最牛逼的秒杀系统,“12306”的架构是如何搭建的?

作者：绘你一世倾城
链接：juejin.im/post/5d84e21f6fb9a06ac8248149

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12306抢票,极限并发带来的思考?

一、大型高并发系统架构

1. 负载均衡简介

• OSPF(开放式最短链路优先)是一个内部网关协议(Interior Gateway Protocol,简称IGP)。OSPF通过路由器之间通告网络接口的状态来建立链路状态数据库，生成最短路径树，OSPF会自动计算路由接口上的Cost值，但也可以通过手工指定该接口的Cost值，手工指定的优先于自动计算的值。OSPF计算的Cost，同样是和接口带宽成反比，带宽越高，Cost值越小。到达目标相同Cost值的路径，可以执行负载均衡，最多6条链路同时执行负载均衡。
• LVS (Linux VirtualServer)，它是一种集群(Cluster)技术，采用IP负载均衡技术和基于内容请求分发技术。调度器具有很好的吞吐率，将请求均衡地转移到不同的服务器上执行，且调度器自动屏蔽掉服务器的故障，从而将一组服务器构成一个高性能的、高可用的虚拟服务器。
• Nginx想必大家都很熟悉了,是一款非常高性能的http代理/反向代理服务器,服务开发中也经常使用它来做负载均衡。Nginx实现负载均衡的方式主要有三种:轮询、加权轮询、ip hash轮询，下面我们就针对Nginx的加权轮询做专门的配置和测试

2. Nginx加权轮询的演示

#配置负载均衡    upstream load_rule {       server 127.0.0.1:3001 weight=1;       server 127.0.0.1:3002 weight=2;       server 127.0.0.1:3003 weight=3;       server 127.0.0.1:3004 weight=4;    }    ...    server {    listen       80;    server_name  load_balance.com www.load_balance.com;    location / {       proxy_pass http://load_rule;    }}

package main
import (    "net/http"    "os"    "strings")
funcmain() {    http.HandleFunc("/buy/ticket", handleReq)    http.ListenAndServe(":3001", nil)}
//处理请求函数,根据请求将响应结果信息写入日志funchandleReq(w http.ResponseWriter, r *http.Request) {    failedMsg :=  "handle in port:"    writeLog(failedMsg, "./stat.log")}
//写入日志funcwriteLog(msg string, logPath string) {    fd, _ := os.OpenFile(logPath, os.O_RDWR|os.O_CREATE|os.O_APPEND, 0644)    defer fd.Close()    content := strings.Join([]string{msg, "\r\n"}, "3001")    buf := []byte(content)    fd.Write(buf)}

ab -n 1000 -c 100 http://www.load_balance.com/buy/ticket

二、秒杀抢购系统选型

1.下单减库存

2.支付减库存

3.预扣库存

三、扣库存的艺术

四、代码演示

1.初始化工作

...//localSpike包结构体定义package localSpike
type LocalSpike struct {    LocalInStock     int64    LocalSalesVolume int64}...//remoteSpike对hash结构的定义和redis连接池package remoteSpike//远程订单存储健值type RemoteSpikeKeys struct {    SpikeOrderHashKey string    //redis中秒杀订单hash结构key    TotalInventoryKey string    //hash结构中总订单库存key    QuantityOfOrderKey string    //hash结构中已有订单数量key}
//初始化redis连接池funcNewPool() *redis.Pool {    return &redis.Pool{        MaxIdle:   10000,        MaxActive: 12000, // max number of connections        Dial: func() (redis.Conn, error) {            c, err := redis.Dial("tcp", ":6379")            if err != nil {                panic(err.Error())            }            return c, err        },    }}...funcinit() {    localSpike = localSpike2.LocalSpike{        LocalInStock:     150,        LocalSalesVolume: 0,    }    remoteSpike = remoteSpike2.RemoteSpikeKeys{        SpikeOrderHashKey:  "ticket_hash_key",        TotalInventoryKey:  "ticket_total_nums",        QuantityOfOrderKey: "ticket_sold_nums",    }    redisPool = remoteSpike2.NewPool()    done = make(chan int, 1)    done <- 1}

2.本地扣库存和统一扣库存

package localSpike//本地扣库存,返回bool值func(spike *LocalSpike) LocalDeductionStock() bool{    spike.LocalSalesVolume = spike.LocalSalesVolume + 1    return spike.LocalSalesVolume < spike.LocalInStock}

package remoteSpike......const LuaScript = `        local ticket_key = KEYS[1]        local ticket_total_key = ARGV[1]        local ticket_sold_key = ARGV[2]        local ticket_total_nums = tonumber(redis.call('HGET', ticket_key, ticket_total_key))        local ticket_sold_nums = tonumber(redis.call('HGET', ticket_key, ticket_sold_key))        -- 查看是否还有余票,增加订单数量,返回结果值       if(ticket_total_nums >= ticket_sold_nums) then            return redis.call('HINCRBY', ticket_key, ticket_sold_key, 1)        end        return 0`//远端统一扣库存func (RemoteSpikeKeys *RemoteSpikeKeys) RemoteDeductionStock(conn redis.Conn) bool {    lua := redis.NewScript(1, LuaScript)    result, err := redis.Int(lua.Do(conn, RemoteSpikeKeys.SpikeOrderHashKey, RemoteSpikeKeys.TotalInventoryKey, RemoteSpikeKeys.QuantityOfOrderKey))    if err != nil {        return false    }    return result != 0}

hmset ticket_hash_key "ticket_total_nums" 10000 "ticket_sold_nums" 0

3.响应用户信息

package main...funcmain() {    http.HandleFunc("/buy/ticket", handleReq)    http.ListenAndServe(":3005", nil)}

package main//处理请求函数,根据请求将响应结果信息写入日志funchandleReq(w http.ResponseWriter, r *http.Request) {    redisConn := redisPool.Get()    LogMsg := ""    <-done    //全局读写锁    if localSpike.LocalDeductionStock() && remoteSpike.RemoteDeductionStock(redisConn) {        util.RespJson(w, 1,  "抢票成功", nil)        LogMsg = LogMsg + "result:1,localSales:" + strconv.FormatInt(localSpike.LocalSalesVolume, 10)    } else {        util.RespJson(w, -1, "已售罄", nil)        LogMsg = LogMsg + "result:0,localSales:" + strconv.FormatInt(localSpike.LocalSalesVolume, 10)    }    done <- 1
    //将抢票状态写入到log中    writeLog(LogMsg, "./stat.log")}
funcwriteLog(msg string, logPath string) {    fd, _ := os.OpenFile(logPath, os.O_RDWR|os.O_CREATE|os.O_APPEND, 0644)    defer fd.Close()    content := strings.Join([]string{msg, "\r\n"}, "")    buf := []byte(content)    fd.Write(buf)}

4.单机服务压测

ab -n 10000 -c 100 http://127.0.0.1:3005/buy/ticket

This is ApacheBench, Version 2.3 <$Revision: 1826891 $>Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/Licensed to The Apache Software Foundation, http://www.apache.org/
Benchmarking 127.0.0.1 (be patient)Completed 1000 requestsCompleted 2000 requestsCompleted 3000 requestsCompleted 4000 requestsCompleted 5000 requestsCompleted 6000 requestsCompleted 7000 requestsCompleted 8000 requestsCompleted 9000 requestsCompleted 10000 requestsFinished 10000 requests

Server Software:Server Hostname:        127.0.0.1Server Port:            3005
Document Path:          /buy/ticketDocument Length:        29 bytes
Concurrency Level:      100Time taken for tests:   2.339 secondsComplete requests:      10000Failed requests:        0Total transferred:      1370000 bytesHTML transferred:       290000 bytesRequests per second:    4275.96 [#/sec] (mean)Time per request:       23.387 [ms] (mean)Time per request:       0.234 [ms] (mean, across all concurrent requests)Transfer rate:          572.08 [Kbytes/sec] received
Connection Times (ms)              min  mean[+/-sd] median   maxConnect:        0    8  14.7      6     223Processing:     2   15  17.6     11     232Waiting:        1   11  13.5      8     225Total:          7   23  22.8     18     239
Percentage of the requests served within a certain time (ms)  50%     18  66%     24  75%     26  80%     28  90%     33  95%     39  98%     45  99%     54 100%    239 (longest request)

//stat.log...result:1,localSales:145result:1,localSales:146result:1,localSales:147result:1,localSales:148result:1,localSales:149result:1,localSales:150result:0,localSales:151result:0,localSales:152result:0,localSales:153result:0,localSales:154result:0,localSales:156...

五、总结回顾

• 负载均衡，分而治之。通过负载均衡，将不同的流量划分到不同的机器上，每台机器处理好自己的请求，将自己的性能发挥到极致，这样系统的整体也就能承受极高的并发了，就像工作的的一个团队，每个人都将自己的价值发挥到了极致，团队成长自然是很大的。
• 合理的使用并发和异步。自epoll网络架构模型解决了c10k问题以来，异步越来被服务端开发人员所接受，能够用异步来做的工作，就用异步来做，在功能拆解上能达到意想不到的效果，这点在nginx、node.js、redis上都能体现，他们处理网络请求使用的epoll模型，用实践告诉了我们单线程依然可以发挥强大的威力。服务器已经进入了多核时代，go语言这种天生为并发而生的语言，完美的发挥了服务器多核优势，很多可以并发处理的任务都可以使用并发来解决，比如go处理http请求时每个请求都会在一个goroutine中执行，总之，怎样合理的压榨CPU,让其发挥出应有的价值，是我们一直需要探索学习的方向。