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.

Query parameters

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desc: (Optional, Boolean) If true, the results are sorted in descending order. Defaults to false.
end: (Optional, date) Returns snapshots with timestamps earlier than this time. Defaults to unset, which means results are not limited to specific timestamps.
from: (Optional, integer) Skips the specified number of snapshots. Defaults to 0.
size: (Optional, integer) Specifies the maximum number of snapshots to obtain. Defaults to 100.
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. Defaults to unset, which means results are not limited to specific timestamps.

Request body

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You can also specify the query parameters in the request body; the exception are from and size, use page instead:

page

Properties of page

from: (Optional, integer) Skips the specified number of snapshots. Defaults to 0.
size: (Optional, integer) Specifies the maximum number of snapshots to obtain. Defaults to 100.

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.

Properties of model_size_stats

assignment_memory_basis

(string) Indicates where to find the memory requirement that is used to decide where the job runs. The possible values are:

model_memory_limit: The job’s memory requirement is calculated on the basis that its model memory will grow to the model_memory_limit specified in the analysis_limits of its config.
current_model_bytes: The job’s memory requirement is calculated on the basis that its current model memory size is a good reflection of what it will be in the future.
peak_model_bytes: The job’s memory requirement is calculated on the basis that its peak model memory size is a good reflection of what the model size will be in the future.

bucket_allocation_failures_count

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

categorized_doc_count

(long) The number of documents that have had a field categorized.

categorization_status

(string) The status of categorization for this job. Contains one of the following values.

ok: Categorization is performing acceptably well (or not being used at all).
warn: Categorization is detecting a distribution of categories that suggests the input data is inappropriate for categorization. Problems could be that there is only one category, more than 90% of categories are rare, the number of categories is greater than 50% of the number of categorized documents, there are no frequently matched categories, or more than 50% of categories are dead.

dead_category_count

(long) The number of categories created by categorization that will never be assigned again because another category’s definition makes it a superset of the dead category. (Dead categories are a side effect of the way categorization has no prior training.)

failed_category_count

(long) The number of times that categorization wanted to create a new category but couldn’t because the job had hit its model_memory_limit. This count does not track which specific categories failed to be created. Therefore you cannot use this value to determine the number of unique categories that were missed.

frequent_category_count

(long) The number of categories that match more than 1% of categorized documents.

job_id

(string) Identifier for the anomaly detection 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.

hard_limit: The internal models require more space than 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_bytes

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

model_bytes_exceeded

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

model_bytes_memory_limit

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

peak_model_bytes

(long) The highest recorded value for the model memory usage.

rare_category_count

(long) The number of categories that match just one categorized document.

result_type

(string) Internal. This value is always 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_category_count

(long) The number of categories created by categorization.

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.

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",
        "categorized_doc_count" : 0,
        "total_category_count" : 0,
        "frequent_category_count" : 0,
        "rare_category_count" : 0,
        "dead_category_count" : 0,
        "categorization_status" : "ok",
        "log_time" : 1575402237000,
        "timestamp" : 1576965600000
      },
      "latest_record_time_stamp" : 1576971072000,
      "latest_result_time_stamp" : 1576965600000,
      "retain" : false
    }
  ]
}

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