Update anomaly detection jobs API
editUpdate anomaly detection jobs API
editUpdates certain properties of an anomaly detection job.
Request
editPOST _ml/anomaly_detectors/<job_id>/_update
Prerequisites
edit-
If the Elasticsearch security features are enabled, you must have
manage_ml
ormanage
cluster privileges to use this API. See Security privileges.
Path parameters
edit-
<job_id>
- (Required, string) Identifier for the anomaly detection job.
Request body
editThe following properties can be updated after the job is created:
-
allow_lazy_open
-
(boolean) Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node. The default value is
false
; if a machine learning node with capacity to run the job cannot immediately be found, the open anomaly detection jobs API returns an error. However, this is also subject to the cluster-widexpack.ml.max_lazy_ml_nodes
setting; see Advanced machine learning settings. If this option is set totrue
, the open anomaly detection jobs API does not return an error and the job waits in theopening
state until sufficient machine learning node capacity is available.If the job is open when you make the update, you must stop the datafeed, close the job, then reopen the job and restart the datafeed for the changes to take effect.
-
analysis_limits
.model_memory_limit
-
(long or string) The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. The default value for jobs created in version 6.1 and later is
1024mb
. This value will need to be increased for jobs that are expected to analyze high cardinality fields, but the default is set to a relatively small size to ensure that high resource usage is a conscious decision. The default value for jobs created in versions earlier than 6.1 is4096mb
.If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit of
b
orkb
and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. For more information about supported byte size units, see Byte size units.If your
elasticsearch.yml
file contains anxpack.ml.max_model_memory_limit
setting, an error occurs when you try to create jobs that havemodel_memory_limit
values greater than that setting. For more information, see Machine learning settings.You can update the
analysis_limits
only while the job is closed. Themodel_memory_limit
property value cannot be decreased below the current usage.If the
memory_status
property in themodel_size_stats
object has a value ofhard_limit
, this means that it was unable to process some data. You might want to re-run the job with an increasedmodel_memory_limit
. -
background_persist_interval
-
(time units) Advanced configuration option. The time between each periodic persistence of the model. The default value is a randomized value between 3 to 4 hours, which avoids all jobs persisting at exactly the same time. The smallest allowed value is 1 hour.
For very large models (several GB), persistence could take 10-20 minutes, so do not set the
background_persist_interval
value too low.If the job is open when you make the update, you must stop the datafeed, close the job, then reopen the job and restart the datafeed for the changes to take effect.
-
custom_settings
- (object) Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information as shown in Adding custom URLs to machine learning results.
-
description
- (string) A description of the job.
-
detectors
-
(array) An array of detector update objects.
-
detectors
.custom_rules
-
(array) An array of custom rule objects, which enable you to customize the way detectors operate. For example, a rule may dictate to the detector conditions under which results should be skipped. For more examples, see Customizing detectors with custom rules.
-
detectors
.custom_rules
.actions
-
(array) The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined. The available actions include:
-
skip_result
: The result will not be created. This is the default value. Unless you also specifyskip_model_update
, the model will be updated as usual with the corresponding series value. -
skip_model_update
: The value for that series will not be used to update the model. Unless you also specifyskip_result
, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results.
-
-
detectors
.custom_rules
.scope
-
(object) An optional scope of series where the rule applies. A rule must either have a non-empty scope or at least one condition. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in
by_field_name
,over_field_name
, orpartition_field_name
. To add a scope for a field, add the field name as a key in the scope object and set its value to an object with the following properties:-
detectors
.custom_rules
.scope
.filter_id
- (string) The id of the filter to be used.
-
detectors
.custom_rules
.scope
.filter_type
-
(string)
Either
include
(the rule applies for values in the filter) orexclude
(the rule applies for values not in the filter). Defaults toinclude
.
-
-
detectors
.custom_rules
.conditions
-
(array) An optional array of numeric conditions when the rule applies. A rule must either have a non-empty scope or at least one condition. Multiple conditions are combined together with a logical
AND
. A condition has the following properties:-
detectors
.custom_rules
.conditions
.applies_to
-
(string)
Specifies the result property to which the condition applies. The available
options are
actual
,typical
,diff_from_typical
,time
. If your detector useslat_long
,metric
,rare
, orfreq_rare
functions, you can only specify conditions that apply totime
. -
detectors
.custom_rules
.conditions
.operator
-
(string)
Specifies the condition operator. The available options are
gt
(greater than),gte
(greater than or equals),lt
(less than) andlte
(less than or equals). -
detectors
.custom_rules
.conditions
.value
-
(double)
The value that is compared against the
applies_to
field using theoperator
.
-
-
-
detectors
.description
-
(string)
A description of the detector. For example,
Low event rate
. -
detectors
.detector_index
-
(integer) A unique identifier for the detector. This identifier is based on the order of the detectors in the
analysis_config
, starting at zero.If you want to update a specific detector, you must use this identifier. You cannot, however, change the
detector_index
value for a detector.
-
-
groups
- (array of strings) A list of job groups. A job can belong to no groups or many.
-
model_plot_config
-
(object) This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
If you enable model plot it can add considerable overhead to the performance of the system; it is not feasible for jobs with many entities.
Model plot provides a simplified and indicative view of the model and its bounds. It does not display complex features such as multivariate correlations or multimodal data. As such, anomalies may occasionally be reported which cannot be seen in the model plot.
Model plot config can be configured when the job is created or updated later. It must be disabled if performance issues are experienced.
-
model_plot_config
.enabled
- (boolean) If true, enables calculation and storage of the model bounds for each entity that is being analyzed. By default, this is not enabled.
-
-
model_snapshot_retention_days
-
(long)
The time in days that model snapshots are retained for the job. Older snapshots
are deleted. The default value is
1
, which means snapshots are retained for one day (twenty-four hours). -
renormalization_window_days
-
(long) Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen. The default value is the longer of 30 days or 100
bucket_spans
.If the job is open when you make the update, you must stop the datafeed, close the job, then reopen the job and restart the datafeed for the changes to take effect.
-
results_retention_days
- (long) Advanced configuration option. The number of days for which job results are retained. Once per day at 00:30 (server time), results older than this period are deleted from Elasticsearch. The default value is null, which means results are retained.
Examples
editPOST _ml/anomaly_detectors/low_request_rate/_update { "description":"An updated job", "detectors": { "detector_index": 0, "description": "An updated detector description" }, "groups": ["kibana_sample_data","kibana_sample_web_logs"], "model_plot_config": { "enabled": true }, "renormalization_window_days": 30, "background_persist_interval": "2h", "model_snapshot_retention_days": 7, "results_retention_days": 60 }
When the anomaly detection job is updated, you receive a summary of the job configuration information, including the updated property values. For example:
{ "job_id" : "low_request_rate", "job_type" : "anomaly_detector", "job_version" : "7.5.1", "groups" : [ "kibana_sample_data", "kibana_sample_web_logs" ], "description" : "An updated job", "create_time" : 1578101716125, "finished_time" : 1578101721816, "analysis_config" : { "bucket_span" : "1h", "summary_count_field_name" : "doc_count", "detectors" : [ { "detector_description" : "An updated detector description", "function" : "low_count", "detector_index" : 0 } ], "influencers" : [ ] }, ... }