Redis单线程已经很快了，为什么6.0要引入多线程？带来什么优势？
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2021-04-06 16:30
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Redis作为一个基于内存的缓存系统，一直以高性能著称，因没有上下文切换以及无锁操作，即使在单线程处理情况下，读速度仍可达到11万次/s，写速度达到8.1万次/s。但是，单线程的设计也给Redis带来一些问题：
只能使用CPU一个核； 如果删除的键过大（比如Set类型中有上百万个对象），会导致服务端阻塞好几秒； QPS难再提高。
Lazy Free以及多线程IO，逐步向多线程过渡，下面将会做详细介绍。单线程原理
文件事件：Redis服务器通过套接字与客户端（或者其他Redis服务器）进行连接，而文件事件就是服务器对套接字操作的抽象；服务器与客户端的通信会产生相应的文件事件，而服务器则通过监听并处理这些事件来完成一系列网络通信操作，比如连接accept，read，write，close等；时间事件：Redis服务器中的一些操作（比如serverCron函数）需要在给定的时间点执行，而时间事件就是服务器对这类定时操作的抽象，比如过期键清理，服务状态统计等。
单线程形式处理，所以说Redis是单线程的。此外，如下图，Redis基于Reactor模式开发了自己的I/O事件处理器，也就是文件事件处理器，Redis在I/O事件处理上，采用了I/O多路复用技术，同时监听多个套接字，并为套接字关联不同的事件处理函数，通过一个线程实现了多客户端并发处理。
Lazy Free机制
Lazy Free，将慢操作异步化，这也是在事件处理上向多线程迈进了一步。慢操作，可以采用渐进式处理，即增加一个时间事件，比如在删除一个具有上百万个对象的Set键时，每次只删除大键中的一部分数据，最终实现大键的删除。但是，该方案可能会导致回收速度赶不上创建速度，最终导致内存耗尽。因此，Redis最终实现上是将大键的删除操作异步化，采用非阻塞删除（对应命令UNLINK），大键的空间回收交由单独线程实现，主线程只做关系解除，可以快速返回，继续处理其他事件，避免服务器长时间阻塞。DEL命令）为例，看看Redis是如何实现的，下面就是删除函数的入口，其中，lazyfree_lazy_user_del是是否修改DEL命令的默认行为，一旦开启，执行DEL时将会以UNLINK形式执行。void delCommand(client *c) {
delGenericCommand(c,server.lazyfree_lazy_user_del);
}
/* This command implements DEL and LAZYDEL. */
void delGenericCommand(client *c, int lazy) {
int numdel = 0, j;
for (j = 1; j < c->argc; j++) {
expireIfNeeded(c->db,c->argv[j]);
// 根据配置确定DEL在执行时是否以lazy形式执行
int deleted = lazy ? dbAsyncDelete(c->db,c->argv[j]) :
dbSyncDelete(c->db,c->argv[j]);
if (deleted) {
signalModifiedKey(c,c->db,c->argv[j]);
notifyKeyspaceEvent(NOTIFY_GENERIC,
"del",c->argv[j],c->db->id);
server.dirty++;
numdel++;
}
}
addReplyLongLong(c,numdel);
}`
String类型，回收收益值就是1，而Set类型，回收收益就是集合中元素个数。/* Delete a key, value, and associated expiration entry if any, from the DB.
* If there are enough allocations to free the value object may be put into
* a lazy free list instead of being freed synchronously. The lazy free list
* will be reclaimed in a different bio.c thread. */
#define LAZYFREE_THRESHOLD 64
int dbAsyncDelete(redisDb *db, robj *key) {
/* Deleting an entry from the expires dict will not free the sds of
* the key, because it is shared with the main dictionary. */
if (dictSize(db->expires) > 0) dictDelete(db->expires,key->ptr);
/* If the value is composed of a few allocations, to free in a lazy way
* is actually just slower... So under a certain limit we just free
* the object synchronously. */
dictEntry *de = dictUnlink(db->dict,key->ptr);
if (de) {
robj *val = dictGetVal(de);
// 计算value的回收收益
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(). */
// 只有回收收益超过一定值，才会执行异步删除，否则还是会退化到同步删除
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;
}
}`
a threaded lazy free，Redis实现了对于Slow Operation的Lazy操作，避免了在大键删除，FLUSHALL，FLUSHDB时导致服务器阻塞。当然，在实现该功能时，不仅引入了lazy free线程，也对Redis聚合类型在存储结构上进行改进。因为Redis内部使用了很多共享对象，比如客户端输出缓存。当然，Redis并未使用加锁来避免线程冲突，锁竞争会导致性能下降，而是去掉了共享对象，直接采用数据拷贝，如下，在3.x和6.x中ZSet节点value的不同实现。// 3.2.5版本ZSet节点实现，value定义robj *obj
/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
robj *obj;
double score;
struct zskiplistNode *backward;
struct zskiplistLevel {
struct zskiplistNode *forward;
unsigned int span;
} level[];
} zskiplistNode;
// 6.0.10版本ZSet节点实现，value定义为sds ele
/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
sds ele;
double score;
struct zskiplistNode *backward;
struct zskiplistLevel {
struct zskiplistNode *forward;
unsigned long span;
} level[];
} zskiplistNode;`
lazy free功能，也为Redis向多线程跨进带来了可能，正如作者所述：Now that values of aggregated data types are fully unshared, and client output buffers don’t contain shared objects as well, there is a lot to exploit. For example it is finally possible to implement threaded I/O in Redis, so that different clients are served by different threads. This means that we’ll have a global lock only when accessing the database, but the clients read/write syscalls and even the parsing of the command the client is sending, can happen in different threads.
多线程I/O及其局限性
Lazy Free，自此Redis有了一个Lazy Free线程专门用于大键的回收，同时，也去掉了聚合类型的共享对象，这为多线程带来可能，Redis也不负众望，在6.0版本实现了多线程I/O。实现原理
事件处理线程每次获取到可读事件时，会将所有就绪的读事件分配给I/O线程，并进行等待，在所有I/O线程完成读操作后，事件处理线程开始执行任务处理，在处理结束后，同样将写事件分配给I/O线程，等待所有I/O线程完成写操作。事件处理线程任务分配流程：int handleClientsWithPendingReadsUsingThreads(void) {
...
/* Distribute the clients across N different lists. */
listIter li;
listNode *ln;
listRewind(server.clients_pending_read,&li);
int item_id = 0;
// 将等待处理的客户端分配给I/O线程
while((ln = listNext(&li))) {
client *c = listNodeValue(ln);
int target_id = item_id % server.io_threads_num;
listAddNodeTail(io_threads_list[target_id],c);
item_id++;
}
...
/* Wait for all the other threads to end their work. */
// 轮训等待所有I/O线程处理完
while(1) {
unsigned long pending = 0;
for (int j = 1; j < server.io_threads_num; j++)
pending += io_threads_pending[j];
if (pending == 0) break;
}
...
return processed;
}`
I/O线程处理流程：void *IOThreadMain(void *myid) {
...
while(1) {
...
// I/O线程执行读写操作
while((ln = listNext(&li))) {
client *c = listNodeValue(ln);
// io_threads_op判断是读还是写事件
if (io_threads_op == IO_THREADS_OP_WRITE) {
writeToClient(c,0);
} else if (io_threads_op == IO_THREADS_OP_READ) {
readQueryFromClient(c->conn);
} else {
serverPanic("io_threads_op value is unknown");
}
}
listEmpty(io_threads_list[id]);
io_threads_pending[id] = 0;
if (tio_debug) printf("[%ld] Done\n", id);
}
}`
局限性
I/O线程只能同时执行读或者同时执行写操作，期间事件处理线程一直处于等待状态，并非流水线模型，有很多轮训等待开销。Tair多线程实现原理
Main Thread负责客户端连接建立等，IO Thread负责请求读取、响应发送、命令解析等，Worker Thread线程专门用于事件处理。IO Thread读取用户的请求并进行解析，之后将解析结果以命令的形式放在队列中发送给Worker Thread处理。Worker Thread将命令处理完成后生成响应，通过另一条队列发送给IO Thread。为了提高线程的并行度，IO Thread和Worker Thread之间采用无锁队列 和管道 进行数据交换，整体性能会更好。
小结
Lazy Free线程，解决了诸如大键删除导致服务器阻塞问题，在6.0版本引入了I/O Thread线程，正式实现了多线程，但相较于Tair，并不太优雅，而且性能提升上并不多，压测看，多线程版本性能是单线程版本的2倍，Tair多线程版本则是单线程版本的3倍。在作者看来，Redis多线程无非两种思路，I/O threading和Slow commands threading，正如作者在其博客中所说：I/O threading is not going to happen in Redis AFAIK, because after much consideration I think it’s a lot of complexity without a good reason. Many Redis setups are network or memory bound actually. Additionally I really believe in a share-nothing setup, so the way I want to scale Redis is by improving the support for multiple Redis instances to be executed in the same host, especially via Redis Cluster.
What instead I really want a lot is slow operations threading, and with the Redis modules system we already are in the right direction. However in the future (not sure if in Redis 6 or 7) we’ll get key-level locking in the module system so that threads can completely acquire control of a key to process slow operations. Now modules can implement commands and can create a reply for the client in a completely separated way, but still to access the shared data set a global lock is needed: this will go away.
I/O threading，尤其是在6.0版本发布的原生Redis Cluster Proxy背景下，使得集群更加易用。slow operations threading（比如4.0版本发布的Lazy Free）来解决多线程问题。后续版本，是否会将IO Thread实现的更加完善，采用Module实现对慢操作的优化，着实值得期待。<END>
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