在 Amazon Personalize 中创建数据集导出作业

使用以下 create_dataset_export_job，将数据集中的数据导出到 Amazon S3 存储桶。为作业命名，将 dataset arn 替换为要导出的数据集的 Amazon 资源名称 (ARN)，然后将 role ARN 替换为您在为 Amazon Personalize 创建 IAM 角色中创建的 Amazon Personalize 服务角色的 ARN。在中 s3DataDestinationkmsKeyArn，可以选择为您的 AWS KMS 密钥提供 ARN，为path提供输出 Amazon S3 存储桶的路径。

对于 ingestionMode，通过以下选项指定要导出的数据：

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'])

使用以下 createDatasetExportJob 方法创建数据集导出作业。将以下内容作为参数传递：a PersonalizeClient、导出任务的名称、要导出的数据集的 ARN、提取模式、输出 Amazon S3 存储桶的路径以及密钥的 ARN。 AWS KMS

ingestionMode 可以是以下一个选项：

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());
    }
}