Anomaly detection API

Use these anomaly detection operations to programmatically create and manage detectors.


Table of contents


Create anomaly detector

Introduced 1.0

Creates an anomaly detector.

This command creates a single-entity detector named test-detector that finds anomalies based on the sum of the value field:

Request

  1. POST _plugins/_anomaly_detection/detectors
  2. {
  3. "name": "test-detector",
  4. "description": "Test detector",
  5. "time_field": "timestamp",
  6. "indices": [
  7. "server_log*"
  8. ],
  9. "feature_attributes": [
  10. {
  11. "feature_name": "test",
  12. "feature_enabled": true,
  13. "aggregation_query": {
  14. "test": {
  15. "sum": {
  16. "field": "value"
  17. }
  18. }
  19. }
  20. }
  21. ],
  22. "filter_query": {
  23. "bool": {
  24. "filter": [
  25. {
  26. "range": {
  27. "value": {
  28. "gt": 1
  29. }
  30. }
  31. }
  32. ],
  33. "adjust_pure_negative": true,
  34. "boost": 1
  35. }
  36. },
  37. "detection_interval": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "window_delay": {
  44. "period": {
  45. "interval": 1,
  46. "unit": "Minutes"
  47. }
  48. }
  49. }

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 1,
  4. "_seq_no": 5,
  5. "anomaly_detector": {
  6. "name": "test-detector",
  7. "description": "Test detector",
  8. "time_field": "timestamp",
  9. "indices": [
  10. "server_log*"
  11. ],
  12. "filter_query": {
  13. "bool": {
  14. "filter": [
  15. {
  16. "range": {
  17. "value": {
  18. "from": 1,
  19. "to": null,
  20. "include_lower": false,
  21. "include_upper": true,
  22. "boost": 1
  23. }
  24. }
  25. }
  26. ],
  27. "adjust_pure_negative": true,
  28. "boost": 1
  29. }
  30. },
  31. "detection_interval": {
  32. "period": {
  33. "interval": 1,
  34. "unit": "Minutes"
  35. }
  36. },
  37. "window_delay": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "shingle_size": 8,
  44. "schema_version": 0,
  45. "feature_attributes": [
  46. {
  47. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  48. "feature_name": "test",
  49. "feature_enabled": true,
  50. "aggregation_query": {
  51. "test": {
  52. "sum": {
  53. "field": "value"
  54. }
  55. }
  56. }
  57. }
  58. ],
  59. "last_update_time": 1633392680364,
  60. "user": {
  61. "name": "admin",
  62. "backend_roles": [
  63. "admin"
  64. ],
  65. "roles": [
  66. "own_index",
  67. "all_access"
  68. ],
  69. "custom_attribute_names": [],
  70. "user_requested_tenant": "__user__"
  71. },
  72. "detector_type": "SINGLE_ENTITY"
  73. },
  74. "_primary_term": 1
  75. }

To create a high cardinality detector by specifying a category field:

Request

  1. POST _plugins/_anomaly_detection/detectors
  2. {
  3. "name": "test-hc-detector",
  4. "description": "Test detector",
  5. "time_field": "timestamp",
  6. "indices": [
  7. "server_log*"
  8. ],
  9. "feature_attributes": [
  10. {
  11. "feature_name": "test",
  12. "feature_enabled": true,
  13. "aggregation_query": {
  14. "test": {
  15. "sum": {
  16. "field": "value"
  17. }
  18. }
  19. }
  20. }
  21. ],
  22. "filter_query": {
  23. "bool": {
  24. "filter": [
  25. {
  26. "range": {
  27. "value": {
  28. "gt": 1
  29. }
  30. }
  31. }
  32. ],
  33. "adjust_pure_negative": true,
  34. "boost": 1
  35. }
  36. },
  37. "detection_interval": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "window_delay": {
  44. "period": {
  45. "interval": 1,
  46. "unit": "Minutes"
  47. }
  48. },
  49. "category_field": [
  50. "ip"
  51. ]
  52. }

Sample response

  1. {
  2. "_id": "b0HRTXwBwf_U8gjUw43R",
  3. "_version": 1,
  4. "_seq_no": 6,
  5. "anomaly_detector": {
  6. "name": "test-hc-detector",
  7. "description": "Test detector",
  8. "time_field": "timestamp",
  9. "indices": [
  10. "server_log*"
  11. ],
  12. "filter_query": {
  13. "bool": {
  14. "filter": [
  15. {
  16. "range": {
  17. "value": {
  18. "from": 1,
  19. "to": null,
  20. "include_lower": false,
  21. "include_upper": true,
  22. "boost": 1
  23. }
  24. }
  25. }
  26. ],
  27. "adjust_pure_negative": true,
  28. "boost": 1
  29. }
  30. },
  31. "detection_interval": {
  32. "period": {
  33. "interval": 1,
  34. "unit": "Minutes"
  35. }
  36. },
  37. "window_delay": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "shingle_size": 8,
  44. "schema_version": 0,
  45. "feature_attributes": [
  46. {
  47. "feature_id": "bkHRTXwBwf_U8gjUw43K",
  48. "feature_name": "test",
  49. "feature_enabled": true,
  50. "aggregation_query": {
  51. "test": {
  52. "sum": {
  53. "field": "value"
  54. }
  55. }
  56. }
  57. }
  58. ],
  59. "last_update_time": 1633393165265,
  60. "category_field": [
  61. "ip"
  62. ],
  63. "user": {
  64. "name": "admin",
  65. "backend_roles": [
  66. "admin"
  67. ],
  68. "roles": [
  69. "own_index",
  70. "all_access"
  71. ],
  72. "custom_attribute_names": [],
  73. "user_requested_tenant": "__user__"
  74. },
  75. "detector_type": "MULTI_ENTITY"
  76. },
  77. "_primary_term": 1
  78. }

You can specify a maximum of two category fields:

  1. "category_field": [
  2. "ip"
  3. ]
  1. "category_field": [
  2. "ip", "error_type"
  3. ]

You can specify the following options.

OptionsDescriptionTypeRequired
nameThe name of the detector.stringYes
descriptionA description of the detector.stringYes
time_fieldThe name of the time field.stringYes
indicesA list of indices to use as the data source.listYes
feature_attributesSpecify a feature_name, set the enabled parameter to true, and specify an aggregation query.listYes
filter_queryProvide an optional filter query for your feature.objectNo
detection_intervalThe time interval for your anomaly detector.objectYes
window_delayAdd extra processing time for data collection.objectNo
category_fieldCategorizes or slices data with a dimension. Similar to GROUP BY in SQL.listNo

Get detector

Introduced 1.0

Returns all information about a detector based on the detector_id.

Request

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 1,
  4. "_primary_term": 1,
  5. "_seq_no": 5,
  6. "anomaly_detector": {
  7. "name": "test-detector",
  8. "description": "Test detector",
  9. "time_field": "timestamp",
  10. "indices": [
  11. "server_log*"
  12. ],
  13. "filter_query": {
  14. "bool": {
  15. "filter": [
  16. {
  17. "range": {
  18. "value": {
  19. "from": 1,
  20. "to": null,
  21. "include_lower": false,
  22. "include_upper": true,
  23. "boost": 1
  24. }
  25. }
  26. }
  27. ],
  28. "adjust_pure_negative": true,
  29. "boost": 1
  30. }
  31. },
  32. "detection_interval": {
  33. "period": {
  34. "interval": 1,
  35. "unit": "Minutes"
  36. }
  37. },
  38. "window_delay": {
  39. "period": {
  40. "interval": 1,
  41. "unit": "Minutes"
  42. }
  43. },
  44. "shingle_size": 8,
  45. "schema_version": 0,
  46. "feature_attributes": [
  47. {
  48. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  49. "feature_name": "test",
  50. "feature_enabled": true,
  51. "aggregation_query": {
  52. "test": {
  53. "sum": {
  54. "field": "value"
  55. }
  56. }
  57. }
  58. }
  59. ],
  60. "last_update_time": 1633392680364,
  61. "user": {
  62. "name": "admin",
  63. "backend_roles": [
  64. "admin"
  65. ],
  66. "roles": [
  67. "own_index",
  68. "all_access"
  69. ],
  70. "custom_attribute_names": [],
  71. "user_requested_tenant": "__user__"
  72. },
  73. "detector_type": "SINGLE_ENTITY"
  74. }
  75. }

A “job” is something that you schedule to run periodically, so it’s only applicable for real-time anomaly detection and not historical analysis that you run just one time.

When you start a real-time detector, the anomaly detection plugin creates a job or if the job already exists updates it. When you start or a restart a real-time detector, the plugin creates a new real-time task that records run-time information like detector configuration snapshot, real-time job states (initializing/running/stopped), init progress, and so on.

A single detector can only have one real-time job (job ID is the same as detector ID), but it can have multiple real-time tasks because each restart of a real-time job creates a new real-time task. You can limit the number of real-time tasks with the plugins.anomaly_detection.max_old_ad_task_docs_per_detector setting.

Historical analysis doesn’t have an associated job. When you start or rerun historical analysis for a detector, the anomaly detection plugin creates a new historical batch task that tracks the historical analysis runtime information like state, coordinating/worker node, task progress, and so on. You can limit the historical task number with the plugins.anomaly_detection.max_old_ad_task_docs_per_detector setting.

Use job=true to get real-time analysis task information.

Request

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>?job=true

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 1,
  4. "_primary_term": 1,
  5. "_seq_no": 5,
  6. "anomaly_detector": {
  7. "name": "test-detector",
  8. "description": "Test detector",
  9. "time_field": "timestamp",
  10. "indices": [
  11. "server_log*"
  12. ],
  13. "filter_query": {
  14. "bool": {
  15. "filter": [
  16. {
  17. "range": {
  18. "value": {
  19. "from": 1,
  20. "to": null,
  21. "include_lower": false,
  22. "include_upper": true,
  23. "boost": 1
  24. }
  25. }
  26. }
  27. ],
  28. "adjust_pure_negative": true,
  29. "boost": 1
  30. }
  31. },
  32. "detection_interval": {
  33. "period": {
  34. "interval": 1,
  35. "unit": "Minutes"
  36. }
  37. },
  38. "window_delay": {
  39. "period": {
  40. "interval": 1,
  41. "unit": "Minutes"
  42. }
  43. },
  44. "shingle_size": 8,
  45. "schema_version": 0,
  46. "feature_attributes": [
  47. {
  48. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  49. "feature_name": "test",
  50. "feature_enabled": true,
  51. "aggregation_query": {
  52. "test": {
  53. "sum": {
  54. "field": "value"
  55. }
  56. }
  57. }
  58. }
  59. ],
  60. "last_update_time": 1633392680364,
  61. "user": {
  62. "name": "admin",
  63. "backend_roles": [
  64. "admin"
  65. ],
  66. "roles": [
  67. "own_index",
  68. "all_access"
  69. ],
  70. "custom_attribute_names": [],
  71. "user_requested_tenant": "__user__"
  72. },
  73. "detector_type": "SINGLE_ENTITY"
  74. },
  75. "anomaly_detector_job": {
  76. "name": "VEHKTXwBwf_U8gjUXY2s",
  77. "schedule": {
  78. "interval": {
  79. "start_time": 1633393656357,
  80. "period": 1,
  81. "unit": "Minutes"
  82. }
  83. },
  84. "window_delay": {
  85. "period": {
  86. "interval": 1,
  87. "unit": "Minutes"
  88. }
  89. },
  90. "enabled": true,
  91. "enabled_time": 1633393656357,
  92. "last_update_time": 1633393656357,
  93. "lock_duration_seconds": 60,
  94. "user": {
  95. "name": "admin",
  96. "backend_roles": [
  97. "admin"
  98. ],
  99. "roles": [
  100. "own_index",
  101. "all_access"
  102. ],
  103. "custom_attribute_names": [],
  104. "user_requested_tenant": "__user__"
  105. }
  106. }
  107. }

Use task=true to get information for both real-time and historical analysis task information.

Request

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>?task=true

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 1,
  4. "_primary_term": 1,
  5. "_seq_no": 5,
  6. "anomaly_detector": {
  7. "name": "test-detector",
  8. "description": "Test detector",
  9. "time_field": "timestamp",
  10. "indices": [
  11. "server_log*"
  12. ],
  13. "filter_query": {
  14. "bool": {
  15. "filter": [
  16. {
  17. "range": {
  18. "value": {
  19. "from": 1,
  20. "to": null,
  21. "include_lower": false,
  22. "include_upper": true,
  23. "boost": 1
  24. }
  25. }
  26. }
  27. ],
  28. "adjust_pure_negative": true,
  29. "boost": 1
  30. }
  31. },
  32. "detection_interval": {
  33. "period": {
  34. "interval": 1,
  35. "unit": "Minutes"
  36. }
  37. },
  38. "window_delay": {
  39. "period": {
  40. "interval": 1,
  41. "unit": "Minutes"
  42. }
  43. },
  44. "shingle_size": 8,
  45. "schema_version": 0,
  46. "feature_attributes": [
  47. {
  48. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  49. "feature_name": "test",
  50. "feature_enabled": true,
  51. "aggregation_query": {
  52. "test": {
  53. "sum": {
  54. "field": "value"
  55. }
  56. }
  57. }
  58. }
  59. ],
  60. "last_update_time": 1633392680364,
  61. "user": {
  62. "name": "admin",
  63. "backend_roles": [
  64. "admin"
  65. ],
  66. "roles": [
  67. "own_index",
  68. "all_access"
  69. ],
  70. "custom_attribute_names": [],
  71. "user_requested_tenant": "__user__"
  72. },
  73. "detector_type": "SINGLE_ENTITY"
  74. },
  75. "realtime_detection_task": {
  76. "task_id": "nkTZTXwBjd8s6RK4QlMq",
  77. "last_update_time": 1633393776375,
  78. "started_by": "admin",
  79. "error": "",
  80. "state": "RUNNING",
  81. "detector_id": "VEHKTXwBwf_U8gjUXY2s",
  82. "task_progress": 0,
  83. "init_progress": 1,
  84. "execution_start_time": 1633393656362,
  85. "is_latest": true,
  86. "task_type": "REALTIME_SINGLE_ENTITY",
  87. "coordinating_node": "SWD7ihu9TaaW1zKwFZNVNg",
  88. "detector": {
  89. "name": "test-detector",
  90. "description": "Test detector",
  91. "time_field": "timestamp",
  92. "indices": [
  93. "server_log*"
  94. ],
  95. "filter_query": {
  96. "bool": {
  97. "filter": [
  98. {
  99. "range": {
  100. "value": {
  101. "from": 1,
  102. "to": null,
  103. "include_lower": false,
  104. "include_upper": true,
  105. "boost": 1
  106. }
  107. }
  108. }
  109. ],
  110. "adjust_pure_negative": true,
  111. "boost": 1
  112. }
  113. },
  114. "detection_interval": {
  115. "period": {
  116. "interval": 1,
  117. "unit": "Minutes"
  118. }
  119. },
  120. "window_delay": {
  121. "period": {
  122. "interval": 1,
  123. "unit": "Minutes"
  124. }
  125. },
  126. "shingle_size": 8,
  127. "schema_version": 0,
  128. "feature_attributes": [
  129. {
  130. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  131. "feature_name": "test",
  132. "feature_enabled": true,
  133. "aggregation_query": {
  134. "test": {
  135. "sum": {
  136. "field": "value"
  137. }
  138. }
  139. }
  140. }
  141. ],
  142. "last_update_time": 1633392680364,
  143. "user": {
  144. "name": "admin",
  145. "backend_roles": [
  146. "admin"
  147. ],
  148. "roles": [
  149. "own_index",
  150. "all_access"
  151. ],
  152. "custom_attribute_names": [],
  153. "user_requested_tenant": "__user__"
  154. },
  155. "detector_type": "SINGLE_ENTITY"
  156. },
  157. "estimated_minutes_left": 0,
  158. "user": {
  159. "name": "admin",
  160. "backend_roles": [
  161. "admin"
  162. ],
  163. "roles": [
  164. "own_index",
  165. "all_access"
  166. ],
  167. "custom_attribute_names": [],
  168. "user_requested_tenant": "__user__"
  169. }
  170. },
  171. "historical_analysis_task": {
  172. "task_id": "99DaTXwB6HknB84StRN1",
  173. "last_update_time": 1633393797040,
  174. "started_by": "admin",
  175. "state": "RUNNING",
  176. "detector_id": "VEHKTXwBwf_U8gjUXY2s",
  177. "task_progress": 0.89285713,
  178. "init_progress": 1,
  179. "current_piece": 1633328940000,
  180. "execution_start_time": 1633393751412,
  181. "is_latest": true,
  182. "task_type": "HISTORICAL_SINGLE_ENTITY",
  183. "coordinating_node": "SWD7ihu9TaaW1zKwFZNVNg",
  184. "worker_node": "2Z4q22BySEyzakYt_A0A2A",
  185. "detector": {
  186. "name": "test-detector",
  187. "description": "Test detector",
  188. "time_field": "timestamp",
  189. "indices": [
  190. "server_log*"
  191. ],
  192. "filter_query": {
  193. "bool": {
  194. "filter": [
  195. {
  196. "range": {
  197. "value": {
  198. "from": 1,
  199. "to": null,
  200. "include_lower": false,
  201. "include_upper": true,
  202. "boost": 1
  203. }
  204. }
  205. }
  206. ],
  207. "adjust_pure_negative": true,
  208. "boost": 1
  209. }
  210. },
  211. "detection_interval": {
  212. "period": {
  213. "interval": 1,
  214. "unit": "Minutes"
  215. }
  216. },
  217. "window_delay": {
  218. "period": {
  219. "interval": 1,
  220. "unit": "Minutes"
  221. }
  222. },
  223. "shingle_size": 8,
  224. "schema_version": 0,
  225. "feature_attributes": [
  226. {
  227. "feature_id": "U0HKTXwBwf_U8gjUXY2m",
  228. "feature_name": "test",
  229. "feature_enabled": true,
  230. "aggregation_query": {
  231. "test": {
  232. "sum": {
  233. "field": "value"
  234. }
  235. }
  236. }
  237. }
  238. ],
  239. "last_update_time": 1633392680364,
  240. "user": {
  241. "name": "admin",
  242. "backend_roles": [
  243. "admin"
  244. ],
  245. "roles": [
  246. "own_index",
  247. "all_access"
  248. ],
  249. "custom_attribute_names": [],
  250. "user_requested_tenant": "__user__"
  251. },
  252. "detector_type": "SINGLE_ENTITY"
  253. },
  254. "detection_date_range": {
  255. "start_time": 1632788951329,
  256. "end_time": 1633393751329
  257. },
  258. "user": {
  259. "name": "admin",
  260. "backend_roles": [
  261. "admin"
  262. ],
  263. "roles": [
  264. "own_index",
  265. "all_access"
  266. ],
  267. "custom_attribute_names": [],
  268. "user_requested_tenant": "__user__"
  269. }
  270. }
  271. }

Update detector

Introduced 1.0

Updates a detector with any changes, including the description or adding or removing of features. To update a detector, you need to first stop both real-time detection and historical analysis.

You can’t update a category field.

Request

  1. PUT _plugins/_anomaly_detection/detectors/<detectorId>
  2. {
  3. "name": "test-detector",
  4. "description": "Test update detector",
  5. "time_field": "timestamp",
  6. "indices": [
  7. "server_log*"
  8. ],
  9. "feature_attributes": [
  10. {
  11. "feature_name": "test",
  12. "feature_enabled": true,
  13. "aggregation_query": {
  14. "test": {
  15. "sum": {
  16. "field": "value"
  17. }
  18. }
  19. }
  20. }
  21. ],
  22. "filter_query": {
  23. "bool": {
  24. "filter": [
  25. {
  26. "range": {
  27. "value": {
  28. "gt": 1
  29. }
  30. }
  31. }
  32. ],
  33. "adjust_pure_negative": true,
  34. "boost": 1
  35. }
  36. },
  37. "detection_interval": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "window_delay": {
  44. "period": {
  45. "interval": 1,
  46. "unit": "Minutes"
  47. }
  48. }
  49. }

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 2,
  4. "_seq_no": 7,
  5. "anomaly_detector": {
  6. "name": "test-detector",
  7. "description": "Test update detector",
  8. "time_field": "timestamp",
  9. "indices": [
  10. "server_log*"
  11. ],
  12. "filter_query": {
  13. "bool": {
  14. "filter": [
  15. {
  16. "range": {
  17. "value": {
  18. "from": 1,
  19. "to": null,
  20. "include_lower": false,
  21. "include_upper": true,
  22. "boost": 1
  23. }
  24. }
  25. }
  26. ],
  27. "adjust_pure_negative": true,
  28. "boost": 1
  29. }
  30. },
  31. "detection_interval": {
  32. "period": {
  33. "interval": 1,
  34. "unit": "Minutes"
  35. }
  36. },
  37. "window_delay": {
  38. "period": {
  39. "interval": 1,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "shingle_size": 8,
  44. "schema_version": 0,
  45. "feature_attributes": [
  46. {
  47. "feature_id": "3kHiTXwBwf_U8gjUlY15",
  48. "feature_name": "test",
  49. "feature_enabled": true,
  50. "aggregation_query": {
  51. "test": {
  52. "sum": {
  53. "field": "value"
  54. }
  55. }
  56. }
  57. }
  58. ],
  59. "last_update_time": 1633394267522,
  60. "user": {
  61. "name": "admin",
  62. "backend_roles": [
  63. "admin"
  64. ],
  65. "roles": [
  66. "own_index",
  67. "all_access"
  68. ],
  69. "custom_attribute_names": [],
  70. "user_requested_tenant": "__user__"
  71. },
  72. "detector_type": "SINGLE_ENTITY"
  73. },
  74. "_primary_term": 1
  75. }

Delete detector

Introduced 1.0

Deletes a detector based on the detector_id. To delete a detector, you need to first stop both real-time detection and historical analysis.

Request

  1. DELETE _plugins/_anomaly_detection/detectors/<detectorId>

Sample response

  1. {
  2. "_index": ".opensearch-anomaly-detectors",
  3. "_type": "_doc",
  4. "_id": "70TxTXwBjd8s6RK4j1Pj",
  5. "_version": 2,
  6. "result": "deleted",
  7. "forced_refresh": true,
  8. "_shards": {
  9. "total": 2,
  10. "successful": 2,
  11. "failed": 0
  12. },
  13. "_seq_no": 9,
  14. "_primary_term": 1
  15. }

Preview detector

Introduced 1.0

Passes a date range to the anomaly detector to return any anomalies within that date range.

To preview a single-entity detector:

Request

  1. POST _plugins/_anomaly_detection/detectors/_preview
  2. {
  3. "period_start": 1633048868000,
  4. "period_end": 1633394468000,
  5. "detector": {
  6. "name": "test-detector",
  7. "description": "Test update detector",
  8. "time_field": "timestamp",
  9. "indices": [
  10. "server_log*"
  11. ],
  12. "feature_attributes": [
  13. {
  14. "feature_name": "test",
  15. "feature_enabled": true,
  16. "aggregation_query": {
  17. "test": {
  18. "sum": {
  19. "field": "value"
  20. }
  21. }
  22. }
  23. }
  24. ],
  25. "filter_query": {
  26. "bool": {
  27. "filter": [
  28. {
  29. "range": {
  30. "value": {
  31. "gt": 1
  32. }
  33. }
  34. }
  35. ],
  36. "adjust_pure_negative": true,
  37. "boost": 1
  38. }
  39. },
  40. "detection_interval": {
  41. "period": {
  42. "interval": 1,
  43. "unit": "Minutes"
  44. }
  45. },
  46. "window_delay": {
  47. "period": {
  48. "interval": 1,
  49. "unit": "Minutes"
  50. }
  51. }
  52. }
  53. }

Sample response

  1. {
  2. "anomaly_result": [
  3. {
  4. "detector_id": null,
  5. "data_start_time": 1633049280000,
  6. "data_end_time": 1633049340000,
  7. "schema_version": 0,
  8. "feature_data": [
  9. {
  10. "feature_id": "8EHmTXwBwf_U8gjU0Y0u",
  11. "feature_name": "test",
  12. "data": 0
  13. }
  14. ],
  15. "anomaly_grade": 0,
  16. "confidence": 0
  17. },
  18. ...
  19. ],
  20. "anomaly_detector": {
  21. "name": "test-detector",
  22. "description": "Test update detector",
  23. "time_field": "timestamp",
  24. "indices": [
  25. "server_log*"
  26. ],
  27. "filter_query": {
  28. "bool": {
  29. "filter": [
  30. {
  31. "range": {
  32. "value": {
  33. "from": 1,
  34. "to": null,
  35. "include_lower": false,
  36. "include_upper": true,
  37. "boost": 1
  38. }
  39. }
  40. }
  41. ],
  42. "adjust_pure_negative": true,
  43. "boost": 1
  44. }
  45. },
  46. "detection_interval": {
  47. "period": {
  48. "interval": 1,
  49. "unit": "Minutes"
  50. }
  51. },
  52. "window_delay": {
  53. "period": {
  54. "interval": 1,
  55. "unit": "Minutes"
  56. }
  57. },
  58. "shingle_size": 8,
  59. "schema_version": 0,
  60. "feature_attributes": [
  61. {
  62. "feature_id": "8EHmTXwBwf_U8gjU0Y0u",
  63. "feature_name": "test",
  64. "feature_enabled": true,
  65. "aggregation_query": {
  66. "test": {
  67. "sum": {
  68. "field": "value"
  69. }
  70. }
  71. }
  72. }
  73. ],
  74. "detector_type": "SINGLE_ENTITY"
  75. }
  76. }

If you specify a category field, each result is associated with an entity:

Request

  1. POST _plugins/_anomaly_detection/detectors/_preview
  2. {
  3. "period_start": 1633048868000,
  4. "period_end": 1633394468000,
  5. "detector": {
  6. "name": "test-detector",
  7. "description": "Test update detector",
  8. "time_field": "timestamp",
  9. "indices": [
  10. "server_log*"
  11. ],
  12. "feature_attributes": [
  13. {
  14. "feature_name": "test",
  15. "feature_enabled": true,
  16. "aggregation_query": {
  17. "test": {
  18. "sum": {
  19. "field": "value"
  20. }
  21. }
  22. }
  23. }
  24. ],
  25. "filter_query": {
  26. "bool": {
  27. "filter": [
  28. {
  29. "range": {
  30. "value": {
  31. "gt": 1
  32. }
  33. }
  34. }
  35. ],
  36. "adjust_pure_negative": true,
  37. "boost": 1
  38. }
  39. },
  40. "detection_interval": {
  41. "period": {
  42. "interval": 1,
  43. "unit": "Minutes"
  44. }
  45. },
  46. "window_delay": {
  47. "period": {
  48. "interval": 1,
  49. "unit": "Minutes"
  50. }
  51. },
  52. "category_field": [
  53. "error_type"
  54. ]
  55. }
  56. }

Sample response

  1. {
  2. "anomaly_result": [
  3. {
  4. "detector_id": null,
  5. "data_start_time": 1633049280000,
  6. "data_end_time": 1633049340000,
  7. "schema_version": 0,
  8. "feature_data": [
  9. {
  10. "feature_id": "tkTpTXwBjd8s6RK4DlOZ",
  11. "feature_name": "test",
  12. "data": 0
  13. }
  14. ],
  15. "anomaly_grade": 0,
  16. "confidence": 0,
  17. "entity": [
  18. {
  19. "name": "error_type",
  20. "value": "error1"
  21. }
  22. ]
  23. },
  24. ...
  25. ],
  26. "anomaly_detector": {
  27. "name": "test-detector",
  28. "description": "Test update detector",
  29. "time_field": "timestamp",
  30. "indices": [
  31. "server_log*"
  32. ],
  33. "filter_query": {
  34. "bool": {
  35. "filter": [
  36. {
  37. "range": {
  38. "value": {
  39. "from": 1,
  40. "to": null,
  41. "include_lower": false,
  42. "include_upper": true,
  43. "boost": 1
  44. }
  45. }
  46. }
  47. ],
  48. "adjust_pure_negative": true,
  49. "boost": 1
  50. }
  51. },
  52. "detection_interval": {
  53. "period": {
  54. "interval": 1,
  55. "unit": "Minutes"
  56. }
  57. },
  58. "window_delay": {
  59. "period": {
  60. "interval": 1,
  61. "unit": "Minutes"
  62. }
  63. },
  64. "shingle_size": 8,
  65. "schema_version": 0,
  66. "feature_attributes": [
  67. {
  68. "feature_id": "tkTpTXwBjd8s6RK4DlOZ",
  69. "feature_name": "test",
  70. "feature_enabled": true,
  71. "aggregation_query": {
  72. "test": {
  73. "sum": {
  74. "field": "value"
  75. }
  76. }
  77. }
  78. }
  79. ],
  80. "category_field": [
  81. "error_type"
  82. ],
  83. "detector_type": "MULTI_ENTITY"
  84. }
  85. }

You can preview a detector with the detector ID:

  1. POST _plugins/_anomaly_detection/detectors/_preview
  2. {
  3. "detector_id": "VEHKTXwBwf_U8gjUXY2s",
  4. "period_start": 1633048868000,
  5. "period_end": 1633394468000
  6. }

Or:

  1. POST _opendistro/_anomaly_detection/detectors/VEHKTXwBwf_U8gjUXY2s/_preview
  2. {
  3. "period_start": 1633048868000,
  4. "period_end": 1633394468000
  5. }

Sample response

  1. {
  2. "anomaly_result": [
  3. {
  4. "detector_id": "VEHKTXwBwf_U8gjUXY2s",
  5. "data_start_time": 1633049280000,
  6. "data_end_time": 1633049340000,
  7. "schema_version": 0,
  8. "feature_data": [
  9. {
  10. "feature_id": "3kHiTXwBwf_U8gjUlY15",
  11. "feature_name": "test",
  12. "data": 0
  13. }
  14. ],
  15. "anomaly_grade": 0,
  16. "confidence": 0,
  17. "user": {
  18. "name": "admin",
  19. "backend_roles": [
  20. "admin"
  21. ],
  22. "roles": [
  23. "own_index",
  24. "all_access"
  25. ],
  26. "custom_attribute_names": [],
  27. "user_requested_tenant": "__user__"
  28. }
  29. },
  30. ...
  31. ],
  32. "anomaly_detector": {
  33. "name": "test-detector",
  34. "description": "Test update detector",
  35. "time_field": "timestamp",
  36. "indices": [
  37. "server_log*"
  38. ],
  39. "filter_query": {
  40. "bool": {
  41. "filter": [
  42. {
  43. "range": {
  44. "value": {
  45. "from": 1,
  46. "to": null,
  47. "include_lower": false,
  48. "include_upper": true,
  49. "boost": 1
  50. }
  51. }
  52. }
  53. ],
  54. "adjust_pure_negative": true,
  55. "boost": 1
  56. }
  57. },
  58. "detection_interval": {
  59. "period": {
  60. "interval": 1,
  61. "unit": "Minutes"
  62. }
  63. },
  64. "window_delay": {
  65. "period": {
  66. "interval": 1,
  67. "unit": "Minutes"
  68. }
  69. },
  70. "shingle_size": 8,
  71. "schema_version": 0,
  72. "feature_attributes": [
  73. {
  74. "feature_id": "3kHiTXwBwf_U8gjUlY15",
  75. "feature_name": "test",
  76. "feature_enabled": true,
  77. "aggregation_query": {
  78. "test": {
  79. "sum": {
  80. "field": "value"
  81. }
  82. }
  83. }
  84. }
  85. ],
  86. "last_update_time": 1633394267522,
  87. "user": {
  88. "name": "admin",
  89. "backend_roles": [
  90. "admin"
  91. ],
  92. "roles": [
  93. "own_index",
  94. "all_access"
  95. ],
  96. "custom_attribute_names": [],
  97. "user_requested_tenant": "__user__"
  98. },
  99. "detector_type": "SINGLE_ENTITY"
  100. }
  101. }

Start detector job

Introduced 1.0

Starts a real-time or historical anomaly detector job.

To start a real-time detector job:

Request

  1. POST _plugins/_anomaly_detection/detectors/<detectorId>/_start

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 3,
  4. "_seq_no": 6,
  5. "_primary_term": 1
  6. }

The _id represents the real-time job ID, which is the same as the detector ID.

To start historical analysis:

  1. POST _plugins/_anomaly_detection/detectors/<detectorId>/_start
  2. {
  3. "start_time": 1633048868000,
  4. "end_time": 1633394468000
  5. }

Sample response

  1. {
  2. "_id": "f9DsTXwB6HknB84SoRTY",
  3. "_version": 1,
  4. "_seq_no": 958,
  5. "_primary_term": 1
  6. }

The _id represents the historical batch task ID, which is a random universally unique identifier (UUID).


Stop detector job

Introduced 1.0

Stops a real-time or historical anomaly detector job.

To stop a real-time detector job:

Request

  1. POST _plugins/_anomaly_detection/detectors/<detectorId>/_stop

Sample response

  1. {
  2. "_id": "VEHKTXwBwf_U8gjUXY2s",
  3. "_version": 0,
  4. "_seq_no": 0,
  5. "_primary_term": 0
  6. }

To stop historical analysis:

Introduced 1.1

  1. POST _plugins/_anomaly_detection/detectors/<detectorId>/_stop?historical=true

Sample response

  1. {
  2. "_id": "f9DsTXwB6HknB84SoRTY",
  3. "_version": 0,
  4. "_seq_no": 0,
  5. "_primary_term": 0
  6. }

Search detector

Introduced 1.0

Returns all anomaly detectors for a search query.

To search detectors using the server_log* index:

Request

  1. GET _plugins/_anomaly_detection/detectors/_search
  2. POST _plugins/_anomaly_detection/detectors/_search
  3. {
  4. "query": {
  5. "wildcard": {
  6. "indices": {
  7. "value": "server_log*"
  8. }
  9. }
  10. }
  11. }

Sample response

  1. {
  2. "took": 2,
  3. "timed_out": false,
  4. "_shards": {
  5. "total": 1,
  6. "successful": 1,
  7. "skipped": 0,
  8. "failed": 0
  9. },
  10. "hits": {
  11. "total": {
  12. "value": 4,
  13. "relation": "eq"
  14. },
  15. "max_score": 1,
  16. "hits": [
  17. {
  18. "_index": ".opensearch-anomaly-detectors",
  19. "_type": "_doc",
  20. "_id": "Zi5zTXwBwf_U8gjUTfJG",
  21. "_version": 1,
  22. "_seq_no": 1,
  23. "_primary_term": 1,
  24. "_score": 1,
  25. "_source": {
  26. "name": "test",
  27. "description": "test",
  28. "time_field": "timestamp",
  29. "indices": [
  30. "server_log"
  31. ],
  32. "filter_query": {
  33. "match_all": {
  34. "boost": 1
  35. }
  36. },
  37. "detection_interval": {
  38. "period": {
  39. "interval": 5,
  40. "unit": "Minutes"
  41. }
  42. },
  43. "window_delay": {
  44. "period": {
  45. "interval": 1,
  46. "unit": "Minutes"
  47. }
  48. },
  49. "shingle_size": 8,
  50. "schema_version": 0,
  51. "feature_attributes": [
  52. {
  53. "feature_id": "ZS5zTXwBwf_U8gjUTfIn",
  54. "feature_name": "test_feature",
  55. "feature_enabled": true,
  56. "aggregation_query": {
  57. "test_feature": {
  58. "sum": {
  59. "field": "value"
  60. }
  61. }
  62. }
  63. }
  64. ],
  65. "last_update_time": 1633386974533,
  66. "category_field": [
  67. "error_type"
  68. ],
  69. "user": {
  70. "name": "admin",
  71. "backend_roles": [
  72. "admin"
  73. ],
  74. "roles": [
  75. "own_index",
  76. "all_access"
  77. ],
  78. "custom_attribute_names": [],
  79. "user_requested_tenant": "__user__"
  80. },
  81. "detector_type": "MULTI_ENTITY"
  82. }
  83. },
  84. ...
  85. ]
  86. }
  87. }

Search detector tasks

Introduced 1.1

Searches detector tasks.

To search for the latest detector level historical analysis task for a high cardinality detector

Request

  1. GET _plugins/_anomaly_detection/detectors/tasks/_search
  2. POST _plugins/_anomaly_detection/detectors/tasks/_search
  3. {
  4. "query": {
  5. "bool": {
  6. "filter": [
  7. {
  8. "term": {
  9. "detector_id": "Zi5zTXwBwf_U8gjUTfJG"
  10. }
  11. },
  12. {
  13. "term": {
  14. "task_type": "HISTORICAL_HC_DETECTOR"
  15. }
  16. },
  17. {
  18. "term": {
  19. "is_latest": "true"
  20. }
  21. }
  22. ]
  23. }
  24. }
  25. }

Sample response

  1. {
  2. "took": 1,
  3. "timed_out": false,
  4. "_shards": {
  5. "total": 1,
  6. "successful": 1,
  7. "skipped": 0,
  8. "failed": 0
  9. },
  10. "hits": {
  11. "total": {
  12. "value": 1,
  13. "relation": "eq"
  14. },
  15. "max_score": 0,
  16. "hits": [
  17. {
  18. "_index": ".opensearch-anomaly-detection-state",
  19. "_type": "_doc",
  20. "_id": "fm-RTXwBYwCbWecgB753",
  21. "_version": 34,
  22. "_seq_no": 928,
  23. "_primary_term": 1,
  24. "_score": 0,
  25. "_source": {
  26. "detector_id": "Zi5zTXwBwf_U8gjUTfJG",
  27. "error": "",
  28. "detection_date_range": {
  29. "start_time": 1630794960000,
  30. "end_time": 1633386960000
  31. },
  32. "task_progress": 1,
  33. "last_update_time": 1633389090738,
  34. "execution_start_time": 1633388922742,
  35. "state": "FINISHED",
  36. "coordinating_node": "2Z4q22BySEyzakYt_A0A2A",
  37. "task_type": "HISTORICAL_HC_DETECTOR",
  38. "execution_end_time": 1633389090738,
  39. "started_by": "admin",
  40. "init_progress": 0,
  41. "is_latest": true,
  42. "detector": {
  43. "category_field": [
  44. "error_type"
  45. ],
  46. "description": "test",
  47. "ui_metadata": {
  48. "features": {
  49. "test_feature": {
  50. "aggregationBy": "sum",
  51. "aggregationOf": "value",
  52. "featureType": "simple_aggs"
  53. }
  54. },
  55. "filters": []
  56. },
  57. "feature_attributes": [
  58. {
  59. "feature_id": "ZS5zTXwBwf_U8gjUTfIn",
  60. "feature_enabled": true,
  61. "feature_name": "test_feature",
  62. "aggregation_query": {
  63. "test_feature": {
  64. "sum": {
  65. "field": "value"
  66. }
  67. }
  68. }
  69. }
  70. ],
  71. "schema_version": 0,
  72. "time_field": "timestamp",
  73. "last_update_time": 1633386974533,
  74. "indices": [
  75. "server_log"
  76. ],
  77. "window_delay": {
  78. "period": {
  79. "unit": "Minutes",
  80. "interval": 1
  81. }
  82. },
  83. "detection_interval": {
  84. "period": {
  85. "unit": "Minutes",
  86. "interval": 5
  87. }
  88. },
  89. "name": "testhc",
  90. "filter_query": {
  91. "match_all": {
  92. "boost": 1
  93. }
  94. },
  95. "shingle_size": 8,
  96. "user": {
  97. "backend_roles": [
  98. "admin"
  99. ],
  100. "custom_attribute_names": [],
  101. "roles": [
  102. "own_index",
  103. "all_access"
  104. ],
  105. "name": "admin",
  106. "user_requested_tenant": "__user__"
  107. },
  108. "detector_type": "MULTI_ENTITY"
  109. },
  110. "user": {
  111. "backend_roles": [
  112. "admin"
  113. ],
  114. "custom_attribute_names": [],
  115. "roles": [
  116. "own_index",
  117. "all_access"
  118. ],
  119. "name": "admin",
  120. "user_requested_tenant": "__user__"
  121. }
  122. }
  123. }
  124. ]
  125. }
  126. }

To search for the latest entity-level tasks for the historical analysis of a high cardinality detector:

Request

  1. GET _plugins/_anomaly_detection/detectors/tasks/_search
  2. POST _plugins/_anomaly_detection/detectors/tasks/_search
  3. {
  4. "query": {
  5. "bool": {
  6. "filter": [
  7. {
  8. "term": {
  9. "detector_id": "Zi5zTXwBwf_U8gjUTfJG"
  10. }
  11. },
  12. {
  13. "term": {
  14. "task_type": "HISTORICAL_HC_ENTITY"
  15. }
  16. },
  17. {
  18. "term": {
  19. "is_latest": "true"
  20. }
  21. }
  22. ]
  23. }
  24. },
  25. "sort": [
  26. {
  27. "execution_start_time": {
  28. "order": "desc"
  29. }
  30. }
  31. ],
  32. "size": 100
  33. }

To search and aggregate states for all entity-level historical tasks:

The parent_task_id is the same as the task ID that you can get with the profile detector API: GET _plugins/_anomaly_detection/detectors/<detector_ID>/_profile/ad_task.

Request

  1. GET _plugins/_anomaly_detection/detectors/tasks/_search
  2. POST _plugins/_anomaly_detection/detectors/tasks/_search
  3. {
  4. "size": 0,
  5. "query": {
  6. "bool": {
  7. "filter": [
  8. {
  9. "term": {
  10. "detector_id": {
  11. "value": "Zi5zTXwBwf_U8gjUTfJG",
  12. "boost": 1
  13. }
  14. }
  15. },
  16. {
  17. "term": {
  18. "parent_task_id": {
  19. "value": "fm-RTXwBYwCbWecgB753",
  20. "boost": 1
  21. }
  22. }
  23. },
  24. {
  25. "terms": {
  26. "task_type": [
  27. "HISTORICAL_HC_ENTITY"
  28. ],
  29. "boost": 1
  30. }
  31. }
  32. ]
  33. }
  34. },
  35. "aggs": {
  36. "test": {
  37. "terms": {
  38. "field": "state",
  39. "size": 100
  40. }
  41. }
  42. }
  43. }

Sample response

  1. {
  2. "took": 2,
  3. "timed_out": false,
  4. "_shards": {
  5. "total": 1,
  6. "successful": 1,
  7. "skipped": 0,
  8. "failed": 0
  9. },
  10. "hits": {
  11. "total": {
  12. "value": 32,
  13. "relation": "eq"
  14. },
  15. "max_score": null,
  16. "hits": []
  17. },
  18. "aggregations": {
  19. "test": {
  20. "doc_count_error_upper_bound": 0,
  21. "sum_other_doc_count": 0,
  22. "buckets": [
  23. {
  24. "key": "FINISHED",
  25. "doc_count": 32
  26. }
  27. ]
  28. }
  29. }
  30. }

Search detector result

Introduced 1.0

Returns all results for a search query.

To search anomaly results for grade greater than 0 for real-time analysis:

Request

  1. GET _plugins/_anomaly_detection/detectors/results/_search
  2. POST _plugins/_anomaly_detection/detectors/results/_search
  3. {
  4. "query": {
  5. "bool": {
  6. "filter": [
  7. {
  8. "term": {
  9. "detector_id": "Zi5zTXwBwf_U8gjUTfJG"
  10. }
  11. },
  12. {
  13. "range": {
  14. "anomaly_grade": {
  15. "gt": 0
  16. }
  17. }
  18. }
  19. ],
  20. "must_not": [
  21. {
  22. "exists": {
  23. "field": "task_id"
  24. }
  25. }
  26. ]
  27. }
  28. }
  29. }

Real-time detection doesn’t persist the task ID in the anomaly result, so the task ID will be null.

Sample response

  1. {
  2. "took": 4,
  3. "timed_out": false,
  4. "_shards": {
  5. "total": 3,
  6. "successful": 3,
  7. "skipped": 0,
  8. "failed": 0
  9. },
  10. "hits": {
  11. "total": {
  12. "value": 90,
  13. "relation": "eq"
  14. },
  15. "max_score": 0,
  16. "hits": [
  17. {
  18. "_index": ".opensearch-anomaly-results-history-2021.10.04-1",
  19. "_type": "_doc",
  20. "_id": "686KTXwB6HknB84SMr6G",
  21. "_version": 1,
  22. "_seq_no": 103622,
  23. "_primary_term": 1,
  24. "_score": 0,
  25. "_source": {
  26. "detector_id": "Zi5zTXwBwf_U8gjUTfJG",
  27. "confidence": 0.918886275269358,
  28. "model_id": "Zi5zTXwBwf_U8gjUTfJG_entity_error16",
  29. "schema_version": 4,
  30. "anomaly_score": 1.1093755891885446,
  31. "execution_start_time": 1633388475001,
  32. "data_end_time": 1633388414989,
  33. "data_start_time": 1633388114989,
  34. "feature_data": [
  35. {
  36. "feature_id": "ZS5zTXwBwf_U8gjUTfIn",
  37. "feature_name": "test_feature",
  38. "data": 0.532
  39. }
  40. ],
  41. "execution_end_time": 1633388475014,
  42. "user": {
  43. "backend_roles": [
  44. "admin"
  45. ],
  46. "custom_attribute_names": [],
  47. "roles": [
  48. "own_index",
  49. "all_access"
  50. ],
  51. "name": "admin",
  52. "user_requested_tenant": "__user__"
  53. },
  54. "anomaly_grade": 0.031023547546561225,
  55. "entity": [
  56. {
  57. "name": "error_type",
  58. "value": "error16"
  59. }
  60. ]
  61. }
  62. },
  63. ...
  64. ]
  65. }
  66. }

You can run historical analysis as many times as you like. So, multiple tasks might exist for the same detector.

You can search for the latest historical batch task first and then search the historical batch task results.

To search anomaly results for grade greater than 0 for historical analysis with the task_id:

Request

  1. GET _plugins/_anomaly_detection/detectors/results/_search
  2. POST _plugins/_anomaly_detection/detectors/results/_search
  3. {
  4. "query": {
  5. "bool": {
  6. "filter": [
  7. {
  8. "term": {
  9. "detector_id": "Zi5zTXwBwf_U8gjUTfJG"
  10. }
  11. },
  12. {
  13. "range": {
  14. "anomaly_grade": {
  15. "gt": 0
  16. }
  17. }
  18. },
  19. {
  20. "term": {
  21. "task_id": "fm-RTXwBYwCbWecgB753"
  22. }
  23. }
  24. ]
  25. }
  26. }
  27. }

Sample response

  1. {
  2. "took": 915,
  3. "timed_out": false,
  4. "_shards": {
  5. "total": 3,
  6. "successful": 3,
  7. "skipped": 0,
  8. "failed": 0
  9. },
  10. "hits": {
  11. "total": {
  12. "value": 4115,
  13. "relation": "eq"
  14. },
  15. "max_score": 0,
  16. "hits": [
  17. {
  18. "_index": ".opensearch-anomaly-results-history-2021.10.04-1",
  19. "_type": "_doc",
  20. "_id": "VRyRTXwBDx7vzPBV8jYC",
  21. "_version": 1,
  22. "_seq_no": 149657,
  23. "_primary_term": 1,
  24. "_score": 0,
  25. "_source": {
  26. "detector_id": "Zi5zTXwBwf_U8gjUTfJG",
  27. "confidence": 0.9642989263957601,
  28. "task_id": "fm-RTXwBYwCbWecgB753",
  29. "model_id": "Zi5zTXwBwf_U8gjUTfJG_entity_error24",
  30. "schema_version": 4,
  31. "anomaly_score": 1.2260712437521946,
  32. "execution_start_time": 1633388982692,
  33. "data_end_time": 1631721300000,
  34. "data_start_time": 1631721000000,
  35. "feature_data": [
  36. {
  37. "feature_id": "ZS5zTXwBwf_U8gjUTfIn",
  38. "feature_name": "test_feature",
  39. "data": 10
  40. }
  41. ],
  42. "execution_end_time": 1633388982709,
  43. "user": {
  44. "backend_roles": [
  45. "admin"
  46. ],
  47. "custom_attribute_names": [],
  48. "roles": [
  49. "own_index",
  50. "all_access"
  51. ],
  52. "name": "admin",
  53. "user_requested_tenant": "__user__"
  54. },
  55. "anomaly_grade": 0.14249628345655782,
  56. "entity": [
  57. {
  58. "name": "error_type",
  59. "value": "error1"
  60. }
  61. ]
  62. }
  63. },
  64. ...
  65. ]
  66. }
  67. }

Get detector stats

Introduced 1.0

Provides information about how the plugin is performing.

To get all stats:

Request

  1. GET _plugins/_anomaly_detection/stats

Sample response

  1. {
  2. "anomaly_detectors_index_status": "green",
  3. "anomaly_detection_state_status": "green",
  4. "single_entity_detector_count": 2,
  5. "detector_count": 5,
  6. "multi_entity_detector_count": 3,
  7. "anomaly_detection_job_index_status": "green",
  8. "models_checkpoint_index_status": "green",
  9. "anomaly_results_index_status": "green",
  10. "nodes": {
  11. "2Z4q22BySEyzakYt_A0A2A": {
  12. "ad_execute_request_count": 95,
  13. "models": [
  14. {
  15. "detector_id": "WTBnTXwBjd8s6RK4b1Sz",
  16. "model_type": "rcf",
  17. "last_used_time": 1633398197185,
  18. "model_id": "WTBnTXwBjd8s6RK4b1Sz_model_rcf_0",
  19. "last_checkpoint_time": 1633396573679
  20. },
  21. ...
  22. ],
  23. "ad_canceled_batch_task_count": 0,
  24. "ad_hc_execute_request_count": 75,
  25. "ad_hc_execute_failure_count": 0,
  26. "model_count": 28,
  27. "ad_execute_failure_count": 1,
  28. "ad_batch_task_failure_count": 0,
  29. "ad_total_batch_task_execution_count": 27,
  30. "ad_executing_batch_task_count": 3
  31. },
  32. "SWD7ihu9TaaW1zKwFZNVNg": {
  33. "ad_execute_request_count": 12,
  34. "models": [
  35. {
  36. "detector_id": "Zi5zTXwBwf_U8gjUTfJG",
  37. "model_type": "entity",
  38. "last_used_time": 1633398375008,
  39. "model_id": "Zi5zTXwBwf_U8gjUTfJG_entity_error13",
  40. "last_checkpoint_time": 1633392973682,
  41. "entity": [
  42. {
  43. "name": "error_type",
  44. "value": "error13"
  45. }
  46. ]
  47. },
  48. ...
  49. ],
  50. "ad_canceled_batch_task_count": 1,
  51. "ad_hc_execute_request_count": 0,
  52. "ad_hc_execute_failure_count": 0,
  53. "model_count": 15,
  54. "ad_execute_failure_count": 2,
  55. "ad_batch_task_failure_count": 0,
  56. "ad_total_batch_task_execution_count": 27,
  57. "ad_executing_batch_task_count": 4
  58. },
  59. "TQDUXEzyTJyV0H6_T4hYUw": {
  60. "ad_execute_request_count": 0,
  61. "models": [
  62. {
  63. "detector_id": "Zi5zTXwBwf_U8gjUTfJG",
  64. "model_type": "entity",
  65. "last_used_time": 1633398375004,
  66. "model_id": "Zi5zTXwBwf_U8gjUTfJG_entity_error24",
  67. "last_checkpoint_time": 1633388177359,
  68. "entity": [
  69. {
  70. "name": "error_type",
  71. "value": "error24"
  72. }
  73. ]
  74. },
  75. ...
  76. ],
  77. "ad_canceled_batch_task_count": 0,
  78. "ad_hc_execute_request_count": 0,
  79. "ad_hc_execute_failure_count": 0,
  80. "model_count": 22,
  81. "ad_execute_failure_count": 0,
  82. "ad_batch_task_failure_count": 0,
  83. "ad_total_batch_task_execution_count": 28,
  84. "ad_executing_batch_task_count": 3
  85. }
  86. }
  87. }

The model_count parameter shows the total number of models running on each node’s memory. For historical analysis, you see the values for the following fields:

  • ad_total_batch_task_execution_count
  • ad_executing_batch_task_count
  • ad_canceled_batch_task_count
  • ad_batch_task_failure_count

If haven’t run any historical analysis, these values show up as 0.

To get all stats for a specific node:

Request

  1. GET _plugins/_anomaly_detection/<nodeId>/stats

To get specific stats for a node:

Request

  1. GET _plugins/_anomaly_detection/<nodeId>/stats/<stat>

For example, to get the ad_execute_request_count value for node SWD7ihu9TaaW1zKwFZNVNg:

  1. GET _plugins/_anomaly_detection/SWD7ihu9TaaW1zKwFZNVNg/stats/ad_execute_request_count

Sample response

  1. {
  2. "nodes": {
  3. "SWD7ihu9TaaW1zKwFZNVNg": {
  4. "ad_execute_request_count": 12
  5. }
  6. }
  7. }

To get a specific type of stats:

Request

  1. GET _plugins/_anomaly_detection/stats/<stat>

For example:

  1. GET _plugins/_anomaly_detection/stats/ad_executing_batch_task_count

Sample response

  1. {
  2. "nodes": {
  3. "2Z4q22BySEyzakYt_A0A2A": {
  4. "ad_executing_batch_task_count": 3
  5. },
  6. "SWD7ihu9TaaW1zKwFZNVNg": {
  7. "ad_executing_batch_task_count": 3
  8. },
  9. "TQDUXEzyTJyV0H6_T4hYUw": {
  10. "ad_executing_batch_task_count": 4
  11. }
  12. }
  13. }

Profile detector

Introduced 1.0

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

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/
  2. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true
  3. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/<type>
  4. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/<type1>,<type2>

Sample Responses

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile
  2. {
  3. "state": "DISABLED",
  4. "error": "Stopped detector: AD models memory usage exceeds our limit."
  5. }
  6. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true&pretty
  7. {
  8. "state": "RUNNING",
  9. "error": "",
  10. "models": [
  11. {
  12. "model_id": "3Dh6TXwBwf_U8gjURE0F_entity_KSLSh0Wv05RQXiBAQHTEZg",
  13. "entity": [
  14. {
  15. "name": "ip",
  16. "value": "192.168.1.1"
  17. },
  18. {
  19. "name": "error_type",
  20. "value": "error8"
  21. }
  22. ],
  23. "model_size_in_bytes": 403491,
  24. "node_id": "2Z4q22BySEyzakYt_A0A2A"
  25. },
  26. ...
  27. ],
  28. "total_size_in_bytes": 12911712,
  29. "init_progress": {
  30. "percentage": "100%"
  31. },
  32. "total_entities": 33,
  33. "active_entities": 32,
  34. "ad_task": {
  35. "ad_task": {
  36. "task_id": "D3I5TnwBYwCbWecg7lN9",
  37. "last_update_time": 1633399993685,
  38. "started_by": "admin",
  39. "state": "RUNNING",
  40. "detector_id": "3Dh6TXwBwf_U8gjURE0F",
  41. "task_progress": 0,
  42. "init_progress": 0,
  43. "execution_start_time": 1633399991933,
  44. "is_latest": true,
  45. "task_type": "HISTORICAL_HC_DETECTOR",
  46. "coordinating_node": "2Z4q22BySEyzakYt_A0A2A",
  47. "detector": {
  48. "name": "testhc-mc",
  49. "description": "test",
  50. "time_field": "timestamp",
  51. "indices": [
  52. "server_log"
  53. ],
  54. "filter_query": {
  55. "match_all": {
  56. "boost": 1
  57. }
  58. },
  59. "detection_interval": {
  60. "period": {
  61. "interval": 5,
  62. "unit": "Minutes"
  63. }
  64. },
  65. "window_delay": {
  66. "period": {
  67. "interval": 1,
  68. "unit": "Minutes"
  69. }
  70. },
  71. "shingle_size": 8,
  72. "schema_version": 0,
  73. "feature_attributes": [
  74. {
  75. "feature_id": "2zh6TXwBwf_U8gjUQ039",
  76. "feature_name": "test",
  77. "feature_enabled": true,
  78. "aggregation_query": {
  79. "test": {
  80. "sum": {
  81. "field": "value"
  82. }
  83. }
  84. }
  85. }
  86. ],
  87. "ui_metadata": {
  88. "features": {
  89. "test": {
  90. "aggregationBy": "sum",
  91. "aggregationOf": "value",
  92. "featureType": "simple_aggs"
  93. }
  94. },
  95. "filters": []
  96. },
  97. "last_update_time": 1633387430916,
  98. "category_field": [
  99. "ip",
  100. "error_type"
  101. ],
  102. "user": {
  103. "name": "admin",
  104. "backend_roles": [
  105. "admin"
  106. ],
  107. "roles": [
  108. "own_index",
  109. "all_access"
  110. ],
  111. "custom_attribute_names": [],
  112. "user_requested_tenant": "__user__"
  113. },
  114. "detector_type": "MULTI_ENTITY"
  115. },
  116. "detection_date_range": {
  117. "start_time": 1632793800000,
  118. "end_time": 1633398600000
  119. },
  120. "user": {
  121. "name": "admin",
  122. "backend_roles": [
  123. "admin"
  124. ],
  125. "roles": [
  126. "own_index",
  127. "all_access"
  128. ],
  129. "custom_attribute_names": [],
  130. "user_requested_tenant": "__user__"
  131. }
  132. },
  133. "node_id": "2Z4q22BySEyzakYt_A0A2A",
  134. "task_id": "D3I5TnwBYwCbWecg7lN9",
  135. "task_type": "HISTORICAL_HC_DETECTOR",
  136. "detector_task_slots": 10,
  137. "total_entities_count": 32,
  138. "pending_entities_count": 22,
  139. "running_entities_count": 10,
  140. "running_entities": [ """[{"name":"ip","value":"192.168.1.1"},{"name":"error_type","value":"error9"}]""",
  141. ...],
  142. "entity_task_profiles": [
  143. {
  144. "shingle_size": 8,
  145. "rcf_total_updates": 1994,
  146. "threshold_model_trained": true,
  147. "threshold_model_training_data_size": 0,
  148. "model_size_in_bytes": 1593240,
  149. "node_id": "2Z4q22BySEyzakYt_A0A2A",
  150. "entity": [
  151. {
  152. "name": "ip",
  153. "value": "192.168.1.1"
  154. },
  155. {
  156. "name": "error_type",
  157. "value": "error7"
  158. }
  159. ],
  160. "task_id": "E3I5TnwBYwCbWecg9FMm",
  161. "task_type": "HISTORICAL_HC_ENTITY"
  162. },
  163. ...
  164. ]
  165. },
  166. "model_count": 32
  167. }
  168. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/total_size_in_bytes
  169. {
  170. "total_size_in_bytes": 13369344
  171. }

You can see the ad_task field only for historical analysis.

The model_count parameter shows the total number of models that a detector runs on each node’s memory. This is useful if you have several models running on your cluster and want to know the count.

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.

For a single-entity detector:

Sample response

  1. {
  2. "state": "INIT",
  3. "total_size_in_bytes": 0,
  4. "init_progress": {
  5. "percentage": "0%",
  6. "needed_shingles": 128
  7. },
  8. "ad_task": {
  9. "ad_task": {
  10. "task_id": "cfUNOXwBFLNqSEcxAlde",
  11. "last_update_time": 1633044731640,
  12. "started_by": "admin",
  13. "state": "RUNNING",
  14. "detector_id": "qL4NOXwB__6eNorTAKtJ",
  15. "task_progress": 0.49603173,
  16. "init_progress": 1,
  17. "current_piece": 1632739800000,
  18. "execution_start_time": 1633044726365,
  19. "is_latest": true,
  20. "task_type": "HISTORICAL_SINGLE_ENTITY",
  21. "coordinating_node": "bCtWtxWPThq0BIn5P5I4Xw",
  22. "worker_node": "dIyavWhmSYWGz65b4u-lpQ",
  23. "detector": {
  24. "name": "detector1",
  25. "description": "test",
  26. "time_field": "timestamp",
  27. "indices": [
  28. "server_log"
  29. ],
  30. "filter_query": {
  31. "match_all": {
  32. "boost": 1
  33. }
  34. },
  35. "detection_interval": {
  36. "period": {
  37. "interval": 5,
  38. "unit": "Minutes"
  39. }
  40. },
  41. "window_delay": {
  42. "period": {
  43. "interval": 1,
  44. "unit": "Minutes"
  45. }
  46. },
  47. "shingle_size": 8,
  48. "schema_version": 0,
  49. "feature_attributes": [
  50. {
  51. "feature_id": "p74NOXwB__6eNorTAKss",
  52. "feature_name": "test-feature",
  53. "feature_enabled": true,
  54. "aggregation_query": {
  55. "test_feature": {
  56. "sum": {
  57. "field": "value"
  58. }
  59. }
  60. }
  61. }
  62. ],
  63. "ui_metadata": {
  64. "features": {
  65. "test-feature": {
  66. "aggregationBy": "sum",
  67. "aggregationOf": "value",
  68. "featureType": "simple_aggs"
  69. }
  70. },
  71. "filters": []
  72. },
  73. "last_update_time": 1633044725832,
  74. "user": {
  75. "name": "admin",
  76. "backend_roles": [
  77. "admin"
  78. ],
  79. "roles": [
  80. "own_index",
  81. "all_access"
  82. ],
  83. "custom_attribute_names": [],
  84. "user_requested_tenant": "__user__"
  85. },
  86. "detector_type": "SINGLE_ENTITY"
  87. },
  88. "detection_date_range": {
  89. "start_time": 1632439925885,
  90. "end_time": 1633044725885
  91. },
  92. "user": {
  93. "name": "admin",
  94. "backend_roles": [
  95. "admin"
  96. ],
  97. "roles": [
  98. "own_index",
  99. "all_access"
  100. ],
  101. "custom_attribute_names": [],
  102. "user_requested_tenant": "__user__"
  103. }
  104. },
  105. "shingle_size": 8,
  106. "rcf_total_updates": 1994,
  107. "threshold_model_trained": true,
  108. "threshold_model_training_data_size": 0,
  109. "model_size_in_bytes": 1593240,
  110. "node_id": "dIyavWhmSYWGz65b4u-lpQ",
  111. "detector_task_slots": 1
  112. }
  113. }

The total_entities parameter shows you the total number of entities including the number of category fields for a detector.

Getting the total count of entities is an expensive operation for real-time analysis of a detector with more than one category field. By default, for a real-time detection profile, a detector counts the number of entities up to a value of 10,000. For historical analysis, the anomaly detection plugin only detects the top 1,000 entities by default and caches the top entities in memory, so it doesn’t cost much to get the total count of entities for historical analysis.

The profile operation also provides information about each entity, such as the entity’s last_sample_timestamp and last_active_timestamp. 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.

If there are no anomaly results for an entity, either the entity doesn’t have any sample data or resources such as memory and disk IO are constrained relative to the number of entities.

Request

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all=true
  2. {
  3. "entity": [
  4. {
  5. "name": "host",
  6. "value": "i-00f28ec1eb8997686"
  7. }
  8. ]
  9. }

Sample Responses

  1. {
  2. "is_active": true,
  3. "last_active_timestamp": 1604026394879,
  4. "last_sample_timestamp": 1604026394879,
  5. "init_progress": {
  6. "percentage": "100%"
  7. },
  8. "model": {
  9. "model_id": "TFUdd3UBBwIAGQeRh5IS_entity_i-00f28ec1eb8997686",
  10. "model_size_in_bytes": 712480,
  11. "node_id": "MQ-bTBW3Q2uU_2zX3pyEQg"
  12. },
  13. "state": "RUNNING"
  14. }

To get profile information for only historical analysis, specify ad_task. Specifying _all is an expensive operation for multi-category high cardinality detectors.

Request

  1. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile?_all
  2. GET _plugins/_anomaly_detection/detectors/<detectorId>/_profile/ad_task

Sample Responses

  1. {
  2. "ad_task": {
  3. "ad_task": {
  4. "task_id": "CHI0TnwBYwCbWecgqgRA",
  5. "last_update_time": 1633399648413,
  6. "started_by": "admin",
  7. "state": "RUNNING",
  8. "detector_id": "3Dh6TXwBwf_U8gjURE0F",
  9. "task_progress": 0,
  10. "init_progress": 0,
  11. "execution_start_time": 1633399646784,
  12. "is_latest": true,
  13. "task_type": "HISTORICAL_HC_DETECTOR",
  14. "coordinating_node": "2Z4q22BySEyzakYt_A0A2A",
  15. "detector": {
  16. "name": "testhc-mc",
  17. "description": "test",
  18. "time_field": "timestamp",
  19. "indices": [
  20. "server_log"
  21. ],
  22. "filter_query": {
  23. "match_all": {
  24. "boost": 1
  25. }
  26. },
  27. "detection_interval": {
  28. "period": {
  29. "interval": 5,
  30. "unit": "Minutes"
  31. }
  32. },
  33. "window_delay": {
  34. "period": {
  35. "interval": 1,
  36. "unit": "Minutes"
  37. }
  38. },
  39. "shingle_size": 8,
  40. "schema_version": 0,
  41. "feature_attributes": [
  42. {
  43. "feature_id": "2zh6TXwBwf_U8gjUQ039",
  44. "feature_name": "test",
  45. "feature_enabled": true,
  46. "aggregation_query": {
  47. "test": {
  48. "sum": {
  49. "field": "value"
  50. }
  51. }
  52. }
  53. }
  54. ],
  55. "ui_metadata": {
  56. "features": {
  57. "test": {
  58. "aggregationBy": "sum",
  59. "aggregationOf": "value",
  60. "featureType": "simple_aggs"
  61. }
  62. },
  63. "filters": []
  64. },
  65. "last_update_time": 1633387430916,
  66. "category_field": [
  67. "ip",
  68. "error_type"
  69. ],
  70. "user": {
  71. "name": "admin",
  72. "backend_roles": [
  73. "admin"
  74. ],
  75. "roles": [
  76. "own_index",
  77. "all_access"
  78. ],
  79. "custom_attribute_names": [],
  80. "user_requested_tenant": "__user__"
  81. },
  82. "detector_type": "MULTI_ENTITY"
  83. },
  84. "detection_date_range": {
  85. "start_time": 1632793800000,
  86. "end_time": 1633398600000
  87. },
  88. "user": {
  89. "name": "admin",
  90. "backend_roles": [
  91. "admin"
  92. ],
  93. "roles": [
  94. "own_index",
  95. "all_access"
  96. ],
  97. "custom_attribute_names": [],
  98. "user_requested_tenant": "__user__"
  99. }
  100. },
  101. "node_id": "2Z4q22BySEyzakYt_A0A2A",
  102. "task_id": "CHI0TnwBYwCbWecgqgRA",
  103. "task_type": "HISTORICAL_HC_DETECTOR",
  104. "detector_task_slots": 10,
  105. "total_entities_count": 32,
  106. "pending_entities_count": 22,
  107. "running_entities_count": 10,
  108. "running_entities" : [
  109. """[{"name":"ip","value":"192.168.1.1"},{"name":"error_type","value":"error9"}]""",
  110. ...
  111. ],
  112. "entity_task_profiles": [
  113. {
  114. "shingle_size": 8,
  115. "rcf_total_updates": 994,
  116. "threshold_model_trained": true,
  117. "threshold_model_training_data_size": 0,
  118. "model_size_in_bytes": 1593240,
  119. "node_id": "2Z4q22BySEyzakYt_A0A2A",
  120. "entity": [
  121. {
  122. "name": "ip",
  123. "value": "192.168.1.1"
  124. },
  125. {
  126. "name": "error_type",
  127. "value": "error6"
  128. }
  129. ],
  130. "task_id": "9XI0TnwBYwCbWecgsAd6",
  131. "task_type": "HISTORICAL_HC_ENTITY"
  132. },
  133. ...
  134. ]
  135. }
  136. }

Delete detector results

Introduced 1.1

Deletes the results of a detector based on a query.

Request

  1. DELETE _plugins/_anomaly_detection/detectors/results
  2. {
  3. "query": {
  4. "bool": {
  5. "filter": [
  6. {
  7. "term": {
  8. "detector_id": {
  9. "value": "rlDtOHwBD5tpxlbyW7Nt"
  10. }
  11. }
  12. },
  13. {
  14. "term": {
  15. "task_id": {
  16. "value": "TM3tOHwBCi2h__AOXlyQ"
  17. }
  18. }
  19. },
  20. {
  21. "range": {
  22. "data_start_time": {
  23. "lte": 1632441600000
  24. }
  25. }
  26. }
  27. ]
  28. }
  29. }
  30. }

Sample response

  1. {
  2. "took": 48,
  3. "timed_out": false,
  4. "total": 28,
  5. "updated": 0,
  6. "created": 0,
  7. "deleted": 28,
  8. "batches": 1,
  9. "version_conflicts": 0,
  10. "noops": 0,
  11. "retries": {
  12. "bulk": 0,
  13. "search": 0
  14. },
  15. "throttled_millis": 0,
  16. "requests_per_second": -1,
  17. "throttled_until_millis": 0,
  18. "failures": []
  19. }

Create monitor

Introduced 1.0

Create a monitor to set up alerts for the detector.

Request

  1. POST _plugins/_alerting/monitors
  2. {
  3. "type": "monitor",
  4. "name": "test-monitor",
  5. "enabled": true,
  6. "schedule": {
  7. "period": {
  8. "interval": 20,
  9. "unit": "MINUTES"
  10. }
  11. },
  12. "inputs": [
  13. {
  14. "search": {
  15. "indices": [
  16. ".opensearch-anomaly-results*"
  17. ],
  18. "query": {
  19. "size": 1,
  20. "query": {
  21. "bool": {
  22. "filter": [
  23. {
  24. "range": {
  25. "data_end_time": {
  26. "from": "||-20m",
  27. "to": "",
  28. "include_lower": true,
  29. "include_upper": true,
  30. "boost": 1
  31. }
  32. }
  33. },
  34. {
  35. "term": {
  36. "detector_id": {
  37. "value": "m4ccEnIBTXsGi3mvMt9p",
  38. "boost": 1
  39. }
  40. }
  41. }
  42. ],
  43. "adjust_pure_negative": true,
  44. "boost": 1
  45. }
  46. },
  47. "sort": [
  48. {
  49. "anomaly_grade": {
  50. "order": "desc"
  51. }
  52. },
  53. {
  54. "confidence": {
  55. "order": "desc"
  56. }
  57. }
  58. ],
  59. "aggregations": {
  60. "max_anomaly_grade": {
  61. "max": {
  62. "field": "anomaly_grade"
  63. }
  64. }
  65. }
  66. }
  67. }
  68. }
  69. ],
  70. "triggers": [
  71. {
  72. "name": "test-trigger",
  73. "severity": "1",
  74. "condition": {
  75. "script": {
  76. "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",
  77. "lang": "painless"
  78. }
  79. },
  80. "actions": [
  81. {
  82. "name": "test-action",
  83. "destination_id": "ld7912sBlQ5JUWWFThoW",
  84. "message_template": {
  85. "source": "This is my message body."
  86. },
  87. "throttle_enabled": false,
  88. "subject_template": {
  89. "source": "TheSubject"
  90. }
  91. }
  92. ]
  93. }
  94. ]
  95. }

Sample response

  1. {
  2. "_id": "OClTEnIBmSf7y6LP11Jz",
  3. "_version": 1,
  4. "_seq_no": 10,
  5. "_primary_term": 1,
  6. "monitor": {
  7. "type": "monitor",
  8. "schema_version": 1,
  9. "name": "test-monitor",
  10. "enabled": true,
  11. "enabled_time": 1589445384043,
  12. "schedule": {
  13. "period": {
  14. "interval": 20,
  15. "unit": "MINUTES"
  16. }
  17. },
  18. "inputs": [
  19. {
  20. "search": {
  21. "indices": [
  22. ".opensearch-anomaly-results*"
  23. ],
  24. "query": {
  25. "size": 1,
  26. "query": {
  27. "bool": {
  28. "filter": [
  29. {
  30. "range": {
  31. "data_end_time": {
  32. "from": "||-20m",
  33. "to": "",
  34. "include_lower": true,
  35. "include_upper": true,
  36. "boost": 1
  37. }
  38. }
  39. },
  40. {
  41. "term": {
  42. "detector_id": {
  43. "value": "m4ccEnIBTXsGi3mvMt9p",
  44. "boost": 1
  45. }
  46. }
  47. }
  48. ],
  49. "adjust_pure_negative": true,
  50. "boost": 1
  51. }
  52. },
  53. "sort": [
  54. {
  55. "anomaly_grade": {
  56. "order": "desc"
  57. }
  58. },
  59. {
  60. "confidence": {
  61. "order": "desc"
  62. }
  63. }
  64. ],
  65. "aggregations": {
  66. "max_anomaly_grade": {
  67. "max": {
  68. "field": "anomaly_grade"
  69. }
  70. }
  71. }
  72. }
  73. }
  74. }
  75. ],
  76. "triggers": [
  77. {
  78. "id": "NilTEnIBmSf7y6LP11Jr",
  79. "name": "test-trigger",
  80. "severity": "1",
  81. "condition": {
  82. "script": {
  83. "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",
  84. "lang": "painless"
  85. }
  86. },
  87. "actions": [
  88. {
  89. "id": "NylTEnIBmSf7y6LP11Jr",
  90. "name": "test-action",
  91. "destination_id": "ld7912sBlQ5JUWWFThoW",
  92. "message_template": {
  93. "source": "This is my message body.",
  94. "lang": "mustache"
  95. },
  96. "throttle_enabled": false,
  97. "subject_template": {
  98. "source": "TheSubject",
  99. "lang": "mustache"
  100. }
  101. }
  102. ]
  103. }
  104. ],
  105. "last_update_time": 1589445384043
  106. }
  107. }