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无论是 LRU 算法还是 LFU 算法，它们在删除淘汰数据时，实际上都会依据 Redis server 的 lazyfree-lazy-eviction 配置项，来决定是否应用 Lazy Free，也就是惰性删除。
(1) 惰性删除是什么
惰性删除是 Redis 4.0 版本后提供的性能，它会应用后盾线程来执行删除数据的工作
(2) 为什么要用惰性删除
能够防止了删除操作对主线程的阻塞。
https://github.com/redis/redi…
(3) 惰性删除怎么用
(3.1) 惰性删除的配置
当 Redis server 须要启动惰性删除时，须要在 redis.conf 配置文件中设置和惰性删除相干的配置项。
其中包含了四个配置项，别离对应了如下的四种场景。
lazyfree-lazy-eviction：对应缓存淘汰时的数据删除场景。
lazyfree-lazy-expire：对应过期 key 的删除场景。
lazyfree-lazy-server-del：对应会隐式进行删除操作的 server 命令执行场景。
replica-lazy-flush：对应从节点实现全量同步后，删除原有旧数据的场景。
这四个配置项的默认值都是 no。所以，如果要在缓存淘汰时启用，就须要将
lazyfree-lazy-eviction 设置为 yes。
(4) 惰性删除原理
(4.1) 被淘汰数据的删除过程
freeMemoryIfNeeded 函数（在 evict.c 文件中）会负责执行数据淘汰的流程。
该函数在筛选出被淘汰的键值对后，就要开始删除被淘汰的数据，这个删除过程次要分成两步。
第一步，freeMemoryIfNeeded 函数会为被淘汰的 key 创立一个 SDS 对象，而后调用 propagateExpire 函数
第二步，freeMemoryIfNeeded 函数会依据 server 是否启用了惰性删除，别离执行
// file: src/evict.c
/* 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.
* Otherwise 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;
// 省略局部代码 ...
// 曾经开释的内存大小 < 打算要开释的内存大小
while (mem_freed < mem_tofree) {
int j, k, i;
// sds
sds bestkey = NULL;
// 省略局部代码
// 最终移除抉择要淘汰的 key
if (bestkey) {
// 抉择对应的 db
db = server.db+bestdbid;
// 创立 redisObject
robj *keyobj = createStringObject(bestkey,sdslen(bestkey));
// 删除
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.
*
* Same for CSC invalidation messages generated by signalModifiedKey.
*
* 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);
// 是否惰性删除
if (server.lazyfree_lazy_eviction)
dbAsyncDelete(db,keyobj); // 异步删除
else
dbSyncDelete(db,keyobj); // 同步删除
latencyEndMonitor(eviction_latency);
latencyAddSampleIfNeeded("eviction-del",eviction_latency);
delta -= (long long) zmalloc_used_memory();
mem_freed += delta;
server.stat_evictedkeys++;
signalModifiedKey(NULL,db,keyobj);
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 {goto cant_free; /* nothing to free... */}
}
result = 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... */
if (result != C_OK) {latencyStartMonitor(lazyfree_latency);
while(bioPendingJobsOfType(BIO_LAZY_FREE)) {if (getMaxmemoryState(NULL,NULL,NULL,NULL) == C_OK) {
result = C_OK;
break;
}
usleep(1000);
}
latencyEndMonitor(lazyfree_latency);
latencyAddSampleIfNeeded("eviction-lazyfree",lazyfree_latency);
}
latencyEndMonitor(latency);
latencyAddSampleIfNeeded("eviction-cycle",latency);
return result;
}
(4.1.1) 流传过期 key-propagateExpire
// file: src/evict.c
/*
* 流传 过期 keys 到 从节点 和 AOF 文件。* 当主节点中的 key 过期时，如果启用()，则会将对此 key 的 DEL 操作发送到 所有从节点 和 AOF 文件。*
* 这样 key 过期集中在一个中央，并且因为 AOF 和 主 -> 从 链接保障操作程序，* 即便咱们容许对过期 key 进行写操作，所有也会保持一致。*
* @param *db redisDb
* @param *key 过期 key 对象(redisObject 格局)
* @param lazy 过期策略
*/
void propagateExpire(redisDb *db, robj *key, int lazy) {robj *argv[2];
argv[0] = lazy ? shared.unlink : shared.del;
argv[1] = key;
// 援用计数 +1
incrRefCount(argv[0]);
// 援用计数 +1
incrRefCount(argv[1]);
// AOF 未敞开
if (server.aof_state != AOF_OFF)
feedAppendOnlyFile(server.delCommand,db->id,argv,2); // 把删除命令追加到 AOF 缓存
// 将删除操作同步给从节点
replicationFeedSlaves(server.slaves,db->id,argv,2);
// 援用计数 -1
decrRefCount(argv[0]);
// 援用计数 -1
decrRefCount(argv[1]);
}(4.1.2) 惰性删除 -dbAsyncDelete
// file: src/evict.c
/*
*/
int freeMemoryIfNeeded(void) {
// 省略局部代码
// 是否惰性删除
if (server.lazyfree_lazy_eviction)
dbAsyncDelete(db,keyobj); // 异步删除
else
dbSyncDelete(db,keyobj); // 同步删除
// 省略局部代码
}(4.2) 数据异步删除 -dbAsyncDelete
// file: src/lazyfree.c
// 从数据库中删除 key、value 和 关联的过期 entry(如果有)。// 如果有足够的内存调配来开释值对象，则能够将其放入惰性开释列表而不是同步开释。// 惰性闲暇列表将在不同的 bio.c 线程中回收。#define LAZYFREE_THRESHOLD 64
/*
* 异步删除
*
* @param *db
* @param *key
*/
int dbAsyncDelete(redisDb *db, robj *key) {// 从 expires 字典中删除 entry(dictEntry)不会开释 key 的 sds，因为它与主字典共享。// 须要删 2 次，第一次删 entry(dictEntry)，第二次删 key
if (dictSize(db->expires) > 0) dictDelete(db->expires,key->ptr);
// 如果该值由一些 allocations 组成，以惰性形式开释实际上会更慢... 所以在肯定限度下咱们只是同步开释对象。// 从字典里删除 key
dictEntry *de = dictUnlink(db->dict,key->ptr);
// 如果节点不为空
if (de) {
// 获取节点的值
robj *val = dictGetVal(de);
//
size_t free_effort = lazyfreeGetFreeEffort(val);
/* If releasing the object is too much work, do it in the background
* by adding the object to the lazy free list.
* Note that if the object is shared, to reclaim it now it is not
* possible. This rarely happens, however sometimes the implementation
* of parts of the Redis core may call incrRefCount() to protect
* objects, and then call dbDelete(). In this case we'll fall
* through and reach the dictFreeUnlinkedEntry() call, that will be
* equivalent to just calling decrRefCount(). */
if (free_effort > LAZYFREE_THRESHOLD && val->refcount == 1) {atomicIncr(lazyfree_objects,1);
bioCreateBackgroundJob(BIO_LAZY_FREE,val,NULL,NULL);
dictSetVal(db->dict,de,NULL);
}
}
/* Release the key-val pair, or just the key if we set the val
* field to NULL in order to lazy free it later. */
if (de) {dictFreeUnlinkedEntry(db->dict,de);
if (server.cluster_enabled) slotToKeyDel(key->ptr);
return 1;
} else {return 0;}
}
/* Return the amount of work needed in order to free an object.
* The return value is not always the actual number of allocations the
* object is composed of, but a number proportional to it.
*
* For strings the function always returns 1.
*
* For aggregated objects represented by hash tables or other data structures
* the function just returns the number of elements the object is composed of.
*
* Objects composed of single allocations are always reported as having a
* single item even if they are actually logical composed of multiple
* elements.
*
* For lists the function returns the number of elements in the quicklist
* representing the list. */
size_t lazyfreeGetFreeEffort(robj *obj) {if (obj->type == OBJ_LIST) {
quicklist *ql = obj->ptr;
return ql->len;
} else if (obj->type == OBJ_SET && obj->encoding == OBJ_ENCODING_HT) {
dict *ht = obj->ptr;
return dictSize(ht);
} else if (obj->type == OBJ_ZSET && obj->encoding == OBJ_ENCODING_SKIPLIST){
zset *zs = obj->ptr;
return zs->zsl->length;
} else if (obj->type == OBJ_HASH && obj->encoding == OBJ_ENCODING_HT) {
dict *ht = obj->ptr;
return dictSize(ht);
} else if (obj->type == OBJ_STREAM) {
size_t effort = 0;
stream *s = obj->ptr;
/* Make a best effort estimate to maintain constant runtime. Every macro
* node in the Stream is one allocation. */
effort += s->rax->numnodes;
/* Every consumer group is an allocation and so are the entries in its
* PEL. We use size of the first group's PEL as an estimate for all
* others. */
if (s->cgroups && raxSize(s->cgroups)) {
raxIterator ri;
streamCG *cg;
raxStart(&ri,s->cgroups);
raxSeek(&ri,"^",NULL,0);
/* There must be at least one group so the following should always
* work. */
serverAssert(raxNext(&ri));
cg = ri.data;
effort += raxSize(s->cgroups)*(1+raxSize(cg->pel));
raxStop(&ri);
}
return effort;
} else {return 1; /* Everything else is a single allocation. */}
}(5) 参考资料
https://weikeqin.com/tags/redis/
Redis 源码分析与实战 学习笔记 Day17 Lazy Free 会影响缓存替换吗？
https://time.geekbang.org/col…
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