Python客户端支持用于在Amazon EMR之上运行Hive

args1 = [u\'s3://us-east-1.elasticmapreduce/libs/hive/hive-script\',
         u\'--base-path\',
         u\'s3://us-east-1.elasticmapreduce/libs/hive/\',
         u\'--install-hive\',
         u\'--hive-versions\',
         u\'0.7\']
args2 = [u\'s3://us-east-1.elasticmapreduce/libs/hive/hive-script\',
         u\'--base-path\',
         u\'s3://us-east-1.elasticmapreduce/libs/hive/\',
         u\'--hive-versions\',
         u\'0.7\',
         u\'--run-hive-script\',
         u\'--args\',
         u\'-f\',
         s3_query_file_uri]
steps = []
for name, args in zip((\'Setup Hive\',\'Run Hive Script\'),(args1,args2)):
    step = JarStep(name,
                   \'s3://us-east-1.elasticmapreduce/libs/script-runner/script-runner.jar\',
                   step_args=args,
                   #action_on_failure=\"CANCEL_AND_WAIT\"
                   )
    #should be inside loop
    steps.append(step)
# Kick off the job
jobid = EmrConnection().run_jobflow(name, s3_log_uri,
                                   steps=steps,
                                   master_instance_type=master_instance_type,
                                   slave_instance_type=slave_instance_type,
                                   num_instances=num_instances,
                                   hadoop_version=\"0.20\")