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tag::aggregations[]
If set, the {dfeed} performs aggregation searches. Support for aggregations is
limited and should be used only with low cardinality data. For more information,
see
{ml-docs}/ml-configuring-aggregation.html[Aggregating data for faster performance].
end::aggregations[]
tag::allow-lazy-open[]
Advanced configuration option. Specifies whether this job can open when there is
insufficient {ml} node capacity for it to be immediately assigned to a node. The
default value is `false`; if a {ml} node with capacity to run the job cannot
immediately be found, the <<ml-open-job,open {anomaly-jobs} API>> returns an
error. However, this is also subject to the cluster-wide
`xpack.ml.max_lazy_ml_nodes` setting; see <<advanced-ml-settings>>. If this
option is set to `true`, the <<ml-open-job,open {anomaly-jobs} API>> does not
return an error and the job waits in the `opening` state until sufficient {ml}
node capacity is available.
end::allow-lazy-open[]
tag::allow-lazy-start[]
Whether this job should be allowed to start when there is insufficient {ml} node
capacity for it to be immediately assigned to a node. The default is `false`,
which means that the <<start-dfanalytics>> will return an error if a {ml} node
with capacity to run the job cannot immediately be found. (However, this is also
subject to the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting - see
<<advanced-ml-settings>>.) If this option is set to `true` then the
<<start-dfanalytics>> will not return an error, and the job will wait in the
`starting` state until sufficient {ml} node capacity is available.
end::allow-lazy-start[]
tag::allow-no-datafeeds[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no {dfeeds} that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches.
The default value is `true`, which returns an empty `datafeeds` array when
there are no matches and the subset of results when there are partial matches.
If this parameter is `false`, the request returns a `404` status code when there
are no matches or only partial matches.
--
end::allow-no-datafeeds[]
tag::allow-no-jobs[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no jobs that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches.
The default value is `true`, which returns an empty `jobs` array
when there are no matches and the subset of results when there are partial
matches. If this parameter is `false`, the request returns a `404` status code
when there are no matches or only partial matches.
--
end::allow-no-jobs[]
tag::allow-no-match[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no {dfanalytics-jobs} that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches.
The default value is `true`, which returns an empty `data_frame_analytics` array
when there are no matches and the subset of results when there are partial
matches. If this parameter is `false`, the request returns a `404` status code
when there are no matches or only partial matches.
--
end::allow-no-match[]
tag::analysis[]
Defines the type of {dfanalytics} you want to perform on your source index. For
example: `outlier_detection`. See <<ml-dfa-analysis-objects>>.
end::analysis[]
tag::analysis-config[]
The analysis configuration, which specifies how to analyze the data. After you
create a job, you cannot change the analysis configuration; all the properties
are informational.
end::analysis-config[]
tag::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 {es} Java processes.
If necessary, you can increase the limits after the job is created.
end::analysis-limits[]
tag::analyzed-fields[]
Specify `includes` and/or `excludes` patterns to select which fields will be
included in the analysis. The patterns specified in `excludes` are applied last,
therefore `excludes` takes precedence. In other words, if the same field is
specified in both `includes` and `excludes`, then the field will not be included
in the analysis.
+
--
The supported fields for each type of analysis are as follows:
* {oldetection-cap} requires numeric or boolean data to analyze. The algorithms
don't support missing values therefore fields that have data types other than
numeric or boolean are ignored. Documents where included fields contain missing
values, null values, or an array are also ignored. Therefore the `dest` index
may contain documents that don't have an {olscore}.
* {regression-cap} supports fields that are numeric, `boolean`, `text`,
`keyword`, and `ip`. It is also tolerant of missing values. Fields that are
supported are included in the analysis, other fields are ignored. Documents
where included fields contain an array with two or more values are also
ignored. Documents in the `dest` index that don’t contain a results field are
not included in the {reganalysis}.
* {classification-cap} supports fields that are numeric, `boolean`, `text`,
`keyword`, and `ip`. It is also tolerant of missing values. Fields that are
supported are included in the analysis, other fields are ignored. Documents
where included fields contain an array with two or more values are also ignored.
Documents in the `dest` index that don’t contain a results field are not
included in the {classanalysis}. {classanalysis-cap} can be improved by mapping
ordinal variable values to a single number. For example, in case of age ranges,
you can model the values as "0-14" = 0, "15-24" = 1, "25-34" = 2, and so on.
If `analyzed_fields` is not set, only the relevant fields will be included. For
example, all the numeric fields for {oldetection}. For more information about
field selection, see <<explain-dfanalytics>>.
--
end::analyzed-fields[]
tag::analyzed-fields-excludes[]
An array of strings that defines the fields that will be excluded from the
analysis. You do not need to add fields with unsupported data types to
`excludes`, these fields are excluded from the analysis automatically.
end::analyzed-fields-excludes[]
tag::analyzed-fields-includes[]
An array of strings that defines the fields that will be included in the analysis.
end::analyzed-fields-includes[]
tag::background-persist-interval[]
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.
--
end::background-persist-interval[]
tag::bucket-span[]
The size of the interval that the analysis is aggregated into, typically between
`5m` and `1h`. The default value is `5m`. If the {anomaly-job} uses a {dfeed}
with {ml-docs}/ml-configuring-aggregation.html[aggregations], this value must be
divisible by the interval of the date histogram aggregation. For more
information, see {ml-docs}/ml-buckets.html[Buckets].
end::bucket-span[]
tag::bucket-span-results[]
The length of the bucket in seconds. This value matches the `bucket_span`
that is specified in the job.
end::bucket-span-results[]
tag::by-field-name[]
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.
end::by-field-name[]
tag::calendar-id[]
A string that uniquely identifies a calendar.
end::calendar-id[]
tag::categorization-analyzer[]
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
<<indices-analyze,Analyze endpoint>>. For more information, see
{ml-docs}/ml-configuring-categories.html[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
<<analysis-analyzers,built-in analyzer>> or one added by another plugin. If it
is an object it has the following properties:
--
`analysis_config`.`categorization_analyzer`.`char_filter`::::
(array of strings or objects)
include::{docdir}/ml/ml-shared.asciidoc[tag=char-filter]
`analysis_config`.`categorization_analyzer`.`tokenizer`::::
(string or object)
include::{docdir}/ml/ml-shared.asciidoc[tag=tokenizer]
`analysis_config`.`categorization_analyzer`.`filter`::::
(array of strings or objects)
include::{docdir}/ml/ml-shared.asciidoc[tag=filter]
end::categorization-analyzer[]
tag::categorization-examples-limit[]
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
{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
--
end::categorization-examples-limit[]
tag::categorization-field-name[]
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
{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
end::categorization-field-name[]
tag::categorization-filters[]
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
{ml-docs}/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.
end::categorization-filters[]
tag::char-filter[]
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>>.
end::char-filter[]
tag::chunking-config[]
{dfeeds-cap} 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 {es} is managed. Chunking configuration controls how the size of these time
chunks are calculated and is an advanced configuration option.
A chunking configuration object has the following properties:
`chunking_config`.`mode`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=mode]
`chunking_config`.`time_span`:::
(<<time-units,time units>>)
include::{docdir}/ml/ml-shared.asciidoc[tag=time-span]
end::chunking-config[]
tag::compute-feature-influence[]
If `true`, the feature influence calculation is enabled. Defaults to `true`.
end::compute-feature-influence[]
tag::custom-rules[]
An array of custom rule objects, which enable you to customize the way detectors
operate. For example, a rule may dictate to the detector conditions under which
results should be skipped. For more examples, see
{ml-docs}/ml-configuring-detector-custom-rules.html[Customizing detectors with custom rules].
end::custom-rules[]
tag::custom-rules-actions[]
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.
end::custom-rules-actions[]
tag::custom-rules-scope[]
An optional 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`. 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:
end::custom-rules-scope[]
tag::custom-rules-scope-filter-id[]
The id of the filter to be used.
end::custom-rules-scope-filter-id[]
tag::custom-rules-scope-filter-type[]
Either `include` (the rule applies for values in the filter) or `exclude` (the
rule applies for values not in the filter). Defaults to `include`.
end::custom-rules-scope-filter-type[]
tag::custom-rules-conditions[]
An optional 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`. A condition has the following properties:
end::custom-rules-conditions[]
tag::custom-rules-conditions-applies-to[]
Specifies the result property to which the condition applies. The available
options are `actual`, `typical`, `diff_from_typical`, `time`. If your detector
uses `lat_long`, `metric`, `rare`, or `freq_rare` functions, you can only
specify conditions that apply to `time`.
end::custom-rules-conditions-applies-to[]
tag::custom-rules-conditions-operator[]
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).
end::custom-rules-conditions-operator[]
tag::custom-rules-conditions-value[]
The value that is compared against the `applies_to` field using the `operator`.
end::custom-rules-conditions-value[]
tag::custom-settings[]
Advanced configuration option. Contains custom meta data about the job. For
example, it can contain custom URL information as shown in
{ml-docs}/ml-configuring-url.html[Adding custom URLs to {ml} results].
end::custom-settings[]
tag::data-description[]
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-detect} are retained.
`data_description`.`format`:::
(string) Only `JSON` format is supported at this time.
`data_description`.`time_field`:::
(string) The name of the field that contains the timestamp.
The default value is `time`.
`data_description`.`time_format`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=time-format]
--
end::data-description[]
tag::data-frame-analytics[]
An array of {dfanalytics-job} resources, which are sorted by the `id` value in
ascending order.
`id`:::
(string) The unique identifier of the {dfanalytics-job}.
`source`:::
(object) The configuration of how the analysis data is sourced. It has an
`index` parameter and optionally a `query` and a `_source`.
`index`::::
(array) Index or indices on which to perform the analysis. It can be a single
index or index pattern as well as an array of indices or patterns.
`query`::::
(object) The query that has been specified for the {dfanalytics-job}. The {es}
query domain-specific language (<<query-dsl,DSL>>). This value corresponds to
the query object in an {es} search POST body. By default, this property has the
following value: `{"match_all": {}}`.
`_source`::::
(object) Contains the specified `includes` and/or `excludes` patterns that
select which fields are present in the destination. Fields that are excluded
cannot be included in the analysis.
`includes`:::::
(array) An array of strings that defines the fields that are included in the
destination.
`excludes`:::::
(array) An array of strings that defines the fields that are excluded from the
destination.
`dest`:::
(string) The destination configuration of the analysis.
`index`::::
(string) The _destination index_ that stores the results of the
{dfanalytics-job}.
`results_field`::::
(string) The name of the field that stores the results of the analysis. Defaults
to `ml`.
`analysis`:::
(object) The type of analysis that is performed on the `source`.
`analyzed_fields`:::
(object) Contains `includes` and/or `excludes` patterns that select which fields
are included in the analysis.
`includes`::::
(Optional, array) An array of strings that defines the fields that are included
in the analysis.
`excludes`::::
(Optional, array) An array of strings that defines the fields that are excluded
from the analysis.
`model_memory_limit`:::
(string) The `model_memory_limit` that has been set to the {dfanalytics-job}.
end::data-frame-analytics[]
tag::data-frame-analytics-stats[]
An array of statistics objects for {dfanalytics-jobs}, which are
sorted by the `id` value in ascending order.
`id`:::
(string) The unique identifier of the {dfanalytics-job}.
`state`:::
(string) Current state of the {dfanalytics-job}.
`progress`:::
(array) The progress report of the {dfanalytics-job} by phase.
`phase`:::
(string) Defines the phase of the {dfanalytics-job}. Possible phases:
`reindexing`, `loading_data`, `analyzing`, and `writing_results`.
`progress_percent`:::
(integer) The progress that the {dfanalytics-job} has made expressed in
percentage.
end::data-frame-analytics-stats[]
tag::datafeed-id[]
A numerical character string that uniquely identifies the
{dfeed}. This identifier can contain lowercase alphanumeric characters (a-z
and 0-9), hyphens, and underscores. It must start and end with alphanumeric
characters.
end::datafeed-id[]
tag::datafeed-id-wildcard[]
Identifier for the {dfeed}. It can be a {dfeed} identifier or a wildcard
expression.
end::datafeed-id-wildcard[]
tag::decompress-definition[]
Specifies whether the included model definition should be returned as a JSON map
(`true`) or in a custom compressed format (`false`). Defaults to `true`.
end::decompress-definition[]
tag::delayed-data-check-config[]
Specifies whether the {dfeed} checks for missing data and the size of the
window. For example: `{"enabled": true, "check_window": "1h"}`.
+
--
The {dfeed} 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 {dfeed} has passed that
moment in time. See
{ml-docs}/ml-delayed-data-detection.html[Working with delayed data].
This check runs only on real-time {dfeeds}.
`delayed_data_check_config`.`enabled`::
(boolean) Specifies whether the {dfeed} periodically checks for delayed data.
Defaults to `true`.
`delayed_data_check_config`.`check_window`::
(<<time-units,time units>>) 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 {dfeed} runs. In particular, the default `check_window` span
calculation is based on the maximum of `2h` or `8 * bucket_span`.
--
end::delayed-data-check-config[]
tag::dependent-variable[]
Defines which field of the document is to be predicted.
This parameter is supplied by field name and must match one of the fields in
the index being used to train. If this field is missing from a document, then
that document will not be used for training, but a prediction with the trained
model will be generated for it. It is also known as continuous target variable.
end::dependent-variable[]
tag::desc-results[]
If true, the results are sorted in descending order.
end::desc-results[]
tag::description-dfa[]
A description of the job.
end::description-dfa[]
tag::dest[]
The destination configuration, consisting of `index` and
optionally `results_field` (`ml` by default).
`index`:::
(Required, string) Defines the _destination index_ to store the results of
the {dfanalytics-job}.
`results_field`:::
(Optional, string) Defines the name of the field in which to store the
results of the analysis. Default to `ml`.
end::dest[]
tag::detector-description[]
A description of the detector. For example, `Low event rate`.
end::detector-description[]
tag::detector-field-name[]
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.
--
end::detector-field-name[]
tag::detector-index[]
A unique identifier for the detector. This identifier is based on the order of
the detectors in the `analysis_config`, starting at zero.
end::detector-index[]
tag::eta[]
Advanced configuration option. The shrinkage applied to the weights. Smaller
values result in larger forests which have better generalization error. However,
the smaller the value the longer the training will take. For more information,
about shrinkage, see
https://en.wikipedia.org/wiki/Gradient_boosting#Shrinkage[this wiki article].
end::eta[]
tag::exclude-frequent[]
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.
end::exclude-frequent[]
tag::exclude-interim-results[]
If `true`, the output excludes interim results. By default, interim results are
included.
end::exclude-interim-results[]
tag::feature-bag-fraction[]
Advanced configuration option. Defines the fraction of features that will be
used when selecting a random bag for each candidate split.
end::feature-bag-fraction[]
tag::feature-influence-threshold[]
The minimum {olscore} that a document needs to have in order to calculate its
{fiscore}. Value range: 0-1 (`0.1` by default).
end::feature-influence-threshold[]
tag::field-selection[]
An array of objects that explain selection for each field, sorted by
the field names. Each object in the array has the following properties:
`name`:::
(string) The field name.
`mapping_types`:::
(string) The mapping types of the field.
`is_included`:::
(boolean) Whether the field is selected to be included in the analysis.
`is_required`:::
(boolean) Whether the field is required.
`feature_type`:::
(string) The feature type of this field for the analysis. May be `categorical`
or `numerical`.
`reason`:::
(string) The reason a field is not selected to be included in the analysis.
end::field-selection[]
tag::filter[]
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.
end::filter[]
tag::filter-id[]
A string that uniquely identifies a filter.
end::filter-id[]
tag::frequency[]
The interval at which scheduled queries are made while the {dfeed} 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 {dfeed} uses aggregations, this value must be divisible
by the interval of the date histogram aggregation.
end::frequency[]
tag::from[]
Skips the specified number of {dfanalytics-jobs}. The default value is `0`.
end::from[]
tag::function[]
The analysis function that is used. For example, `count`, `rare`, `mean`, `min`,
`max`, and `sum`. For more information, see
{ml-docs}/ml-functions.html[Function reference].
end::function[]
tag::gamma[]
Advanced configuration option. Regularization parameter to prevent overfitting
on the training dataset. Multiplies a linear penalty associated with the size of
individual trees in the forest. The higher the value the more training will
prefer smaller trees. The smaller this parameter the larger individual trees
will be and the longer train will take.
end::gamma[]
tag::groups[]
A list of job groups. A job can belong to no groups or many.
end::groups[]
tag::include-model-definition[]
Specifies if the model definition should be returned in the response. Defaults
to `false`. When `true`, only a single model must match the ID patterns
provided, otherwise a bad request is returned.
end::include-model-definition[]
tag::tags[]
A comma delimited string of tags. A {infer} model can have many tags, or none.
When supplied, only {infer} models that contain all the supplied tags are
returned.
end::tags[]
tag::indices[]
An array of index names. Wildcards are supported. For example:
`["it_ops_metrics", "server*"]`.
tag::num-top-feature-importance-values[]
Advanced configuration option. If set, feature importance for the top
most important features will be computed. Importance is calculated
using the SHAP (SHapley Additive exPlanations) method as described in
https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf[Lundberg, S. M., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017.].
end::num-top-feature-importance-values[]
+
--
NOTE: If any indices are in remote clusters then `cluster.remote.connect` must
not be set to `false` on any {ml} nodes.
--
end::indices[]
tag::influencers[]
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.
end::influencers[]
tag::is-interim[]
If `true`, this is an interim result. In other words, the results are calculated
based on partial input data.
end::is-interim[]
tag::job-id-anomaly-detection[]
Identifier for the {anomaly-job}.
end::job-id-anomaly-detection[]
tag::job-id-anomaly-detection-default[]
Identifier for the {anomaly-job}. It can be a job identifier, a group name, or a
wildcard expression. If you do not specify one of these options, the API returns
information for all {anomaly-jobs}.
end::job-id-anomaly-detection-default[]
tag::job-id-anomaly-detection-define[]
Identifier for the {anomaly-job}. This identifier can contain lowercase
alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start
and end with alphanumeric characters.
end::job-id-anomaly-detection-define[]
tag::job-id-anomaly-detection-list[]
An identifier for the {anomaly-jobs}. It can be a job
identifier, a group name, or a comma-separated list of jobs or groups.
end::job-id-anomaly-detection-list[]
tag::job-id-anomaly-detection-wildcard[]
Identifier for the {anomaly-job}. It can be a job identifier, a group name, or a
wildcard expression.
end::job-id-anomaly-detection-wildcard[]
tag::job-id-anomaly-detection-wildcard-list[]
Identifier for the {anomaly-job}. It can be a job identifier, a group name, a
comma-separated list of jobs or groups, or a wildcard expression.
end::job-id-anomaly-detection-wildcard-list[]
tag::job-id-data-frame-analytics[]
Identifier for the {dfanalytics-job}.
end::job-id-data-frame-analytics[]
tag::job-id-data-frame-analytics-default[]
Identifier for the {dfanalytics-job}. If you do not specify this option, the API
returns information for the first hundred {dfanalytics-jobs}.
end::job-id-data-frame-analytics-default[]
tag::job-id-data-frame-analytics-define[]
Identifier for the {dfanalytics-job}. This identifier can contain lowercase
alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start
and end with alphanumeric characters.
end::job-id-data-frame-analytics-define[]
tag::job-id-datafeed[]
The unique identifier for the job to which the {dfeed} sends data.
end::job-id-datafeed[]
tag::jobs-stats-anomaly-detection[]
An array of {anomaly-job} statistics objects.
For more information, see <<ml-jobstats>>.
end::jobs-stats-anomaly-detection[]
tag::lambda[]
Advanced configuration option. Regularization parameter to prevent overfitting
on the training dataset. Multiplies an L2 regularisation term which applies to
leaf weights of the individual trees in the forest. The higher the value the
more training will attempt to keep leaf weights small. This makes the prediction
function smoother at the expense of potentially not being able to capture
relevant relationships between the features and the {depvar}. The smaller this
parameter the larger individual trees will be and the longer train will take.
end::lambda[]
tag::latency[]
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>>.
+
--
NOTE: Latency is only applicable when you send data by using
the <<ml-post-data,post data>> API.
--
end::latency[]
tag::max-empty-searches[]
If a real-time {dfeed} 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 {dfeed} with no end time that sees no data will remain started
until it is explicitly stopped. By default this setting is not set.
end::max-empty-searches[]
tag::maximum-number-trees[]
Advanced configuration option. Defines the maximum number of trees the forest is
allowed to contain. The maximum value is 2000.
end::maximum-number-trees[]
tag::memory-estimation[]
An object containing the memory estimates. The object has the
following properties:
`expected_memory_without_disk`:::
(string) Estimated memory usage under the assumption that the whole
{dfanalytics} should happen in memory (i.e. without overflowing to disk).
`expected_memory_with_disk`:::
(string) Estimated memory usage under the assumption that overflowing to disk is
allowed during {dfanalytics}. `expected_memory_with_disk` is usually smaller
than `expected_memory_without_disk` as using disk allows to limit the main
memory needed to perform {dfanalytics}.
end::memory-estimation[]
tag::method[]
Sets the method that {oldetection} uses. If the method is not set {oldetection}
uses an ensemble of different methods and normalises and combines their
individual {olscores} to obtain the overall {olscore}. We recommend to use the
ensemble method. Available methods are `lof`, `ldof`, `distance_kth_nn`,
`distance_knn`.
end::method[]
tag::mode[]
There are three available modes:
+
--
* `auto`: The chunk size is dynamically calculated. This is the default and
recommended value.
* `manual`: Chunking is applied according to the specified `time_span`.
* `off`: No chunking is applied.
--
end::mode[]
tag::model-id[]
The unique identifier of the trained {infer} model.
end::model-id[]
tag::model-memory-limit[]
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>>.
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>>.
--
end::model-memory-limit[]
tag::model-memory-limit-dfa[]
The approximate maximum amount of memory resources that are permitted for
analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If
your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
setting, an error occurs when you try to create {dfanalytics-jobs} that have
`model_memory_limit` values greater than that setting. For more information, see
<<ml-settings>>.
end::model-memory-limit-dfa[]
tag::model-plot-config[]
This advanced configuration option stores model information along with the
results. It provides a more detailed view into {anomaly-detect}.
+
--
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.
--
end::model-plot-config[]
tag::model-plot-config-enabled[]
If true, enables calculation and storage of the model bounds for each entity
that is being analyzed. By default, this is not enabled.
end::model-plot-config-enabled[]
tag::model-plot-config-terms[]
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.
end::model-plot-config-terms[]
tag::model-snapshot-retention-days[]
Advanced configuration option. The period of time (in days) that model snapshots are retained.
Age is calculated relative to the timestamp of the newest model snapshot.
The default value is `1`, which means snapshots that are one day (twenty-four hours)
older than the newest snapshot are deleted.
end::model-snapshot-retention-days[]
tag::multivariate-by-fields[]
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.
--
end::multivariate-by-fields[]
tag::n-neighbors[]
Defines the value for how many nearest neighbors each method of
{oldetection} will use to calculate its {olscore}. When the value is not set,
different values will be used for different ensemble members. This helps
improve diversity in the ensemble. Therefore, only override this if you are
confident that the value you choose is appropriate for the data set.
end::n-neighbors[]
tag::num-top-classes[]
Defines the number of categories for which the predicted
probabilities are reported. It must be non-negative. If it is greater than the
total number of categories (in the {version} version of the {stack}, it's two)
to predict then we will report all category probabilities. Defaults to 2.
end::num-top-classes[]
tag::over-field-name[]
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 {ml-docs}/ml-configuring-pop.html[Performing population analysis].
end::over-field-name[]
tag::outlier-fraction[]
Sets the proportion of the data set that is assumed to be outlying prior to
{oldetection}. For example, 0.05 means it is assumed that 5% of values are real
outliers and 95% are inliers.
end::outlier-fraction[]
tag::partition-field-name[]
The field used to segment the analysis. When you use this property, you have
completely independent baselines for each value of this field.
end::partition-field-name[]
tag::prediction-field-name[]
Defines the name of the prediction field in the results.
Defaults to `<dependent_variable>_prediction`.
end::prediction-field-name[]
tag::query[]
The {es} query domain-specific language (DSL). This value corresponds to the
query object in an {es} search POST body. All the options that are supported by
{es} can be used, as this object is passed verbatim to {es}. By default, this
property has the following value: `{"match_all": {"boost": 1}}`.
end::query[]
tag::query-delay[]
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 {es} 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. For more information, see
{ml-docs}/ml-delayed-data-detection.html[Handling delayed data].
end::query-delay[]
tag::randomize-seed[]
Defines the seed to the random generator that is used to pick
which documents will be used for training. By default it is randomly generated.
Set it to a specific value to ensure the same documents are used for training
assuming other related parameters (e.g. `source`, `analyzed_fields`, etc.) are
the same.
end::randomize-seed[]
tag::renormalization-window-days[]
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`.
end::renormalization-window-days[]
tag::results-index-name[]
A text string that affects the name of the {ml} results index. The default value
is `shared`, which generates an index named `.ml-anomalies-shared`.
end::results-index-name[]
tag::results-retention-days[]
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 {es}. The default value is null, which means all
results are retained.
end::results-retention-days[]
tag::retain[]
If `true`, this snapshot will not be deleted during automatic cleanup of
snapshots older than `model_snapshot_retention_days`. However, this snapshot
will be deleted when the job is deleted. The default value is `false`.
end::retain[]
tag::script-fields[]
Specifies scripts that evaluate custom expressions and returns script fields to
the {dfeed}. The detector configuration objects in a job can contain functions
that use these script fields. For more information, see
{ml-docs}/ml-configuring-transform.html[Transforming data with script fields]
and <<request-body-search-script-fields,Script fields>>.
end::script-fields[]
tag::scroll-size[]
The `size` parameter that is used in {es} searches. The default value is `1000`.
end::scroll-size[]
tag::size[]
Specifies the maximum number of {dfanalytics-jobs} to obtain. The default value
is `100`.
end::size[]
tag::snapshot-id[]
A numerical character string that uniquely identifies the model snapshot.
end::snapshot-id[]
tag::source-put-dfa[]
The configuration of how to source the analysis data. It requires an
`index`. Optionally, `query` and `_source` may be specified.
`index`:::
(Required, string or array) Index or indices on which to perform the
analysis. It can be a single index or index pattern as well as an array of
indices or patterns.
`query`:::
(Optional, object) The {es} query domain-specific language
(<<query-dsl,DSL>>). This value corresponds to the query object in an {es}
search POST body. All the options that are supported by {es} can be used,
as this object is passed verbatim to {es}. By default, this property has
the following value: `{"match_all": {}}`.
`_source`:::
(Optional, object) Specify `includes` and/or `excludes` patterns to select
which fields will be present in the destination. Fields that are excluded
cannot be included in the analysis.
`includes`::::
(array) An array of strings that defines the fields that will be
included in the destination.
`excludes`::::
(array) An array of strings that defines the fields that will be
excluded from the destination.
end::source-put-dfa[]
tag::standardization-enabled[]
If `true`, then the following operation is performed on the columns before
computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to `true`. For
more information, see
https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
end::standardization-enabled[]
tag::summary-count-field-name[]
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.
--
end::summary-count-field-name[]
tag::time-format[]
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.
--
end::time-format[]
tag::time-span[]
The time span that each search will be querying. This setting is only applicable
when the mode is set to `manual`. For example: `3h`.
end::time-span[]
tag::timeout-start[]
Controls the amount of time to wait until the {dfanalytics-job} starts. Defaults
to 20 seconds.
end::timeout-start[]
tag::timeout-stop[]
Controls the amount of time to wait until the {dfanalytics-job} stops. Defaults
to 20 seconds.
end::timeout-stop[]
tag::timestamp-results[]
The start time of the bucket for which these results were calculated.
end::timestamp-results[]
tag::tokenizer[]
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`.
end::tokenizer[]
tag::training-percent[]
Defines what percentage of the eligible documents that will
be used for training. Documents that are ignored by the analysis (for example
those that contain arrays with more than one value) won’t be included in the
calculation for used percentage. Defaults to `100`.
end::training-percent[]
tag::use-null[]
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`.
end::use-null[]
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