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zvFnT3f!oZNeNh7P3}?&w=x*?K1<+tn8_=yuJ6^8?_e()-LrxW-(GEX$ uZG}%KCxKkow)>qHNE(FbYRrfK47aDw+C7ic)(Uti6@#a%pUXO@geCye*4(xL literal 0 HcmV?d00001 diff --git a/doc/wiki-cn/ratelimit.md b/doc/wiki-cn/ratelimit.md new file mode 100644 index 000000000..9b343d729 --- /dev/null +++ b/doc/wiki-cn/ratelimit.md @@ -0,0 +1,57 @@ +# 自适应限流保护 + +kratos 借鉴了 Sentinel 项目的自适应限流系统,通过综合分析服务的 cpu 使用率、请求成功的 qps 和请求成功的 rt 来做自适应限流保护。 + + +## 核心目标 + +* 自动嗅探负载和 qps,减少人工配置 +* 削顶,保证超载时系统不被拖垮,并能以高水位 qps 继续运行 + + +## 限流规则 + +1,指标介绍 + +|指标名称|指标含义| +|---|---| +|cpu|最近 1s 的 CPU 使用率均值,使用滑动平均计算,采样周期是 250ms| +|inflight|当前处理中正在处理的请求数量| +|pass|请求处理成功的量| +|rt|请求成功的响应耗时| + + +2,滑动窗口 + +在自适应限流保护中,采集到的指标的时效性非常强,系统只需要采集最近一小段时间内的 qps、rt 即可,对于较老的数据,会自动丢弃。为了实现这个效果,kratos 使用了滑动窗口来保存采样数据。 + +![ratelimit-rolling-window](/doc/img/ratelimit-rolling-window.png) + +如上图,展示了一个具有两个桶(bucket)的滑动窗口(rolling window)。整个滑动窗口用来保存最近 1s 的采样数据,每个小的桶用来保存 500ms 的采样数据。 +当时间流动之后,过期的桶会自动被新桶的数据覆盖掉,在图中,在 1000-1500ms 时,bucket 1 的数据因为过期而被丢弃,之后 bucket 3 的数据填到了窗口的头部。 + + +3,限流公式 + +判断是否丢弃当前请求的算法如下: + +`cpu > 800 AND (Now - PrevDrop) < 1s AND (MaxPass * MinRt * windows / 1000) < InFlight` + +MaxPass 表示最近 5s 内,单个采样窗口中最大的请求数。 +MinRt 表示最近 5s 内,单个采样窗口中最小的响应时间。 +windows 表示一秒内采样窗口的数量,默认配置中是 5s 50 个采样,那么 windows 的值为 10。 + +## 压测报告 + +场景1,请求以每秒增加1个的速度不停上升,压测效果如下: + +![ratelimit-benchmark-up-1](/doc/img/ratelimit-benchmark-up-1.png) + +左测是没有限流的压测效果,右侧是带限流的压测效果。 +可以看到,没有限流的场景里,系统在 700qps 时开始抖动,在 1k qps 时被拖垮,几乎没有新的请求能被放行,然而在使用限流之后,系统请求能够稳定在 600 qps 左右,rt 没有暴增,服务也没有被打垮,可见,限流有效的保护了服务。 + + +参考资料: + +[Sentinel 系统自适应限流](https://github.com/alibaba/Sentinel/wiki/%E7%B3%BB%E7%BB%9F%E8%87%AA%E9%80%82%E5%BA%94%E9%99%90%E6%B5%81) +