在 Amazon Personalize 化中創建數據集導出任務

使用以下指令create_dataset_export_job將資料集中的資料匯出到 Amazon S3 儲存貯體。為任務命名，取代dataset arn為您要匯出的資料集的 Amazon 資源名稱 (ARN)，然後取代role ARN為您在中建立ARN的 Amazon Personalize 服務角色為 Amazon Personalize 化創建IAM角色。在中s3DataDestination，針對kmsKeyArn選擇性地提ARN供您的 AWS KMS 密鑰，並為您的輸出 Amazon S3 存儲桶path提供路徑。

對於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工作。將下列項目當作參數傳遞：a PersonalizeClient、匯出任務的名稱、要匯出的資料集、擷取模式、輸出 Amazon S3 儲存貯體的路徑，以ARN及 ARN AWS KMS 索引鍵。

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());
    }
}