Redis系列之淘汰策略介绍

Redis系列之淘汰策略介绍

为什么需要Redis淘汰策略？

由于Redis内存是有大小的，当内存快满的时候，又没有过期数据，这个时候就会导致内存被占满，内存满了，自然就不能再放入新的数据。所以，就需要Redis的淘汰策略来保证可用性。

Redis淘汰策略分类

在Redis中提供了好几种淘汰策略，查看官方文档

https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/，找到如下几种淘汰策略：

我们发现redis提供了8种不同的策略，只要在我们的config中配置maxmemory-policy即可指定相关的淘汰策略。

maxmemory-policy noeviction # 默认淘汰策略，只能读不能写

Redis数据淘汰流程

淘汰流程：

1. 首先，我们会有一个淘汰池，默认大小是16，并且里面的数据都是末尾淘汰机制。
2. 每次指令操作的时候，会自旋判断当前的内存是否满足指令所需要的内存，内存满足，继续指令操作
3. 如果当前内存不能满足时，判断淘汰机制是否为noeviction，是默认的noeviction机制，OOM报错给用户，只能读不能写，如果不是默认的noeviction机制会从淘汰池中的尾部拿取一个最适合淘汰的数据。
   1. 取样，从Redis中随机获取取样的数据，不一次性读取所有的数据。
   2. 在取样的数据中，根据淘汰算法，找到最适合淘汰的数据
   3. 将最合适淘汰的取样数据跟淘汰池中的数据比较，是否比淘汰池中的数据更适合淘汰，如果更合适，才放入淘汰池
   4. 淘汰池按照适合的程度进行排序，最适合的数据放在尾部
4. 将需要淘汰的数据从redis中删除，并且从淘汰池移除

源码验证淘汰流程

每次执行操作指令都会走freeMemoryIfNeeded函数(evict.c文件)

/* 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.* Otherwise 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. *//* 从库是否忽略内存淘汰机制，server.masterhost有配置，说明是从库 */if (server.masterhost && server.repl_slave_ignore_maxmemory) return C_OK;size_t mem_reported, mem_tofree, mem_freed;mstime_t latency, eviction_latency, lazyfree_latency;long long delta;int slaves = listLength(server.slaves);int result = C_ERR;/* 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;/* 判断内存是否满，如果没有超过内存，直接返回 */if (getMaxmemoryState(&mem_reported,NULL,&mem_tofree,NULL) == C_OK)return C_OK;mem_freed = 0;latencyStartMonitor(latency);/* 如果策略为noeviction，默认不淘汰数据，直接报错OOM */if (server.maxmemory_policy == MAXMEMORY_NO_EVICTION)goto cant_free; /* We need to free memory, but policy forbids. *//* 内存不够的情况，一直自旋释放内存 */while (mem_freed < mem_tofree) {int j, k, i;static unsigned int next_db = 0;sds bestkey = NULL; // 定义最好的删除keyint bestdbid;redisDb *db;dict *dict;dictEntry *de;if (server.maxmemory_policy & (MAXMEMORY_FLAG_LRU|MAXMEMORY_FLAG_LFU) ||server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL){ // 如果淘汰算法是LRU、LFU、TTLstruct evictionPoolEntry *pool = EvictionPoolLRU; // 淘汰池，默认大小为16// 自旋，找到合适的要淘汰的keywhile(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. *//* 去不同的DB查找 */for (i = 0; i < server.dbnum; i++) {db = server.db+i;dict = (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) ?db->dict : db->expires; // 判断需要淘汰的范围，是所有数据还是过期的数据if ((keys = dictSize(dict)) != 0) { evictionPoolPopulate(i, dict, db->dict, pool);// 关键方法，从范围中取样，拿到最适合淘汰的数据total_keys += keys;}}if (!total_keys) break; /* No keys to evict. */ /*没有过期的key*//* 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. *//* 移除这个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.** Same for CSC invalidation messages generated by signalModifiedKey.** 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);/* 如果是异步淘汰，进行异步淘汰*/if (server.lazyfree_lazy_eviction)dbAsyncDelete(db,keyobj);// 异步淘汰机制elsedbSyncDelete(db,keyobj); // 同步淘汰机制latencyEndMonitor(eviction_latency);latencyAddSampleIfNeeded("eviction-del",eviction_latency);delta -= (long long) zmalloc_used_memory();mem_freed += delta;server.stat_evictedkeys++;signalModifiedKey(NULL,db,keyobj);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 {goto cant_free; /* nothing to free... */}}result = 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... */if (result != C_OK) {latencyStartMonitor(lazyfree_latency);while(bioPendingJobsOfType(BIO_LAZY_FREE)) {if (getMaxmemoryState(NULL,NULL,NULL,NULL) == C_OK) {result = C_OK;break;}usleep(1000);}latencyEndMonitor(lazyfree_latency);latencyAddSampleIfNeeded("eviction-lazyfree",lazyfree_latency);}latencyEndMonitor(latency);latencyAddSampleIfNeeded("eviction-cycle",latency);return result;
}

evictionPoolPopulate方法(evict.c文件)

/* This is an helper function for freeMemoryIfNeeded(), it is used in order* to populate the evictionPool with a few entries every time we want to* expire a key. Keys with idle time smaller than one of the current* keys are added. Keys are always added if there are free entries.** We insert keys on place in ascending order, so keys with the smaller* idle time are on the left, and keys with the higher idle time on the* right. */void evictionPoolPopulate(int dbid, dict *sampledict, dict *keydict, struct evictionPoolEntry *pool) {int j, k, count;// 需要取样的数据dictEntry *samples[server.maxmemory_samples];// 随机从需要取样的范围中得到取样的数据count = dictGetSomeKeys(sampledict,samples,server.maxmemory_samples);// 循环取样数据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) { // 如果是ttl，只能从带有过期时间的数据中获取，所以不需要获取对象，其它的淘汰策略都需要去我们的键值对中获取值对象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) { // 如果是LRU算法，采用LRU算法得到最长时间没访问的idle = estimateObjectIdleTime(o);} else if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) { // 如果是LFU算法，根据LFU算法得到最少访问的，idle越大，越容易淘汰，因为是用255-LFUDecrAndReturn(o);/* 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) { // ttl 直接根据时间来/* 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. *//* 取样的数据，计算好淘汰的idle后，放入淘汰池中 */k = 0;while (k < EVPOOL_SIZE &&pool[k].key &&pool[k].idle < idle) k++; // 自旋，找到淘汰池中比当前key的idle小的最后一个下标// k=0说明上面循环没进，也就是淘汰池中的所有数据都比当前数据的idle大，并且淘汰池的最后一个不为空，说明淘汰池也是满的，所以优先淘汰淘汰池中的数据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) { // 插入到桶后面/* Inserting into empty position. No setup needed before insert. */} else { // 插入到中间，会进行淘汰池的数据移动/* 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));// 假如当前数据比淘汰池的有些数据大，那么淘汰最小的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 optimization bla bla bla. *//* 将当前的放入淘汰池 */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;}
}

简要看了一遍源码，我们对redis数据的淘汰机制有了一定的理解，并且知道淘汰算法有8种，所以下面主要介绍一下Redis中比较重要的LRU算法和LFU算法

Redis中的LRU算法

LRU，Least Recently Used翻译过来就是最久未使用，LRU算法根据使用时间淘汰数据，越久没使用的数据越容易淘汰。

• 实现原理

1. 首先，LRU算法是根据这个对象的操作访问时间来进行淘汰的，所以我们就需要知道这个对象最后的访问时间。
2. 知道了对象的最后访问时间后，我们就需要跟当前的系统时间进行对比，计算出这个对象已经多久没访问

• 源码验证

在Redis源码中，有一个redisObject对象，这个对象就是我们redis中所有数据结构的对外对象，它里面有个字段叫做lru

redisObject对象 (server.h文件)

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;

看注释，大概也能猜出来，redis去实现lru淘汰算法跟这个lru对象有关，这个字段大小为24bit，记录的是对象操作访问时候的秒单位的后24位(bit)，然后怎么获取秒单位的后24位？看一下例子：

long currentTimeMillis = System.currentTimeMillis();
System.out.println(currentTimeMillis/1000); // 获取当前秒
System.out.println(currentTimeMillis/1000 & ((1<<24)-1));// 获取秒的后24位

控制台打印一下，得到两个10进制参数

用二进制转换平台转换一下，1715915460二进制1100110010001101100101011000100

4639428二进制10001101100101011000100

两个参数对比一下，确实是拿到了最后24位

currentTimeMillis/1000 & ((1<<24)-1)为什么能获取到当前时间（二进制）的最后24位？还是画图看看，一个数和24个1进行二进制的与运算，就是获取最后24位数，如图所示

然后怎么获取24个1？细心的读者可能已经知道了，没错，就是(1<<24)-1，1左移24位再减1，如图所示：

二进制不熟悉，可以参考二进制运算

生活中的例子：

场景一：数据在5月份被访问，现在是8月份，我们可以通过8-3=5，得到这个对象3个月没访问

场景二：数据在5月份被访问，现在是3月份，我们可以通过：3+12-5得到这个对象10个月没访问

同理：

如果redisObject.lru < lruclock，直接通过lruclock-redisObject.lru得到这个对象多久没访问。

如果redisObject.lru > lruclock，直接通过lruclock+(24bit的最大值-redisObject.lru)

通过redis源码验证一下，发现源码的思路和我们上面所说是差不多的，查看estimateObjectIdleTime方法(evict.c)

/* 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) {// 获取秒单位时间的最后24位unsigned long long lruclock = LRU_CLOCK();// 因为只有24位，所有最大值为2的24次方-1// 超过最大值从0开始，所以需要判断lruclock(当前系统时间)跟缓存对象的lru字段的大小if (lruclock >= o->lru) {// 如果lruclock>=robj.lru，返回lruclock->lru,再转换单位return (lruclock - o->lru) * LRU_CLOCK_RESOLUTION;} else {// 否则，lruclock+(LRU_CLOCK_MAX - o->lru)，得到的对象的值越小，返回的值越大，越大越容易被淘汰return (lruclock + (LRU_CLOCK_MAX - o->lru)) *LRU_CLOCK_RESOLUTION;}
}

Redis中的LFU算法

LFU，英文Least Frequently Used，翻译成中文就是最不常用的优先淘汰。不常用，它的衡量标准就是次数，次数越少的越容易淘汰。

• LFU的时效性问题

LFU算法有个问题需要去考虑，就是这个时效性问题，什么是时效性问题？就是去统计这个次数的时候，不能仅仅只考虑数量，而不考虑时间

举个例子，假如去年有一个新闻，很火，假如点击量是3000w，那么今年再有一个新闻出来，刚出来，点击量是1000w，本来我们应该让今年这个新闻显示出来的，去年的新闻虽然太火，但是也是去年的，我们推荐系统肯定不希望这个新闻继续上热搜的，所以推荐系统就需要考虑到数量同时兼顾这个时间问题

所以，如果根据LFU来做的话，仅根据使用次数来淘汰数据，很容易淘汰今年的新闻，所以容易导致新的数据进不去，旧的数据出不来，不过Redis里的LFU算法肯定是有考虑到这个问题的，具体是怎么实现的？

• 源码分析

来看redisObject的结构体，在server.h代码里，看里面注释，大概也知道在LFU算法的时候，里面这个lru，它前面16位代表的是时间，后8位代表的是一个数值，frequenct频率，应该就是代表这个对象的访问次数，我们先给它叫做counter

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;

前16bits代表时间，有啥用？跟时间相关，可以猜想应该和时效性有关。大胆猜测，这个时间是不是去记录对象多久没访问，如果多久没访问，就去减少对应的次数

找到Redis源码里的evict.c的LFUDecrAndReturn函数：

/* 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) {// lru字段右移8位，得到前面16位的时间unsigned long ldt = o->lru >> 8;// lru字段与255进行&运算，255代表8位的最大值，也就是二进制的8个1，得到8位counter值unsigned long counter = o->lru & 255;// 如果配置了lfu_decay_time，用LFUTimeElapsed(ldt)除以配置的值，总的没访问的分钟时间除以配置值，得到每分钟没访问，需要减少多少访问次数unsigned long num_periods = server.lfu_decay_time ? LFUTimeElapsed(ldt) / server.lfu_decay_time : 0;if (num_periods)// 不能减少为负数counter = (num_periods > counter) ? 0 : counter - num_periods;return counter;
}

redis配置

lfu-decay-time 1 // 多少分钟没操作访问就减1次

而对应8bit的次数，最大值是255，可以看下redis源码LFULongIncr函数，在evict.c

/* 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) {// 如果等于255，直接返回255,8位的最大值if (counter == 255) return 255;// 得到随机数，0到1之间double r = (double)rand()/RAND_MAX;// LFU_INIT_VAL表示基数值，默认为5，在server.h配置double baseval = counter - LFU_INIT_VAL;// 如果当前counter小于基数，那么p=1，r肯定小于p，所以counter肯定加if (baseval < 0) baseval = 0;// 不然，按照几率来校验counter是否加，跟baseval和lfu_log_factor这两个参数相关，因为都是在分母，所以两个值越大，p越小，也就是counter++的概率越小double p = 1.0/(baseval*server.lfu_log_factor+1);if (r < p) counter++;// p越小，counter++的几率就越小，反之亦然return counter;
}

所以，LFU的实现逻辑，可以总结一下：

1. 如果达到255最大值，counter就不加，因为达到255的几率不是很高，可以支撑很大的数据量
2. counter是随机添加，添加的概率和已有的counter值和配置的lfu-log-factor两个参数相关，已有的counter值越大，添加的几率越小，配置的lfu-log-factor值越大，添加的几率也越小

在redis官网找到如图的压测数据图，里面facror就是配置的lfu_log_factor，可以看到配置的值越大，需要达到255的最大值就需要更多的hits