AWS SDK を使用して Amazon EMR ジョブフローを実行する - AWS SDK コードサンプル

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AWS SDK を使用して Amazon EMR ジョブフローを実行する

次のコード例は、Amazon EMR ジョブフローを実行する方法を示しています。

SDK for Python (Boto3)

については、こちらを参照してください GitHub。用例一覧を検索し、AWS コードサンプルリポジトリでの設定と実行の方法を確認してください。

def run_job_flow( name, log_uri, keep_alive, applications, job_flow_role, service_role, security_groups, steps, emr_client, ): """ Runs a job flow with the specified steps. A job flow creates a cluster of instances and adds steps to be run on the cluster. Steps added to the cluster are run as soon as the cluster is ready. This example uses the 'emr-5.30.1' release. A list of recent releases can be found here: :param name: The name of the cluster. :param log_uri: The URI where logs are stored. This can be an Amazon S3 bucket URL, such as 's3://my-log-bucket'. :param keep_alive: When True, the cluster is put into a Waiting state after all steps are run. When False, the cluster terminates itself when the step queue is empty. :param applications: The applications to install on each instance in the cluster, such as Hive or Spark. :param job_flow_role: The IAM role assumed by the cluster. :param service_role: The IAM role assumed by the service. :param security_groups: The security groups to assign to the cluster instances. Amazon EMR adds all needed rules to these groups, so they can be empty if you require only the default rules. :param steps: The job flow steps to add to the cluster. These are run in order when the cluster is ready. :param emr_client: The Boto3 EMR client object. :return: The ID of the newly created cluster. """ try: response = emr_client.run_job_flow( Name=name, LogUri=log_uri, ReleaseLabel="emr-5.30.1", Instances={ "MasterInstanceType": "m5.xlarge", "SlaveInstanceType": "m5.xlarge", "InstanceCount": 3, "KeepJobFlowAliveWhenNoSteps": keep_alive, "EmrManagedMasterSecurityGroup": security_groups["manager"].id, "EmrManagedSlaveSecurityGroup": security_groups["worker"].id, }, Steps=[ { "Name": step["name"], "ActionOnFailure": "CONTINUE", "HadoopJarStep": { "Jar": "command-runner.jar", "Args": [ "spark-submit", "--deploy-mode", "cluster", step["script_uri"], *step["script_args"], ], }, } for step in steps ], Applications=[{"Name": app} for app in applications],,, EbsRootVolumeSize=10, VisibleToAllUsers=True, ) cluster_id = response["JobFlowId"]"Created cluster %s.", cluster_id) except ClientError: logger.exception("Couldn't create cluster.") raise else: return cluster_id
  • API の詳細については、RunJobFlowAWS「 SDK for Python (Boto3) API リファレンス」の「」を参照してください。