diff --git a/docs/build.gradle b/docs/build.gradle index 01643c1399d..4f417e8fc2b 100644 --- a/docs/build.gradle +++ b/docs/build.gradle @@ -26,7 +26,7 @@ apply plugin: 'elasticsearch.rest-resources' /* List of files that have snippets that will not work until platinum tests can occur ... */ buildRestTests.expectedUnconvertedCandidates = [ - 'reference/ml/anomaly-detection/transforms.asciidoc', + 'reference/ml/anomaly-detection/ml-configuring-transform.asciidoc', 'reference/ml/anomaly-detection/apis/delete-calendar-event.asciidoc', 'reference/ml/anomaly-detection/apis/get-bucket.asciidoc', 'reference/ml/anomaly-detection/apis/get-category.asciidoc', diff --git a/docs/reference/ml/anomaly-detection/configuring.asciidoc b/docs/reference/ml/anomaly-detection/configuring.asciidoc deleted file mode 100644 index 759c0e21535..00000000000 --- a/docs/reference/ml/anomaly-detection/configuring.asciidoc +++ /dev/null @@ -1,52 +0,0 @@ -[role="xpack"] -[[ml-configuring]] -== Configuring machine learning - -If you want to use {ml-features}, there must be at least one {ml} node in -your cluster and all master-eligible nodes must have {ml} enabled. By default, -all nodes are {ml} nodes. For more information about these settings, see -{ref}/modules-node.html#ml-node[{ml} nodes]. - -To use the {ml-features} to analyze your data, you can create an {anomaly-job} -and send your data to that job. - -* If your data is stored in {es}: - -** You can create a {dfeed}, which retrieves data from {es} for analysis. -** You can use {kib} to expedite the creation of jobs and {dfeeds}. - -* If your data is not stored in {es}, you can -{ref}/ml-post-data.html[POST data] from any source directly to an API. - -The results of {ml} analysis are stored in {es} and you can use {kib} to help -you visualize and explore the results. - -//For a tutorial that walks you through these configuration steps, -//see <>. - -Though it is quite simple to analyze your data and provide quick {ml} results, -gaining deep insights might require some additional planning and configuration. -The scenarios in this section describe some best practices for generating useful -{ml} results and insights from your data. - -* <> -* <> -* <> -* <> -* <> -* <> -* <> - -include::customurl.asciidoc[] - -include::aggregations.asciidoc[] - -include::detector-custom-rules.asciidoc[] - -include::categories.asciidoc[] - -include::populations.asciidoc[] - -include::transforms.asciidoc[] - -include::delayed-data-detection.asciidoc[] \ No newline at end of file diff --git a/docs/reference/ml/anomaly-detection/functions/count.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-count-functions.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/functions/count.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-count-functions.asciidoc index f1f39ef0840..2fa0ac3a02c 100644 --- a/docs/reference/ml/anomaly-detection/functions/count.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-count-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-count-functions]] -=== Count functions += Count functions Count functions detect anomalies when the number of events in a bucket is anomalous. @@ -22,7 +22,7 @@ The {ml-features} include the following count functions: [float] [[ml-count]] -===== Count, high_count, low_count +== Count, high_count, low_count The `count` function detects anomalies when the number of events in a bucket is anomalous. @@ -145,7 +145,7 @@ and the `summary_count_field_name` property. For more information, see [float] [[ml-nonzero-count]] -===== Non_zero_count, high_non_zero_count, low_non_zero_count +== Non_zero_count, high_non_zero_count, low_non_zero_count The `non_zero_count` function detects anomalies when the number of events in a bucket is anomalous, but it ignores cases where the bucket count is zero. Use @@ -215,7 +215,7 @@ data is sparse, use the `count` functions, which are optimized for that scenario [float] [[ml-distinct-count]] -===== Distinct_count, high_distinct_count, low_distinct_count +== Distinct_count, high_distinct_count, low_distinct_count The `distinct_count` function detects anomalies where the number of distinct values in one field is unusual. diff --git a/docs/reference/ml/anomaly-detection/functions.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-functions.asciidoc similarity index 88% rename from docs/reference/ml/anomaly-detection/functions.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-functions.asciidoc index e90a1281d6c..b0481001dd5 100644 --- a/docs/reference/ml/anomaly-detection/functions.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-functions]] -== Function reference += Function reference The {ml-features} include analysis functions that provide a wide variety of flexible ways to analyze data for anomalies. @@ -41,17 +41,3 @@ These functions effectively ignore empty buckets. * <> * <> * <> - -include::functions/count.asciidoc[] - -include::functions/geo.asciidoc[] - -include::functions/info.asciidoc[] - -include::functions/metric.asciidoc[] - -include::functions/rare.asciidoc[] - -include::functions/sum.asciidoc[] - -include::functions/time.asciidoc[] diff --git a/docs/reference/ml/anomaly-detection/functions/geo.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-geo-functions.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/functions/geo.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-geo-functions.asciidoc index d42b9ea12f4..9d790217bee 100644 --- a/docs/reference/ml/anomaly-detection/functions/geo.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-geo-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-geo-functions]] -=== Geographic functions += Geographic functions The geographic functions detect anomalies in the geographic location of the input data. @@ -13,7 +13,7 @@ geographic functions. [float] [[ml-lat-long]] -==== Lat_long +== Lat_long The `lat_long` function detects anomalies in the geographic location of the input data. diff --git a/docs/reference/ml/anomaly-detection/functions/info.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-info-functions.asciidoc similarity index 97% rename from docs/reference/ml/anomaly-detection/functions/info.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-info-functions.asciidoc index 61913e539ce..cf365525b39 100644 --- a/docs/reference/ml/anomaly-detection/functions/info.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-info-functions.asciidoc @@ -1,5 +1,5 @@ [[ml-info-functions]] -=== Information Content Functions += Information Content Functions The information content functions detect anomalies in the amount of information that is contained in strings within a bucket. These functions can be used as @@ -12,7 +12,7 @@ The {ml-features} include the following information content functions: [float] [[ml-info-content]] -==== Info_content, High_info_content, Low_info_content +== Info_content, High_info_content, Low_info_content The `info_content` function detects anomalies in the amount of information that is contained in strings in a bucket. diff --git a/docs/reference/ml/anomaly-detection/functions/metric.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-metric-functions.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/functions/metric.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-metric-functions.asciidoc index 35aba1ee40b..883d6e959c8 100644 --- a/docs/reference/ml/anomaly-detection/functions/metric.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-metric-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-metric-functions]] -=== Metric functions += Metric functions The metric functions include functions such as mean, min and max. These values are calculated for each bucket. Field values that cannot be converted to @@ -20,7 +20,7 @@ function. [float] [[ml-metric-min]] -==== Min +== Min The `min` function detects anomalies in the arithmetic minimum of a value. The minimum value is calculated for each bucket. @@ -55,7 +55,7 @@ entry mistakes. It models the minimum amount for each product over time. [float] [[ml-metric-max]] -==== Max +== Max The `max` function detects anomalies in the arithmetic maximum of a value. The maximum value is calculated for each bucket. @@ -113,7 +113,7 @@ response times for each bucket. [float] [[ml-metric-median]] -==== Median, high_median, low_median +== Median, high_median, low_median The `median` function detects anomalies in the statistical median of a value. The median value is calculated for each bucket. @@ -151,7 +151,7 @@ median `responsetime` is unusual compared to previous `responsetime` values. [float] [[ml-metric-mean]] -==== Mean, high_mean, low_mean +== Mean, high_mean, low_mean The `mean` function detects anomalies in the arithmetic mean of a value. The mean value is calculated for each bucket. @@ -221,7 +221,7 @@ values. [float] [[ml-metric-metric]] -==== Metric +== Metric 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 @@ -258,7 +258,7 @@ when the mean, min, or max `responsetime` is unusual compared to previous [float] [[ml-metric-varp]] -==== Varp, high_varp, low_varp +== Varp, high_varp, low_varp The `varp` function detects anomalies in the variance of a value which is a measure of the variability and spread in the data. diff --git a/docs/reference/ml/anomaly-detection/functions/rare.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-rare-functions.asciidoc similarity index 99% rename from docs/reference/ml/anomaly-detection/functions/rare.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-rare-functions.asciidoc index a6b91dcfbba..e7ed9a69dc9 100644 --- a/docs/reference/ml/anomaly-detection/functions/rare.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-rare-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-rare-functions]] -=== Rare functions += Rare functions The rare functions detect values that occur rarely in time or rarely for a population. @@ -35,7 +35,7 @@ The {ml-features} include the following rare functions: [float] [[ml-rare]] -==== Rare +== Rare The `rare` function detects values that occur rarely in time or rarely for a population. It detects anomalies according to the number of distinct rare values. @@ -93,7 +93,7 @@ is rare, even if it occurs for that client IP in every bucket. [float] [[ml-freq-rare]] -==== Freq_rare +== Freq_rare The `freq_rare` function detects values that occur rarely for a population. It detects anomalies according to the number of times (frequency) that rare diff --git a/docs/reference/ml/anomaly-detection/functions/sum.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-sum-functions.asciidoc similarity index 97% rename from docs/reference/ml/anomaly-detection/functions/sum.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-sum-functions.asciidoc index 387769c80f3..e84116aa200 100644 --- a/docs/reference/ml/anomaly-detection/functions/sum.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-sum-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-sum-functions]] -=== Sum functions += Sum functions The sum functions detect anomalies when the sum of a field in a bucket is anomalous. @@ -19,7 +19,7 @@ The {ml-features} include the following sum functions: [float] [[ml-sum]] -==== Sum, high_sum, low_sum +== Sum, high_sum, low_sum The `sum` function detects anomalies where the sum of a field in a bucket is anomalous. @@ -75,7 +75,7 @@ to find users that are abusing internet privileges. [float] [[ml-nonnull-sum]] -==== Non_null_sum, high_non_null_sum, low_non_null_sum +== Non_null_sum, high_non_null_sum, low_non_null_sum The `non_null_sum` function is useful if your data is sparse. Buckets without values are ignored and buckets with a zero value are analyzed. diff --git a/docs/reference/ml/anomaly-detection/functions/time.asciidoc b/docs/reference/ml/anomaly-detection/functions/ml-time-functions.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/functions/time.asciidoc rename to docs/reference/ml/anomaly-detection/functions/ml-time-functions.asciidoc index cdf11cba447..deeddaefa04 100644 --- a/docs/reference/ml/anomaly-detection/functions/time.asciidoc +++ b/docs/reference/ml/anomaly-detection/functions/ml-time-functions.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-time-functions]] -=== Time functions += Time functions The time functions detect events that happen at unusual times, either of the day or of the week. These functions can be used to find unusual patterns of behavior, @@ -37,7 +37,7 @@ step change in behavior and the new times will be learned quickly. [float] [[ml-time-of-day]] -==== Time_of_day +== Time_of_day The `time_of_day` function detects when events occur that are outside normal usage patterns. For example, it detects unusual activity in the middle of the @@ -73,7 +73,7 @@ its past behavior. [float] [[ml-time-of-week]] -==== Time_of_week +== Time_of_week The `time_of_week` function detects when events occur that are outside normal usage patterns. For example, it detects login events on the weekend. diff --git a/docs/reference/ml/anomaly-detection/aggregations.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-aggregations.asciidoc similarity index 97% rename from docs/reference/ml/anomaly-detection/aggregations.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-aggregations.asciidoc index a12f50a4702..b6ee3e48661 100644 --- a/docs/reference/ml/anomaly-detection/aggregations.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-aggregations.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-configuring-aggregation]] -=== Aggregating data for faster performance += Aggregating data for faster performance By default, {dfeeds} fetch data from {es} using search and scroll requests. It can be significantly more efficient, however, to aggregate data in {es} @@ -17,7 +17,7 @@ search and scroll behavior. [discrete] [[aggs-limits-dfeeds]] -==== Requirements and limitations +== Requirements and limitations There are some limitations to using aggregations in {dfeeds}. Your aggregation must include a `date_histogram` aggregation, which in turn must contain a `max` @@ -48,7 +48,7 @@ functions, set the interval to the same value as the bucket span. [discrete] [[aggs-include-jobs]] -==== Including aggregations in {anomaly-jobs} +== Including aggregations in {anomaly-jobs} When you create or update an {anomaly-job}, you can include the names of aggregations, for example: @@ -134,7 +134,7 @@ that match values in the job configuration are fed to the job. [discrete] [[aggs-dfeeds]] -==== Nested aggregations in {dfeeds} +== Nested aggregations in {dfeeds} {dfeeds-cap} support complex nested aggregations. This example uses the `derivative` pipeline aggregation to find the first order derivative of the @@ -180,7 +180,7 @@ counter `system.network.out.bytes` for each value of the field `beat.name`. [discrete] [[aggs-single-dfeeds]] -==== Single bucket aggregations in {dfeeds} +== Single bucket aggregations in {dfeeds} {dfeeds-cap} not only supports multi-bucket aggregations, but also single bucket aggregations. The following shows two `filter` aggregations, each gathering the @@ -232,7 +232,7 @@ number of unique entries for the `error` field. [discrete] [[aggs-define-dfeeds]] -==== Defining aggregations in {dfeeds} +== Defining aggregations in {dfeeds} When you define an aggregation in a {dfeed}, it must have the following form: diff --git a/docs/reference/ml/anomaly-detection/categories.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-categories.asciidoc similarity index 99% rename from docs/reference/ml/anomaly-detection/categories.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-categories.asciidoc index c2f55f975cc..afb9fc39366 100644 --- a/docs/reference/ml/anomaly-detection/categories.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-categories.asciidoc @@ -1,7 +1,7 @@ [role="xpack"] [testenv="platinum"] [[ml-configuring-categories]] -=== Detecting anomalous categories of data += Detecting anomalous categories of data Categorization is a {ml} process that tokenizes a text field, clusters similar data together, and classifies it into categories. It works best on @@ -100,7 +100,7 @@ SQL statement from the categorization algorithm. [discrete] [[ml-configuring-analyzer]] -==== Customizing the categorization analyzer +== Customizing the categorization analyzer Categorization uses English dictionary words to identify log message categories. By default, it also uses English tokenization rules. For this reason, if you use diff --git a/docs/reference/ml/anomaly-detection/detector-custom-rules.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc similarity index 97% rename from docs/reference/ml/anomaly-detection/detector-custom-rules.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc index a757c9036a1..c8f1e3b5479 100644 --- a/docs/reference/ml/anomaly-detection/detector-custom-rules.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-configuring-detector-custom-rules]] -=== Customizing detectors with custom rules += Customizing detectors with custom rules <> enable you to change the behavior of anomaly detectors based on domain-specific knowledge. @@ -15,7 +15,7 @@ scope and conditions. For the full list of specification details, see the {anomaly-jobs} API. [[ml-custom-rules-scope]] -==== Specifying custom rule scope +== Specifying custom rule scope Let us assume we are configuring an {anomaly-job} in order to detect DNS data exfiltration. Our data contain fields "subdomain" and "highest_registered_domain". @@ -131,7 +131,7 @@ Such a detector will skip results when the values of all 3 scoped fields are included in the referenced filters. [[ml-custom-rules-conditions]] -==== Specifying custom rule conditions +== Specifying custom 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 @@ -211,7 +211,7 @@ PUT _ml/anomaly_detectors/rule_with_range // TEST[skip:needs-licence] [[ml-custom-rules-lifecycle]] -==== Custom rules in the lifecycle of a job +== Custom rules in the lifecycle of a job Custom rules only affect results created after the rules were applied. Let us imagine that we have configured an {anomaly-job} and it has been running @@ -222,7 +222,7 @@ 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. [[ml-custom-rules-filtering]] -==== Using custom rules vs. filtering data +== Using custom rules vs. filtering data It might appear like using rules is just another way of filtering the data that feeds into an {anomaly-job}. For example, a rule that skips results when diff --git a/docs/reference/ml/anomaly-detection/populations.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-populations.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/populations.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-populations.asciidoc index 7df0d2ffbc2..907d1ca6bf7 100644 --- a/docs/reference/ml/anomaly-detection/populations.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-populations.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] -[[ml-configuring-pop]] -=== Performing population analysis +[[ml-configuring-populations]] += Performing population analysis Entities or events in your data can be considered anomalous when: diff --git a/docs/reference/ml/anomaly-detection/transforms.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-transform.asciidoc similarity index 99% rename from docs/reference/ml/anomaly-detection/transforms.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-transform.asciidoc index 6cda51caaa5..c60677b4969 100644 --- a/docs/reference/ml/anomaly-detection/transforms.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-transform.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-configuring-transform]] -=== Transforming data with script fields += Transforming data with script fields If you use {dfeeds}, you can add scripts to transform your data before it is analyzed. {dfeeds-cap} contain an optional `script_fields` property, where @@ -190,7 +190,7 @@ the **Edit JSON** tab. For example: image::images/ml-scriptfields.jpg[Adding script fields to a {dfeed} in {kib}] [[ml-configuring-transform-examples]] -==== Common script field examples +== Common script field examples While the possibilities are limitless, there are a number of common scenarios where you might use script fields in your {dfeeds}. diff --git a/docs/reference/ml/anomaly-detection/customurl.asciidoc b/docs/reference/ml/anomaly-detection/ml-configuring-url.asciidoc similarity index 98% rename from docs/reference/ml/anomaly-detection/customurl.asciidoc rename to docs/reference/ml/anomaly-detection/ml-configuring-url.asciidoc index 1c0c463e9e8..abd8ba80498 100644 --- a/docs/reference/ml/anomaly-detection/customurl.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-configuring-url.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-configuring-url]] -=== Adding custom URLs to machine learning results += Adding custom URLs to machine learning results When you create an advanced {anomaly-job} or edit any {anomaly-jobs} in {kib}, you can optionally attach one or more custom URLs. @@ -49,7 +49,7 @@ You can also specify these custom URL settings when you create or update [float] [[ml-configuring-url-strings]] -==== String substitution in custom URLs +== String substitution in custom URLs You can use dollar sign ($) delimited tokens in a custom URL. These tokens are substituted for the values of the corresponding fields in the anomaly records. diff --git a/docs/reference/ml/anomaly-detection/delayed-data-detection.asciidoc b/docs/reference/ml/anomaly-detection/ml-delayed-data-detection.asciidoc similarity index 95% rename from docs/reference/ml/anomaly-detection/delayed-data-detection.asciidoc rename to docs/reference/ml/anomaly-detection/ml-delayed-data-detection.asciidoc index 53f1756a4ec..372df97a1f6 100644 --- a/docs/reference/ml/anomaly-detection/delayed-data-detection.asciidoc +++ b/docs/reference/ml/anomaly-detection/ml-delayed-data-detection.asciidoc @@ -1,6 +1,6 @@ [role="xpack"] [[ml-delayed-data-detection]] -=== Handling delayed data += Handling delayed data Delayed data are documents that are indexed late. That is to say, it is data related to a time that the {dfeed} has already processed. @@ -15,7 +15,7 @@ if it is set too high, analysis drifts farther away from real-time. The balance that is struck depends upon each use case and the environmental factors of the cluster. -==== Why worry about delayed data? +== Why worry about delayed data? This is a particularly prescient question. If data are delayed randomly (and consequently are missing from analysis), the results of certain types of @@ -27,7 +27,7 @@ however, {anomaly-jobs} with a `low_count` function may provide false positives. In this situation, it would be useful to see if data comes in after an anomaly is recorded so that you can determine a next course of action. -==== How do we detect delayed data? +== How do we detect delayed data? In addition to the `query_delay` field, there is a delayed data check config, which enables you to configure the datafeed to look in the past for delayed data. @@ -41,7 +41,7 @@ arrived since the analysis. If there is indeed missing data due to their ingest delay, the end user is notified. For example, you can see annotations in {kib} for the periods where these delays occur. -==== What to do about delayed data? +== What to do about delayed data? The most common course of action is to simply to do nothing. For many functions and situations, ignoring the data is acceptable. However, if the amount of diff --git a/docs/reference/ml/anomaly-detection/stopping-ml.asciidoc b/docs/reference/ml/anomaly-detection/stopping-ml.asciidoc deleted file mode 100644 index 9902ef59857..00000000000 --- a/docs/reference/ml/anomaly-detection/stopping-ml.asciidoc +++ /dev/null @@ -1,88 +0,0 @@ -[role="xpack"] -[[stopping-ml]] -== Stopping {ml} {anomaly-detect} - -An orderly shutdown ensures that: - -* {dfeeds-cap} are stopped -* Buffers are flushed -* Model history is pruned -* Final results are calculated -* Model snapshots are saved -* {anomaly-jobs-cap} are closed - -This process ensures that jobs are in a consistent state in case you want to -subsequently re-open them. - -[float] -[[stopping-ml-datafeeds]] -=== Stopping {dfeeds} - -When you stop a {dfeed}, it ceases to retrieve data from {es}. You can stop a -{dfeed} by using {kib} or the -{ref}/ml-stop-datafeed.html[stop {dfeeds} API]. For example, the following -request stops the `feed1` {dfeed}: - -[source,console] --------------------------------------------------- -POST _ml/datafeeds/feed1/_stop --------------------------------------------------- -// TEST[skip:setup:server_metrics_startdf] - -NOTE: You must have `manage_ml`, or `manage` cluster privileges to stop {dfeeds}. -For more information, see {ref}/security-privileges.html[Security privileges] - -A {dfeed} can be started and stopped multiple times throughout its lifecycle. - -//For examples of stopping {dfeeds} in {kib}, see <>. - -[float] -[[stopping-all-ml-datafeeds]] -==== Stopping all {dfeeds} - -If you are upgrading your cluster, you can use the following request to stop all -{dfeeds}: - -[source,console] ----------------------------------- -POST _ml/datafeeds/_all/_stop ----------------------------------- -// TEST[skip:needs-licence] - -[float] -[[closing-ml-jobs]] -=== Closing {anomaly-jobs} - -When you close an {anomaly-job}, it cannot receive data or perform analysis -operations. If a job is associated with a {dfeed}, you must stop the {dfeed} -before you can close the job. If the {dfeed} has an end date, the job closes -automatically on that end date. - -You can close a job by using the -{ref}/ml-close-job.html[close {anomaly-job} API]. For -example, the following request closes the `job1` job: - -[source,console] --------------------------------------------------- -POST _ml/anomaly_detectors/job1/_close --------------------------------------------------- -// TEST[skip:setup:server_metrics_openjob] - -NOTE: You must have `manage_ml`, or `manage` cluster privileges to stop {dfeeds}. -For more information, see {ref}/security-privileges.html[Security privileges] - -{anomaly-jobs-cap} can be opened and closed multiple times throughout their -lifecycle. - -[float] -[[closing-all-ml-datafeeds]] -==== Closing all {anomaly-jobs} - -If you are upgrading your cluster, you can use the following request to close -all open {anomaly-jobs} on the cluster: - -[source,console] ----------------------------------- -POST _ml/anomaly_detectors/_all/_close ----------------------------------- -// TEST[skip:needs-licence] diff --git a/docs/reference/ml/ml-shared.asciidoc b/docs/reference/ml/ml-shared.asciidoc index 4a7a03db11a..be4ca91e565 100644 --- a/docs/reference/ml/ml-shared.asciidoc +++ b/docs/reference/ml/ml-shared.asciidoc @@ -1115,7 +1115,7 @@ 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]. +see {ml-docs}/ml-configuring-populations.html[Performing population analysis]. end::over-field-name[] tag::partition-field-name[]