🔄 Auto Scaling ライフサイクル

import boto3
import json

autoscaling = boto3.client('autoscaling')
s3 = boto3.client('s3')

def lambda_handler(event, context):
    # EventBridgeからのLifeCycle Hookイベントを処理
    
    detail = event['detail']
    instance_id = detail['EC2InstanceId']
    lifecycle_hook_name = detail['LifecycleHookName']
    asg_name = detail['AutoScalingGroupName']
    lifecycle_token = detail['LifecycleActionToken']
    
    try:
        # カスタム処理を実行（例：アプリケーションデプロイ）
        if 'launching' in lifecycle_hook_name.lower():
            # 起動時の処理
            deploy_application(instance_id)
            
        elif 'terminating' in lifecycle_hook_name.lower():
            # 終了時の処理
            save_logs_to_s3(instance_id)
        
        # 処理成功 - CONTINUEを送信
        autoscaling.complete_lifecycle_action(
            LifecycleHookName=lifecycle_hook_name,
            AutoScalingGroupName=asg_name,
            LifecycleActionToken=lifecycle_token,
            LifecycleActionResult='CONTINUE',
            InstanceId=instance_id
        )
        
        return {
            'statusCode': 200,
            'body': json.dumps('LifeCycle Hook completed successfully')
        }
        
    except Exception as e:
        print(f"Error: {str(e)}")
        
        # 処理失敗 - ABANDONを送信
        autoscaling.complete_lifecycle_action(
            LifecycleHookName=lifecycle_hook_name,
            AutoScalingGroupName=asg_name,
            LifecycleActionToken=lifecycle_token,
            LifecycleActionResult='ABANDON',
            InstanceId=instance_id
        )
        
        raise

def deploy_application(instance_id):
    # アプリケーションデプロイロジック
    print(f"Deploying application to {instance_id}")
    # SSM Run Commandなどを使用して実行
    pass

def save_logs_to_s3(instance_id):
    # ログをS3に保存するロジック
    print(f"Saving logs from {instance_id} to S3")
    pass

# 起動時のLifeCycle Hook作成
aws autoscaling put-lifecycle-hook \
  --lifecycle-hook-name launching-hook \
  --auto-scaling-group-name my-asg \
  --lifecycle-transition autoscaling:EC2_INSTANCE_LAUNCHING \
  --default-result CONTINUE \
  --heartbeat-timeout 600 \
  --notification-target-arn arn:aws:events:region:account:rule/my-rule

# 終了時のLifeCycle Hook作成
aws autoscaling put-lifecycle-hook \
  --lifecycle-hook-name terminating-hook \
  --auto-scaling-group-name my-asg \
  --lifecycle-transition autoscaling:EC2_INSTANCE_TERMINATING \
  --default-result CONTINUE \
  --heartbeat-timeout 300 \
  --notification-target-arn arn:aws:events:region:account:rule/my-rule

# ========================================
# Step 1: LifeCycle Hook の設定（初回のみ）
# ========================================

aws autoscaling put-lifecycle-hook \
  --lifecycle-hook-name "my-launching-hook" \
  --auto-scaling-group-name "my-asg" \
  --lifecycle-transition "autoscaling:EC2_INSTANCE_LAUNCHING" \
  --default-result "ABANDON" \
  --heartbeat-timeout 1800 \
  --notification-target-arn "arn:aws:events:ap-northeast-1:123456789012:rule/my-lifecycle-rule"

# ========================================
# Step 2: Lambda関数での処理（インスタンスごと）
# ========================================

import boto3
import time

autoscaling = boto3.client('autoscaling')

def lambda_handler(event, context):
    # イベントから必要な情報を取得
    detail = event['detail']
    instance_id = detail['EC2InstanceId']
    hook_name = detail['LifecycleHookName']
    asg_name = detail['AutoScalingGroupName']
    token = detail['LifecycleActionToken']
    
    try:
        # 長時間処理の場合、ハートビートを送信
        for i in range(3):
            # 処理を実行
            perform_deployment_step(instance_id, i)
            
            # タイムアウト前にハートビート送信
            if i < 2:  # 最後は不要
                autoscaling.record_lifecycle_action_heartbeat(
                    LifecycleHookName=hook_name,
                    AutoScalingGroupName=asg_name,
                    LifecycleActionToken=token,
                    InstanceId=instance_id
                )
                print(f"Heartbeat sent for step {i+1}")
            
            time.sleep(300)  # 5分待機
        
        # 処理成功 - CONTINUE で完了通知
        autoscaling.complete_lifecycle_action(
            LifecycleHookName=hook_name,
            AutoScalingGroupName=asg_name,
            LifecycleActionToken=token,
            LifecycleActionResult='CONTINUE',
            InstanceId=instance_id
        )
        
        return {'status': 'success'}
        
    except Exception as e:
        print(f"Error: {str(e)}")
        
        # 処理失敗 - ABANDON で完了通知
        autoscaling.complete_lifecycle_action(
            LifecycleHookName=hook_name,
            AutoScalingGroupName=asg_name,
            LifecycleActionToken=token,
            LifecycleActionResult='ABANDON',
            InstanceId=instance_id
        )
        
        raise

def perform_deployment_step(instance_id, step):
    print(f"Performing deployment step {step} for {instance_id}")
    # デプロイ処理の実装
    pass