[role="xpack"]
[testenv="platinum"]
[[ml-estimate-model-memory]]
= Estimate {anomaly-jobs} model memory API
++++
<titleabbrev>Estimate model memory</titleabbrev>
++++

Makes an estimation of the memory usage for an {anomaly-job} model. It 
is based on analysis configuration details for the job and cardinality estimates for the 
fields it references.


[[ml-estimate-model-memory-request]]
== {api-request-title}

`POST _ml/anomaly_detectors/_estimate_model_memory`

[[ml-estimate-model-memory-prereqs]]
== {api-prereq-title}

If the {es} {security-features} are enabled, you must have the following 
equivalent privileges:

* `manage_ml` or cluster: `manage`

For more information, see <<security-privileges>>.


[[ml-estimate-model-memory-request-body]]
== {api-request-body-title}

`analysis_config`::
(Required, object) 
For a list of the properties that you can specify in the `analysis_config` 
component of the body of this API, see <<put-analysisconfig,`analysis_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 `influencers` has no effect on the estimation. +
^*^It can be omitted from the request if there are no `influencers`.

`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_name` and 
`partition_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 a `by_field_name`, 
`over_field_name` or `partition_field_name`.

[[ml-estimate-model-memory-example]]
== {api-examples-title}

[source,console]
--------------------------------------------------
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
  }
}
--------------------------------------------------
// TEST[skip:needs-licence]

The estimate returns the following result:

[source,console-result]
----
{
  "model_memory_estimate": "21mb"
}
----