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docs: add native Japanese translation of ECC documentation (ja-JP)
Translate everything-claude-code repository to Japanese including: - 17 root documentation files - 60 agent documentation files - 80 command documentation files - 99 rule files across 18 language directories (common, angular, arkts, cpp, csharp, dart, fsharp, golang, java, kotlin, perl, php, python, ruby, rust, swift, typescript, web) - 199 skill documentation files Total: 455 files translated to Japanese with: - Consistent terminology glossary applied throughout - YAML field names preserved in English (name, description, etc.) - Code blocks and examples untouched (comments translated) - Markdown structure and relative links preserved - Professional translation maintaining technical accuracy This translation expands ECC accessibility to Japanese-speaking developers and teams. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
Claude
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docs/ja-JP/skills/redis-patterns/SKILL.md
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docs/ja-JP/skills/redis-patterns/SKILL.md
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---
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name: redis-patterns
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description: Redisデータ構造パターン、キャッシング戦略、分散ロック、レート制限、Pub/Sub、本番アプリケーション用コネクション管理。
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origin: ECC
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---
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# Redis Patterns
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一般的なバックエンド使用例に対するRedisベストプラクティスの参考資料。
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## How It Works
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Redisはメモリ内データ構造ストアで、文字列、ハッシュ、リスト、セット、ソート済みセット、ストリームなどをサポートします。単一インスタンスでは個々のRedisコマンドは原子的ですが、マルチステップワークフローはLuaスクリプト、MULTI/EXECトランザクション、または明示的な同期化が必要です。RDBスナップショットまたはAOFログを通じてデータをオプションで永続化します。クライアントはRESPプロトコルを使用してTCP経由で通信します。接続プール不可欠でリクエストごとのハンドシェイクオーバーヘッドを回避します。
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## When to Activate
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- アプリケーションにキャッシング追加
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- レート制限またはスロットリング実装
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- 分散ロックまたはコーディネーション構築
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- セッションまたはトークンストレージ設定
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- Pub/SubまたはRedis Streams for messaging使用
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- 本番環境でRedis設定（プール、削除、クラスタリング）
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## Data Structure Cheat Sheet
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| Use Case | Structure | Example Key |
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|----------|-----------|-------------|
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| Simple cache | String | `product:123` |
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| User session | Hash | `session:abc` |
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| Leaderboard | Sorted Set | `scores:weekly` |
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| Unique visitors | Set | `visitors:2024-01-01` |
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| Activity feed | List | `feed:user:456` |
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| Event stream | Stream | `events:orders` |
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| Counters / rate limits | String (INCR) | `ratelimit:user:123` |
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| Bloom filter / HLL | HyperLogLog | `hll:pageviews` |
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## Core Patterns
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### Cache-Aside (Lazy Loading)
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```python
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import redis
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import json
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r = redis.Redis(host='localhost', port=6379, decode_responses=True)
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def get_product(product_id: int):
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cache_key = f"product:{product_id}"
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cached = r.get(cache_key)
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if cached:
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return json.loads(cached)
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product = db.query("SELECT * FROM products WHERE id = %s", product_id)
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r.setex(cache_key, 3600, json.dumps(product)) # TTL: 1 hour
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return product
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```
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### Write-Through Cache
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```python
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def update_product(product_id: int, data: dict):
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# DB書き込み先
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db.execute("UPDATE products SET ... WHERE id = %s", product_id)
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# キャッシュを即座に更新
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cache_key = f"product:{product_id}"
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r.setex(cache_key, 3600, json.dumps(data))
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```
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### Cache Invalidation
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```python
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# タグベース削除 — セット内で関連キーをグループ化
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def cache_product(product_id: int, category_id: int, data: dict):
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key = f"product:{product_id}"
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tag = f"tag:category:{category_id}"
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pipe = r.pipeline(transaction=True)
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pipe.setex(key, 3600, json.dumps(data))
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pipe.sadd(tag, key)
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pipe.expire(tag, 3600)
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pipe.execute()
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def invalidate_category(category_id: int):
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tag = f"tag:category:{category_id}"
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keys = r.smembers(tag)
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if keys:
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r.delete(*keys)
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r.delete(tag)
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```
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### Session Storage
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```python
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import time
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import uuid
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def create_session(user_id: int, ttl: int = 86400) -> str:
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session_id = str(uuid.uuid4())
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key = f"session:{session_id}"
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pipe = r.pipeline(transaction=True)
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pipe.hset(key, mapping={
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"user_id": user_id,
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"created_at": int(time.time()),
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})
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pipe.expire(key, ttl)
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pipe.execute()
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return session_id
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def get_session(session_id: str) -> dict | None:
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data = r.hgetall(f"session:{session_id}")
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return data if data else None
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def delete_session(session_id: str):
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r.delete(f"session:{session_id}")
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```
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## Rate Limiting
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### Fixed Window (Simple)
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```python
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def is_rate_limited(user_id: int, limit: int = 100, window: int = 60) -> bool:
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key = f"ratelimit:{user_id}:{int(time.time()) // window}"
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pipe = r.pipeline(transaction=True)
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pipe.incr(key)
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pipe.expire(key, window)
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count, _ = pipe.execute()
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return count > limit
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```
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### Sliding Window (Lua — Atomic)
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```lua
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-- sliding_window.lua
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local key = KEYS[1]
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local now = tonumber(ARGV[1])
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local window = tonumber(ARGV[2])
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local limit = tonumber(ARGV[3])
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redis.call('ZREMRANGEBYSCORE', key, 0, now - window)
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local count = redis.call('ZCARD', key)
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if count < limit then
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-- Use unique member (now + sequence) to avoid collisions within the same millisecond
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local seq_key = key .. ':seq'
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local seq = redis.call('INCR', seq_key)
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redis.call('EXPIRE', seq_key, math.ceil(window / 1000))
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redis.call('ZADD', key, now, now .. '-' .. seq)
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redis.call('EXPIRE', key, math.ceil(window / 1000))
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return 1
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end
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return 0
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```
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```python
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sliding_window = r.register_script(open('sliding_window.lua').read())
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def allow_request(user_id: int) -> bool:
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key = f"ratelimit:sliding:{user_id}"
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now = int(time.time() * 1000)
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return bool(sliding_window(keys=[key], args=[now, 60000, 100]))
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```
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## Distributed Locks
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### Distributed Lock (Single Node — SET NX PX)
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```python
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import uuid
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def acquire_lock(resource: str, ttl_ms: int = 5000) -> str | None:
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lock_key = f"lock:{resource}"
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token = str(uuid.uuid4())
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acquired = r.set(lock_key, token, px=ttl_ms, nx=True)
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return token if acquired else None
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def release_lock(resource: str, token: str) -> bool:
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release_script = """
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if redis.call('get', KEYS[1]) == ARGV[1] then
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return redis.call('del', KEYS[1])
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else
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return 0
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end
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"""
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result = r.eval(release_script, 1, f"lock:{resource}", token)
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return bool(result)
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# Usage
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token = acquire_lock("order:payment:123")
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if token:
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try:
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process_payment()
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finally:
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release_lock("order:payment:123", token)
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```
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> マルチノード設定の場合、フルRedlockアルゴリズムを実装する `redlock-py` ライブラリを使用してください。
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## Pub/Sub & Streams
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### Pub/Sub (Fire-and-Forget)
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```python
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# Publisher
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def publish_event(channel: str, payload: dict):
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r.publish(channel, json.dumps(payload))
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# Subscriber (blocking — run in separate thread/process)
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def subscribe_events(channel: str):
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pubsub = r.pubsub()
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pubsub.subscribe(channel)
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for message in pubsub.listen():
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if message['type'] == 'message':
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handle(json.loads(message['data']))
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```
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### Redis Streams (Durable Queue)
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```python
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# Producer
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def emit(stream: str, event: dict):
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r.xadd(stream, event, maxlen=10000) # Cap stream length
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# Consumer group — guarantees at-least-once delivery
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try:
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r.xgroup_create('events:orders', 'processor', id='0', mkstream=True)
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except Exception:
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pass # Group already exists
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def consume(stream: str, group: str, consumer: str):
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while True:
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messages = r.xreadgroup(group, consumer, {stream: '>'}, count=10, block=2000)
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for _, entries in (messages or []):
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for msg_id, data in entries:
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process(data)
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r.xack(stream, group, msg_id)
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```
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> 配信保証、コンシューマーグループ、または再生が必要な場合、Pub/Sub代わりに**Streams**を優先してください。
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## Key Design
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### Naming Conventions
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```
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# Pattern: resource:id:field
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user:123:profile
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order:456:status
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cache:product:789
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# Pattern: namespace:resource:id
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myapp:session:abc123
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myapp:ratelimit:user:123
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# Pattern: resource:date (time-bound keys)
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stats:pageviews:2024-01-01
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```
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### TTL Strategy
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| Data Type | Suggested TTL |
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|-----------|--------------|
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| User session | 24h (`86400`) |
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| API response cache | 5–15 min |
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| Rate limit window | Match window size |
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| Short-lived tokens | 5–10 min |
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| Leaderboard | 1h–24h |
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| Static/reference data | 1h–1 week |
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常にTTLを設定してください。TTLなしのキーは無限に蓄積してメモリ圧力を引き起こします。
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## Connection Management
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### Connection Pooling
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```python
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from redis import ConnectionPool, Redis
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pool = ConnectionPool(
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host='localhost',
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port=6379,
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db=0,
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max_connections=20,
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decode_responses=True,
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socket_connect_timeout=2,
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socket_timeout=2,
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)
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r = Redis(connection_pool=pool)
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```
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### Cluster Mode
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```python
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from redis.cluster import RedisCluster
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r = RedisCluster(
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startup_nodes=[{"host": "redis-1", "port": 6379}],
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decode_responses=True,
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skip_full_coverage_check=True,
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)
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```
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### Sentinel (High Availability)
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```python
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from redis.sentinel import Sentinel
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sentinel = Sentinel(
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[('sentinel-1', 26379), ('sentinel-2', 26379)],
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socket_timeout=0.5,
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)
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master = sentinel.master_for('mymaster', decode_responses=True)
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replica = sentinel.slave_for('mymaster', decode_responses=True)
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```
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## Eviction Policies
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| Policy | Behavior | Best For |
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|--------|----------|----------|
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| `noeviction` | Error on write when full | Queues / critical data |
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| `allkeys-lru` | Evict least recently used | General cache |
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| `volatile-lru` | LRU only among keys with TTL | Mixed data store |
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| `allkeys-lfu` | Evict least frequently used | Skewed access patterns |
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| `volatile-ttl` | Evict soonest-to-expire | Prioritize long-lived data |
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`redis.conf`を通じて設定：`maxmemory-policy allkeys-lru`
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## Anti-Patterns
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| Anti-Pattern | Problem | Fix |
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| Keys with no TTL | Memory grows unbounded | Always set TTL |
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| `KEYS *` in production | Blocks the server (O(N)) | Use `SCAN` cursor |
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| Storing large blobs (>100KB) | Slow serialization, memory pressure | Store reference + fetch from object store |
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| Single Redis for everything | No isolation between cache & queue | Use separate DBs or instances |
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| Ignoring connection pool limits | Connection exhaustion under load | Size pool to workload |
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| Not handling cache miss stampede | Thundering herd on cold start | Use locks or probabilistic early expiry |
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| `FLUSHALL` without thought | Wipes entire instance | Scope deletes by key pattern |
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### Cache Miss Stampede Prevention
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```python
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import threading
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_locks: dict[str, threading.Lock] = {}
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_locks_mutex = threading.Lock()
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def get_with_lock(key: str, fetch_fn, ttl: int = 300):
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cached = r.get(key)
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if cached:
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return json.loads(cached)
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with _locks_mutex:
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if key not in _locks:
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_locks[key] = threading.Lock()
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lock = _locks[key]
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with lock:
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cached = r.get(key) # Re-check after acquiring lock
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if cached:
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return json.loads(cached)
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value = fetch_fn()
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r.setex(key, ttl, json.dumps(value))
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return value
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```
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> マルチプロセスデプロイメント：インプロセスロックを上記の分散ロックセクション から `acquire_lock`/`release_lock` に置き換えてください。
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## Examples
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**Django/Flask APIエンドポイントにキャッシング追加：**
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レスポンスに5分TTLでCache-asideを使用。リクエストパラメータでキーを指定。
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**ユーザーごとにAPIレート制限：**
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低トラフィックエンドポイントに固定ウィンドウを `pipeline(transaction=True)` で使用；正確なユーザーごと制限にはsliding-windowの Lua使用。
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**ワーカー間のバックグラウンドジョブ調整：**
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予想ジョブ期間を超えるTTLで `acquire_lock` を使用。常に `finally` ブロックでリリース。
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**複数購読者への通知のファンアウト：**
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ファイアアンドフォーゲットにPub/Subを使用。保証配信または再生が必要な場合、Streamsに切り替え。
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## Quick Reference
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| Pattern | When to Use |
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|---------|-------------|
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| Cache-aside | Read-heavy, tolerate slight staleness |
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| Write-through | Strong consistency required |
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| Distributed lock | Prevent concurrent access to a resource |
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| Sliding window rate limit | Accurate per-user throttling |
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| Redis Streams | Durable event queue with consumer groups |
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| Pub/Sub | Broadcast with no delivery guarantees needed |
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| Sorted Set leaderboard | Ranked scoring, pagination |
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| HyperLogLog | Approximate unique count at low memory |
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## Related
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- Skill: `postgres-patterns` — リレーショナルデータパターン
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- Skill: `backend-patterns` — APIおよびサービスレイヤーパターン
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- Skill: `database-migrations` — スキーマバージョニング
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- Skill: `django-patterns` — Djangoキャッシュフレームワーク統合
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- Agent: `database-reviewer` — 全データベースレビューワークフロー
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