Créer une règle de masquage des données

Crée une règle de données pour appliquer une règle de masquage de données à une colonne de table BigQuery spécifique. Il s'agit d'un mécanisme principal pour implémenter la sécurité au niveau des colonnes dans BigQuery.

Exemple de code

Node.js

Avant d'essayer cet exemple, suivez les instructions de configuration pour Node.js du guide de démarrage rapide de BigQuery : Utiliser les bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API BigQuery pour Node.js.

Pour vous authentifier auprès de BigQuery, configurez le service Identifiants par défaut de l'application. Pour en savoir plus, consultez la page Configurer l'authentification pour les bibliothèques clientes.

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

Avant d'essayer cet exemple, suivez les instructions de configuration pour Python du guide de démarrage rapide de BigQuery à l'aide de bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API BigQuery pour Python.

Pour vous authentifier auprès de BigQuery, configurez le service Identifiants par défaut de l'application. Pour en savoir plus, consultez la page Configurer l'authentification pour les bibliothèques clientes.

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}")

Étape suivante

Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud , consultez l'explorateur d'exemplesGoogle Cloud .