def create( config_path, cluster_mode, destination_system, destination_database, destination_environment, algorithm_instance, ext_params_str ): data_system = DataSystem( config_path, cluster_mode, destination_system, destination_database, destination_environment ) if data_system.database_type == DataSystem.DatabaseType.EMR: config = AlgorithmConfigurationHadoop.create_with_ext_params( config_path, cluster_mode, destination_database, destination_environment, algorithm_instance, ext_params_str ) execution_system = EMRSystem.from_data_system(data_system, config.get_emr_cluster_id()) return AlgorithmExecutorHadoop(execution_system, config) else: raise M3DUnsupportedDatabaseTypeException(data_system.database_type)
def create(config_path, cluster_mode, destination_system, destination_database, destination_environment, destination_table, load_type, emr_cluster_id, spark_params_str): data_system = DataSystem(config_path, cluster_mode, destination_system, destination_database, destination_environment) if data_system.database_type == DataSystem.DatabaseType.EMR: execution_system = EMRSystem.from_data_system( data_system, emr_cluster_id) spark_params_dict = json.loads(spark_params_str) return LoadExecutorHadoop(execution_system, load_type, destination_table, spark_params_dict) else: raise M3DUnsupportedDatabaseTypeException( data_system.database_type)