[ML][DOCS] Add documentation for detector rules and filters (#32013)

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Dimitris Athanasiou 2018-07-25 16:10:32 +01:00 committed by GitHub
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16 changed files with 648 additions and 12 deletions

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@ -310,6 +310,16 @@ setups['farequote_datafeed'] = setups['farequote_job'] + '''
"indexes":"farequote" "indexes":"farequote"
} }
''' '''
setups['ml_filter_safe_domains'] = '''
- do:
xpack.ml.put_filter:
filter_id: "safe_domains"
body: >
{
"description": "A list of safe domains",
"items": ["*.google.com", "wikipedia.org"]
}
'''
setups['server_metrics_index'] = ''' setups['server_metrics_index'] = '''
- do: - do:
indices.create: indices.create:

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@ -47,6 +47,15 @@ The main {ml} resources can be accessed with a variety of endpoints:
* {ref}/ml-delete-calendar-job.html[DELETE /calendars/<calendar_id+++>+++/jobs/<job_id+++>+++]: Disassociate a job from a calendar * {ref}/ml-delete-calendar-job.html[DELETE /calendars/<calendar_id+++>+++/jobs/<job_id+++>+++]: Disassociate a job from a calendar
* {ref}/ml-delete-calendar.html[DELETE /calendars/<calendar_id+++>+++]: Delete a calendar * {ref}/ml-delete-calendar.html[DELETE /calendars/<calendar_id+++>+++]: Delete a calendar
[float]
[[ml-api-filters]]
=== /filters/
* {ref}/ml-put-filter.html[PUT /filters/<filter_id+++>+++]: Create a filter
* {ref}/ml-update-filter.html[POST /filters/<filter_id+++>+++/_update]: Update a filter
* {ref}/ml-get-filter.html[GET /filters/<filter_id+++>+++]: List filters
* {ref}/ml-delete-filter.html[DELETE /filter/<filter_id+++>+++]: Delete a filter
[float] [float]
[[ml-api-datafeeds]] [[ml-api-datafeeds]]
=== /datafeeds/ === /datafeeds/

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@ -34,6 +34,7 @@ The scenarios in this section describe some best practices for generating useful
* <<ml-configuring-categories>> * <<ml-configuring-categories>>
* <<ml-configuring-pop>> * <<ml-configuring-pop>>
* <<ml-configuring-transform>> * <<ml-configuring-transform>>
* <<ml-configuring-detector-custom-rules>>
:edit_url: https://github.com/elastic/elasticsearch/edit/{branch}/x-pack/docs/en/ml/customurl.asciidoc :edit_url: https://github.com/elastic/elasticsearch/edit/{branch}/x-pack/docs/en/ml/customurl.asciidoc
include::customurl.asciidoc[] include::customurl.asciidoc[]
@ -49,3 +50,6 @@ include::populations.asciidoc[]
:edit_url: https://github.com/elastic/elasticsearch/edit/{branch}/x-pack/docs/en/ml/transforms.asciidoc :edit_url: https://github.com/elastic/elasticsearch/edit/{branch}/x-pack/docs/en/ml/transforms.asciidoc
include::transforms.asciidoc[] include::transforms.asciidoc[]
:edit_url: https://github.com/elastic/elasticsearch/edit/{branch}/x-pack/docs/en/ml/detector-custom-rules.asciidoc
include::detector-custom-rules.asciidoc[]

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@ -0,0 +1,230 @@
[role="xpack"]
[[ml-configuring-detector-custom-rules]]
=== Customizing detectors with rules and filters
<<ml-rules,Rules and filters>> enable you to change the behavior of anomaly
detectors based on domain-specific knowledge.
Rules describe _when_ a detector should take a certain _action_ instead
of following its default behavior. To specify the _when_ a rule uses
a `scope` and `conditions`. You can think of `scope` as the categorical
specification of a rule, while `conditions` are the numerical part.
A rule can have a scope, one or more conditions, or a combination of
scope and conditions.
Let us see how those can be configured by examples.
==== Specifying rule scope
Let us assume we are configuring a job in order to DNS data exfiltration.
Our data contain fields "subdomain" and "highest_registered_domain".
We can use a detector that looks like `high_info_content(subdomain) over highest_registered_domain`.
If we run such a job it is possible that we discover a lot of anomalies on
frequently used domains that we have reasons to trust. As security analysts, we
are not interested in such anomalies. Ideally, we could instruct the detector to
skip results for domains that we consider safe. Using a rule with a scope allows
us to achieve this.
First, we need to create a list with our safe domains. Those lists are called
`filters` in {ml}. Filters can be shared across jobs.
We create our filter using the {ref}/ml-put-filter.html[put filter API]:
[source,js]
----------------------------------
PUT _xpack/ml/filters/safe_domains
{
"description": "Our list of safe domains",
"items": ["safe.com", "trusted.com"]
}
----------------------------------
// CONSOLE
Now, we can create our job specifying a scope that uses the filter for the
`highest_registered_domain` field:
[source,js]
----------------------------------
PUT _xpack/ml/anomaly_detectors/dns_exfiltration_with_rule
{
"analysis_config" : {
"bucket_span":"5m",
"detectors" :[{
"function":"high_info_content",
"field_name": "subdomain",
"over_field_name": "highest_registered_domain",
"custom_rules": [{
"actions": ["skip_result"],
"scope": {
"highest_registered_domain": {
"filter_id": "safe_domains",
"filter_type": "include"
}
}
}]
}]
},
"data_description" : {
"time_field":"timestamp"
}
}
----------------------------------
// CONSOLE
As time advances and we see more data and more results, we might encounter new
domains that we want to add in the filter. We can do that by using the
{ref}/ml-update-filter.html[update filter API]:
[source,js]
----------------------------------
POST _xpack/ml/filters/safe_domains/_update
{
"add_items": ["another-safe.com"]
}
----------------------------------
// CONSOLE
// TEST[setup:ml_filter_safe_domains]
Note that we can provide scope for any of the partition/over/by fields.
In the following example we scope multiple fields:
[source,js]
----------------------------------
PUT _xpack/ml/anomaly_detectors/scoping_multiple_fields
{
"analysis_config" : {
"bucket_span":"5m",
"detectors" :[{
"function":"count",
"partition_field_name": "my_partition",
"over_field_name": "my_over",
"by_field_name": "my_by",
"custom_rules": [{
"actions": ["skip_result"],
"scope": {
"my_partition": {
"filter_id": "filter_1"
},
"my_over": {
"filter_id": "filter_2"
},
"my_by": {
"filter_id": "filter_3"
}
}
}]
}]
},
"data_description" : {
"time_field":"timestamp"
}
}
----------------------------------
// CONSOLE
Such a detector will skip results when the values of all 3 scoped fields
are included in the referenced filters.
==== Specifying rule conditions
Imagine a detector that looks for anomalies in CPU utilization.
Given a machine that is idle for long enough, small movement in CPU could
result in anomalous results where the `actual` value is quite small, for
example, 0.02. Given our knowledge about how CPU utilization behaves we might
determine that anomalies with such small actual values are not interesting for
investigation.
Let us now configure a job with a rule that will skip results where CPU
utilization is less than 0.20.
[source,js]
----------------------------------
PUT _xpack/ml/anomaly_detectors/cpu_with_rule
{
"analysis_config" : {
"bucket_span":"5m",
"detectors" :[{
"function":"high_mean",
"field_name": "cpu_utilization",
"custom_rules": [{
"actions": ["skip_result"],
"conditions": [
{
"applies_to": "actual",
"operator": "lt",
"value": 0.20
}
]
}]
}]
},
"data_description" : {
"time_field":"timestamp"
}
}
----------------------------------
// CONSOLE
When there are multiple conditions they are combined with a logical `and`.
This is useful when we want the rule to apply to a range. We simply create
a rule with two conditions, one for each end of the desired range.
Here is an example where a count detector will skip results when the count
is greater than 30 and less than 50:
[source,js]
----------------------------------
PUT _xpack/ml/anomaly_detectors/rule_with_range
{
"analysis_config" : {
"bucket_span":"5m",
"detectors" :[{
"function":"count",
"custom_rules": [{
"actions": ["skip_result"],
"conditions": [
{
"applies_to": "actual",
"operator": "gt",
"value": 30
},
{
"applies_to": "actual",
"operator": "lt",
"value": 50
}
]
}]
}]
},
"data_description" : {
"time_field":"timestamp"
}
}
----------------------------------
// CONSOLE
==== Rules in the life-cycle of a job
Rules only affect results created after the rules were applied.
Let us imagine that we have configured a job and it has been running
for some time. After observing its results we decide that we can employ
rules in order to get rid of some uninteresting results. We can use
the update-job API to do so. However, the rule we added will only be in effect
for any results created from the moment we added the rule onwards. Past results
will remain unaffected.
==== Using rules VS filtering data
It might appear like using rules is just another way of filtering the data
that feeds into a job. For example, a rule that skips results when the
partition field value is in a filter sounds equivalent to having a query
that filters out such documents. But it is not. There is a fundamental
difference. When the data is filtered before reaching a job it is as if they
never existed for the job. With rules, the data still reaches the job and
affects its behavior (depending on the rule actions).
For example, a rule with the `skip_result` action means all data will still
be modeled. On the other hand, a rule with the `skip_model_update` action means
results will still be created even though the model will not be updated by
data matched by a rule.

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@ -8,6 +8,8 @@ input data.
The {xpackml} features include the following geographic function: `lat_long`. The {xpackml} features include the following geographic function: `lat_long`.
NOTE: You cannot create forecasts for jobs that contain geographic functions. NOTE: You cannot create forecasts for jobs that contain geographic functions.
You also cannot add rules with conditions to detectors that use geographic
functions.
[float] [float]
[[ml-lat-long]] [[ml-lat-long]]

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@ -15,6 +15,9 @@ The {xpackml} features include the following metric functions:
* <<ml-metric-metric,`metric`>> * <<ml-metric-metric,`metric`>>
* xref:ml-metric-varp[`varp`, `high_varp`, `low_varp`] * xref:ml-metric-varp[`varp`, `high_varp`, `low_varp`]
NOTE: You cannot add rules with conditions to detectors that use the `metric`
function.
[float] [float]
[[ml-metric-min]] [[ml-metric-min]]
==== Min ==== Min
@ -221,7 +224,6 @@ mean `responsetime` for each application over time. It detects when the mean
The `metric` function combines `min`, `max`, and `mean` functions. You can use The `metric` function combines `min`, `max`, and `mean` functions. You can use
it as a shorthand for a combined analysis. If you do not specify a function in it as a shorthand for a combined analysis. If you do not specify a function in
a detector, this is the default function. a detector, this is the default function.
//TBD: Is that default behavior still true?
High- and low-sided functions are not applicable. You cannot use this function High- and low-sided functions are not applicable. You cannot use this function
when a `summary_count_field_name` is specified. when a `summary_count_field_name` is specified.

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@ -15,6 +15,8 @@ number of times (frequency) rare values occur.
`exclude_frequent`. `exclude_frequent`.
* You cannot create forecasts for jobs that contain `rare` or `freq_rare` * You cannot create forecasts for jobs that contain `rare` or `freq_rare`
functions. functions.
* You cannot add rules with conditions to detectors that use `rare` or
`freq_rare` functions.
* Shorter bucket spans (less than 1 hour, for example) are recommended when * Shorter bucket spans (less than 1 hour, for example) are recommended when
looking for rare events. The functions model whether something happens in a looking for rare events. The functions model whether something happens in a
bucket at least once. With longer bucket spans, it is more likely that bucket at least once. With longer bucket spans, it is more likely that

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@ -8,6 +8,7 @@ job configuration options.
* <<ml-calendar-resource,Calendars>> * <<ml-calendar-resource,Calendars>>
* <<ml-datafeed-resource,{dfeeds-cap}>> * <<ml-datafeed-resource,{dfeeds-cap}>>
* <<ml-datafeed-counts,{dfeed-cap} counts>> * <<ml-datafeed-counts,{dfeed-cap} counts>>
* <<ml-filter-resource,Filters>>
* <<ml-job-resource,Jobs>> * <<ml-job-resource,Jobs>>
* <<ml-jobstats,Job statistics>> * <<ml-jobstats,Job statistics>>
* <<ml-snapshot-resource,Model snapshots>> * <<ml-snapshot-resource,Model snapshots>>
@ -19,6 +20,8 @@ include::ml/calendarresource.asciidoc[]
[role="xpack"] [role="xpack"]
include::ml/datafeedresource.asciidoc[] include::ml/datafeedresource.asciidoc[]
[role="xpack"] [role="xpack"]
include::ml/filterresource.asciidoc[]
[role="xpack"]
include::ml/jobresource.asciidoc[] include::ml/jobresource.asciidoc[]
[role="xpack"] [role="xpack"]
include::ml/jobcounts.asciidoc[] include::ml/jobcounts.asciidoc[]

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@ -15,6 +15,14 @@ machine learning APIs and in advanced job configuration options in Kibana.
* <<ml-post-calendar-event,Add scheduled events to calendar>>, <<ml-delete-calendar-event,Delete scheduled events from calendar>> * <<ml-post-calendar-event,Add scheduled events to calendar>>, <<ml-delete-calendar-event,Delete scheduled events from calendar>>
* <<ml-get-calendar,Get calendars>>, <<ml-get-calendar-event,Get scheduled events>> * <<ml-get-calendar,Get calendars>>, <<ml-get-calendar-event,Get scheduled events>>
[float]
[[ml-api-filter-endpoint]]
=== Filters
* <<ml-put-filter,Create filter>>, <<ml-delete-filter,Delete filter>>
* <<ml-update-filter,Update filters>>
* <<ml-get-filter,Get filters>>
[float] [float]
[[ml-api-datafeed-endpoint]] [[ml-api-datafeed-endpoint]]
=== {dfeeds-cap} === {dfeeds-cap}
@ -69,11 +77,13 @@ include::ml/close-job.asciidoc[]
//CREATE //CREATE
include::ml/put-calendar.asciidoc[] include::ml/put-calendar.asciidoc[]
include::ml/put-datafeed.asciidoc[] include::ml/put-datafeed.asciidoc[]
include::ml/put-filter.asciidoc[]
include::ml/put-job.asciidoc[] include::ml/put-job.asciidoc[]
//DELETE //DELETE
include::ml/delete-calendar.asciidoc[] include::ml/delete-calendar.asciidoc[]
include::ml/delete-datafeed.asciidoc[] include::ml/delete-datafeed.asciidoc[]
include::ml/delete-calendar-event.asciidoc[] include::ml/delete-calendar-event.asciidoc[]
include::ml/delete-filter.asciidoc[]
include::ml/delete-job.asciidoc[] include::ml/delete-job.asciidoc[]
include::ml/delete-calendar-job.asciidoc[] include::ml/delete-calendar-job.asciidoc[]
include::ml/delete-snapshot.asciidoc[] include::ml/delete-snapshot.asciidoc[]
@ -93,6 +103,7 @@ include::ml/get-job.asciidoc[]
include::ml/get-job-stats.asciidoc[] include::ml/get-job-stats.asciidoc[]
include::ml/get-snapshot.asciidoc[] include::ml/get-snapshot.asciidoc[]
include::ml/get-calendar-event.asciidoc[] include::ml/get-calendar-event.asciidoc[]
include::ml/get-filter.asciidoc[]
include::ml/get-record.asciidoc[] include::ml/get-record.asciidoc[]
//OPEN //OPEN
include::ml/open-job.asciidoc[] include::ml/open-job.asciidoc[]
@ -107,6 +118,7 @@ include::ml/start-datafeed.asciidoc[]
include::ml/stop-datafeed.asciidoc[] include::ml/stop-datafeed.asciidoc[]
//UPDATE //UPDATE
include::ml/update-datafeed.asciidoc[] include::ml/update-datafeed.asciidoc[]
include::ml/update-filter.asciidoc[]
include::ml/update-job.asciidoc[] include::ml/update-job.asciidoc[]
include::ml/update-snapshot.asciidoc[] include::ml/update-snapshot.asciidoc[]
//VALIDATE //VALIDATE

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@ -0,0 +1,53 @@
[role="xpack"]
[[ml-delete-filter]]
=== Delete Filter API
++++
<titleabbrev>Delete Filter</titleabbrev>
++++
Deletes a filter.
==== Request
`DELETE _xpack/ml/filters/<filter_id>`
==== Description
This API deletes a {stack-ov}/ml-rules.html[filter].
If a {ml} job references the filter, you cannot delete the filter. You must
update or delete the job before you can delete the filter.
==== Path Parameters
`filter_id` (required)::
(string) Identifier for the filter.
==== Authorization
You must have `manage_ml`, or `manage` cluster privileges to use this API.
For more information, see {xpack-ref}/security-privileges.html[Security Privileges].
==== Examples
The following example deletes the `safe_domains` filter:
[source,js]
--------------------------------------------------
DELETE _xpack/ml/filters/safe_domains
--------------------------------------------------
// CONSOLE
// TEST[setup:ml_filter_safe_domains]
When the filter is deleted, you receive the following results:
[source,js]
----
{
"acknowledged": true
}
----
//TESTRESPONSE

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@ -0,0 +1,16 @@
[role="xpack"]
[[ml-filter-resource]]
=== Filter Resources
A filter resource has the following properties:
`filter_id`::
(string) A string that uniquely identifies the filter.
`description`::
(array) A description of the filter.
`items`::
(array of strings) An array of strings which is the filter item list.
For more information, see {stack-ov}/ml-rules.html[Machine learning rules and filters].

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@ -0,0 +1,84 @@
[role="xpack"]
[[ml-get-filter]]
=== Get Filters API
++++
<titleabbrev>Get Filters</titleabbrev>
++++
Retrieves filters.
==== Request
`GET _xpack/ml/filters/<filter_id>` +
`GET _xpack/ml/filters/`
===== Description
You can get a single filter or all filters. For more information, see
{stack-ov}/ml-rules.html[Machine learning rules and filters].
==== Path Parameters
`filter_id`::
(string) Identifier for the filter.
==== Request Body
`from`:::
(integer) Skips the specified number of filters.
`size`:::
(integer) Specifies the maximum number of filters to obtain.
==== Results
The API returns the following information:
`filters`::
(array) An array of filter resources.
For more information, see <<ml-filter-resource>>.
==== Authorization
You must have `monitor_ml`, `monitor`, `manage_ml`, or `manage` cluster
privileges to use this API. For more information, see
{xpack-ref}/security-privileges.html[Security Privileges].
==== Examples
The following example gets configuration information for the `safe_domains`
filter:
[source,js]
--------------------------------------------------
GET _xpack/ml/filters/safe_domains
--------------------------------------------------
// CONSOLE
// TEST[setup:ml_filter_safe_domains]
The API returns the following results:
[source,js]
----
{
"count": 1,
"filters": [
{
"filter_id": "safe_domains",
"description": "A list of safe domains",
"items": [
"*.google.com",
"wikipedia.org"
]
}
]
}
----
//TESTRESPONSE

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@ -262,7 +262,12 @@ NOTE: The `field_name` cannot contain double quotes or backslashes.
`use_null`:: `use_null`::
(boolean) Defines whether a new series is used as the null series (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`. + when there is no value for the by or partition fields. The default value is `false`.
`custom_rules`::
(array) An array of 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' IMPORTANT: Field names are case sensitive, for example a field named 'Bytes'
@ -270,9 +275,9 @@ is different from one named 'bytes'.
-- --
After you create a job, the only property you can change in the detector After you create a job, the only properties you can change in the detector
configuration object is the `detector_description`; all other properties are configuration object are the `detector_description` and the `custom_rules`;
informational. all other properties are informational.
[float] [float]
[[ml-datadescription]] [[ml-datadescription]]
@ -408,6 +413,64 @@ the categorization field value came from.
For more information, see For more information, see
{xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages]. {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 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 partition/by/over fields.
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 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 cannot specify `conditions` for your rule.
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 `applied_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] [float]
[[ml-apilimits]] [[ml-apilimits]]
==== Analysis Limits ==== Analysis Limits

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@ -0,0 +1,68 @@
[role="xpack"]
[[ml-put-filter]]
=== Create Filter API
++++
<titleabbrev>Create Filter</titleabbrev>
++++
Instantiates a filter.
==== Request
`PUT _xpack/ml/filters/<filter_id>`
===== Description
A {stack-ov}/ml-rules.html[filter] contains a list of strings.
It can be used by one or more jobs. Specifically, filters are referenced in
the `custom_rules` property of <<ml-detectorconfig,detector configuration objects>>.
==== Path Parameters
`filter_id` (required)::
(string) Identifier for the filter.
==== Request Body
`description`::
(string) A description of the filter.
`items`::
(array of strings) The items of the filter.
A wildcard `*` can be used at the beginning
or the end of an item. Up to 10000 items
are allowed in each filter.
==== Authorization
You must have `manage_ml`, or `manage` cluster privileges to use this API.
For more information, see
{xpack-ref}/security-privileges.html[Security Privileges].
==== Examples
The following example creates the `safe_domains` filter:
[source,js]
--------------------------------------------------
PUT _xpack/ml/filters/safe_domains
{
"description": "A list of safe domains",
"items": ["*.google.com", "wikipedia.org"]
}
--------------------------------------------------
// CONSOLE
When the filter is created, you receive the following response:
[source,js]
----
{
"filter_id": "safe_domains",
"description": "A list of safe domains",
"items": ["*.google.com", "wikipedia.org"]
}
----
//TESTRESPONSE

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@ -0,0 +1,67 @@
[role="xpack"]
[[ml-update-filter]]
=== Update Filter API
++++
<titleabbrev>Update Filter</titleabbrev>
++++
Updates the description of a filter, adds items, or removes items.
==== Request
`POST _xpack/ml/filters/<filter_id>/_update`
//==== Description
==== Path Parameters
`filter_id` (required)::
(string) Identifier for the filter.
==== Request Body
`description`::
(string) A description for the filter. See <<ml-filter-resource>>.
`add_items`::
(array of strings) The items to add to the filter.
`remove_items`::
(array of strings) The items to remove from the filter.
==== Authorization
You must have `manage_ml`, or `manage` cluster privileges to use this API.
For more information, see
{xpack-ref}/security-privileges.html[Security Privileges].
==== Examples
You can change the description, add and remove items to the `safe_domains` filter as follows:
[source,js]
--------------------------------------------------
POST _xpack/ml/filters/safe_domains/_update
{
"description": "Updated list of domains",
"add_items": ["*.myorg.com"],
"remove_items": ["wikipedia.org"]
}
--------------------------------------------------
// CONSOLE
// TEST[setup:ml_filter_safe_domains]
The API returns the following results:
[source,js]
----
{
"filter_id": "safe_domains",
"description": "Updated list of domains",
"items": ["*.google.com", "*.myorg.com"]
}
----
//TESTRESPONSE

View File

@ -35,6 +35,8 @@ each periodic persistence of the model. See <<ml-job-resource>>. | Yes
|`description` |A description of the job. See <<ml-job-resource>>. | No |`description` |A description of the job. See <<ml-job-resource>>. | No
|`detectors` |An array of <<ml-detector-update, detector update objects>>. | No
|`groups` |A list of job groups. See <<ml-job-resource>>. | No |`groups` |A list of job groups. See <<ml-job-resource>>. | No
|`model_plot_config`: `enabled` |If true, enables calculation and storage of the |`model_plot_config`: `enabled` |If true, enables calculation and storage of the
@ -58,12 +60,6 @@ if the job is open when you make the update, you must stop the data feed, close
the job, then restart the data feed and open the job for the changes to take the job, then restart the data feed and open the job for the changes to take
effect. effect.
//|`analysis_config`: `detectors`: `detector_index` | A unique identifier of the
//detector. Matches the order of detectors returned by
//<<ml-get-job,GET job>>, starting from 0. | No
//|`analysis_config`: `detectors`: `detector_description` |A description of the
//detector. See <<ml-analysisconfig>>. | No
[NOTE] [NOTE]
-- --
* You can update the `analysis_limits` only while the job is closed. * You can update the `analysis_limits` only while the job is closed.
@ -73,6 +69,21 @@ of `hard_limit`, this means that it was unable to process some data. You might
want to re-run this job with an increased `model_memory_limit`. want to re-run this job with an increased `model_memory_limit`.
-- --
[[ml-detector-update]]
==== Detector Update Objects
A detector update object has the following properties:
`detector_index`::
(integer) The identifier of the detector to update.
`description`::
(string) The new description for the detector.
`custom_rules`::
(array) The new list of <<ml-detector-custom-rule, rules>> for the detector.
No other detector property can be updated.
==== Authorization ==== Authorization