Redis + Caffeine多級緩存電商場景深度解析

一、實施目的

1. 性能優化

   • 降低商品詳情頁訪問延遲
   • 提升系統整體吞吐量

2. 成本控制

   • 減少Redis集群規模
   • 降低數據庫查詢壓力

3. 穩定性保障

   • 應對秒殺等高并發場景
   • 實現故障自動降級

二、具體實施

2.1 架構設計

用戶請求 → Nginx → 應用服務器(Caffeine) → Redis集群 → MySQL/分庫分表

2.2 組件配置

Caffeine配置：

spring.cache.caffeine.spec=maximumSize=50000,expireAfterWrite=60s,refreshAfterWrite=30s,recordStats

Redis集群配置：

spring.redis.cluster.nodes=192.168.1.101:6379,192.168.1.102:6379
spring.redis.lettuce.pool.max-active=500
spring.redis.lettuce.pool.max-wait=2000

2.3 核心代碼實現

商品查詢服務：

public ProductDetail getProductDetail(Long productId) {// 本地緩存查詢ProductDetail detail = caffeineCache.get(productId, k -> {// Redis查詢String redisKey = "pd:" + productId;ProductDetail value = redisTemplate.opsForValue().get(redisKey);if (value == null) {// 數據庫查詢value = productDao.findById(productId);// 異步寫入Redisexecutor.execute(() -> {redisTemplate.opsForValue().set(redisKey, value,60 + ThreadLocalRandom.current().nextInt(30),TimeUnit.MINUTES);});}return value;});return detail;
}

庫存扣減服務：

public boolean deductStock(Long productId, int num) {// 本地庫存標記if (!localStockMark.tryLock(productId)) {return false;}try {// Redis原子扣減Long remain = redisTemplate.execute(new DefaultRedisScript<>("local stock = tonumber(redis.call('GET', KEYS[1]))\n" +"if stock >= tonumber(ARGV[1]) then\n" +"    return redis.call('DECRBY', KEYS[1], ARGV[1])\n" +"else\n" +"    return -1\n" +"end",Long.class),Collections.singletonList("stock:" + productId),String.valueOf(num));if (remain >= 0) {// 異步記錄庫存變更mq.sendStockMessage(productId, num);return true;}return false;} finally {localStockMark.unlock(productId);}
}

三、實施效果

3.1 性能指標對比

指標	單Redis架構	多級緩存架構	提升幅度
平均響應時間	68ms	9ms	655%
峰值QPS	12萬	85萬	608%
數據庫查詢量	100%	8%	減少92%

3.2 業務指標改善

1. 秒殺場景：

   • 下單成功率從35%提升至89%
   • 超賣問題完全杜絕

2. 常規場景：

   • 商品詳情頁加載時間從1.2s→180ms
   • 服務器成本降低40%

3.3 系統穩定性

1. Redis故障時：

   • 核心商品仍可提供服務
   • 系統存活時間從<1分鐘延長至30分鐘

2. 大促期間：

   • 流量波動減少70%
   • CPU負載降低55%

四、關鍵策略

4.1 緩存預熱

@Scheduled(cron = "0 0 3 * * ?")
public void dailyPreheat() {List<Long> hotItems = hotProductService.predictHotItems();hotItems.parallelStream().forEach(id -> {ProductDetail detail = productDao.getDetail(id);caffeineCache.put(id, detail);redisTemplate.opsForValue().set("pd:" + id,detail,6, TimeUnit.HOURS);});
}

4.2 一致性保障

@Transactional
public void updateProduct(Product product) {// 1.刪除緩存caffeineCache.invalidate(product.getId());redisTemplate.delete("pd:" + product.getId());// 2.更新數據庫productDao.update(product);// 3.延遲雙刪executor.schedule(() -> {redisTemplate.delete("pd:" + product.getId());}, 1, TimeUnit.SECONDS);
}

4.3 監控配置

Prometheus監控指標

 name: cache_hit_rateexpr: rate(caffeine_cache_hits_total[5m]) / (rate(caffeine_cache_hits_total[5m]) +  rate(caffeine_cache_misses_total[5m]))name: redis_latencyexpr: histogram_quantile(0.99, rate(redis_command_duration_seconds_bucket[1m]))

電商多級緩存完整實現方案

1. 基礎配置

1.1 Maven依賴

<dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId></dependency><dependency><groupId>com.github.ben-manes.caffeine</groupId><artifactId>caffeine</artifactId><version>3.1.8</version></dependency>
</dependencies>

1.2 配置文件

spring:redis:host: 127.0.0.1port: 6379password: yourpasswordlettuce:pool:max-active: 16max-wait: 1000msmax-idle: 8caffeine:specs:productCache: maximumSize=10000,expireAfterWrite=60s,recordStatsstockCache: maximumSize=5000,expireAfterWrite=10s

2. 核心實現類

2.1 緩存配置類

@Configuration
@EnableCaching
public class CacheConfig {@Beanpublic CacheManager cacheManager() {CaffeineCacheManager cacheManager = new CaffeineCacheManager();cacheManager.registerCustomCache("products", Caffeine.from(caffeineProperties().getSpec("productCache")).build());cacheManager.registerCustomCache("stocks",Caffeine.from(caffeineProperties().getSpec("stockCache")).build());return cacheManager;}@Beanpublic RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory factory) {RedisTemplate<String, Object> template = new RedisTemplate<>();template.setConnectionFactory(factory);template.setKeySerializer(new StringRedisSerializer());template.setValueSerializer(new GenericJackson2JsonRedisSerializer());return template;}
}

2.2 商品服務實現

@Service
public class ProductServiceImpl implements ProductService {@Autowiredprivate RedisTemplate<String, Object> redisTemplate;@Cacheable(cacheNames = "products", key = "#productId")@Overridepublic ProductDetail getProductDetail(Long productId) {String redisKey = "product:" + productId;ProductDetail detail = (ProductDetail) redisTemplate.opsForValue().get(redisKey);if (detail == null) {detail = loadFromDB(productId);redisTemplate.opsForValue().set(redisKey, detail,60 + ThreadLocalRandom.current().nextInt(30),TimeUnit.MINUTES);}return detail;}private ProductDetail loadFromDB(Long productId) {// 數據庫查詢實現return productRepository.findById(productId);}
}

2.3 庫存服務實現

@Service
public class StockServiceImpl implements StockService {@Autowiredprivate RedisTemplate<String, Object> redisTemplate;@Cacheable(cacheNames = "stocks", key = "#productId")@Overridepublic Integer getStock(Long productId) {String redisKey = "stock:" + productId;Integer stock = (Integer) redisTemplate.opsForValue().get(redisKey);if (stock == null) {stock = loadStockFromDB(productId);redisTemplate.opsForValue().set(redisKey,stock,10, TimeUnit.SECONDS);}return stock;}@CacheEvict(cacheNames = "stocks", key = "#productId")@Overridepublic boolean deductStock(Long productId, int quantity) {// 庫存扣減邏輯return stockRepository.deductStock(productId, quantity);}
}

3. 輔助組件

3.1 緩存預熱

@Component
public class CacheWarmUp implements CommandLineRunner {@Autowiredprivate ProductService productService;@Overridepublic void run(String... args) {List<Long> hotProducts = Arrays.asList(1001L, 1002L, 1003L);hotProducts.parallelStream().forEach(productService::getProductDetail);}
}

3.2 監控端點

@RestController
@RequestMapping("/cache")
public class CacheMonitorController {@Autowiredprivate CacheManager cacheManager;@GetMapping("/stats")public Map<String, Object> getCacheStats() {Map<String, Object> stats = new HashMap<>();CaffeineCache productsCache = (CaffeineCache) cacheManager.getCache("products");if (productsCache != null) {stats.put("products", productsCache.getNativeCache().stats());}return stats;}
}

關鍵點說明

1. 多級緩存流程

1. 先查Caffeine本地緩存
2. 未命中則查Redis
3. 仍未命中則查數據庫
4. 回填各級緩存

2. 緩存策略

數據類型	本地緩存TTL	Redis TTL
商品數據	60秒	30-90分鐘(隨機)
庫存數據	10秒	10秒

3. 一致性保障

1. 使用@CacheEvict保證更新時緩存失效
2. 庫存采用短過期時間自動刷新

4. 監控能力

1. 通過/cache/stats端點暴露緩存命中率
2. 集成Spring Boot Actuator

5. 性能優化

1. 并行預熱熱點數據
2. Redis連接池配置
3. 本地緩存大小控制