Create anomaly detection jobs API

(object or string) 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 syntax is very similar to that used to define the analyzer in the Analyze endpoint. For more information, see Categorizing log messages.

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 it is an object it has the following properties:

(array) An array of detector configuration objects. 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.

(time units) The size of the window in which to expect data that is out of time order. The default value is 0 (no latency). If you specify a non-zero value, it must be greater than or equal to one second. For more information about time units, see Time units.

(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, then 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.

(Optional, object) Settings related to how categorization interacts with partition fields.

(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.

(long) The maximum number of examples stored per category in memory and in the results data store. The default value is 4. 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.

(long or 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. The default value for jobs created in version 6.1 and later is 1024mb. If the xpack.ml.max_model_memory_limit setting has a value greater than 0 and less than 1024mb, however, that value is used instead. 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. For more information about supported byte size units, see Byte size units.

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. For more information, see Machine learning settings.

(string) The time format, which can be epoch, epoch_ms, or a custom pattern. The default value is epoch, which 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.

(Optional, 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.

(Optional, 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.

Defaults to the same ID as the anomaly detection job.

(Optional, object) Specifies whether the datafeed checks for missing data and the size of the window. For example: {"enabled": true, "check_window": "1h"}.

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. See Working with delayed data.

This check runs only on real-time datafeeds.

(Required, array) An array of index names. Wildcards are supported. For example: ["it_ops_metrics", "server*"].

(Optional, object) Specifies index expansion options that are used during search.

For example:

{
   "expand_wildcards": ["all"],
   "ignore_unavailable": true,
   "allow_no_indices": "false",
   "ignore_throttled": true
}

For more information about these options, see Multi-target syntax.

(Optional, object) Specifies runtime fields for the datafeed search.

For example:

{
  "day_of_week": {
    "type": "keyword",
    "script": {
      "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))"
    }
  }
}