前言

在生产中已有实践,本组件仅做个人学习交流分享使用。github:https://github.com/axinSoochow/redis-caffeine-cache-starter
个人水平有限,欢迎大家在评论区轻喷。

所谓二级缓存

平时我们会将数据存储到磁盘上,如:数据库。如果每次都从数据库里去读取,会因为磁盘本身的IO影响读取速度,所以就有了像redis这种的内存缓存。可以将数据读取出来放到内存里,这样当需要获取数据时,就能够直接从内存中拿到数据返回,能够很大程度的提高速度。
但是一般redis是单独部署成集群,所以会有网络IO上的消耗,虽然与redis集群的链接已经有连接池这种工具,但是数据传输上也还是会有一定消耗。所以就有了进程内缓存,如:caffeine。当应用内缓存有符合条件的数据时,就可以直接使用,而不用通过网络到redis中去获取,这样就形成了两级缓存。应用内缓存叫做一级缓存,远程缓存(如redis)叫做二级缓存。

系统是否需要缓存

  • CPU占用:如果你有某些应用需要消耗大量的cpu去计算获得结果。
  • 数据库IO占用:如果你发现你的数据库连接池比较空闲,那么不应该用缓存。但是如果数据库连接池比较繁忙,甚至经常报出连接不够的报警,那么是时候应该考虑缓存了。

分布式二级缓存的优势

Redis用来存储热点数据,Redis中没有的数据则直接去数据库访问。
已经有Redis了,干嘛还需要了解Guava,Caffeine这些进程缓存呢:

  • Redis如果不可用,这个时候我们只能访问数据库,很容易造成雪崩,但一般不会出现这种情况
  • 访问Redis会有一定的网络I/O以及序列化反序列化开销,虽然性能很高但是其终究没有本地方法快,可以将最热的数据存放在本地,以便进一步加快访问速度。这个思路并不是我们做互联网架构独有的,在计算机系统中使用L1,L2,L3多级缓存,用来减少对内存的直接访问,从而加快访问速度。

所以如果仅仅是使用Redis,能满足我们大部分需求,但是当需要追求更高的性能以及更高的可用性的时候,那就不得不了解多级缓存。

二级缓存操作过程

如何使用组件?

组件是基于Spring Cache框架上改造的,在项目中使用分布式缓存,仅仅需要在缓存注解上增加:cacheManager ="L2_CacheManager",或者 cacheManager = CacheRedisCaffeineAutoConfiguration.分布式二级缓存

//这个方法会使用分布式二级缓存来提供查询
@Cacheable(cacheNames = CacheNames.CACHE_12HOUR, cacheManager = "L2_CacheManager")
public Config getAllValidateConfig() {
}

如果你想既使用分布式缓存,又想用分布式二级缓存组件,那你需要向Spring注入一个 @Primary 的 CacheManager bean

@Primary
@Bean("deaultCacheManager")
public RedisCacheManager cacheManager(RedisConnectionFactory factory) {
    // 生成一个默认配置,通过config对象即可对缓存进行自定义配置
    RedisCacheConfiguration config = RedisCacheConfiguration.defaultCacheConfig();
    // 设置缓存的默认过期时间,也是使用Duration设置
    config = config.entryTtl(Duration.ofMinutes(2)).disableCachingNullValues();

    // 设置一个初始化的缓存空间set集合
    Set<String> cacheNames =  new HashSet<>();
    cacheNames.add(CacheNames.CACHE_15MINS);
    cacheNames.add(CacheNames.CACHE_30MINS);

    // 对每个缓存空间应用不同的配置
    Map<String, RedisCacheConfiguration> configMap = new HashMap<>();
    configMap.put(CacheNames.CACHE_15MINS, config.entryTtl(Duration.ofMinutes(15)));
    configMap.put(CacheNames.CACHE_30MINS, config.entryTtl(Duration.ofMinutes(30)));

    // 使用自定义的缓存配置初始化一个cacheManager
    RedisCacheManager cacheManager = RedisCacheManager.builder(factory)
        .initialCacheNames(cacheNames)  // 注意这两句的调用顺序,一定要先调用该方法设置初始化的缓存名,再初始化相关的配置
        .withInitialCacheConfigurations(configMap)
        .build();
    return cacheManager;
}

然后:

//这个方法会使用分布式二级缓存
@Cacheable(cacheNames = CacheNames.CACHE_12HOUR, cacheManager = "L2_CacheManager")
public Config getAllValidateConfig() {
}

//这个方法会使用分布式缓存
@Cacheable(cacheNames = CacheNames.CACHE_12HOUR)
public Config getAllValidateConfig2() {
}

核心实现方法

核心其实就是实现 org.springframework.cache.CacheManager接口与继承org.springframework.cache.support.AbstractValueAdaptingCache,在Spring缓存框架下实现缓存的读与写。

RedisCaffeineCacheManager实现CacheManager 接口

RedisCaffeineCacheManager.class 主要来管理缓存实例,根据不同的 CacheNames 生成对应的缓存管理bean,然后放入一个map中。

package com.axin.idea.rediscaffeinecachestarter.support;

import com.axin.idea.rediscaffeinecachestarter.CacheRedisCaffeineProperties;
import com.github.benmanes.caffeine.cache.Caffeine;
import com.github.benmanes.caffeine.cache.stats.CacheStats;
import lombok.extern.slf4j.Slf4j;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.cache.Cache;
import org.springframework.cache.CacheManager;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.util.CollectionUtils;

import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.TimeUnit;

@Slf4j
public class RedisCaffeineCacheManager implements CacheManager {

    private final Logger logger = LoggerFactory.getLogger(RedisCaffeineCacheManager.class);

    private static ConcurrentMap<String, Cache> cacheMap = new ConcurrentHashMap<String, Cache>();

    private CacheRedisCaffeineProperties cacheRedisCaffeineProperties;

    private RedisTemplate<Object, Object> stringKeyRedisTemplate;

    private boolean dynamic = true;

    private Set<String> cacheNames;
    {
        cacheNames = new HashSet<>();
        cacheNames.add(CacheNames.CACHE_15MINS);
        cacheNames.add(CacheNames.CACHE_30MINS);
        cacheNames.add(CacheNames.CACHE_60MINS);
        cacheNames.add(CacheNames.CACHE_180MINS);
        cacheNames.add(CacheNames.CACHE_12HOUR);
    }
    public RedisCaffeineCacheManager(CacheRedisCaffeineProperties cacheRedisCaffeineProperties,
                                     RedisTemplate<Object, Object> stringKeyRedisTemplate) {
        super();
        this.cacheRedisCaffeineProperties = cacheRedisCaffeineProperties;
        this.stringKeyRedisTemplate = stringKeyRedisTemplate;
        this.dynamic = cacheRedisCaffeineProperties.isDynamic();
    }

    //——————————————————————— 进行缓存工具 ——————————————————————
    /**
    * 清除所有进程缓存
    */
    public void clearAllCache() {
        stringKeyRedisTemplate.convertAndSend(cacheRedisCaffeineProperties.getRedis().getTopic(), new CacheMessage(null, null));
    }

    /**
    * 返回所有进程缓存(二级缓存)的统计信息
    * result:{"缓存名称":统计信息}
    * @return
    */
    public static Map<String, CacheStats> getCacheStats() {
        if (CollectionUtils.isEmpty(cacheMap)) {
            return null;
        }

        Map<String, CacheStats> result = new LinkedHashMap<>();
        for (Cache cache : cacheMap.values()) {
            RedisCaffeineCache caffeineCache = (RedisCaffeineCache) cache;
            result.put(caffeineCache.getName(), caffeineCache.getCaffeineCache().stats());
        }
        return result;
    }

    //—————————————————————————— core —————————————————————————
    @Override
    public Cache getCache(String name) {
        Cache cache = cacheMap.get(name);
        if(cache != null) {
            return cache;
        }
        if(!dynamic && !cacheNames.contains(name)) {
            return null;
        }

        cache = new RedisCaffeineCache(name, stringKeyRedisTemplate, caffeineCache(name), cacheRedisCaffeineProperties);
        Cache oldCache = cacheMap.putIfAbsent(name, cache);
        logger.debug("create cache instance, the cache name is : {}", name);
        return oldCache == null ? cache : oldCache;
    }

    @Override
    public Collection<String> getCacheNames() {
        return this.cacheNames;
    }

    public void clearLocal(String cacheName, Object key) {
        //cacheName为null 清除所有进程缓存
        if (cacheName == null) {
            log.info("清除所有本地缓存");
            cacheMap = new ConcurrentHashMap<>();
            return;
        }

        Cache cache = cacheMap.get(cacheName);
        if(cache == null) {
            return;
        }

        RedisCaffeineCache redisCaffeineCache = (RedisCaffeineCache) cache;
        redisCaffeineCache.clearLocal(key);
    }

    /**
    * 实例化本地一级缓存
    * @param name
    * @return
    */
    private com.github.benmanes.caffeine.cache.Cache<Object, Object> caffeineCache(String name) {
        Caffeine<Object, Object> cacheBuilder = Caffeine.newBuilder();
        CacheRedisCaffeineProperties.CacheDefault cacheConfig;
        switch (name) {
            case CacheNames.CACHE_15MINS:
                cacheConfig = cacheRedisCaffeineProperties.getCache15m();
                break;
            case CacheNames.CACHE_30MINS:
                cacheConfig = cacheRedisCaffeineProperties.getCache30m();
                break;
            case CacheNames.CACHE_60MINS:
                cacheConfig = cacheRedisCaffeineProperties.getCache60m();
                break;
            case CacheNames.CACHE_180MINS:
                cacheConfig = cacheRedisCaffeineProperties.getCache180m();
                break;
            case CacheNames.CACHE_12HOUR:
                cacheConfig = cacheRedisCaffeineProperties.getCache12h();
                break;
            default:
                cacheConfig = cacheRedisCaffeineProperties.getCacheDefault();
        }
        long expireAfterAccess = cacheConfig.getExpireAfterAccess();
        long expireAfterWrite = cacheConfig.getExpireAfterWrite();
        int initialCapacity = cacheConfig.getInitialCapacity();
        long maximumSize = cacheConfig.getMaximumSize();
        long refreshAfterWrite = cacheConfig.getRefreshAfterWrite();

        log.debug("本地缓存初始化:");
        if (expireAfterAccess > 0) {
            log.debug("设置本地缓存访问后过期时间,{}秒", expireAfterAccess);
            cacheBuilder.expireAfterAccess(expireAfterAccess, TimeUnit.SECONDS);
        }
        if (expireAfterWrite > 0) {
            log.debug("设置本地缓存写入后过期时间,{}秒", expireAfterWrite);
            cacheBuilder.expireAfterWrite(expireAfterWrite, TimeUnit.SECONDS);
        }
        if (initialCapacity > 0) {
            log.debug("设置缓存初始化大小{}", initialCapacity);
            cacheBuilder.initialCapacity(initialCapacity);
        }
        if (maximumSize > 0) {
            log.debug("设置本地缓存最大值{}", maximumSize);
            cacheBuilder.maximumSize(maximumSize);
        }
        if (refreshAfterWrite > 0) {
            cacheBuilder.refreshAfterWrite(refreshAfterWrite, TimeUnit.SECONDS);
        }
        cacheBuilder.recordStats();
        return cacheBuilder.build();
    }
}

RedisCaffeineCache 继承 AbstractValueAdaptingCache

核心是get方法与put方法。

package com.axin.idea.rediscaffeinecachestarter.support;

import com.axin.idea.rediscaffeinecachestarter.CacheRedisCaffeineProperties;
import com.github.benmanes.caffeine.cache.Cache;
import lombok.Getter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.cache.support.AbstractValueAdaptingCache;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.util.StringUtils;

import java.time.Duration;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.locks.ReentrantLock;

public class RedisCaffeineCache extends AbstractValueAdaptingCache {

    private final Logger logger = LoggerFactory.getLogger(RedisCaffeineCache.class);

    private String name;

    private RedisTemplate<Object, Object> redisTemplate;

    @Getter
    private Cache<Object, Object> caffeineCache;

    private String cachePrefix;

    /**
     * 默认key超时时间 3600s
     */
    private long defaultExpiration = 3600;

    private Map<String, Long> defaultExpires = new HashMap<>();
    {
        defaultExpires.put(CacheNames.CACHE_15MINS, TimeUnit.MINUTES.toSeconds(15));
        defaultExpires.put(CacheNames.CACHE_30MINS, TimeUnit.MINUTES.toSeconds(30));
        defaultExpires.put(CacheNames.CACHE_60MINS, TimeUnit.MINUTES.toSeconds(60));
        defaultExpires.put(CacheNames.CACHE_180MINS, TimeUnit.MINUTES.toSeconds(180));
        defaultExpires.put(CacheNames.CACHE_12HOUR, TimeUnit.HOURS.toSeconds(12));
    }

    private String topic;
    private Map<String, ReentrantLock> keyLockMap = new ConcurrentHashMap();

    protected RedisCaffeineCache(boolean allowNullValues) {
        super(allowNullValues);
    }

    public RedisCaffeineCache(String name, RedisTemplate<Object, Object> redisTemplate,
                              Cache<Object, Object> caffeineCache, CacheRedisCaffeineProperties cacheRedisCaffeineProperties) {
        super(cacheRedisCaffeineProperties.isCacheNullValues());
        this.name = name;
        this.redisTemplate = redisTemplate;
        this.caffeineCache = caffeineCache;
        this.cachePrefix = cacheRedisCaffeineProperties.getCachePrefix();
        this.defaultExpiration = cacheRedisCaffeineProperties.getRedis().getDefaultExpiration();
        this.topic = cacheRedisCaffeineProperties.getRedis().getTopic();
        defaultExpires.putAll(cacheRedisCaffeineProperties.getRedis().getExpires());
    }

    @Override
    public String getName() {
        return this.name;
    }

    @Override
    public Object getNativeCache() {
        return this;
    }

    @Override
    public <T> T get(Object key, Callable<T> valueLoader) {
        Object value = lookup(key);
        if (value != null) {
            return (T) value;
        }
        //key在redis和缓存中均不存在
        ReentrantLock lock = keyLockMap.get(key.toString());

        if (lock == null) {
            logger.debug("create lock for key : {}", key);
            keyLockMap.putIfAbsent(key.toString(), new ReentrantLock());
            lock = keyLockMap.get(key.toString());
        }
        try {
            lock.lock();
            value = lookup(key);
            if (value != null) {
                return (T) value;
            }
            //执行原方法获得value
            value = valueLoader.call();
            Object storeValue = toStoreValue(value);
            put(key, storeValue);
            return (T) value;
        } catch (Exception e) {
            throw new ValueRetrievalException(key, valueLoader, e.getCause());
        } finally {
            lock.unlock();
        }
    }

    @Override
    public void put(Object key, Object value) {
        if (!super.isAllowNullValues() && value == null) {
            this.evict(key);
            return;
        }
        long expire = getExpire();
        logger.debug("put:{},expire:{}", getKey(key), expire);
        redisTemplate.opsForValue().set(getKey(key), toStoreValue(value), expire, TimeUnit.SECONDS);

        //缓存变更时通知其他节点清理本地缓存
        push(new CacheMessage(this.name, key));
        //此处put没有意义,会收到自己发送的缓存key失效消息
//        caffeineCache.put(key, value);
    }

    @Override
    public ValueWrapper putIfAbsent(Object key, Object value) {
        Object cacheKey = getKey(key);
        // 使用setIfAbsent原子性操作
        long expire = getExpire();
        boolean setSuccess;
        setSuccess = redisTemplate.opsForValue().setIfAbsent(getKey(key), toStoreValue(value), Duration.ofSeconds(expire));

        Object hasValue;
        //setNx结果
        if (setSuccess) {
            push(new CacheMessage(this.name, key));
            hasValue = value;
        }else {
            hasValue = redisTemplate.opsForValue().get(cacheKey);
        }

        caffeineCache.put(key, toStoreValue(value));
        return toValueWrapper(hasValue);
    }

    @Override
    public void evict(Object key) {
        // 先清除redis中缓存数据,然后清除caffeine中的缓存,避免短时间内如果先清除caffeine缓存后其他请求会再从redis里加载到caffeine中
        redisTemplate.delete(getKey(key));

        push(new CacheMessage(this.name, key));

        caffeineCache.invalidate(key);
    }

    @Override
    public void clear() {
        // 先清除redis中缓存数据,然后清除caffeine中的缓存,避免短时间内如果先清除caffeine缓存后其他请求会再从redis里加载到caffeine中
        Set<Object> keys = redisTemplate.keys(this.name.concat(":*"));
        for (Object key : keys) {
            redisTemplate.delete(key);
        }

        push(new CacheMessage(this.name, null));
        caffeineCache.invalidateAll();
    }

    /**
     * 取值逻辑
     * @param key
     * @return
     */
    @Override
    protected Object lookup(Object key) {
        Object cacheKey = getKey(key);
        Object value = caffeineCache.getIfPresent(key);
        if (value != null) {
            logger.debug("从本地缓存中获得key, the key is : {}", cacheKey);
            return value;
        }

        value = redisTemplate.opsForValue().get(cacheKey);

        if (value != null) {
            logger.debug("从redis中获得值,将值放到本地缓存中, the key is : {}", cacheKey);
            caffeineCache.put(key, value);
        }
        return value;
    }

    /**
     * @description 清理本地缓存
     */
    public void clearLocal(Object key) {
        logger.debug("clear local cache, the key is : {}", key);
        if (key == null) {
            caffeineCache.invalidateAll();
        } else {
            caffeineCache.invalidate(key);
        }
    }

    //————————————————————————————私有方法——————————————————————————

    private Object getKey(Object key) {
        String keyStr = this.name.concat(":").concat(key.toString());
        return StringUtils.isEmpty(this.cachePrefix) ? keyStr : this.cachePrefix.concat(":").concat(keyStr);
    }

    private long getExpire() {
        long expire = defaultExpiration;
        Long cacheNameExpire = defaultExpires.get(this.name);
        return cacheNameExpire == null ? expire : cacheNameExpire.longValue();
    }

    /**
     * @description 缓存变更时通知其他节点清理本地缓存
     */
    private void push(CacheMessage message) {
        redisTemplate.convertAndSend(topic, message);
    }

}

关于分布式本地缓存失效

现在的线上生产的都是多个节点,如果本节点的缓存失效了,是需要通过中间件来通知其他节点失效消息的。本组件考虑到学习分享让大家引入的依赖少点,就直接通过 redis 来发送消息了,实际生产过程中换成成熟的消息中间件(kafka、RocketMQ)来做通知更为稳妥。

08-05 23:04