redis系列介绍八-淘汰策略

LRU

说完了过期策略再说下淘汰策略,redis 使用的策略是近似的 lru 策略,为什么是近似的呢,先来看下什么是 lru,看下 wiki 的介绍
,图中一共有四个槽的存储空间,依次访问顺序是 A B C D E D F,
当第一次访问 D 时刚好占满了坑,并且值是 4,这个值越小代表越先被淘汰,当 E 进来时,看了下已经存在的四个里 A 是最小的,代表是最早存在并且最早被访问的,那就先淘汰它了,E 占领了 A 的位置,并设置值为 4,然后又访问 D 了,D 已经存在了,不过又被访问到了,得更新值为 5,然后是 F 进来了,这时 B 是最老的且最近未被访问,所以就淘汰它了。以上是一个 lru 的简要说明,但是 redis 没有严格按照这个去执行,理由跟前面过期策略一致,最严格的过期策略应该是每个 key 都有对应的定时器,当超时时马上就能清除,但是问题是这样的cpu 消耗太大,所换来的内存效率不太值得,淘汰策略也是这样,类似于上图,要维护所有 key 的一个有序 lru 值,并且遍历将最小的淘汰,redis 采用的是抽样的形式,最初的实现方式是随机从 dict 抽取 5 个 key,淘汰一个 lru 最小的,这样子勉强能达到淘汰的目的,但是效果不是特别好,后面在 redis 3.0开始,将随机抽取改成了维护一个 pool,pool 的大小默认是 16,每次放入的都是按lru 值有序排列好,每一次放入的必须是 lru小于 pool 中最小的 lru 才允许放入,直到放满,后面再有新的就会将大的踢出。
redis 针对这个策略的改进做了一个实验,这里借用下图

首先背景是这图中的所有点都对应一个 redis 的 key,灰色部分加入后被顺序访问过一遍,然后又加入了绿色部分,那么按照理论的 lru 算法,应该是图左上中,浅灰色部分全都被淘汰,那么对比来看看图右上,左下和右下,左下表示 2.8 版本就是随机抽样 5 个 key,淘汰其中 lru 最小的一个,发现是灰色和浅灰色的都有被淘汰的,右下的 3.0 版本抽样数量不变的情况下,稍好一些,当 3.0 版本的抽样数量调整成 10 后,已经较为接近理论上的 lru 策略了,通过代码来简要分析下

1
2
3
4
5
6
7
8
9
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;

对于 lru 策略来说,lru 字段记录的就是redisObj 的LRU time,
redis 在访问数据时,都会调用lookupKey方法

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
/* Low level key lookup API, not actually called directly from commands
* implementations that should instead rely on lookupKeyRead(),
* lookupKeyWrite() and lookupKeyReadWithFlags(). */
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 (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
// 这个是后面一节的内容
updateLFU(val);
} else {
// 对于这个分支,访问时就会去更新 lru 值
val->lru = LRU_CLOCK();
}
}
return val;
} else {
return NULL;
}
}
/* 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) {
// 如果服务器的频率server.hz大于 1 时就是用系统预设的 lruclock
lruclock = server.lruclock;
} else {
lruclock = getLRUClock();
}
return lruclock;
}
/* 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;
}

redis 处理命令是在这里processCommand

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
/* If this function gets called we already read a whole
* command, arguments are in the client argv/argc fields.
* processCommand() execute the command or prepare the
* server for a bulk read from the client.
*
* If C_OK is returned the client is still alive and valid and
* other operations can be performed by the caller. Otherwise
* if C_ERR is returned the client was destroyed (i.e. after QUIT). */
int processCommand(client *c) {
moduleCallCommandFilters(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 &&
c->cmd->proc != discardCommand)))
{
flagTransaction(c);
addReply(c, shared.oomerr);
return C_OK;
}
}
}

这里只摘了部分,当需要清理内存时就会调用, 然后调用了freeMemoryIfNeededAndSafe

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
/* 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();
}
/* This function is periodically called to see if there is memory to free
* according to the current "maxmemory" settings. In case we are over the
* memory limit, the function will try to free some memory to return back
* under the limit.
*
* The function returns C_OK if we are under the memory limit or if we
* were over the limit, but the attempt to free memory was successful.
* Otehrwise if we are over the memory limit, but not enough memory
* was freed to return back under the limit, the function returns C_ERR. */
int freeMemoryIfNeeded(void) {
int keys_freed = 0;
/* 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;
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;
}
}
} else {
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;
}

这里就是根据具体策略去淘汰 key,首先是要往 pool 更新 key,更新key 的方法是evictionPoolPopulate

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
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poolpool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:

1
2
3
4
5
6
7
8
9
10
11
/* 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;
}
}

空闲时间第一种是 lurclock 大于对象的 lru,那么就是减一下乘以精度,因为 lruclock 有可能是已经预生成的,所以会可能走下面这个

LFU

上面介绍了LRU 的算法,但是考虑一种场景

1
2
3
4
~~~~~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|

可以发现,当采用 lru 的淘汰策略的时候,D 是最新的,会被认为是最值得保留的,但是事实上还不如 A 跟 B,然后 antirez 大神就想到了LFU (Least Frequently Used) 这个算法, 显然对于上面的四个 key 的访问频率,保留优先级应该是 B > A > C = D
那要怎么来实现这个 LFU 算法呢,其实像LRU,理想的情况就是维护个链表,把最新访问的放到头上去,但是这个会影响访问速度,注意到前面代码的应该可以看到,redisObject 的 lru 字段其实是两用的,当策略是 LFU 时,这个字段就另作他用了,它的 24 位长度被分成两部分

1
2
3
4
      16 bits      8 bits
+----------------+--------+
+ Last decr time | LOG_C |
+----------------+--------+

前16位字段是最后一次递减时间,因此Redis知道 上一次计数器递减,后8位是 计数器 counter。
LFU 的主体策略就是当这个 key 被访问的次数越多频率越高他就越容易被保留下来,并且是最近被访问的频率越高。这其实有两个事情要做,一个是在访问的时候增加计数值,在一定长时间不访问时进行衰减,所以这里用了两个值,前 16 位记录上一次衰减的时间,后 8 位记录具体的计数值。
Redis4.0之后为maxmemory_policy淘汰策略添加了两个LFU模式:

volatile-lfu:对有过期时间的key采用LFU淘汰策略
allkeys-lfu:对全部key采用LFU淘汰策略
还有2个配置可以调整LFU算法:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
lfu-log-factor 10
lfu-decay-time 1
```
`lfu-log-factor` 可以调整计数器counter的增长速度,lfu-log-factor越大,counter增长的越慢。

`lfu-decay-time`是一个以分钟为单位的数值,可以调整counter的减少速度
这里有个问题是 8 位大小够计么,访问一次加 1 的话的确不够,不过大神就是大神,才不会这么简单的加一。往下看代码
```C
/* Low level key lookup API, not actually called directly from commands
* implementations that should instead rely on lookupKeyRead(),
* lookupKeyWrite() and lookupKeyReadWithFlags(). */
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 (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
// 当淘汰策略是 LFU 时,就会调用这个updateLFU
updateLFU(val);
} else {
val->lru = LRU_CLOCK();
}
}
return val;
} else {
return NULL;
}
}

updateLFU 这个其实个入口,调用了两个重要的方法

1
2
3
4
5
6
7
8
/* Update LFU when an object is accessed.
* Firstly, decrement the counter if the decrement time is reached.
* Then logarithmically increment the counter, and update the access time. */
void updateLFU(robj *val) {
unsigned long counter = LFUDecrAndReturn(val);
counter = LFULogIncr(counter);
val->lru = (LFUGetTimeInMinutes()<<8) | counter;
}

首先来看看LFUDecrAndReturn,这个方法的作用是根据上一次衰减时间和系统配置的 lfu-decay-time 参数来确定需要将 counter 减去多少

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
/* If the object decrement time is reached decrement the LFU counter but
* do not update LFU fields of the object, we update the access time
* and counter in an explicit way when the object is really accessed.
* And we will times halve the counter according to the times of
* elapsed time than server.lfu_decay_time.
* Return the object frequency counter.
*
* This function is used in order to scan the dataset for the best object
* to fit: as we check for the candidate, we incrementally decrement the
* counter of the scanned objects if needed. */
unsigned long LFUDecrAndReturn(robj *o) {
// 右移 8 位,拿到上次衰减时间
unsigned long ldt = o->lru >> 8;
// 对 255 做与操作,拿到 counter 值
unsigned long counter = o->lru & 255;
// 根据lfu_decay_time来算出过了多少个衰减周期
unsigned long num_periods = server.lfu_decay_time ? LFUTimeElapsed(ldt) / server.lfu_decay_time : 0;
if (num_periods)
counter = (num_periods > counter) ? 0 : counter - num_periods;
return counter;
}

然后是加,调用了LFULogIncr

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
/* Logarithmically increment a counter. The greater is the current counter value
* the less likely is that it gets really implemented. Saturate it at 255. */
uint8_t LFULogIncr(uint8_t counter) {
// 最大值就是 255,到顶了就不加了
if (counter == 255) return 255;
// 生成个随机小数
double r = (double)rand()/RAND_MAX;
// 减去个基础值,LFU_INIT_VAL = 5,防止刚进来就被逐出
double baseval = counter - LFU_INIT_VAL;
// 如果是小于 0,
if (baseval < 0) baseval = 0;
// 如果 baseval 是 0,那么 p 就是 1了,后面 counter 直接加一,如果不是的话,得看系统参数lfu_log_factor,这个越大,除出来的 p 越小,那么 counter++的可能性也越小,这样子就把前面的疑问给解决了,不是直接+1 的
double p = 1.0/(baseval*server.lfu_log_factor+1);
if (r < p) counter++;
return counter;
}

大概的变化速度可以参考

1
2
3
4
5
6
7
8
9
10
11
+--------+------------+------------+------------+------------+------------+
| factor | 100 hits | 1000 hits | 100K hits | 1M hits | 10M hits |
+--------+------------+------------+------------+------------+------------+
| 0 | 104 | 255 | 255 | 255 | 255 |
+--------+------------+------------+------------+------------+------------+
| 1 | 18 | 49 | 255 | 255 | 255 |
+--------+------------+------------+------------+------------+------------+
| 10 | 10 | 18 | 142 | 255 | 255 |
+--------+------------+------------+------------+------------+------------+
| 100 | 8 | 11 | 49 | 143 | 255 |
+--------+------------+------------+------------+------------+------------+

简而言之就是 lfu_log_factor 越大变化的越慢

总结

总结一下,redis 实现了近似的 lru 淘汰策略,通过增加了淘汰 key 的池子(pool),并且增大每次抽样的 key 的数量来将淘汰效果更进一步地接近于 lru,这是 lru 策略,但是对于前面举的一个例子,其实 lru 并不能保证 key 的淘汰就如我们预期,所以在后期又引入了 lfu 的策略,lfu的策略比较巧妙,复用了 redis 对象的 lru 字段,并且使用了factor 参数来控制计数器递增的速度,防止 8 位的计数器太早溢出。