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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:
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:
淘汰策略
淘汰策略即maxmemory_policy的賦值有以下幾種:
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:
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
總結
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