---
title: "GET /v1/projects/{projectsId}/locations/{locationsId}/featurestores:searchFeatures"
method: GET
path: "/v1/projects/{projectsId}/locations/{locationsId}/featurestores:searchFeatures"
tags: ["projects"]
---

# GET /v1/projects/{projectsId}/locations/{locationsId}/featurestores:searchFeatures

`GET /v1/projects/{projectsId}/locations/{locationsId}/featurestores:searchFeatures`

## Request

- Base URL: `https://aiplatform.googleapis.com`
- URL: `https://aiplatform.googleapis.com/v1/projects/{projectsId}/locations/{locationsId}/featurestores:searchFeatures`
- Auth: OAuth 2 and OAuth 2
  - Implicit: `https://accounts.google.com/o/oauth2/auth`
  - Scopes: `https://www.googleapis.com/auth/cloud-platform`
  - Authorization code: `https://accounts.google.com/o/oauth2/auth`
  - Token URL: `https://accounts.google.com/o/oauth2/token` scopes: `https://www.googleapis.com/auth/cloud-platform`

Searches Features matching a query in a given project.

## Path parameters

- `projectsId` string, required
- `locationsId` string, required

## Query parameters

- `pageSize` integer
- `pageToken` string
- `query` string

## Response `200`

Successful response

- GoogleCloudAiplatformV1SearchFeaturesResponse: Response message for FeaturestoreService.SearchFeatures.
  - `features` GoogleCloudAiplatformV1Feature[]: The Features matching the request. Fields returned: * `name` * `description` * `labels` * `create_time` * `update_time`
    - `createTime` string, google-datetime: Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    - `description` string: Description of the Feature.
    - `disableMonitoring` boolean: Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    - `etag` string: Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    - `labels` object: Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    - `monitoringStatsAnomalies` GoogleCloudAiplatformV1FeatureMonitoringStatsAnomaly[]: Output only. Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
      - `featureStatsAnomaly` GoogleCloudAiplatformV1FeatureStatsAnomaly: Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.
        - `anomalyDetectionThreshold` number, double: This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
        - `anomalyUri` string: Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
        - `distributionDeviation` number, double: Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
        - `endTime` string, google-datetime: The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
        - `score` number, double: Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
        - `startTime` string, google-datetime: The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
        - `statsUri` string: Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message [tensorflow.metadata.v0.FeatureNameStatistics](https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/statistics.proto).
      - `objective` 'OBJECTIVE_UNSPECIFIED' | 'IMPORT_FEATURE_ANALYSIS' | 'SNAPSHOT_ANALYSIS': Output only. The objective for each stats.
    - `name` string: Immutable. Name of the Feature. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature}` `projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}` The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    - `pointOfContact` string: Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs.
    - `updateTime` string, google-datetime: Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    - `valueType` 'VALUE_TYPE_UNSPECIFIED' | 'BOOL' | 'BOOL_ARRAY' | 'DOUBLE' | 'DOUBLE_ARRAY' | 'INT64' | 'INT64_ARRAY' | 'STRING' | 'STRING_ARRAY' | 'BYTES' | 'STRUCT': Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    - `versionColumnName` string: Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use feature_id.
  - `nextPageToken` string: A token, which can be sent as SearchFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

---

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