Export feature values

Export feature values for all entities of a single entity type to a BigQuery table or a Cloud Storage bucket. You can choose to get a snapshot or to fully export feature values. A snapshot returns a single value per feature compared to a full export, which can return multiple values per feature. You can't select particular entity IDs or include multiple entity types when exporting feature values.

Exporting feature values is useful for archiving or for performing ad hoc analysis on your data. For example, you can store regular snapshots of your featurestore to save its state at different points in time. If you need to get feature values for building a training dataset, use batch serving instead.

Snapshot and full export comparison

Both the snapshot and full export options let you query data by specifying a single timestamp (either the start time or end time) or both timestamps. For snapshots, Vertex AI Feature Store (Legacy) returns the latest feature value within a given time range. In the output, the associated timestamp with each feature value is the snapshot timestamp (not the feature value timestamp).

For full exports, Vertex AI Feature Store (Legacy) returns all feature values within a given time range. In the output, the associated timestamp with each feature value is the feature timestamp (the specified timestamp when the feature value was ingested).

The following table summarizes what Vertex AI Feature Store (Legacy) returns based on the option that you choose and the timestamps that you provide.

Option	Start time only (inclusive)	End time only (inclusive)	Start and end time (inclusive)
Snapshot	Starting with the current time (when the request was received), returns the latest value, looking back until the start time. The snapshot timestamp is set to the current time.	Starting with the end time, returns the latest value, looking back to the very first value for each feature. The snapshot timestamp is set to the specified end time.	Returns the latest value within the specified time range. The snapshot timestamp is set to the specified end time.
Full export	Returns all values on and after the start time and up to the current time (when the request was sent).	Returns all values up to the end time, going all the way back to the very first value for each feature.	Returns all values within the specified time range.

Null values

For snapshots, if the latest feature value is null at a given timestamp, Vertex AI Feature Store (Legacy) returns the previous non-null feature value. If there are no previous non-null values, Vertex AI Feature Store (Legacy) returns null.

For full exports, if a feature value is null at a given timestamp, Vertex AI Feature Store (Legacy) returns null for that timestamp.

Examples

As an example, assume you had the following values in a featurestore, where the values for Feature_A and Feature_B share the same timestamp:

Entity ID	Feature value timestamp	Feature_A	Feature_B
123	T1	A_T1	B_T1
123	T2	A_T2	NULL
123	T3	A_T3	NULL
123	T4	A_T4	B_T4
123	T5	NULL	B_T5

Snapshot

For snapshots, Vertex AI Feature Store (Legacy) returns the following values based on the given timestamp values:

If only the starttime is set to T3 , the snapshot returns the following values:

Entity ID	Snapshot timestamp	Feature_A	Feature_B
123	CURRENT_TIME	A_T4	B_T5

If only the endtime is set to T3 , the snapshot returns the following values:

Entity ID	Snapshot timestamp	Feature_A	Feature_B
123	T3	A_T3	B_T1

If the startand endtimes are set to T2 and T3 , the snapshot returns the following values:

Entity ID	Snapshot timestamp	Feature_A	Feature_B
123	T3	A_T3	NULL

Full export

For full exports, Vertex AI Feature Store (Legacy) returns the following values based on the given timestamp values:

If only the starttime is set to T3 , the full export returns the following values:

Entity ID	Feature value timestamp	Feature_A	Feature_B
123	T3	A_T3	NULL
123	T4	A_T4	B_T4
123	T5	NULL	B_T5

If only the endtime is set to T3 , the full export returns the following values:

Entity ID	Feature value timestamp	Feature_A	Feature_B
123	T1	A_T1	B_T1
123	T2	A_T2	NULL
123	T3	A_T3	NULL

If the startand endtimes are set to T2 and T4 , the full export returns the following values:

Entity ID	Feature value timestamp	Feature_A	Feature_B
123	T2	A_T2	NULL
123	T3	A_T3	NULL
123	T4	A_T4	B_T4

Export feature values

When you export feature values, you choose which features to query and whether it is a snapshot or a full export. The following sections show a sample for each option.

For both options, the output destination must be in the same region as the source featurestore. For example, if your featurestore is in us-central1 , then the destination Cloud Storage bucket or BigQuery table must also be in us-central1 .

Snapshot

Export the latest feature values for a given time range.

Web UI

Use another method. You cannot export feature values from the Google Cloud console.

REST

To export feature values, send a POST request by using the entityTypes.exportFeatureValues method.

The following sample outputs a BigQuery table, but you can also output to a Cloud Storage bucket. Each output destination might have some prerequisites before you can submit a request. For example, if you specify a table name for the bigqueryDestination field, you must have an existing dataset. These requirements are documented in the API reference.

Before using any of the request data, make the following replacements:

LOCATION_ID : Region where the featurestore is located. For example, us-central1 .
PROJECT_ID : Your project ID .
FEATURESTORE_ID : ID of the featurestore.
ENTITY_TYPE_ID : ID of the entity type.
START_TIME and END_TIME : (Optional) If you specify the start time only, returns the latest value starting from the current time (when the request is sent) and looking back until the start time. If you specify the end time only, returns the latest value starting from the end time (inclusive) and looking back to the very first value. If you specify a start time and end time, returns the latest value within the specified time range (inclusive). If you specify neither, returns the latest values for each feature, starting from the current time and looking back to the very first value.
DATASET_NAME : Name of the destination BigQuery dataset.
TABLE_NAME : Name of the destination BigQuery table.
FEATURE_ID : ID of one or more features. Specify a single * (asterisk) to select all features.

HTTP method and URL:

POST https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues

Request JSON body:

{
  "snapshotExport": {
    "start_time": " START_TIME 
",
    "snapshot_time": " END_TIME 
"
  },
  "destination" : {
    "bigqueryDestination": {
      "outputUri": "bq:// PROJECT_ID 
. DATASET_NAME 
. TABLE_NAME 
"
    }
  },
  "featureSelector": {
    "idMatcher": {
      "ids": [" FEATURE_ID 
", ...]
    }
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.ExportFeatureValuesOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-12-03T22:55:25.974976Z",
      "updateTime": "2021-12-03T22:55:25.974976Z"
    }
  }
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. BigQueryDestination 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EntityTypeName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesRequest 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesRequest 
. SnapshotExport 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesResponse 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeatureSelector 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeatureValueDestination 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. IdMatcher 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.Arrays 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 ExportFeatureValuesSnapshotSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 featurestoreId 
  
 = 
  
 "YOUR_FEATURESTORE_ID" 
 ; 
  
 String 
  
 entityTypeId 
  
 = 
  
 "YOUR_ENTITY_TYPE_ID" 
 ; 
  
 String 
  
 destinationTableUri 
  
 = 
  
 "YOUR_DESTINATION_TABLE_URI" 
 ; 
  
 List<String> 
  
 featureSelectorIds 
  
 = 
  
 Arrays 
 . 
 asList 
 ( 
 "title" 
 , 
  
 "genres" 
 , 
  
 "average_rating" 
 ); 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 endpoint 
  
 = 
  
 "us-central1-aiplatform.googleapis.com:443" 
 ; 
  
 int 
  
 timeout 
  
 = 
  
 300 
 ; 
  
 exportFeatureValuesSnapshotSample 
 ( 
  
 project 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 , 
  
 destinationTableUri 
 , 
  
 featureSelectorIds 
 , 
  
 location 
 , 
  
 endpoint 
 , 
  
 timeout 
 ); 
  
 } 
  
 static 
  
 void 
  
 exportFeatureValuesSnapshotSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 featurestoreId 
 , 
  
 String 
  
 entityTypeId 
 , 
  
 String 
  
 destinationTableUri 
 , 
  
 List<String> 
  
 featureSelectorIds 
 , 
  
 String 
  
 location 
 , 
  
 String 
  
 endpoint 
 , 
  
 int 
  
 timeout 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  FeaturestoreServiceSettings 
 
  
 featurestoreServiceSettings 
  
 = 
  
  FeaturestoreServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 endpoint 
 ). 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  FeaturestoreServiceClient 
 
  
 featurestoreServiceClient 
  
 = 
  
  FeaturestoreServiceClient 
 
 . 
 create 
 ( 
 featurestoreServiceSettings 
 )) 
  
 { 
  
  FeatureSelector 
 
  
 featureSelector 
  
 = 
  
  FeatureSelector 
 
 . 
 newBuilder 
 () 
  
 . 
  setIdMatcher 
 
 ( 
  IdMatcher 
 
 . 
 newBuilder 
 (). 
 addAllIds 
 ( 
 featureSelectorIds 
 ). 
 build 
 ()) 
  
 . 
 build 
 (); 
  
  ExportFeatureValuesRequest 
 
  
 exportFeatureValuesRequest 
  
 = 
  
  ExportFeatureValuesRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setEntityType 
 ( 
  
  EntityTypeName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 ). 
 toString 
 ()) 
  
 . 
 setDestination 
 ( 
  
  FeatureValueDestination 
 
 . 
 newBuilder 
 () 
  
 . 
 setBigqueryDestination 
 ( 
  
  BigQueryDestination 
 
 . 
 newBuilder 
 (). 
 setOutputUri 
 ( 
 destinationTableUri 
 ))) 
  
 . 
 setFeatureSelector 
 ( 
 featureSelector 
 ) 
  
 . 
  setSnapshotExport 
 
 ( 
  SnapshotExport 
 
 . 
 newBuilder 
 ()) 
  
 . 
 build 
 (); 
  
 OperationFuture<ExportFeatureValuesResponse 
 , 
  
 ExportFeatureValuesOperationMetadata 
>  
 exportFeatureValuesFuture 
  
 = 
  
 featurestoreServiceClient 
 . 
  exportFeatureValuesAsync 
 
 ( 
 exportFeatureValuesRequest 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s%n" 
 , 
  
 exportFeatureValuesFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  ExportFeatureValuesResponse 
 
  
 exportFeatureValuesResponse 
  
 = 
  
 exportFeatureValuesFuture 
 . 
  get 
 
 ( 
 timeout 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Snapshot Export Feature Values Response" 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 exportFeatureValuesResponse 
 ); 
  
 featurestoreServiceClient 
 . 
 close 
 (); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const project = 'YOUR_PROJECT_ID'; 
 // const featurestoreId = 'YOUR_FEATURESTORE_ID'; 
 // const entityTypeId = 'YOUR_ENTITY_TYPE_ID'; 
 // const destinationTableUri = 'YOUR_BQ_DESTINATION_TABLE_URI'; 
 // const timestamp = <STARTING_TIMESTAMP_OF_SNAPSHOT_IN_SECONDS>; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // const apiEndpoint = 'YOUR_API_ENDPOINT'; 
 // const timeout = <TIMEOUT_IN_MILLI_SECONDS>; 
 // Imports the Google Cloud Featurestore Service Client library 
 const 
  
 { 
 FeaturestoreServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ). 
 v1 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 apiEndpoint 
 , 
 }; 
 // Instantiates a client 
 const 
  
 featurestoreServiceClient 
  
 = 
  
 new 
  
  FeaturestoreServiceClient 
 
 ( 
  
 clientOptions 
 ); 
 async 
  
 function 
  
 exportFeatureValuesSnapshot 
 () 
  
 { 
  
 // Configure the entityType resource 
  
 const 
  
 entityType 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /featurestores/ 
 ${ 
 featurestoreId 
 } 
 /entityTypes/ 
 ${ 
 entityTypeId 
 } 
 ` 
 ; 
  
 const 
  
 destination 
  
 = 
  
 { 
  
 bigqueryDestination 
 : 
  
 { 
  
 // # Output to BigQuery table created earlier 
  
 outputUri 
 : 
  
 destinationTableUri 
 , 
  
 }, 
  
 }; 
  
 const 
  
 featureSelector 
  
 = 
  
 { 
  
 idMatcher 
 : 
  
 { 
  
 ids 
 : 
  
 [ 
 'age' 
 , 
  
 'gender' 
 , 
  
 'liked_genres' 
 ], 
  
 }, 
  
 }; 
  
 const 
  
 snapshotExport 
  
 = 
  
 { 
  
 startTime 
 : 
  
 { 
  
 seconds 
 : 
  
 Number 
 ( 
 timestamp 
 ), 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 entityType 
 : 
  
 entityType 
 , 
  
 destination 
 : 
  
 destination 
 , 
  
 featureSelector 
 : 
  
 featureSelector 
 , 
  
 snapshotExport 
 : 
  
 snapshotExport 
 , 
  
 }; 
  
 // Export Feature Values Request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 featurestoreServiceClient 
 . 
 exportFeatureValues 
 ( 
  
 request 
 , 
  
 { 
 timeout 
 : 
  
 Number 
 ( 
 timeout 
 )} 
  
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 'Export feature values snapshot response' 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 exportFeatureValuesSnapshot 
 ();

Additional languages

To learn how to install and use the Vertex AI SDK for Python, see Use the Vertex AI SDK for Python . For more information, see the Vertex AI SDK for Python API reference documentation.

Full export

Export all feature values within a given time range.

Web UI

Use another method. You cannot export feature values from the Google Cloud console.

REST

To export feature values, send a POST request by using the entityTypes.exportFeatureValues method.

Before using any of the request data, make the following replacements:

LOCATION_ID : Region where the featurestore is located. For example, us-central1 .
PROJECT_ID : .
FEATURESTORE_ID : ID of the featurestore.
ENTITY_TYPE_ID : ID of the entity type.
START_TIME and END_TIME : (Optional) If you specify the start time only, returns all values between the current time (when the request is sent) and the start time (inclusive). If you specify the end time only, returns all values between the end time (inclusive) and the very first value timestamp (for each feature). If you specify a start time and end time, returns all values within the specified time range (inclusive). If you specify neither, returns all values between the current time and the very first value timestamp (for each feature).
DATASET_NAME : Name of the destination BigQuery dataset.
TABLE_NAME : Name of the destination BigQuery table.
FEATURE_ID : ID of one or more features. Specify a single * (asterisk) to select all features.

HTTP method and URL:

POST https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues

Request JSON body:

{
  "fullExport": {
    "start_time": " START_TIME 
",
    "end_time": " END_TIME 
"
  },
  "destination" : {
    "bigqueryDestination": {
      "outputUri": "bq:// PROJECT 
. DATASET_NAME 
. TABLE_NAME 
"
    }
  },
  "featureSelector": {
    "idMatcher": {
      "ids": [" FEATURE_ID 
", ...]
    }
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues"

PowerShell

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
:exportFeatureValues" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.ExportFeatureValuesOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-12-03T22:55:25.974976Z",
      "updateTime": "2021-12-03T22:55:25.974976Z"
    }
  }
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. BigQueryDestination 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EntityTypeName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesRequest 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesRequest 
. FullExport 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportFeatureValuesResponse 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeatureSelector 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeatureValueDestination 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. IdMatcher 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.Arrays 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 ExportFeatureValuesSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 featurestoreId 
  
 = 
  
 "YOUR_FEATURESTORE_ID" 
 ; 
  
 String 
  
 entityTypeId 
  
 = 
  
 "YOUR_ENTITY_TYPE_ID" 
 ; 
  
 String 
  
 destinationTableUri 
  
 = 
  
 "YOUR_DESTINATION_TABLE_URI" 
 ; 
  
 List<String> 
  
 featureSelectorIds 
  
 = 
  
 Arrays 
 . 
 asList 
 ( 
 "title" 
 , 
  
 "genres" 
 , 
  
 "average_rating" 
 ); 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 endpoint 
  
 = 
  
 "us-central1-aiplatform.googleapis.com:443" 
 ; 
  
 int 
  
 timeout 
  
 = 
  
 300 
 ; 
  
 exportFeatureValuesSample 
 ( 
  
 project 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 , 
  
 destinationTableUri 
 , 
  
 featureSelectorIds 
 , 
  
 location 
 , 
  
 endpoint 
 , 
  
 timeout 
 ); 
  
 } 
  
 static 
  
 void 
  
 exportFeatureValuesSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 featurestoreId 
 , 
  
 String 
  
 entityTypeId 
 , 
  
 String 
  
 destinationTableUri 
 , 
  
 List<String> 
  
 featureSelectorIds 
 , 
  
 String 
  
 location 
 , 
  
 String 
  
 endpoint 
 , 
  
 int 
  
 timeout 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  FeaturestoreServiceSettings 
 
  
 featurestoreServiceSettings 
  
 = 
  
  FeaturestoreServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 endpoint 
 ). 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  FeaturestoreServiceClient 
 
  
 featurestoreServiceClient 
  
 = 
  
  FeaturestoreServiceClient 
 
 . 
 create 
 ( 
 featurestoreServiceSettings 
 )) 
  
 { 
  
  FeatureSelector 
 
  
 featureSelector 
  
 = 
  
  FeatureSelector 
 
 . 
 newBuilder 
 () 
  
 . 
  setIdMatcher 
 
 ( 
  IdMatcher 
 
 . 
 newBuilder 
 (). 
 addAllIds 
 ( 
 featureSelectorIds 
 ). 
 build 
 ()) 
  
 . 
 build 
 (); 
  
  ExportFeatureValuesRequest 
 
  
 exportFeatureValuesRequest 
  
 = 
  
  ExportFeatureValuesRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setEntityType 
 ( 
  
  EntityTypeName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 ). 
 toString 
 ()) 
  
 . 
 setDestination 
 ( 
  
  FeatureValueDestination 
 
 . 
 newBuilder 
 () 
  
 . 
 setBigqueryDestination 
 ( 
  
  BigQueryDestination 
 
 . 
 newBuilder 
 (). 
 setOutputUri 
 ( 
 destinationTableUri 
 ))) 
  
 . 
 setFeatureSelector 
 ( 
 featureSelector 
 ) 
  
 . 
  setFullExport 
 
 ( 
  FullExport 
 
 . 
 newBuilder 
 ()) 
  
 . 
 build 
 (); 
  
 OperationFuture<ExportFeatureValuesResponse 
 , 
  
 ExportFeatureValuesOperationMetadata 
>  
 exportFeatureValuesFuture 
  
 = 
  
 featurestoreServiceClient 
 . 
  exportFeatureValuesAsync 
 
 ( 
 exportFeatureValuesRequest 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s%n" 
 , 
  
 exportFeatureValuesFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  ExportFeatureValuesResponse 
 
  
 exportFeatureValuesResponse 
  
 = 
  
 exportFeatureValuesFuture 
 . 
  get 
 
 ( 
 timeout 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Export Feature Values Response" 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 exportFeatureValuesResponse 
 ); 
  
 featurestoreServiceClient 
 . 
 close 
 (); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const project = 'YOUR_PROJECT_ID'; 
 // const featurestoreId = 'YOUR_FEATURESTORE_ID'; 
 // const entityTypeId = 'YOUR_ENTITY_TYPE_ID'; 
 // const destinationTableUri = 'YOUR_BQ_DESTINATION_TABLE_URI'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // const apiEndpoint = 'YOUR_API_ENDPOINT'; 
 // const timeout = <TIMEOUT_IN_MILLI_SECONDS>; 
 // Imports the Google Cloud Featurestore Service Client library 
 const 
  
 { 
 FeaturestoreServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ). 
 v1 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 apiEndpoint 
 , 
 }; 
 // Instantiates a client 
 const 
  
 featurestoreServiceClient 
  
 = 
  
 new 
  
  FeaturestoreServiceClient 
 
 ( 
  
 clientOptions 
 ); 
 async 
  
 function 
  
 exportFeatureValues 
 () 
  
 { 
  
 // Configure the entityType resource 
  
 const 
  
 entityType 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /featurestores/ 
 ${ 
 featurestoreId 
 } 
 /entityTypes/ 
 ${ 
 entityTypeId 
 } 
 ` 
 ; 
  
 const 
  
 destination 
  
 = 
  
 { 
  
 bigqueryDestination 
 : 
  
 { 
  
 // # Output to BigQuery table created earlier 
  
 outputUri 
 : 
  
 destinationTableUri 
 , 
  
 }, 
  
 }; 
  
 const 
  
 featureSelector 
  
 = 
  
 { 
  
 idMatcher 
 : 
  
 { 
  
 ids 
 : 
  
 [ 
 'age' 
 , 
  
 'gender' 
 , 
  
 'liked_genres' 
 ], 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 entityType 
 : 
  
 entityType 
 , 
  
 destination 
 : 
  
 destination 
 , 
  
 featureSelector 
 : 
  
 featureSelector 
 , 
  
 fullExport 
 : 
  
 {}, 
  
 }; 
  
 // Export Feature Values Request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 featurestoreServiceClient 
 . 
 exportFeatureValues 
 ( 
  
 request 
 , 
  
 { 
 timeout 
 : 
  
 Number 
 ( 
 timeout 
 )} 
  
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 'Export feature values response' 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 exportFeatureValues 
 ();

Additional languages

To learn how to install and use the Vertex AI SDK for Python, see Use the Vertex AI SDK for Python . For more information, see the Vertex AI SDK for Python API reference documentation.

What's next

Learn how to batch ingest feature values .
Learn how to serve features through online serving .
View the Vertex AI Feature Store (Legacy) concurrent batch job quota .
Troubleshoot common Vertex AI Feature Store (Legacy) issues.

Export feature values Stay organized with collections Save and categorize content based on your preferences.

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Export feature values

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Additional languages

Full export

Web UI

REST

curl

PowerShell

Java

Node.js

Additional languages

What's next

Export feature values