Estimate job model memory usage Added in 7.7.0
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.
Body Required
-
analysis_config object
Additional properties are allowed.
-
max_bucket_cardinality 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
influencers
has no effect on the estimation. -
overall_cardinality 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_name
andpartition_field_name
of 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_name
orpartition_field_name
.
curl \
-X POST http://api.example.com/_ml/anomaly_detectors/_estimate_model_memory \
-H "Content-Type: application/json" \
-d '{"analysis_config":{"bucket_span":"string","":"string","categorization_field_name":"string","categorization_filters":["string"],"detectors":[{"by_field_name":"string","custom_rules":[{"actions":["skip_result"],"conditions":[{}],"scope":{"additionalProperty1":{},"additionalProperty2":{}}}],"detector_description":"string","detector_index":42.0,"exclude_frequent":"all","field_name":"string","function":"string","over_field_name":"string","partition_field_name":"string","use_null":true}],"influencers":["string"],"latency":"string","model_prune_window":"string","multivariate_by_fields":true,"per_partition_categorization":{"enabled":true,"stop_on_warn":true},"summary_count_field_name":"string"},"max_bucket_cardinality":{"additionalProperty1":42.0,"additionalProperty2":42.0},"overall_cardinality":{"additionalProperty1":42.0,"additionalProperty2":42.0}}'
{
"analysis_config": {
"bucket_span": "string",
"": "string",
"categorization_field_name": "string",
"categorization_filters": [
"string"
],
"detectors": [
{
"by_field_name": "string",
"custom_rules": [
{
"actions": [
"skip_result"
],
"conditions": [
{}
],
"scope": {
"additionalProperty1": {},
"additionalProperty2": {}
}
}
],
"detector_description": "string",
"detector_index": 42.0,
"exclude_frequent": "all",
"field_name": "string",
"function": "string",
"over_field_name": "string",
"partition_field_name": "string",
"use_null": true
}
],
"influencers": [
"string"
],
"latency": "string",
"model_prune_window": "string",
"multivariate_by_fields": true,
"per_partition_categorization": {
"enabled": true,
"stop_on_warn": true
},
"summary_count_field_name": "string"
},
"max_bucket_cardinality": {
"additionalProperty1": 42.0,
"additionalProperty2": 42.0
},
"overall_cardinality": {
"additionalProperty1": 42.0,
"additionalProperty2": 42.0
}
}
{
"model_memory_estimate": "string"
}