深入剖析 redis 数据淘汰策略

概述

在 redis 中,允许用户设置最大使用内存大小 server.maxmemory,在内存限定的情况下是很有用的。譬如,在一台 8G 机子上部署了 4 个 redis 服务点,每一个服务点分配 1.5G 的内存大小,减少内存紧张的情况,由此获取更为稳健的服务。

redis 内存数据集大小上升到一定大小的时候,就会施行数据淘汰策略。redis 提供 6种数据淘汰策略:

  1. volatile-lru:从已设置过期时间的数据集(server.db[i].expires)中挑选最近最少使用的数据淘汰
  2. volatile-ttl:从已设置过期时间的数据集(server.db[i].expires)中挑选将要过期的数据淘汰
  3. volatile-random:从已设置过期时间的数据集(server.db[i].expires)中任意选择数据淘汰
  4. allkeys-lru:从数据集(server.db[i].dict)中挑选最近最少使用的数据淘汰
  5. allkeys-random:从数据集(server.db[i].dict)中任意选择数据淘汰
  6. no-enviction(驱逐):禁止驱逐数据

redis 确定驱逐某个键值对后,会删除这个数据并,并将这个数据变更消息发布到本地(AOF 持久化)和从机(主从连接)。

LRU 数据淘汰机制

在服务器配置中保存了 lru 计数器 server.lrulock,会定时(redis 定时程序 serverCorn())更新,server.lrulock 的值是根据 server.unixtime 计算出来的。

另外,从 struct redisObject 中可以发现,每一个 redis 对象都会设置相应的 lru。可以想象的是,每一次访问数据的时候,会更新 redisObject.lru。

LRU 数据淘汰机制是这样的:在数据集中随机挑选几个键值对,取出其中 lru 最大的键值对淘汰。所以,你会发现,redis 并不是保证取得所有数据集中最近最少使用(LRU)的键值对,而只是随机挑选的几个键值对中的。

// redisServer 保存了 lru 计数器
struct redisServer {
    ...
    unsigned lruclock:22;       /* Clock incrementing every minute, for LRU */
    ...
};

// 每一个 redis 对象都保存了 lru
#define REDIS_LRU_CLOCK_MAX ((1<<21)-1) /* Max value of obj->lru */
#define REDIS_LRU_CLOCK_RESOLUTION 10 /* LRU clock resolution in seconds */
typedef struct redisObject {
    // 刚刚好 32 bits

    // 对象的类型,字符串/列表/集合/哈希表
    unsigned type:4;
    // 未使用的两个位
    unsigned notused:2;     /* Not used */
    // 编码的方式,redis 为了节省空间,提供多种方式来保存一个数据
    // 譬如:“123456789” 会被存储为整数 123456789
    unsigned encoding:4;
    unsigned lru:22;        /* lru time (relative to server.lruclock) */

    // 引用数
    int refcount;

    // 数据指针
    void *ptr;
} robj;

// redis 定时执行程序。联想:linux cron
int serverCron(struct aeEventLoop *eventLoop, long long id, void *clientData) {
    ......
    /* We have just 22 bits per object for LRU information.
     * So we use an (eventually wrapping) LRU clock with 10 seconds resolution.
     * 2^22 bits with 10 seconds resolution is more or less 1.5 years.
     *
     * Note that even if this will wrap after 1.5 years it's not a problem,
     * everything will still work but just some object will appear younger
     * to Redis. But for this to happen a given object should never be touched
     * for 1.5 years.
     *
     * Note that you can change the resolution altering the
     * REDIS_LRU_CLOCK_RESOLUTION define.
     */
    updateLRUClock();
    ......
}

// 更新服务器的 lru 计数器
void updateLRUClock(void) {
    server.lruclock = (server.unixtime/REDIS_LRU_CLOCK_RESOLUTION) &
                                                REDIS_LRU_CLOCK_MAX;
}

 

TTL 数据淘汰机制

redis 数据集数据结构中保存了键值对过期时间的表,即 redisDb.expires。和 LRU 数据淘汰机制类似,TTL 数据淘汰机制是这样的:从过期时间的表中随机挑选几个键值对,取出其中 ttl 最大的键值对淘汰。同样你会发现,redis 并不是保证取得所有过期时间的表中最快过期的键值对,而只是随机挑选的几个键值对中的。

总结

redis 每服务客户端执行一个命令的时候,会检测使用的内存是否超额。如果超额,即进行数据淘汰。

// 执行命令
int processCommand(redisClient *c) {
    ......
    // 内存超额
    /* Handle the maxmemory directive.
     *
     * First we try to free some memory if possible (if there are volatile
     * keys in the dataset). If there are not the only thing we can do
     * is returning an error. */
    if (server.maxmemory) {
        int retval = freeMemoryIfNeeded();
        if ((c->cmd->flags & REDIS_CMD_DENYOOM) && retval == REDIS_ERR) {
            flagTransaction(c);
            addReply(c, shared.oomerr);
            return REDIS_OK;
        }
    }
    ......
}

// 如果需要,是否一些内存
int freeMemoryIfNeeded(void) {
    size_t mem_used, mem_tofree, mem_freed;
    int slaves = listLength(server.slaves);

    // redis 从机回复空间和 AOF 内存大小不计算入 redis 内存大小
    /* Remove the size of slaves output buffers and AOF buffer from the
     * count of used memory. */
    mem_used = zmalloc_used_memory();

    // 从机回复空间大小
    if (slaves) {
        listIter li;
        listNode *ln;

        listRewind(server.slaves,&li);
        while((ln = listNext(&li))) {
            redisClient *slave = listNodeValue(ln);
            unsigned long obuf_bytes = getClientOutputBufferMemoryUsage(slave);
            if (obuf_bytes > mem_used)
                mem_used = 0;
            else
                mem_used -= obuf_bytes;
        }
    }
    // server.aof_buf && server.aof_rewrite_buf_blocks
    if (server.aof_state != REDIS_AOF_OFF) {
        mem_used -= sdslen(server.aof_buf);
        mem_used -= aofRewriteBufferSize();
    }

    // 内存是否超过设置大小
    /* Check if we are over the memory limit. */
    if (mem_used <= server.maxmemory) return REDIS_OK;

    // redis 中可以设置内存超额策略
    if (server.maxmemory_policy == REDIS_MAXMEMORY_NO_EVICTION)
        return REDIS_ERR; /* We need to free memory, but policy forbids. */

    /* Compute how much memory we need to free. */
    mem_tofree = mem_used - server.maxmemory;
    mem_freed = 0;
    while (mem_freed < mem_tofree) {
        int j, k, keys_freed = 0;

        // 遍历所有数据集
        for (j = 0; j < server.dbnum; j++) {
            long bestval = 0; /* just to prevent warning */
            sds bestkey = NULL;
            struct dictEntry *de;
            redisDb *db = server.db+j;
            dict *dict;

            // 不同的策略,选择的数据集不一样
            if (server.maxmemory_policy == REDIS_MAXMEMORY_ALLKEYS_LRU ||
                server.maxmemory_policy == REDIS_MAXMEMORY_ALLKEYS_RANDOM)
            {
                dict = server.db[j].dict;
            } else {
                dict = server.db[j].expires;
            }

            // 数据集为空,继续下一个数据集
            if (dictSize(dict) == 0) continue;

            // 随机淘汰随机策略:随机挑选
            /* volatile-random and allkeys-random policy */
            if (server.maxmemory_policy == REDIS_MAXMEMORY_ALLKEYS_RANDOM ||
                server.maxmemory_policy == REDIS_MAXMEMORY_VOLATILE_RANDOM)
            {
                de = dictGetRandomKey(dict);
                bestkey = dictGetKey(de);
            }

            // LRU 策略:挑选最近最少使用的数据
            /* volatile-lru and allkeys-lru policy */
            else if (server.maxmemory_policy == REDIS_MAXMEMORY_ALLKEYS_LRU ||
                server.maxmemory_policy == REDIS_MAXMEMORY_VOLATILE_LRU)
            {
                // server.maxmemory_samples 为随机挑选键值对次数
                // 随机挑选 server.maxmemory_samples个键值对,驱逐最近最少使用的数据
                for (k = 0; k < server.maxmemory_samples; k++) {
                    sds thiskey;
                    long thisval;
                    robj *o;

                    // 随机挑选键值对
                    de = dictGetRandomKey(dict);

                    // 获取键
                    thiskey = dictGetKey(de);

                    /* When policy is volatile-lru we need an additional lookup
                     * to locate the real key, as dict is set to db->expires. */
                    if (server.maxmemory_policy == REDIS_MAXMEMORY_VOLATILE_LRU)
                        de = dictFind(db->dict, thiskey);
                    o = dictGetVal(de);

                    // 计算数据的空闲时间
                    thisval = estimateObjectIdleTime(o);

                    // 当前键值空闲时间更长,则记录
                    /* Higher idle time is better candidate for deletion */
                    if (bestkey == NULL || thisval > bestval) {
                        bestkey = thiskey;
                        bestval = thisval;
                    }
                }
            }

            // TTL 策略:挑选将要过期的数据
            /* volatile-ttl */
            else if (server.maxmemory_policy == REDIS_MAXMEMORY_VOLATILE_TTL) {
                // server.maxmemory_samples 为随机挑选键值对次数
                // 随机挑选 server.maxmemory_samples个键值对,驱逐最快要过期的数据
                for (k = 0; k < server.maxmemory_samples; k++) {
                    sds thiskey;
                    long thisval;

                    de = dictGetRandomKey(dict);
                    thiskey = dictGetKey(de);
                    thisval = (long) dictGetVal(de);

                    /* Expire sooner (minor expire unix timestamp) is better
                     * candidate for deletion */
                    if (bestkey == NULL || thisval < bestval) {
                        bestkey = thiskey;
                        bestval = thisval;
                    }
                }
            }

            // 删除选定的键值对
            /* Finally remove the selected key. */
            if (bestkey) {
                long long delta;

                robj *keyobj = createStringObject(bestkey,sdslen(bestkey));

                // 发布数据更新消息,主要是 AOF 持久化和从机
                propagateExpire(db,keyobj);

                // 注意, propagateExpire() 可能会导致内存的分配, propagateExpire() 
提前执行就是因为 redis 只计算 dbDelete() 释放的内存大小。倘若同时计算 dbDelete() 释放的内存
和 propagateExpire() 分配空间的大小,与此同时假设分配空间大于释放空间,就有可能永远退不出这个循环。
                // 下面的代码会同时计算 dbDelete() 释放的内存和 propagateExpire() 分配空间的大小:
                // propagateExpire(db,keyobj);
                // delta = (long long) zmalloc_used_memory();
                // dbDelete(db,keyobj);
                // delta -= (long long) zmalloc_used_memory();
                // mem_freed += delta;
                /////////////////////////////////////////

                /* We compute the amount of memory freed by dbDelete() alone.
                 * It is possible that actually the memory needed to propagate
                 * the DEL in AOF and replication link is greater than the one
                 * we are freeing removing the key, but we can't account for
                 * that otherwise we would never exit the loop.
                 *
                 * AOF and Output buffer memory will be freed eventually so
                 * we only care about memory used by the key space. */
                // 只计算 dbDelete() 释放内存的大小
                delta = (long long) zmalloc_used_memory();
                dbDelete(db,keyobj);
                delta -= (long long) zmalloc_used_memory();
                mem_freed += delta;

                server.stat_evictedkeys++;

                // 将数据的删除通知所有的订阅客户端
                notifyKeyspaceEvent(REDIS_NOTIFY_EVICTED, "evicted",
                    keyobj, db->id);
                decrRefCount(keyobj);
                keys_freed++;

                // 将从机回复空间中的数据及时发送给从机
                /* When the memory to free starts to be big enough, we may
                 * start spending so much time here that is impossible to
                 * deliver data to the slaves fast enough, so we force the
                 * transmission here inside the loop. */
                if (slaves) flushSlavesOutputBuffers();
            }
        }

        // 未能释放空间,且此时 redis 使用的内存大小依旧超额,失败返回
        if (!keys_freed) return REDIS_ERR; /* nothing to free... */
    }
    return REDIS_OK;
}

 

捣乱 2014-5-27

文章来自:http://daoluan.net/blog/redis-data-elimination/

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