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Redis中的LRU淘汰策略分析

大圣归来 2019-05-29 17:47 204 查看 https://www.cnblogs.com/linxiy

Redis
作为缓存使用时,一些场景下要考虑内存的空间消耗问题。
Redis
会删除过期键以释放空间,过期键的删除策略有两种:

  • 惰性删除:每次从键空间中获取键时,都检查取得的键是否过期,如果过期的话,就删除该键;如果没有过期,就返回该键。
  • 定期删除:每隔一段时间,程序就对数据库进行一次检查,删除里面的过期键。

另外,

Redis
也可以开启
LRU
功能来自动淘汰一些键值对。

LRU算法

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
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
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
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。

Redis
3.0之后又改善了算法的性能,会提供一个候选key的
pool
,里面默认有16个key,按照空闲时间排好序,新key只会在
pool
不满或者空闲时间大于
pool
里最小的,才能进池。

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
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

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