D'autres exemples de AWS SDK sont disponibles dans le référentiel [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub . Les traductions sont fournies par des outils de traduction automatique. En cas de conflit entre le contenu d'une traduction et celui de la version originale en anglais, la version anglaise prévaudra. # Utilisation `AddJobFlowSteps` avec un AWS SDK Les exemples de code suivants illustrent comment utiliser `AddJobFlowSteps`. ------ #### [ Python ] **Kit SDK for Python (Boto3)** Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). Ajoutez une étape Spark, qui est exécutée par le cluster dès son ajout. ``` def add_step(cluster_id, name, script_uri, script_args, emr_client): """ Adds a job step to the specified cluster. This example adds a Spark step, which is run by the cluster as soon as it is added. :param cluster_id: The ID of the cluster. :param name: The name of the step. :param script_uri: The URI where the Python script is stored. :param script_args: Arguments to pass to the Python script. :param emr_client: The Boto3 EMR client object. :return: The ID of the newly added step. """ try: response = emr_client.add_job_flow_steps( JobFlowId=cluster_id, Steps=[ { "Name": name, "ActionOnFailure": "CONTINUE", "HadoopJarStep": { "Jar": "command-runner.jar", "Args": [ "spark-submit", "--deploy-mode", "cluster", script_uri, *script_args, ], }, } ], ) step_id = response["StepIds"][0] logger.info("Started step with ID %s", step_id) except ClientError: logger.exception("Couldn't start step %s with URI %s.", name, script_uri) raise else: return step_id ``` Exécutez une commande Amazon EMR File System (EMRFS) en tant qu’étape de travail sur un cluster. Cela peut être utilisé pour automatiser les commandes EMRFS sur un cluster au lieu d’exécuter des commandes manuellement via une connexion SSH. ``` import boto3 from botocore.exceptions import ClientError def add_emrfs_step(command, bucket_url, cluster_id, emr_client): """ Add an EMRFS command as a job flow step to an existing cluster. :param command: The EMRFS command to run. :param bucket_url: The URL of a bucket that contains tracking metadata. :param cluster_id: The ID of the cluster to update. :param emr_client: The Boto3 Amazon EMR client object. :return: The ID of the added job flow step. Status can be tracked by calling the emr_client.describe_step() function. """ job_flow_step = { "Name": "Example EMRFS Command Step", "ActionOnFailure": "CONTINUE", "HadoopJarStep": { "Jar": "command-runner.jar", "Args": ["/usr/bin/emrfs", command, bucket_url], }, } try: response = emr_client.add_job_flow_steps( JobFlowId=cluster_id, Steps=[job_flow_step] ) step_id = response["StepIds"][0] print(f"Added step {step_id} to cluster {cluster_id}.") except ClientError: print(f"Couldn't add a step to cluster {cluster_id}.") raise else: return step_id def usage_demo(): emr_client = boto3.client("emr") # Assumes the first waiting cluster has EMRFS enabled and has created metadata # with the default name of 'EmrFSMetadata'. cluster = emr_client.list_clusters(ClusterStates=["WAITING"])["Clusters"][0] add_emrfs_step( "sync", "s3://elasticmapreduce/samples/cloudfront", cluster["Id"], emr_client ) if __name__ == "__main__": usage_demo() ``` + Pour plus de détails sur l'API, consultez [AddJobFlowSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/AddJobFlowSteps)le *AWS manuel de référence de l'API SDK for Python (Boto3*). ------ #### [ SAP ABAP ] **Kit SDK pour SAP ABAP** Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). ``` TRY. " Build args list for Spark submit DATA lt_args TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist. APPEND NEW /aws1/cl_emrxmlstringlist_w( 'spark-submit' ) TO lt_args. APPEND NEW /aws1/cl_emrxmlstringlist_w( '--deploy-mode' ) TO lt_args. APPEND NEW /aws1/cl_emrxmlstringlist_w( 'cluster' ) TO lt_args. APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_script_uri ) TO lt_args. APPEND LINES OF it_script_args TO lt_args. " Create step configuration DATA(lo_hadoop_jar_step) = NEW /aws1/cl_emrhadoopjarstepcfg( iv_jar = 'command-runner.jar' it_args = lt_args ). DATA(lo_step_config) = NEW /aws1/cl_emrstepconfig( iv_name = iv_name iv_actiononfailure = 'CONTINUE' io_hadoopjarstep = lo_hadoop_jar_step ). DATA lt_steps TYPE /aws1/cl_emrstepconfig=>tt_stepconfiglist. APPEND lo_step_config TO lt_steps. DATA(lo_result) = lo_emr->addjobflowsteps( iv_jobflowid = iv_cluster_id it_steps = lt_steps ). " Get first step ID DATA(lt_step_ids) = lo_result->get_stepids( ). READ TABLE lt_step_ids INDEX 1 INTO DATA(lo_step_id_obj). IF sy-subrc = 0. ov_step_id = lo_step_id_obj->get_value( ). MESSAGE |Step added with ID { ov_step_id }| TYPE 'I'. ENDIF. CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error). DATA(lv_error) = lo_internal_error->if_message~get_text( ). MESSAGE lv_error TYPE 'E'. ENDTRY. ``` + Pour plus de détails sur l'API, reportez-vous [AddJobFlowSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*. ------