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Update an anomaly detection job

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

Updates certain properties of an anomaly detection job.

Required privileges

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.

background_persist_interval union oneOf

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.

Values: -1 0
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_prune_window union oneOf
Values: -1 0
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.

Response

application/json
allow_lazy_open boolean required
analysis_config object required
bucket_span union oneOf · 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.

Values: -1 0
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.

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 union oneOf · 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.

Values: -1 0
model_prune_window union oneOf

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.

Values: -1 0
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.

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 union oneOf
Values: -1 0
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
description string
groups string[]
job_id string required
job_type string required
job_version string required
model_snapshot_id string
model_snapshot_retention_days number required
renormalization_window_days number
results_index_name string required
results_retention_days number