Path parameters

• job_id string Required

  Identifier for the job.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • allow_lazy_open boolean Required
  • analysis_config object Required
    Hide analysis_config attributes Show analysis_config attributes object

    • bucket_span string 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.

      External documentation
    • detectors array[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.

      Hide detectors attributes Show detectors attributes object

      • function string Required

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

      • by_field_name
      • custom_rules array[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.

      • 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
      • field_name
      • over_field_name
      • partition_field_name
      • use_null boolean

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

        Default value is false.

    • influencers array[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 string | object

      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:

      string-1 string CategorizationAnalyzerDefinition object
    • 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 array[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

      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.

      External documentation
    • 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.

      External documentation
    • 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.

      Hide per_partition_categorization attributes Show per_partition_categorization attributes object

      • 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
    Hide analysis_limits attributes Show analysis_limits attributes object

    • categorization_examples_limit number

      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.

      Default value is 4.

    • model_memory_limit number | 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. 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.

      One of:

      number-1 number string-2 string
  • background_persist_interval string

    A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.

    External documentation
  • create_time number Required

    Time unit for milliseconds

  • finished_time number

    Time unit for milliseconds

  • custom_settings object
    Hide custom_settings attribute Show custom_settings attribute object

    • * string Additional properties
  • daily_model_snapshot_retention_after_days number Required
  • data_description object Required
    Hide data_description attributes Show data_description attributes object

    • format string

      Only JSON format is supported at this time.

    • time_field string

      The name of the field that contains the timestamp.

    • time_format string

      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.

      Default value is epoch.

    • field_delimiter string
  • datafeed_config object
    Hide datafeed_config attributes Show datafeed_config attributes object

    • aggregations object
    • 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.

      Hide authorization attributes Show authorization attributes object

      • roles array[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
    • 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.

      External documentation
    • indices array[string] Required
    • indexes array[string]
    • job_id string Required
    • max_empty_searches number
    • query_delay string

      A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.

      External documentation
    • script_fields object
      Hide script_fields attribute Show script_fields attribute object

      • * object Additional properties
        Hide * attribute Show * attribute object

        • ignore_failure boolean
    • scroll_size number
    • delayed_data_check_config object Required
      Hide delayed_data_check_config attribute Show delayed_data_check_config attribute object

      • enabled boolean Required

        Specifies whether the datafeed periodically checks for delayed data.

    • runtime_mappings object
      Hide runtime_mappings attribute Show runtime_mappings attribute object

      • * object Additional properties
    • indices_options object

      Controls how to deal with unavailable concrete indices (closed or missing), how wildcard expressions are expanded to actual indices (all, closed or open indices) and how to deal with wildcard expressions that resolve to no indices.

      Hide indices_options attributes Show indices_options attributes 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.

      • ignore_unavailable boolean

        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.

        Default value is false.

      • ignore_throttled boolean

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

        Default value is true.

    • 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 array[string]
  • job_id string Required
  • job_type string Required
  • job_version string Required
  • model_plot_config object
    Hide model_plot_config attributes Show model_plot_config attributes object

    • annotations_enabled boolean Generally available

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

      Default value is true.

    • enabled boolean

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

      Default value is false.

    • 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