直接從資料來源提供地圖項目,不必建立地圖項目群組和地圖項目:您可以在地圖檢視畫面中指定資料來源的 URI。請注意,在這種情況下,您無法將資料格式化為時間序列,也無法在 BigQuery 來源中納入歷來資料。每個資料列都必須包含與專屬 ID 相對應的最新特徵值。不支援在不同列中出現多個相同實體 ID。
Vertex AI 特徵儲存庫可讓您在 BigQuery 中維護特徵資料,並提供來自 BigQuery 資料集來源的特徵,因此無須將特徵匯入或複製到離線儲存庫。
資料來源準備指南
在 BigQuery 中準備資料來源時,請按照下列規範瞭解結構定義和限制:
在資料來源中加入下列資料欄:
實體 ID 欄:資料來源至少須包含一個含有 string 或 int 值的實體 ID 欄。這個資料欄的預設名稱為 entity_id。您可以為這個欄使用其他名稱。這個欄中的每個值大小不得超過 4 KB。
請注意,您也可以使用多個資料欄中的特徵來建構實體 ID,藉此指定特徵記錄。在這種情況下,您可以在資料來源中加入多個實體 ID 資料欄。每個實體 ID 欄的名稱都必須不重複。如果您是透過建立地圖項目群組來註冊資料來源,請為每個地圖項目群組設定實體 ID 欄。否則,如果您直接將資料來源與地圖項目檢視畫面建立關聯,請設定地圖項目檢視畫面,指定實體 ID 欄。
請注意,您可以在資料來源中加入多個 ID 欄。在這種情況下,每個實體 ID 欄的名稱都必須是唯一的。您可以設定特徵群組或特徵檢視,以便使用特徵記錄的每個資料欄值建構實體 ID。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-09-04 (世界標準時間)。"],[],[],null,["# Prepare data source\n\nBefore you can start serving features online using\nVertex AI Feature Store, you need to set up your feature data source in\nBigQuery, as follows:\n\n1. Create a BigQuery table or view using your feature data. To load\n feature data into a BigQuery table or view, you can create a\n BigQuery dataset using the data, create a BigQuery\n table, and then load the feature data from the dataset into the table.\n\n2. After you load the feature data into the BigQuery table or\n view, you need to make this data source available to\n Vertex AI Feature Store for online serving. There are two ways in\n which you can connect the data source to online serving resources, such as\n online stores and feature view instances:\n\n - **Register the data source by creating feature groups and features:**\n You can associate feature groups and features with feature view instances\n in your online store. You can format the data in either of the following ways:\n\n - Format your data as a time series by including a feature timestamp\n column. Vertex AI Feature Store serves only the latest\n feature values for each unique entity ID, based on the feature\n timestamp in this column.\n\n - Format the data without including a feature timestamp columns.\n Vertex AI Feature Store manages the timestamps and serves\n only the latest feature values for each unique entity ID.\n\n For information about how to create feature groups, see\n [Create a feature group](/vertex-ai/docs/featurestore/latest/create-featuregroup). For\n information about how to create features within a feature group, see\n [create a feature](/vertex-ai/docs/featurestore/latest/create-feature).\n - **Directly serve features from the data source without creating feature groups and features:**\n You can specify the URI of the data source in the feature view.\n Note that in this scenario, you can't format your data as a time series or\n include historical data in the BigQuery source. Each row must contain\n the latest feature values corresponding to a unique ID. Multiple occurrences\n of the same entity ID in different rows are not supported.\n\nSince Vertex AI Feature Store lets you maintain feature data\nin BigQuery and serves features from the BigQuery\ndata source, there's no need to import or copy the features to an offline\nstore.\n\nData source preparation guidelines\n----------------------------------\n\nFollow these guidelines to understand the schema and constraints while preparing\nthe data source in BigQuery:\n\n1. Include the following columns in the data source:\n\n - **Entity ID columns** : The data source must have at least one entity ID\n column with `string` or `int` values. The default name for this column is\n `entity_id`. You can optionally use a different name for this column. The\n size of each value in this column must be less than 4 KB.\n\n Note that you can also designate a feature record by constructing the entity\n ID using features from multiple columns. In this scenario, you can include\n multiple entity ID columns in the data source. The name of each entity ID\n column must be unique. If you register the data source by creating feature\n groups, set the entity ID columns for each feature group.\n Otherwise, if you directly associate the data source with a feature view,\n configure the feature views to specify the entity ID columns.\n\n Note that you can include multiple ID columns in a data source. In such a\n scenario, the name of each entity ID column must be unique. You can\n configure your feature groups or feature views to construct the entity ID\n using the values from each column for a feature record.\n - **Feature timestamp column** : Optional. If you register the data source\n using feature groups and features, and need to format the data as a time\n series, include a feature timestamp column. The timestamp column contains\n values of type `timestamp`. The default name for the timestamp column is\n `feature_timestamp`. If you want to use a different column name, use the\n `time_series` parameter to set the timestamp column for the feature group.\n\n If you don't specify a timestamp column to format your data as a time series,\n Vertex AI Feature Store manages the timestamps for the features\n and serves the latest feature values.\n\n If you directly associate a BigQuery data source with a feature\n view, the `feature_timestamp` column isn't required. In this scenario, you\n must include only the latest feature values in the data source and\n Vertex AI Feature Store doesn't look up the timestamp.\n - **Embedding and filtering columns**: Optional. If you want to use embedding\n management in an online store created for Optimized online serving, the\n data source must contain the following columns:\n\n - An `embedding` column containing arrays of type `float`.\n\n - Optional: One or more filtering columns of type `string` or `string` array.\n\n - Optional: A crowding column of type `int`.\n\n2. Each row in data source is a complete record of feature values associated\n with an entity ID. If a feature value is missing in one of the columns, then\n it's considered a null value.\n\n3. Each column of the BigQuery table or view represents a feature.\n Provide the values for each feature in a separate column. If you're associating\n the data source with a feature group and features, associate each column with a separate feature.\n\n4. Supported data types for feature values include `bool`, `int`, `float`,\n `string`, `timestamp`, arrays of these data types, and bytes. Note that during\n [data sync](/vertex-ai/docs/featurestore/latest/sync-data), feature values of type `timestamp` are converted to\n `int64`.\n\n5. The data source must be located in the same region as the online store\n instance, or in a multi-region that includes or overlaps with the region for the\n online store. For example, if the online store is in `us-central`, the\n BigQuery source might be located in `us-central` or `US`.\n\n6. [Sync the data in a feature view](/vertex-ai/docs/featurestore/latest/create-featureview#sync_featuredata)\n before online serving to ensure that you serve only the latest feature values.\n If you're using scheduled data sync, you might need to [manually sync the data\n in the feature view](/vertex-ai/docs/featurestore/latest/sync-data).\n However, if you're using continuous data sync with Optimized online serving,\n then you don't need to manually sync the data.\n\nWhat's next\n-----------\n\n- Learn how to create [feature groups](/vertex-ai/docs/featurestore/latest/create-featuregroup) and [features](/vertex-ai/docs/featurestore/latest/create-feature).\n\n- Learn how to [create a feature view](/vertex-ai/docs/featurestore/latest/create-featureview).\n\n- [Online serving types](/vertex-ai/docs/featurestore/latest/online-serving-types) in Vertex AI Feature Store."]]