[role="xpack"]
[[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. 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.
//* The smallest allowed value is 3600 (1 hour).

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.

`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.

[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.