id": "cneh7HEBHPICjJIdXdrR_model_threshold",
"node_id": "Og23iUroTdKrkwS-y89zLw"
},
{
"model_id": "cneh7HEBHPICjJIdXdrR_model_rcf_0",
"model_size_in_bytes": 4456448,
"node_id": "Og23iUroTdKrkwS-y89zLw"
}
],
"shingle_size": 8,
"coordinating_node": "Og23iUroTdKrkwS-y89zLw",
"total_size_in_bytes": 13369344,
"init_progress": {
"percentage": "70%",
"estimated_minutes_left": 77,
"needed_shingles": 77
}
}
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/total_size_in_bytes
{
"total_size_in_bytes" : 13369344
}
If you configured the category field, you can see the number of unique values in the field and all active entities with models running in memory.
You can use this data to estimate how much memory is required for anomaly detection so you can decide how to size your cluster. For example, if a detector has one million entities and only 10 of them are active in memory, you need to scale your cluster up or out.
Request
GET /_plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true&pretty
{
"state": "RUNNING",
"models": [
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997684",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997685",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997686",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997680",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997681",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997682",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
},
{
"model_id": "T4c3dXUBj-2IZN7itix__entity_i-00f28ec1eb8997683",
"model_size_in_bytes": 712480,
"node_id": "g6pmr547QR-CfpEvO67M4g"
}
],
"total_size_in_bytes": 4987360,
"init_progress": {
"percentage": "100%"
},
"total_entities": 7,
"active_entities": 7
}
The profile operation also provides information about each entity, such as the entity’s last_sample_timestamp and last_active_timestamp.
If there are no anomaly results for an entity, either the entity doesn't have any sample data or its model is removed from the model cache.
last_sample_timestamp shows the last document in the input data source index containing the entity, while last_active_timestamp shows the timestamp when the entity’s model was last seen in the model cache.
Request
GET /_plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true&entity=i-00f28ec1eb8997686
{
"category_field": "host",
"value": "i-00f28ec1eb8997686",
"is_active": true,
"last_active_timestamp": 1604026394879,
"last_sample_timestamp": 1604026394879,
"init_progress": {
"percentage": "100%"
},
"model": {
"model_id": "TFUdd3UBBwIAGQeRh5IS_entity_i-00f28ec1eb8997686",
"model_size_in_bytes": 712480,
"node_id": "MQ-bTBW3Q2uU_2zX3pyEQg"
},
"state": "RUNNING"
}
For a historical detector, specify _all or ad_task to see information about its latest task:
Request
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all
GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/ad_task
Sample Responses
{
"ad_task": {
"ad_task": {
"task_id": "JXxyG3YBv5IHYYfMlFS2",
"last_update_time": 1606778263543,
"state": "STOPPED",
"detector_id": "SwvxCHYBPhugfWD9QAL6",
"task_progress": 0.010480972,
"init_progress": 1,
"current_piece": 1578140400000,
"execution_start_time": 1606778262709,
"is_latest": true,
"task_type": "HISTORICAL",
"detector": {
"name": "historical_test1",
"description": "test",
"time_field": "timestamp",
"indices": [
"nab_art_daily_jumpsdown"
],
"filter_query": {
"match_all": {
"boost": 1
}
},
"detection_interval": {
"period": {
"interval": 5,
"unit": "Minutes"
}
},
"window_delay": {
"period": {
"interval": 1,
"unit": "Minutes"
}
},
"shingle_size": 8,
"schema_version": 0,
"feature_attributes": [
{
"feature_id": "zgvyCHYBPhugfWD9Ap_F",
"feature_name": "sum",
"feature_enabled": true,
"aggregation_query": {
"sum": {
"sum": {
"field": "value"
}
}
}
},
{
"feature_id": "zwvyCHYBPhugfWD9Ap_G",
"feature_name": "max",
"feature_enabled": true,
"aggregation_query": {
"max": {
"max": {
"field": "value"
}
}
}
}
],
"ui_metadata": {
"features": {
"max": {
"aggregationBy": "max",
"aggregationOf": "value",
"featureType": "simple_aggs"
},
"sum": {
"aggregationBy": "sum",
"aggregationOf": "value",
"featureType": "simple_aggs"
}
},
"filters": [],
"filterType": "simple_filter"
},
"last_update_time": 1606467935713,
"detector_type": "HISTORICAL_SIGLE_ENTITY",
"detection_date_range": {
"start_time": 1577840400000,
"end_time": 1606463775000
}
}
},
"shingle_size": 8,
"rcf_total_updates": 1994,
"threshold_model_trained": true,
"threshold_model_training_data_size": 0,
"node_id": "Q9yznwxvTz-yJxtz7rJlLg"
}
}