[英]Lambda not working for ec2 with auto-scaling
AWS Lambda 對不屬於任何自動擴展組的 ec2 實例按預期工作 [即 ec2 實例停止和啟動],但它不適用於屬於自動擴展組的 ec2 實例。
對於屬於自動擴展組的 ec2 實例,ec2 實例將重新啟動並再次運行。
AWS Lambda 代碼如下;
import boto3
ec2 = boto3.client('ec2')
def lambda_handler(event, context):
action_handler(event['action'])
def get_ec2_instances():
ec2_int = ec2.describe_instances(
Filters=[{
'Name': 'ops',
'Values': [
'cost-save'
]
}]
)
return ec2_int
def action_handler(action):
ec2_instances = get_ec2_instances()
for reservation in ec2_instances['Reservations']:
for ec2_instance in reservation['Instances']:
if action == "stop":
stop_ec2(ec2_instance)
elif action == "start":
start_ec2(ec2_instance)
def stop_ec2(ec2_int):
if ec2_int['State']['Name'] == 'running':
ec2.stop_instances(InstanceIds=[ec2_int['InstanceId']])
def start_ec2(ec2_int):
if ec2_int['State']['Name'] == 'stopped':
ec2.start_instances(InstanceIds=[ec2_int['InstanceId']])
你能幫忙解決這個問題嗎? 我想確保作為自動縮放一部分的 ec2 實例也必須停止和啟動。
以下是一種解決方法。 您可能需要根據您的設置重構一些代碼。
import boto3
ec2 = boto3.client('ec2')
auto_scaling_group_client = boto3.client('autoscaling')
# TODO Refactor auto_scaling_group_processes list as per your setup
auto_scaling_group_processes = ['Launch',
'Terminate',
'HealthCheck',
'ReplaceUnhealthy',
'ScheduledActions',
'AddToLoadBalancer',
'AlarmNotification',
'AZRebalance']
def lambda_handler(event, context):
action_handler(event['action'])
def get_ec2_instances():
ec2_int = ec2.describe_instances(
Filters=[{
'Name': 'ops',
'Values': [
'cost-save'
]
}]
)
return ec2_int
def action_handler(action):
ec2_instances = get_ec2_instances()
auto_scaling_groups_to_resume = set()
# TODO Can refactor logic added related to auto_scaling
for reservation in ec2_instances['Reservations']:
for ec2_instance in reservation['Instances']:
auto_scaling_group_name = get_auto_scaling_group_name(ec2_instance['InstanceId'])
if action == "stop":
suspend_processes(auto_scaling_group_name)
stop_ec2(ec2_instance)
elif action == "start":
auto_scaling_groups_to_resume.add(auto_scaling_group_name)
start_ec2(ec2_instance)
resume_auto_scaling_group_processes(auto_scaling_groups_to_resume)
def stop_ec2(ec2_int):
if ec2_int['State']['Name'] == 'running':
ec2.stop_instances(InstanceIds=[ec2_int['InstanceId']])
def start_ec2(ec2_int):
if ec2_int['State']['Name'] == 'stopped':
ec2.start_instances(InstanceIds=[ec2_int['InstanceId']])
def resume_processes(auto_scaling_group_name):
if auto_scaling_group_name is not None:
auto_scaling_group_client.resume_processes(
AutoScalingGroupName=auto_scaling_group_name,
ScalingProcesses=auto_scaling_group_processes
)
# TODO Can refactor
def get_auto_scaling_group_name(ec_int_id):
auto_scaling_group = auto_scaling_group_client.describe_auto_scaling_instances(
InstanceIds=[
ec_int_id
]
)
for auto_scaling_int in auto_scaling_group['AutoScalingInstances']:
return auto_scaling_int['AutoScalingGroupName']
# TODO Can refactor
def resume_auto_scaling_group_processes(auto_scaling_groups):
for auto_scaling_group in auto_scaling_groups:
resume_processes(auto_scaling_group)
def suspend_processes(auto_scaling_group_name):
if auto_scaling_group_name is not None:
auto_scaling_group_client.suspend_processes(
AutoScalingGroupName=auto_scaling_group_name,
ScalingProcesses=auto_scaling_group_processes
)
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