105 lines
3.6 KiB
Plaintext
105 lines
3.6 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[ml-snapshot-resource]]
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=== Model Snapshot Resources
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Model snapshots are saved to disk periodically.
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By default, this is occurs approximately every 3 hours to 4 hours and is
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configurable with the `background_persist_interval` property.
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By default, model snapshots are retained for one day (twenty-four hours). You
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can change this behavior by updating the `model_snapshot_retention_days` for the
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job. When choosing a new value, consider the following:
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* Persistence enables resilience in the event of a system failure.
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* Persistence enables snapshots to be reverted.
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* The time taken to persist a job is proportional to the size of the model in memory.
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A model snapshot resource has the following properties:
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`description`::
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(string) An optional description of the job.
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`job_id`::
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(string) A numerical character string that uniquely identifies the job that
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the snapshot was created for.
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`min_version`::
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(string) The minimum version required to be able to restore the model snapshot.
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`latest_record_time_stamp`::
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(date) The timestamp of the latest processed record.
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`latest_result_time_stamp`::
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(date) The timestamp of the latest bucket result.
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`model_size_stats`::
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(object) Summary information describing the model.
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See <<ml-snapshot-stats,Model Size Statistics>>.
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`retain`::
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(boolean) If true, this snapshot will not be deleted during automatic cleanup
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of snapshots older than `model_snapshot_retention_days`.
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However, this snapshot will be deleted when the job is deleted.
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The default value is false.
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`snapshot_id`::
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(string) A numerical character string that uniquely identifies the model
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snapshot. For example: "1491852978".
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`snapshot_doc_count`::
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(long) For internal use only.
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`timestamp`::
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(date) The creation timestamp for the snapshot.
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NOTE: All of these properties are informational with the exception of
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`description` and `retain`.
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[float]
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[[ml-snapshot-stats]]
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==== Model Size Statistics
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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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(long) The number of buckets for which entities were not processed due to
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memory limit constraints.
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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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(date) The timestamp that the `model_size_stats` were recorded, according to
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server-time.
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`memory_status`::
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(string) The status of the memory in relation to its `model_memory_limit`.
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Contains one of the following values.
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`ok`::: The internal models stayed below the configured value.
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`soft_limit`::: The internal models require more than 60% of the configured
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memory limit and more aggressive pruning will
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be performed in order to try to reclaim space.
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`hard_limit`::: The internal models require more space that the configured
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memory limit. Some incoming data could not be processed.
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`model_bytes`::
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(long) An approximation of the memory resources required for this analysis.
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`result_type`::
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(string) Internal. This value is always set to "model_size_stats".
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`timestamp`::
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(date) The timestamp that the `model_size_stats` were recorded, according to the bucket timestamp of the data.
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`total_by_field_count`::
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(long) The number of _by_ field values analyzed. Note that these are counted separately for each detector and partition.
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`total_over_field_count`::
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(long) The number of _over_ field values analyzed. Note that these are counted separately for each detector and partition.
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`total_partition_field_count`::
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(long) The number of _partition_ field values analyzed.
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NOTE: All of these properties are informational; you cannot change their values.
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