https://github.com/GuoZhaoran/spikeSystem

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轮询
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加权轮询
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IP Hash 轮询
#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
https://www.kancloud.cn/digest/understandingnginx/202607

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在极限并发情况下任何一个内存操作的细节都至关影响性能尤其像创建订单这种逻辑一般都需要存储到磁盘数据库的对数据库压力可想而知。 -
如果存在恶意下单只下单不支付库存就会变少少卖很多订单虽然服务端可以限制IP 和 用户购买数量这也不算是一个好方法。



Redis 库使用的是 Redigo,下面是代码实现:
...
//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(chanint, 1)
done <- 1
}
本地扣库存和统一扣库存
本地扣库存逻辑非常简单,用户请求过来,添加销量,然后对比销量是否大于本地库存,返回 Bool 值:
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
}
注意这里对共享数据 LocalSalesVolume 的操作是要使用锁来实现的,但是因为本地扣库存和统一扣库存是一个原子性操作,所以在最上层使用 Channel 来实现,这块后边会讲。
统一扣库存操作 Redis,因为 Redis 是单线程的,而我们要实现从中取数据,写数据并计算一些列步骤,我们要配合 Lua 脚本打包命令,保证操作的原子性:
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
return0
`
//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 {
returnfalse
}
return result != 0
}
我们使用 Hash 结构存储总库存和总销量的信息,用户请求过来时,判断总销量是否大于库存,然后返回相关的 Bool 值。
hmset ticket_hash_key "ticket_total_nums"10000"ticket_sold_nums"0
响应用户信息
我们开启一个 HTTP 服务,监听在一个端口上:
package main
...
funcmain() {
http.HandleFunc("/buy/ticket", handleReq)
http.ListenAndServe(":3005", nil)
}
上面我们做完了所有的初始化工作,接下来 handleReq 的逻辑非常清晰,判断是否抢票成功,返回给用户信息就可以了。
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)
}
前边提到我们扣库存时要考虑竞态条件,我们这里是使用 Channel 避免并发的读写,保证了请求的高效顺序执行。我们将接口的返回信息写入到了 ./stat.log 文件方便做压测统计。
开启服务,我们使用 AB 压测工具进行测试:
ab -n 10000 -c 100 http://127.0.0.1:3005/buy/ticket
下面是我本地低配 Mac 的压测信息:
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 requests
Completed 2000 requests
Completed 3000 requests
Completed 4000 requests
Completed 5000 requests
Completed 6000 requests
Completed 7000 requests
Completed 8000 requests
Completed 9000 requests
Completed 10000 requests
Finished 10000 requests
Server Software:
Server Hostname: 127.0.0.1
Server Port: 3005
Document Path: /buy/ticket
Document Length: 29 bytes
Concurrency Level: 100
Time taken for tests: 2.339 seconds
Complete requests: 10000
Failed requests: 0
Total transferred: 1370000 bytes
HTML transferred: 290000 bytes
Requests 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 max
Connect: 0 8 14.7 6 223
Processing: 2 15 17.6 11 232
Waiting: 1 11 13.5 8 225
Total: 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)
而且查看日志发现整个服务过程中,请求都很正常,流量均匀,Redis 也很正常:
//stat.log
...
result:1,localSales:145
result:1,localSales:146
result:1,localSales:147
result:1,localSales:148
result:1,localSales:149
result:1,localSales:150
result:0,localSales:151
result:0,localSales:152
result:0,localSales:153
result:0,localSales:154
result:0,localSales:156
...
总体来说,秒杀系统是非常复杂的。我们这里只是简单介绍模拟了一下单机如何优化到高性能,集群如何避免单点故障,保证订单不超卖、不少卖的一些策略。
完整的订单系统还有订单进度的查看,每台服务器上都有一个任务,定时的从总库存同步余票和库存信息展示给用户,还有用户在订单有效期内不支付,释放订单,补充到库存等等。
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