Update an anomaly detection job
- POST /_ml/anomaly_detectors/{job_id}/_update
Updates certain properties of an anomaly detection job.
Required authorization
- Cluster privileges manage_ml
Request body
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allow_lazy_openboolean · default: false -
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. If
falseand 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_nodessetting. If this option is set totrue, the open anomaly detection jobs API does not return an error and the job waits in the opening state until sufficient machine learning node capacity is available. -
background_persist_intervalstring -
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 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.
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custom_settingsmap string to 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.
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daily_model_snapshot_retention_after_daysnumber · default: 1 -
Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies a period of time (in days) after which only the first snapshot per day is retained. This period is relative to the timestamp of the most recent snapshot for this job. Valid values range from 0 to
model_snapshot_retention_days. For jobs created before version 7.8.0, the default value matchesmodel_snapshot_retention_days. -
model_snapshot_retention_daysnumber · default: 10 -
Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies the maximum period of time (in days) that snapshots are retained. This period is relative to the timestamp of the most recent snapshot for this job.
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renormalization_window_daysnumber -
Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
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results_retention_daysnumber -
Advanced configuration option. The period of time (in days) that results are retained. Age is calculated relative to the timestamp of the latest bucket result. If this property has a non-null value, once per day at 00:30 (server time), results that are the specified number of days older than the latest bucket result are deleted from Elasticsearch. The default value is null, which means all results are retained.
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detectorsobject -
An array of detector update objects.
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detector_indexnumberrequired -
A unique identifier for the detector. This identifier is based on the order of the detectors in the
analysis_config, starting at zero. -
custom_rulesobject -
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. Kibana refers to custom rules as job rules.
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actions· default: [ "skip_result" ] -
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.
Supported values include:
skip_result: The result will not be created. 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.
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Values:
skip_resultskip_model_update -
conditionsobject -
An 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.
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scopemap string to object -
A 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. -
per_partition_categorizationobject -
Settings related to how categorization interacts with partition fields.
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enabledboolean -
To enable this setting, you must also set the
partition_field_nameproperty to the same value in every detector that uses the keywordmlcategory. Otherwise, job creation fails. -
stop_on_warnboolean -
This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly.
Response
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analysis_configobjectrequired -
bucket_spanstring · default: 5mrequired -
The size of the interval that the analysis is aggregated into, typically between
5mand1h. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation. -
detectorsobjectrequired -
Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned.
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functionstringrequired -
The analysis function that is used. For example,
count,rare,mean,min,max, orsum. -
by_field_namestring -
The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
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custom_rulesobject -
Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules.
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actions· default: [ "skip_result" ] -
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.
Supported values include:
skip_result: The result will not be created. 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.
-
Values:
skip_resultskip_model_update -
conditionsobject -
An 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.
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scopemap string to object -
A 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. -
detector_indexnumber -
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 specify a value for this property, it is ignored. -
exclude_frequent -
If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, you can set
exclude_frequenttoallfor both fields, or tobyoroverfor those specific fields. -
Values:
allnonebyover -
field_namestring -
The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field. The
field_namecannot contain double quotes or backslashes. -
over_field_namestring -
The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
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influencersstringrequired -
A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity.
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categorization_analyzerunion -
If
categorization_field_nameis specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time ascategorization_filters. The categorization analyzer specifies how thecategorization_fieldis interpreted by the categorization process. Thecategorization_analyzerfield can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin. -
categorization_field_namestring -
If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting
by_field_name,over_field_name, orpartition_field_nameto the keywordmlcategory. -
categorization_filtersstring -
If
categorization_field_nameis specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time ascategorization_analyzer. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use thecategorization_analyzerproperty instead and include the filters as pattern_replace character filters. The effect is exactly the same. -
latencystring · default: 0 -
The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second. NOTE: Latency is applicable only when you send data by using the post data API.
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model_prune_windowstring -
Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the
bucket_span. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of30dor 20 timesbucket_span. -
multivariate_by_fieldsboolean -
This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features. If set to
true, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use themultivariate_by_fieldsproperty, you must also specifyby_field_namein your detector. -
per_partition_categorizationobject -
Settings related to how categorization interacts with partition fields.
-
enabledboolean -
To enable this setting, you must also set the
partition_field_nameproperty to the same value in every detector that uses the keywordmlcategory. Otherwise, job creation fails. -
stop_on_warnboolean -
This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly.
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summary_count_field_namestring -
If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same
summary_count_field_nameapplies to all detectors in the job. NOTE: Thesummary_count_field_nameproperty cannot be used with themetricfunction. -
analysis_limitsobjectrequired -
categorization_examples_limitnumber · default: 4 -
The maximum number of examples stored per category in memory and in the results data store. If you increase this value, more examples are available, however it requires that you have more storage available. If you set this value to 0, no examples are stored. NOTE: The
categorization_examples_limitapplies only to analysis that uses categorization. -
model_memory_limitnumber | string · default: 1024mb -
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. If the
xpack.ml.max_model_memory_limitsetting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. 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 ofborkband 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. If you specify a value for thexpack.ml.max_model_memory_limitsetting, an error occurs when you try to create jobs that havemodel_memory_limitvalues greater than that setting value. -
data_descriptionobjectrequired -
time_formatstring · default: epoch -
The time format, which can be
epoch,epoch_ms, or a custom pattern. The valueepochrefers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The valueepoch_msindicates that time is measured in milliseconds since the epoch. Theepochandepoch_mstime formats accept either integer or real values. Custom patterns must conform to the Java DateTimeFormatter class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example:yyyy-MM-dd'T'HH:mm:ssX. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails. -
datafeed_configobject -
aggregationsmap string to Aggregation -
The security privileges that the datafeed uses to run its queries. If Elastic Stack security features were disabled at the time of the most recent update to the datafeed, this property is omitted.
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If an API key was used for the most recent update to the datafeed, its name and identifier are listed in the response.
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The identifier for the API key.
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The name of the API key.
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If a user ID was used for the most recent update to the datafeed, its roles at the time of the update are listed in the response.
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If a service account was used for the most recent update to the datafeed, the account name is listed in the response.
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chunking_configobject -
moderequired -
If the mode is
auto, the chunk size is dynamically calculated; this is the recommended value when the datafeed does not use aggregations. If the mode ismanual, chunking is applied according to the specifiedtime_span; use this mode when the datafeed uses aggregations. If the mode isoff, no chunking is applied. -
Values:
automanualoff -
delayed_data_check_configobjectrequired -
check_windowstring -
The window of time that is searched for late data. This window of time ends with the latest finalized bucket. It defaults to null, which causes an appropriate
check_windowto be calculated when the real-time datafeed runs. In particular, the defaultcheck_windowspan calculation is based on the maximum of2hor8 * bucket_span. -
indices_optionsobject -
allow_no_indicesboolean -
If false, the request returns an error if any wildcard expression, index alias, or
_allvalue targets only missing or closed indices. This behavior applies even if the request targets other open indices. For example, a request targetingfoo*,bar*returns an error if an index starts withfoobut no index starts withbar. -
expand_wildcardsstring -
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as
open,hidden.Supported values include:
all: Match any data stream or index, including hidden ones.open: Match open, non-hidden indices. Also matches any non-hidden data stream.closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.hidden: Match hidden data streams and hidden indices. Must be combined withopen,closed, orboth.none: Wildcard expressions are not accepted.
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Values:
allopenclosedhiddennone -
If true, missing or closed indices are not included in the response.
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queryobjectrequired -
The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value:
{"match_all": {"boost": 1}}. - Query DSL