Como calcular estatísticas numéricas e categóricas

É possível usar a Proteção de dados sensíveis para calcular estatísticas numéricas e categóricas numéricas para colunas individuais em tabelas do BigQuery. A Proteção de Dados Sensíveis pode calcular:

• o valor mínimo da coluna
• o valor máximo da coluna
• os valores de quantil da coluna
• um histograma de frequências de valor na coluna

Calcular estatísticas numéricas

É possível determinar valores mínimos, máximos e de quantil para colunas individuais do BigQuery. Para calcular esses valores, configure um DlpJob, definindo a métrica de privacidade NumericalStatsConfig como o nome. da coluna a ser verificada. Quando você executa o job, a proteção de dados sensíveis calcula estatísticas para a coluna especificada, retornando os resultados no objeto NumericalStatsResult. A Proteção de Dados Sensíveis pode calcular estatísticas para os seguintes tipos de números:

• integer
• flutuante
• data
• data/hora
• carimbo de data/hora
• time

As estatísticas apresentadas pela verificação incluem o valor mínimo, o valor máximo e 99 valores de quantil que particionam o conjunto de valores de campo em 100 buckets de tamanhos iguais.

Exemplos de código

O exemplo de código a seguir demonstra, em várias linguagens, como usar a proteção de dados sensíveis para calcular estatísticas numéricas.

Importante: o código nesta página exige a configuração inicial de um cliente da Proteção de Dados Sensíveis. Para mais informações sobre como instalar e criar um cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis. O envio de JSON para endpoints REST da Proteção de dados sensíveis não requer uma biblioteca de cliente.

C#

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.Action.Types;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types;

public class RiskAnalysisCreateNumericalStats
{
    public static AnalyzeDataSourceRiskDetails.Types.NumericalStatsResult NumericalStats(
        string callingProjectId,
        string tableProjectId,
        string datasetId,
        string tableId,
        string topicId,
        string subscriptionId,
        string columnName)
    {
        var dlp = DlpServiceClient.Create();

        // Construct + submit the job
        var config = new RiskAnalysisJobConfig
        {
            PrivacyMetric = new PrivacyMetric
            {
                NumericalStatsConfig = new NumericalStatsConfig
                {
                    Field = new FieldId { Name = columnName }
                }
            },
            SourceTable = new BigQueryTable
            {
                ProjectId = tableProjectId,
                DatasetId = datasetId,
                TableId = tableId
            },
            Actions =
                {
                    new Google.Cloud.Dlp.V2.Action
                    {
                        PubSub = new PublishToPubSub
                        {
                            Topic = $"projects/{callingProjectId}/topics/{topicId}"
                        }
                    }
                }
        };

        var submittedJob = dlp.CreateDlpJob(
            new CreateDlpJobRequest
            {
                ParentAsProjectName = new ProjectName(callingProjectId),
                RiskJob = config
            });

        // Listen to pub/sub for the job
        var subscriptionName = new SubscriptionName(callingProjectId, subscriptionId);
        var subscriber = SubscriberClient.CreateAsync(
            subscriptionName).Result;

        // SimpleSubscriber runs your message handle function on multiple
        // threads to maximize throughput.
        var done = new ManualResetEventSlim(false);
        subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == submittedJob.Name)
            {
                Thread.Sleep(500); // Wait for DLP API results to become consistent
                done.Set();
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
        subscriber.StopAsync(CancellationToken.None).Wait();

        // Process results
        var resultJob = dlp.GetDlpJob(
            new GetDlpJobRequest
            {
                DlpJobName = DlpJobName.Parse(submittedJob.Name)
            });

        var result = resultJob.RiskDetails.NumericalStatsResult;

        // 'UnpackValue(x)' is a prettier version of 'x.toString()'
        Console.WriteLine($"Value Range: [{UnpackValue(result.MinValue)}, {UnpackValue(result.MaxValue)}]");
        var lastValue = string.Empty;
        for (var quantile = 0; quantile < result.QuantileValues.Count; quantile++)
        {
            var currentValue = UnpackValue(result.QuantileValues[quantile]);
            if (lastValue != currentValue)
            {
                Console.WriteLine($"Value at {quantile + 1}% quantile: {currentValue}");
            }
            lastValue = currentValue;
        }

        return result;
    }

    public static string UnpackValue(Value protoValue)
    {
        var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
        return jsonValue.Values.ElementAt(0).ToString();
    }
}

Go

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import (
	"context"
	"fmt"
	"io"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// riskNumerical computes the numerical risk of the given column.
func riskNumerical(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID, columnName string) error {
	// projectID := "my-project-id"
	// dataProject := "bigquery-public-data"
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// datasetID := "nhtsa_traffic_fatalities"
	// tableID := "accident_2015"
	// columnName := "state_number"
	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	topicExists, err := t.Exists(ctx)
	if err != nil {
		return err
	}
	if !topicExists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	subExists, err := s.Exists(ctx)
	if err != nil {
		return err
	}
	if !subExists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: &dlppb.RiskAnalysisJobConfig{
				// PrivacyMetric configures what to compute.
				PrivacyMetric: &dlppb.PrivacyMetric{
					Type: &dlppb.PrivacyMetric_NumericalStatsConfig_{
						NumericalStatsConfig: &dlppb.PrivacyMetric_NumericalStatsConfig{
							Field: &dlppb.FieldId{
								Name: columnName,
							},
						},
					},
				},
				// SourceTable describes where to find the data.
				SourceTable: &dlppb.BigQueryTable{
					ProjectId: dataProject,
					DatasetId: datasetID,
					TableId:   tableID,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the risk job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the risk job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()
		time.Sleep(500 * time.Millisecond)
		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "GetDlpJob: %v", err)
			return
		}
		n := resp.GetRiskDetails().GetNumericalStatsResult()
		fmt.Fprintf(w, "Value range: [%v, %v]\n", n.GetMinValue(), n.GetMaxValue())
		var tmp string
		for p, v := range n.GetQuantileValues() {
			if v.String() != tmp {
				fmt.Fprintf(w, "Value at %v quantile: %v\n", p, v)
				tmp = v.String()
			}
		}
		// Stop listening for more messages.
		cancel()
	})
	if err != nil {
		return fmt.Errorf("Recieve: %w", err)
	}
	return nil
}

Java

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.PublishToPubSub;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.NumericalStatsResult;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.NumericalStatsConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.ProjectTopicName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

class RiskAnalysisNumericalStats {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    String topicId = "pub-sub-topic";
    String subscriptionId = "pub-sub-subscription";
    numericalStatsAnalysis(projectId, datasetId, tableId, topicId, subscriptionId);
  }

  public static void numericalStatsAnalysis(
      String projectId, String datasetId, String tableId, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {

    // 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 (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {

      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setTableId(tableId)
              .setDatasetId(datasetId)
              .setProjectId(projectId)
              .build();

      // This represents the name of the column to analyze, which must contain numerical data
      String columnName = "Age";

      // Configure the privacy metric for the job
      FieldId fieldId = FieldId.newBuilder().setName(columnName).build();
      NumericalStatsConfig numericalStatsConfig =
          NumericalStatsConfig.newBuilder().setField(fieldId).build();
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setNumericalStatsConfig(numericalStatsConfig).build();

      // Create action to publish job status notifications over Google Cloud Pub/Sub
      ProjectTopicName topicName = ProjectTopicName.of(projectId, topicId);
      PublishToPubSub publishToPubSub =
          PublishToPubSub.newBuilder().setTopic(topicName.toString()).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      // Retrieve completed job status
      DlpJob completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      NumericalStatsResult result = completedJob.getRiskDetails().getNumericalStatsResult();

      System.out.printf(
          "Value range : [%.3f, %.3f]\n",
          result.getMinValue().getFloatValue(), result.getMaxValue().getFloatValue());

      int percent = 1;
      Double lastValue = null;
      for (Value quantileValue : result.getQuantileValuesList()) {
        Double currentValue = quantileValue.getFloatValue();
        if (lastValue == null || !lastValue.equals(currentValue)) {
          System.out.printf("Value at %s %% quantile : %.3f", percent, currentValue);
        }
        lastValue = currentValue;
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const tableProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The name of the column to compute risk metrics for, e.g. 'age'
// Note that this column must be a numeric data type
// const columnName = 'firstName';

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

async function numericalRiskAnalysis() {
  const sourceTable = {
    projectId: tableProjectId,
    datasetId: datasetId,
    tableId: tableId,
  };

  // Construct request for creating a risk analysis job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: {
      privacyMetric: {
        numericalStatsConfig: {
          field: {
            name: columnName,
          },
        },
      },
      sourceTable: sourceTable,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Run risk analysis job
  const [topicResponse] = await pubsub.topic(topicId).get();
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  console.log(`Job created. Job name: ${jobName}`);
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  setTimeout(() => {
    console.log(' Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  const results = job.riskDetails.numericalStatsResult;

  console.log(
    `Value Range: [${getValue(results.minValue)}, ${getValue(
      results.maxValue
    )}]`
  );

  // Print unique quantile values
  let tempValue = null;
  results.quantileValues.forEach((result, percent) => {
    const value = getValue(result);

    // Only print new values
    if (
      tempValue !== value &&
      !(tempValue && tempValue.equals && tempValue.equals(value))
    ) {
      console.log(`Value at ${percent}% quantile: ${value}`);
      tempValue = value;
    }
  });
}

await numericalRiskAnalysis();

PHP

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\NumericalStatsConfig;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Computes risk metrics of a column of numbers in a Google BigQuery table.
 *
 * @param string $callingProjectId  The project ID to run the API call under
 * @param string $dataProjectId     The project ID containing the target Datastore
 * @param string $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string $datasetId         The ID of the BigQuery dataset to inspect
 * @param string $tableId           The ID of the BigQuery table to inspect
 * @param string $columnName        The name of the column to compute risk metrics for, e.g. "age"
 */
function numerical_stats(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $datasetId,
    string $tableId,
    string $columnName
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Construct risk analysis config
    $columnField = (new FieldId())
        ->setName($columnName);

    $statsConfig = (new NumericalStatsConfig())
        ->setField($columnField);

    $privacyMetric = (new PrivacyMetric())
        ->setNumericalStatsConfig($statsConfig);

    // Construct items to be analyzed
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($dataProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct risk analysis job config to run
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setRiskJob($riskJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Helper function to convert Protobuf values to strings
    $valueToString = function ($value) {
        $json = json_decode($value->serializeToJsonString(), true);
        return array_shift($json);
    };

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $results = $job->getRiskDetails()->getNumericalStatsResult();
            printf(
                'Value range: [%s, %s]' . PHP_EOL,
                $valueToString($results->getMinValue()),
                $valueToString($results->getMaxValue())
            );

            // Only print unique values
            $lastValue = null;
            foreach ($results->getQuantileValues() as $percent => $quantileValue) {
                $value = $valueToString($quantileValue);
                if ($value != $lastValue) {
                    printf('Value at %s quantile: %s' . PHP_EOL, $percent, $value);
                    $lastValue = $value;
                }
            }

            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import concurrent.futures

import google.cloud.dlp
import google.cloud.pubsub


def numerical_risk_analysis(
    project: str,
    table_project_id: str,
    dataset_id: str,
    table_id: str,
    column_name: str,
    topic_id: str,
    subscription_id: str,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to compute risk metrics of a column
       of numerical data in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        dataset_id: The id of the dataset to inspect.
        table_id: The id of the table to inspect.
        column_name: The name of the column to compute risk metrics for.
        topic_id: The name of the Pub/Sub topic to notify once the job
            completes.
        subscription_id: The name of the Pub/Sub subscription to use when
            listening for job completion notifications.
        timeout: The number of seconds to wait for a response from the API.

    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Convert the project id into full resource ids.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Location info of the BigQuery table.
    source_table = {
        "project_id": table_project_id,
        "dataset_id": dataset_id,
        "table_id": table_id,
    }

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Configure risk analysis job
    # Give the name of the numeric column to compute risk metrics for
    risk_job = {
        "privacy_metric": {"numerical_stats_config": {"field": {"name": column_name}}},
        "source_table": source_table,
        "actions": actions,
    }

    # Call API to start risk analysis job
    operation = dlp.create_dlp_job(request={"parent": parent, "risk_job": risk_job})

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        if message.attributes["DlpJobName"] == operation.name:
            # This is the message we're looking for, so acknowledge it.
            message.ack()

            # Now that the job is done, fetch the results and print them.
            job = dlp.get_dlp_job(request={"name": operation.name})
            print(f"Job name: {job.name}")
            results = job.risk_details.numerical_stats_result
            print(
                "Value Range: [{}, {}]".format(
                    results.min_value.integer_value,
                    results.max_value.integer_value,
                )
            )
            prev_value = None
            for percent, result in enumerate(results.quantile_values):
                value = result.integer_value
                if prev_value != value:
                    print(f"Value at {percent}% quantile: {value}")
                    prev_value = value
            subscription.set_result(None)
        else:
            # This is not the message we're looking for.
            message.drop()

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)
    subscription = subscriber.subscribe(subscription_path, callback)

    try:
        subscription.result(timeout=timeout)
    except concurrent.futures.TimeoutError:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )
        subscription.close()

Calcular estatísticas categóricas numéricas

É possível calcular estatísticas categóricas numéricas para os buckets de histograma individuais em uma coluna do BigQuery, incluindo:

• limite superior na frequência de valor de um determinado bucket;
• limite inferior na frequência de valor de um determinado bucket;
• tamanho de um determinado bucket;
• uma amostra de frequências de valor de um determinado bucket (máximo de 20).

Para calcular esses valores, configure um DlpJob, definindo a métrica de privacidade CategoricalStatsConfig como o nome. da coluna a ser verificada. Quando você executa o job, a proteção de dados sensíveis calcula estatísticas para a coluna especificada, retornando os resultados no objeto CategoricalStatsResult.

Observação: é possível aplicar CategoricalStatsConfig a todos os tipos de coluna, exceto matrizes e estruturas. Se os tipos de colunas consistirem apenas nos tipos especificados em Calcular estatísticas numéricas, talvez seja mais informativo verificar essa métrica.

Exemplos de código

A amostra de código a seguir demonstra em várias linguagens como usar a proteção de dados sensíveis para calcular estatísticas categóricas.

Importante: o código nesta página exige a configuração inicial de um cliente da Proteção de Dados Sensíveis. Para mais informações sobre como instalar e criar um cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis. O envio de JSON para endpoints REST da Proteção de dados sensíveis não requer uma biblioteca de cliente.

C#

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.Action.Types;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types;

public class RiskAnalysisCreateCategoricalStats
{
    public static AnalyzeDataSourceRiskDetails.Types.CategoricalStatsResult CategoricalStats(
        string callingProjectId,
        string tableProjectId,
        string datasetId,
        string tableId,
        string topicId,
        string subscriptionId,
        string columnName)
    {
        var dlp = DlpServiceClient.Create();

        // Construct + submit the job
        var config = new RiskAnalysisJobConfig
        {
            PrivacyMetric = new PrivacyMetric
            {
                CategoricalStatsConfig = new CategoricalStatsConfig()
                {
                    Field = new FieldId { Name = columnName }
                }
            },
            SourceTable = new BigQueryTable
            {
                ProjectId = tableProjectId,
                DatasetId = datasetId,
                TableId = tableId
            },
            Actions =
            {
                new Google.Cloud.Dlp.V2.Action
                {
                    PubSub = new PublishToPubSub
                    {
                        Topic = $"projects/{callingProjectId}/topics/{topicId}"
                    }
                }
            }
        };

        var submittedJob = dlp.CreateDlpJob(new CreateDlpJobRequest
        {
            ParentAsProjectName = new ProjectName(callingProjectId),
            RiskJob = config
        });

        // Listen to pub/sub for the job
        var subscriptionName = new SubscriptionName(callingProjectId, subscriptionId);
        var subscriber = SubscriberClient.CreateAsync(
            subscriptionName).Result;

        // SimpleSubscriber runs your message handle function on multiple
        // threads to maximize throughput.
        var done = new ManualResetEventSlim(false);
        subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == submittedJob.Name)
            {
                Thread.Sleep(500); // Wait for DLP API results to become consistent
                done.Set();
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
        subscriber.StopAsync(CancellationToken.None).Wait();

        // Process results
        var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
        {
            DlpJobName = DlpJobName.Parse(submittedJob.Name)
        });

        var result = resultJob.RiskDetails.CategoricalStatsResult;

        for (var bucketIdx = 0; bucketIdx < result.ValueFrequencyHistogramBuckets.Count; bucketIdx++)
        {
            var bucket = result.ValueFrequencyHistogramBuckets[bucketIdx];
            Console.WriteLine($"Bucket {bucketIdx}");
            Console.WriteLine($"  Most common value occurs {bucket.ValueFrequencyUpperBound} time(s).");
            Console.WriteLine($"  Least common value occurs {bucket.ValueFrequencyLowerBound} time(s).");
            Console.WriteLine($"  {bucket.BucketSize} unique value(s) total.");

            foreach (var bucketValue in bucket.BucketValues)
            {
                // 'UnpackValue(x)' is a prettier version of 'x.toString()'
                Console.WriteLine($"  Value {UnpackValue(bucketValue.Value)} occurs {bucketValue.Count} time(s).");
            }
        }

        return result;
    }

    public static string UnpackValue(Value protoValue)
    {
        var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
        return jsonValue.Values.ElementAt(0).ToString();
    }
}

Go

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import (
	"context"
	"fmt"
	"io"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// riskCategorical computes the categorical risk of the given data.
func riskCategorical(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID, columnName string) error {
	// projectID := "my-project-id"
	// dataProject := "bigquery-public-data"
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// datasetID := "nhtsa_traffic_fatalities"
	// tableID := "accident_2015"
	// columnName := "state_number"
	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	topicExists, err := t.Exists(ctx)
	if err != nil {
		return err
	}
	if !topicExists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	subExists, err := s.Exists(ctx)
	if err != nil {
		return err
	}
	if !subExists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: &dlppb.RiskAnalysisJobConfig{
				// PrivacyMetric configures what to compute.
				PrivacyMetric: &dlppb.PrivacyMetric{
					Type: &dlppb.PrivacyMetric_CategoricalStatsConfig_{
						CategoricalStatsConfig: &dlppb.PrivacyMetric_CategoricalStatsConfig{
							Field: &dlppb.FieldId{
								Name: columnName,
							},
						},
					},
				},
				// SourceTable describes where to find the data.
				SourceTable: &dlppb.BigQueryTable{
					ProjectId: dataProject,
					DatasetId: datasetID,
					TableId:   tableID,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the risk job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the risk job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()
		time.Sleep(500 * time.Millisecond)
		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "GetDlpJob: %v", err)
			return
		}
		h := resp.GetRiskDetails().GetCategoricalStatsResult().GetValueFrequencyHistogramBuckets()
		for i, b := range h {
			fmt.Fprintf(w, "Histogram bucket %v\n", i)
			fmt.Fprintf(w, "  Most common value occurs %v times\n", b.GetValueFrequencyUpperBound())
			fmt.Fprintf(w, "  Least common value occurs %v times\n", b.GetValueFrequencyLowerBound())
			fmt.Fprintf(w, "  %v unique values total\n", b.GetBucketSize())
			for _, v := range b.GetBucketValues() {
				fmt.Fprintf(w, "    Value %v occurs %v times\n", v.GetValue(), v.GetCount())
			}
		}
		// Stop listening for more messages.
		cancel()
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

Java

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.PublishToPubSub;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.CategoricalStatsResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.CategoricalStatsResult.CategoricalStatsHistogramBucket;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.CategoricalStatsConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.ValueFrequency;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.ProjectTopicName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

class RiskAnalysisCategoricalStats {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    String topicId = "pub-sub-topic";
    String subscriptionId = "pub-sub-subscription";
    categoricalStatsAnalysis(projectId, datasetId, tableId, topicId, subscriptionId);
  }

  public static void categoricalStatsAnalysis(
      String projectId, String datasetId, String tableId, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // 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 (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {
      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId(tableId)
              .build();

      // The name of the column to analyze, which doesn't need to contain numerical data
      String columnName = "Mystery";

      // Configure the privacy metric for the job
      FieldId fieldId = FieldId.newBuilder().setName(columnName).build();
      CategoricalStatsConfig categoricalStatsConfig =
          CategoricalStatsConfig.newBuilder().setField(fieldId).build();
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setCategoricalStatsConfig(categoricalStatsConfig).build();

      // Create action to publish job status notifications over Google Cloud Pub/Sub
      ProjectTopicName topicName = ProjectTopicName.of(projectId, topicId);
      PublishToPubSub publishToPubSub =
          PublishToPubSub.newBuilder().setTopic(topicName.toString()).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      // Build the job creation request to be sent by the client
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      // Retrieve completed job status
      DlpJob completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      CategoricalStatsResult result = completedJob.getRiskDetails().getCategoricalStatsResult();
      List<CategoricalStatsHistogramBucket> histogramBucketList =
          result.getValueFrequencyHistogramBucketsList();

      for (CategoricalStatsHistogramBucket bucket : histogramBucketList) {
        long mostCommonFrequency = bucket.getValueFrequencyUpperBound();
        System.out.printf("Most common value occurs %d time(s).\n", mostCommonFrequency);

        long leastCommonFrequency = bucket.getValueFrequencyLowerBound();
        System.out.printf("Least common value occurs %d time(s).\n", leastCommonFrequency);

        for (ValueFrequency valueFrequency : bucket.getBucketValuesList()) {
          System.out.printf(
              "Value %s occurs %d time(s).\n",
              valueFrequency.getValue().toString(), valueFrequency.getCount());
        }
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const tableProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

// The name of the column to compute risk metrics for, e.g. 'firstName'
// const columnName = 'firstName';
async function categoricalRiskAnalysis() {
  const sourceTable = {
    projectId: tableProjectId,
    datasetId: datasetId,
    tableId: tableId,
  };

  // Construct request for creating a risk analysis job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: {
      privacyMetric: {
        categoricalStatsConfig: {
          field: {
            name: columnName,
          },
        },
      },
      sourceTable: sourceTable,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Run risk analysis job
  const [topicResponse] = await pubsub.topic(topicId).get();
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  console.log(`Job created. Job name: ${jobName}`);
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  setTimeout(() => {
    console.log(' Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  const histogramBuckets =
    job.riskDetails.categoricalStatsResult.valueFrequencyHistogramBuckets;
  histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
    console.log(`Bucket ${histogramBucketIdx}:`);

    // Print bucket stats
    console.log(
      `  Most common value occurs ${histogramBucket.valueFrequencyUpperBound} time(s)`
    );
    console.log(
      `  Least common value occurs ${histogramBucket.valueFrequencyLowerBound} time(s)`
    );

    // Print bucket values
    console.log(`${histogramBucket.bucketSize} unique values total.`);
    histogramBucket.bucketValues.forEach(valueBucket => {
      console.log(
        `  Value ${getValue(valueBucket.value)} occurs ${
          valueBucket.count
        } time(s).`
      );
    });
  });
}

await categoricalRiskAnalysis();

PHP

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\CategoricalStatsConfig;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Computes risk metrics of a column of data in a Google BigQuery table.
 *
 * @param string $callingProjectId The project ID to run the API call under
 * @param string $dataProjectId    The project ID containing the target Datastore
 * @param string $topicId          The name of the Pub/Sub topic to notify once the job completes
 * @param string $subscriptionId   The name of the Pub/Sub subscription to use when listening for job
 * @param string $datasetId        The ID of the dataset to inspect
 * @param string $tableId          The ID of the table to inspect
 * @param string $columnName       The name of the column to compute risk metrics for, e.g. "age"
 */
function categorical_stats(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $datasetId,
    string $tableId,
    string $columnName
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Construct risk analysis config
    $columnField = (new FieldId())
        ->setName($columnName);

    $statsConfig = (new CategoricalStatsConfig())
        ->setField($columnField);

    $privacyMetric = (new PrivacyMetric())
        ->setCategoricalStatsConfig($statsConfig);

    // Construct items to be analyzed
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($dataProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct risk analysis job config to run
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setRiskJob($riskJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $histBuckets = $job->getRiskDetails()->getCategoricalStatsResult()->getValueFrequencyHistogramBuckets();

            foreach ($histBuckets as $bucketIndex => $histBucket) {
                // Print bucket stats
                printf('Bucket %s:' . PHP_EOL, $bucketIndex);
                printf('  Most common value occurs %s time(s)' . PHP_EOL, $histBucket->getValueFrequencyUpperBound());
                printf('  Least common value occurs %s time(s)' . PHP_EOL, $histBucket->getValueFrequencyLowerBound());
                printf('  %s unique value(s) total.', $histBucket->getBucketSize());

                // Print bucket values
                foreach ($histBucket->getBucketValues() as $percent => $quantile) {
                    printf(
                        '  Value %s occurs %s time(s).' . PHP_EOL,
                        $quantile->getValue()->serializeToJsonString(),
                        $quantile->getCount()
                    );
                }
            }

            break;
        case JobState::FAILED:
            $errors = $job->getErrors();
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state.');
    }
}

Python

Para saber como instalar e usar a biblioteca de cliente da Proteção de dados sensíveis, consulte Bibliotecas de cliente da Proteção de dados sensíveis.

Para autenticar na Proteção de dados sensíveis, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import concurrent.futures

import google.cloud.dlp
import google.cloud.pubsub


def categorical_risk_analysis(
    project: str,
    table_project_id: str,
    dataset_id: str,
    table_id: str,
    column_name: str,
    topic_id: str,
    subscription_id: str,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to compute risk metrics of a column
       of categorical data in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        dataset_id: The id of the dataset to inspect.
        table_id: The id of the table to inspect.
        column_name: The name of the column to compute risk metrics for.
        topic_id: The name of the Pub/Sub topic to notify once the job
            completes.
        subscription_id: The name of the Pub/Sub subscription to use when
            listening for job completion notifications.
        timeout: The number of seconds to wait for a response from the API.

    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Convert the project id into full resource ids.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Location info of the BigQuery table.
    source_table = {
        "project_id": table_project_id,
        "dataset_id": dataset_id,
        "table_id": table_id,
    }

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Configure risk analysis job
    # Give the name of the numeric column to compute risk metrics for
    risk_job = {
        "privacy_metric": {
            "categorical_stats_config": {"field": {"name": column_name}}
        },
        "source_table": source_table,
        "actions": actions,
    }

    # Call API to start risk analysis job
    operation = dlp.create_dlp_job(request={"parent": parent, "risk_job": risk_job})

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        if message.attributes["DlpJobName"] == operation.name:
            # This is the message we're looking for, so acknowledge it.
            message.ack()

            # Now that the job is done, fetch the results and print them.
            job = dlp.get_dlp_job(request={"name": operation.name})
            print(f"Job name: {job.name}")
            histogram_buckets = (
                job.risk_details.categorical_stats_result.value_frequency_histogram_buckets  # noqa: E501
            )
            # Print bucket stats
            for i, bucket in enumerate(histogram_buckets):
                print(f"Bucket {i}:")
                print(
                    "   Most common value occurs {} time(s)".format(
                        bucket.value_frequency_upper_bound
                    )
                )
                print(
                    "   Least common value occurs {} time(s)".format(
                        bucket.value_frequency_lower_bound
                    )
                )
                print(f"   {bucket.bucket_size} unique values total.")
                for value in bucket.bucket_values:
                    print(
                        "   Value {} occurs {} time(s)".format(
                            value.value.integer_value, value.count
                        )
                    )
            subscription.set_result(None)
        else:
            # This is not the message we're looking for.
            message.drop()

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)
    subscription = subscriber.subscribe(subscription_path, callback)

    try:
        subscription.result(timeout=timeout)
    except concurrent.futures.TimeoutError:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )
        subscription.close()