IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Model snapshot resources

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Model snapshot resources

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Model snapshots are saved to disk periodically. By default, this 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 (twenty-four hours). 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.

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

All of these properties are informational with the exception of description and retain.

Model Size Statistics

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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.

All of these properties are informational; you cannot change their values.

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