OpenSearch/docs/reference/ml/apis/jobresource.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-job-resource]]
=== Job Resources

A job resource has the following properties:

`analysis_config`::
  (object) The analysis configuration, which specifies how to analyze the data.
  See <<ml-analysisconfig, analysis configuration objects>>.

`analysis_limits`::
  (object) Defines approximate limits on the memory resource requirements for the job.
  See <<ml-apilimits,analysis limits>>.

`background_persist_interval`::
  (time units) 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.
+
--
TIP: For very large models (several GB), persistence could take 10-20 minutes,
so do not set the `background_persist_interval` value too low.

--

`create_time`::
  (string) The time the job was created. For example, `1491007356077`. This
  property is informational; you cannot change its value.

`custom_settings`::
  (object) Advanced configuration option. Contains custom meta data about the
  job. For example, it can contain custom URL information as shown in
  {xpack-ref}/ml-configuring-url.html[Adding Custom URLs to Machine Learning Results].

`data_description`::
  (object) Describes the data format and how APIs parse timestamp fields.
  See <<ml-datadescription,data description objects>>.

`description`::
  (string) An optional description of the job.

`established_model_memory`::
  (long) The approximate amount of memory resources that have been used for
  analytical processing. This field is present only when the analytics have used
  a stable amount of memory for several consecutive buckets.

`finished_time`::
  (string) If the job closed or failed, this is the time the job finished,
  otherwise it is `null`. This property is informational; you cannot change its
  value.

`groups`::
  (array of strings) A list of job groups.  A job can belong to no groups or
  many. For example, `["group1", "group2"]`.

`job_id`::
  (string) The unique identifier for the job. This identifier can contain
  lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It
  must start and end with alphanumeric characters. This property is
  informational; you cannot change the identifier for existing jobs.

`job_type`::
  (string) Reserved for future use, currently set to `anomaly_detector`.

`job_version`::
 (string) The version of {es} that existed on the node when the job was created.

`model_plot_config`::
  (object) Configuration properties for storing additional model information.
  See <<ml-apimodelplotconfig, model plot configuration>>.

`model_snapshot_id`::
  (string) A numerical character string that uniquely identifies the model
  snapshot. For example, `1491007364`. This property is informational; you
  cannot change its value. For more information about model snapshots, see
  <<ml-snapshot-resource>>.

`model_snapshot_retention_days`::
  (long) The time in days that model snapshots are retained for the job.
  Older snapshots are deleted. The default value is `1`, which means snapshots
  are retained for one day (twenty-four hours).

`renormalization_window_days`::
  (long) 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) The name of the index in which to store the {ml} results.
  The default value is `shared`,
  which corresponds to the index name `.ml-anomalies-shared`

`results_retention_days`::
  (long) Advanced configuration option.
  The number of days for which job results are retained.
  Once per day at 00:30 (server time), results older than this period are
  deleted from Elasticsearch. The default value is null, which means results
  are retained.

[[ml-analysisconfig]]
==== Analysis Configuration Objects

An analysis configuration object has the following properties:

`bucket_span`::
  (time units) The size of the interval that the analysis is aggregated into,
  typically between `5m` and `1h`. The default value is `5m`. For more 
  information about time units, see <<time-units,Common options>>.

`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`. For more information, see
  {xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages].

`categorization_filters`::
  (array of strings) 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. For more information, see
  {xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages].
  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.

`categorization_analyzer`::
  (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`. See
  <<ml-categorizationanalyzer,categorization analyzer>>.

`detectors`::
  (array) An array of detector configuration objects,
  which describe the anomaly detectors that are used in the job.
  See <<ml-detectorconfig,detector configuration objects>>. +
+
--
NOTE: If the `detectors` array does not contain at least one detector,
no analysis can occur and an error is returned.

--

`influencers`::
  (array of strings) 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`::
  (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,Common options>>.
+
--
NOTE: Latency is only applicable when you send data by using
the <<ml-post-data,post data>> API.

--

`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, 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.

NOTE: 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.

--

After you create a job, you cannot change the analysis configuration object; all
the properties are informational.

[float]
[[ml-detectorconfig]]
==== 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.
Each detector has the following properties:

`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. For example, `Low event rate`.

`detector_index`::
  (integer) A unique identifier for the detector. This identifier is based on
  the order of the detectors in the `analysis_config`, starting at zero. You can
  use this identifier when you want to update a specific detector.

`exclude_frequent`::
  (string) Contains one of the following values: `all`, `none`, `by`, or `over`.
  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, then you can set `exclude_frequent`
  to `all` for both fields, or to `by` or `over` for those specific fields.

`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. +
+
--
NOTE: The `field_name` cannot contain double quotes or backslashes.

--

`function`::
  (string) The analysis function that is used. 
  For example, `count`, `rare`, `mean`, `min`, `max`, and `sum`. For more
  information, see {xpack-ref}/ml-functions.html[Function Reference].

`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. For more information, see
  {xpack-ref}/ml-configuring-pop.html[Performing Population Analysis].

`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. The default value is `false`.

`custom_rules`::
  (array) An array of custom rule objects, which enable customizing how the detector works.
  For example, a rule may dictate to the detector conditions under which results should be skipped.
  For more information see <<ml-detector-custom-rule,detector custom rule objects>>. +
+
--
IMPORTANT: Field names are case sensitive, for example a field named 'Bytes'
is different from one named 'bytes'.

--

After you create a job, the only properties you can change in the detector
configuration object are the `detector_description` and the `custom_rules`;
all other properties are informational.

[float]
[[ml-datadescription]]
==== Data Description Objects

The data description defines the format of the input data when you send data to
the job by using the <<ml-post-data,post data>> API. Note that when configure
a {dfeed}, these properties are automatically set.

When data is received via the <<ml-post-data,post data>> API, it is not stored
in {es}. Only the results for anomaly detection are retained.

A data description object has the following properties:

`format`::
  (string) Only `JSON` format is supported at this time.

`time_field`::
  (string) The name of the field that contains the timestamp.
  The default value is `time`.

`time_format`::
  (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. +
+
--
NOTE: 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.

--

[float]
[[ml-categorizationanalyzer]]
==== Categorization Analyzer

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 <<indices-analyze,Analyze endpoint>>.

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 <<analysis-analyzers,built-in analyzer>> or
one added by another plugin.

If it is an object it has the following properties:

`char_filter`::
  (array of strings or objects) One or more
  <<analysis-charfilters,character filters>>. In addition to the built-in
  character filters, other plugins can provide more character filters. This
  property is optional. If it 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
  <<analysis-pattern-replace-charfilter,pattern replace character filters>>.

`tokenizer`::
  (string or object) The name or definition of the
  <<analysis-tokenizers,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_classic` that
  tokenizes in the same way as the non-customizable tokenizer in older versions
  of the product. If you want to use that tokenizer but change the character or
  token filters, specify `"tokenizer": "ml_classic"` in your
  `categorization_analyzer`.

`filter`::
  (array of strings or objects) One or more
  <<analysis-tokenfilters,token filters>>. In addition to the built-in token
  filters, other plugins can provide more token filters. This property is
  optional. If it is not specified, no token filters are applied prior to
  categorization.

If you omit the `categorization_analyzer`, the following default values are used:

[source,js]
--------------------------------------------------
POST _xpack/ml/anomaly_detectors/_validate
{
  "analysis_config" : {
    "categorization_analyzer" : {
      "tokenizer" : "ml_classic",
      "filter" : [
        { "type" : "stop", "stopwords": [
          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
          "GMT", "UTC"
        ] }
      ]
    },
    "categorization_field_name": "message",
    "detectors" :[{
      "function":"count",
      "by_field_name": "mlcategory"
    }]
  },
  "data_description" : {
  }
}
--------------------------------------------------
// CONSOLE

If you specify any part of the `categorization_analyzer`, however, any omitted
sub-properties are _not_ set to default values.

If you are categorizing non-English messages in a language where words are
separated by spaces, you might get better results if you change the day or month
words in the stop token filter to the appropriate words in your language. If you
are categorizing messages in a language where words are not separated by spaces,
you must use a different tokenizer as well in order to get sensible
categorization results.

It is important to be aware that analyzing for categorization of machine
generated log messages is a little different from tokenizing for search.
Features that work well for search, such as stemming, synonym substitution, and
lowercasing are likely to make the results of categorization worse. However, in
order for drill down from {ml} results to work correctly, the tokens that the
categorization analyzer produces must be similar to those produced by the search
analyzer. If they are sufficiently similar, when you search for the tokens that
the categorization analyzer produces then you find the original document that
the categorization field value came from.

For more information, see
{xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages].

[float]
[[ml-detector-custom-rule]]
==== Detector Custom Rule

{stack-ov}/ml-rules.html[Custom rules] enable you to customize the way detectors 
operate. 

A custom rule has the following properties:

`actions`::
  (array) 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.
  The available actions include: +
  `skip_result`::: The result will not be created. This is the default value.
  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.
  
`scope`::
  (object) An optional scope of series where the rule applies. 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`.
  To add a scope for a field, add the field name as a key in the scope object and
  set its value to an object with the following properties:  
`filter_id`:::
  (string) The id of the <<ml-filter-resource,filter>> to be used.

`filter_type`:::
  (string) Either `include` (the rule applies for values in the filter)
  or `exclude` (the rule applies for values not in the filter). Defaults
  to `include`.    

`conditions`::
  (array) An optional array of numeric conditions when the rule applies.
  Multiple conditions are combined together with a logical `AND`.
+
--
NOTE: If your detector uses `lat_long`, `metric`, `rare`, or `freq_rare` 
functions, you can only specify `conditions` that apply to `time`.

  
A condition has the following properties: 

`applies_to`:::
    (string) Specifies the result property to which the condition applies.
    The available options are `actual`, `typical`, `diff_from_typical`, `time`.
`operator`:::
    (string) Specifies the condition operator. The available options are
    `gt` (greater than), `gte` (greater than or equals), `lt` (less than) and `lte` (less than or equals).
`value`:::
    (double) The value that is compared against the `applies_to` field using the `operator`.
--

A rule is required to either have a non-empty scope or at least one condition.
For more examples see 
{stack-ov}/ml-configuring-detector-custom-rules.html[Configuring Detector Custom Rules].

[float]
[[ml-apilimits]]
==== Analysis Limits

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.
If necessary, you can increase the limits after the job is created.

The `analysis_limits` object has the following properties:

`categorization_examples_limit`::
  (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. +
+
--
NOTE: The `categorization_examples_limit` only applies to analysis that uses categorization.
For more information, see
{xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages].

--

`model_memory_limit`::
  (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`.
  This value will need to be increased for jobs that are expected to analyze high
  cardinality fields, but the default is set to a relatively small size to ensure
  that high resource usage is a conscious decision. The default value for jobs
  created in versions earlier than 6.1 is `4096mb`.
+
--
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-units,Common options>>.

If your `elasticsearch.yml` file contains an `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. For more information,
see <<ml-settings>>.
--

[float]
[[ml-apimodelplotconfig]]
==== Model Plot Config

This advanced configuration option stores model information along with the
results. It provides a more detailed view into anomaly detection.

WARNING: 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.

The `model_plot_config` object has the following properties:

`enabled`::
  (boolean) If true, enables calculation and storage of the model bounds for
  each entity that is being analyzed. By default, this is not enabled.

`terms`::
  experimental[] (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. For example, "CPU,NetworkIn,DiskWrites". 
  Wildcards are not supported. Only the specified `terms` can be viewed when 
  using the Single Metric Viewer.