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Redis中LRU淘汰策略的深入分析

2020-10-28 21:30:19
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前言

Redis作為緩存使用時,一些場景下要考慮內存的空間消耗問題。Redis會刪除過期鍵以釋放空間,過期鍵的刪除策略有兩種:

  • 惰性刪除:每次從鍵空間中獲取鍵時,都檢查取得的鍵是否過期,如果過期的話,就刪除該鍵;如果沒有過期,就返回該鍵。
  • 定期刪除:每隔一段時間,程序就對數據庫進行一次檢查,刪除里面的過期鍵。

另外,Redis也可以開啟LRU功能來自動淘汰一些鍵值對。

LRU算法

當需要從緩存中淘汰數據時,我們希望能淘汰那些將來不可能再被使用的數據,保留那些將來還會頻繁訪問的數據,但最大的問題是緩存并不能預言未來。一個解決方法就是通過LRU進行預測:最近被頻繁訪問的數據將來被訪問的可能性也越大。緩存中的數據一般會有這樣的訪問分布:一部分數據擁有絕大部分的訪問量。當訪問模式很少改變時,可以記錄每個數據的最后一次訪問時間,擁有最少空閑時間的數據可以被認為將來最有可能被訪問到。

舉例如下的訪問模式,A每5s訪問一次,B每2s訪問一次,C與D每10s訪問一次,|代表計算空閑時間的截止點:

~~~~~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對于A、B、C工作的很好,完美預測了將來被訪問到的概率B>A>C,但對于D卻預測了最少的空閑時間。

但是,總體來說,LRU算法已經是一個性能足夠好的算法了

LRU配置參數

Redis配置中和LRU有關的有三個:

  • maxmemory: 配置Redis存儲數據時指定限制的內存大小,比如100m。當緩存消耗的內存超過這個數值時, 將觸發數據淘汰。該數據配置為0時,表示緩存的數據量沒有限制, 即LRU功能不生效。64位的系統默認值為0,32位的系統默認內存限制為3GB
  • maxmemory_policy: 觸發數據淘汰后的淘汰策略
  • maxmemory_samples: 隨機采樣的精度,也就是隨即取出key的數目。該數值配置越大, 越接近于真實的LRU算法,但是數值越大,相應消耗也變高,對性能有一定影響,樣本值默認為5。

淘汰策略

淘汰策略即maxmemory_policy的賦值有以下幾種:

  • noeviction:如果緩存數據超過了maxmemory限定值,并且客戶端正在執行的命令(大部分的寫入指令,但DEL和幾個指令例外)會導致內存分配,則向客戶端返回錯誤響應
  • allkeys-lru: 對所有的鍵都采取LRU淘汰
  • volatile-lru: 僅對設置了過期時間的鍵采取LRU淘汰
  • allkeys-random: 隨機回收所有的鍵
  • volatile-random: 隨機回收設置過期時間的鍵
  • volatile-ttl: 僅淘汰設置了過期時間的鍵---淘汰生存時間TTL(Time To Live)更小的鍵

volatile-lru, volatile-random和volatile-ttl這三個淘汰策略使用的不是全量數據,有可能無法淘汰出足夠的內存空間。在沒有過期鍵或者沒有設置超時屬性的鍵的情況下,這三種策略和noeviction差不多。

一般的經驗規則:

  • 使用allkeys-lru策略:當預期請求符合一個冪次分布(二八法則等),比如一部分的子集元素比其它其它元素被訪問的更多時,可以選擇這個策略。
  • 使用allkeys-random:循環連續的訪問所有的鍵時,或者預期請求分布平均(所有元素被訪問的概率都差不多)
  • 使用volatile-ttl:要采取這個策略,緩存對象的TTL值最好有差異

volatile-lru 和 volatile-random策略,當你想要使用單一的Redis實例來同時實現緩存淘汰和持久化一些經常使用的鍵集合時很有用。未設置過期時間的鍵進行持久化保存,設置了過期時間的鍵參與緩存淘汰。不過一般運行兩個實例是解決這個問題的更好方法。

為鍵設置過期時間也是需要消耗內存的,所以使用allkeys-lru這種策略更加節省空間,因為這種策略下可以不為鍵設置過期時間。

近似LRU算法

我們知道,LRU算法需要一個雙向鏈表來記錄數據的最近被訪問順序,但是出于節省內存的考慮,Redis的LRU算法并非完整的實現。Redis并不會選擇最久未被訪問的鍵進行回收,相反它會嘗試運行一個近似LRU的算法,通過對少量鍵進行取樣,然后回收其中的最久未被訪問的鍵。通過調整每次回收時的采樣數量maxmemory-samples,可以實現調整算法的精度。

根據Redis作者的說法,每個Redis Object可以擠出24 bits的空間,但24 bits是不夠存儲兩個指針的,而存儲一個低位時間戳是足夠的,Redis Object以秒為單位存儲了對象新建或者更新時的unix time,也就是LRU clock,24 bits數據要溢出的話需要194天,而緩存的數據更新非常頻繁,已經足夠了。

Redis的鍵空間是放在一個哈希表中的,要從所有的鍵中選出一個最久未被訪問的鍵,需要另外一個數據結構存儲這些源信息,這顯然不劃算。最初,Redis只是隨機的選3個key,然后從中淘汰,后來算法改進到了N個key的策略,默認是5個。

Redis3.0之后又改善了算法的性能,會提供一個待淘汰候選key的pool,里面默認有16個key,按照空閑時間排好序。更新時從Redis鍵空間隨機選擇N個key,分別計算它們的空閑時間idle,key只會在pool不滿或者空閑時間大于pool里最小的時,才會進入pool,然后從pool中選擇空閑時間最大的key淘汰掉。

真實LRU算法與近似LRU的算法可以通過下面的圖像對比:

淺灰色帶是已經被淘汰的對象,灰色帶是沒有被淘汰的對象,綠色帶是新添加的對象。可以看出,maxmemory-samples值為5時Redis 3.0效果比Redis 2.8要好。使用10個采樣大小的Redis 3.0的近似LRU算法已經非常接近理論的性能了。

數據訪問模式非常接近冪次分布時,也就是大部分的訪問集中于部分鍵時,LRU近似算法會處理得很好。

在模擬實驗的過程中,我們發現如果使用冪次分布的訪問模式,真實LRU算法和近似LRU算法幾乎沒有差別。

LRU源碼分析

Redis中的鍵與值都是redisObject對象:

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記錄了redisObj的LRU time。

Redis命令訪問緩存的數據時,均會調用函數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; }}

該函數在策略為LRU(非LFU)時會更新對象的lru值, 設置為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()取決于LRU_CLOCK_RESOLUTION(默認值1000),LRU_CLOCK_RESOLUTION代表了LRU算法的精度,即一個LRU的單位是多長。server.hz代表服務器刷新的頻率,如果服務器的時間更新精度值比LRU的精度值要小,LRU_CLOCK()直接使用服務器的時間,減小開銷。

Redis處理命令的入口是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;  } }}

只列出了釋放內存空間的部分,freeMemoryIfNeededAndSafe為釋放內存的函數:

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();}

幾種淘汰策略maxmemory_policy就是在這個函數里面實現的。

當采用LRU時,可以看到,從0號數據庫開始(默認16個),根據不同的策略,選擇redisDb的dict(全部鍵)或者expires(有過期時間的鍵),用來更新候選鍵池子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隨機選擇maxmemory_samples數量的key,然后計算這些key的空閑時間idle time,當滿足條件時(比pool中的某些鍵的空閑時間還大)就可以進pool。pool更新之后,就淘汰pool中空閑時間最大的鍵。

estimateObjectIdleTime用來計算Redis對象的空閑時間:

/* 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; }}

空閑時間基本就是就是對象的lru和全局的LRU_CLOCK()的差值乘以精度LRU_CLOCK_RESOLUTION,將秒轉化為了毫秒。

參考鏈接

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

總結

以上就是這篇文章的全部內容了,希望本文的內容對大家的學習或者工作具有一定的參考學習價值,謝謝大家對武林網的支持。

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