[role="xpack"]
[[ml-revert-snapshot]]
=== Revert Model Snapshots API
++++
<titleabbrev>Revert Model Snapshots</titleabbrev>
++++

This API enables you to revert to a specific snapshot.

==== Request

`POST _xpack/ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>/_revert`


==== Description

The {ml} feature in {xpack} reacts quickly to anomalous input, learning new
behaviors in data. Highly anomalous input increases the variance in the models
whilst the system learns whether this is a new step-change in behavior or a
one-off event. In the case where this anomalous input is known to be a one-off,
then it might be appropriate to reset the model state to a time before this
event. For example, you might consider reverting to a saved snapshot after Black
Friday or a critical system failure.

////
To revert to a saved snapshot, you must follow this sequence:
. Close the job
. Revert to a snapshot
. Open the job
. Send new data to the job

When reverting to a snapshot, there is a choice to make about whether or not
you want to keep the results that were created between the time of the snapshot
and the current time. In the case of Black Friday for instance, you might want
to keep the results and carry on processing data from the current time,
though without the models learning the one-off behavior and compensating for it.
However, say in the event of a critical system failure and you decide to reset
and models to a previous known good state and process data from that time,
it makes sense to delete the intervening results for the known bad period and
resend data from that earlier time.

Any gaps in data since the snapshot time will be treated as nulls and not modeled.
If there is a partial bucket at the end of the snapshot and/or at the beginning
of the new input data, then this will be ignored and treated as a gap.

For jobs with many entities, the model state may be very large.
If a model state is several GB, this could take 10-20 mins to revert depending
upon machine spec and resources. If this is the case, please ensure this time
is planned for.
Model size (in bytes) is available as part of the Job Resource Model Size Stats.
////
IMPORTANT: Before you revert to a saved snapshot, you must close the job.


==== Path Parameters

`job_id` (required)::
  (string) Identifier for the job

`snapshot_id` (required)::
  (string) Identifier for the model snapshot

==== Request Body

`delete_intervening_results`::
  (boolean) If true, deletes the results in the time period between the
  latest results and the time of the reverted snapshot. It also resets the
  model to accept records for this time period. The default value is false.

NOTE: If you choose not to delete intervening results when reverting a snapshot,
the job will not accept input data that is older than the current time.
If you want to resend data, then delete the intervening results.


==== Authorization

You must have `manage_ml`, or `manage` cluster privileges to use this API.
For more information, see
{xpack-ref}/security-privileges.html[Security Privileges].
//<<privileges-list-cluster>>.


==== Examples

The following example reverts to the `1491856080` snapshot for the
`it_ops_new_kpi` job:

[source,js]
--------------------------------------------------
POST
_xpack/ml/anomaly_detectors/it_ops_new_kpi/model_snapshots/1491856080/_revert
{
  "delete_intervening_results": true
}
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]

When the operation is complete, you receive the following results:
[source,js]
----
{
  "model": {
    "job_id": "it_ops_new_kpi",
    "timestamp": 1491856080000,
    "description": "State persisted due to job close at 2017-04-10T13:28:00-0700",
    "snapshot_id": "1491856080",
    "snapshot_doc_count": 1,
    "model_size_stats": {
      "job_id": "it_ops_new_kpi",
      "result_type": "model_size_stats",
      "model_bytes": 29518,
      "total_by_field_count": 3,
      "total_over_field_count": 0,
      "total_partition_field_count": 2,
      "bucket_allocation_failures_count": 0,
      "memory_status": "ok",
      "log_time": 1491856080000,
      "timestamp": 1455318000000
    },
    "latest_record_time_stamp": 1455318669000,
    "latest_result_time_stamp": 1455318000000,
    "retain": false
  }
}
----