建立資料遮蓋政策

建立資料政策，將資料遮蓋規則套用至特定 BigQuery 資料表欄。這是 BigQuery 導入資料欄層級安全防護機制的主要機制。

深入探索

如需包含這個程式碼範例的詳細說明文件，請參閱下列文章：

• 遮蓋欄資料

程式碼範例

Node.js

在試用這個範例之前，請先按照「使用用戶端程式庫的 BigQuery 快速入門導覽課程」中的 Node.js 設定說明操作。詳情請參閱 BigQuery Node.js API 參考說明文件。

如要向 BigQuery 進行驗證，請設定應用程式預設憑證。詳情請參閱「設定用戶端程式庫的驗證作業」。

const datapolicy = require('@google-cloud/bigquery-datapolicies');
const {DataPolicyServiceClient} = datapolicy.v2;
const protos = datapolicy.protos.google.cloud.bigquery.datapolicies.v2;
const {status} = require('@grpc/grpc-js');

const dataPolicyServiceClient = new DataPolicyServiceClient();

/**
 * Creates a data policy to apply a data masking rule to a specific BigQuery table column.
 * This is a primary mechanism for implementing column-level security in BigQuery.
 *
 * @param {string} projectId The Google Cloud project ID (for example, 'example-project-id')
 * @param {string} location The Google Cloud location. Example: 'us'
 * @param {string} dataPolicyId The user-assigned ID of the data policy. Example: 'example-data-policy-id'
 */
async function createDataPolicy(projectId, location, dataPolicyId) {
  const parent = `projects/${projectId}/locations/${location}`;

  const dataPolicy = {
    dataPolicyType: protos.DataPolicy.DataPolicyType.DATA_MASKING_POLICY,
    dataMaskingPolicy: {
      predefinedExpression:
        protos.DataMaskingPolicy.PredefinedExpression.SHA256,
    },
  };

  const request = {
    parent,
    dataPolicyId,
    dataPolicy,
  };

  try {
    const [response] = await dataPolicyServiceClient.createDataPolicy(request);
    console.log(`Successfully created data policy: ${response.name}`);
    console.log(`Data policy ID: ${response.dataPolicyId}`);
    console.log(`Data policy type: ${response.dataPolicyType}`);
    if (response.dataMaskingPolicy) {
      console.log(
        `Data masking expression: ${response.dataMaskingPolicy.predefinedExpression}`,
      );
    }
  } catch (err) {
    if (err.code === status.ALREADY_EXISTS) {
      console.log(
        `Data policy '${dataPolicyId}' already exists in location '${location}' of project '${projectId}'.`,
      );
      console.log(
        'Consider updating the existing data policy or using a different dataPolicyId.',
      );
    } else {
      console.error('Error creating data policy:', err.message);
    }
  }
}

Python

在試用這個範例之前，請先按照「使用用戶端程式庫的 BigQuery 快速入門導覽課程」中的 Python 設定說明操作。詳情請參閱 BigQuery Python API 參考說明文件。

如要向 BigQuery 進行驗證，請設定應用程式預設憑證。詳情請參閱「設定用戶端程式庫的驗證作業」。

from google.api_core import exceptions
from google.cloud import bigquery_datapolicies_v2

client = bigquery_datapolicies_v2.DataPolicyServiceClient()


def create_data_policy(project_id: str, location: str, data_policy_id: str) -> None:
    """Creates a data policy to apply a data masking rule to a specific BigQuery table column.
    This is a primary mechanism for implementing column-level security in BigQuery.

    Args:
        project_id (str): The Google Cloud project ID.
        location (str): The geographic location of the data policy (for example, "us-central1").
        data_policy_id (str): The ID for the new data policy.
    """

    parent = f"projects/{project_id}/locations/{location}"

    # Define the data masking policy.
    # Here, we specify a SHA-256 predefined expression for data masking.
    data_masking_policy = bigquery_datapolicies_v2.DataMaskingPolicy(
        predefined_expression=bigquery_datapolicies_v2.DataMaskingPolicy.PredefinedExpression.SHA256
    )

    # Create the DataPolicy object.
    # We set the type to DATA_MASKING_POLICY and assign the defined masking policy.
    data_policy = bigquery_datapolicies_v2.DataPolicy(
        data_policy_type=bigquery_datapolicies_v2.DataPolicy.DataPolicyType.DATA_MASKING_POLICY,
        data_masking_policy=data_masking_policy,
    )

    request = bigquery_datapolicies_v2.CreateDataPolicyRequest(
        parent=parent,
        data_policy_id=data_policy_id,
        data_policy=data_policy,
    )

    try:
        response = client.create_data_policy(request=request)
        print(f"Successfully created data policy: {response.name}")
        print(f"Data Policy ID: {response.data_policy_id}")
        print(f"Data Policy Type: {response.data_policy_type.name}")
        print(
            "Data Masking Predefined Expression:"
            f" {response.data_masking_policy.predefined_expression.name}"
        )
    except exceptions.AlreadyExists as e:
        print(
            f"Error: Data policy '{data_policy_id}' already exists in project"
            f" '{project_id}' in location '{location}'. Use a unique ID or"
            " update the existing policy if needed."
        )

    except exceptions.NotFound as e:
        print(
            f"Error: The specified project '{project_id}' or location '{location}'"
            " was not found or is inaccessible. Make sure the project ID and"
            " location are correct and you have the necessary permissions."
        )
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

後續步驟

如要搜尋及篩選其他 Google Cloud 產品的程式碼範例，請參閱Google Cloud 範例瀏覽工具。