개별 BigQuery 열의 최소, 최대, 백분위수 값을 확인할 수 있습니다. 이러한 값을 계산하려면 NumericalStatsConfig 개인정보 보호 측정항목을 스캔 대상 열의 이름으로 설정하여 DlpJob을 구성합니다. 작업을 실행하면 Cloud DLP가 지정된 열의 통계를 계산하고 결과를 NumericalStatsResult 객체에 반환합니다.
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이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.
코드 샘플
C#
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
// 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
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
민감한 정보 보호의 클라이언트 라이브러리를 설치하고 사용하는 방법은 민감한 정보 보호 클라이언트 라이브러리를 참조하세요.
Sensitive Data Protection에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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()
다음 단계
다른 Google Cloud 제품의 코드 샘플을 검색하고 필터링하려면 Google Cloud 샘플 브라우저를 참조하세요.