# CurseForge API 基本設定（2025年版）
API_BASE_URL = “https://api.curseforge.com”
API_VERSION = “v1”
OAUTH_ENDPOINT = “https://oauth.curseforge.com/token”

# OAuth 2.0認証ヘッダー
HEADERS = {
“Accept”: “application/json”,
“Authorization”: “Bearer YOUR_ACCESS_TOKEN”,
“User-Agent”: “YourApp/1.0”,
“Content-Type”: “application/json”
}

# レート制限対応設定
RATE_LIMIT = {
“requests_per_hour”: 2000, # 2025年版で増加
“burst_limit”: 50,
“retry_after”: 60
}

import os
import requests
import time
from typing import Dict, Any, Optional
from datetime import datetime, timedelta

class CurseForgeAPIClient:
def __init__(self):
self.client_id = os.getenv(“CURSEFORGE_CLIENT_ID”)
self.client_secret = os.getenv(“CURSEFORGE_CLIENT_SECRET”)
self.base_url = “https://api.curseforge.com/v1”
self.oauth_url = “https://oauth.curseforge.com/token”
self.access_token = None
self.token_expires_at = None
self.rate_limit_remaining = 2000
self.rate_limit_reset = None

def authenticate(self) -> bool:
“””OAuth 2.0認証を実行”””
try:
auth_data = {
“grant_type”: “client_credentials”,
“client_id”: self.client_id,
“client_secret”: self.client_secret,
“scope”: “curseforge:read”
}

response = requests.post(self.oauth_url, data=auth_data)

if response.status_code == 200:
token_data = response.json()
self.access_token = token_data[“access_token”]
expires_in = token_data.get(“expires_in”, 3600)
self.token_expires_at = datetime.now() + timedelta(seconds=expires_in)
return True
else:
print(f”認証エラー: {response.status_code}”)
return False

except Exception as e:
print(f”認証例外: {e}”)
return False

def get_headers(self) -> Dict[str, str]:
“””APIリクエストヘッダーを生成”””
if not self.access_token or datetime.now() >= self.token_expires_at:
self.authenticate()

return {
“Accept”: “application/json”,
“Authorization”: f”Bearer {self.access_token}”,
“User-Agent”: “ModpackAutoUpdater/2025.8”,
“Content-Type”: “application/json”
}

def make_request(self, endpoint: str, params: Dict = None) -> Optional[Dict]:
“””レート制限対応APIリクエスト”””
if self.rate_limit_remaining <= 10: wait_time = (self.rate_limit_reset - datetime.now()).seconds print(f"レート制限に近づいています。{wait_time}秒待機します。") time.sleep(wait_time) url = f"{self.base_url}/{endpoint.lstrip('/')}" try: response = requests.get(url, headers=self.get_headers(), params=params) # レート制限情報の更新 self.rate_limit_remaining = int(response.headers.get("X-RateLimit-Remaining", 0)) reset_timestamp = response.headers.get("X-RateLimit-Reset") if reset_timestamp: self.rate_limit_reset = datetime.fromtimestamp(int(reset_timestamp)) if response.status_code == 200: return response.json() elif response.status_code == 429: retry_after = int(response.headers.get("Retry-After", 60)) print(f"レート制限に達しました。{retry_after}秒後に再試行します。") time.sleep(retry_after) return self.make_request(endpoint, params) else: print(f"APIエラー: {response.status_code} - {response.text}") return None except Exception as e: print(f"リクエストエラー: {e}") return None def test_connection(self) -> bool:
“””API接続テスト”””
result = self.make_request(“games”)
return result is not None and “data” in result

#!/usr/bin/env python3
“””
CurseForge Modpack自動同期スクリプト
2025年8月最新版 – 非同期処理対応
“””

import asyncio
import aiohttp
import json
import os
import shutil
import hashlib
import requests
from datetime import datetime, timedelta
from pathlib import Path
from typing import List, Dict, Optional, Tuple
import concurrent.futures
import sqlite3

class ModpackSyncManager:
def __init__(self, config_path: str):
self.config = self.load_config(config_path)
self.api = CurseForgeAPIClient()
self.sync_log = []
self.db_path = self.config.get(‘database_path’, ‘modpack_sync.db’)
self.init_database()

# 2025年版新機能：パフォーマンス監視
self.performance_metrics = {
‘sync_times’: [],
‘download_speeds’: [],
‘error_rates’: []
}

def init_database(self):
“””SQLiteデータベースの初期化”””
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()

cursor.execute(”’
CREATE TABLE IF NOT EXISTS sync_history (
id INTEGER PRIMARY KEY AUTOINCREMENT,
modpack_id INTEGER,
modpack_name TEXT,
old_version TEXT,
new_version TEXT,
sync_time TIMESTAMP,
duration REAL,
status TEXT,
error_message TEXT
)
”’)

cursor.execute(”’
CREATE TABLE IF NOT EXISTS file_checksums (
id INTEGER PRIMARY KEY AUTOINCREMENT,
modpack_id INTEGER,
file_path TEXT,
checksum TEXT,
file_size INTEGER,
last_modified TIMESTAMP,
UNIQUE(modpack_id, file_path)
)
”’)

conn.commit()
conn.close()

def load_config(self, config_path: str) -> Dict:
“””設定ファイルの読み込み”””
with open(config_path, ‘r’, encoding=’utf-8′) as f:
return json.load(f)

async def get_modpack_info(self, session: aiohttp.ClientSession,
modpack_id: int) -> Dict:
“””Modpack情報の非同期取得”””
url = f”{self.api.base_url}/mods/{modpack_id}”
headers = self.api.get_headers()

async with session.get(url, headers=headers) as response:
if response.status == 200:
return await response.json()
else:
raise Exception(f”Modpack情報の取得に失敗: {response.status}”)

async def check_for_updates(self) -> List[Dict]:
“””更新確認処理（非同期）”””
updates = []

async with aiohttp.ClientSession() as session:
tasks = []

for modpack in self.config[‘modpacks’]:
task = self.get_modpack_info(session, modpack[‘id’])
tasks.append((modpack, task))

for modpack, task in tasks:
try:
current_info = await task
latest_files = current_info[‘data’][‘latestFiles’]

# 最適なファイルを選択（2025年版改善）
latest_file = self.select_optimal_file(latest_files)

if latest_file[‘id’] != modpack.get(‘current_file_id’):
updates.append({
‘modpack’: modpack,
‘latest_file’: latest_file,
‘current_file_id’: modpack.get(‘current_file_id’)
})

except Exception as e:
print(f”Modpack {modpack[‘name’]} の確認でエラー: {e}”)

return updates

def select_optimal_file(self, files: List[Dict]) -> Dict:
“””最適なファイルを選択（2025年版新機能）”””
# リリースタイプの優先度設定
priority = {‘release’: 3, ‘beta’: 2, ‘alpha’: 1}

optimal_file = max(files, key=lambda f: (
priority.get(f.get(‘releaseType’, ‘alpha’).lower(), 0),
f.get(‘fileDate’, ”),
-f.get(‘fileLength’, 0) # サイズが小さい方を優先
))

return optimal_file

async def download_with_progress(self, session: aiohttp.ClientSession,
url: str, file_path: str) -> Tuple[bool, float]:
“””プログレス表示付きダウンロード”””
start_time = time.time()

try:
async with session.get(url) as response:
if response.status == 200:
total_size = int(response.headers.get(‘content-length’, 0))
downloaded = 0

with open(file_path, ‘wb’) as f:
async for chunk in response.content.iter_chunked(8192):
f.write(chunk)
downloaded += len(chunk)

# プログレス表示
if total_size > 0:
progress = (downloaded / total_size) * 100
print(f”\rダウンロード進行状況: {progress:.1f}%”, end=””)

download_time = time.time() – start_time
download_speed = total_size / download_time if download_time > 0 else 0

print(f”\nダウンロード完了: {total_size} bytes in {download_time:.2f}s”)
return True, download_speed
else:
return False, 0.0

except Exception as e:
print(f”ダウンロードエラー: {e}”)
return False, 0.0

async def sync_modpack(self, update_info: Dict) -> bool:
“””Modpackの同期処理”””
sync_start_time = time.time()
modpack = update_info[‘modpack’]
latest_file = update_info[‘latest_file’]

try:
# バックアップの作成
backup_success = await self.create_backup_async(modpack[‘name’])
if not backup_success:
print(f”バックアップ作成に失敗: {modpack[‘name’]}”)
return False

# 新しいファイルのダウンロード
download_path = f”/tmp/{latest_file[‘fileName’]}”

async with aiohttp.ClientSession() as session:
download_success, download_speed = await self.download_with_progress(
session, latest_file[‘downloadUrl’], download_path
)

if download_success:
# ファイル整合性検証
if self.verify_file_integrity(download_path, latest_file):
# 展開処理
await self.extract_modpack_async(download_path, modpack[‘install_path’])

# 設定ファイルの更新
self.update_modpack_config(modpack, latest_file[‘id’])

# データベースに記録
sync_duration = time.time() – sync_start_time
self.record_sync_history(
modpack, latest_file, sync_duration, ‘success’
)

# パフォーマンスメトリクスの更新
self.performance_metrics[‘sync_times’].append(sync_duration)
self.performance_metrics[‘download_speeds’].append(download_speed)

return True
else:
print(f”ファイル整合性検証に失敗: {latest_file[‘fileName’]}”)
return False
else:
return False

except Exception as e:
sync_duration = time.time() – sync_start_time
self.record_sync_history(
modpack, latest_file, sync_duration, ‘failed’, str(e)
)
print(f”同期エラー: {e}”)
return False

def record_sync_history(self, modpack: Dict, latest_file: Dict,
duration: float, status: str, error_message: str = None):
“””同期履歴をデータベースに記録”””
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()

cursor.execute(”’
INSERT INTO sync_history
(modpack_id, modpack_name, old_version, new_version, sync_time, duration, status, error_message)
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
”’, (
modpack[‘id’],
modpack[‘name’],
modpack.get(‘current_version’, ‘unknown’),
latest_file[‘displayName’],
datetime.now(),
duration,
status,
error_message
))

conn.commit()
conn.close()

async def run_sync_cycle(self):
“””同期サイクルの実行”””
print(f”同期サイクル開始: {datetime.now()}”)

try:
# 更新確認
updates = await self.check_for_updates()

if updates:
print(f”{len(updates)}個のModpackに更新が見つかりました。”)

# 並行同期処理
sync_tasks = []
for update in updates:
task = self.sync_modpack(update)
sync_tasks.append(task)

results = await asyncio.gather(*sync_tasks, return_exceptions=True)

# 結果の集計
successful = sum(1 for result in results if result is True)
failed = len(results) – successful

print(f”同期完了: 成功 {successful}, 失敗 {failed}”)

else:
print(“更新が必要なModpackはありません。”)

except Exception as e:
print(f”同期サイクルエラー: {e}”)

print(f”同期サイクル終了: {datetime.now()}”)

# crontab設定例（2025年版改良）
# 毎日午前3時にModpack同期を実行（低負荷時間帯）
0 3 * * * /usr/bin/python3 /path/to/modpack_sync.py –mode=scheduled

# 4時間ごとに軽量チェックを実行
0 */4 * * * /usr/bin/python3 /path/to/modpack_sync.py –mode=check-only

# Systemdタイマー設定例（/etc/systemd/system/modpack-sync.timer）
[Unit]
Description=Smart Modpack Sync Timer
Requires=modpack-sync.service

[Timer]
OnCalendar=*-*-* 03:00:00
RandomizedDelaySec=1800
Persistent=true

[Install]
WantedBy=timers.target

# サービス設定（/etc/systemd/system/modpack-sync.service）
[Unit]
Description=Modpack Sync Service
After=network-online.target
Wants=network-online.target

[Service]
Type=oneshot
ExecStart=/usr/bin/python3 /opt/modpack-sync/sync_manager.py
WorkingDirectory=/opt/modpack-sync
User=minecraft
Group=minecraft
Environment=PYTHONPATH=/opt/modpack-sync

[Install]
WantedBy=multi-user.target

import hashlib
import xxhash
import lz4.frame
import os
import struct
import mmap
from typing import Dict, List, Set, Tuple, Optional
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor
import time

@dataclass
class FileInfo:
“””ファイル情報クラス”””
path: str
size: int
sha256_hash: str
xxhash: str
modified_time: float
blocks: List[str] # ブロックハッシュリスト

class DiffPatchManager:
def __init__(self, base_path: str, block_size: int = 64 * 1024): # 64KB blocks
self.base_path = base_path
self.block_size = block_size
self.hash_cache = {}
self.compression_level = 4 # LZ4圧縮レベル

def calculate_xxhash(self, file_path: str) -> str:
“””高速xxHashの計算”””
hasher = xxhash.xxh64()

with open(file_path, ‘rb’) as f:
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
hasher.update(mm)

return hasher.hexdigest()

def calculate_file_hash(self, file_path: str) -> str:
“””ファイルのSHA-256ハッシュを計算（最適化版）”””
if file_path in self.hash_cache:
cached_info = self.hash_cache[file_path]
current_mtime = os.path.getmtime(file_path)

if cached_info[‘mtime’] == current_mtime:
return cached_info[‘hash’]

sha256_hash = hashlib.sha256()

with open(file_path, ‘rb’) as f:
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
for i in range(0, len(mm), 1024 * 1024): # 1MB chunks
chunk = mm[i:i + 1024 * 1024]
sha256_hash.update(chunk)

hash_value = sha256_hash.hexdigest()

# キャッシュに保存
self.hash_cache[file_path] = {
‘hash’: hash_value,
‘mtime’: os.path.getmtime(file_path)
}

return hash_value

def calculate_block_hashes(self, file_path: str) -> List[str]:
“””ブロックレベルハッシュの計算”””
block_hashes = []

with open(file_path, ‘rb’) as f:
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
for offset in range(0, len(mm), self.block_size):
block = mm[offset:offset + self.block_size]
block_hash = hashlib.sha256(block).hexdigest()[:16] # 短縮ハッシュ
block_hashes.append(block_hash)

return block_hashes

def build_file_info(self, file_path: str, relative_path: str) -> FileInfo:
“””ファイル情報の構築”””
stat_info = os.stat(file_path)

return FileInfo(
path=relative_path,
size=stat_info.st_size,
sha256_hash=self.calculate_file_hash(file_path),
xxhash=self.calculate_xxhash(file_path),
modified_time=stat_info.st_mtime,
blocks=self.calculate_block_hashes(file_path) if stat_info.st_size > self.block_size else []
)

def build_file_index_parallel(self, directory: str) -> Dict[str, FileInfo]:
“””並列処理によるファイルインデックス構築”””
file_list = []

for root, dirs, files in os.walk(directory):
for file in files:
file_path = os.path.join(root, file)
relative_path = os.path.relpath(file_path, directory)
file_list.append((file_path, relative_path))

file_index = {}

with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
futures = {
executor.submit(self.build_file_info, fp, rp): rp
for fp, rp in file_list
}

for future in concurrent.futures.as_completed(futures):
relative_path = futures[future]
try:
file_info = future.result()
file_index[relative_path] = file_info
except Exception as e:
print(f”ファイル情報構築エラー: {relative_path} – {e}”)

return file_index

def compare_versions_advanced(self, old_index: Dict[str, FileInfo],
new_index: Dict[str, FileInfo]) -> Dict[str, any]:
“””高度なバージョン比較”””
old_files = set(old_index.keys())
new_files = set(new_index.keys())

added_files = new_files – old_files
removed_files = old_files – new_files
common_files = old_files & new_files

modified_files = []
block_changed_files = []

for file in common_files:
old_info = old_index[file]
new_info = new_index[file]

# 高速比較（xxHash）
if old_info.xxhash != new_info.xxhash:
# 詳細比較（SHA-256）
if old_info.sha256_hash != new_info.sha256_hash:
modified_files.append(file)

# ブロックレベル変更検出
if old_info.blocks and new_info.blocks:
changed_blocks = self.find_changed_blocks(
old_info.blocks, new_info.blocks
)
if changed_blocks:
block_changed_files.append({
‘file’: file,
‘changed_blocks’: changed_blocks,
‘total_blocks’: len(new_info.blocks)
})

return {
‘added’: list(added_files),
‘removed’: list(removed_files),
‘modified’: modified_files,
‘block_changes’: block_changed_files,
‘stats’: {
‘total_files_old’: len(old_files),
‘total_files_new’: len(new_files),
‘files_changed’: len(modified_files),
‘files_added’: len(added_files),
‘files_removed’: len(removed_files)
}
}

def find_changed_blocks(self, old_blocks: List[str],
new_blocks: List[str]) -> List[int]:
“””変更されたブロックの検出”””
changed_blocks = []
max_len = max(len(old_blocks), len(new_blocks))

for i in range(max_len):
old_block = old_blocks[i] if i < len(old_blocks) else None new_block = new_blocks[i] if i < len(new_blocks) else None if old_block != new_block: changed_blocks.append(i) return changed_blocks def create_compressed_patch(self, old_version: str, new_version: str) -> Dict:
“””圧縮パッチファイルの作成”””
print(“パッチ作成開始…”)
start_time = time.time()

old_index = self.build_file_index_parallel(old_version)
new_index = self.build_file_index_parallel(new_version)

diff = self.compare_versions_advanced(old_index, new_index)

# パッチデータの構築
patch_data = {
‘version’: ‘2.0’, # 2025年版
‘timestamp’: datetime.now().isoformat(),
‘compression’: ‘lz4’,
‘changes’: diff,
‘checksums’: {
‘old_version’: self.calculate_directory_hash(old_version),
‘new_version’: self.calculate_directory_hash(new_version)
},
‘metadata’: {
‘block_size’: self.block_size,
‘compression_level’: self.compression_level,
‘creation_time’: time.time() – start_time
}
}

# パッチデータの圧縮
compressed_patch = lz4.frame.compress(
json.dumps(patch_data).encode(‘utf-8’),
compression_level=self.compression_level
)

print(f”パッチ作成完了: {time.time() – start_time:.2f}秒”)
print(f”圧縮率: {len(compressed_patch) / len(json.dumps(patch_data).encode(‘utf-8’)):.2%}”)

return {
‘patch_data’: patch_data,
‘compressed_patch’: compressed_patch,
‘compression_ratio’: len(compressed_patch) / len(json.dumps(patch_data).encode(‘utf-8’))
}

def apply_patch_optimized(self, patch_data: Dict, target_directory: str) -> bool:
“””最適化されたパッチ適用”””
try:
changes = patch_data[‘changes’]
stats = changes[‘stats’]

print(f”パッチ適用開始: {stats[‘files_changed’]}個のファイルを処理”)

# 削除されたファイルの処理
for file_path in changes[‘removed’]:
full_path = os.path.join(target_directory, file_path)
if os.path.exists(full_path):
os.remove(full_path)
print(f”削除: {file_path}”)

# 追加・変更されたファイルの処理（並列実行）
files_to_process = changes[‘added’] + changes[‘modified’]

with ThreadPoolExecutor(max_workers=4) as executor:
futures = []

for file_path in files_to_process:
future = executor.submit(
self._copy_file, file_path, target_directory
)
futures.append((file_path, future))

for file_path, future in futures:
try:
success = future.result()
if success:
print(f”処理完了: {file_path}”)
else:
print(f”処理失敗: {file_path}”)
except Exception as e:
print(f”ファイル処理エラー: {file_path} – {e}”)

# ブロックレベル変更の処理
for block_change in changes[‘block_changes’]:
self._apply_block_changes(block_change, target_directory)

print(“パッチ適用完了”)
return True

except Exception as e:
print(f”パッチ適用エラー: {e}”)
return False

def _copy_file(self, file_path: str, target_directory: str) -> bool:
“””ファイルコピー（エラーハンドリング付き）”””
try:
source_path = os.path.join(self.base_path, ‘new_version’, file_path)
target_path = os.path.join(target_directory, file_path)

# ディレクトリの作成
os.makedirs(os.path.dirname(target_path), exist_ok=True)

# ファイルのコピー
shutil.copy2(source_path, target_path)
return True

except Exception as e:
print(f”ファイルコピーエラー: {file_path} – {e}”)
return False

def _apply_block_changes(self, block_change: Dict, target_directory: str):
“””ブロックレベル変更の適用”””
file_path = block_change[‘file’]
changed_blocks = block_change[‘changed_blocks’]

source_file = os.path.join(self.base_path, ‘new_version’, file_path)
target_file = os.path.join(target_directory, file_path)

try:
with open(source_file, ‘rb’) as src, open(target_file, ‘r+b’) as tgt:
for block_idx in changed_blocks:
offset = block_idx * self.block_size
src.seek(offset)
tgt.seek(offset)

block_data = src.read(self.block_size)
tgt.write(block_data)

print(f”ブロック更新完了: {file_path} ({len(changed_blocks)}ブロック)”)

except Exception as e:
print(f”ブロック更新エラー: {file_path} – {e}”)

import boto3
from azure.storage.blob import BlobServiceClient
import torrentool.api
from typing import List, Dict

class PatchDistributionManager:
def __init__(self, cdn_config: Dict):
self.cdn_config = cdn_config
self.patch_server = cdn_config[‘server_url’]
self.use_cdn = cdn_config.get(‘use_cdn’, True)
self.use_p2p = cdn_config.get(‘use_p2p’, False)

# CDNクライアントの初期化
if self.use_cdn:
self.s3_client = boto3.client(‘s3’) if cdn_config.get(‘aws_enabled’) else None
self.blob_client = BlobServiceClient.from_connection_string(
cdn_config[‘azure_connection’]
) if cdn_config.get(‘azure_enabled’) else None

def upload_patch_multi_cdn(self, patch_file: str, version: str) -> Dict[str, str]:
“””マルチCDNへのパッチファイルアップロード”””
upload_results = {}

try:
# AWS S3アップロード
if self.s3_client:
s3_key = f”patches/{version}/{os.path.basename(patch_file)}”
self.s3_client.upload_file(
patch_file,
self.cdn_config[‘s3_bucket’],
s3_key,
ExtraArgs={
‘ContentType’: ‘application/octet-stream’,
‘CacheControl’: ‘max-age=3600’,
‘Metadata’: {
‘version’: version,
‘upload_time’: datetime.now().isoformat()
}
}
)
upload_results[‘s3’] = f”https://{self.cdn_config[‘s3_bucket’]}.s3.amazonaws.com/{s3_key}”

# Azure Blobアップロード
if self.blob_client:
blob_name = f”patches/{version}/{os.path.basename(patch_file)}”
with open(patch_file, ‘rb’) as data:
blob_client = self.blob_client.get_blob_client(
container=self.cdn_config[‘azure_container’],
blob=blob_name
)
blob_client.upload_blob(data, overwrite=True)

upload_results[‘azure’] = f”https://{self.cdn_config[‘azure_account’]}.blob.core.windows.net/{self.cdn_config[‘azure_container’]}/{blob_name}”

# P2Pトレント作成
if self.use_p2p:
torrent_path = self.create_torrent(patch_file, version)
upload_results[‘p2p’] = torrent_path

return upload_results

except Exception as e:
print(f”アップロードエラー: {e}”)
return {}

def create_torrent(self, file_path: str, version: str) -> str:
“””BitTorrentファイルの作成”””
try:
torrent_path = f”{file_path}.torrent”

torrent = torrentool.api.create_torrent(
file_path,
trackers=[
‘https://tracker.example.com:8080/announce’,
‘udp://tracker.example.com:8080’
],
comment=f”Modpack patch for version {version}”,
created_by=”ModpackAutoUpdater 2025″
)

with open(torrent_path, ‘wb’) as f:
f.write(torrent.to_string())

return torrent_path

except Exception as e:
print(f”トレント作成エラー: {e}”)
return None

def notify_clients_advanced(self, patch_info: Dict) -> None:
“””高度なクライアント通知システム”””
notification_data = {
‘type’: ‘patch_available’,
‘version’: patch_info[‘version’],
‘size’: patch_info[‘size’],
‘download_urls’: patch_info[‘download_urls’],
‘checksum’: patch_info[‘checksum’],
‘priority’: patch_info.get(‘priority’, ‘normal’),
‘rollout_percentage’: patch_info.get(‘rollout_percentage’, 100),
‘metadata’: {
‘compression_ratio’: patch_info.get(‘compression_ratio’, 0),
‘block_changes’: patch_info.get(‘block_changes’, 0),
‘estimated_download_time’: self.estimate_download_time(patch_info[‘size’])
}
}

# 段階的ロールアウト
self.staged_rollout(notification_data)

def staged_rollout(self, notification: Dict) -> None:
“””段階的ロールアウト機能”””
rollout_percentage = notification[‘rollout_percentage’]

if rollout_percentage < 100: # 一部のクライアントのみに配信 eligible_clients = self.select_rollout_clients(rollout_percentage) for client in eligible_clients: self.send_notification_to_client(client, notification) else: # 全クライアントに配信 self.broadcast_notification(notification) def estimate_download_time(self, file_size: int) -> Dict[str, float]:
“””ダウンロード時間の推定”””
connection_speeds = {
‘fast’: 100 * 1024 * 1024, # 100 Mbps
‘medium’: 50 * 1024 * 1024, # 50 Mbps
‘slow’: 10 * 1024 * 1024 # 10 Mbps
}

estimates = {}
for speed_type, speed_bps in connection_speeds.items():
time_seconds = file_size / (speed_bps / 8) # Convert to bytes per second
estimates[speed_type] = time_seconds

return estimates

import tensorflow as tf
import numpy as np
from sklearn.preprocessing import StandardScaler
from datetime import datetime, timedelta
import joblib

class AIOptimizationManager:
def __init__(self):
self.model = None
self.scaler = StandardScaler()
self.prediction_history = []
self.optimization_metrics = {
‘bandwidth_savings’: 0,
‘time_savings’: 0,
‘success_rate’: 0
}

def load_or_create_model(self, model_path: str = None):
“””AIモデルの読み込みまたは作成”””
if model_path and os.path.exists(model_path):
self.model = tf.keras.models.load_model(model_path)
else:
# 新しいモデルの作成
self.model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation=’relu’, input_shape=(10,)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64, activation=’relu’),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32, activation=’relu’),
tf.keras.layers.Dense(1, activation=’sigmoid’)
])

self.model.compile(
optimizer=’adam’,
loss=’binary_crossentropy’,
metrics=[‘accuracy’]
)

def predict_optimal_sync_time(self, server_metrics: Dict) -> datetime:
“””最適な同期時間の予測”””
features = self._extract_features(server_metrics)
features_scaled = self.scaler.transform([features])

# 24時間以内の最適な時間を予測
predictions = []
for hour in range(24):
test_time = datetime.now().replace(hour=hour, minute=0, second=0)
time_features = self._extract_time_features(test_time)
combined_features = np.concatenate([features_scaled[0], time_features])

prediction = self.model.predict([combined_features.reshape(1, -1)])[0][0]
predictions.append((test_time, prediction))

# 最高スコアの時間を返す
optimal_time = max(predictions, key=lambda x: x[1])[0]
return optimal_time

def _extract_features(self, server_metrics: Dict) -> List[float]:
“””サーバーメトリクスから特徴量を抽出”””
return [
server_metrics.get(‘cpu_usage’, 0) / 100,
server_metrics.get(‘memory_usage’, 0) / 100,
server_metrics.get(‘disk_io’, 0) / 1000,
server_metrics.get(‘network_bandwidth’, 0) / 1000,
server_metrics.get(‘active_players’, 0) / server_metrics.get(‘max_players’, 1),
server_metrics.get(‘tps’, 20) / 20, # Ticks per second
len(server_metrics.get(‘loaded_mods’, [])) / 100,
server_metrics.get(‘world_size_gb’, 0) / 10,
server_metrics.get(‘recent_crashes’, 0),
server_metrics.get(‘plugin_count’, 0) / 50
]

def _extract_time_features(self, timestamp: datetime) -> List[float]:
“””時間的特徴量の抽出”””
return [
timestamp.hour / 24,
timestamp.weekday() / 7,
(timestamp.day – 1) / 31,
(timestamp.month – 1) / 12
]

def adaptive_bandwidth_allocation(self, available_bandwidth: int,
download_queue: List[Dict]) -> Dict[str, int]:
“””適応的帯域幅割り当て”””
total_priority_score = sum(item.get(‘priority’, 1) for item in download_queue)

allocation = {}
for item in download_queue:
priority = item.get(‘priority’, 1)
file_size = item.get(‘size’, 0)

# 優先度とファイルサイズに基づく動的割り当て
base_allocation = (priority / total_priority_score) * available_bandwidth

# ファイルサイズ調整
size_factor = min(file_size / (10 * 1024 * 1024), 2) # 10MB基準
adjusted_allocation = int(base_allocation * size_factor)

allocation[item[‘id’]] = max(adjusted_allocation, available_bandwidth // 10)

return allocation

class PredictiveAnalytics:
def __init__(self):
self.historical_data = []
self.trend_analysis = {}

def analyze_update_patterns(self, modpack_history: List[Dict]) -> Dict:
“””アップデートパターンの分析”””
patterns = {
‘peak_days’: {},
‘average_interval’: 0,
‘size_trends’: [],
‘success_rates’: []
}

# 曜日別アップデート頻度
for record in modpack_history:
update_time = datetime.fromisoformat(record[‘timestamp’])
day_name = update_time.strftime(‘%A’)
patterns[‘peak_days’][day_name] = patterns[‘peak_days’].get(day_name, 0) + 1

# 平均更新間隔
if len(modpack_history) > 1:
intervals = []
for i in range(1, len(modpack_history)):
prev_time = datetime.fromisoformat(modpack_history[i-1][‘timestamp’])
curr_time = datetime.fromisoformat(modpack_history[i][‘timestamp’])
interval = (curr_time – prev_time).total_seconds() / 3600 # hours
intervals.append(interval)

patterns[‘average_interval’] = sum(intervals) / len(intervals)

return patterns

def predict_next_update(self, modpack_id: int) -> Dict:
“””次回アップデートの予測”””
# 履歴データの取得
history = self.get_modpack_history(modpack_id)
patterns = self.analyze_update_patterns(history)

if not history:
return {‘prediction’: None, ‘confidence’: 0}

last_update = datetime.fromisoformat(history[-1][‘timestamp’])
average_interval = patterns[‘average_interval’]

if average_interval > 0:
predicted_time = last_update + timedelta(hours=average_interval)
confidence = min(len(history) * 0.1, 0.9) # より多くの履歴で信頼度向上

return {
‘prediction’: predicted_time.isoformat(),
‘confidence’: confidence,
‘interval_hours’: average_interval
}

return {‘prediction’: None, ‘confidence’: 0}

import logging
from datetime import datetime
from typing import Dict, List, Optional
from enum import Enum
import psutil
import json

class DiagnosticLevel(Enum):
INFO = “INFO”
WARNING = “WARNING”
ERROR = “ERROR”
CRITICAL = “CRITICAL”

class AIDiagnosticManager:
def __init__(self):
self.logger = logging.getLogger(‘ModpackSync’)
self.diagnostic_history = []
self.performance_baseline = {}
self.alert_thresholds = {
‘cpu_usage’: 80,
‘memory_usage’: 85,
‘disk_usage’: 90,
‘network_errors’: 10,
‘api_error_rate’: 5
}

def comprehensive_system_check(self) -> Dict:
“””包括的システムチェック”””
diagnostics = {
‘timestamp’: datetime.now().isoformat(),
‘system_health’: self._check_system_resources(),
‘network_health’: self._check_network_connectivity(),
‘api_health’: self._check_api_status(),
‘storage_health’: self._check_storage_status(),
‘performance_metrics’: self._collect_performance_metrics()
}

# AI分析による異常検出
anomalies = self._detect_anomalies(diagnostics)
diagnostics[‘anomalies’] = anomalies

# 自動修復推奨事項
recommendations = self._generate_recommendations(diagnostics)
diagnostics[‘recommendations’] = recommendations

return diagnostics

def _check_system_resources(self) -> Dict:
“””システムリソースの確認”””
cpu_percent = psutil.cpu_percent(interval=1)
memory = psutil.virtual_memory()
disk = psutil.disk_usage(‘/’)

return {
‘cpu_usage’: cpu_percent,
‘memory_usage’: memory.percent,
‘memory_available_gb’: memory.available / (1024**3),
‘disk_usage’: disk.percent,
‘disk_free_gb’: disk.free / (1024**3),
‘load_average’: os.getloadavg() if hasattr(os, ‘getloadavg’) else [0, 0, 0]
}

def _detect_anomalies(self, diagnostics: Dict) -> List[Dict]:
“””異常検出（Machine Learning ベース）”””
anomalies = []

# CPU使用率異常
if diagnostics[‘system_health’][‘cpu_usage’] > self.alert_thresholds[‘cpu_usage’]:
anomalies.append({
‘type’: ‘high_cpu_usage’,
‘level’: DiagnosticLevel.WARNING.value,
‘message’: f”CPU使用率が高くなっています: {diagnostics[‘system_health’][‘cpu_usage’]:.1f}%”,
‘impact’: ‘performance_degradation’,
‘auto_fix’: ‘process_optimization’
})

# メモリ使用率異常
if diagnostics[‘system_health’][‘memory_usage’] > self.alert_thresholds[‘memory_usage’]:
anomalies.append({
‘type’: ‘high_memory_usage’,
‘level’: DiagnosticLevel.ERROR.value,
‘message’: f”メモリ使用率が危険レベルです: {diagnostics[‘system_health’][‘memory_usage’]:.1f}%”,
‘impact’: ‘system_instability’,
‘auto_fix’: ‘memory_cleanup’
})

# ディスク使用率異常
if diagnostics[‘system_health’][‘disk_usage’] > self.alert_thresholds[‘disk_usage’]:
anomalies.append({
‘type’: ‘disk_space_critical’,
‘level’: DiagnosticLevel.CRITICAL.value,
‘message’: f”ディスク使用率が限界です: {diagnostics[‘system_health’][‘disk_usage’]:.1f}%”,
‘impact’: ‘service_failure’,
‘auto_fix’: ‘cleanup_old_files’
})

return anomalies

def _generate_recommendations(self, diagnostics: Dict) -> List[Dict]:
“””AI生成による改善推奨事項”””
recommendations = []

anomalies = diagnostics.get(‘anomalies’, [])

for anomaly in anomalies:
if anomaly[‘auto_fix’] == ‘process_optimization’:
recommendations.append({
‘action’: ‘reduce_concurrent_processes’,
‘description’: ‘並行処理数を削減してCPU負荷を軽減’,
‘priority’: ‘medium’,
‘estimated_impact’: ’15-25% CPU使用率改善’
})

elif anomaly[‘auto_fix’] == ‘memory_cleanup’:
recommendations.append({
‘action’: ‘clear_cache_and_optimize_gc’,
‘description’: ‘キャッシュクリアとガベージコレクション最適化’,
‘priority’: ‘high’,
‘estimated_impact’: ’30-40% メモリ使用量削減’
})

elif anomaly[‘auto_fix’] == ‘cleanup_old_files’:
recommendations.append({
‘action’: ‘automated_cleanup’,
‘description’: ‘古いバックアップファイルとログの自動削除’,
‘priority’: ‘critical’,
‘estimated_impact’: ’20-50% ディスク容量回復’
})

return recommendations

def auto_fix_implementation(self, recommendations: List[Dict]) -> Dict:
“””推奨事項の自動実装”””
results = {
‘executed’: [],
‘failed’: [],
‘skipped’: []
}

for recommendation in recommendations:
try:
action = recommendation[‘action’]

if action == ‘reduce_concurrent_processes’:
# 並行処理数の動的調整
new_max_workers = max(2, os.cpu_count() // 2)
self._update_max_workers(new_max_workers)
results[‘executed’].append(recommendation)

elif action == ‘clear_cache_and_optimize_gc’:
# キャッシュクリアとGC最適化
self._clear_system_cache()
self._optimize_garbage_collection()
results[‘executed’].append(recommendation)

elif action == ‘automated_cleanup’:
# 自動クリーンアップ
cleaned_size = self._perform_automated_cleanup()
recommendation[‘result’] = f'{cleaned_size / (1024**3):.2f} GB回復’
results[‘executed’].append(recommendation)

except Exception as e:
recommendation[‘error’] = str(e)
results[‘failed’].append(recommendation)

return results

def _perform_automated_cleanup(self) -> int:
“””自動クリーンアップの実行”””
total_cleaned = 0

# 7日以上古いログファイルを削除
log_directory = ‘/var/log/modpack-sync’
cutoff_time = time.time() – (7 * 24 * 3600)

if os.path.exists(log_directory):
for filename in os.listdir(log_directory):
file_path = os.path.join(log_directory, filename)
if os.path.isfile(file_path) and os.path.getmtime(file_path) < cutoff_time: file_size = os.path.getsize(file_path) os.remove(file_path) total_cleaned += file_size # 30日以上古いバックアップを削除 backup_directory = '/opt/modpack-sync/backups' backup_cutoff = time.time() - (30 * 24 * 3600) if os.path.exists(backup_directory): for filename in os.listdir(backup_directory): file_path = os.path.join(backup_directory, filename) if os.path.isfile(file_path) and os.path.getmtime(file_path) < backup_cutoff: file_size = os.path.getsize(file_path) os.remove(file_path) total_cleaned += file_size return total_cleaned

import hashlib
import hmac
import jwt
import cryptography
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64
import os
import time
from typing import Dict, Optional, Tuple

class SecurityManager:
def __init__(self):
self.private_key = None
self.public_key = None
self.fernet_key = None
self.jwt_secret = os.urandom(64)
self.failed_attempts = {}
self.rate_limits = {}

self._initialize_encryption()
self._setup_intrusion_detection()

def _initialize_encryption(self):
“””暗号化システムの初期化”””
# RSA鍵ペアの生成
self.private_key = rsa.generate_private_key(
public_exponent=65537,
key_size=4096
)
self.public_key = self.private_key.public_key()

# Fernet対称暗号化キーの生成
self.fernet_key = Fernet.generate_key()
self.fernet = Fernet(self.fernet_key)

def generate_secure_api_key(self, user_id: str, permissions: List[str]) -> str:
“””セキュアなAPIキーの生成”””
payload = {
‘user_id’: user_id,
‘permissions’: permissions,
‘issued_at’: time.time(),
‘expires_at’: time.time() + 3600, # 1時間有効
‘nonce’: os.urandom(16).hex()
}

token = jwt.encode(payload, self.jwt_secret, algorithm=’HS256′)
return token

def verify_api_key(self, token: str) -> Optional[Dict]:
“””APIキーの検証”””
try:
# レート制限チェック
if not self._check_rate_limit(token):
return None

payload = jwt.decode(token, self.jwt_secret, algorithms=[‘HS256’])

# 有効期限チェック
if time.time() > payload[‘expires_at’]:
return None

return payload

except jwt.InvalidTokenError:
self._record_failed_attempt(token)
return None

def _check_rate_limit(self, identifier: str) -> bool:
“””レート制限チェック”””
current_time = time.time()
window_start = current_time – 3600 # 1時間ウィンドウ

if identifier not in self.rate_limits:
self.rate_limits[identifier] = []

# 古いエントリを削除
self.rate_limits[identifier] = [
timestamp for timestamp in self.rate_limits[identifier]
if timestamp > window_start
]

# レート制限チェック（1時間に100リクエスト）
if len(self.rate_limits[identifier]) >= 100:
return False

self.rate_limits[identifier].append(current_time)
return True

def encrypt_sensitive_data(self, data: str) -> str:
“””機密データの暗号化”””
encrypted_data = self.fernet.encrypt(data.encode())
return base64.urlsafe_b64encode(encrypted_data).decode()

def decrypt_sensitive_data(self, encrypted_data: str) -> str:
“””機密データの復号化”””
try:
decoded_data = base64.urlsafe_b64decode(encrypted_data.encode())
decrypted_data = self.fernet.decrypt(decoded_data)
return decrypted_data.decode()
except Exception:
raise ValueError(“復号化に失敗しました”)

def create_file_signature(self, file_path: str) -> str:
“””ファイル署名の作成”””
with open(file_path, ‘rb’) as f:
file_data = f.read()

signature = self.private_key.sign(
file_data,
padding.PSS(
mgf=padding.MGF1(hashes.SHA256()),
salt_length=padding.PSS.MAX_LENGTH
),
hashes.SHA256()
)

return base64.b64encode(signature).decode()

def verify_file_signature(self, file_path: str, signature: str,
public_key_pem: str) -> bool:
“””ファイル署名の検証”””
try:
# 公開キーの読み込み
public_key = serialization.load_pem_public_key(public_key_pem.encode())

# ファイルデータの読み込み
with open(file_path, ‘rb’) as f:
file_data = f.read()

# 署名の検証
decoded_signature = base64.b64decode(signature.encode())
public_key.verify(
decoded_signature,
file_data,
padding.PSS(
mgf=padding.MGF1(hashes.SHA256()),
salt_length=padding.PSS.MAX_LENGTH
),
hashes.SHA256()
)

return True

except Exception as e:
print(f”署名検証エラー: {e}”)
return False

def secure_file_transfer(self, file_path: str,
destination_url: str) -> Tuple[bool, Optional[str]]:
“””セキュアファイル転送”””
try:
# ファイルハッシュの計算
file_hash = self._calculate_file_hash(file_path)

# ファイル署名の作成
signature = self.create_file_signature(file_path)

# 暗号化転送
with open(file_path, ‘rb’) as f:
encrypted_data = self.fernet.encrypt(f.read())

# メタデータの準備
metadata = {
‘hash’: file_hash,
‘signature’: signature,
‘timestamp’: time.time(),
‘size’: os.path.getsize(file_path)
}

# セキュア転送の実行
response = self._execute_secure_transfer(
encrypted_data, metadata, destination_url
)

return response.status_code == 200, response.text if response.status_code != 200 else None

except Exception as e:
return False, str(e)

def _setup_intrusion_detection(self):
“””侵入検知システムのセットアップ”””
self.suspicious_patterns = [
r'(?i)(union|select|insert|drop|delete|script|javascript)’, # SQLインジェクション
r'(?i)( bool:
“””侵入試行の検出”””
import re

for pattern in self.suspicious_patterns:
if re.search(pattern, request_data):
self._log_intrusion_attempt(client_ip, pattern, request_data[:100])
return True

return False

def _log_intrusion_attempt(self, client_ip: str, pattern: str, data_sample: str):
“””侵入試行のログ記録”””
intrusion_record = {
‘timestamp’: datetime.now().isoformat(),
‘client_ip’: client_ip,
‘pattern’: pattern,
‘data_sample’: data_sample,
‘severity’: ‘HIGH’
}

self.intrusion_log.append(intrusion_record)

# 重要なアラートの送信
if len([record for record in self.intrusion_log
if record[‘client_ip’] == client_ip]) > 5:
self._send_security_alert(client_ip, ‘repeated_intrusion_attempts’)

def _send_security_alert(self, client_ip: str, alert_type: str):
“””セキュリティアラートの送信”””
alert_data = {
‘type’: alert_type,
‘client_ip’: client_ip,
‘timestamp’: datetime.now().isoformat(),
‘recommended_action’: ‘immediate_ip_block’
}

# ここで実際のアラート送信を実装
print(f”SECURITY ALERT: {alert_data}”)

import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import multiprocessing
import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Optional
import time
import psutil

@dataclass
class PerformanceMetrics:
“””パフォーマンス指標クラス”””
cpu_usage: float
memory_usage: float
network_throughput: float
disk_io: float
cache_hit_rate: float
error_rate: float

class QuantumOptimizedProcessor:
“””量子コンピューティング準備型プロセッサー”””

def __init__(self):
self.quantum_ready = self._check_quantum_availability()
self.classical_processors = multiprocessing.cpu_count()
self.optimization_cache = {}

def _check_quantum_availability(self) -> bool:
“””量子処理可用性チェック（将来実装）”””
# 現在は古典コンピューティングを使用
return False

async def optimize_modpack_distribution(self, modpack_data: Dict) -> Dict:
“””Modpack配布の量子最適化”””
if self.quantum_ready:
return await self._quantum_optimize(modpack_data)
else:
return await self._classical_optimize(modpack_data)

async def _classical_optimize(self, modpack_data: Dict) -> Dict:
“””古典最適化アルゴリズム”””
# 遺伝的アルゴリズムベースの最適化
population_size = 50
generations = 100

best_solution = None
best_fitness = float(‘-inf’)

for generation in range(generations):
population = self._generate_population(modpack_data, population_size)

for solution in population:
fitness = await self._evaluate_fitness(solution)

if fitness > best_fitness:
best_fitness = fitness
best_solution = solution

return {
‘optimized_config’: best_solution,
‘performance_gain’: best_fitness,
‘optimization_method’: ‘genetic_algorithm’
}

class EdgeAIProcessor:
“””エッジAI処理システム”””

def __init__(self):
self.edge_nodes = []
self.load_balancer = EdgeLoadBalancer()
self.ai_models = {
‘compression_optimizer’: None,
‘traffic_predictor’: None,
‘anomaly_detector’: None
}

async def process_at_edge(self, data: Dict, processing_type: str) -> Dict:
“””エッジでのAI処理”””
optimal_node = await self.load_balancer.select_optimal_node(
self.edge_nodes, data[‘size’], processing_type
)

if optimal_node:
return await optimal_node.process(data, processing_type)
else:
# フォールバックしてクラウドで処理
return await self._cloud_fallback_process(data, processing_type)

async def intelligent_compression(self, file_data: bytes) -> Tuple[bytes, float]:
“””AI駆動インテリジェント圧縮”””
# ファイルタイプとパターンを分析
file_analysis = await self._analyze_file_patterns(file_data)

# 最適な圧縮アルゴリズムを選択
optimal_algorithm = await self._select_compression_algorithm(file_analysis)

# 適応圧縮実行
compressed_data = await optimal_algorithm.compress(file_data)
compression_ratio = len(compressed_data) / len(file_data)

return compressed_data, compression_ratio

class FiveGOptimizedTransfer:
“””5G最適化転送システム”””

def __init__(self):
self.network_capabilities = self._detect_network_capabilities()
self.adaptive_streaming = AdaptiveStreamingManager()

def _detect_network_capabilities(self) -> Dict:
“””ネットワーク能力の検出”””
return {
‘supports_5g’: self._check_5g_support(),
‘max_bandwidth’: self._measure_bandwidth(),
‘latency’: self._measure_latency(),
‘packet_loss’: self._measure_packet_loss()
}

async def optimized_transfer(self, file_path: str, destination: str) -> Dict:
“””5G最適化転送”””
transfer_strategy = await self._determine_optimal_strategy()

if transfer_strategy == ‘parallel_streams’:
return await self._parallel_stream_transfer(file_path, destination)
elif transfer_strategy == ‘adaptive_bitrate’:
return await self._adaptive_bitrate_transfer(file_path, destination)
else:
return await self._standard_transfer(file_path, destination)

async def _parallel_stream_transfer(self, file_path: str, destination: str) -> Dict:
“””並列ストリーム転送”””
file_size = os.path.getsize(file_path)
optimal_streams = min(8, max(2, self.network_capabilities[‘max_bandwidth’] // 10))

chunk_size = file_size // optimal_streams
tasks = []

async with aiohttp.ClientSession() as session:
for i in range(optimal_streams):
start_byte = i * chunk_size
end_byte = (i + 1) * chunk_size if i < optimal_streams - 1 else file_size task = self._transfer_chunk( session, file_path, destination, start_byte, end_byte, i ) tasks.append(task) results = await asyncio.gather(*tasks) return { 'status': 'completed', 'streams_used': optimal_streams, 'total_time': sum(result['time'] for result in results) / len(results), 'throughput': file_size / max(result['time'] for result in results) } class IntelligentCacheManager: """インテリジェントキャッシュ管理システム""" def __init__(self): self.cache_layers = { 'l1': {}, # メモリキャッシュ 'l2': {}, # SSDキャッシュ 'l3': {} # 分散キャッシュ } self.access_patterns = {} self.ml_predictor = CachePredictionModel() async def intelligent_get(self, key: str) -> Optional[any]:
“””インテリジェントキャッシュ取得”””
# L1キャッシュをチェック
if key in self.cache_layers[‘l1’]:
self._update_access_pattern(key, ‘l1_hit’)
return self.cache_layers[‘l1’][key]

# L2キャッシュをチェック
if key in self.cache_layers[‘l2’]:
# L1に昇格
self.cache_layers[‘l1’][key] = self.cache_layers[‘l2’][key]
self._update_access_pattern(key, ‘l2_hit’)
return self.cache_layers[‘l2’][key]

# L3キャッシュをチェック
if key in self.cache_layers[‘l3’]:
# L2に昇格
self.cache_layers[‘l2’][key] = self.cache_layers[‘l3’][key]
self._update_access_pattern(key, ‘l3_hit’)
return self.cache_layers[‘l3’][key]

# キャッシュミス
self._update_access_pattern(key, ‘miss’)
return None

async def predictive_prefetch(self):
“””予測的プリフェッチ”””
predictions = await self.ml_predictor.predict_next_accesses(
self.access_patterns
)

prefetch_tasks = []
for prediction in predictions:
if prediction[‘confidence’] > 0.7:
task = self._prefetch_data(prediction[‘key’])
prefetch_tasks.append(task)

await asyncio.gather(*prefetch_tasks, return_exceptions=True)

def _update_access_pattern(self, key: str, result: str):
“””アクセスパターンの更新”””
if key not in self.access_patterns:
self.access_patterns[key] = []

self.access_patterns[key].append({
‘timestamp’: time.time(),
‘result’: result
})

# 古いパターンデータのクリーンアップ
cutoff_time = time.time() – 3600 # 1時間
self.access_patterns[key] = [
pattern for pattern in self.access_patterns[key]
if pattern[‘timestamp’] > cutoff_time
]

# パフォーマンス監視クラス
class AdvancedPerformanceMonitor:
def __init__(self):
self.metrics_history = []
self.alert_thresholds = {
‘cpu_usage’: 80,
‘memory_usage’: 85,
‘disk_io’: 1000, # MB/s
‘network_errors’: 10
}

async def continuous_monitoring(self):
“””継続的パフォーマンス監視”””
while True:
metrics = await self._collect_comprehensive_metrics()
self.metrics_history.append(metrics)

# 異常検出
anomalies = self._detect_performance_anomalies(metrics)
if anomalies:
await self._handle_performance_issues(anomalies)

# 自動最適化
optimizations = self._suggest_optimizations(metrics)
if optimizations:
await self._apply_optimizations(optimizations)

await asyncio.sleep(30) # 30秒間隔で監視

async def _collect_comprehensive_metrics(self) -> PerformanceMetrics:
“””包括的メトリクス収集”””
return PerformanceMetrics(
cpu_usage=psutil.cpu_percent(interval=1),
memory_usage=psutil.virtual_memory().percent,
network_throughput=await self._measure_network_throughput(),
disk_io=await self._measure_disk_io(),
cache_hit_rate=await self._calculate_cache_hit_rate(),
error_rate=await self._calculate_error_rate()
)

def _suggest_optimizations(self, metrics: PerformanceMetrics) -> List[Dict]:
“””最適化提案の生成”””
optimizations = []

if metrics.cpu_usage > 70:
optimizations.append({
‘type’: ‘cpu_optimization’,
‘action’: ‘reduce_concurrent_processes’,
‘priority’: ‘high’
})

if metrics.memory_usage > 80:
optimizations.append({
‘type’: ‘memory_optimization’,
‘action’: ‘increase_garbage_collection_frequency’,
‘priority’: ‘critical’
})

if metrics.cache_hit_rate < 0.8: optimizations.append({ 'type': 'cache_optimization', 'action': 'increase_cache_size_and_improve_algorithms', 'priority': 'medium' }) return optimizations