Get model snapshots info

POST also GET /_ml/anomaly_detectors/{job_id}/model_snapshots/{snapshot_id}
POST also GET /_ml/anomaly_detectors/{job_id}/model_snapshots

Auth

Required authorization

Cluster privileges monitor_ml

Path parameters

job_id string required

Identifier for the anomaly detection job.

snapshot_id string optional

A numerical character string that uniquely identifies the model snapshot. You can get information for multiple snapshots by using a comma-separated list or a wildcard expression. You can get all snapshots by using _all, by specifying * as the snapshot ID, or by omitting the snapshot ID.

Leave it out to use /_ml/anomaly_detectors/{job_id}/model_snapshots.

Query parameters

desc boolean · default: false: If true, the results are sorted in descending order.
end union oneOf: Returns snapshots with timestamps earlier than this time.; One of: string or number
from number · default: 0: Skips the specified number of snapshots.
size number · default: 100: Specifies the maximum number of snapshots to obtain.
sort string: Specifies the sort field for the requested snapshots. By default, the snapshots are sorted by their timestamp.
start union oneOf: Returns snapshots with timestamps after this time.; One of: string or number

Request body

desc boolean · default: false: Refer to the description for the desc query parameter.
end union oneOf: Refer to the description for the end query parameter.; One of: string or number
page object
sort string: Refer to the description for the sort query parameter.
start union oneOf: Refer to the description for the start query parameter.; One of: string or number

Response

application/json

count number required
model_snapshots object[] required

Examples

Request Console 1 / 8 languages

An example body for a GET _ml/anomaly_detectors/high_sum_total_sales/model_snapshots request.

Console Kibana Dev Tools

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

JavaScript Elasticsearch JavaScript Client

const response = await client.ml.getModelSnapshots({
  job_id: "high_sum_total_sales",
  start: 1575402236000,
});

Python Elasticsearch Python Client

resp = client.ml.get_model_snapshots(
    job_id="high_sum_total_sales",
    start="1575402236000",
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_ml/anomaly_detectors/high_sum_total_sales/model_snapshots" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "start": "1575402236000"
  }'

Java Elasticsearch Java Client

client.ml().getModelSnapshots(g -> g
    .jobId("high_sum_total_sales")
    .start(DateTime.of("1575402236000"))
);

C# Elasticsearch .NET Client

var response = await client.MachineLearning
    .GetModelSnapshotsAsync(new GetModelSnapshotsRequest()
    {
        JobId = "high_sum_total_sales",
        Start = DateTimeOffset.Parse("2019-12-03T19:43:56.0000000+00:00", CultureInfo.InvariantCulture, DateTimeStyles.RoundtripKind)
    });

PHP Elasticsearch PHP Client

$resp = $client->ml()->getModelSnapshots([
    "job_id" => "high_sum_total_sales",
    "body" => [
        "start" => "1575402236000",
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.ml.get_model_snapshots(
  job_id: "high_sum_total_sales",
  body: {
    "start": "1575402236000"
  }
)

Response

{
  "count": 0,
  "model_snapshots": [
    {
      "description": "string",
      "job_id": "string",
      "latest_record_time_stamp": 0,
      "latest_result_time_stamp": 0,
      "min_version": "string",
      "model_size_stats": {
        "bucket_allocation_failures_count": 0,
        "job_id": "string",
        "log_time": "string",
        "memory_status": "ok",
        "model_bytes": "string",
        "model_bytes_exceeded": "string",
        "model_bytes_memory_limit": "string",
        "output_memory_allocator_bytes": "string",
        "peak_model_bytes": "string",
        "assignment_memory_basis": "string",
        "result_type": "string",
        "total_by_field_count": 0,
        "total_over_field_count": 0,
        "total_partition_field_count": 0,
        "categorization_status": "ok",
        "categorized_doc_count": 0,
        "dead_category_count": 0,
        "failed_category_count": 0,
        "frequent_category_count": 0,
        "rare_category_count": 0,
        "total_category_count": 0,
        "timestamp": 0
      },
      "retain": false,
      "snapshot_doc_count": 0,
      "snapshot_id": "string",
      "timestamp": 0
    }
  ]
}

Generated from the API spec