Estimate anomaly detection jobs model memory APIedit
Makes an estimation of the memory usage for an anomaly detection job model. It is based on analysis configuration details for the job and cardinality estimates for the fields it references.
Requestedit
POST _ml/anomaly_detectors/_estimate_model_memory
Prerequisitesedit
Requires the manage_ml cluster privilege. This privilege is included in the
machine_learning_admin built-in role.
Request bodyedit
-
analysis_config -
(Required, object)
For a list of the properties that you can specify in the
analysis_configcomponent of the body of this API, seeanalysis_config. -
max_bucket_cardinality -
(Required*, object)
Estimates of the highest cardinality in a single bucket that is observed for
influencer fields over the time period that the job analyzes data. To produce a
good answer, values must be provided for all influencer fields. Providing values
for fields that are not listed as
influencershas no effect on the estimation.
*It can be omitted from the request if there are noinfluencers. -
overall_cardinality -
(Required*, object)
Estimates of the cardinality that is observed for fields over the whole time
period that the job analyzes data. To produce a good answer, values must be
provided for fields referenced in the
by_field_name,over_field_nameandpartition_field_nameof any detectors. Providing values for other fields has no effect on the estimation.
*It can be omitted from the request if no detectors have aby_field_name,over_field_nameorpartition_field_name.
Examplesedit
POST _ml/anomaly_detectors/_estimate_model_memory
{
"analysis_config": {
"bucket_span": "5m",
"detectors": [
{
"function": "sum",
"field_name": "bytes",
"by_field_name": "status",
"partition_field_name": "app"
}
],
"influencers": [ "source_ip", "dest_ip" ]
},
"overall_cardinality": {
"status": 10,
"app": 50
},
"max_bucket_cardinality": {
"source_ip": 300,
"dest_ip": 30
}
}
The estimate returns the following result:
{
"model_memory_estimate": "21mb"
}