OpenSearch/docs/reference/ml/apis/snapshotresource.asciidoc

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