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# redis系列介绍八-淘汰策略

Posted on 2020-04-18 Edited on 2020-04-19 In Redis , 数据结构 , 源码 , C , Redis Views: Views: Disqus:

### LRU

说完了过期策略再说下淘汰策略，redis 使用的策略是近似的 lru 策略，为什么是近似的呢，先来看下什么是 lru，看下 wiki 的介绍
，图中一共有四个槽的存储空间，依次访问顺序是 A B C D E D F，
当第一次访问 D 时刚好占满了坑，并且值是 4，这个值越小代表越先被淘汰，当 E 进来时，看了下已经存在的四个里 A 是最小的，代表是最早存在并且最早被访问的，那就先淘汰它了，E 占领了 A 的位置，并设置值为 4，然后又访问 D 了，D 已经存在了，不过又被访问到了，得更新值为 5，然后是 F 进来了，这时 B 是最老的且最近未被访问，所以就淘汰它了。以上是一个 lru 的简要说明，但是 redis 没有严格按照这个去执行，理由跟前面过期策略一致，最严格的过期策略应该是每个 key 都有对应的定时器，当超时时马上就能清除，但是问题是这样的cpu 消耗太大，所换来的内存效率不太值得，淘汰策略也是这样，类似于上图，要维护所有 key 的一个有序 lru 值，并且遍历将最小的淘汰，redis 采用的是抽样的形式，最初的实现方式是随机从 dict 抽取 5 个 key，淘汰一个 lru 最小的，这样子勉强能达到淘汰的目的，但是效果不是特别好，后面在 redis 3.0开始，将随机抽取改成了维护一个 pool，pool 的大小默认是 16，每次放入的都是按lru 值有序排列好，每一次放入的必须是 lru小于 pool 中最小的 lru 才允许放入，直到放满，后面再有新的就会将大的踢出。
redis 针对这个策略的改进做了一个实验，这里借用下图
首先背景是这图中的所有点都对应一个 redis 的 key，灰色部分加入后被顺序访问过一遍，然后又加入了绿色部分，那么按照理论的 lru 算法，应该是图左上中，浅灰色部分全都被淘汰，那么对比来看看图右上，左下和右下，左下表示 2.8 版本就是随机抽样 5 个 key，淘汰其中 lru 最小的一个，发现是灰色和浅灰色的都有被淘汰的，右下的 3.0 版本抽样数量不变的情况下，稍好一些，当 3.0 版本的抽样数量调整成 10 后，已经较为接近理论上的 lru 策略了，通过代码来简要分析下

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    typedef struct redisObject {
        unsigned type:4;
        unsigned encoding:4;
        unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                                * LFU data (least significant 8 bits frequency
                                * and most significant 16 bits access time). */
        int refcount;
        void *ptr;
    } robj;

对于 lru 策略来说，lru 字段记录的就是`redisObj` 的LRU time，
redis 在访问数据时，都会调用`lookupKey`方法

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    /* Low level key lookup API, not actually called directly from commands
     * implementations that should instead rely on lookupKeyRead(),
     * lookupKeyWrite() and lookupKeyReadWithFlags(). */
    robj *lookupKey(redisDb *db, robj *key, int flags) {
        dictEntry *de = dictFind(db->dict,key->ptr);
        if (de) {
            robj *val = dictGetVal(de);

            /* Update the access time for the ageing algorithm.
             * Don't do it if we have a saving child, as this will trigger
             * a copy on write madness. */
            if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
                if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                    // 这个是后面一节的内容
                    updateLFU(val);
                } else {
                    //  对于这个分支，访问时就会去更新 lru 值
                    val->lru = LRU_CLOCK();
                }
            }
            return val;
        } else {
            return NULL;
        }
    }
    /* This function is used to obtain the current LRU clock.
     * If the current resolution is lower than the frequency we refresh the
     * LRU clock (as it should be in production servers) we return the
     * precomputed value, otherwise we need to resort to a system call. */
    unsigned int LRU_CLOCK(void) {
        unsigned int lruclock;
        if (1000/server.hz <= LRU_CLOCK_RESOLUTION) {
            // 如果服务器的频率server.hz大于 1 时就是用系统预设的 lruclock
            lruclock = server.lruclock;
        } else {
            lruclock = getLRUClock();
        }
        return lruclock;
    }
    /* Return the LRU clock, based on the clock resolution. This is a time
     * in a reduced-bits format that can be used to set and check the
     * object->lru field of redisObject structures. */
    unsigned int getLRUClock(void) {
        return (mstime()/LRU_CLOCK_RESOLUTION) & LRU_CLOCK_MAX;
    }

redis 处理命令是在这里`processCommand`

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    /* If this function gets called we already read a whole
     * command, arguments are in the client argv/argc fields.
     * processCommand() execute the command or prepare the
     * server for a bulk read from the client.
     *
     * If C_OK is returned the client is still alive and valid and
     * other operations can be performed by the caller. Otherwise
     * if C_ERR is returned the client was destroyed (i.e. after QUIT). */
    int processCommand(client *c) {
        moduleCallCommandFilters(c);

        /* Handle the maxmemory directive.
         *
         * Note that we do not want to reclaim memory if we are here re-entering
         * the event loop since there is a busy Lua script running in timeout
         * condition, to avoid mixing the propagation of scripts with the
         * propagation of DELs due to eviction. */
        if (server.maxmemory && !server.lua_timedout) {
            int out_of_memory = freeMemoryIfNeededAndSafe() == C_ERR;
            /* freeMemoryIfNeeded may flush slave output buffers. This may result
             * into a slave, that may be the active client, to be freed. */
            if (server.current_client == NULL) return C_ERR;

            /* It was impossible to free enough memory, and the command the client
             * is trying to execute is denied during OOM conditions or the client
             * is in MULTI/EXEC context? Error. */
            if (out_of_memory &&
                (c->cmd->flags & CMD_DENYOOM ||
                 (c->flags & CLIENT_MULTI &&
                  c->cmd->proc != execCommand &&
                  c->cmd->proc != discardCommand)))
            {
                flagTransaction(c);
                addReply(c, shared.oomerr);
                return C_OK;
            }
        }
    }

这里只摘了部分，当需要清理内存时就会调用, 然后调用了`freeMemoryIfNeededAndSafe`

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    /* This is a wrapper for freeMemoryIfNeeded() that only really calls the
     * function if right now there are the conditions to do so safely:
     *
     * - There must be no script in timeout condition.
     * - Nor we are loading data right now.
     *
     */
    int freeMemoryIfNeededAndSafe(void) {
        if (server.lua_timedout || server.loading) return C_OK;
        return freeMemoryIfNeeded();
    }
    /* This function is periodically called to see if there is memory to free
     * according to the current "maxmemory" settings. In case we are over the
     * memory limit, the function will try to free some memory to return back
     * under the limit.
     *
     * The function returns C_OK if we are under the memory limit or if we
     * were over the limit, but the attempt to free memory was successful.
     * Otehrwise if we are over the memory limit, but not enough memory
     * was freed to return back under the limit, the function returns C_ERR. */
    int freeMemoryIfNeeded(void) {
        int keys_freed = 0;
        /* By default replicas should ignore maxmemory
         * and just be masters exact copies. */
        if (server.masterhost && server.repl_slave_ignore_maxmemory) return C_OK;

        size_t mem_reported, mem_tofree, mem_freed;
        mstime_t latency, eviction_latency;
        long long delta;
        int slaves = listLength(server.slaves);

        /* When clients are paused the dataset should be static not just from the
         * POV of clients not being able to write, but also from the POV of
         * expires and evictions of keys not being performed. */
        if (clientsArePaused()) return C_OK;
        if (getMaxmemoryState(&mem_reported,NULL,&mem_tofree,NULL) == C_OK)
            return C_OK;

        mem_freed = 0;

        if (server.maxmemory_policy == MAXMEMORY_NO_EVICTION)
            goto cant_free; /* We need to free memory, but policy forbids. */

        latencyStartMonitor(latency);
        while (mem_freed < mem_tofree) {
            int j, k, i;
            static unsigned int next_db = 0;
            sds bestkey = NULL;
            int bestdbid;
            redisDb *db;
            dict *dict;
            dictEntry *de;

            if (server.maxmemory_policy & (MAXMEMORY_FLAG_LRU|MAXMEMORY_FLAG_LFU) ||
                server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL)
            {
                struct evictionPoolEntry *pool = EvictionPoolLRU;

                while(bestkey == NULL) {
                    unsigned long total_keys = 0, keys;

                    /* We don't want to make local-db choices when expiring keys,
                     * so to start populate the eviction pool sampling keys from
                     * every DB. */
                    for (i = 0; i < server.dbnum; i++) {
                        db = server.db+i;
                        dict = (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) ?
                                db->dict : db->expires;
                        if ((keys = dictSize(dict)) != 0) {
                            evictionPoolPopulate(i, dict, db->dict, pool);
                            total_keys += keys;
                        }
                    }
                    if (!total_keys) break; /* No keys to evict. */

                    /* Go backward from best to worst element to evict. */
                    for (k = EVPOOL_SIZE-1; k >= 0; k--) {
                        if (pool[k].key == NULL) continue;
                        bestdbid = pool[k].dbid;

                        if (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) {
                            de = dictFind(server.db[pool[k].dbid].dict,
                                pool[k].key);
                        } else {
                            de = dictFind(server.db[pool[k].dbid].expires,
                                pool[k].key);
                        }

                        /* Remove the entry from the pool. */
                        if (pool[k].key != pool[k].cached)
                            sdsfree(pool[k].key);
                        pool[k].key = NULL;
                        pool[k].idle = 0;

                        /* If the key exists, is our pick. Otherwise it is
                         * a ghost and we need to try the next element. */
                        if (de) {
                            bestkey = dictGetKey(de);
                            break;
                        } else {
                            /* Ghost... Iterate again. */
                        }
                    }
                }
            }

            /* volatile-random and allkeys-random policy */
            else if (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM ||
                     server.maxmemory_policy == MAXMEMORY_VOLATILE_RANDOM)
            {
                /* When evicting a random key, we try to evict a key for
                 * each DB, so we use the static 'next_db' variable to
                 * incrementally visit all DBs. */
                for (i = 0; i < server.dbnum; i++) {
                    j = (++next_db) % server.dbnum;
                    db = server.db+j;
                    dict = (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM) ?
                            db->dict : db->expires;
                    if (dictSize(dict) != 0) {
                        de = dictGetRandomKey(dict);
                        bestkey = dictGetKey(de);
                        bestdbid = j;
                        break;
                    }
                }
            }

            /* Finally remove the selected key. */
            if (bestkey) {
                db = server.db+bestdbid;
                robj *keyobj = createStringObject(bestkey,sdslen(bestkey));
                propagateExpire(db,keyobj,server.lazyfree_lazy_eviction);
                /* We compute the amount of memory freed by db*Delete() 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. */
                delta = (long long) zmalloc_used_memory();
                latencyStartMonitor(eviction_latency);
                if (server.lazyfree_lazy_eviction)
                    dbAsyncDelete(db,keyobj);
                else
                    dbSyncDelete(db,keyobj);
                latencyEndMonitor(eviction_latency);
                latencyAddSampleIfNeeded("eviction-del",eviction_latency);
                latencyRemoveNestedEvent(latency,eviction_latency);
                delta -= (long long) zmalloc_used_memory();
                mem_freed += delta;
                server.stat_evictedkeys++;
                notifyKeyspaceEvent(NOTIFY_EVICTED, "evicted",
                    keyobj, db->id);
                decrRefCount(keyobj);
                keys_freed++;

                /* 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();

                /* Normally our stop condition is the ability to release
                 * a fixed, pre-computed amount of memory. However when we
                 * are deleting objects in another thread, it's better to
                 * check, from time to time, if we already reached our target
                 * memory, since the "mem_freed" amount is computed only
                 * across the dbAsyncDelete() call, while the thread can
                 * release the memory all the time. */
                if (server.lazyfree_lazy_eviction && !(keys_freed % 16)) {
                    if (getMaxmemoryState(NULL,NULL,NULL,NULL) == C_OK) {
                        /* Let's satisfy our stop condition. */
                        mem_freed = mem_tofree;
                    }
                }
            } else {
                latencyEndMonitor(latency);
                latencyAddSampleIfNeeded("eviction-cycle",latency);
                goto cant_free; /* nothing to free... */
            }
        }
        latencyEndMonitor(latency);
        latencyAddSampleIfNeeded("eviction-cycle",latency);
        return C_OK;

    cant_free:
        /* We are here if we are not able to reclaim memory. There is only one
         * last thing we can try: check if the lazyfree thread has jobs in queue
         * and wait... */
        while(bioPendingJobsOfType(BIO_LAZY_FREE)) {
            if (((mem_reported - zmalloc_used_memory()) + mem_freed) >= mem_tofree)
                break;
            usleep(1000);
        }
        return C_ERR;
    }

这里就是根据具体策略去淘汰 key，首先是要往 pool 更新 key，更新key 的方法是`evictionPoolPopulate`

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    void evictionPoolPopulate(int dbid, dict *sampledict, dict *keydict, struct evictionPoolEntry *pool) {
        int j, k, count;
        dictEntry *samples[server.maxmemory_samples];

        count = dictGetSomeKeys(sampledict,samples,server.maxmemory_samples);
        for (j = 0; j < count; j++) {
            unsigned long long idle;
            sds key;
            robj *o;
            dictEntry *de;

            de = samples[j];
            key = dictGetKey(de);

            /* If the dictionary we are sampling from is not the main
             * dictionary (but the expires one) we need to lookup the key
             * again in the key dictionary to obtain the value object. */
            if (server.maxmemory_policy != MAXMEMORY_VOLATILE_TTL) {
                if (sampledict != keydict) de = dictFind(keydict, key);
                o = dictGetVal(de);
            }

            /* Calculate the idle time according to the policy. This is called
             * idle just because the code initially handled LRU, but is in fact
             * just a score where an higher score means better candidate. */
            if (server.maxmemory_policy & MAXMEMORY_FLAG_LRU) {
                idle = estimateObjectIdleTime(o);
            } else if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                /* When we use an LRU policy, we sort the keys by idle time
                 * so that we expire keys starting from greater idle time.
                 * However when the policy is an LFU one, we have a frequency
                 * estimation, and we want to evict keys with lower frequency
                 * first. So inside the pool we put objects using the inverted
                 * frequency subtracting the actual frequency to the maximum
                 * frequency of 255. */
                idle = 255-LFUDecrAndReturn(o);
            } else if (server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL) {
                /* In this case the sooner the expire the better. */
                idle = ULLONG_MAX - (long)dictGetVal(de);
            } else {
                serverPanic("Unknown eviction policy in evictionPoolPopulate()");
            }

            /* Insert the element inside the pool.
             * First, find the first empty bucket or the first populated
             * bucket that has an idle time smaller than our idle time. */
            k = 0;
            while (k < EVPOOL_SIZE &&
                   pool[k].key &&
                   pool[k].idle < idle) k++;
            if (k == 0 && pool[EVPOOL_SIZE-1].key != NULL) {
                /* Can't insert if the element is < the worst element we have
                 * and there are no empty buckets. */
                continue;
            } else if (k < EVPOOL_SIZE && pool[k].key == NULL) {
                /* Inserting into empty position. No setup needed before insert. */
            } else {
                /* Inserting in the middle. Now k points to the first element
                 * greater than the element to insert.  */
                if (pool[EVPOOL_SIZE-1].key == NULL) {
                    /* Free space on the right? Insert at k shifting
                     * all the elements from k to end to the right. */

                    /* Save SDS before overwriting. */
                    sds cached = pool[EVPOOL_SIZE-1].cached;
                    memmove(pool+k+1,pool+k,
                        sizeof(pool[0])*(EVPOOL_SIZE-k-1));
                    pool[k].cached = cached;
                } else {
                    /* No free space on right? Insert at k-1 */
                    k--;
                    /* Shift all elements on the left of k (included) to the
                     * left, so we discard the element with smaller idle time. */
                    sds cached = pool[0].cached; /* Save SDS before overwriting. */
                    if (pool[0].key != pool[0].cached) sdsfree(pool[0].key);
                    memmove(pool,pool+1,sizeof(pool[0])*k);
                    pool[k].cached = cached;
                }
            }

            /* Try to reuse the cached SDS string allocated in the pool entry,
             * because allocating and deallocating this object is costly
             * (according to the profiler, not my fantasy. Remember:
             * premature optimizbla bla bla bla. */
            int klen = sdslen(key);
            if (klen > EVPOOL_CACHED_SDS_SIZE) {
                pool[k].key = sdsdup(key);
            } else {
                memcpy(pool[k].cached,key,klen+1);
                sdssetlen(pool[k].cached,klen);
                pool[k].key = pool[k].cached;
            }
            pool[k].idle = idle;
            pool[k].dbid = dbid;
        }
    }

`Redis`随机选择`maxmemory_samples`数量的key，然后计算这些`key`的空闲时间`idle time`，当满足条件时(比pool中的某些键的空闲时间还大)就可以进`pool`。`pool`更新之后，就淘汰`pool`中空闲时间最大的键。

`estimateObjectIdleTime`用来计算Redis对象的空闲时间：

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    /* Given an object returns the min number of milliseconds the object was never
     * requested, using an approximated LRU algorithm. */
    unsigned long long estimateObjectIdleTime(robj *o) {
        unsigned long long lruclock = LRU_CLOCK();
        if (lruclock >= o->lru) {
            return (lruclock - o->lru) * LRU_CLOCK_RESOLUTION;
        } else {
            return (lruclock + (LRU_CLOCK_MAX - o->lru)) *
                        LRU_CLOCK_RESOLUTION;
        }
    }

空闲时间第一种是 lurclock 大于对象的 lru，那么就是减一下乘以精度，因为 lruclock 有可能是已经预生成的，所以会可能走下面这个

### LFU

上面介绍了LRU 的算法，但是考虑一种场景

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    ~~~~~A~~~~~A~~~~~A~~~~A~~~~~A~~~~~A~~|
    ~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~|
    ~~~~~~~~~~C~~~~~~~~~C~~~~~~~~~C~~~~~~|
    ~~~~~D~~~~~~~~~~D~~~~~~~~~D~~~~~~~~~D|

可以发现，当采用 lru 的淘汰策略的时候，D 是最新的，会被认为是最值得保留的，但是事实上还不如 A 跟 B，然后 antirez 大神就想到了LFU (Least Frequently Used) 这个算法, 显然对于上面的四个 key 的访问频率，保留优先级应该是 B > A > C = D
那要怎么来实现这个 LFU 算法呢，其实像LRU，理想的情况就是维护个链表，把最新访问的放到头上去，但是这个会影响访问速度，注意到前面代码的应该可以看到，redisObject 的 lru 字段其实是两用的，当策略是 LFU 时，这个字段就另作他用了，它的 24 位长度被分成两部分

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          16 bits      8 bits
    +----------------+--------+
    + Last decr time | LOG_C  |
    +----------------+--------+

前16位字段是最后一次递减时间，因此Redis知道 上一次计数器递减，后8位是 计数器 counter。
LFU 的主体策略就是当这个 key 被访问的次数越多频率越高他就越容易被保留下来，并且是最近被访问的频率越高。这其实有两个事情要做，一个是在访问的时候增加计数值，在一定长时间不访问时进行衰减，所以这里用了两个值，前 16 位记录上一次衰减的时间，后 8 位记录具体的计数值。
Redis4.0之后为maxmemory_policy淘汰策略添加了两个LFU模式：

`volatile-lfu`：对有过期时间的key采用LFU淘汰策略
`allkeys-lfu`：对全部key采用LFU淘汰策略
还有2个配置可以调整LFU算法：

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    lfu-log-factor 10
    lfu-decay-time 1
    ```
    `lfu-log-factor` 可以调整计数器counter的增长速度，lfu-log-factor越大，counter增长的越慢。

    `lfu-decay-time`是一个以分钟为单位的数值，可以调整counter的减少速度
    这里有个问题是 8 位大小够计么，访问一次加 1 的话的确不够，不过大神就是大神，才不会这么简单的加一。往下看代码
    ```C
    /* Low level key lookup API, not actually called directly from commands
     * implementations that should instead rely on lookupKeyRead(),
     * lookupKeyWrite() and lookupKeyReadWithFlags(). */
    robj *lookupKey(redisDb *db, robj *key, int flags) {
        dictEntry *de = dictFind(db->dict,key->ptr);
        if (de) {
            robj *val = dictGetVal(de);

            /* Update the access time for the ageing algorithm.
             * Don't do it if we have a saving child, as this will trigger
             * a copy on write madness. */
            if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
                if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                    // 当淘汰策略是 LFU 时，就会调用这个updateLFU
                    updateLFU(val);
                } else {
                    val->lru = LRU_CLOCK();
                }
            }
            return val;
        } else {
            return NULL;
        }
    }

`updateLFU` 这个其实个入口，调用了两个重要的方法

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    /* Update LFU when an object is accessed.
     * Firstly, decrement the counter if the decrement time is reached.
     * Then logarithmically increment the counter, and update the access time. */
    void updateLFU(robj *val) {
        unsigned long counter = LFUDecrAndReturn(val);
        counter = LFULogIncr(counter);
        val->lru = (LFUGetTimeInMinutes()<<8) | counter;
    }

首先来看看`LFUDecrAndReturn`，这个方法的作用是根据上一次衰减时间和系统配置的 `lfu-decay-time` 参数来确定需要将 counter 减去多少

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    /* If the object decrement time is reached decrement the LFU counter but
     * do not update LFU fields of the object, we update the access time
     * and counter in an explicit way when the object is really accessed.
     * And we will times halve the counter according to the times of
     * elapsed time than server.lfu_decay_time.
     * Return the object frequency counter.
     *
     * This function is used in order to scan the dataset for the best object
     * to fit: as we check for the candidate, we incrementally decrement the
     * counter of the scanned objects if needed. */
    unsigned long LFUDecrAndReturn(robj *o) {
        // 右移 8 位，拿到上次衰减时间
        unsigned long ldt = o->lru >> 8;
        // 对 255 做与操作，拿到 counter 值
        unsigned long counter = o->lru & 255;
        // 根据lfu_decay_time来算出过了多少个衰减周期
        unsigned long num_periods = server.lfu_decay_time ? LFUTimeElapsed(ldt) / server.lfu_decay_time : 0;
        if (num_periods)
            counter = (num_periods > counter) ? 0 : counter - num_periods;
        return counter;
    }

然后是加，调用了`LFULogIncr`

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    /* Logarithmically increment a counter. The greater is the current counter value
     * the less likely is that it gets really implemented. Saturate it at 255. */
    uint8_t LFULogIncr(uint8_t counter) {
        // 最大值就是 255，到顶了就不加了
        if (counter == 255) return 255;
        // 生成个随机小数
        double r = (double)rand()/RAND_MAX;
        // 减去个基础值，LFU_INIT_VAL = 5，防止刚进来就被逐出
        double baseval = counter - LFU_INIT_VAL;
        // 如果是小于 0，
        if (baseval < 0) baseval = 0;
        // 如果 baseval 是 0，那么 p 就是 1了，后面 counter 直接加一，如果不是的话，得看系统参数lfu_log_factor，这个越大，除出来的 p 越小，那么 counter++的可能性也越小，这样子就把前面的疑问给解决了，不是直接+1 的
        double p = 1.0/(baseval*server.lfu_log_factor+1);
        if (r < p) counter++;
        return counter;
    }

大概的变化速度可以参考

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    +--------+------------+------------+------------+------------+------------+
    | factor | 100 hits   | 1000 hits  | 100K hits  | 1M hits    | 10M hits   |
    +--------+------------+------------+------------+------------+------------+
    | 0      | 104        | 255        | 255        | 255        | 255        |
    +--------+------------+------------+------------+------------+------------+
    | 1      | 18         | 49         | 255        | 255        | 255        |
    +--------+------------+------------+------------+------------+------------+
    | 10     | 10         | 18         | 142        | 255        | 255        |
    +--------+------------+------------+------------+------------+------------+
    | 100    | 8          | 11         | 49         | 143        | 255        |
    +--------+------------+------------+------------+------------+------------+

简而言之就是 lfu_log_factor 越大变化的越慢

### 总结

总结一下，redis 实现了近似的 lru 淘汰策略，通过增加了淘汰 key 的池子(pool)，并且增大每次抽样的 key 的数量来将淘汰效果更进一步地接近于 lru，这是 lru 策略，但是对于前面举的一个例子，其实 lru 并不能保证 key 的淘汰就如我们预期，所以在后期又引入了 lfu 的策略，lfu的策略比较巧妙，复用了 redis 对象的 lru 字段，并且使用了factor 参数来控制计数器递增的速度，防止 8 位的计数器太早溢出。

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- Post author: Nicksxs
- Post link: https://nicksxs.me/2020/04/18/redis系列介绍八/
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