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Get model snapshots API

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IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.

Get model snapshots API

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Retrieves information about model snapshots.

Request

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GET _ml/anomaly_detectors/<job_id>/model_snapshots

GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>

Prerequisites

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If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API. See Security privileges.

Path parameters

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<job_id>

(Required, string) Identifier for the anomaly detection job.

<snapshot_id>

(Optional, string) A numerical character string that uniquely identifies the model snapshot.

If you do not specify this optional parameter, the API returns information about all model snapshots.

Request body

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desc: (Optional, boolean) If true, the results are sorted in descending order.
end: (Optional, date) Returns snapshots with timestamps earlier than this time.
from: (Optional, integer) Skips the specified number of snapshots.
size: (Optional, integer) Specifies the maximum number of snapshots to obtain.
sort: (Optional, string) Specifies the sort field for the requested snapshots. By default, the snapshots are sorted by their timestamp.
start: (Optional, string) Returns snapshots with timestamps after this time.

Response body

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The API returns an array of model snapshot objects, which have 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.

min_version

(string) The minimum version required to be able to restore the model snapshot.

model_size_stats

(object) Summary information describing the model.

model_size_stats.bucket_allocation_failures_count

(long) The number of buckets for which entities were not processed due to memory limit constraints.

model_size_stats.job_id

(string) Identifier for the anomaly detection job.

model_size_stats.log_time

(date) The timestamp that the model_size_stats were recorded, according to server-time.

model_size_stats.memory_status

(string) The status of the memory in relation to its model_memory_limit. Contains one of the following values.

hard_limit: The internal models require more space that the configured memory limit. Some incoming data could not be processed.
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.

model_size_stats.model_bytes

(long) An approximation of the memory resources required for this analysis.

model_size_stats.model_bytes_exceeded

(long) The number of bytes over the high limit for memory usage at the last allocation failure.

model_size_stats.model_bytes_memory_limit

(long) The upper limit for memory usage, checked on increasing values.

model_size_stats.result_type

(string) Internal. This value is always set to model_size_stats.

model_size_stats.timestamp

(date) The timestamp that the model_size_stats were recorded, according to the bucket timestamp of the data.

model_size_stats.total_by_field_count

(long) The number of by field values analyzed. Note that these are counted separately for each detector and partition.

model_size_stats.total_over_field_count

(long) The number of over field values analyzed. Note that these are counted separately for each detector and partition.

model_size_stats.total_partition_field_count

(long) The number of partition field values analyzed.

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.

snapshot_doc_count

(long) For internal use only.

timestamp

(date) The creation timestamp for the snapshot.

Examples

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GET _ml/anomaly_detectors/high_sum_total_sales/model_snapshots
{
  "start": "1575402236000"
}

In this example, the API provides a single result:

{
  "count" : 1,
  "model_snapshots" : [
    {
      "job_id" : "high_sum_total_sales",
      "min_version" : "6.4.0",
      "timestamp" : 1575402237000,
      "description" : "State persisted due to job close at 2019-12-03T19:43:57+0000",
      "snapshot_id" : "1575402237",
      "snapshot_doc_count" : 1,
      "model_size_stats" : {
        "job_id" : "high_sum_total_sales",
        "result_type" : "model_size_stats",
        "model_bytes" : 1638816,
        "model_bytes_exceeded" : 0,
        "model_bytes_memory_limit" : 10485760,
        "total_by_field_count" : 3,
        "total_over_field_count" : 3320,
        "total_partition_field_count" : 2,
        "bucket_allocation_failures_count" : 0,
        "memory_status" : "ok",
        "log_time" : 1575402237000,
        "timestamp" : 1576965600000
      },
      "latest_record_time_stamp" : 1576971072000,
      "latest_result_time_stamp" : 1576965600000,
      "retain" : false
    }
  ]
}

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