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当需要从缓存中淘汰数据时，我们希望能淘汰那些将来不可能再被使用的数据，保留那些将来还会频繁访问的数据，但最大的问题是缓存并不能预言未来。一个解决方法就是通过LRU进行预测：最近被频繁访问的数据将来被访问的可能性也越大。缓存中的数据一般会有这样的访问分布：一部分数据拥有绝大部分的访问量。当访问模式很少改变时，可以记录每个数据的最后一次访问时间，拥有最少空闲时间的数据可以被认为将来最有可能被访问到。

举例如下的访问模式，A每5s访问一次，B每2s访问一次，C与D每10s访问一次，|代表计算空闲时间的截止点：

~~~~~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|

可以看到，LRU对于A、B、C工作的很好，完美预测了将来被访问到的概率B>A>C，但对于D却预测了最少的空闲时间。

但是，总体来说，LRU算法已经是一个性能足够好的算法了

LRU配置参数

Redis配置中和LRU有关的有三个：

maxmemory: 配置Redis存储数据时指定限制的内存大小，比如100m。当缓存消耗的内存超过这个数值时, 将触发数据淘汰。该数据配置为0时，表示缓存的数据量没有限制, 即LRU功能不生效。64位的系统默认值为0，32位的系统默认内存限制为3GB maxmemory_policy: 触发数据淘汰后的淘汰策略 maxmemory_samples: 随机采样的精度，也就是随即取出key的数目。该数值配置越大, 越接近于真实的LRU算法，但是数值越大，相应消耗也变高，对性能有一定影响，样本值默认为5。

淘汰策略

淘汰策略即maxmemory_policy的赋值有以下几种：

noeviction:如果缓存数据超过了maxmemory限定值，并且客户端正在执行的命令(大部分的写入指令，但DEL和几个指令例外)会导致内存分配，则向客户端返回错误响应 allkeys-lru: 对所有的键都采取LRU淘汰 volatile-lru: 仅对设置了过期时间的键采取LRU淘汰 allkeys-random: 随机回收所有的键 volatile-random: 随机回收设置过期时间的键 volatile-ttl: 仅淘汰设置了过期时间的键---淘汰生存时间TTL(Time To Live)更小的键

volatile-lru, volatile-random和volatile-ttl这三个淘汰策略使用的不是全量数据，有可能无法淘汰出足够的内存空间。在没有过期键或者没有设置超时属性的键的情况下，这三种策略和noeviction差不多。

一般的经验规则:

使用allkeys-lru策略：当预期请求符合一个幂次分布(二八法则等)，比如一部分的子集元素比其它其它元素被访问的更多时，可以选择这个策略。使用allkeys-random：循环连续的访问所有的键时，或者预期请求分布平均（所有元素被访问的概率都差不多）使用volatile-ttl：要采取这个策略，缓存对象的TTL值最好有差异

volatile-lru 和 volatile-random策略，当你想要使用单一的Redis实例来同时实现缓存淘汰和持久化一些经常使用的键集合时很有用。未设置过期时间的键进行持久化保存，设置了过期时间的键参与缓存淘汰。不过一般运行两个实例是解决这个问题的更好方法。

为键设置过期时间也是需要消耗内存的，所以使用allkeys-lru这种策略更加节省空间，因为这种策略下可以不为键设置过期时间。

近似LRU算法

我们知道，LRU算法需要一个双向链表来记录数据的最近被访问顺序，但是出于节省内存的考虑，Redis的LRU算法并非完整的实现。Redis并不会选择最久未被访问的键进行回收，相反它会尝试运行一个近似LRU的算法，通过对少量键进行取样，然后回收其中的最久未被访问的键。通过调整每次回收时的采样数量maxmemory-samples，可以实现调整算法的精度。

根据Redis作者的说法，每个Redis Object可以挤出24 bits的空间，但24 bits是不够存储两个指针的，而存储一个低位时间戳是足够的，Redis Object以秒为单位存储了对象新建或者更新时的unix time，也就是LRU clock，24 bits数据要溢出的话需要194天，而缓存的数据更新非常频繁，已经足够了。

Redis的键空间是放在一个哈希表中的，要从所有的键中选出一个最久未被访问的键，需要另外一个数据结构存储这些源信息，这显然不划算。最初，Redis只是随机的选3个key，然后从中淘汰，后来算法改进到了N个key的策略，默认是5个。

Redis3.0之后又改善了算法的性能，会提供一个待淘汰候选key的pool，里面默认有16个key，按照空闲时间排好序。更新时从Redis键空间随机选择N个key，分别计算它们的空闲时间idle，key只会在pool不满或者空闲时间大于pool里最小的时，才会进入pool，然后从pool中选择空闲时间最大的key淘汰掉。

真实LRU算法与近似LRU的算法可以通过下面的图像对比：

浅灰色带是已经被淘汰的对象，灰色带是没有被淘汰的对象，绿色带是新添加的对象。可以看出，maxmemory-samples值为5时Redis 3.0效果比Redis 2.8要好。使用10个采样大小的Redis 3.0的近似LRU算法已经非常接近理论的性能了。

数据访问模式非常接近幂次分布时，也就是大部分的访问集中于部分键时，LRU近似算法会处理得很好。

在模拟实验的过程中，我们发现如果使用幂次分布的访问模式，真实LRU算法和近似LRU算法几乎没有差别。

LRU源码分析

Redis中的键与值都是redisObject对象：

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;

unsigned的低24 bits的lru记录了redisObj的LRU time。

Redis命令访问缓存的数据时,均会调用函数lookupKey:

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 (server.rdb_child_pid == -1 &&   server.aof_child_pid == -1 &&   !(flags & LOOKUP_NOTOUCH))  {   if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {    updateLFU(val);   } else {    val->lru = LRU_CLOCK();   }  }  return val; } else {  return NULL; }}

该函数在策略为LRU(非LFU)时会更新对象的lru值, 设置为LRU_CLOCK()值:

/* 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;}/* 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) {  atomicGet(server.lruclock,lruclock); } else {  lruclock = getLRUClock(); } return lruclock;}

LRU_CLOCK()取决于LRU_CLOCK_RESOLUTION(默认值1000)，LRU_CLOCK_RESOLUTION代表了LRU算法的精度，即一个LRU的单位是多长。server.hz代表服务器刷新的频率，如果服务器的时间更新精度值比LRU的精度值要小，LRU_CLOCK()直接使用服务器的时间，减小开销。

Redis处理命令的入口是processCommand:

int processCommand(client *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))) {   flagTransaction(c);   addReply(c, shared.oomerr);   return C_OK;  } }}

只列出了释放内存空间的部分，freeMemoryIfNeededAndSafe为释放内存的函数：

int freeMemoryIfNeeded(void) { /* 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, keys_freed = 0;  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;    }   }  }  if (!keys_freed) {   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;}/* 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();}

几种淘汰策略maxmemory_policy就是在这个函数里面实现的。

当采用LRU时，可以看到，从0号数据库开始(默认16个)，根据不同的策略，选择redisDb的dict(全部键)或者expires(有过期时间的键)，用来更新候选键池子pool，pool更新策略是evictionPoolPopulate:

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：

/* 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; }}

空闲时间基本就是就是对象的lru和全局的LRU_CLOCK()的差值乘以精度LRU_CLOCK_RESOLUTION，将秒转化为了毫秒。

参考链接

Random notes on improving the Redis LRU algorithm Using Redis as an LRU cache

总结

以上就是这篇文章的全部内容了，希望本文的内容对大家的学习或者工作具有一定的参考学习价值，谢谢大家对脚本之家的支持。

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