diff --git a/x-pack/docs/build.gradle b/x-pack/docs/build.gradle index 0d1def2b4f5..e97faf12a6c 100644 --- a/x-pack/docs/build.gradle +++ b/x-pack/docs/build.gradle @@ -9,13 +9,6 @@ apply plugin: 'elasticsearch.docs-test' * only remove entries from this list. When it is empty we'll remove it * entirely and have a party! There will be cake and everything.... */ buildRestTests.expectedUnconvertedCandidates = [ - 'en/ml/functions/count.asciidoc', - 'en/ml/functions/geo.asciidoc', - 'en/ml/functions/info.asciidoc', - 'en/ml/functions/metric.asciidoc', - 'en/ml/functions/rare.asciidoc', - 'en/ml/functions/sum.asciidoc', - 'en/ml/functions/time.asciidoc', 'en/rest-api/watcher/put-watch.asciidoc', 'en/security/authentication/user-cache.asciidoc', 'en/security/authorization/field-and-document-access-control.asciidoc', @@ -56,7 +49,6 @@ buildRestTests.expectedUnconvertedCandidates = [ 'en/watcher/troubleshooting.asciidoc', 'en/rest-api/license/delete-license.asciidoc', 'en/rest-api/license/update-license.asciidoc', - 'en/ml/api-quickref.asciidoc', 'en/rest-api/ml/delete-snapshot.asciidoc', 'en/rest-api/ml/forecast.asciidoc', 'en/rest-api/ml/get-bucket.asciidoc', diff --git a/x-pack/docs/en/ml/aggregations.asciidoc b/x-pack/docs/en/ml/aggregations.asciidoc index f3b8e6b3e34..5ff54b76f01 100644 --- a/x-pack/docs/en/ml/aggregations.asciidoc +++ b/x-pack/docs/en/ml/aggregations.asciidoc @@ -1,5 +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} diff --git a/x-pack/docs/en/ml/api-quickref.asciidoc b/x-pack/docs/en/ml/api-quickref.asciidoc index 9602379c374..dc87a6ba209 100644 --- a/x-pack/docs/en/ml/api-quickref.asciidoc +++ b/x-pack/docs/en/ml/api-quickref.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[ml-api-quickref]] -== API Quick Reference +== API quick reference All {ml} endpoints have the following base: @@ -7,6 +8,7 @@ All {ml} endpoints have the following base: ---- /_xpack/ml/ ---- +// NOTCONSOLE The main {ml} resources can be accessed with a variety of endpoints: diff --git a/x-pack/docs/en/ml/categories.asciidoc b/x-pack/docs/en/ml/categories.asciidoc index bb217e2e186..21f71b871cb 100644 --- a/x-pack/docs/en/ml/categories.asciidoc +++ b/x-pack/docs/en/ml/categories.asciidoc @@ -1,3 +1,4 @@ +[role="xpack"] [[ml-configuring-categories]] === Categorizing log messages @@ -77,7 +78,7 @@ NOTE: To add the `categorization_examples_limit` property, you must use the [float] [[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 @@ -213,7 +214,7 @@ API examples above. [float] [[ml-viewing-categories]] -==== Viewing Categorization Results +==== Viewing categorization results After you open the job and start the {dfeed} or supply data to the job, you can view the categorization results in {kib}. For example: diff --git a/x-pack/docs/en/ml/configuring.asciidoc b/x-pack/docs/en/ml/configuring.asciidoc index ba965a08b04..c2c6e69a711 100644 --- a/x-pack/docs/en/ml/configuring.asciidoc +++ b/x-pack/docs/en/ml/configuring.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[ml-configuring]] -== Configuring Machine Learning +== Configuring machine learning If you want to use {xpackml} features, there must be at least one {ml} node in your cluster and all master-eligible nodes must have {ml} enabled. By default, diff --git a/x-pack/docs/en/ml/customurl.asciidoc b/x-pack/docs/en/ml/customurl.asciidoc index 7c773c4b9bf..7c197084c0e 100644 --- a/x-pack/docs/en/ml/customurl.asciidoc +++ b/x-pack/docs/en/ml/customurl.asciidoc @@ -48,7 +48,7 @@ using the {ml} APIs. [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/x-pack/docs/en/ml/functions.asciidoc b/x-pack/docs/en/ml/functions.asciidoc index ae5f768e056..e32470c6827 100644 --- a/x-pack/docs/en/ml/functions.asciidoc +++ b/x-pack/docs/en/ml/functions.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[ml-functions]] -== Function Reference +== Function reference The {xpackml} features include analysis functions that provide a wide variety of flexible ways to analyze data for anomalies. diff --git a/x-pack/docs/en/ml/functions/count.asciidoc b/x-pack/docs/en/ml/functions/count.asciidoc index 4b70f80933d..a2dc5645b61 100644 --- a/x-pack/docs/en/ml/functions/count.asciidoc +++ b/x-pack/docs/en/ml/functions/count.asciidoc @@ -1,5 +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. @@ -21,7 +22,7 @@ The {xpackml} 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. @@ -44,8 +45,20 @@ see {ref}/ml-job-resource.html#ml-detectorconfig[Detector Configuration Objects] .Example 1: Analyzing events with the count function [source,js] -------------------------------------------------- -{ "function" : "count" } +PUT _xpack/ml/anomaly_detectors/example1 +{ + "analysis_config": { + "detectors": [{ + "function" : "count" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } +} -------------------------------------------------- +// CONSOLE This example is probably the simplest possible analysis. It identifies time buckets during which the overall count of events is higher or lower than @@ -57,12 +70,22 @@ and detects when the event rate is unusual compared to its past behavior. .Example 2: Analyzing errors with the high_count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example2 { - "function" : "high_count", - "by_field_name" : "error_code", - "over_field_name": "user" + "analysis_config": { + "detectors": [{ + "function" : "high_count", + "by_field_name" : "error_code", + "over_field_name": "user" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE If you use this `high_count` function in a detector in your job, it models the event rate for each error code. It detects users that generate an @@ -72,11 +95,21 @@ unusually high count of error codes compared to other users. .Example 3: Analyzing status codes with the low_count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example3 { - "function" : "low_count", - "by_field_name" : "status_code" + "analysis_config": { + "detectors": [{ + "function" : "low_count", + "by_field_name" : "status_code" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE In this example, the function detects when the count of events for a status code is lower than usual. @@ -88,22 +121,30 @@ compared to its past behavior. .Example 4: Analyzing aggregated data with the count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example4 { - "summary_count_field_name" : "events_per_min", - "detectors" [ - { "function" : "count" } - ] -} + "analysis_config": { + "summary_count_field_name" : "events_per_min", + "detectors": [{ + "function" : "count" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } +} -------------------------------------------------- +// CONSOLE If you are analyzing an aggregated `events_per_min` field, do not use a sum function (for example, `sum(events_per_min)`). Instead, use the count function -and the `summary_count_field_name` property. -//TO-DO: For more information, see <>. +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 @@ -144,11 +185,21 @@ The `non_zero_count` function models only the following data: .Example 5: Analyzing signatures with the high_non_zero_count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example5 { - "function" : "high_non_zero_count", - "by_field_name" : "signaturename" + "analysis_config": { + "detectors": [{ + "function" : "high_non_zero_count", + "by_field_name" : "signaturename" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE If you use this `high_non_zero_count` function in a detector in your job, it models the count of events for the `signaturename` field. It ignores any buckets @@ -163,7 +214,7 @@ 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. @@ -187,11 +238,21 @@ see {ref}/ml-job-resource.html#ml-detectorconfig[Detector Configuration Objects] .Example 6: Analyzing users with the distinct_count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example6 { - "function" : "distinct_count", - "field_name" : "user" + "analysis_config": { + "detectors": [{ + "function" : "distinct_count", + "field_name" : "user" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE This `distinct_count` function detects when a system has an unusual number of logged in users. When you use this function in a detector in your job, it @@ -201,12 +262,22 @@ users is unusual compared to the past. .Example 7: Analyzing ports with the high_distinct_count function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example7 { - "function" : "high_distinct_count", - "field_name" : "dst_port", - "over_field_name": "src_ip" + "analysis_config": { + "detectors": [{ + "function" : "high_distinct_count", + "field_name" : "dst_port", + "over_field_name": "src_ip" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE This example detects instances of port scanning. When you use this function in a detector in your job, it models the distinct count of ports. It also detects the diff --git a/x-pack/docs/en/ml/functions/geo.asciidoc b/x-pack/docs/en/ml/functions/geo.asciidoc index cc98e95bf20..e9685b46e16 100644 --- a/x-pack/docs/en/ml/functions/geo.asciidoc +++ b/x-pack/docs/en/ml/functions/geo.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[ml-geo-functions]] -=== Geographic Functions +=== Geographic functions The geographic functions detect anomalies in the geographic location of the input data. @@ -28,12 +29,22 @@ see {ref}/ml-job-resource.html#ml-detectorconfig[Detector Configuration Objects] .Example 1: Analyzing transactions with the lat_long function [source,js] -------------------------------------------------- +PUT _xpack/ml/anomaly_detectors/example1 { - "function" : "lat_long", - "field_name" : "transactionCoordinates", - "by_field_name" : "creditCardNumber" + "analysis_config": { + "detectors": [{ + "function" : "lat_long", + "field_name" : "transactionCoordinates", + "by_field_name" : "creditCardNumber" + }] + }, + "data_description": { + "time_field":"timestamp", + "time_format": "epoch_ms" + } } -------------------------------------------------- +// CONSOLE If you use this `lat_long` function in a detector in your job, it detects anomalies where the geographic location of a credit card transaction is @@ -54,6 +65,7 @@ For example, JSON data might contain the following transaction coordinates: "creditCardNumber": "1234123412341234" } -------------------------------------------------- +// NOTCONSOLE In {es}, location data is likely to be stored in `geo_point` fields. For more information, see {ref}/geo-point.html[Geo-point datatype]. This data type is not @@ -64,7 +76,15 @@ format. For example, the following Painless script transforms [source,js] -------------------------------------------------- +PUT _xpack/ml/datafeeds/datafeed-test2 { + "job_id": "farequote", + "indices": ["farequote"], + "query": { + "match_all": { + "boost": 1 + } + }, "script_fields": { "lat-lon": { "script": { @@ -75,5 +95,7 @@ format. For example, the following Painless script transforms } } -------------------------------------------------- +// CONSOLE +// TEST[setup:farequote_job] For more information, see <>. diff --git a/x-pack/docs/en/ml/functions/info.asciidoc b/x-pack/docs/en/ml/functions/info.asciidoc index f964d4eb3ec..2c3117e0e56 100644 --- a/x-pack/docs/en/ml/functions/info.asciidoc +++ b/x-pack/docs/en/ml/functions/info.asciidoc @@ -40,6 +40,7 @@ For more information about those properties, see "over_field_name" : "highest_registered_domain" } -------------------------------------------------- +// NOTCONSOLE If you use this `info_content` function in a detector in your job, it models information that is present in the `subdomain` string. It detects anomalies @@ -60,6 +61,7 @@ choice. "over_field_name" : "src_ip" } -------------------------------------------------- +// NOTCONSOLE If you use this `high_info_content` function in a detector in your job, it models information content that is held in the DNS query string. It detects @@ -77,6 +79,7 @@ information content is higher than expected. "by_field_name" : "logfilename" } -------------------------------------------------- +// NOTCONSOLE If you use this `low_info_content` function in a detector in your job, it models information content that is present in the message string for each diff --git a/x-pack/docs/en/ml/functions/metric.asciidoc b/x-pack/docs/en/ml/functions/metric.asciidoc index 495fc6f3335..3ee51797027 100644 --- a/x-pack/docs/en/ml/functions/metric.asciidoc +++ b/x-pack/docs/en/ml/functions/metric.asciidoc @@ -1,5 +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 @@ -42,6 +43,7 @@ For more information about those properties, see "by_field_name" : "product" } -------------------------------------------------- +// NOTCONSOLE If you use this `min` function in a detector in your job, it detects where the smallest transaction is lower than previously observed. You can use this @@ -76,6 +78,7 @@ For more information about those properties, see "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `max` function in a detector in your job, it detects where the longest `responsetime` is longer than previously observed. You can use this @@ -98,6 +101,7 @@ to previous applications. "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE The analysis in the previous example can be performed alongside `high_mean` functions by application. By combining detectors and using the same influencer @@ -106,7 +110,7 @@ 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. @@ -136,6 +140,7 @@ For more information about those properties, see "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `median` function in a detector in your job, it models the median `responsetime` for each application over time. It detects when the median @@ -143,7 +148,7 @@ median `responsetime` for each application over time. It detects when the median [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. @@ -173,6 +178,7 @@ For more information about those properties, see "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `mean` function in a detector in your job, it models the mean `responsetime` for each application over time. It detects when the mean @@ -187,6 +193,7 @@ If you use this `mean` function in a detector in your job, it models the mean "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `high_mean` function in a detector in your job, it models the mean `responsetime` for each application over time. It detects when the mean @@ -201,6 +208,7 @@ mean `responsetime` for each application over time. It detects when the mean "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `low_mean` function in a detector in your job, it models the mean `responsetime` for each application over time. It detects when the mean @@ -237,6 +245,7 @@ For more information about those properties, see "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `metric` function in a detector in your job, it models the mean, min, and max `responsetime` for each application over time. It detects @@ -245,7 +254,7 @@ 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. @@ -273,6 +282,7 @@ For more information about those properties, see "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `varp` function in a detector in your job, it models the variance in values of `responsetime` for each application over time. It detects @@ -288,6 +298,7 @@ behavior. "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `high_varp` function in a detector in your job, it models the variance in values of `responsetime` for each application over time. It detects @@ -303,6 +314,7 @@ behavior. "by_field_name" : "application" } -------------------------------------------------- +// NOTCONSOLE If you use this `low_varp` function in a detector in your job, it models the variance in values of `responsetime` for each application over time. It detects diff --git a/x-pack/docs/en/ml/functions/rare.asciidoc b/x-pack/docs/en/ml/functions/rare.asciidoc index 2485605557c..fc30918b508 100644 --- a/x-pack/docs/en/ml/functions/rare.asciidoc +++ b/x-pack/docs/en/ml/functions/rare.asciidoc @@ -1,5 +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. @@ -54,6 +55,7 @@ For more information about those properties, see "by_field_name" : "status" } -------------------------------------------------- +// NOTCONSOLE If you use this `rare` function in a detector in your job, it detects values that are rare in time. It models status codes that occur over time and detects @@ -69,6 +71,7 @@ status codes in a web access log that have never (or rarely) occurred before. "over_field_name" : "clientip" } -------------------------------------------------- +// NOTCONSOLE If you use this `rare` function in a detector in your job, it detects values that are rare in a population. It models status code and client IP interactions @@ -111,6 +114,7 @@ For more information about those properties, see "over_field_name" : "clientip" } -------------------------------------------------- +// NOTCONSOLE If you use this `freq_rare` function in a detector in your job, it detects values that are frequently rare in a population. It models URI paths and diff --git a/x-pack/docs/en/ml/functions/sum.asciidoc b/x-pack/docs/en/ml/functions/sum.asciidoc index 3a0f0b264e9..7a95ad63fcc 100644 --- a/x-pack/docs/en/ml/functions/sum.asciidoc +++ b/x-pack/docs/en/ml/functions/sum.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. @@ -16,16 +16,9 @@ The {xpackml} features include the following sum functions: * xref:ml-sum[`sum`, `high_sum`, `low_sum`] * xref:ml-nonnull-sum[`non_null_sum`, `high_non_null_sum`, `low_non_null_sum`] -//// -TBD: Incorporate from prelert docs?: -Input data may contain pre-calculated fields giving the total count of some value e.g. transactions per minute. -Ensure you are familiar with our advice on Summarization of Input Data, as this is likely to provide -a more appropriate method to using the sum function. -//// - [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. @@ -54,6 +47,7 @@ For more information about those properties, see "over_field_name" : "employee" } -------------------------------------------------- +// NOTCONSOLE If you use this `sum` function in a detector in your job, it models total expenses per employees for each cost center. For each time bucket, @@ -69,6 +63,7 @@ to other employees. "over_field_name" : "cs_host" } -------------------------------------------------- +// NOTCONSOLE If you use this `high_sum` function in a detector in your job, it models total `cs_bytes`. It detects `cs_hosts` that transfer unusually high @@ -79,7 +74,7 @@ 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. @@ -110,6 +105,7 @@ is not applicable for this function. "byFieldName" : "employee" } -------------------------------------------------- +// NOTCONSOLE If you use this `high_non_null_sum` function in a detector in your job, it models the total `amount_approved` for each employee. It ignores any buckets diff --git a/x-pack/docs/en/ml/functions/time.asciidoc b/x-pack/docs/en/ml/functions/time.asciidoc index a8067e2ca13..ac8199307f1 100644 --- a/x-pack/docs/en/ml/functions/time.asciidoc +++ b/x-pack/docs/en/ml/functions/time.asciidoc @@ -1,5 +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, @@ -60,6 +61,7 @@ For more information about those properties, see "by_field_name" : "process" } -------------------------------------------------- +// NOTCONSOLE If you use this `time_of_day` function in a detector in your job, it models when events occur throughout a day for each process. It detects when an @@ -91,6 +93,7 @@ For more information about those properties, see "over_field_name" : "workstation" } -------------------------------------------------- +// NOTCONSOLE If you use this `time_of_week` function in a detector in your job, it models when events occur throughout the week for each `eventcode`. It detects diff --git a/x-pack/docs/en/ml/populations.asciidoc b/x-pack/docs/en/ml/populations.asciidoc index 53e10ce8d41..bf0dd2ad7d7 100644 --- a/x-pack/docs/en/ml/populations.asciidoc +++ b/x-pack/docs/en/ml/populations.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[ml-configuring-pop]] -=== Performing Population Analysis +=== Performing population analysis Entities or events in your data can be considered anomalous when: diff --git a/x-pack/docs/en/ml/stopping-ml.asciidoc b/x-pack/docs/en/ml/stopping-ml.asciidoc index 862fe5cf050..c0be2d947cd 100644 --- a/x-pack/docs/en/ml/stopping-ml.asciidoc +++ b/x-pack/docs/en/ml/stopping-ml.asciidoc @@ -1,5 +1,6 @@ +[role="xpack"] [[stopping-ml]] -== Stopping Machine Learning +== Stopping machine learning An orderly shutdown of {ml} ensures that: @@ -24,10 +25,10 @@ request stops the `feed1` {dfeed}: [source,js] -------------------------------------------------- -POST _xpack/ml/datafeeds/feed1/_stop +POST _xpack/ml/datafeeds/datafeed-total-requests/_stop -------------------------------------------------- // CONSOLE -// TEST[skip:todo] +// TEST[setup:server_metrics_startdf] NOTE: You must have `manage_ml`, or `manage` cluster privileges to stop {dfeeds}. For more information, see <>. @@ -63,10 +64,10 @@ example, the following request closes the `job1` job: [source,js] -------------------------------------------------- -POST _xpack/ml/anomaly_detectors/job1/_close +POST _xpack/ml/anomaly_detectors/total-requests/_close -------------------------------------------------- // CONSOLE -// TEST[skip:todo] +// TEST[setup:server_metrics_openjob] NOTE: You must have `manage_ml`, or `manage` cluster privileges to stop {dfeeds}. For more information, see <>. diff --git a/x-pack/docs/en/ml/transforms.asciidoc b/x-pack/docs/en/ml/transforms.asciidoc index 9789518081b..c4b4d560297 100644 --- a/x-pack/docs/en/ml/transforms.asciidoc +++ b/x-pack/docs/en/ml/transforms.asciidoc @@ -1,5 +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 @@ -602,10 +603,3 @@ The preview {dfeed} API returns the following results, which show that ] ---------------------------------- // TESTRESPONSE - -//// -==== Configuring Script Fields in {dfeeds-cap} - -//TO-DO: Add Kibana steps from -//https://github.com/elastic/prelert-legacy/wiki/Transforming-data-with-script_fields#transforming-geo_point-data-to-a-workable-string-format -////