Dynamic Start and Stop Scheduler Lambda Functions

start_scheduler_function.py

import boto3, os, json, time

REGION = os.environ['REGION']
ASG_NAME = os.environ['ASG_NAME']
SSM_PARAMETER_NAME = os.environ['SSM_PARAMETER_NAME']

ssm = boto3.client('ssm', region_name=REGION)
ec2 = boto3.client('ec2', region_name=REGION)
autoscaling = boto3.client('autoscaling', region_name=REGION)
def lambda_handler(event, context):
  # get parameter_store instance id value
  INSTANCE_ID = ssm.get_parameter(Name=SSM_PARAMETER_NAME)['Parameter']['Value']
 
  #start instance
  ec2.start_instances(
    InstanceIds=[INSTANCE_ID]
  )
  #wait until instance is in Running state
  waiter_running = ec2.get_waiter('instance_running')
  waiter_running.wait(
    InstanceIds=[INSTANCE_ID]
  )
  print('INFO: Started ' + INSTANCE_ID)
  #wait until instance health is OK
  waiter_healthcheck = ec2.get_waiter('instance_status_ok')
  waiter_healthcheck.wait(
    InstanceIds=[INSTANCE_ID]
  )
  print('INFO: Instance ' + INSTANCE_ID + ' is healthy...')
  time.sleep(60)
  #set instance to InService
  autoscaling.exit_standby(
    InstanceIds=[INSTANCE_ID],
    AutoScalingGroupName=ASG_NAME
  )
  #wait until LifecycleState is InService
  response = autoscaling.describe_auto_scaling_instances(
    InstanceIds=[INSTANCE_ID]
  )
  while str(json.dumps(response["AutoScalingInstances"][0]["LifecycleState"])).replace('"', '') != "InService":
    time.sleep(60)
    response = autoscaling.describe_auto_scaling_instances(
      InstanceIds=[INSTANCE_ID]
    )
  print('INFO: Instance set to InService...')
  # set desired capacity to 1
  autoscaling.set_desired_capacity(
    AutoScalingGroupName=ASG_NAME,
    DesiredCapacity=1
  )
  print('INFO: Set desired capacity to 1...')