[DOCS] Change "data feed" to "datafeed" in Machine Learning documentation (elastic/x-pack-elasticsearch#1277)

* [DOCS] Add xpackml attribute to XPack Reference

* [DOCS] Use attribute for datafeed terms

Original commit: elastic/x-pack-elasticsearch@f37bf48ee4
This commit is contained in:
Lisa Cawley 2017-05-02 12:45:42 -07:00 committed by GitHub
parent 3a0bc504a9
commit 9b2fb6ac16
22 changed files with 181 additions and 192 deletions

View File

@ -35,15 +35,15 @@ The main {ml} resources can be accessed with a variety of endpoints:
[[ml-api-datafeeds]]
=== /datafeeds/
* <<ml-put-datafeed,PUT /datafeeds/<datafeed_id+++>+++>>: Create a data feed
* <<ml-start-datafeed,POST /datafeeds/<datafeed_id>/_start>>: Start a data feed
* <<ml-get-datafeed,GET /datafeeds>>: List data feeds
* <<ml-get-datafeed,GET /datafeeds/<datafeed_id+++>+++>>: Get data feed details
* <<ml-get-datafeed-stats,GET /datafeeds/<datafeed_id>/_stats>>: Get statistical information for data feeds
* <<ml-preview-datafeed,GET /datafeeds/<datafeed_id>/_preview>>: Get a preview of a data feed
* <<ml-update-datafeed,POST /datafeeds/<datafeedid>/_update>>: Update certain settings for a data feed
* <<ml-stop-datafeed,POST /datafeeds/<datafeed_id>/_stop>>: Stop a data feed
* <<ml-delete-datafeed,DELETE /datafeeds/<datafeed_id+++>+++>>: Delete data feed
* <<ml-put-datafeed,PUT /datafeeds/<datafeed_id+++>+++>>: Create a {dfeed}
* <<ml-start-datafeed,POST /datafeeds/<datafeed_id>/_start>>: Start a {dfeed}
* <<ml-get-datafeed,GET /datafeeds>>: List {dfeeds}
* <<ml-get-datafeed,GET /datafeeds/<datafeed_id+++>+++>>: Get {dfeed} details
* <<ml-get-datafeed-stats,GET /datafeeds/<datafeed_id>/_stats>>: Get statistical information for {dfeeds}
* <<ml-preview-datafeed,GET /datafeeds/<datafeed_id>/_preview>>: Get a preview of a {dfeed}
* <<ml-update-datafeed,POST /datafeeds/<datafeedid>/_update>>: Update certain settings for a {dfeed}
* <<ml-stop-datafeed,POST /datafeeds/<datafeed_id>/_stop>>: Stop a {dfeed}
* <<ml-delete-datafeed,DELETE /datafeeds/<datafeed_id+++>+++>>: Delete {dfeed}
[float]
[[ml-api-results]]

View File

@ -2,14 +2,14 @@
== Getting Started
////
{xpack} {ml} features automatically detect:
{xpackml} features automatically detect:
* Anomalies in single or multiple time series
* Outliers in a population (also known as _entity profiling_)
* Rare events (also known as _log categorization_)
This tutorial is focuses on an anomaly detection scenario in single time series.
////
Ready to get some hands-on experience with the {xpack} {ml} features? This
Ready to get some hands-on experience with the {xpackml} features? This
tutorial shows you how to:
* Load a sample data set into {es}
@ -40,7 +40,7 @@ viewing jobs +
//ll {ml} features are available to use as an API, however this tutorial
//will focus on using the {ml} tab in the {kib} UI.
WARNING: The {xpack} {ml} features are in beta and subject to change.
WARNING: The {xpackml} features are in beta and subject to change.
Beta features are not subject to the same support SLA as GA features,
and deployment in production is at your own risk.
@ -66,9 +66,9 @@ activity related to jobs, see <<ml-settings>>.
[[ml-gs-users]]
==== Users, Roles, and Privileges
The {xpack} {ml} features implement cluster privileges and built-in roles to
The {xpackml} features implement cluster privileges and built-in roles to
make it easier to control which users have authority to view and manage the jobs,
data feeds, and results.
{dfeeds}, and results.
By default, you can perform all of the steps in this tutorial by using the
built-in `elastic` super user. The default password for the `elastic` user is
@ -87,7 +87,7 @@ For more information, see <<built-in-roles>> and <<privileges-list-cluster>>.
For the purposes of this tutorial, we provide sample data that you can play with
and search in {es}. When you consider your own data, however, it's important to
take a moment and think about where the {xpack} {ml} features will be most
take a moment and think about where the {xpackml} features will be most
impactful.
The first consideration is that it must be time series data. The {ml} features
@ -104,12 +104,12 @@ insights.
The final consideration is where the data is located. This tutorial assumes that
your data is stored in {es}. It guides you through the steps required to create
a _data feed_ that passes data to a job. If your own data is outside of {es},
a _{dfeed}_ that passes data to a job. If your own data is outside of {es},
analysis is still possible by using a post data API.
IMPORTANT: If you want to create {ml} jobs in {kib}, you must use data feeds.
IMPORTANT: If you want to create {ml} jobs in {kib}, you must use {dfeeds}.
That is to say, you must store your input data in {es}. When you create
a job, you select an existing index pattern and {kib} configures the data feed
a job, you select an existing index pattern and {kib} configures the {dfeed}
for you under the covers.
@ -168,7 +168,7 @@ particular time. If your data is stored in {es}, you can generate
this type of sum or average by using aggregations. One of the benefits of
summarizing data this way is that {es} automatically distributes
these calculations across your cluster. You can then feed this summarized data
into {xpack} {ml} instead of raw results, which reduces the volume
into {xpackml} instead of raw results, which reduces the volume
of data that must be considered while detecting anomalies. For the purposes of
this tutorial, however, these summary values are stored in {es},
rather than created using the {ref}/search-aggregations.html[_aggregations framework_].
@ -319,7 +319,7 @@ This tutorial uses {kib} to create jobs and view results, but you can
alternatively use APIs to accomplish most tasks.
For API reference information, see <<ml-apis>>.
The {xpack} {ml} features in {kib} use pop-ups. You must configure your
The {xpackml} features in {kib} use pop-ups. You must configure your
web browser so that it does not block pop-up windows or create an
exception for your Kibana URL.
--
@ -406,7 +406,7 @@ NOTE: Some functions such as `count` and `rare` do not require fields.
interval that the analysis is aggregated into.
+
--
The {xpack} {ml} features use the concept of a bucket to divide up the time series
The {xpackml} features use the concept of a bucket to divide up the time series
into batches for processing. For example, if you are monitoring
the total number of requests in the system,
//and receive a data point every 10 minutes
@ -432,7 +432,7 @@ typical anomalies and the frequency at which alerting is required.
. Determine whether you want to process all of the data or only part of it. If
you want to analyze all of the existing data, click
**Use full server-metrics* data**. If you want to see what happens when you
stop and start data feeds and process additional data over time, click the time
stop and start {dfeeds} and process additional data over time, click the time
picker in the {kib} toolbar. Since the sample data spans a period of time
between March 23, 2017 and April 22, 2017, click **Absolute**. Set the start
time to March 23, 2017 and the end time to April 1, 2017, for example. Once
@ -440,7 +440,7 @@ you've got the time range set up, click the **Go** button. +
+
--
[role="screenshot"]
image::images/ml-gs-job1-time.jpg["Setting the time range for the data feed"]
image::images/ml-gs-job1-time.jpg["Setting the time range for the {dfeed}"]
--
+
--
@ -462,7 +462,7 @@ As the job is created, the graph is updated to give a visual representation of
the progress of {ml} as the data is processed. This view is only available whilst the
job is running.
TIP: The `create_single_metic.sh` script creates a similar job and data feed by
TIP: The `create_single_metic.sh` script creates a similar job and {dfeed} by
using the {ml} APIs. You can download that script by clicking
here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_single_metric.sh[create_single_metric.sh]
For API reference information, see <<ml-apis>>.
@ -511,12 +511,12 @@ A closing job cannot accept further data.
`failed`::: The job did not finish successfully due to an error.
This situation can occur due to invalid input data.
If the job had irrevocably failed, it must be force closed and then deleted.
If the data feed can be corrected, the job can be closed and then re-opened.
If the {dfeed} can be corrected, the job can be closed and then re-opened.
Datafeed state::
The status of the data feed, which can be one of the following values: +
started::: The data feed is actively receiving data.
stopped::: The data feed is stopped and will not receive data until it is
{dfeed-cap} state::
The status of the {dfeed}, which can be one of the following values: +
started::: The {dfeed} is actively receiving data.
stopped::: The {dfeed} is stopped and will not receive data until it is
re-started.
Latest timestamp::
@ -527,22 +527,22 @@ If you click the arrow beside the name of job, you can show or hide additional
information, such as the settings, configuration information, or messages for
the job.
You can also click one of the **Actions** buttons to start the data feed, edit
the job or data feed, and clone or delete the job, for example.
You can also click one of the **Actions** buttons to start the {dfeed}, edit
the job or {dfeed}, and clone or delete the job, for example.
[float]
[[ml-gs-job1-datafeed]]
==== Managing Data Feeds
==== Managing {dfeeds-cap}
A data feed can be started and stopped multiple times throughout its lifecycle.
If you want to retrieve more data from {es} and the data feed is
stopped, you must restart it.
A {dfeed} can be started and stopped multiple times throughout its lifecycle.
If you want to retrieve more data from {es} and the {dfeed} is stopped, you must
restart it.
For example, if you did not use the full data when you created the job, you can
now process the remaining data by restarting the data feed:
now process the remaining data by restarting the {dfeed}:
. In the **Machine Learning** / **Job Management** tab, click the following
button to start the data feed: image:images/ml-start-feed.jpg["Start data feed"]
button to start the {dfeed}: image:images/ml-start-feed.jpg["Start {dfeed}"]
. Choose a start time and end time. For example,
@ -553,20 +553,20 @@ otherwise you might miss anomalies. +
+
--
[role="screenshot"]
image::images/ml-gs-job1-datafeed.jpg["Restarting a data feed"]
image::images/ml-gs-job1-datafeed.jpg["Restarting a {dfeed}"]
--
The data feed state changes to `started`, the job state changes to `opened`,
The {dfeed} state changes to `started`, the job state changes to `opened`,
and the number of processed records increases as the new data is analyzed. The
latest timestamp information also increases. For example:
[role="screenshot"]
image::images/ml-gs-job1-manage2.jpg["Job opened and data feed started"]
image::images/ml-gs-job1-manage2.jpg["Job opened and {dfeed} started"]
TIP: If your data is being loaded continuously, you can continue running the job
in real time. For this, start your data feed and select **No end time**.
in real time. For this, start your {dfeed} and select **No end time**.
If you want to stop the data feed at this point, you can click the following
button: image:images/ml-stop-feed.jpg["Stop data feed"]
If you want to stop the {dfeed} at this point, you can click the following
button: image:images/ml-stop-feed.jpg["Stop {dfeed}"]
Now that you have processed all the data, let's start exploring the job results.
@ -574,7 +574,7 @@ Now that you have processed all the data, let's start exploring the job results.
[[ml-gs-jobresults]]
=== Exploring Job Results
The {xpack} {ml} features analyze the input stream of data, model its behavior,
The {xpackml} features analyze the input stream of data, model its behavior,
and perform analysis based on the detectors you defined in your job. When an
event occurs outside of the model, that event is identified as an anomaly.

View File

@ -3,7 +3,7 @@
[partintro]
--
The {xpack} {ml} features automate the analysis of time-series data by creating
The {xpackml} features automate the analysis of time-series data by creating
accurate baselines of normal behaviors in the data and identifying anomalous
patterns in that data.
@ -37,9 +37,9 @@ Jobs::
necessary to perform an analytics task. For a list of the properties associated
with a job, see <<ml-job-resource, Job Resources>>.
Data feeds::
{dfeeds-cap}::
Jobs can analyze either a one-off batch of data or continuously in real time.
Data feeds retrieve data from {es} for analysis. Alternatively you can
{dfeeds-cap} retrieve data from {es} for analysis. Alternatively you can
<<ml-post-data,POST data>> from any source directly to an API.
Detectors::
@ -51,7 +51,7 @@ Detectors::
see <<ml-detectorconfig, Detector Configuration Objects>>.
Buckets::
The {xpack} {ml} features use the concept of a bucket to divide the time
The {xpackml} features use the concept of a bucket to divide the time
series into batches for processing. The _bucket span_ is part of the
configuration information for a job. It defines the time interval that is used
to summarize and model the data. This is typically between 5 minutes to 1 hour
@ -63,7 +63,7 @@ Buckets::
Machine learning nodes::
A {ml} node is a node that has `xpack.ml.enabled` and `node.ml` set to `true`,
which is the default behavior. If you set `node.ml` to `false`, the node can
service API requests but it cannot run jobs. If you want to use {xpack} {ml}
service API requests but it cannot run jobs. If you want to use {xpackml}
features, there must be at least one {ml} node in your cluster. For more
information about this setting, see <<ml-settings>>.

View File

@ -8,7 +8,7 @@ The following limitations and known problems apply to the {version} release of
=== Pop-ups must be enabled in browsers
//See x-pack-elasticsearch/#844
The {xpack} {ml} features in Kibana use pop-ups. You must configure your
The {xpackml} features in Kibana use pop-ups. You must configure your
web browser so that it does not block pop-up windows or create an
exception for your Kibana URL.
@ -17,8 +17,8 @@ exception for your Kibana URL.
=== Jobs must be re-created at GA
//See x-pack-elasticsearch/#844
The models that you create in the {xpack} {ml} Beta cannot be upgraded.
After the {xpack} {ml} features become generally available, you must
The models that you create in the {xpackml} Beta cannot be upgraded.
After the {xpackml} features become generally available, you must
re-create your jobs. If you have data sets and job configurations that
you work with extensively in the beta, make note of all the details so
that you can re-create them successfully.
@ -39,15 +39,15 @@ represented as a single dot. If there are only two data points, they are joined
by a line.
[float]
=== Jobs close on the data feed end date
=== Jobs close on the {dfeed} end date
//See x-pack-elasticsearch/#1037
If you start a data feed and specify an end date, it will close the job when
the data feed stops. This behavior avoids having numerous open one-time jobs.
If you start a {dfeed} and specify an end date, it will close the job when
the {dfeed} stops. This behavior avoids having numerous open one-time jobs.
If you do not specify an end date when you start a data feed, the job
remains open when you stop the data feed. This behavior avoids the overhead
of closing and re-opening large jobs when there are pauses in the data feed.
If you do not specify an end date when you start a {dfeed}, the job
remains open when you stop the {dfeed}. This behavior avoids the overhead
of closing and re-opening large jobs when there are pauses in the {dfeed}.
[float]
=== Post data API requires JSON format
@ -111,14 +111,13 @@ Likewise, when you create a single or multi-metric job in {kib}, in some cases
it uses aggregations on the data that it retrieves from {es}. One of the
benefits of summarizing data this way is that {es} automatically distributes
these calculations across your cluster. This summarized data is then fed into
{xpack} {ml} instead of raw results, which reduces the volume of data that must
{xpackml} instead of raw results, which reduces the volume of data that must
be considered while detecting anomalies. However, if you have two jobs, one of
which uses pre-aggregated data and another that does not, their results might
differ. This difference is due to the difference in precision of the input data.
The {ml} analytics are designed to be aggregation-aware and the likely increase
in performance that is gained by pre-aggregating the data makes the potentially
poorer precision worthwhile. If you want to view or change the aggregations
that are used in your job, refer to the `aggregations` property in your data
feed.
that are used in your job, refer to the `aggregations` property in your {dfeed}.
For more information, see <<ml-datafeed-resource>>.

View File

@ -3,23 +3,23 @@
Use machine learning to detect anomalies in time series data.
* <<ml-api-datafeed-endpoint,Datafeeds>>
* <<ml-api-datafeed-endpoint,{dfeeds-cap}>>
* <<ml-api-job-endpoint,Jobs>>
* <<ml-api-snapshot-endpoint, Model Snapshots>>
* <<ml-api-result-endpoint,Results>>
* <<ml-api-definitions, Definitions>>
[[ml-api-datafeed-endpoint]]
=== Data Feeds
=== {dfeeds-cap}
* <<ml-put-datafeed,Create data feed>>
* <<ml-delete-datafeed,Delete data feed>>
* <<ml-get-datafeed,Get data feed info>>
* <<ml-get-datafeed-stats,Get data feed statistics>>
* <<ml-preview-datafeed,Preview data feed>>
* <<ml-start-datafeed,Start data feed>>
* <<ml-stop-datafeed,Stop data feed>>
* <<ml-update-datafeed,Update data feed>>
* <<ml-put-datafeed,Create {dfeed}>>
* <<ml-delete-datafeed,Delete {dfeed}>>
* <<ml-get-datafeed,Get {dfeed} info>>
* <<ml-get-datafeed-stats,Get {dfeed} statistics>>
* <<ml-preview-datafeed,Preview {dfeed}>>
* <<ml-start-datafeed,Start {dfeed}>>
* <<ml-stop-datafeed,Stop {dfeed}>>
* <<ml-update-datafeed,Update {dfeed}>>
include::ml/put-datafeed.asciidoc[]
include::ml/delete-datafeed.asciidoc[]
@ -88,8 +88,8 @@ include::ml/get-record.asciidoc[]
[[ml-api-definitions]]
=== Definitions
* <<ml-datafeed-resource,Data feeds>>
* <<ml-datafeed-counts,Data feed counts>>
* <<ml-datafeed-resource,{dfeeds-cap}>>
* <<ml-datafeed-counts,{dfeed-cap} counts>>
* <<ml-job-resource,Jobs>>
* <<ml-jobstats,Job statistics>>
* <<ml-snapshot-resource,Model snapshots>>

View File

@ -27,7 +27,7 @@ After it is closed, the job has a minimal overhead on the cluster except for
maintaining its meta data. Therefore it is a best practice to close jobs that
are no longer required to process data.
When a data feed that has a specified end date stops, it automatically closes
When a {dfeed} that has a specified end date stops, it automatically closes
the job.
NOTE: If you use the `force` query parameter, the request returns before the

View File

@ -1,11 +1,11 @@
//lcawley Verified example output 2017-04-11
[[ml-datafeed-resource]]
==== Data Feed Resources
==== {dfeed-cap} Resources
A data feed resource has the following properties:
A {dfeed} resource has the following properties:
`aggregations`::
(object) If set, the data feed performs aggregation searches.
(object) If set, the {dfeed} performs aggregation searches.
For syntax information, see {ref}/search-aggregations.html[Aggregations].
Support for aggregations is limited and should only be used with
low cardinality data.
@ -22,11 +22,11 @@ A data feed resource has the following properties:
For example: {"mode": "manual", "time_span": "3h"}
`datafeed_id`::
(string) A numerical character string that uniquely identifies the data feed.
(string) A numerical character string that uniquely identifies the {dfeed}.
`frequency`::
(time units) The interval at which scheduled queries are made while the data
feed runs in real time. The default value is either the bucket span for short
(time units) 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"
@ -34,7 +34,7 @@ A data feed resource has the following properties:
(array) An array of index names. For example: ["it_ops_metrics"]
`job_id` (required)::
(string) The unique identifier for the job to which the data feed sends data.
(string) The unique identifier for the job to which the {dfeed} sends data.
`query`::
(object) The {es} query domain-specific language (DSL). This value
@ -59,7 +59,7 @@ A data feed resource has the following properties:
[[ml-datafeed-chunking-config]]
===== Chunking Configuration Objects
Data feeds might be required to search over long time periods, for several months
{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.
@ -80,21 +80,21 @@ A chunking configuration object has the following properties:
[float]
[[ml-datafeed-counts]]
==== Data Feed Counts
==== {dfeed-cap} Counts
The get data feed statistics API provides information about the operational
progress of a data feed. For example:
The get {dfeed} statistics API provides information about the operational
progress of a {dfeed}. For example:
`assignment_explanation`::
(string) For started data feeds only, contains messages relating to the
(string) For started {dfeeds} only, contains messages relating to the
selection of a node.
`datafeed_id`::
(string) A numerical character string that uniquely identifies the data feed.
(string) A numerical character string that uniquely identifies the {dfeed}.
`node`::
(object) The node upon which the data feed is started. The data feed and
job will be on the same node.
(object) The node upon which the {dfeed} is started. The {dfeed} and job will
be on the same node.
`id`::: The unique identifier of the node. For example,
"0-o0tOoRTwKFZifatTWKNw".
`name`::: The node name. For example, "0-o0tOo".
@ -104,7 +104,7 @@ progress of a data feed. For example:
`attributes`::: For example, {"max_running_jobs": "10"}.
`state`::
(string) The status of the data feed, which can be one of the following values: +
`started`::: The data feed is actively receiving data.
`stopped`::: The data feed is stopped and will not receive data until it is
(string) The status of the {dfeed}, which can be one of the following values: +
`started`::: The {dfeed} is actively receiving data.
`stopped`::: The {dfeed} is stopped and will not receive data until it is
re-started.

View File

@ -1,8 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-delete-datafeed]]
==== Delete Data Feeds
==== Delete {dfeeds-cap}
The delete data feed API enables you to delete an existing data feed.
The delete {dfeed} API enables you to delete an existing {dfeed}.
===== Request
@ -12,23 +12,13 @@ The delete data feed API enables you to delete an existing data feed.
===== Description
NOTE: You must stop the data feed before you can delete it.
NOTE: You must stop the {dfeed} before you can delete it.
===== Path Parameters
`feed_id` (required)::
(string) Identifier for the data feed
////
===== Responses
200
(EmptyResponse) The cluster has been successfully deleted
404
(BasicFailedReply) The cluster specified by {cluster_id} cannot be found (code: clusters.cluster_not_found)
412
(BasicFailedReply) The Elasticsearch cluster has not been shutdown yet (code: clusters.cluster_plan_state_error)
////
(string) Identifier for the {dfeed}
===== Authorization
@ -39,7 +29,7 @@ For more information, see <<privileges-list-cluster>>.
===== Examples
The following example deletes the `datafeed-it-ops` data feed:
The following example deletes the `datafeed-it-ops` {dfeed}:
[source,js]
--------------------------------------------------
@ -48,7 +38,7 @@ DELETE _xpack/ml/datafeeds/datafeed-it-ops
// CONSOLE
// TEST[skip:todo]
When the data feed is deleted, you receive the following results:
When the {dfeed} is deleted, you receive the following results:
[source,js]
----
{

View File

@ -19,8 +19,8 @@ IMPORTANT: Deleting a job must be done via this API only. Do not delete the
DELETE Document API. When {security} is enabled, make sure no `write`
privileges are granted to anyone over the `.ml-*` indices.
Before you can delete a job, you must delete the data feeds that are associated
with it. See <<ml-delete-datafeed,Delete Data Feeds>>.
Before you can delete a job, you must delete the {dfeeds} that are associated
with it. See <<ml-delete-datafeed,Delete {dfeeds-cap}>>.
It is not currently possible to delete multiple jobs using wildcards or a comma
separated list.
@ -28,7 +28,7 @@ separated list.
===== Path Parameters
`job_id` (required)::
(string) Identifier for the job
(string) Identifier for the job
===== Authorization

View File

@ -1,9 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-get-datafeed-stats]]
==== Get Data Feed Statistics
==== Get {dfeed-cap} Statistics
The get data feed statistics API enables you to retrieve usage information for
data feeds.
The get {dfeed} statistics API enables you to retrieve usage information for
{dfeeds}.
===== Request
@ -15,16 +15,16 @@ data feeds.
===== Description
If the data feed is stopped, the only information you receive is the
If the {dfeed} is stopped, the only information you receive is the
`datafeed_id` and the `state`.
===== Path Parameters
`feed_id`::
(string) Identifier for the data feed.
(string) Identifier for the {dfeed}.
This parameter does not support wildcards, but you can specify `_all` or
omit the `feed_id` to get information about all data feeds.
omit the `feed_id` to get information about all {dfeeds}.
===== Results
@ -32,8 +32,8 @@ If the data feed is stopped, the only information you receive is the
The API returns the following information:
`datafeeds`::
(array) An array of data feed count objects.
For more information, see <<ml-datafeed-counts,Data Feed Counts>>.
(array) An array of {dfeed} count objects.
For more information, see <<ml-datafeed-counts>>.
===== Authorization
@ -45,7 +45,7 @@ privileges to use this API. For more information, see <<privileges-list-cluster>
===== Examples
The following example gets usage information for the
`datafeed-farequote` data feed:
`datafeed-farequote` {dfeed}:
[source,js]
--------------------------------------------------

View File

@ -1,9 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-get-datafeed]]
==== Get Data Feeds
==== Get {dfeeds-cap}
The get data feeds API enables you to retrieve configuration information for
data feeds.
The get {dfeeds} API enables you to retrieve configuration information for
{dfeeds}.
===== Request
@ -16,9 +16,9 @@ data feeds.
===== Path Parameters
`feed_id`::
(string) Identifier for the data feed.
(string) Identifier for the {dfeed}.
This parameter does not support wildcards, but you can specify `_all` or
omit the `feed_id` to get information about all data feeds.
omit the `feed_id` to get information about all {dfeeds}.
===== Results
@ -26,8 +26,8 @@ data feeds.
The API returns the following information:
`datafeeds`::
(array) An array of data feed objects.
For more information, see <<ml-datafeed-resource,data feed resources>>.
(array) An array of {dfeed} objects.
For more information, see <<ml-datafeed-resource>>.
===== Authorization
@ -39,7 +39,7 @@ privileges to use this API. For more information, see <<privileges-list-cluster>
===== Examples
The following example gets configuration information for the
`datafeed-it-ops-kpi` data feed:
`datafeed-it-ops-kpi` {dfeed}:
[source,js]
--------------------------------------------------

View File

@ -39,7 +39,7 @@ progress of a job.
`failed`::: The job did not finish successfully due to an error.
This situation can occur due to invalid input data.
If the job had irrevocably failed, it must be force closed and then deleted.
If the data feed can be corrected, the job can be closed and then re-opened.
If the {dfeed} can be corrected, the job can be closed and then re-opened.
[float]
[[ml-datacounts]]
@ -97,7 +97,7 @@ or old results are deleted, the job counts are not reset.
it is possible that not all fields are missing. The value of
`processed_record_count` includes this count. +
NOTE: If you are using data feeds or posting data to the job in JSON format, a
NOTE: If you are using {dfeeds} or posting data to the job in JSON format, a
high `missing_field_count` is often not an indication of data issues. It is not
necessarily a cause for concern.
@ -117,9 +117,9 @@ necessarily a cause for concern.
(long) The number of records that have been processed by the job.
This value includes records with missing fields, since they are nonetheless
analyzed. +
If you use data feeds and have aggregations in your search query,
If you use {dfeeds} and have aggregations in your search query,
the `processed_record_count` will be the number of aggregated records
processed, not the number of {es} documents.
processed, not the number of {es} documents.
`sparse_bucket_count`::
(long) The number of buckets that contained few data points compared to the

View File

@ -212,10 +212,10 @@ LEAVE UNDOCUMENTED
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 data feed, these properties are automatically set.
a {dfeed}, these properties are automatically set.
When data is received via the <<ml-post-data,post data>> API, it is not stored
in Elasticsearch. Only the results for anomaly detection are retained.
in {es}. Only the results for anomaly detection are retained.
A data description object has the following properties:

View File

@ -1,8 +1,8 @@
//lcawley: Verified example output 2017-04-11
[[ml-preview-datafeed]]
==== Preview Data Feeds
==== Preview {dfeeds-cap}
The preview data feed API enables you to preview a data feed.
The preview {dfeed} API enables you to preview a {dfeed}.
===== Request
@ -13,14 +13,14 @@ The preview data feed API enables you to preview a data feed.
===== Description
The API returns the first "page" of results from the `search` that is created
by using the current data feed settings. This preview shows the structure of
by using the current {dfeed} settings. This preview shows the structure of
the data that will be passed to the anomaly detection engine.
===== Path Parameters
`datafeed_id` (required)::
(string) Identifier for the data feed
(string) Identifier for the {dfeed}
===== Authorization
@ -31,7 +31,7 @@ privileges to use this API. For more information, see <<privileges-list-cluster>
===== Examples
The following example obtains a preview of the `datafeed-farequote` data feed:
The following example obtains a preview of the `datafeed-farequote` {dfeed}:
[source,js]
--------------------------------------------------

View File

@ -1,8 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-put-datafeed]]
==== Create Data Feeds
==== Create {dfeeds-cap}
The create data feed API enables you to instantiate a data feed.
The create {dfeed} API enables you to instantiate a {dfeed}.
===== Request
@ -12,20 +12,20 @@ The create data feed API enables you to instantiate a data feed.
===== Description
You must create a job before you create a data feed. You can associate only one
data feed to each job.
You must create a job before you create a {dfeed}. You can associate only one
{dfeed} to each job.
===== Path Parameters
`feed_id` (required)::
(string) A numerical character string that uniquely identifies the data feed.
(string) A numerical character string that uniquely identifies the {dfeed}.
===== Request Body
`aggregations`::
(object) If set, the data feed performs aggregation searches.
(object) If set, the {dfeed} performs aggregation searches.
For more information, see <<ml-datafeed-resource>>.
`chunking_config`::
@ -33,8 +33,8 @@ data feed to each job.
See <<ml-datafeed-chunking-config>>.
`frequency`::
(time units) The interval at which scheduled queries are made while the data
feed runs in real time. The default value is either the bucket span for short
(time units) 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".
@ -65,7 +65,7 @@ data feed to each job.
For example: ["network","sql","kpi"].
For more information about these properties,
see <<ml-datafeed-resource, Data Feed Resources>>.
see <<ml-datafeed-resource>>.
===== Authorization
@ -75,7 +75,7 @@ For more information, see <<privileges-list-cluster>>.
===== Examples
The following example creates the `datafeed-it-ops-kpi` data feed:
The following example creates the `datafeed-it-ops-kpi` {dfeed}:
[source,js]
--------------------------------------------------
@ -94,7 +94,7 @@ PUT _xpack/ml/datafeeds/datafeed-it-ops-kpi
// CONSOLE
// TEST[skip:todo]
When the data feed is created, you receive the following results:
When the {dfeed} is created, you receive the following results:
[source,js]
----
{

View File

@ -271,7 +271,7 @@ probability of this occurrence.
There can be many anomaly records depending on the characteristics and size of
the input data. In practice, there are often too many to be able to manually
process them. The {xpack} {ml} features therefore perform a sophisticated
process them. The {xpackml} features therefore perform a sophisticated
aggregation of the anomaly records into buckets.
The number of record results depends on the number of anomalies found in each

View File

@ -1,9 +1,9 @@
//lcawley Verified example output 2017-04
[[ml-start-datafeed]]
==== Start Data Feeds
==== Start {dfeeds-cap}
A data feed must be started in order to retrieve data from {es}.
A data feed can be started and stopped multiple times throughout its lifecycle.
A {dfeed} must be started in order to retrieve data from {es}.
A {dfeed} can be started and stopped multiple times throughout its lifecycle.
===== Request
@ -11,21 +11,21 @@ A data feed can be started and stopped multiple times throughout its lifecycle.
===== Description
NOTE: Before you can start a data feed, the job must be open. Otherwise, an error
NOTE: Before you can start a {dfeed}, the job must be open. Otherwise, an error
occurs.
When you start a data feed, you can specify a start time. This enables you to
When you start a {dfeed}, you can specify a start time. This enables you to
include a training period, providing you have this data available in {es}.
If you want to analyze from the beginning of a dataset, you can specify any date
earlier than that beginning date.
If you do not specify a start time and the data feed is associated with a new
If you do not specify a start time and the {dfeed} is associated with a new
job, the analysis starts from the earliest time for which data is available.
When you start a data feed, you can also specify an end time. If you do so, the
When you start a {dfeed}, you can also specify an end time. If you do so, the
job analyzes data from the start time until the end time, at which point the
analysis stops. This scenario is useful for a one-off batch analysis. If you
do not specify an end time, the data feed runs continuously.
do not specify an end time, the {dfeed} runs continuously.
The `start` and `end` times can be specified by using one of the
following formats: +
@ -40,12 +40,12 @@ designator, where Z is accepted as an abbreviation for UTC time.
NOTE: When a URL is expected (for example, in browsers), the `+` used in time
zone designators must be encoded as `%2B`.
If the system restarts, any jobs that had data feeds running are also restarted.
If the system restarts, any jobs that had {dfeeds} running are also restarted.
When a stopped data feed is restarted, it continues processing input data from
When a stopped {dfeed} is restarted, it continues processing input data from
the next millisecond after it was stopped. If your data contains the same
timestamp (for example, it is summarized by minute), then data loss is possible
for the timestamp value when the data feed stopped. This situation can occur
for the timestamp value when the {dfeed} stopped. This situation can occur
because the job might not have completely processed all data for that millisecond.
If you specify a `start` value that is earlier than the timestamp of the latest
processed record, that value is ignored.
@ -54,20 +54,20 @@ processed record, that value is ignored.
===== Path Parameters
`feed_id` (required)::
(string) Identifier for the data feed
(string) Identifier for the {dfeed}
===== Request Body
`end`::
(string) The time that the data feed should end. This value is exclusive.
(string) The time that the {dfeed} should end. This value is exclusive.
The default value is an empty string.
`start`::
(string) The time that the data feed should begin. This value is inclusive.
(string) The time that the {dfeed} should begin. This value is inclusive.
The default value is an empty string.
`timeout`::
(time) Controls the amount of time to wait until a data feed starts.
(time) Controls the amount of time to wait until a {dfeed} starts.
The default value is 20 seconds.
@ -79,7 +79,7 @@ For more information, see <<privileges-list-cluster>>.
===== Examples
The following example opens the `datafeed-it-ops-kpi` data feed:
The following example starts the `datafeed-it-ops-kpi` {dfeed}:
[source,js]
--------------------------------------------------
@ -91,7 +91,7 @@ POST _xpack/ml/datafeeds/datafeed-it-ops-kpi/_start
// CONSOLE
// TEST[skip:todo]
When the job opens, you receive the following results:
When the {dfeed} starts, you receive the following results:
[source,js]
----
{

View File

@ -1,9 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-stop-datafeed]]
==== Stop Data Feeds
==== Stop {dfeeds-cap}
A data feed that is stopped ceases to retrieve data from {es}.
A data feed can be started and stopped multiple times throughout its lifecycle.
A {dfeed} that is stopped ceases to retrieve data from {es}.
A {dfeed} can be started and stopped multiple times throughout its lifecycle.
===== Request
@ -14,15 +14,15 @@ A data feed can be started and stopped multiple times throughout its lifecycle.
===== Path Parameters
`feed_id` (required)::
(string) Identifier for the data feed
(string) Identifier for the {dfeed}
===== Request Body
`force`::
(boolean) If true, the data feed is stopped forcefully.
(boolean) If true, the {dfeed} is stopped forcefully.
`timeout`::
(time) Controls the amount of time to wait until a data feed stops.
(time) Controls the amount of time to wait until a {dfeed} stops.
The default value is 20 seconds.
@ -33,7 +33,7 @@ For more information, see <<privileges-list-cluster>>.
===== Examples
The following example stops the `datafeed-it-ops-kpi` data feed:
The following example stops the `datafeed-it-ops-kpi` {dfeed}:
[source,js]
--------------------------------------------------
@ -45,7 +45,7 @@ POST _xpack/ml/datafeeds/datafeed-it-ops-kpi/_stop
// CONSOLE
// TEST[skip:todo]
When the data feed stops, you receive the following results:
When the {dfeed} stops, you receive the following results:
[source,js]
----
{

View File

@ -1,8 +1,8 @@
//lcawley Verified example output 2017-04
[[ml-update-datafeed]]
==== Update Data Feeds
==== Update {dfeeds-cap}
The update data feed API enables you to update certain properties of a data feed.
The update {dfeed} API enables you to update certain properties of a {dfeed}.
===== Request
@ -13,14 +13,14 @@ The update data feed API enables you to update certain properties of a data feed
===== Path Parameters
`feed_id` (required)::
(string) Identifier for the data feed
(string) Identifier for the {dfeed}
===== Request Body
The following properties can be updated after the data feed is created:
The following properties can be updated after the {dfeed} is created:
`aggregations`::
(object) If set, the data feed performs aggregation searches.
(object) If set, the {dfeed} performs aggregation searches.
For more information, see <<ml-datafeed-resource>>.
`chunking_config`::
@ -28,8 +28,8 @@ The following properties can be updated after the data feed is created:
See <<ml-datafeed-chunking-config>>.
`frequency`::
(time units) The interval at which scheduled queries are made while the data
feed runs in real time. The default value is either the bucket span for short
(time units) 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".
@ -60,7 +60,7 @@ The following properties can be updated after the data feed is created:
For example: ["network","sql","kpi"].
For more information about these properties,
see <<ml-datafeed-resource, Data Feed Resources>>.
see <<ml-datafeed-resource>>.
===== Authorization
@ -70,7 +70,7 @@ For more information, see <<privileges-list-cluster>>.
===== Examples
The following example updates the `it-ops-kpi` job:
The following example updates the `datafeed-it-ops-kpi` {dfeed}:
[source,js]
--------------------------------------------------
@ -114,7 +114,7 @@ POST _xpack/ml/datafeeds/datafeed-it-ops-kpi/_update
// CONSOLE
// TEST[skip:todo]
When the data feed is updated, you receive the following results:
When the {dfeed} is updated, you receive the following results:
[source,js]
----
{

View File

@ -92,7 +92,7 @@ Grants `manage_ml` cluster privileges and read access to the `.ml-*` indices.
[[built-in-roles-ml-user]]
`machine_learning_user`::
Grants the minimum privileges required to view {xpack} {ml} configuration,
Grants the minimum privileges required to view {xpackml} configuration,
status, and results. This role grants `monitor_ml` cluster privileges and
read access to the `.ml-notifications` and `.ml-anomalies*` indices,
which store {ml} results.

View File

@ -16,7 +16,7 @@ All cluster read-only operations, like cluster health & state, hot threads, node
info, node & cluster stats, snapshot/restore status, pending cluster tasks.
`monitor_ml`::
All read only {ml} operations, such as getting information about data feeds, jobs,
All read only {ml} operations, such as getting information about {dfeeds}, jobs,
model snapshots, or results.
`monitor_watcher`::
@ -24,14 +24,14 @@ All read only watcher operations, such as getting a watch and watcher stats.
`manage`::
Builds on `monitor` and adds cluster operations that change values in the cluster.
This includes snapshotting,updating settings, and rerouting. This privilege does
This includes snapshotting, updating settings, and rerouting. This privilege does
not include the ability to manage security.
`manage_index_templates`::
All operations on index templates.
`manage_ml`::
All {ml} operations, such as creating and deleting data feeds, jobs, and model
All {ml} operations, such as creating and deleting {dfeeds}, jobs, and model
snapshots.
`manage_pipeline`::

View File

@ -10,8 +10,8 @@ You do not need to configure any settings to use {ml}. It is enabled by default.
Set to `true` (default) to enable {ml}. +
+
If set to `false` in `elasticsearch.yml`, the {ml} APIs are disabled.
You also cannot open jobs or start data feeds.
If set to `false` in `kibana.yml`, the {ml} icon is not visible in Kibana. +
You also cannot open jobs or start {dfeeds}.
If set to `false` in `kibana.yml`, the {ml} icon is not visible in {kib}. +
+
TIP: If you want to use {ml} features in your cluster, you must enable {ml} on
all master-eligible nodes. This is the default behavior.