Redis中的LRU淘汰策略分析
阿新 • • 發佈：2019-05-29
Redis作為快取使用時，一些場景下要考慮記憶體的空間消耗問題。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，程式就對資料庫進行一次檢查，刪除裡面的過期鍵。
另外，Redis也可以開啟LRU功能來自動淘汰一些鍵值對。
LRU演算法
當需要從快取中淘汰資料時，我們希望能淘汰那些將來不可能再被使用的資料，保留那些將來還會頻繁訪問的資料，但最大的問題是快取並不能預言未來。一個解決方法就是通過LRU進行預測：最近被頻繁訪問的資料將來被訪問的可能性也越大。快取中的資料一般會有這樣的訪問分佈：一部分資料擁有絕大部分的訪問量。當訪問模式很少改變時，可以記錄每個資料的最後一次訪問時間，擁有最少空閒時間的資料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，|代表計算空閒時間的截止點：
~~~~~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對於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，但對於D卻預測了最少的空閒時間。
但是，總體來說，LRU演算法已經是一個性能足夠好的演算法了
LRU配置引數
Redis配置中和LRU有關的有三個：
maxmemory: 配置Redis儲存資料時指定限制的記憶體大小，比如100m。當快取消耗的記憶體超過這個數值時, 將觸發資料淘汰。該資料配置為0時，表示快取的資料量沒有限制, 即LRU功能不生效。64位的系統預設值為0，32位的系統預設記憶體限制為3GBmaxmemory_policy: 觸發資料淘汰後的淘汰策略maxmemory_samples: 隨機取樣的精度，也就是隨即取出key的數目。該數值配置越大, 越接近於真實的LRU演算法，但是數值越大，相應消耗也變高，對效能有一定影響，樣本值預設為5。
淘汰策略
淘汰策略即maxmemory_policy
noeviction:如果快取資料超過了maxmemory限定值，並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)會導致記憶體分配，則向客戶端返回錯誤響應allkeys-lru: 對所有的鍵都採取LRU淘汰volatile-lru: 僅對設定了過期時間的鍵採取LRU淘汰allkeys-random: 隨機回收所有的鍵volatile-random: 隨機回收設定過期時間的鍵volatile-ttl: 僅淘汰設定了過期時間的鍵---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資料，有可能無法淘汰出足夠的記憶體空間。在沒有過期鍵或者沒有設定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佈(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：迴圈連續的訪問所有的鍵時，或者預期請求分佈平均（所有元素被訪問的概率都差不多） - 使用
volatile-ttl：要採取這個策略，快取物件的TTL值最好有差異
volatile-lru 和 volatile-random策略，當你想要使用單一的Redis例項來同時實現快取淘汰和持久化一些經常使用的鍵集合時很有用。未設定過期時間的鍵進行持久化儲存，設定了過期時間的鍵參與快取淘汰。不過一般執行兩個例項是解決這個問題的更好方法。
為鍵設定過期時間也是需要消耗記憶體的，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定過期時間。
近似LRU演算法
我們知道，LRU演算法需要一個雙向連結串列來記錄資料的最近被訪問順序，但是出於節省記憶體的考慮，Redis的LRU演算法並非完整的實現。Redis並不會選擇最久未被訪問的鍵進行回收，相反它會嘗試執行一個近似LRU的演算法，通過對少量鍵進行取樣，然後回收其中的最久未被訪問的鍵。通過調整每次回收時的取樣數量maxmemory-samples，可以實現調整演算法的精度。
根據Redis作者的說法，每個Redis Object可以擠出24 bits的空間，但24 bits是不夠儲存兩個指標的，而儲存一個低位時間戳是足夠的，Redis Object以秒為單位儲存了物件新建或者更新時的unix time，也就是LRU clock，24 bits資料要溢位的話需要194天，而快取的資料更新非常頻繁，已經足夠了。
Redis的鍵空間是放在一個雜湊表中的，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儲存這些源資訊，這顯然不划算。最初，Redis只是隨機的選3個key，然後從中淘汰，後來演算法改進到了N個key的策略，預設是5個。
Redis3.0之後又改善了演算法的效能，會提供一個候選key的pool，裡面預設有16個key，按照空閒時間排好序，新key只會在pool不滿或者空閒時間大於pool裡最小的，才能進池。
真實LRU演算法與近似LRU的演算法可以通過下面的影象對比： 
淺灰色帶是已經被淘汰的物件，灰色帶是沒有被淘汰的物件，綠色帶是新新增的物件。可以看出，maxmemory-samples值為5時Redis 3.0效果比Redis 2.8要好。使用10個取樣大小的Redis 3.0的近似LRU演算法已經非常接近理論的效能了。
資料訪問模式非常接近冪次分佈時，也就是大部分的訪問集中於部分鍵時，LRU近似演算法會處理得很好。
在模擬實驗的過程中，我們發現如果使用冪次分佈的訪問模式，真實LRU演算法和近似LRU演算法幾乎沒有差別。
LRU原始碼分析
Redis中的鍵與值都是redisObject物件：
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;
unsigned的低24 bits的lru記錄了redisObj的LRU time。
Redis命令訪問快取的資料時,均會呼叫函式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;
}
}
該函式在策略為LRU(非LFU)時會更新物件的lru值, 設定為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()取決於LRU_CLOCK_RESOLUTION(預設值1000)，LRU_CLOCK_RESOLUTION代表了LRU演算法的精度，即一個LRU的單位是多長。server.hz代表伺服器重新整理的頻率，如果伺服器的時間更新精度值比LRU的精度值要小，LRU_CLOCK()直接使用伺服器的時間，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;
}
}
}
只列出了釋放記憶體空間的部分，freeMemoryIfNeededAndSafe為釋放記憶體的函式：
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就是在這個函式裡面實現的。
當採用LRU時，可以看到，從0號資料庫開始(預設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，然後計算這些key的空閒時間idle time，當滿足條件時(比pool中的某些鍵的空閒時間還大)就可以進pool。pool更新之後，就淘汰pool中空閒時間最大的鍵。
estimateObjectIdleTime用來計算Redis物件的空閒時間：
/* 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;
}
}
空閒時間基本就是就是物件的lru和全域性的LRU_CLOCK()的差值乘以精度LRU_CLOCK_RESOLUTION，將秒轉化為了毫秒。
參考連結
- Random notes on improving the Redis LRU algorithm
- Using Redis as an LRU cache