# 可用性計算
#
# "nines" は慣習的な表現であり、文字列処理で機械的に数えると誤解を招きます。
# 代表的な目標値は参照表として扱い、必要に応じて要件として明示します。
availability_levels = {
    99.0: "2 nines - 基本的なサービス",
    99.9: "3 nines - 標準的なビジネスアプリケーション",
    99.95: "3.5 nines - 重要なビジネスアプリケーション",
    99.99: "4 nines - ミッションクリティカルなサービス",
    99.999: "5 nines - 金融・医療などの最重要システム",
}

def calculate_availability_metrics(availability_percentage):
    """
    可用性（%）から各種メトリクスを計算（availability_percentage は 0〜100 の数値を想定）
    """
    # 年間の総分数
    minutes_per_year = 365.25 * 24 * 60
    
    # ダウンタイム計算
    downtime_percentage = 100 - availability_percentage
    downtime_minutes_per_year = minutes_per_year * (downtime_percentage / 100)
    
    # 各期間でのダウンタイム
    downtime_per_month = downtime_minutes_per_year / 12
    downtime_per_week = downtime_minutes_per_year / 52.18
    downtime_per_day = downtime_minutes_per_year / 365.25
    
    return {
        "availability": f"{availability_percentage}%",
        "availability_level": availability_levels.get(availability_percentage),
        "downtime_per_year": format_duration(downtime_minutes_per_year),
        "downtime_per_month": format_duration(downtime_per_month),
        "downtime_per_week": format_duration(downtime_per_week),
        "downtime_per_day": format_duration(downtime_per_day)
    }

def format_duration(minutes):
    """分数を人間が読みやすい形式に変換"""
    if minutes >= 1440:  # 1日以上
        days = minutes / 1440
        return f"{days:.1f} days"
    elif minutes >= 60:  # 1時間以上
        hours = minutes / 60
        return f"{hours:.1f} hours"
    else:
        return f"{minutes:.1f} minutes"

# 実際の計算結果
# 99.9% = 年間8.76時間 = 月間43.8分
# 99.95% = 年間4.38時間 = 月間21.9分
# 99.99% = 年間52.6分 = 月間4.38分
# 99.999% = 年間5.26分 = 月間26.3秒

# scripts/dr_failover.py
#!/usr/bin/env python3
"""
Pilot Light DRのフェイルオーバースクリプト
"""

import boto3
import time
import json
from datetime import datetime

class DisasterRecoveryManager:
    def __init__(self, primary_region, dr_region):
        self.primary_region = primary_region
        self.dr_region = dr_region
        self.ec2_primary = boto3.client('ec2', region_name=primary_region)
        self.ec2_dr = boto3.client('ec2', region_name=dr_region)
        self.route53 = boto3.client('route53')
        self.rds_dr = boto3.client('rds', region_name=dr_region)
        
    def check_primary_health(self):
        """プライマリリージョンの健全性チェック"""
        try:
            # ALBのヘルスチェック
            elb = boto3.client('elbv2', region_name=self.primary_region)
            response = elb.describe_target_health(
                TargetGroupArn=self.primary_target_group_arn
            )
            
            healthy_targets = [
                t for t in response['TargetHealthDescriptions']
                if t['TargetHealth']['State'] == 'healthy'
            ]
            
            if len(healthy_targets) == 0:
                return False, "No healthy targets in primary region"
                
            return True, f"{len(healthy_targets)} healthy targets"
            
        except Exception as e:
            return False, f"Health check failed: {str(e)}"
    
    def activate_dr_site(self):
        """DRサイトのアクティベーション"""
        print(f"[{datetime.now()}] Starting DR activation...")
        
        # 1. RDSレプリカのプロモート
        print("Promoting RDS read replica...")
        self.promote_rds_replica()
        
        # 2. EC2インスタンスの起動
        print("Starting EC2 instances...")
        self.start_dr_instances()
        
        # 3. Auto Scaling Groupの調整
        print("Adjusting Auto Scaling Groups...")
        self.scale_dr_environment()
        
        # 4. Route 53のフェイルオーバー
        print("Updating Route 53 records...")
        self.update_dns_records()
        
        # 5. 監視アラートの更新
        print("Updating monitoring...")
        self.update_monitoring()
        
        print(f"[{datetime.now()}] DR activation completed!")
        
    def promote_rds_replica(self):
        """RDSリードレプリカをマスターに昇格"""
        response = self.rds_dr.promote_read_replica(
            DBInstanceIdentifier=self.dr_db_instance_id,
            BackupRetentionPeriod=7
        )
        
        # プロモーション完了を待つ
        waiter = self.rds_dr.get_waiter('db_instance_available')
        waiter.wait(DBInstanceIdentifier=self.dr_db_instance_id)
        
        print(f"RDS replica promoted: {self.dr_db_instance_id}")
        
    def start_dr_instances(self):
        """事前に作成されたDRインスタンスを起動"""
        # タグでDRインスタンスを検索
        response = self.ec2_dr.describe_instances(
            Filters=[
                {'Name': 'tag:DR-Role', 'Values': ['standby']},
                {'Name': 'instance-state-name', 'Values': ['stopped']}
            ]
        )
        
        instance_ids = []
        for reservation in response['Reservations']:
            for instance in reservation['Instances']:
                instance_ids.append(instance['InstanceId'])
        
        if instance_ids:
            self.ec2_dr.start_instances(InstanceIds=instance_ids)
            
            # 起動完了を待つ
            waiter = self.ec2_dr.get_waiter('instance_running')
            waiter.wait(InstanceIds=instance_ids)
            
            print(f"Started {len(instance_ids)} DR instances")
            
    def scale_dr_environment(self):
        """Auto Scaling Groupを本番相当にスケール"""
        autoscaling = boto3.client('autoscaling', region_name=self.dr_region)
        
        # DRのASGを本番レベルにスケール
        autoscaling.set_desired_capacity(
            AutoScalingGroupName=self.dr_asg_name,
            DesiredCapacity=self.prod_desired_capacity,
            HonorCooldown=False
        )
        
        print(f"Scaled ASG to {self.prod_desired_capacity} instances")
        
    def update_dns_records(self):
        """Route 53のレコードをDRサイトに向ける"""
        # ヘルスチェックの作成
        health_check_response = self.route53.create_health_check(
            CallerReference=f"dr-health-{int(time.time())}",
            HealthCheckConfig={
                'Type': 'HTTPS',
                'ResourcePath': '/health',
                'FullyQualifiedDomainName': self.dr_alb_dns_name,
                'Port': 443,
                'RequestInterval': 30,
                'FailureThreshold': 3
            }
        )
        
        # Weighted Routingポリシーの更新
        self.route53.change_resource_record_sets(
            HostedZoneId=self.hosted_zone_id,
            ChangeBatch={
                'Changes': [
                    {
                        'Action': 'UPSERT',
                        'ResourceRecordSet': {
                            'Name': self.domain_name,
                            'Type': 'A',
                            'SetIdentifier': 'DR-Site',
                            'Weight': 100,  # 全トラフィックをDRへ
                            'AliasTarget': {
                                'HostedZoneId': self.dr_alb_zone_id,
                                'DNSName': self.dr_alb_dns_name,
                                'EvaluateTargetHealth': True
                            }
                        }
                    },
                    {
                        'Action': 'UPSERT',
                        'ResourceRecordSet': {
                            'Name': self.domain_name,
                            'Type': 'A',
                            'SetIdentifier': 'Primary-Site',
                            'Weight': 0,  # プライマリへのトラフィックを停止
                            'AliasTarget': {
                                'HostedZoneId': self.primary_alb_zone_id,
                                'DNSName': self.primary_alb_dns_name,
                                'EvaluateTargetHealth': True
                            }
                        }
                    }
                ]
            }
        )
        
        print("DNS records updated for DR failover")
        
    def generate_runbook(self):
        """DR実行手順書の生成"""
        runbook = {
            "title": "Disaster Recovery Runbook",
            "generated_at": datetime.now().isoformat(),
            "steps": [
                {
                    "step": 1,
                    "action": "Verify Primary Failure",
                    "command": "python dr_manager.py check-health",
                    "expected_duration": "2 minutes",
                    "rollback": "N/A"
                },
                {
                    "step": 2,
                    "action": "Initiate DR Failover",
                    "command": "python dr_manager.py failover --confirm",
                    "expected_duration": "15-20 minutes",
                    "rollback": "python dr_manager.py failback"
                },
                {
                    "step": 3,
                    "action": "Verify DR Site Health",
                    "command": "python dr_manager.py verify-dr",
                    "expected_duration": "5 minutes",
                    "rollback": "Investigate specific failures"
                },
                {
                    "step": 4,
                    "action": "Update External Dependencies",
                    "manual_steps": [
                        "Update CDN origin to DR site",
                        "Notify third-party integrations",
                        "Update monitoring dashboards"
                    ],
                    "expected_duration": "10 minutes"
                },
                {
                    "step": 5,
                    "action": "Communication",
                    "manual_steps": [
                        "Send notification to stakeholders",
                        "Update status page",
                        "Prepare RCA timeline"
                    ],
                    "expected_duration": "Ongoing"
                }
            ],
            "contacts": {
                "primary": "oncall@company.com",
                "escalation": "management@company.com",
                "vendor_support": {
                    "aws": "enterprise-support-case",
                    "datadog": "priority-support"
                }
            }
        }
        
        with open('dr_runbook.json', 'w') as f:
            json.dump(runbook, f, indent=2)
            
        print("DR runbook generated: dr_runbook.json")

# 使用例
if __name__ == "__main__":
    dr_manager = DisasterRecoveryManager(
        primary_region='us-east-1',
        dr_region='us-west-2'
    )
    
    # ヘルスチェック
    healthy, message = dr_manager.check_primary_health()
    if not healthy:
        print(f"Primary site unhealthy: {message}")
        dr_manager.activate_dr_site()

# hybrid_cloud_connector.py
import boto3
import requests
from azure.identity import DefaultAzureCredential
from azure.mgmt.network import NetworkManagementClient
from google.cloud import compute_v1

class HybridCloudConnector:
    """
    ハイブリッドクラウド接続を管理するクラス
    """
    
    def __init__(self, config):
        self.config = config
        self.aws_client = boto3.client('ec2')
        self.azure_credential = DefaultAzureCredential()
        self.gcp_client = compute_v1.VpnGatewaysClient()
        
    def establish_site_to_site_vpn(self, on_premise_config):
        """
        オンプレミスと各クラウドプロバイダー間のVPN接続を確立
        """
        connections = {}
        
        # AWS Site-to-Site VPN
        aws_vpn = self.create_aws_vpn(on_premise_config)
        connections['aws'] = aws_vpn
        
        # Azure VPN Gateway
        azure_vpn = self.create_azure_vpn(on_premise_config)
        connections['azure'] = azure_vpn
        
        # GCP Cloud VPN
        gcp_vpn = self.create_gcp_vpn(on_premise_config)
        connections['gcp'] = gcp_vpn
        
        # BGPルーティングの設定
        self.configure_bgp_routing(connections)
        
        return connections
    
    def create_aws_vpn(self, on_premise_config):
        """AWS Site-to-Site VPNの作成"""
        # カスタマーゲートウェイの作成
        cgw_response = self.aws_client.create_customer_gateway(
            BgpAsn=on_premise_config['bgp_asn'],
            PublicIp=on_premise_config['public_ip'],
            Type='ipsec.1',
            TagSpecifications=[{
                'ResourceType': 'customer-gateway',
                'Tags': [
                    {'Key': 'Name', 'Value': f"{self.config['project']}-on-premise-cgw"}
                ]
            }]
        )
        
        # VPN接続の作成
        vpn_response = self.aws_client.create_vpn_connection(
            CustomerGatewayId=cgw_response['CustomerGateway']['CustomerGatewayId'],
            Type='ipsec.1',
            VpnGatewayId=self.config['aws_vpn_gateway_id'],
            Options={
                'StaticRoutesOnly': False,  # BGPを使用
                'TunnelOptions': [
                    {
                        'TunnelInsideCidr': '169.254.10.0/30',
                        'PreSharedKey': self.generate_psk()
                    },
                    {
                        'TunnelInsideCidr': '169.254.11.0/30',
                        'PreSharedKey': self.generate_psk()
                    }
                ]
            }
        )
        
        return {
            'vpn_connection_id': vpn_response['VpnConnection']['VpnConnectionId'],
            'tunnel_1_address': vpn_response['VpnConnection']['Options']['TunnelOptions'][0]['TunnelInsideCidr'],
            'tunnel_2_address': vpn_response['VpnConnection']['Options']['TunnelOptions'][1]['TunnelInsideCidr']
        }
    
    def configure_hybrid_dns(self):
        """
        ハイブリッド環境でのDNS解決設定
        """
        # Route 53 Resolverエンドポイントの作成
        route53resolver = boto3.client('route53resolver')
        
        # インバウンドエンドポイント（オンプレミス→AWS）
        inbound_endpoint = route53resolver.create_resolver_endpoint(
            CreatorRequestId=f"{self.config['project']}-inbound",
            Name=f"{self.config['project']}-inbound-endpoint",
            SecurityGroupIds=self.config['resolver_security_groups'],
            Direction='INBOUND',
            IpAddresses=[
                {'SubnetId': subnet_id}
                for subnet_id in self.config['resolver_subnet_ids']
            ]
        )
        
        # アウトバウンドエンドポイント（AWS→オンプレミス）
        outbound_endpoint = route53resolver.create_resolver_endpoint(
            CreatorRequestId=f"{self.config['project']}-outbound",
            Name=f"{self.config['project']}-outbound-endpoint",
            SecurityGroupIds=self.config['resolver_security_groups'],
            Direction='OUTBOUND',
            IpAddresses=[
                {'SubnetId': subnet_id}
                for subnet_id in self.config['resolver_subnet_ids']
            ]
        )
        
        # フォワーディングルールの作成
        route53resolver.create_resolver_rule(
            CreatorRequestId=f"{self.config['project']}-forward-rule",
            Name="on-premise-forward",
            RuleType='FORWARD',
            DomainName=self.config['on_premise_domain'],
            TargetIps=[
                {'Ip': ip, 'Port': 53}
                for ip in self.config['on_premise_dns_servers']
            ],
            ResolverEndpointId=outbound_endpoint['ResolverEndpoint']['Id']
        )
        
    def implement_data_sync(self):
        """
        ハイブリッド環境でのデータ同期実装
        """
        # AWS DataSyncタスクの作成
        datasync = boto3.client('datasync')
        
        # オンプレミスNFSロケーション
        on_premise_location = datasync.create_location_nfs(
            ServerHostname=self.config['nfs_server'],
            Subdirectory=self.config['nfs_path'],
            OnPremConfig={
                'AgentArns': [self.config['datasync_agent_arn']]
            }
        )
        
        # S3ロケーション
        s3_location = datasync.create_location_s3(
            S3BucketArn=f"arn:aws:s3:::{self.config['s3_bucket']}",
            Subdirectory=self.config['s3_prefix'],
            S3Config={
                'BucketAccessRoleArn': self.config['datasync_role_arn']
            }
        )
        
        # 同期タスクの作成
        sync_task = datasync.create_task(
            SourceLocationArn=on_premise_location['LocationArn'],
            DestinationLocationArn=s3_location['LocationArn'],
            Name=f"{self.config['project']}-hybrid-sync",
            Options={
                'VerifyMode': 'ONLY_FILES_TRANSFERRED',
                'OverwriteMode': 'ALWAYS',
                'Atime': 'NONE',
                'Mtime': 'PRESERVE',
                'PreserveDeletedFiles': 'PRESERVE',
                'PreserveDevices': 'NONE',
                'PosixPermissions': 'PRESERVE',
                'TaskQueueing': 'ENABLED'
            },
            Schedule={
                'ScheduleExpression': 'rate(1 hour)'
            }
        )
        
        return sync_task

# event_driven_architecture.py
import json
import asyncio
from abc import ABC, abstractmethod
from typing import Dict, List, Any
import aioboto3
from dataclasses import dataclass, asdict
from datetime import datetime

@dataclass
class DomainEvent:
    """ドメインイベントの基底クラス"""
    event_id: str
    event_type: str
    aggregate_id: str
    occurred_at: datetime
    version: int = 1
    
    def to_dict(self) -> Dict[str, Any]:
        data = asdict(self)
        data['occurred_at'] = self.occurred_at.isoformat()
        return data

@dataclass
class OrderCreatedEvent(DomainEvent):
    """注文作成イベント"""
    order_id: str
    customer_id: str
    items: List[Dict[str, Any]]
    total_amount: float

class EventPublisher:
    """イベント発行者"""
    def __init__(self, event_bus_name: str):
        self.event_bus_name = event_bus_name
        self.session = aioboto3.Session()
        
    async def publish(self, event: DomainEvent):
        """イベントをEventBridgeに発行"""
        async with self.session.client('events') as events:
            response = await events.put_events(
                Entries=[{
                    'Source': f'com.example.{event.event_type}',
                    'DetailType': event.__class__.__name__,
                    'Detail': json.dumps(event.to_dict()),
                    'EventBusName': self.event_bus_name
                }]
            )
            
            if response['FailedEntryCount'] > 0:
                raise Exception(f"Failed to publish event: {response['Entries']}")

class EventHandler(ABC):
    """イベントハンドラーの基底クラス"""
    
    @abstractmethod
    async def handle(self, event: Dict[str, Any]):
        pass

class InventoryReservationHandler(EventHandler):
    """在庫予約ハンドラー"""
    
    def __init__(self, inventory_service):
        self.inventory_service = inventory_service
        
    async def handle(self, event: Dict[str, Any]):
        """注文作成イベントを受けて在庫を予約"""
        order_data = json.loads(event['detail'])
        
        try:
            # 各商品の在庫を予約
            reservations = []
            for item in order_data['items']:
                reservation = await self.inventory_service.reserve_stock(
                    product_id=item['product_id'],
                    quantity=item['quantity'],
                    order_id=order_data['order_id']
                )
                reservations.append(reservation)
            
            # 在庫予約完了イベントを発行
            await self.publish_reservation_completed(
                order_id=order_data['order_id'],
                reservations=reservations
            )
            
        except InsufficientStockError as e:
            # 在庫不足イベントを発行
            await self.publish_insufficient_stock(
                order_id=order_data['order_id'],
                product_id=e.product_id,
                requested=e.requested,
                available=e.available
            )

class SagaOrchestrator:
    """分散トランザクションを管理するSagaオーケストレーター"""
    
    def __init__(self, state_machine_arn: str):
        self.state_machine_arn = state_machine_arn
        self.session = aioboto3.Session()
        
    async def start_order_saga(self, order_data: Dict[str, Any]):
        """注文処理のSagaを開始"""
        async with self.session.client('stepfunctions') as sfn:
            response = await sfn.start_execution(
                stateMachineArn=self.state_machine_arn,
                name=f"order-saga-{order_data['order_id']}",
                input=json.dumps({
                    'orderId': order_data['order_id'],
                    'customerId': order_data['customer_id'],
                    'items': order_data['items'],
                    'totalAmount': order_data['total_amount'],
                    'sagaStatus': 'STARTED'
                })
            )
            
            return response['executionArn']

# AWS Step Functions の状態マシン定義
saga_state_machine = {
    "Comment": "Order processing saga",
    "StartAt": "ReserveInventory",
    "States": {
        "ReserveInventory": {
            "Type": "Task",
            "Resource": "arn:aws:states:::lambda:invoke",
            "Parameters": {
                "FunctionName": "inventory-reservation-function",
                "Payload.$": "$"
            },
            "Retry": [{
                "ErrorEquals": ["States.TaskFailed"],
                "IntervalSeconds": 2,
                "MaxAttempts": 3,
                "BackoffRate": 2
            }],
            "Catch": [{
                "ErrorEquals": ["InsufficientStockError"],
                "Next": "HandleInventoryFailure"
            }],
            "Next": "ProcessPayment"
        },
        "ProcessPayment": {
            "Type": "Task",
            "Resource": "arn:aws:states:::lambda:invoke",
            "Parameters": {
                "FunctionName": "payment-processing-function",
                "Payload.$": "$"
            },
            "Catch": [{
                "ErrorEquals": ["PaymentFailedError"],
                "Next": "CompensateInventory"
            }],
            "Next": "ConfirmOrder"
        },
        "ConfirmOrder": {
            "Type": "Task",
            "Resource": "arn:aws:states:::lambda:invoke",
            "Parameters": {
                "FunctionName": "order-confirmation-function",
                "Payload.$": "$"
            },
            "End": true
        },
        "HandleInventoryFailure": {
            "Type": "Task",
            "Resource": "arn:aws:states:::lambda:invoke",
            "Parameters": {
                "FunctionName": "inventory-failure-handler",
                "Payload.$": "$"
            },
            "Next": "OrderFailed"
        },
        "CompensateInventory": {
            "Type": "Task",
            "Resource": "arn:aws:states:::lambda:invoke",
            "Parameters": {
                "FunctionName": "inventory-compensation-function",
                "Payload.$": "$"
            },
            "Next": "OrderFailed"
        },
        "OrderFailed": {
            "Type": "Fail",
            "Error": "OrderProcessingFailed",
            "Cause": "Failed to process order due to inventory or payment issues"
        }
    }
}

# cqrs_implementation.py
from typing import Optional, List, Dict, Any
from dataclasses import dataclass
from datetime import datetime
import asyncio
import aioboto3

# コマンド側の実装
@dataclass
class CreateOrderCommand:
    customer_id: str
    items: List[Dict[str, Any]]
    shipping_address: Dict[str, str]

class OrderCommandHandler:
    """注文コマンドハンドラー"""
    
    def __init__(self, write_db, event_store):
        self.write_db = write_db
        self.event_store = event_store
        
    async def handle_create_order(self, command: CreateOrderCommand) -> str:
        """注文作成コマンドの処理"""
        # ビジネスロジックの実行
        order = Order(
            customer_id=command.customer_id,
            items=command.items,
            shipping_address=command.shipping_address,
            status=OrderStatus.PENDING
        )
        
        # 書き込みDBに保存
        await self.write_db.save_order(order)
        
        # イベントストアにイベントを保存
        event = OrderCreatedEvent(
            order_id=order.id,
            customer_id=order.customer_id,
            occurred_at=datetime.utcnow()
        )
        await self.event_store.append(event)
        
        # 読み取りモデルの更新を非同期で実行
        await self.publish_event_for_projection(event)
        
        return order.id

# クエリ側の実装
class OrderQueryService:
    """注文クエリサービス"""
    
    def __init__(self, read_db):
        self.read_db = read_db
        
    async def get_order_by_id(self, order_id: str) -> Optional[Dict[str, Any]]:
        """注文IDによる検索（読み取り最適化されたビュー）"""
        return await self.read_db.find_order_view(order_id)
        
    async def get_customer_orders(
        self,
        customer_id: str,
        status: Optional[str] = None,
        from_date: Optional[datetime] = None,
        to_date: Optional[datetime] = None,
        limit: int = 100
    ) -> List[Dict[str, Any]]:
        """顧客の注文履歴検索（複雑なクエリに最適化）"""
        query = {
            'customer_id': customer_id
        }
        
        if status:
            query['status'] = status
            
        if from_date or to_date:
            query['created_at'] = {}
            if from_date:
                query['created_at']['$gte'] = from_date
            if to_date:
                query['created_at']['$lte'] = to_date
                
        return await self.read_db.find_orders(query, limit=limit)

# プロジェクション（読み取りモデル）の更新
class OrderProjectionHandler:
    """イベントから読み取りモデルを構築"""
    
    def __init__(self, read_db, cache):
        self.read_db = read_db
        self.cache = cache
        
    async def handle_order_created(self, event: OrderCreatedEvent):
        """注文作成イベントから読み取りビューを更新"""
        # 詳細な注文ビューを作成
        order_view = {
            'order_id': event.order_id,
            'customer_id': event.customer_id,
            'created_at': event.occurred_at,
            'status': 'PENDING',
            'total_amount': self.calculate_total(event.items),
            'item_count': len(event.items),
            # 検索用の非正規化データ
            'customer_name': await self.get_customer_name(event.customer_id),
            'product_names': await self.get_product_names(event.items)
        }
        
        # 読み取りDBに保存
        await self.read_db.save_order_view(order_view)
        
        # キャッシュも更新
        await self.cache.set(
            f"order:{event.order_id}",
            order_view,
            expire=3600  # 1時間
        )
        
        # 顧客別の集計ビューも更新
        await self.update_customer_statistics(event.customer_id)

# troubleshooting_framework.py
import asyncio
import logging
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
import boto3

@dataclass
class Symptom:
    """観測された症状"""
    description: str
    severity: str  # LOW, MEDIUM, HIGH, CRITICAL
    first_observed: datetime
    frequency: str  # ONCE, INTERMITTENT, CONSTANT
    affected_components: List[str]

@dataclass
class Hypothesis:
    """問題の仮説"""
    description: str
    probability: float  # 0.0 - 1.0
    test_method: str
    required_data: List[str]

class TroubleshootingEngine:
    """体系的なトラブルシューティングエンジン"""
    
    def __init__(self):
        self.cloudwatch = boto3.client('cloudwatch')
        self.logs = boto3.client('logs')
        self.xray = boto3.client('xray')
        
    async def diagnose(self, symptom: Symptom) -> Dict[str, Any]:
        """症状から問題を診断"""
        
        # 1. データ収集
        data = await self.collect_diagnostic_data(symptom)
        
        # 2. 仮説生成
        hypotheses = self.generate_hypotheses(symptom, data)
        
        # 3. 仮説検証
        verified_hypotheses = await self.verify_hypotheses(hypotheses, data)
        
        # 4. 根本原因分析
        root_cause = self.identify_root_cause(verified_hypotheses)
        
        # 5. 解決策の提案
        solutions = self.propose_solutions(root_cause)
        
        return {
            'symptom': symptom,
            'collected_data': data,
            'hypotheses': hypotheses,
            'verified_hypotheses': verified_hypotheses,
            'root_cause': root_cause,
            'proposed_solutions': solutions,
            'diagnosis_timestamp': datetime.utcnow()
        }
    
    async def collect_diagnostic_data(self, symptom: Symptom) -> Dict[str, Any]:
        """診断に必要なデータを収集"""
        
        data = {}
        time_range = self.determine_time_range(symptom)
        
        # メトリクスデータの収集
        for component in symptom.affected_components:
            metrics = await self.collect_metrics(component, time_range)
            data[f'{component}_metrics'] = metrics
            
        # ログデータの収集
        logs = await self.collect_logs(symptom.affected_components, time_range)
        data['logs'] = logs
        
        # トレースデータの収集
        traces = await self.collect_traces(time_range)
        data['traces'] = traces
        
        # 関連イベントの収集
        events = await self.collect_events(time_range)
        data['events'] = events
        
        return data
    
    def generate_hypotheses(self, symptom: Symptom, data: Dict[str, Any]) -> List[Hypothesis]:
        """症状とデータから仮説を生成"""
        
        hypotheses = []
        
        # パフォーマンス劣化の場合
        if "slow" in symptom.description.lower() or "latency" in symptom.description.lower():
            hypotheses.extend([
                Hypothesis(
                    description="CPU使用率の上昇によるパフォーマンス劣化",
                    probability=self.calculate_cpu_hypothesis_probability(data),
                    test_method="CPU使用率とレスポンスタイムの相関分析",
                    required_data=["cpu_utilization", "response_time"]
                ),
                Hypothesis(
                    description="データベースのスロークエリによる遅延",
                    probability=self.calculate_db_hypothesis_probability(data),
                    test_method="データベースクエリログの分析",
                    required_data=["db_query_logs", "db_cpu_utilization"]
                ),
                Hypothesis(
                    description="ネットワーク遅延による影響",
                    probability=self.calculate_network_hypothesis_probability(data),
                    test_method="ネットワークレイテンシの測定",
                    required_data=["network_latency", "packet_loss"]
                )
            ])
        
        # エラー増加の場合
        if "error" in symptom.description.lower() or "fail" in symptom.description.lower():
            hypotheses.extend([
                Hypothesis(
                    description="依存サービスの障害",
                    probability=self.calculate_dependency_hypothesis_probability(data),
                    test_method="依存サービスのヘルスチェック",
                    required_data=["dependency_health", "circuit_breaker_status"]
                ),
                Hypothesis(
                    description="リソース枯渇によるエラー",
                    probability=self.calculate_resource_hypothesis_probability(data),
                    test_method="リソース使用状況の分析",
                    required_data=["memory_utilization", "disk_space", "connection_pool"]
                )
            ])
        
        # 確率でソート
        hypotheses.sort(key=lambda h: h.probability, reverse=True)
        
        return hypotheses
    
    async def verify_hypotheses(
        self,
        hypotheses: List[Hypothesis],
        data: Dict[str, Any]
    ) -> List[Tuple[Hypothesis, bool, Dict[str, Any]]]:
        """仮説を検証"""
        
        verified = []
        
        for hypothesis in hypotheses:
            # 必要なデータが揃っているか確認
            if all(key in data for key in hypothesis.required_data):
                # 仮説に応じた検証を実行
                if "CPU" in hypothesis.description:
                    result = self.verify_cpu_hypothesis(data)
                elif "データベース" in hypothesis.description:
                    result = await self.verify_database_hypothesis(data)
                elif "ネットワーク" in hypothesis.description:
                    result = self.verify_network_hypothesis(data)
                elif "依存サービス" in hypothesis.description:
                    result = await self.verify_dependency_hypothesis(data)
                else:
                    result = (False, {})
                
                verified.append((hypothesis, result[0], result[1]))
        
        return verified
    
    def identify_root_cause(
        self,
        verified_hypotheses: List[Tuple[Hypothesis, bool, Dict[str, Any]]]
    ) -> Optional[Dict[str, Any]]:
        """根本原因を特定"""
        
        # 検証された仮説から最も可能性の高いものを選択
        confirmed = [(h, evidence) for h, verified, evidence in verified_hypotheses if verified]
        
        if not confirmed:
            return None
        
        # 因果関係を分析
        root_cause = {
            'primary_cause': confirmed[0][0].description,
            'evidence': confirmed[0][1],
            'confidence': self.calculate_confidence(confirmed),
            'contributing_factors': [h.description for h, _ in confirmed[1:]]
        }
        
        return root_cause
    
    def propose_solutions(self, root_cause: Optional[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """解決策を提案"""
        
        if not root_cause:
            return [{
                'action': '追加調査',
                'description': '根本原因を特定できませんでした。より詳細なログとメトリクスの収集が必要です。',
                'priority': 'HIGH'
            }]
        
        solutions = []
        
        # 原因に応じた解決策
        if "CPU" in root_cause['primary_cause']:
            solutions.extend([
                {
                    'action': 'スケールアウト',
                    'description': 'インスタンス数を増やしてCPU負荷を分散',
                    'priority': 'HIGH',
                    'implementation': 'aws autoscaling set-desired-capacity --desired-capacity +2'
                },
                {
                    'action': 'コード最適化',
                    'description': 'CPU使用率の高い処理を特定し最適化',
                    'priority': 'MEDIUM',
                    'implementation': 'プロファイリングツールで特定後、アルゴリズム改善'
                }
            ])
        
        elif "データベース" in root_cause['primary_cause']:
            solutions.extend([
                {
                    'action': 'クエリ最適化',
                    'description': 'スロークエリを特定し、インデックスを追加',
                    'priority': 'HIGH',
                    'implementation': 'EXPLAIN ANALYZEで実行計画を確認し、適切なインデックスを作成'
                },
                {
                    'action': 'リードレプリカ追加',
                    'description': '読み取り負荷を分散',
                    'priority': 'MEDIUM',
                    'implementation': 'RDSリードレプリカを作成し、読み取りクエリを振り分け'
                }
            ])
        
        return solutions

# 実践的なトラブルシューティングスクリプト
async def investigate_production_issue():
    """本番環境の問題を調査"""
    
    engine = TroubleshootingEngine()
    
    # 観測された症状
    symptom = Symptom(
        description="APIレスポンスタイムが通常の3倍に増加",
        severity="HIGH",
        first_observed=datetime.utcnow() - timedelta(hours=2),
        frequency="CONSTANT",
        affected_components=["api-gateway", "app-server", "database"]
    )
    
    # 診断実行
    diagnosis = await engine.diagnose(symptom)
    
    # 結果のレポート生成
    report = generate_diagnosis_report(diagnosis)
    
    # Slackに通知
    await notify_slack(report)
    
    # 自動修復可能な場合は実行
    if diagnosis['proposed_solutions']:
        auto_remediation_solutions = [
            s for s in diagnosis['proposed_solutions']
            if s.get('auto_remediation', False)
        ]
        
        for solution in auto_remediation_solutions:
            await execute_remediation(solution)

# 段階的移行戦略
migration_phases = {
    "Phase 1": {
        "duration": "3 months",
        "scope": "Development and Testing environments",
        "approach": "Lift and shift",
        "risk": "LOW"
    },
    "Phase 2": {
        "duration": "6 months",
        "scope": "Non-critical applications",
        "approach": "Re-platform with containers",
        "risk": "MEDIUM"
    },
    "Phase 3": {
        "duration": "12 months",
        "scope": "Core banking applications",
        "approach": "Hybrid with data replication",
        "risk": "HIGH",
        "special_considerations": [
            "Real-time data sync",
            "Encryption in transit and at rest",
            "Compliance validation"
        ]
    }
}

# データ同期の実装
class HybridDataSync:
    def __init__(self, on_premise_db, cloud_db):
        self.on_premise_db = on_premise_db
        self.cloud_db = cloud_db
        self.cdc_client = self.setup_cdc()
        
    def setup_cdc(self):
        """Change Data Captureのセットアップ"""
        # AWS DMS を使用したリアルタイムレプリケーション
        dms = boto3.client('dms')
        
        # レプリケーションインスタンスの作成
        replication_instance = dms.create_replication_instance(
            ReplicationInstanceIdentifier='finance-hybrid-sync',
            ReplicationInstanceClass='dms.r5.large',
            MultiAZ=True,
            PubliclyAccessible=False,
            VpcSecurityGroupIds=['sg-secure-dms']
        )
        
        # ソースとターゲットのエンドポイント作成
        source_endpoint = dms.create_endpoint(
            EndpointIdentifier='on-premise-oracle',
            EndpointType='source',
            EngineName='oracle',
            ServerName='10.0.1.100',
            Port=1521,
            DatabaseName='FINCORE',
            SslMode='require'
        )
        
        target_endpoint = dms.create_endpoint(
            EndpointIdentifier='cloud-aurora',
            EndpointType='target',
            EngineName='aurora-postgresql',
            ServerName='finance-aurora.cluster-xxx.amazonaws.com',
            Port=5432,
            DatabaseName='fincore'
        )
        
        return dms