本文整理汇总了Python中boto.emr.connection.EmrConnection.set_termination_protection方法的典型用法代码示例。如果您正苦于以下问题：Python EmrConnection.set_termination_protection方法的具体用法？Python EmrConnection.set_termination_protection怎么用？Python EmrConnection.set_termination_protection使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类boto.emr.connection.EmrConnection的用法示例。
在下文中一共展示了EmrConnection.set_termination_protection方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: start_hadoop_cluster
# 需要导入模块: from boto.emr.connection import EmrConnection [as 别名]
# 或者: from boto.emr.connection.EmrConnection import set_termination_protection [as 别名]
def start_hadoop_cluster(nodenum):
try:
hadoop_params = ['-m','mapred.tasktracker.map.tasks.maximum=1',
'-m', 'mapred.child.java.opts=-Xmx10g']
configure_hadoop_action = BootstrapAction('configure_hadoop', 's3://elasticmapreduce/bootstrap-actions/configure-hadoop', hadoop_params)
emr_connection = EmrConnection()
bucket_name = "udk-bucket"
steps = []
copy_jar_step = JarStep(name='copy-jar',
jar='s3n://' + bucket_name + '/copy-to-hdfs.jar',
step_args=['s3n://' + bucket_name + '/pipeline.pear',
'/mnt/pipeline.pear'])
steps.append(copy_jar_step)
jobflow_id = emr_connection.run_jobflow(name='udk',
log_uri='s3://udk-bucket/jobflow_logs',
master_instance_type='m2.xlarge',
slave_instance_type='m2.xlarge',
num_instances=nodenum,
keep_alive=True,
enable_debugging=False,
bootstrap_actions=[configure_hadoop_action],
hadoop_version='1.0.3',
steps=steps)
emr_connection.set_termination_protection(jobflow_id, True)
return jobflow_id
except Exception, e:
return "none" 示例2: terminate
# 需要导入模块: from boto.emr.connection import EmrConnection [as 别名]
# 或者: from boto.emr.connection.EmrConnection import set_termination_protection [as 别名]
def terminate(cluster_id):
try:
emr_connection = EmrConnection()
emr_connection.set_termination_protection(cluster_id, False)
emr_connection.terminate_jobflow(cluster_id)
return True
except Exception, e:
print e
return False示例3: EmrLauncher
# 需要导入模块: from boto.emr.connection import EmrConnection [as 别名]
# 或者: from boto.emr.connection.EmrConnection import set_termination_protection [as 别名]
class EmrLauncher(object):
# Default constructor of the class.
def __init__(self):
try:
self.zone_name = "ap-southeast-1"
self.access_key = "xxxxxx"
self.private_key = "xxxxxxx"
self.ec2_keyname = "xxxxxxxx"
self.base_bucket = "s3://emr-bucket/"
self.bootstrap_script = "custom-bootstrap.sh"
self.log_dir = "Logs"
self.emr_status_wait = 20
self.conn = ""
self.cluster_name = "MyFirstEmrCluster"
# Establishing EmrConnection
self.conn = EmrConnection(self.access_key, self.private_key,
region=RegionInfo(name=self.zone_name,
endpoint=self.zone_name + '.elasticmapreduce.amazonaws.com'))
self.log_bucket_name = self.base_bucket + self.log_dir
self.bootstrap_script_name = self.base_bucket + self.bootstrap_script
def launch_emr_cluster(self, master_type, slave_type, num_instance, ami_version):
try:
#Custom Bootstrap step
bootstrap_step = BootstrapAction("CustomBootStrap", self.bootstrap_script_name, None)
#Modifyting block size to 256 MB
block_size_conf = 'dfs.block.size=256'
hadoop_config_params = ['-h', block_size_conf, '-h']
hadoop_config_bootstrapper = BootstrapAction('hadoop-config',
's3://elasticmapreduce/bootstrap-actions/configure-hadoop',
hadoop_config_params)
#Bootstrapping Ganglia
hadoop_monitor_bootstrapper = BootstrapAction('ganglia-config',
's3://elasticmapreduce/bootstrap-actions/install-ganglia', '')
#Bootstrapping Impala
impala_install_params = ['--install-impala','--base-path', 's3://elasticmapreduce', '--impala-version', 'latest']
bootstrap_impala_install_step = BootstrapAction("ImpalaInstall", "s3://elasticmapreduce/libs/impala/setup-impala",
impala_install_params)
#Hive installation
hive_install_step = InstallHiveStep();
#Pig Installation
pig_install_step = InstallPigStep();
#Launching the cluster
jobid = self.conn.run_jobflow(
self.cluster_name,
self.log_bucket_name,
bootstrap_actions=[hadoop_config_bootstrapper, hadoop_monitor_bootstrapper, bootstrap_step,
bootstrap_impala_install_step],
ec2_keyname=self.ec2_keyname,
steps=[hive_install_step, pig_install_step],
keep_alive=True,
action_on_failure = 'CANCEL_AND_WAIT',
master_instance_type=master_type,
slave_instance_type=slave_type,
num_instances=num_instance,
ami_version=ami_version)
#Enabling the termination protection
self.conn.set_termination_protection(jobid, True)
#Checking the state of EMR cluster
state = self.conn.describe_jobflow(jobid).state
while state != u'COMPLETED' and state != u'SHUTTING_DOWN' and state != u'FAILED' and state != u'WAITING':
#sleeping to recheck for status.
time.sleep(int(self.emr_status_wait))
state = self.conn.describe_jobflow(jobid).state
if state == u'SHUTTING_DOWN' or state == u'FAILED':
logging.error("Launching EMR cluster failed")
return "ERROR"
#Check if the state is WAITING. Then launch the next steps
if state == u'WAITING':
#Finding the master node dns of EMR cluster
master_dns = self.conn.describe_jobflow(jobid).masterpublicdnsname
logging.info("Launched EMR Cluster Successfully")
logging.info("Master node DNS of EMR " + master_dns)
return "SUCCESS"
except:
logging.error("Launching EMR cluster failed")
return "FAILED"
def main(self):
try:
master_type = 'm3.xlarge'
slave_type = 'm3.xlarge'
num_instance = 3
ami_version = '2.4.8'
emr_status = self.launch_emr_cluster(master_type, slave_type, num_instance, ami_version)
if emr_status == 'SUCCESS':
logging.info("Emr cluster launched successfully")
#.........这里部分代码省略.........