pos-system/docs/en/architecture/caching-architecture.md

Caching Architecture / Kiến trúc Caching

EN: Multi-layer caching strategy for optimal performance VI: Chiến lược caching nhiều tầng để tối ưu hiệu suất

Overview Diagram / Sơ đồ Tổng quan

graph TD
    Request[API Request] --> L1{L1 Cache<br/>Memory}
    
    L1 -->|Hit| Return1[Return<br/>< 1ms]
    L1 -->|Miss| L2{L2 Cache<br/>Redis}
    
    L2 -->|Hit| WarmL1[Warm L1]
    WarmL1 --> Return2[Return<br/>< 5ms]
    
    L2 -->|Miss| DB[(Database)]
    DB --> StoreL2[Store L2 + L1]
    StoreL2 --> Return3[Return<br/>< 50ms]
    
    style L1 fill:#d4edda
    style L2 fill:#fff4e1
    style DB fill:#f0e1ff

Architecture Description / Mô tả Kiến trúc

EN: Multi-Layer Caching

GoodGo platform uses 2-layer caching for performance:

L1 Cache (Memory):

• In-memory cache per service instance
• Very fast access (< 1ms)
• Limited capacity (10k keys default)
• Short TTL (60 seconds default, max 5 minutes)
• Not shared across instances

L2 Cache (Redis):

• Shared distributed cache
• Fast access (< 5ms)
• Large capacity
• Longer TTL (configurable, typically 5-15 minutes)
• Shared across all service instances

Cache Flow:

Request → L1 → L2 → Database
  ↓        ↓    ↓      ↓
40-50%  80-90% 10-20%  Cache miss
hit rate hit rate        rate

VI: Caching Nhiều Tầng

Nền tảng GoodGo sử dụng caching 2 tầng để tối ưu hiệu suất:

L1 Cache (Memory):

• In-memory cache trên mỗi service instance
• Truy cập rất nhanh (< 1ms)
• Dung lượng giới hạn (10k keys mặc định)
• TTL ngắn (60 giây mặc định, tối đa 5 phút)
• Không share giữa instances

L2 Cache (Redis):

• Shared distributed cache
• Truy cập nhanh (< 5ms)
• Dung lượng lớn
• TTL dài hơn (configurable, thường 5-15 phút)
• Share giữa tất cả service instances

Cache Implementation / Triển khai Cache

Multi-Layer Cache Service

// Multi-layer cache implementation
export class MultiLayerCache {
  private l1Cache: NodeCache;
  private l2Cache: Redis;
  
  constructor() {
    // L1: Memory cache
    this.l1Cache = new NodeCache({
      stdTTL: 60,        // 60 seconds default
      maxKeys: 10000,    // Max 10k keys
      checkperiod: 120   // Check for expired keys every 2min
    });
    
    // L2: Redis cache
    this.l2Cache = new Redis({
      host: process.env.REDIS_HOST,
      port: parseInt(process.env.REDIS_PORT),
      db: 0
    });
  }
  
  async get<T>(key: string): Promise<T | null> {
    // Try L1 first
    const l1Value = this.l1Cache.get<T>(key);
    if (l1Value) {
      logger.debug('L1 cache hit', { key });
      return l1Value;
    }
    
    // Try L2
    const l2Value = await this.l2Cache.get(key);
    if (l2Value) {
      logger.debug('L2 cache hit', { key });
      const parsed = JSON.parse(l2Value) as T;
      
      // Warm L1 cache
      this.l1Cache.set(key, parsed);
      return parsed;
    }
    
    logger.debug('Cache miss', { key });
    return null;
  }
  
  async set(key: string, value: any, ttl: number = 300): Promise<void> {
    // Store in both L1 and L2
    this.l1Cache.set(key, value, Math.min(ttl, 300)); // L1 max 5min
    await this.l2Cache.setex(key, ttl, JSON.stringify(value));
  }
  
  async del(key: string): Promise<void> {
    this.l1Cache.del(key);
    await this.l2Cache.del(key);
  }
  
  async invalidatePattern(pattern: string): Promise<void> {
    // L1: Clear all (simple approach)
    this.l1Cache.flushAll();
    
    // L2: Delete by pattern
    const keys = await this.l2Cache.keys(pattern);
    if (keys.length > 0) {
      await this.l2Cache.del(...keys);
    }
  }
}

Cache Key Naming / Quy ước Đặt tên Key

Pattern: {service}:{entity}:{identifier}:{sub-resource}

Examples:

// User cache keys
const keys = {
  user: (userId: string) => `iam:user:${userId}`,
  userPermissions: (userId: string) => `iam:user:${userId}:permissions`,
  userRoles: (userId: string) => `iam:user:${userId}:roles`,
  session: (sessionId: string) => `iam:session:${sessionId}`,
};

// Usage
const user = await cache.get(keys.user('user_123'));
const permissions = await cache.get(keys.userPermissions('user_123'));

TTL Strategies / Chiến lược TTL

graph LR
    subgraph "TTL Tiers"
        Short[Short TTL<br/>60-300s<br/>Frequently changing]
        Medium[Medium TTL<br/>300-1800s<br/>Moderately changing]
        Long[Long TTL<br/>1800-3600s<br/>Rarely changing]
    end
    
    Short --> Permissions[User Permissions]
    Short --> Sessions[Session Data]
    
    Medium --> UserProfiles[User Profiles]
    Medium --> OrgData[Organization Data]
    
    Long --> Config[Static Config]
    Long --> RefData[Reference Data]
    
    style Short fill:#f8d7da
    style Medium fill:#fff3cd
    style Long fill:#d4edda

TTL Guidelines:

Data Type	TTL	Reason
User permissions	5 min	Security-sensitive
Session data	Varies	Based on session length
User profiles	10 min	Moderate update frequency
Organization data	15 min	Infrequent updates
Static config	30-60 min	Very stable
Reference data	1-2 hours	Almost never changes

Cache Invalidation / Vô hiệu hóa Cache

sequenceDiagram
    participant API
    participant Service
    participant Cache
    participant DB
    
    API->>Service: Update User
    Service->>DB: UPDATE user
    DB-->>Service: Success
    
    Service->>Cache: Invalidate user:123
    Service->>Cache: Invalidate user:123:permissions
    Service->>Cache: Invalidate user:123:roles
    Cache-->>Service: Cleared
    
    Service-->>API: Success
    
    Note over Service,Cache: Next request will fetch fresh data

Invalidation Strategies:

// 1. Single key invalidation
async updateUser(userId: string, data: UpdateUserDto): Promise<User> {
  const user = await userRepository.update(userId, data);
  
  // Invalidate user cache
  await cache.del(cacheKeys.user(userId));
  
  return user;
}

// 2. Pattern-based invalidation
async updateUserRole(userId: string, roleId: string): Promise<void> {
  await userRoleRepository.assign(userId, roleId);
  
  // Invalidate all user-related cache
  await cache.invalidatePattern(`iam:user:${userId}:*`);
}

// 3. Time-based invalidation (TTL expiry)
// Automatically handled by cache

Cache Warming / Làm ấm Cache

// Preload frequently accessed data
async warmCache(): Promise<void> {
  logger.info('Starting cache warming');
  
  // Warm user permissions for active users
  const activeUsers = await userRepository.findActive({ limit: 1000 });
  
  for (const user of activeUsers) {
    const permissions = await rbacService.getUserPermissions(user.id);
    
    await cache.set(
      cacheKeys.userPermissions(user.id),
      permissions,
      300 // 5 minutes
    );
  }
  
  logger.info('Cache warming completed', { count: activeUsers.length });
}

// Run on service startup
warmCache().catch(err => logger.error('Cache warming failed', { err }));

Performance Metrics / Chỉ số Hiệu suất

Cache Hit Rates:

// Track cache performance
export class CacheMetrics {
  private hits = new Counter({
    name: 'cache_hits_total',
    help: 'Total cache hits',
    labelNames: ['layer', 'key_prefix']
  });
  
  private misses = new Counter({
    name: 'cache_misses_total',
    help: 'Total cache misses',
    labelNames: ['layer', 'key_prefix']
  });
  
  recordHit(layer: 'l1' | 'l2', key: string): void {
    const prefix = key.split(':')[0];
    this.hits.inc({ layer, key_prefix: prefix });
  }
  
  recordMiss(key: string): void {
    const prefix = key.split(':')[0];
    this.misses.inc({ layer: 'db', key_prefix: prefix });
  }
}

Expected Performance:

Metric	L1 Cache	L2 Cache	Database
Latency	< 1ms	< 5ms	< 50ms
Hit Rate	40-50%	80-90%	-
Capacity	10k keys	Unlimited	-

Best Practices / Best Practices

DO:

• ✅ Use cache for frequently accessed data
• ✅ Set appropriate TTLs based on data change frequency
• ✅ Invalidate cache on data updates
• ✅ Use cache key namespacing
• ✅ Monitor cache hit rates
• ✅ Warm cache on startup for critical data

DON'T:

• ❌ Cache data that changes very frequently
• ❌ Set TTL too long (stale data risk)
• ❌ Set TTL too short (negates cache benefit)
• ❌ Cache sensitive data without encryption
• ❌ Ignore cache invalidation on updates
• ❌ Use cache as primary data store

Related Documentation / Tài liệu Liên quan

• System Design - Overall architecture with caching
• Data Consistency Patterns - Cache invalidation patterns

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Last Updated: 2024-01-15
Authors: GoodGo Architecture Team