来自：https://juejin.cn/post/6928407842009546766

• 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。

| 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。
  
  

| Lazy Free机制

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 = , 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);
}

/* 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) > ) 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 ;
    }
}

// 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;

Now that values of aggregated data * 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

实现原理

int handleClientsWithPendingReadsUsingThreads(void) {
    ...

    /* Distribute the clients across N different lists. */
    listIter li;
    listNode *ln;
    listRewind(server.clients_pending_read,&li);
    int item_id = ;
    // 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 = ;
        for (int j = 1; j < server.io_threads_num; j++)
            pending += io_threads_pending[j];
        if (pending == ) break;
    }

    ...

    return processed;
}

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,);
            } 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] = ;

        if (tio_debug) printf("[%ld] Done\n", id);
    }
}

局限性

Tair多线程实现原理

| 小结

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.