Update an anomaly detection job

  • POST /_ml/anomaly_detectors/{job_id}/_update
Auth

Updates certain properties of an anomaly detection job.

Required authorization

Cluster privileges manage_ml

Path parameters

job_id string required

Identifier for the job.

Request body

allow_lazy_open boolean · 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 false and 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-wide xpack.ml.max_lazy_ml_nodes setting. If this option is set to true, 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.

analysis_limits object
model_memory_limit string required

Limits can be applied for the resources required to hold the mathematical models in memory. These limits are approximate and can be set per job. They do not control the memory used by other processes, for example the Elasticsearch Java processes.

background_persist_interval string

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.

custom_settings map 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.

categorization_filters string[]
description string

A description of the job.

model_plot_config object
annotations_enabled boolean · default: true

If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.

enabled boolean · default: false

If true, enables calculation and storage of the model bounds for each entity that is being analyzed.

terms string

Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer.

model_prune_window string
daily_model_snapshot_retention_after_days number · 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 matches model_snapshot_retention_days.

model_snapshot_retention_days number · 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.

renormalization_window_days number

Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.

results_retention_days number

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.

groups string[]

A list of job groups. A job can belong to no groups or many.

detectors union oneOf

A detector update object or an array of detector update objects.

object or object[]
detector_index number required

A unique identifier for the detector. This identifier is based on the order of the detectors in the analysis_config, starting at zero.

description string

A description of the detector.

custom_rules object[]

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.

actions enum[] · 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 specify skip_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 specify skip_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_result skip_model_update
conditions object[]

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.

applies_to enum required

Specifies the result property to which the condition applies. If your detector uses lat_long, metric, rare, or freq_rare functions, you can only specify conditions that apply to time.

Values: actual typical diff_from_typical time
operator enum required

Specifies the condition operator. The available options are greater than, greater than or equals, less than, and less than or equals.

Values: gt gte lt lte
value number required

The value that is compared against the applies_to field using the operator.

scope map 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, or partition_field_name.

<string> object
filter_id string required

The identifier for the filter.

filter_type enum · default: include

If set to include, the rule applies for values in the filter. If set to exclude, the rule applies for values not in the filter.

Values: include exclude
per_partition_categorization object

Settings related to how categorization interacts with partition fields.

enabled boolean

To enable this setting, you must also set the partition_field_name property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.

stop_on_warn boolean

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

application/json
allow_lazy_open boolean required
analysis_config object required
bucket_span string · default: 5m required

The size of the interval that the analysis is aggregated into, typically between 5m and 1h. 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.

detectors object[] required

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.

function string required

The analysis function that is used. For example, count, rare, mean, min, max, or sum.

by_field_name string

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.

custom_rules object[]

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.

actions enum[] · 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 specify skip_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 specify skip_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_result skip_model_update
conditions object[]

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.

applies_to enum required

Specifies the result property to which the condition applies. If your detector uses lat_long, metric, rare, or freq_rare functions, you can only specify conditions that apply to time.

Values: actual typical diff_from_typical time
operator enum required

Specifies the condition operator. The available options are greater than, greater than or equals, less than, and less than or equals.

Values: gt gte lt lte
value number required

The value that is compared against the applies_to field using the operator.

scope map 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, or partition_field_name.

<string> object
filter_id string required

The identifier for the filter.

filter_type enum · default: include

If set to include, the rule applies for values in the filter. If set to exclude, the rule applies for values not in the filter.

Values: include exclude
detector_description string

A description of the detector.

detector_index number

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 enum

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_frequent to all for both fields, or to by or over for those specific fields.

Values: all none by over
field_name string

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_name cannot contain double quotes or backslashes.

over_field_name string

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.

partition_field_name string

The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.

use_null boolean · default: false

Defines whether a new series is used as the null series when there is no value for the by or partition fields.

influencers string[] required

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.

categorization_analyzer union oneOf

If categorization_field_name is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as categorization_filters. The categorization analyzer specifies how the categorization_field is interpreted by the categorization process. The categorization_analyzer field 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.

One of:
categorization_field_name string

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, or partition_field_name to the keyword mlcategory.

categorization_filters string[]

If categorization_field_name is 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 as categorization_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 the categorization_analyzer property instead and include the filters as pattern_replace character filters. The effect is exactly the same.

latency string · 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.

model_prune_window string

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 of 30d or 20 times bucket_span.

multivariate_by_fields boolean

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 the multivariate_by_fields property, you must also specify by_field_name in your detector.

per_partition_categorization object

Settings related to how categorization interacts with partition fields.

enabled boolean

To enable this setting, you must also set the partition_field_name property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.

stop_on_warn boolean

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.

summary_count_field_name string

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_name applies to all detectors in the job. NOTE: The summary_count_field_name property cannot be used with the metric function.

analysis_limits object required
categorization_examples_limit number · 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_limit applies only to analysis that uses categorization.

model_memory_limit number | 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_limit setting 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 of b or kb 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. If you specify a value for the xpack.ml.max_model_memory_limit setting, an error occurs when you try to create jobs that have model_memory_limit values greater than that setting value.

background_persist_interval string
create_time number required
finished_time number
custom_settings map string to string
daily_model_snapshot_retention_after_days number required
data_description object required
format string

Only JSON format is supported at this time.

time_field string · default: time

The name of the field that contains the timestamp.

time_format string · default: epoch

The time format, which can be epoch, epoch_ms, or a custom pattern. The value epoch refers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The value epoch_ms indicates that time is measured in milliseconds since the epoch. The epoch and epoch_ms time 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.

field_delimiter string
datafeed_config object
aggregations map string to {} Aggregation
authorization object

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.

api_key object

If an API key was used for the most recent update to the datafeed, its name and identifier are listed in the response.

id string required

The identifier for the API key.

name string required

The name of the API key.

roles string[]

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.

service_account string

If a service account was used for the most recent update to the datafeed, the account name is listed in the response.

chunking_config object
mode enum required

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 is manual, chunking is applied according to the specified time_span; use this mode when the datafeed uses aggregations. If the mode is off, no chunking is applied.

Values: auto manual off
time_span string · default: 3h

The time span that each search will be querying. This setting is applicable only when the mode is set to manual.

datafeed_id string required
frequency string

The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. For example: 150s. When frequency is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation.

indices string[] required
indexes string[]
job_id string required
max_empty_searches number
query_delay string
script_fields map string to object
<string> object
script string required
ignore_failure boolean
scroll_size number
delayed_data_check_config object required
check_window string

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_window to be calculated when the real-time datafeed runs. In particular, the default check_window span calculation is based on the maximum of 2h or 8 * bucket_span.

enabled boolean required

Specifies whether the datafeed periodically checks for delayed data.

runtime_mappings object
indices_options object
allow_no_indices boolean

A setting that does two separate checks on the index expression. If false, the request returns an error (1) if any wildcard expression (including _all and *) resolves to zero matching indices or (2) if the complete set of resolved indices, aliases or data streams is empty after all expressions are evaluated. If true, index expressions that resolve to no indices are allowed and the request returns an empty result.

expand_wildcards union oneOf

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 with open, closed, or both.
  • none: Wildcard expressions are not accepted.
Values: all open closed hidden none
ignore_unavailable boolean · default: false

If false, the request returns an error if it targets a concrete (non-wildcarded) index, alias, or data stream that is missing, closed, or otherwise unavailable. If true, unavailable concrete targets are silently ignored.

ignore_throttled boolean · default: true

If true, concrete, expanded or aliased indices are ignored when frozen.

query object required

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
description string
groups string[]
job_id string required
job_type string required
job_version string required
model_plot_config object
annotations_enabled boolean · default: true

If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.

enabled boolean · default: false

If true, enables calculation and storage of the model bounds for each entity that is being analyzed.

terms string

Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer.

model_snapshot_id string
model_snapshot_retention_days number required
renormalization_window_days number
results_index_name string required
results_retention_days number