[DOCS] Review of API docs part 1 (elastic/x-pack-elasticsearch#1118)
* [DOCS] Review of close job and job stats * [DOCS] Add force close * [DOCS] Remove invalid params from get records * [DOCS] Remove invalid params from get buckets * [DOCS] Job resource corrections Original commit: elastic/x-pack-elasticsearch@bc68d05097
This commit is contained in:
Sophie Chang
lcawley
parent
7ee48846ec
commit
e2cc00ab8e
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@ -91,7 +91,7 @@ include::ml/get-record.asciidoc[]
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* <<ml-datafeed-resource,Data feeds>>
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* <<ml-datafeed-counts,Data feed counts>>
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* <<ml-job-resource,Jobs>>
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* <<ml-jobcounts,Job counts>>
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* <<ml-jobstats,Job Stats>>
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* <<ml-snapshot-resource,Model snapshots>>
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* <<ml-results-resource,Results>>
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@ -5,6 +5,9 @@
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The close job API enables you to close a job.
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A job can be opened and closed multiple times throughout its lifecycle.
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A closed job cannot receive data or perform analysis
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operations, but you can still explore and navigate results.
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===== Request
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`POST _xpack/ml/anomaly_detectors/<job_id>/_close`
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@ -18,14 +21,15 @@ flushing buffers, calculating final results and persisting the model snapshots.
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Depending upon the size of the job, it could take several minutes to close and
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the equivalent time to re-open.
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After it is closed, the job has almost no overhead on the cluster except for
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maintaining its meta data. A closed job cannot receive data or perform analysis
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operations, but you can still explore and navigate results.
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After it is closed, the job has a minimal overhead on the cluster except for
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maintaining its meta data.
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Therefore it is best practice to close jobs that are no longer required to process data.
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When a datafeed that has a specified end date stops, it will automatically close the job.
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You must have `manage_ml`, or `manage` cluster privileges to use this API.
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For more information, see <<privileges-list-cluster>>.
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//NOTE: TBD
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//OUTDATED?: If using the {prelert} UI, the job will be automatically closed when stopping a datafeed job.
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===== Path Parameters
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@ -35,9 +39,14 @@ For more information, see <<privileges-list-cluster>>.
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===== Query Parameters
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`close_timeout`::
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(time) Controls the time to wait until a job has closed.
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(time units) Controls the time to wait until a job has closed.
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The default value is 30 minutes.
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`force`::
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(boolean) Use to close a failed job, or to forcefully close a job which has not
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responded to its initial close request.
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////
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===== Responses
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@ -29,7 +29,7 @@ The API returns the following information:
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`jobs`::
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(array) An array of job count objects.
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For more information, see <<ml-jobcounts,Job Counts>>.
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For more information, see <<ml-jobstats,Job Stats>>.
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////
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===== Responses
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@ -27,10 +27,6 @@ privileges to use this API. For more information, see <<privileges-list-cluster>
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`end`::
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(string) Returns records with timestamps earlier than this time.
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`expand`::
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(boolean) TBD
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//This field did not work on older build.
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`from`::
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(integer) Skips the specified number of records.
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@ -1,12 +1,12 @@
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//lcawley Verified example output 2017-04-11
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[[ml-jobcounts]]
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==== Job Counts
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[[ml-jobstats]]
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==== Job Stats
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The get job statistics API provides information about the operational
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progress of a job.
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NOTE: Job count values are cumulative for the lifetime of a job. If a model snapshot is reverted
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or old results are deleted, the job counts are not reset.
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`assignment_explanation`::
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(string) For open jobs only, contains messages relating to the selection of an executing node.
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`data_counts`::
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(object) An object that describes the number of records processed and any related error counts.
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@ -19,31 +19,36 @@ or old results are deleted, the job counts are not reset.
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(object) An object that provides information about the size and contents of the model.
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See <<ml-modelsizestats,model size stats objects>>
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`node`::
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(object) For open jobs only, contains information about the executing node.
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See <<ml-stats-node,node object>>.
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`open_time`::
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(string) For open jobs only, the elapsed time for which the job has been open.
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E.g. `28746386s`.
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`state`::
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(string) The status of the job, which can be one of the following values:
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`closed`::: The job finished successfully with its model state persisted.
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The job is still available to accept further data. +
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+
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--
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NOTE: If you send data in a periodic cycle and close the job at the end of
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each transaction, the job is marked as closed in the intervals between
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when data is sent. For example, if data is sent every minute and it takes
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1 second to process, the job has a closed state for 59 seconds.
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--
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`closing`::: TBD. The job is in the process of closing?
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`open`::: The job is available to receive and process data.
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`closed`::: The job finished successfully with its model state persisted.
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The job must be opened before it can accept further data.
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`closing`::: The job close action is in progress and has not yet completed.
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A closing job cannot accept further data.
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`failed`::: The job did not finish successfully due to an error.
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This situation can occur due to invalid input data. In this case,
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sending corrected data to a failed job re-opens the job and
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resets it to an open state.
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`open`::: The job is actively receiving and processing data.
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This situation can occur due to invalid input data.
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If the job had irrevecobly failed, it must be force closed and then deleted.
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If the datafeed can be corrected, the job can be closed and then re-opened.
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[float]
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[[ml-datacounts]]
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===== Data Counts Objects
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The `data_counts` object describes the number of records processed
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and any related error counts. It has the following properties:
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and any related error counts.
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The `data_count` values are cumulative for the lifetime of a job. If a model snapshot is reverted
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or old results are deleted, the job counts are not reset.
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`bucket_count`::
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(long) The number of bucket results produced by the job.
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@ -53,7 +58,9 @@ and any related error counts. It has the following properties:
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The datetime string is in ISO 8601 format.
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`empty_bucket_count`::
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() TBD
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(long) The number of buckets which did not contain any data. If your data contains many
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empty buckets, consider increasing your `bucket_span` or using functions that are tolerant
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to gaps in data such as `mean`, `non_null_sum` or `non_zero_count`.
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`input_bytes`::
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(long) The number of raw bytes read by the job.
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@ -72,16 +79,16 @@ and any related error counts. It has the following properties:
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(string) A numerical character string that uniquely identifies the job.
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`last_data_time`::
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() TBD
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(datetime) The timestamp at which data was last analyzed, according to server time.
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`latest_empty_bucket_timestamp`::
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(date) The timestamp of the last bucket that did not contain any data.
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`latest_record_timestamp`::
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(string) The timestamp of the last chronologically ordered record.
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If the records are not in strict chronological order, this value might not be
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the same as the timestamp of the last record.
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The datetime string is in ISO 8601 format.
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(date) The timestamp of the last processed record.
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`latest_sparse_bucket_timestamp`::
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() TBD
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(date) The timestamp of the last bucket that was considered sparse.
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`missing_field_count`::
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(long) The number of records that are missing a field that the job is configured to analyze.
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@ -97,6 +104,7 @@ necessarily a cause for concern.
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`out_of_order_timestamp_count`::
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(long) The number of records that are out of time sequence and outside of the latency window.
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This is only applicable when using the `_data` endpoint.
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These records are discarded, since jobs require time series data to be in ascending chronological order.
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`processed_field_count`::
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@ -108,13 +116,16 @@ necessarily a cause for concern.
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(long) The number of records that have been processed by the job.
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This value includes records with missing fields, since they are nonetheless analyzed.
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+
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The following records are not processed:
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When using datafeeds, the `processed_record_count` will differ from the `input_record_count`
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if you are using aggregations in your search query.
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+
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When posting to the `/_data` endpoint, the following records are not processed:
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* Records not in chronological order and outside the latency window
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* Records with invalid timestamp
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* Records filtered by an exclude transform
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`sparse_bucket_count`::
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() TBD
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(long) The number of buckets which contained few data points compared to the expected number
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of data points. If your data contains many sparse buckets, consider using a longer `bucket_span`.
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[float]
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[[ml-modelsizestats]]
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@ -123,13 +134,14 @@ necessarily a cause for concern.
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The `model_size_stats` object has the following properties:
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`bucket_allocation_failures_count`::
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() TBD
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(long) The number of buckets for which new entites in incoming data was not processed due to a
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insufficient model memory as signified by a `hard_limit` `memory_status`.
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`job_id`::
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(string) A numerical character string that uniquely identifies the job.
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`log_time`::
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() TBD
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(date) The timestamp of the `model_size_stats` according to server time.
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`memory_status`::
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(string) The status of the mathematical models. This property can have one of the following values:
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@ -142,24 +154,46 @@ The `model_size_stats` object has the following properties:
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last time the model was persisted. If the job is closed, this value indicates the latest size.
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`result_type`::
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TBD
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(string) For internal use. The type of result.
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`total_by_field_count`::
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(long) The number of `by` field values that were analyzed by the models. +
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(long) The number of `by` field values that were analyzed by the models.
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+
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--
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NOTE: The `by` field values are counted separately for each detector and partition.
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--
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`total_over_field_count`::
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(long) The number of `over` field values that were analyzed by the models. +
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(long) The number of `over` field values that were analyzed by the models.
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+
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--
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NOTE: The `over` field values are counted separately for each detector and partition.
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--
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`total_partition_field_count`::
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(long) The number of `partition` field values that were analyzed by the models.
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`timestamp`::
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TBD
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(date) The timestamp of the `model_size_stats` according to the timestamp of the data.
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[float]
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[[ml-stats-node]]
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===== Node Objects
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The `node` objects contains properties of the executing node and is only available for open jobs.
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`id`::
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(string) The unique identifier of the executing node.
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`name`::
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(string) The node's name.
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`ephemeral_id`::
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`transport_address`::
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(string) Host and port where transport HTTP connections are accepted.
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`attributes`::
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(object) {ml} attributes.
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`max_running_jobs`::: The maximum number of concurrently open jobs allowed per node.
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@ -5,10 +5,11 @@
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A job resource has the following properties:
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`analysis_config`::
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(object) The analysis configuration, which specifies how to analyze the data. See <<ml-analysisconfig, analysis configuration objects>>.
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(object) The analysis configuration, which specifies how to analyze the data.
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See <<ml-analysisconfig, analysis configuration objects>>.
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`analysis_limits`::
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(object) Defines limits on the number of field values and time buckets to be analyzed.
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(object) Defines approximate limits on the memory resource requirements for the job.
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See <<ml-apilimits,analysis limits>>.
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`create_time`::
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@ -21,17 +22,17 @@ A job resource has the following properties:
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(string) An optional description of the job.
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`finished_time`::
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(string) If the job closed of failed, this is the time the job finished, in ISO 8601 format.
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Otherwise, it is `null`. For example, `1491007365347`.
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(string) If the job closed or failed, this is the time the job finished, otherwise it is `null`.
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`job_id`::
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(string) A numerical character string that uniquely identifies the job.
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(string) The unique identifier for the job.
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`job_type`::
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(string) TBD. For example: "anomaly_detector".
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(string) Reserved for future use, currently set to `anomaly_detector`.
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`model_plot_config`:: TBD
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`enabled`:: TBD. For example, `true`.
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`model_plot_config`::
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(object) Configuration properties for storing additional model information.
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See <<ml-apimodelplotconfig, model plot configuration>>.
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`model_snapshot_id`::
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(string) A numerical character string that uniquely identifies the model
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@ -42,7 +43,8 @@ A job resource has the following properties:
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Older snapshots are deleted. The default value is 1 day.
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`results_index_name`::
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() TBD. For example, `shared`.
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(string) The name of the index in which to store the results generated by {ml} results.
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The default value is `shared` which corresponds to the index name `.ml-anomalies-shared`
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[[ml-analysisconfig]]
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===== Analysis Configuration Objects
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@ -50,8 +52,8 @@ A job resource has the following properties:
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An analysis configuration object has the following properties:
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`bucket_span` (required)::
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(unsigned integer) The size of the interval that the analysis is aggregated into, measured in seconds. The default value is 5 minutes.
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//TBD: Is this now measured in minutes?
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(time units) The size of the interval that the analysis is aggregated into, typically between `5m` and `1h`.
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The default value is `5m`.
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`categorization_field_name`::
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(string) If not null, the values of the specified field will be categorized.
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@ -84,9 +86,9 @@ and an error is returned.
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the use of influencers is recommended as it aggregates results for each influencer entity.
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`latency`::
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(unsigned integer) The size of the window, in seconds, in which to expect data that is out of time order. The default value is 0 milliseconds (no latency). +
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+
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--
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(unsigned integer) The size of the window, in seconds, in which to expect data that is out of time order.
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The default value is 0 (no latency).
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NOTE: Latency is only applicable when you send data by using the <<ml-post-data, Post Data to Jobs>> API.
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--
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@ -103,10 +105,10 @@ NOTE: Latency is only applicable when you send data by using the <<ml-post-data,
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--
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NOTE: To use the `multivariate_by_fields` property, you must also specify `by_field_name` in your detector.
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--
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`overlapping_buckets`::
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(boolean) If set to `true`, an additional analysis occurs that runs out of phase by half a bucket length.
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This requires more system resources and enhances detection of anomalies that span bucket boundaries.
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// LEAVE UNDOCUMENTED
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// `overlapping_buckets`::
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// (boolean) If set to `true`, an additional analysis occurs that runs out of phase by half a bucket length.
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// This requires more system resources and enhances detection of anomalies that span bucket boundaries.
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`summary_count_field_name`::
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(string) If not null, the data fed to the job is expected to be pre-summarized.
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@ -115,10 +117,11 @@ NOTE: To use the `multivariate_by_fields` property, you must also specify `by_fi
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+
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--
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NOTE: The `summary_count_field_name` property cannot be used with the `metric` function.
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--
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`use_per_partition_normalization`::
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() TBD
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// LEAVE UNDOCUMENTED
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// `use_per_partition_normalization`::
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// () TBD
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[[ml-detectorconfig]]
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===== Detector Configuration Objects
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@ -134,10 +137,11 @@ Each detector has the following properties:
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It is used for finding unusual values in the context of the split.
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`detector_description`::
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(string) A description of the detector. For example, `low_sum(events_per_min)`.
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(string) A description of the detector. For example, `Low event rate`.
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`detector_rules`::
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(array) TBD
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// LEAVE UNDOCUMENTED
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// `detector_rules`::
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// (array) TBD
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`exclude_frequent`::
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(string) Contains one of the following values: `all`, `none`, `by`, or `over`.
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@ -152,19 +156,12 @@ Each detector has the following properties:
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+
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--
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NOTE: The `field_name` cannot contain double quotes or backslashes.
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--
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`function` (required)::
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(string) The analysis function that is used.
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For example, `count`, `rare`, `mean`, `min`, `max`, and `sum`.
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The default function is `metric`, which looks for anomalies in all of `min`, `max`,
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and `mean`. +
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+
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--
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NOTE: You cannot use the `metric` function with pre-summarized input. If `summary_count_field_name`
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is not null, you must specify a function other than `metric`.
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--
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`over_field_name`::
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(string) The field used to split the data.
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In particular, this property is used for analyzing the splits with respect to the history of all splits.
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@ -180,33 +177,21 @@ NOTE: You cannot use the `metric` function with pre-summarized input. If `summar
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+
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--
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IMPORTANT: Field names are case sensitive, for example a field named 'Bytes' is different to one named 'bytes'.
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--
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[[ml-datadescription]]
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===== Data Description Objects
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The data description settings define the format of the input data.
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When data is read from Elasticsearch, the datafeed must be configured.
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This defines which index data will be taken from, and over what time period.
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The data description define the format of the input data when posting time-ordered data to the `_data` endpoint.
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Please note that when configuring a datafeed, these are automatically set.
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When data is received via the <<ml-post-data, Post Data to Jobs>> API,
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you must specify the data format (for example, JSON or CSV). In this scenario,
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the data posted is not stored in Elasticsearch. Only the results for anomaly detection are retained.
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|
||||
When you create a job, by default it accepts data in tab-separated-values format and expects
|
||||
an Epoch time value in a field named `time`. The `time` field must be measured in seconds from the Epoch.
|
||||
If, however, your data is not in this format, you can provide a data description object that specifies the
|
||||
format of your data.
|
||||
|
||||
A data description object has the following properties:
|
||||
|
||||
`fieldDelimiter`::
|
||||
() TBD
|
||||
|
||||
`format`::
|
||||
() TBD
|
||||
(string) Only `JSON` format is supported at this time.
|
||||
|
||||
`time_field`::
|
||||
(string) The name of the field that contains the timestamp.
|
||||
|
|
@ -215,23 +200,22 @@ A data description object has the following properties:
|
|||
`time_format`::
|
||||
(string) The time format, which can be `epoch`, `epoch_ms`, or a custom pattern.
|
||||
The default value is `epoch`, which refers to UNIX or Epoch time (the number of seconds
|
||||
since 1 Jan 1970) and corresponds to the time_t type in C and C++.
|
||||
since 1 Jan 1970).
|
||||
The value `epoch_ms` indicates that time is measured in milliseconds since the epoch.
|
||||
The `epoch` and `epoch_ms` time formats accept either integer or real values. +
|
||||
+
|
||||
--
|
||||
NOTE: Custom patterns must conform to the Java `DateTimeFormatter` class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails.
|
||||
|
||||
--
|
||||
`quotecharacter`::
|
||||
() TBD
|
||||
|
||||
|
||||
[[ml-apilimits]]
|
||||
===== Analysis Limits
|
||||
|
||||
Limits can be applied for the size of the mathematical models that are held in memory.
|
||||
These limits can be set per job and do not control the memory used by other processes.
|
||||
If necessary, the limits can also be updated after the job is created.
|
||||
Limits can be applied for the resources required to hold the mathematical models in memory.
|
||||
These limits are approximate and can be set per job.
|
||||
They do not control the memory used by other processes, for example the elasticsearch Java processes.
|
||||
If necessary, the limits can be increased after the job is created.
|
||||
|
||||
The `analysis_limits` object has the following properties:
|
||||
|
||||
|
|
@ -241,10 +225,33 @@ The `analysis_limits` object has the following properties:
|
|||
more examples are available, however it requires that you have more storage available.
|
||||
If you set this value to `0`, no examples are stored.
|
||||
|
||||
////
|
||||
NOTE: The `categorization_examples_limit` only applies to analysis that uses categorization.
|
||||
////
|
||||
|
||||
`model_memory_limit`::
|
||||
(long) The maximum amount of memory, in MiB, that the mathematical models can use.
|
||||
Once this limit is approached, data pruning becomes more aggressive.
|
||||
Upon exceeding this limit, new entities are not modeled. The default value is 4096.
|
||||
|
||||
[[ml-apimodelplotconfig]]
|
||||
===== Model Plot Config
|
||||
|
||||
This advanced configuration option will store model information along with results allowing a more detailed view into anomaly detection.
|
||||
Enabling this can add considerable overhead to the performance of the system and is not feasible for jobs with many entities.
|
||||
|
||||
Model plot provides a simplified and indicative view of the model and its bounds.
|
||||
It does not display complex features such as multivariate correlations or multimodal data.
|
||||
As such, anomalies may occassionally be reported which cannot be seen in the model plot.
|
||||
|
||||
Model plot config can be configured when the job is created or updated later. It must be disabled if performance issues are experienced.
|
||||
|
||||
The `model_plot_config` object has the following properties:
|
||||
|
||||
`enabled`::
|
||||
(boolean) If true, will enable calculation and storage of the model bounds for each entity being analyzed.
|
||||
By default, this is not enabled.
|
||||
|
||||
`terms`::
|
||||
(string) Limits data collection to this comma separated list of _partition_ or _by_ field names.
|
||||
If terms are not specified or is an empty string, no filtering is applied.
|
||||
E.g. `"CPU,NetworkIn,DiskWrites"`
|
||||
|
||||
|
|
|
|||
|
|
@ -88,4 +88,4 @@ For example:
|
|||
}
|
||||
----
|
||||
|
||||
For more information about these properties, see <<ml-jobcounts,Job Counts>>.
|
||||
For more information about these properties, see <<ml-jobstats,Job Stats>>.
|
||||
|
|
|
|||
Loading…
Reference in New Issue