Body Required

• 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. By default, 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-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.

  Default value is false.

• analysis_config object Required

  Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.

  Hide analysis_config attributes Show analysis_config attributes object

  • bucket_span string

    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.

  • 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

    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.

    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.

    Hide attributes Show attributes

    • char_filter array

      One or more character filters. In addition to the built-in character filters, other plugins can provide more character filters. If this property is not specified, no character filters are applied prior to categorization. If you are customizing some other aspect of the analyzer and you need to achieve the equivalent of categorization_filters (which are not permitted when some other aspect of the analyzer is customized), add them here as pattern replace character filters.

      External documentation
    • filter array

      One or more token filters. In addition to the built-in token filters, other plugins can provide more token filters. If this property is not specified, no token filters are applied prior to categorization.

      External documentation

    • tokenizer object | string

      The name or definition of the tokenizer to use after character filters are applied. This property is compulsory if categorization_analyzer is specified as an object. Machine learning provides a tokenizer called ml_standard that tokenizes in a way that has been determined to produce good categorization results on a variety of log file formats for logs in English. If you want to use that tokenizer but change the character or token filters, specify "tokenizer": "ml_standard" in your categorization_analyzer. Additionally, the ml_classic tokenizer is available, which tokenizes in the same way as the non-customizable tokenizer in old versions of the product (before 6.2). ml_classic was the default categorization tokenizer in versions 6.2 to 7.13, so if you need categorization identical to the default for jobs created in these versions, specify "tokenizer": "ml_classic" in your categorization_analyzer.

      One of:

      object-1 object string-2 string
  • 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.

  • 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

    • 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 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.

      Hide custom_rules attributes Show custom_rules attributes object

      • actions array[string]

        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 are skip_result or skip_model_update. Default value is ["skip_result"].

      • conditions array[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.

      • scope 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.

    • 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 string

      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 are all, none, by, or 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.

    • function string

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

    • 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

      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]

    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.

  • 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.

  • 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.

    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

  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.

  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

    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.

    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

  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.

• custom_settings object

  Advanced configuration option. Contains custom meta data about the job.

• daily_model_snapshot_retention_after_days number

  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.

  Default value is 1.

• data_description object Required

  Defines the format of the input data when you send data to the job by using the post data API. Note that when configure a datafeed, these properties are automatically set. When data is received via the post data API, it is not stored in Elasticsearch. Only the results for anomaly detection are retained.

  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

  Defines a datafeed for the anomaly detection job. If Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.

  Hide datafeed_config attributes Show datafeed_config attributes object

  • aggregations object

    If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.

  • chunking_config object

    Datafeeds might be required to search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated and is an advanced configuration option.

    Hide chunking_config attributes Show chunking_config attributes object

    • mode string 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 are auto, manual, or off.

    • time_span string

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

  • datafeed_id string

    A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. The default value is the job identifier.

  • delayed_data_check_config object

    Specifies whether the datafeed checks for missing data and the size of the window. The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the query_delay option is set too low and the data is being indexed after the datafeed has passed that moment in time. This check runs only on real-time datafeeds.

    Hide delayed_data_check_config attributes Show delayed_data_check_config attributes object

    • 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.

  • 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 | array[string]

    An array of index names. Wildcards are supported. If any indices are in remote clusters, the machine learning nodes must have the remote_cluster_client role.

  • indices_options object

    Specifies index expansion options that are used during search.

    Hide indices_options attributes Show indices_options attributes object

    • allow_no_indices boolean

      If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices. For example, a request targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.

    • expand_wildcards string | array[string]

      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.
    • ignore_unavailable boolean

      If true, missing or closed indices are not included in the response.

      Default value is false.

    • ignore_throttled boolean

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

      Default value is true.

  • job_id string
  • max_empty_searches number

    If a real-time datafeed has never seen any data (including during any initial training period) then it will automatically stop itself and close its associated job after this many real-time searches that return no documents. In other words, it will stop after frequency times max_empty_searches of real-time operation. If not set then a datafeed with no end time that sees no data will remain started until it is explicitly stopped.

  • query object

    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.

    External documentation
  • query_delay string

    The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between 60s and 120s. This randomness improves the query performance when there are multiple jobs running on the same node.

  • runtime_mappings object

    Specifies runtime fields for the datafeed search.

    Hide runtime_mappings attribute Show runtime_mappings attribute object

    • * object Additional properties
      Hide * attributes Show * attributes object

      • fields object

        For type composite

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        • * object Additional properties
      • fetch_fields array[object]

        For type lookup
      • format string

        A custom format for date type runtime fields.

      • input_field string

        For type lookup

      • target_field string

        For type lookup

      • target_index string

        For type lookup

      • script object

        Painless script executed at query time.

      • type string Required

        Field type, which can be: boolean, composite, date, double, geo_point, ip,keyword, long, or lookup.

        Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.

  • script_fields object

    Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields.

    Hide script_fields attribute Show script_fields attribute object

    • * object Additional properties
      Hide * attributes Show * attributes object

      • script object Required
        Hide script attributes Show script attributes object

        • params object

          Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.

        • options object
      • ignore_failure boolean
  • scroll_size number

    The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of index.max_result_window, which is 10,000 by default.

    Default value is 1000.

• description string

  A description of the job.

• job_id string

  The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

• groups array[string]

  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.

  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_retention_days number

  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. By default, snapshots ten days older than the newest snapshot are deleted.

  Default value is 10.

• renormalization_window_days number

  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.

• results_index_name string

  A text string that affects the name of the machine learning results index. By default, the job generates an index named .ml-anomalies-shared.

• 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. Annotations generated by the system also count as results for retention purposes; they are deleted after the same number of days as results. Annotations added by users are retained forever.