--- type: api title: Estimate job model memory usage description: Make an estimation of the memory usage for an anomaly detection job model. url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-ml-estimate-model-memory resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-ml-estimate-model-memory products: - Elasticsearch Serverless --- # Estimate job model memory usage Availability: ## Paths - `POST` `/_ml/anomaly_detectors/_estimate_model_memory` - [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/authentication#apikeyauth) ## Description Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references. ## Required authorization * Cluster privileges: `manage_ml` ## Request Body `application/json` - `analysis_config` ({} AnalysisConfig) — optional For a list of the properties that you can specify in the `analysis_config` component of the body of this API. - `bucket_span` (string Duration · default: 5m) — optional The size of the interval that the analysis is aggregated into, typically between `5m` and `1h`. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation. - `categorization_analyzer` (CategorizationAnalyzer) — optional If `categorization_field_name` is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as `categorization_filters`. The categorization analyzer specifies how the `categorization_field` is interpreted by the categorization process. The `categorization_analyzer` field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin. - `categorization_field_name` (string Field) — optional If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword `mlcategory`. - `categorization_filters` ([] string) — optional If `categorization_field_name` is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time as `categorization_analyzer`. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the `categorization_analyzer` property instead and include the filters as pattern_replace character filters. The effect is exactly the same. - `detectors` ([] Detector) — required Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned. - `by_field_name` (string Field) — optional The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split. - `custom_rules` ([] DetectionRule) — optional Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules. - `actions` ([] enum RuleAction · default: [ "skip_result" ]) — optional The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined. Supported values include: - `skip_result`: The result will not be created. Unless you also specify `skip_model_update`, the model will be updated as usual with the corresponding series value. - `skip_model_update`: The value for that series will not be used to update the model. Unless you also specify `skip_result`, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results. Values: `skip_result`, `skip_model_update` - `conditions` ([] RuleCondition) — optional An array of numeric conditions when the rule applies. A rule must either have a non-empty scope or at least one condition. Multiple conditions are combined together with a logical AND. - `applies_to` (enum AppliesTo) — required Specifies the result property to which the condition applies. If your detector uses `lat_long`, `metric`, `rare`, or `freq_rare` functions, you can only specify conditions that apply to time. Values: `actual`, `typical`, `diff_from_typical`, `time` - `operator` (enum ConditionOperator) — required Specifies the condition operator. The available options are greater than, greater than or equals, less than, and less than or equals. Values: `gt`, `gte`, `lt`, `lte` - `value` (number) — required The value that is compared against the `applies_to` field using the operator. - `scope` (map string to FilterRef) — optional A scope of series where the rule applies. A rule must either have a non-empty scope or at least one condition. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in `by_field_name`, `over_field_name`, or `partition_field_name`. Map values ({} FilterRef) - `filter_id` (string Id) — required The identifier for the filter. - `filter_type` (enum FilterType · default: include) — optional If set to `include`, the rule applies for values in the filter. If set to `exclude`, the rule applies for values not in the filter. Values: `include`, `exclude` - `detector_description` (string) — optional A description of the detector. - `detector_index` (number) — optional A unique identifier for the detector. This identifier is based on the order of the detectors in the `analysis_config`, starting at zero. If you specify a value for this property, it is ignored. - `exclude_frequent` (enum ExcludeFrequent) — optional If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, you can set `exclude_frequent` to `all` for both fields, or to `by` or `over` for those specific fields. Values: `all`, `none`, `by`, `over` - `field_name` (string Field) — optional The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field. The `field_name` cannot contain double quotes or backslashes. - `function` (string) — optional The analysis function that is used. For example, `count`, `rare`, `mean`, `min`, `max`, or `sum`. - `over_field_name` (string Field) — optional The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits. - `partition_field_name` (string Field) — optional The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field. - `use_null` (boolean · default: false) — optional Defines whether a new series is used as the null series when there is no value for the by or partition fields. - `influencers` ([] string Field) — optional A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity. - `latency` (string Duration · default: 0) — optional The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second. NOTE: Latency is applicable only when you send data by using the post data API. - `model_prune_window` (string Duration) — optional Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the `bucket_span`. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of `30d` or 20 times `bucket_span`. - `multivariate_by_fields` (boolean) — optional This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features. If set to `true`, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use the `multivariate_by_fields` property, you must also specify `by_field_name` in your detector. - `per_partition_categorization` ({} PerPartitionCategorization) — optional Settings related to how categorization interacts with partition fields. - `enabled` (boolean) — optional To enable this setting, you must also set the `partition_field_name` property to the same value in every detector that uses the keyword `mlcategory`. Otherwise, job creation fails. - `stop_on_warn` (boolean) — optional This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly. - `summary_count_field_name` (string Field) — optional If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same `summary_count_field_name` applies to all detectors in the job. NOTE: The `summary_count_field_name` property cannot be used with the `metric` function. - `max_bucket_cardinality` (map string to number) — optional 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` (map string to number) — optional 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`. ## Response `application/json` - `model_memory_estimate` (string) ## Code Examples #### Console ```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 } } ``` #### JavaScript ```javascript const response = await client.ml.estimateModelMemory({ 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, }, }); ``` #### Python ```python resp = client.ml.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 }, ) ``` #### curl ```curl curl -X POST "$ELASTICSEARCH_URL/_ml/anomaly_detectors/_estimate_model_memory" \ -H "Authorization: ApiKey $ELASTIC_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "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 } }' ``` #### Java ```java client.ml().estimateModelMemory(e -> e .analysisConfig(a -> a .bucketSpan(b -> b .time("5m") ) .detectors(d -> d .byFieldName("status") .fieldName("bytes") .function("sum") .partitionFieldName("app") ) .influencers(List.of("source_ip","dest_ip")) ) .maxBucketCardinality(Map.of("dest_ip", 30L,"source_ip", 300L)) .overallCardinality(Map.of("app", 50L,"status", 10L)) ); ``` #### C# ```c# var response = await client.MachineLearning .EstimateModelMemoryAsync(d1 => d1 .AnalysisConfig(d2 => d2 .BucketSpan("5m") .Detectors(d3 => d3 .ByFieldName(x => x.Status) .FieldName(x => x.Bytes) .Function("sum") .PartitionFieldName(x => x.App) ) .Influencers(x => x.SourceIp, x => x.DestIp) ) .MaxBucketCardinality(new Dictionary() { { global::Elastic.Clients.Elasticsearch.Infer.Field(x => x.SourceIp), 300L }, { global::Elastic.Clients.Elasticsearch.Infer.Field(x => x.DestIp), 30L } }) .OverallCardinality(new Dictionary() { { global::Elastic.Clients.Elasticsearch.Infer.Field(x => x.Status), 10L }, { global::Elastic.Clients.Elasticsearch.Infer.Field(x => x.App), 50L } }) ); ``` #### PHP ```php $resp = $client->ml()->estimateModelMemory([ "body" => [ "analysis_config" => [ "bucket_span" => "5m", "detectors" => array( [ "function" => "sum", "field_name" => "bytes", "by_field_name" => "status", "partition_field_name" => "app", ], ), "influencers" => array( "source_ip", "dest_ip", ), ], "overall_cardinality" => [ "status" => 10, "app" => 50, ], "max_bucket_cardinality" => [ "source_ip" => 300, "dest_ip" => 30, ], ], ]); ``` #### Ruby ```ruby response = client.ml.estimate_model_memory( body: { "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 } } ) ``` ## Response Examples #### Ml estimate model memory response example1 A successful response from `POST _ml/anomaly_detectors/_estimate_model_memory`. ```json { "model_memory_estimate": "21mb" } ```