在 Amazon Personalize 中建立資料集匯出任務

使用下列項目將資料集中的資料create_dataset_export_job匯出至 Amazon S3 儲存貯體。為任務命名、dataset arn將 取代為您要匯出之資料集的 Amazon Resource Name (ARN)，並將 取代role ARN為您在 中建立的 Amazon Personalize 服務角色的 ARN為 Amazon Personalize 建立 IAM 角色。在 中s3DataDestination，對於 kmsKeyArn，選擇性地為您的 AWS KMS 金鑰提供 ARN，對於 ，path則提供輸出 Amazon S3 儲存貯體的路徑。

針對 ingestionMode，指定要從下列選項匯出的資料：

import boto3

personalize = boto3.client('personalize')

response = personalize.create_dataset_export_job(
    jobName = 'job name',
    datasetArn = 'dataset ARN',
    jobOutput = {
      "s3DataDestination": {
        "kmsKeyArn": "kms key ARN",
        "path": "s3://amzn-s3-demo-bucket/folder-name/"
      }
    },
    roleArn = 'role ARN',
    ingestionMode = 'PUT'
)

dsej_arn = response['datasetExportJobArn']

print ('Dataset Export Job arn: ' + dsej_arn)

description = personalize.describe_dataset_export_job(
    datasetExportJobArn = dsej_arn)['datasetExportJob']

print('Name: ' + description['jobName'])
print('ARN: ' + description['datasetExportJobArn'])
print('Status: ' + description['status'])

使用下列createDatasetExportJob方法建立資料集匯出任務。傳遞下列參數：PersonalizeClient、匯出任務的名稱、您要匯出之資料集的 ARN、擷取模式、輸出 Amazon S3 儲存貯體的路徑，以及 AWS KMS 金鑰的 ARN。

ingestionMode 可以是下列其中一個選項：

public static void createDatasetExportJob(PersonalizeClient personalizeClient, 
                                        String jobName,
                                        String datasetArn, 
                                        IngestionMode ingestionMode, 
                                        String roleArn,
                                        String s3BucketPath,
                                        String kmsKeyArn) {

    long waitInMilliseconds = 30 * 1000; // 30 seconds
    String status = null;

    try {
        S3DataConfig exportS3DataConfig = S3DataConfig.builder()
            .path(s3BucketPath)
            .kmsKeyArn(kmsKeyArn)
            .build();
            
        DatasetExportJobOutput jobOutput = DatasetExportJobOutput.builder()
            .s3DataDestination(exportS3DataConfig)
            .build();

        CreateDatasetExportJobRequest createRequest = CreateDatasetExportJobRequest.builder()
            .jobName(jobName)
            .datasetArn(datasetArn)
            .ingestionMode(ingestionMode)
            .jobOutput(jobOutput)
            .roleArn(roleArn)
            .build();

        String datasetExportJobArn = personalizeClient.createDatasetExportJob(createRequest).datasetExportJobArn();

        DescribeDatasetExportJobRequest describeDatasetExportJobRequest = DescribeDatasetExportJobRequest.builder()
            .datasetExportJobArn(datasetExportJobArn)
            .build();

        long maxTime = Instant.now().getEpochSecond() + 3 * 60 * 60;

        while (Instant.now().getEpochSecond() < maxTime) {

            DatasetExportJob datasetExportJob = personalizeClient.describeDatasetExportJob(describeDatasetExportJobRequest)
                .datasetExportJob();

            status = datasetExportJob.status();
            System.out.println("Export job status: " + status);

            if (status.equals("ACTIVE") || status.equals("CREATE FAILED")) {
                break;
            }
            try {
                Thread.sleep(waitInMilliseconds);
            } catch (InterruptedException e) {
                System.out.println(e.getMessage());
            }
        }
    } catch (PersonalizeException e) {
        System.out.println(e.awsErrorDetails().errorMessage());
    }
}