honeymoose
/
OpenSearch
mirror of https://github.com/honeymoose/OpenSearch.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[DOCS] Adds cat anomaly detectors API (#52866) (#52970)

This commit is contained in:
Lisa Cawley 2020-03-02 07:28:55 -08:00 committed by GitHub
parent a328a8eaf1
commit 4fbe1b0550
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 751 additions and 272 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/reference/cat.asciidoc
View File

@ -227,6 +227,8 @@ include::cat/alias.asciidoc[]
include::cat/allocation.asciidoc[]
include::cat/anomaly-detectors.asciidoc[]
include::cat/count.asciidoc[]
include::cat/dataframeanalytics.asciidoc[]

280
docs/reference/cat/anomaly-detectors.asciidoc Normal file
View File

@ -0,0 +1,280 @@
[role="xpack"]
[testenv="platinum"]
[[cat-anomaly-detectors]]
=== cat anomaly detectors API
++++
<titleabbrev>cat anomaly detectors</titleabbrev>
++++
Returns configuration and usage information about {anomaly-jobs}.
[[cat-anomaly-detectors-request]]
==== {api-request-title}
`GET /_cat/ml/anomaly_detectors/<job_id>` +
`GET /_cat/ml/anomaly_detectors`
[[cat-anomaly-detectors-prereqs]]
==== {api-prereq-title}
* If the {es} {security-features} are enabled, you must have `monitor_ml`,
`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. See
<<security-privileges>> and {ml-docs}/setup.html[Set up {ml-features}].
[[cat-anomaly-detectors-desc]]
==== {api-description-title}
See {ml-docs}/ml-jobs.html[{anomaly-jobs-cap}].
NOTE: This API returns a maximum of 10,000 jobs.
[[cat-anomaly-detectors-path-params]]
==== {api-path-parms-title}
`<job_id>`::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
[[cat-anomaly-detectors-query-params]]
==== {api-query-parms-title}
`allow_no_jobs`::
(Optional, boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-jobs]
include::{docdir}/rest-api/common-parms.asciidoc[tag=bytes]
include::{docdir}/rest-api/common-parms.asciidoc[tag=http-format]
include::{docdir}/rest-api/common-parms.asciidoc[tag=cat-h]
+
If you do not specify which columns to include, the API returns the default
columns. If you explicitly specify one or more columns, it returns only the
specified columns.
+
Valid columns are:
`assignment_explanation`, `ae`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation-anomaly-jobs]
`buckets.count`, `bc`, `bucketsCount`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count-anomaly-jobs]
`buckets.time.exp_avg`, `btea`, `bucketsTimeExpAvg`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-exponential-average]
`buckets.time.exp_avg_hour`, `bteah`, `bucketsTimeExpAvgHour`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-exponential-average-hour]
`buckets.time.max`, `btmax`, `bucketsTimeMax`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-maximum]
`buckets.time.min`, `btmin`, `bucketsTimeMin`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-minimum]
`buckets.time.total`, `btt`, `bucketsTimeTotal`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-total]
`data.buckets`, `db`, `dataBuckets`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count]
`data.earliest_record`, `der`, `dataEarliestRecord`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=earliest-record-timestamp]
`data.empty_buckets`, `deb`, `dataEmptyBuckets`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=empty-bucket-count]
`data.input_bytes`, `dib`, `dataInputBytes`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=input-bytes]
`data.input_fields`, `dif`, `dataInputFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=input-field-count]
`data.input_records`, `dir`, `dataInputRecords`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=input-record-count]
`data.invalid_dates`, `did`, `dataInvalidDates`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=invalid-date-count]
`data.last`, `dl`, `dataLast`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=last-data-time]
`data.last_empty_bucket`, `dleb`, `dataLastEmptyBucket`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-empty-bucket-timestamp]
`data.last_sparse_bucket`, `dlsb`, `dataLastSparseBucket`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-sparse-record-timestamp]
`data.latest_record`, `dlr`, `dataLatestRecord`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-record-timestamp]
`data.missing_fields`, `dmf`, `dataMissingFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=missing-field-count]
`data.out_of_order_timestamps`, `doot`, `dataOutOfOrderTimestamps`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=out-of-order-timestamp-count]
`data.processed_fields`, `dpf`, `dataProcessedFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=processed-field-count]
`data.processed_records`, `dpr`, `dataProcessedRecords`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=processed-record-count]
`data.sparse_buckets`, `dsb`, `dataSparseBuckets`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=sparse-bucket-count]
`forecasts.memory.avg`, `fmavg`, `forecastsMemoryAvg`:::
The average memory usage in bytes for forecasts related to the {anomaly-job}.
`forecasts.memory.max`, `fmmax`, `forecastsMemoryMax`:::
The maximum memory usage in bytes for forecasts related to the {anomaly-job}.
`forecasts.memory.min`, `fmmin`, `forecastsMemoryMin`:::
The minimum memory usage in bytes for forecasts related to the {anomaly-job}.
`forecasts.memory.total`, `fmt`, `forecastsMemoryTotal`:::
The total memory usage in bytes for forecasts related to the {anomaly-job}.
`forecasts.records.avg`, `fravg`, `forecastsRecordsAvg`:::
The average number of `model_forecast` documents written for forecasts related
to the {anomaly-job}.
`forecasts.records.max`, `frmax`, `forecastsRecordsMax`:::
The maximum number of `model_forecast` documents written for forecasts related
to the {anomaly-job}.
`forecasts.records.min`, `frmin`, `forecastsRecordsMin`:::
The minimum number of `model_forecast` documents written for forecasts related
to the {anomaly-job}.
`forecasts.records.total`, `frt`, `forecastsRecordsTotal`:::
The total number of `model_forecast` documents written for forecasts related to
the {anomaly-job}.
`forecasts.time.avg`, `ftavg`, `forecastsTimeAvg`:::
The average runtime in milliseconds for forecasts related to the {anomaly-job}.
`forecasts.time.max`, `ftmax`, `forecastsTimeMax`:::
The maximum runtime in milliseconds for forecasts related to the {anomaly-job}.
`forecasts.time.min`, `ftmin`, `forecastsTimeMin`:::
The minimum runtime in milliseconds for forecasts related to the {anomaly-job}.
`forecasts.time.total`, `ftt`, `forecastsTimeTotal`:::
The total runtime in milliseconds for forecasts related to the {anomaly-job}.
`forecasts.total`, `ft`, `forecastsTotal`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=forecast-total]
`id`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
`model.bucket_allocation_failures`, `mbaf`, `modelBucketAllocationFailures`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-allocation-failures-count]
`model.by_fields`, `mbf`, `modelByFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=total-by-field-count]
`model.bytes`, `mb`, `modelBytes`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-bytes]
`model.bytes_exceeded`, `mbe`, `modelBytesExceeded`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=model-bytes-exceeded]
`model.categorization_status`, `mcs`, `modelCategorizationStatus`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-status]
`model.categorized_doc_count`, `mcdc`, `modelCategorizedDocCount`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=categorized-doc-count]
`model.dead_category_count`, `mdcc`, `modelDeadCategoryCount`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=dead-category-count]
`model.frequent_category_count`, `mfcc`, `modelFrequentCategoryCount`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=frequent-category-count]
`model.log_time`, `mlt`, `modelLogTime`:::
The timestamp when the model stats were gathered, according to server time.
`model.memory_limit`, `mml`, `modelMemoryLimit`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit-anomaly-jobs]
`model.memory_status`, `mms`, `modelMemoryStatus`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-status]
`model.over_fields`, `mof`, `modelOverFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=total-over-field-count]
`model.partition_fields`, `mpf`, `modelPartitionFields`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=total-partition-field-count]
`model.rare_category_count`, `mrcc`, `modelRareCategoryCount`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=rare-category-count]
`model.timestamp`, `mt`, `modelTimestamp`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=model-timestamp]
`model.total_category_count`, `mtcc`, `modelTotalCategoryCount`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=total-category-count]
`node.address`, `na`, `nodeAddress`:::
The network address of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-jobs]
`node.ephemeral_id`, `ne`, `nodeEphemeralId`:::
The ephemeral ID of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-jobs]
`node.id`, `ni`, `nodeId`:::
The unique identifier of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-jobs]
`node.name`, `nn`, `nodeName`:::
The node name.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-jobs]
`opened_time`, `ot`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=open-time]
`state`, `s`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=state-anomaly-job]
include::{docdir}/rest-api/common-parms.asciidoc[tag=help]
include::{docdir}/rest-api/common-parms.asciidoc[tag=cat-s]
include::{docdir}/rest-api/common-parms.asciidoc[tag=time]
include::{docdir}/rest-api/common-parms.asciidoc[tag=cat-v]
[[cat-anomaly-detectors-example]]
==== {api-examples-title}
[source,console]
--------------------------------------------------
GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v
--------------------------------------------------
// TEST[skip:kibana sample data]
[source,console-result]
----
id s dpr mb
high_sum_total_sales closed 14022 1.5mb
low_request_rate closed 1216 40.5kb
response_code_rates closed 28146 132.7kb
url_scanning closed 28146 501.6kb
----
// TESTRESPONSE[skip:kibana sample data]

144
docs/reference/cat/datafeeds.asciidoc
View File

@ -22,12 +22,14 @@ Returns configuration and usage information about {dfeeds}.
`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. See
<<security-privileges>> and {ml-docs}/setup.html[Set up {ml-features}].
////
[[cat-datafeeds-desc]]
==== {api-description-title}
TBD: This API returns a maximum of 10,000 {dfeeds}.
////
{dfeeds-cap} retrieve data from {es} for analysis by {anomaly-jobs}. For more
information, see {ml-docs}/ml-dfeeds.html[{dfeeds-cap}].
NOTE: This API returns a maximum of 10,000 jobs.
[[cat-datafeeds-path-params]]
==== {api-path-parms-title}
@ -46,6 +48,60 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-datafeeds]
include::{docdir}/rest-api/common-parms.asciidoc[tag=http-format]
include::{docdir}/rest-api/common-parms.asciidoc[tag=cat-h]
+
If you do not specify which columns to include, the API returns the default
columns. If you explicitly specify one or more columns, it returns only the
specified columns.
+
Valid columns are:
`assignment_explanation`, `ae`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation-datafeeds]
`buckets.count`, `bc`, `bucketsCount`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count]
`id`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=datafeed-id]
`node.address`, `na`, `nodeAddress`:::
The network address of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
`node.ephemeral_id`, `ne`, `nodeEphemeralId`:::
The ephemeral ID of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
`node.id`, `ni`, `nodeId`:::
The unique identifier of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
`node.name`, `nn`, `nodeName`:::
The node name.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
`search.bucket_avg`, `sba`, `searchBucketAvg`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-bucket-avg]
`search.count`, `sc`, `searchCount`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=search-count]
`search.exp_avg_hour`, `seah`, `searchExpAvgHour`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-exp-avg-hour]
`search.time`, `st`, `searchTime`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-time]
`state`, `s`:::
(Default)
include::{docdir}/ml/ml-shared.asciidoc[tag=state-datafeed]
include::{docdir}/rest-api/common-parms.asciidoc[tag=help]
@ -55,86 +111,6 @@ include::{docdir}/rest-api/common-parms.asciidoc[tag=time]
include::{docdir}/rest-api/common-parms.asciidoc[tag=cat-v]
[[cat-datafeeds-results]]
==== {api-response-body-title}
`assignment_explanation`::
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation]
+
To retrieve this information, specify the `ae` column in the `h` query parameter.
`bucket.count`::
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count]
+
To retrieve this information, specify the `bc` or `bucketCount` column in the
`h` query parameter.
`id`::
include::{docdir}/ml/ml-shared.asciidoc[tag=datafeed-id]
+
To retrieve this information, specify the `id` column in the `h` query parameter.
`node.address`::
The network address of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node]
+
To retrieve this information, specify the `na` or `nodeAddress` column in the
`h` query parameter.
`node.ephemeral_id`::
The ephemeral ID of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node]
+
To retrieve this information, specify the `ne` or `nodeEphemeralId` column in
the `h` query parameter.
`node.id`::
The unique identifier of the node.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node]
+
To retrieve this information, specify the `ni` or `nodeId` column in the `h`
query parameter.
`node.name`::
The node name.
+
include::{docdir}/ml/ml-shared.asciidoc[tag=node]
+
To retrieve this information, specify the `nn` or `nodeName` column in the `h`
query parameter.
`search.bucket_avg`::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-bucket-avg]
+
To retrieve this information, specify the `sba` or `searchBucketAvg` column in
the `h` query parameter.
`search.count`::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-count]
+
To retrieve this information, specify the `sc` or `searchCount` column in the
`h` query parameter.
`search.exp_avg_hour`::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-exp-avg-hour]
+
To retrieve this information, specify the `seah` or `searchExpAvgHour` column in
the `h` query parameter.
`search.time`::
include::{docdir}/ml/ml-shared.asciidoc[tag=search-time]
+
To retrieve this information, specify the `st` or `searchTime` column in the `h`
query parameter.
`state`::
include::{docdir}/ml/ml-shared.asciidoc[tag=state-datafeed]
+
To retrieve this information, specify the `s` column in the `h` query parameter.
[[cat-datafeeds-example]]
==== {api-examples-title}
@ -146,7 +122,7 @@ GET _cat/ml/datafeeds?v
[source,console-result]
----
id state bucket.count search.count
id state buckets.count search.count
datafeed-high_sum_total_sales stopped 743 7
datafeed-low_request_rate stopped 1457 3
datafeed-response_code_rates stopped 1460 18

18
docs/reference/ml/anomaly-detection/apis/get-datafeed-stats.asciidoc
View File

@ -68,7 +68,7 @@ informational; you cannot update their values.
`assignment_explanation`::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation]
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation-datafeeds]
`datafeed_id`::
(string)
@ -76,12 +76,18 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=datafeed-id]
`node`::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=node]
`node`.`id`::: The unique identifier of the node. For example, "0-o0tOoRTwKFZifatTWKNw".
include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
`node`.`id`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
`node`.`name`::: The node name. For example, `0-o0tOo`.
`node`.`ephemeral_id`::: The node ephemeral ID.
`node`.`transport_address`::: The host and port where transport HTTP connections
are accepted. For example, `127.0.0.1:9300`.
`node`.`ephemeral_id`:::
include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]
`node`.`transport_address`::: The host and port where transport HTTP connections are
accepted. For example, `127.0.0.1:9300`.
`node`.`attributes`::: For example, `{"ml.machine_memory": "17179869184"}`.
`state`::

216
docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc
View File

@ -57,8 +57,8 @@ The API returns the following information about the operational progress of a
job:
`assignment_explanation`::
(string) For open jobs only, contains messages relating to the selection of a
node to run the job.
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=assignment-explanation-anomaly-jobs]
[[datacounts]]`data_counts`::
(object) An object that describes the quantity of input to the job and any
@ -67,85 +67,73 @@ a job. If a model snapshot is reverted or old results are deleted, the job
counts are not reset.
`data_counts`.`bucket_count`:::
(long) The number of bucket results produced by the job.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count-anomaly-jobs]
`data_counts`.`earliest_record_timestamp`:::
(date) The timestamp of the earliest chronologically input document.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=earliest-record-timestamp]
`data_counts`.`empty_bucket_count`:::
(long) The number of buckets which did not contain any data. If your data
contains many empty buckets, consider increasing your `bucket_span` or using
functions that are tolerant to gaps in data such as `mean`, `non_null_sum` or
`non_zero_count`.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=empty-bucket-count]
`data_counts`.`input_bytes`:::
(long) The number of raw bytes read by the job.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=input-bytes]
`data_counts`.`input_field_count`:::
(long) The total number of fields in input documents posted to the job. This
count includes fields that are not used in the analysis. However, be aware that
if you are using a {dfeed}, it extracts only the required fields from the
documents it retrieves before posting them to the job.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=input-field-count]
`data_counts`.`input_record_count`:::
(long) The number of data records read by the job.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=input-record-count]
`data_counts`.`invalid_date_count`:::
(long) The number of records with either a missing date field or a date that
could not be parsed.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=invalid-date-count]
`data_counts`.`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
`data_counts`.`last_data_time`:::
(date) The timestamp at which data was last analyzed, according to server time.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=last-data-time]
`data_counts`.`latest_empty_bucket_timestamp`:::
(date) The timestamp of the last bucket that did not contain any data.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-empty-bucket-timestamp]
`data_counts`.`latest_record_timestamp`:::
(date) The timestamp of the latest chronologically input document.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-record-timestamp]
`data_counts`.`latest_sparse_bucket_timestamp`:::
(date) The timestamp of the last bucket that was considered sparse.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=latest-sparse-record-timestamp]
`data_counts`.`missing_field_count`:::
(long) The number of input documents that are missing a field that the job is
configured to analyze. Input documents with missing fields are still processed
because it is possible that not all fields are missing. The value of
`processed_record_count` includes this count.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=missing-field-count]
+
--
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.
--
The value of `processed_record_count` includes this count.
`data_counts`.`out_of_order_timestamp_count`:::
(long) The number of input documents that are out of time sequence and outside
of the latency window. This information is applicable only when you provide data
to the job by using the <<ml-post-data,post data API>>. These out of order
documents are discarded, since jobs require time series data to be in ascending
chronological order.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=out-of-order-timestamp-count]
`data_counts`.`processed_field_count`:::
(long) The total number of fields in all the documents that have been processed
by the job. Only fields that are specified in the detector configuration object
contribute to this count. The time stamp is not included in this count.
include::{docdir}/ml/ml-shared.asciidoc[tag=processed-field-count]
`data_counts`.`processed_record_count`:::
(long) The number of input documents that have been processed by the job. This
value includes documents with missing fields, since they are nonetheless
analyzed. If you use {dfeeds} and have aggregations in your search query, the
`processed_record_count` will be the number of aggregation results processed,
not the number of {es} documents.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=processed-record-count]
`data_counts`.`sparse_bucket_count`:::
(long) The number of buckets that contained few data points compared to the
expected number of data points. If your data contains many sparse buckets,
consider using a longer `bucket_span`.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=sparse-bucket-count]
[[forecastsstats]]`forecasts_stats`::
(object) An object that provides statistical information about forecasts
@ -171,8 +159,8 @@ related to this job. If there are no forecasts, this property is omitted.
maximum, average and total.
`forecasts_stats`.`records`:::
(object) The `avg`, `min`, `max` and `total` number of model_forecast documents
written for forecasts related to this job. If there are no forecasts, this
(object) The `avg`, `min`, `max` and `total` number of `model_forecast` documents
written for forecasts related to this job. If there are no forecasts, this
property is omitted.
`forecasts_stats`.`processing_time_ms`:::
@ -186,8 +174,8 @@ omitted.
omitted.
`forecasts_stats`.`total`:::
(long) The number of individual forecasts currently available for this job. A
value of `1` or more indicates that forecasts exist.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=forecast-total]
`job_id`::
(string)
@ -198,38 +186,24 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
model. It has the following properties:
`model_size_stats`.`bucket_allocation_failures_count`:::
(long) The number of buckets for which new entities in incoming data were not
processed due to insufficient model memory. This situation is also signified
by a `hard_limit: memory_status` property value.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-allocation-failures-count]
`model_size_stats`.`categorized_doc_count`:::
(long) The number of documents that have had a field categorized.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=categorized-doc-count]
`model_size_stats`.`categorization_status`:::
(string) The status of categorization for this job.
Contains one of the following values.
+
--
* `ok`: Categorization is performing acceptably well (or not being
used at all).
* `warn`: Categorization is detecting a distribution of categories
that suggests the input data is inappropriate for categorization.
Problems could be that there is only one category, more than 90% of
categories are rare, the number of categories is greater than 50% of
the number of categorized documents, there are no frequently
matched categories, or more than 50% of categories are dead.
--
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-status]
`model_size_stats`.`dead_category_count`:::
(long) The number of categories created by categorization that will
never be assigned again because another category's definition
makes it a superset of the dead category. (Dead categories are a
side effect of the way categorization has no prior training.)
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=dead-category-count]
`model_size_stats`.`frequent_category_count`:::
(long) The number of categories that match more than 1% of categorized
documents.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=frequent-category-count]
`model_size_stats`.`job_id`:::
(string)
@ -239,53 +213,47 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
(date) The timestamp of the `model_size_stats` according to server time.
`model_size_stats`.`memory_status`:::
(string) The status of the mathematical models. This property can have one of
the following values:
+
--
* `ok`: The models stayed below the configured value.
* `soft_limit`: The models used more than 60% of the configured memory limit and
older unused models will be pruned to free up space.
* `hard_limit`: The models used more space than the configured memory limit. As
a result, not all incoming data was processed.
--
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-status]
`model_size_stats`.`model_bytes`:::
(long) The number of bytes of memory used by the models. This is the maximum
value since the last time the model was persisted. If the job is closed,
this value indicates the latest size.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-bytes]
`model_size_stats`.`model_bytes_exceeded`:::
(long) The number of bytes over the high limit for memory usage at the last
allocation failure.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-bytes-exceeded]
`model_size_stats`.`model_bytes_memory_limit`:::
(long) The upper limit for memory usage, checked on increasing values.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit-anomaly-jobs]
`model_size_stats`.`rare_category_count`:::
(long) The number of categories that match just one categorized document.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=rare-category-count]
`model_size_stats`.`result_type`:::
(string) For internal use. The type of result.
`model_size_stats`.`total_by_field_count`:::
(long) The number of `by` field values that were analyzed by the models. This
value is cumulative for all detectors.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=total-by-field-count]
`model_size_stats`.`total_category_count`:::
(long) The number of categories created by categorization.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=total-category-count]
`model_size_stats`.`total_over_field_count`:::
(long) The number of `over` field values that were analyzed by the models. This
value is cumulative for all detectors.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=total-over-field-count]
`model_size_stats`.`total_partition_field_count`:::
(long) The number of `partition` field values that were analyzed by the models.
This value is cumulative for all detectors.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=total-partition-field-count]
`model_size_stats`.`timestamp`:::
(date) The timestamp of the `model_size_stats` according to the timestamp of the
data.
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-timestamp]
[[stats-node]]`node`::
(object) Contains properties for the node that runs the job. This information is
@ -296,10 +264,12 @@ available only for open jobs.
`{"ml.machine_memory": "17179869184"}`.
`node`.`ephemeral_id`:::
(string) The ephemeral id of the node.
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]
`node`.`id`:::
(string) The unique identifier of the node.
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
`node`.`name`:::
(string) The node name.
@ -308,24 +278,12 @@ available only for open jobs.
(string) The host and port where transport HTTP connections are accepted.
`open_time`::
(string) For open jobs only, the elapsed time for which the job has been open.
For example, `28746386s`.
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=open-time]
`state`::
(string) The status of the job, which can be one of the following values:
+
--
* `closed`: The job finished successfully with its model state persisted. The
job must be opened before it can accept further data.
* `closing`: The job close action is in progress and has not yet completed. 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 {dfeed} can be corrected, the job can
be closed and then re-opened.
* `opened`: The job is available to receive and process data.
* `opening`: The job open action is in progress and has not yet completed.
--
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=state-anomaly-job]
[[timingstats]]`timing_stats`::
(object) An object that provides statistical information about timing aspect of
@ -335,28 +293,32 @@ this job. It has the following properties:
(double) Average of all bucket processing times in milliseconds.
`timing_stats`.`bucket_count`:::
(long) The number of buckets processed.
(long)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-count]
`timing_stats`.`exponential_average_bucket_processing_time_ms`:::
(double) Exponential moving average of all bucket processing times in
milliseconds.
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-exponential-average]
`timing_stats`.`exponential_average_bucket_processing_time_per_hour_ms`:::
(double) Exponentially-weighted moving average of bucket processing times
calculated in a 1 hour time window.
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-exponential-average-hour]
`timing_stats`.`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
`timing_stats`.`maximum_bucket_processing_time_ms`:::
(double) Maximum among all bucket processing times in milliseconds.
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-maximum]
`timing_stats`.`minimum_bucket_processing_time_ms`:::
(double) Minimum among all bucket processing times in milliseconds.
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-minimum]
`timing_stats`.`total_bucket_processing_time_ms`:::
(double) Sum of all bucket processing times in milliseconds.
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-time-total]
[[ml-get-job-stats-response-codes]]
==== {api-response-codes-title}

273
docs/reference/ml/ml-shared.asciidoc
View File

@ -136,9 +136,14 @@ tag::analyzed-fields-includes[]
An array of strings that defines the fields that will be included in the analysis.
end::analyzed-fields-includes[]
tag::assignment-explanation[]
tag::assignment-explanation-anomaly-jobs[]
For open {anomaly-jobs} only, contains messages relating to the selection
of a node to run the job.
end::assignment-explanation-anomaly-jobs[]
tag::assignment-explanation-datafeeds[]
For started {dfeeds} only, contains messages relating to the selection of a node.
end::assignment-explanation[]
end::assignment-explanation-datafeeds[]
tag::assignment-explanation-dfanalytics[]
Contains messages relating to the selection of a node.
@ -157,10 +162,20 @@ so do not set the `background_persist_interval` value too low.
--
end::background-persist-interval[]
tag::bucket-allocation-failures-count[]
The number of buckets for which new entities in incoming data were not processed
due to insufficient model memory. This situation is also signified by a
`hard_limit: memory_status` property value.
end::bucket-allocation-failures-count[]
tag::bucket-count[]
The number of buckets processed.
end::bucket-count[]
tag::bucket-count-anomaly-jobs[]
The number of bucket results produced by the job.
end::bucket-count-anomaly-jobs[]
tag::bucket-span[]
The size of the interval that the analysis is aggregated into, typically between
`5m` and `1h`. The default value is `5m`. If the {anomaly-job} uses a {dfeed}
@ -174,6 +189,27 @@ The length of the bucket in seconds. This value matches the `bucket_span`
that is specified in the job.
end::bucket-span-results[]
tag::bucket-time-exponential-average[]
Exponential moving average of all bucket processing times, in milliseconds.
end::bucket-time-exponential-average[]
tag::bucket-time-exponential-average-hour[]
Exponentially-weighted moving average of bucket processing times
calculated in a 1 hour time window, in milliseconds.
end::bucket-time-exponential-average-hour[]
tag::bucket-time-maximum[]
Maximum among all bucket processing times, in milliseconds.
end::bucket-time-maximum[]
tag::bucket-time-minimum[]
Minimum among all bucket processing times, in milliseconds.
end::bucket-time-minimum[]
tag::bucket-time-total[]
Sum of all bucket processing times, in milliseconds.
end::bucket-time-total[]
tag::by-field-name[]
The field used to split the data. In particular, this property is used for
analyzing the splits with respect to their own history. It is used for finding
@ -251,6 +287,24 @@ customize the tokenizer or post-tokenization filtering, use the
`pattern_replace` character filters. The effect is exactly the same.
end::categorization-filters[]
tag::categorization-status[]
The status of categorization for the job. Contains one of the following values:
+
--
* `ok`: Categorization is performing acceptably well (or not being used at all).
* `warn`: Categorization is detecting a distribution of categories that suggests
the input data is inappropriate for categorization. Problems could be that there
is only one category, more than 90% of categories are rare, the number of
categories is greater than 50% of the number of categorized documents, there are
no frequently matched categories, or more than 50% of categories are dead.
--
end::categorization-status[]
tag::categorized-doc-count[]
The number of documents that have had a field categorized.
end::categorized-doc-count[]
tag::char-filter[]
One or more <<analysis-charfilters,character filters>>. In addition to the
built-in character filters, other plugins can provide more character filters.
@ -482,6 +536,13 @@ Identifier for the {dfeed}. It can be a {dfeed} identifier or a wildcard
expression.
end::datafeed-id-wildcard[]
tag::dead-category-count[]
The number of categories created by categorization that will never be assigned
again because another category's definition makes it a superset of the dead
category. (Dead categories are a side effect of the way categorization has no
prior training.)
end::dead-category-count[]
tag::decompress-definition[]
Specifies whether the included model definition should be returned as a JSON map
(`true`) or in a custom compressed format (`false`). Defaults to `true`.
@ -562,6 +623,17 @@ A unique identifier for the detector. This identifier is based on the order of
the detectors in the `analysis_config`, starting at zero.
end::detector-index[]
tag::earliest-record-timestamp[]
The timestamp of the earliest chronologically input document.
end::earliest-record-timestamp[]
tag::empty-bucket-count[]
The number of buckets which did not contain any data. If your data
contains many empty buckets, consider increasing your `bucket_span` or using
functions that are tolerant to gaps in data such as `mean`, `non_null_sum` or
`non_zero_count`.
end::empty-bucket-count[]
tag::eta[]
Advanced configuration option. The shrinkage applied to the weights. Smaller
values result in larger forests which have better generalization error. However,
@ -628,6 +700,11 @@ tag::filter-id[]
A string that uniquely identifies a filter.
end::filter-id[]
tag::forecast-total[]
The number of individual forecasts currently available for the job. A value of
`1` or more indicates that forecasts exist.
end::forecast-total[]
tag::frequency[]
The interval at which scheduled queries are made while the {dfeed} runs in real
time. The default value is either the bucket span for short bucket spans, or,
@ -638,6 +715,10 @@ bucket results. If the {dfeed} uses aggregations, this value must be divisible
by the interval of the date histogram aggregation.
end::frequency[]
tag::frequent-category-count[]
The number of categories that match more than 1% of categorized documents.
end::frequent-category-count[]
tag::from[]
Skips the specified number of {dfanalytics-jobs}. The default value is `0`.
end::from[]
@ -698,6 +779,26 @@ is available as part of the input data. When you use multiple detectors, the use
of influencers is recommended as it aggregates results for each influencer entity.
end::influencers[]
tag::input-bytes[]
The number of bytes of input data posted to the {anomaly-job}.
end::input-bytes[]
tag::input-field-count[]
The total number of fields in input documents posted to the {anomaly-job}. This
count includes fields that are not used in the analysis. However, be aware that
if you are using a {dfeed}, it extracts only the required fields from the
documents it retrieves before posting them to the job.
end::input-field-count[]
tag::input-record-count[]
The number of input documents posted to the {anomaly-job}.
end::input-record-count[]
tag::invalid-date-count[]
The number of input documents with either a missing date field or a date that
could not be parsed.
end::invalid-date-count[]
tag::is-interim[]
If `true`, this is an interim result. In other words, the results are calculated
based on partial input data.
@ -768,6 +869,10 @@ relevant relationships between the features and the {depvar}. The smaller this
parameter the larger individual trees will be and the longer train will take.
end::lambda[]
tag::last-data-time[]
The timestamp at which data was last analyzed, according to server time.
end::last-data-time[]
tag::latency[]
The size of the window in which to expect data that is out of time order. The
default value is 0 (no latency). If you specify a non-zero value, it must be
@ -781,6 +886,18 @@ the <<ml-post-data,post data>> API.
--
end::latency[]
tag::latest-empty-bucket-timestamp[]
The timestamp of the last bucket that did not contain any data.
end::latest-empty-bucket-timestamp[]
tag::latest-record-timestamp[]
The timestamp of the latest chronologically input document.
end::latest-record-timestamp[]
tag::latest-sparse-record-timestamp[]
The timestamp of the last bucket that was considered sparse.
end::latest-sparse-record-timestamp[]
tag::max-empty-searches[]
If a real-time {dfeed} has never seen any data (including during any initial
training period) then it will automatically stop itself and close its associated
@ -818,6 +935,19 @@ ensemble method. Available methods are `lof`, `ldof`, `distance_kth_nn`,
`distance_knn`.
end::method[]
tag::missing-field-count[]
The number of input documents that are missing a field that the {anomaly-job} is
configured to analyze. Input documents with missing fields are still processed
because it is possible that not all fields are missing.
+
--
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.
--
end::missing-field-count[]
tag::mode[]
There are three available modes:
+
@ -829,6 +959,17 @@ recommended value.
--
end::mode[]
tag::model-bytes[]
The number of bytes of memory used by the models. This is the maximum value
since the last time the model was persisted. If the job is closed, this value
indicates the latest size.
end::model-bytes[]
tag::model-bytes-exceeded[]
The number of bytes over the high limit for memory usage at the last allocation
failure.
end::model-bytes-exceeded[]
tag::model-id[]
The unique identifier of the trained {infer} model.
end::model-id[]
@ -858,6 +999,10 @@ see <<ml-settings>>.
--
end::model-memory-limit[]
tag::model-memory-limit-anomaly-jobs[]
The upper limit for model memory usage, checked on increasing values.
end::model-memory-limit-anomaly-jobs[]
tag::model-memory-limit-dfa[]
The approximate maximum amount of memory resources that are permitted for
analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If
@ -867,6 +1012,19 @@ setting, an error occurs when you try to create {dfanalytics-jobs} that have
<<ml-settings>>.
end::model-memory-limit-dfa[]
tag::model-memory-status[]
The status of the mathematical models, which can have one of the following
values:
+
--
* `ok`: The models stayed below the configured value.
* `soft_limit`: The models used more than 60% of the configured memory limit
and older unused models will be pruned to free up space.
* `hard_limit`: The models used more space than the configured memory limit.
As a result, not all incoming data was processed.
--
end::model-memory-status[]
tag::model-plot-config[]
This advanced configuration option stores model information along with the
results. It provides a more detailed view into {anomaly-detect}.
@ -904,6 +1062,10 @@ The default value is `1`, which means snapshots that are one day (twenty-four ho
older than the newest snapshot are deleted.
end::model-snapshot-retention-days[]
tag::model-timestamp[]
The timestamp of the last record when the model stats were gathered.
end::model-timestamp[]
tag::multivariate-by-fields[]
This functionality is reserved for internal use. It is not supported for use in
customer environments and is not subject to the support SLA of official GA
@ -934,10 +1096,27 @@ improve diversity in the ensemble. Therefore, only override this if you are
confident that the value you choose is appropriate for the data set.
end::n-neighbors[]
tag::node[]
tag::node-address[]
The network address of the node.
end::node-address[]
tag::node-datafeeds[]
For started {dfeeds} only, this information pertains to the node upon which the
{dfeed} is started.
end::node[]
end::node-datafeeds[]
tag::node-ephemeral-id[]
The ephemeral ID of the node.
end::node-ephemeral-id[]
tag::node-id[]
The unique identifier of the node.
end::node-id[]
tag::node-jobs[]
Contains properties for the node that runs the job. This information is
available only for open jobs.
end::node-jobs[]
tag::num-top-classes[]
Defines the number of categories for which the predicted
@ -946,12 +1125,17 @@ total number of categories (in the {version} version of the {stack}, it's two)
to predict then we will report all category probabilities. Defaults to 2.
end::num-top-classes[]
tag::over-field-name[]
The field used to split the data. In particular, this property is used for
analyzing the splits with respect to the history of all splits. It is used for
finding unusual values in the population of all splits. For more information,
see {ml-docs}/ml-configuring-pop.html[Performing population analysis].
end::over-field-name[]
tag::open-time[]
For open jobs only, the elapsed time for which the job has been open.
end::open-time[]
tag::out-of-order-timestamp-count[]
The number of input documents that are out of time sequence and outside
of the latency window. This information is applicable only when you provide data
to the {anomaly-job} by using the <<ml-post-data,post data API>>. These out of
order documents are discarded, since jobs require time series data to be in
ascending chronological order.
end::out-of-order-timestamp-count[]
tag::outlier-fraction[]
Sets the proportion of the data set that is assumed to be outlying prior to
@ -959,6 +1143,13 @@ Sets the proportion of the data set that is assumed to be outlying prior to
outliers and 95% are inliers.
end::outlier-fraction[]
tag::over-field-name[]
The field used to split the data. In particular, this property is used for
analyzing the splits with respect to the history of all splits. It is used for
finding unusual values in the population of all splits. For more information,
see {ml-docs}/ml-configuring-pop.html[Performing population analysis].
end::over-field-name[]
tag::partition-field-name[]
The field used to segment the analysis. When you use this property, you have
completely independent baselines for each value of this field.
@ -969,6 +1160,20 @@ Defines the name of the prediction field in the results.
Defaults to `<dependent_variable>_prediction`.
end::prediction-field-name[]
tag::processed-field-count[]
The total number of fields in all the documents that have been processed by the
{anomaly-job}. Only fields that are specified in the detector configuration
object contribute to this count. The timestamp is not included in this count.
end::processed-field-count[]
tag::processed-record-count[]
The number of input documents that have been processed by the {anomaly-job}.
This value includes documents with missing fields, since they are nonetheless
analyzed. If you use {dfeeds} and have aggregations in your search query, the
`processed_record_count` is the number of aggregation results processed, not the
number of {es} documents.
end::processed-record-count[]
tag::query[]
The {es} query domain-specific language (DSL). This value corresponds to the
query object in an {es} search POST body. All the options that are supported by
@ -993,6 +1198,10 @@ assuming other related parameters (e.g. `source`, `analyzed_fields`, etc.) are
the same.
end::randomize-seed[]
tag::rare-category-count[]
The number of categories that match just one categorized document.
end::rare-category-count[]
tag::renormalization-window-days[]
Advanced configuration option. The period over which adjustments to the score
are applied, as new data is seen. The default value is the longer of 30 days or
@ -1086,6 +1295,12 @@ The configuration of how to source the analysis data. It requires an
excluded from the destination.
end::source-put-dfa[]
tag::sparse-bucket-count[]
The number of buckets that contained few data points compared to the expected
number of data points. If your data contains many sparse buckets, consider using
a longer `bucket_span`.
end::sparse-bucket-count[]
tag::standardization-enabled[]
If `true`, then the following operation is performed on the columns before
computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to `true`. For
@ -1093,6 +1308,25 @@ more information, see
https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
end::standardization-enabled[]
tag::state-anomaly-job[]
The status of the {anomaly-job}, which can be one of the following values:
+
--
* `closed`: The job finished successfully with its model state persisted. The
job must be opened before it can accept further data.
* `closing`: The job close action is in progress and has not yet completed. 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, a fatal error occurring during the analysis,
or an external interaction such as the process being killed by the Linux out of
memory (OOM) killer. If the job had irrevocably failed, it must be force closed
and then deleted. If the {dfeed} can be corrected, the job can be closed and
then re-opened.
* `opened`: The job is available to receive and process data.
* `opening`: The job open action is in progress and has not yet completed.
--
end::state-anomaly-job[]
tag::state-datafeed[]
The status of the {dfeed}, which can be one of the following values:
+
@ -1168,6 +1402,25 @@ that tokenizer but change the character or token filters, specify
`"tokenizer": "ml_classic"` in your `categorization_analyzer`.
end::tokenizer[]
tag::total-by-field-count[]
The number of `by` field values that were analyzed by the models. This value is
cumulative for all detectors in the job.
end::total-by-field-count[]
tag::total-category-count[]
The number of categories created by categorization.
end::total-category-count[]
tag::total-over-field-count[]
The number of `over` field values that were analyzed by the models. This value
is cumulative for all detectors in the job.
end::total-over-field-count[]
tag::total-partition-field-count[]
The number of `partition` field values that were analyzed by the models. This
value is cumulative for all detectors in the job.
end::total-partition-field-count[]
tag::training-percent[]
Defines what percentage of the eligible documents that will
be used for training. Documents that are ignored by the analysis (for example

4
x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/rest/cat/RestCatDatafeedsAction.java
View File

@ -82,9 +82,9 @@ public class RestCatDatafeedsAction extends AbstractCatAction {
.build());
// Timing stats
table.addCell("bucket.count",
table.addCell("buckets.count",
TableColumnAttributeBuilder.builder("bucket count")
.setAliases("bc", "bucketCount")
.setAliases("bc", "bucketsCount")
.build());
table.addCell("search.count",
TableColumnAttributeBuilder.builder("number of searches ran by the datafeed")

76
x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/rest/cat/RestCatJobsAction.java
View File

@ -97,7 +97,7 @@ public class RestCatJobsAction extends AbstractCatAction {
.build());
table.addCell("data.processed_fields",
TableColumnAttributeBuilder.builder("number of processed fields", false)
.setAliases("dpr", "dataProcessedFields")
.setAliases("dpf", "dataProcessedFields")
.build());
table.addCell("data.input_bytes",
TableColumnAttributeBuilder.builder("total input bytes", false)
@ -223,55 +223,55 @@ public class RestCatJobsAction extends AbstractCatAction {
.build());
// Forecast Stats
table.addCell("forecast." + ForecastStats.Fields.TOTAL,
TableColumnAttributeBuilder.builder("total number of forecasts").setAliases("ft", "forecastTotal").build());
table.addCell("forecast.memory.min",
table.addCell("forecasts." + ForecastStats.Fields.TOTAL,
TableColumnAttributeBuilder.builder("total number of forecasts").setAliases("ft", "forecastsTotal").build());
table.addCell("forecasts.memory.min",
TableColumnAttributeBuilder.builder("minimum memory used by forecasts", false)
.setAliases("fmmin", "forecastMemoryMin")
.setAliases("fmmin", "forecastsMemoryMin")
.build());
table.addCell("forecast.memory.max",
table.addCell("forecasts.memory.max",
TableColumnAttributeBuilder.builder("maximum memory used by forecasts", false)
.setAliases("fmmax", "forecastsMemoryMax")
.build());
table.addCell("forecast.memory.avg",
table.addCell("forecasts.memory.avg",
TableColumnAttributeBuilder.builder("average memory used by forecasts", false)
.setAliases("fmavg", "forecastMemoryAvg")
.setAliases("fmavg", "forecastsMemoryAvg")
.build());
table.addCell("forecast.memory.total",
table.addCell("forecasts.memory.total",
TableColumnAttributeBuilder.builder("total memory used by all forecasts", false)
.setAliases("fmt", "forecastMemoryTotal")
.setAliases("fmt", "forecastsMemoryTotal")
.build());
table.addCell("forecast." + ForecastStats.Fields.RECORDS + ".min",
table.addCell("forecasts." + ForecastStats.Fields.RECORDS + ".min",
TableColumnAttributeBuilder.builder("minimum record count for forecasts", false)
.setAliases("frmin", "forecastRecordsMin")
.setAliases("frmin", "forecastsRecordsMin")
.build());
table.addCell("forecast." + ForecastStats.Fields.RECORDS + ".max",
table.addCell("forecasts." + ForecastStats.Fields.RECORDS + ".max",
TableColumnAttributeBuilder.builder("maximum record count for forecasts", false)
.setAliases("frmax", "forecastRecordsMax")
.setAliases("frmax", "forecastsRecordsMax")
.build());
table.addCell("forecast." + ForecastStats.Fields.RECORDS + ".avg",
table.addCell("forecasts." + ForecastStats.Fields.RECORDS + ".avg",
TableColumnAttributeBuilder.builder("average record count for forecasts", false)
.setAliases("fravg", "forecastRecordsAvg")
.setAliases("fravg", "forecastsRecordsAvg")
.build());
table.addCell("forecast." + ForecastStats.Fields.RECORDS + ".total",
table.addCell("forecasts." + ForecastStats.Fields.RECORDS + ".total",
TableColumnAttributeBuilder.builder("total record count for all forecasts", false)
.setAliases("frt", "forecastRecordsTotal")
.setAliases("frt", "forecastsRecordsTotal")
.build());
table.addCell("forecast.time.min",
table.addCell("forecasts.time.min",
TableColumnAttributeBuilder.builder("minimum runtime for forecasts", false)
.setAliases("ftmin", "forecastTimeMin")
.setAliases("ftmin", "forecastsTimeMin")
.build());
table.addCell("forecast.time.max",
table.addCell("forecasts.time.max",
TableColumnAttributeBuilder.builder("maximum run time for forecasts", false)
.setAliases("ftmax", "forecastTimeMax")
.setAliases("ftmax", "forecastsTimeMax")
.build());
table.addCell("forecast.time.avg",
table.addCell("forecasts.time.avg",
TableColumnAttributeBuilder.builder("average runtime for all forecasts (milliseconds)", false)
.setAliases("ftavg", "forecastTimeAvg")
.setAliases("ftavg", "forecastsTimeAvg")
.build());
table.addCell("forecast.time.total",
table.addCell("forecasts.time.total",
TableColumnAttributeBuilder.builder("total runtime for all forecasts", false)
.setAliases("ftt", "forecastTimeTotal").build());
.setAliases("ftt", "forecastsTimeTotal").build());
//Node info
table.addCell("node.id",
@ -292,29 +292,29 @@ public class RestCatJobsAction extends AbstractCatAction {
.build());
//Timing Stats
table.addCell("bucket.count",
table.addCell("buckets.count",
TableColumnAttributeBuilder.builder("bucket count")
.setAliases("bc", "bucketCount")
.setAliases("bc", "bucketsCount")
.build());
table.addCell("bucket.time.total",
table.addCell("buckets.time.total",
TableColumnAttributeBuilder.builder("total bucket processing time", false)
.setAliases("btt", "bucketTimeTotal")
.setAliases("btt", "bucketsTimeTotal")
.build());
table.addCell("bucket.time.min",
table.addCell("buckets.time.min",
TableColumnAttributeBuilder.builder("minimum bucket processing time", false)
.setAliases("btmin", "bucketTimeMin")
.setAliases("btmin", "bucketsTimeMin")
.build());
table.addCell("bucket.time.max",
table.addCell("buckets.time.max",
TableColumnAttributeBuilder.builder("maximum bucket processing time", false)
.setAliases("btmax", "bucketTimeMax")
.setAliases("btmax", "bucketsTimeMax")
.build());
table.addCell("bucket.time.exp_avg",
table.addCell("buckets.time.exp_avg",
TableColumnAttributeBuilder.builder("exponential average bucket processing time (milliseconds)", false)
.setAliases("btea", "bucketTimeExpAvg")
.setAliases("btea", "bucketsTimeExpAvg")
.build());
table.addCell("bucket.time.exp_avg_hour",
table.addCell("buckets.time.exp_avg_hour",
TableColumnAttributeBuilder.builder("exponential average bucket processing time by hour (milliseconds)", false)
.setAliases("bteah", "bucketTimeExpAvgHour")
.setAliases("bteah", "bucketsTimeExpAvgHour")
.build());
table.endHeaders();

2
x-pack/plugin/src/test/resources/rest-api-spec/api/cat.ml_jobs.json
View File

@ -1,7 +1,7 @@
{
"cat.ml_jobs":{
"documentation":{
"url":"http://www.elastic.co/guide/en/elasticsearch/reference/current/ml-get-job-stats.html"
"url":"http://www.elastic.co/guide/en/elasticsearch/reference/current/cat-anomaly-detectors.html"
},
"stability":"stable",
"url":{

4
x-pack/plugin/src/test/resources/rest-api-spec/test/ml/datafeed_cat_apis.yml
View File

@ -86,7 +86,7 @@ setup:
datafeed_id: datafeed-job-stats-test
- match:
$body: |
/ #id state bucket.count search.count
/ #id state buckets.count search.count
^ (datafeed\-job\-stats\-test \s+ \w+ \s+ \d+ \s+ \d+ \n)+ $/
- do:
@ -95,7 +95,7 @@ setup:
datafeed_id: datafeed-job-stats-test
- match:
$body: |
/^ id \s+ state \s+ bucket\.count \s+ search\.count \n
/^ id \s+ state \s+ buckets\.count \s+ search\.count \n
(datafeed\-job\-stats\-test \s+ \w+ \s+ \d+ \s+ \d+ \n)+ $/
- do:

4
x-pack/plugin/src/test/resources/rest-api-spec/test/ml/job_cat_apis.yml
View File

@ -90,7 +90,7 @@ setup:
job_id: job-stats-test
- match:
$body: |
/ #id state data.processed_records model.bytes model.memory_status forecast.total bucket.count
/ #id state data.processed_records model.bytes model.memory_status forecasts.total buckets.count
^ (job\-stats\-test \s+ \w+ \s+ \d+ \s+ .*? \s+ \w+ \s+ \d+ \s+ \d+ \n)+ $/
- do:
@ -99,7 +99,7 @@ setup:
job_id: job-stats-test
- match:
$body: |
/^ id \s+ state \s+ data\.processed_records \s+ model\.bytes \s+ model\.memory_status \s+ forecast\.total \s+ bucket\.count \n
/^ id \s+ state \s+ data\.processed_records \s+ model\.bytes \s+ model\.memory_status \s+ forecasts\.total \s+ buckets\.count \n
(job\-stats\-test \s+ \w+ \s+ \d+ \s+ .*? \s+ \w+ \s+ \d+ \s+ \d+ \n)+ $/
- do: