前言
之前学习和实际生产环境的flask都是用app.run()的默认方式启动的，因为只是公司内部服务，请求量不高，一直也没出过什么性能问题。最近接管其它小组的服务时，发现他们的服务使用Gunicorn + Flask的方式运行的，本地开发用的gevent的WSGIServer。对于Gunicorn之前只是耳闻，没实际用过，正好捣鼓下看看到底能有多少性能提升。本文简单记录flask在各种配置参数和运行方式的性能，后面也会跟其他语言和框架做个对比。
- python版本：3.11
- flask版本：3.0.3
- Gunicorn：23.0.0
- wrk作为性能测试工具
- 运行环境：vbox虚拟机，debian 12, 4C4G的硬件配置
wrk测试脚本
wrk支持用lua脚本对请求的响应结果进行验证，以下脚本对响应码和响应内容进行校验
wrk.method = "GET" wrk.host = "127.0.0.1:8080" wrk.path = "/health" wrk.timeout = 1.0 response = function(status, headers, body) if status ~= 200 then print("Error: expected 200 but got " .. status) end if not body:find("ok") then print("Error: response does not contain expected content.") end end Flask框架的测试记录
- 先测试默认运行方式，且没有sleep的情况下的并发性能。
from flask import Flask app = Flask(__name__) @app.get("/health") def health(): return "ok" if __name__ == "__main__": app.run(host="0.0.0.0", port=8080) 使用命令nohup python demo.py > /dev/null启动，以下为wrk测试结果，可以看到已经出现超时请求。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 65.47ms 81.07ms 1.99s 97.69% Req/Sec 575.99 107.20 1.08k 66.71% 137538 requests in 1.00m, 22.82MB read Socket errors: connect 0, read 114, write 0, timeout 144 Requests/sec: 2288.49 Transfer/sec: 388.86KB - 还是默认启动方式，增加等待时间，模拟处理任务的时间消耗。后续测试都会增加等待时间。
from flask import Flask from time import sleep app = Flask(__name__) @app.get("/health") def health(): sleep(0.1) return "ok" if __name__ == "__main__": app.run(host="0.0.0.0", port=8080) wrk测试结果，不出所料性能会有所下降。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 201.90ms 239.99ms 2.00s 95.05% Req/Sec 479.79 185.87 1.82k 68.19% 114440 requests in 1.00m, 18.99MB read Socket errors: connect 0, read 2, write 0, timeout 1833 Requests/sec: 1904.45 Transfer/sec: 323.62KB - flask 更新到版本2后支持使用异步函数（需要安装异步相关依赖
python -m pip install -U flask[async]）
from flask import Flask import asyncio app = Flask(__name__) @app.route('/health') async def health(): await asyncio.sleep(0.1) return "ok" if __name__ == "__main__": app.run(host="0.0.0.0", port=8080) wrk测试结果，性能相较于同步函数甚至还下降了，QPS几乎砍半，看来异步版Flask还有待增强。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 275.49ms 190.22ms 2.00s 95.46% Req/Sec 242.86 104.25 720.00 65.91% 57896 requests in 1.00m, 9.61MB read Socket errors: connect 0, read 48, write 0, timeout 611 Requests/sec: 964.31 Transfer/sec: 163.86KB - 接管的Flask应用在本地使用gevent的WSGIServer运行，所以也来试试。
from gevent.pywsgi import WSGIServer from flask import Flask from time import sleep app = Flask(__name__) @app.route('/health') def health(): sleep(0.1) return "ok" if __name__ == "__main__": http_server = WSGIServer(('0.0.0.0', 8080), app) http_server.serve_forever() wrk测试结果，惨不忍睹，像是单线程在挨个处理请求，每个请求都会阻塞住。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 185.36ms 221.17ms 1.93s 96.30% Req/Sec 5.54 3.39 10.00 46.55% 592 requests in 1.00m, 67.64KB read Socket errors: connect 0, read 0, write 0, timeout 322 Requests/sec: 9.85 Transfer/sec: 1.13KB - 按网上搜的结果加上了monkey patch
from gevent.pywsgi import WSGIServer from gevent import monkey from flask import Flask from time import sleep monkey.patch_all() app = Flask(__name__) @app.route('/health') def health(): sleep(0.1) return "ok" if __name__ == "__main__": http_server = WSGIServer(('0.0.0.0', 8080), app) http_server.serve_forever() wrk测试结果，加上monkey patch后似乎也没什么作用。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 182.89ms 209.48ms 1.82s 96.07% Req/Sec 5.55 3.50 10.00 47.89% 592 requests in 1.00m, 67.64KB read Socket errors: connect 0, read 0, write 0, timeout 312 Requests/sec: 9.85 Transfer/sec: 1.13KB - 正式上gunicorn，代码没有任何改动，也不需要引用gevent的WSGServer。
from flask import Flask from time import sleep app = Flask(__name__) @app.get("/health") def health(): sleep(0.1) return "ok" if __name__ == "__main__": app.run(host="0.0.0.0", port=8080) 运行命令。指定-k gevent。demo:app中的demo是代码文件名。--worker-connections默认为1000
gunicorn demo:app -b 0.0.0.0:8080 -w 4 -k gevent --worker-connections 2000 wrk测试结果。性能相较于默认启动方式有了接近10倍的提升，请求响应时间也很稳定，最大响应时间也只有310.48。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 126.18ms 9.52ms 310.48ms 84.68% Req/Sec 3.98k 165.70 4.53k 77.34% 948506 requests in 1.00m, 143.83MB read Requests/sec: 15799.31 Transfer/sec: 2.40MB 其它框架和语言
在t4c2000的wrk配置下，flask+unicorn的每个进程基本都占用了85+%的CPU，再提高就得加CPU核心数了，不过这样的性能已经能满足公司内部服务的需求了，而且实际业务中，短板更可能是网络IO。
这里也测测其它语言和框架，看看Flask在Gunicorn的加持下能否打出python的牌面。
Golang
上来先试试最熟悉的Go， version: 1.22.4，使用标准库。（编译打包出来就能直接运行，不需要jvm这样的虚拟机，也不需要python这样的解释器，更不需要docker这样的容器运行时，特喜欢Go这一点）
package main import ( "fmt" "net/http" "time" ) func MyHandler(w http.ResponseWriter, r *http.Request) { time.Sleep(time.Millisecond * 100) w.Write([]byte("ok")) } func main() { http.HandleFunc("/health", MyHandler) err := http.ListenAndServe("0.0.0.0:8080", nil) if err != nil { fmt.Println(err) } } wrk结果如下，请求量是目前测试以来第一个突破百万，而且也没有timeout的出现。使用top观察资源消耗，CPU只占用了约30%，而且还只有一个进程。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 101.56ms 1.48ms 121.62ms 77.58% Req/Sec 4.94k 191.07 5.05k 93.49% 1180108 requests in 1.00m, 132.80MB read Requests/sec: 19643.94 Transfer/sec: 2.21MB 不断加大连接，直到系统平均负载到达4（虚拟机CPU核心数为4）。连接数加了10倍，QPS差不多也是10倍于Flask + Gunicorn。这时候实际上wrk也占用了不少CPU资源，服务端的性能并没到瓶颈。
$ wrk -s bm.lua -t 4 -c20000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 20000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 167.56ms 43.45ms 397.95ms 60.37% Req/Sec 29.48k 8.84k 48.26k 63.05% 6867733 requests in 1.00m, 772.85MB read Requests/sec: 114258.70 Transfer/sec: 12.86MB FastAPI
Go的性能已经很不错了，就性能来说还不是Flask+Gunicorn能媲美的。再来试试号称性能并肩Go的FastAPI（官网features里面写的）。FastAPI版本：0.115.4
纯uvicorn启动，用的是同步函数。
from fastapi import FastAPI import uvicorn from fastapi.responses import PlainTextResponse from time import sleep app = FastAPI() @app.get("/health") def index(): sleep(0.1) return PlainTextResponse(status_code=200,content="ok") if __name__ == '__main__': uvicorn.run(app, host="127.0.0.1", port=8080, access_log=False) wrk测试结果，可以看到相当低下，甚至还不如flask的默认运行方式，超时请求数都过2w了。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 1.22s 438.35ms 1.95s 60.00% Req/Sec 115.03 57.31 410.00 86.69% 23494 requests in 1.00m, 3.02MB read Socket errors: connect 0, read 0, write 0, timeout 22894 Requests/sec: 390.92 Transfer/sec: 51.54KB 改用异步函数再试试。
from fastapi import FastAPI import uvicorn from fastapi.responses import PlainTextResponse from time import sleep import asyncio app = FastAPI() @app.get("/health") async def health(): await asyncio.sleep(0.1) return PlainTextResponse(status_code=200,content="ok") if __name__ == '__main__': uvicorn.run(app, host="127.0.0.1", port=8080, access_log=False) wrk测试结果，可以看到性能好很多了，而且没有timeout。QPS是Flask默认启动方式的2倍，但实际性能应该不止2倍。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 484.53ms 63.18ms 654.48ms 61.26% Req/Sec 1.09k 698.29 3.13k 63.78% 246744 requests in 1.00m, 31.77MB read Requests/sec: 4106.80 Transfer/sec: 541.43KB uvicorn支持指定worker数，这里设置为CPU核心数。
from fastapi import FastAPI import uvicorn from fastapi.responses import PlainTextResponse from time import sleep import asyncio app = FastAPI() @app.get("/health") async def health(): await asyncio.sleep(0.1) return PlainTextResponse(status_code=200,content="ok") if __name__ == '__main__': uvicorn.run(app="demo2:app", host="127.0.0.1", port=8080, access_log=False, workers=4) wrk测试结果，响应时间还是非常稳的，完全没有timeout的情况，延迟还更低。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 164.52ms 13.57ms 273.27ms 73.49% Req/Sec 3.05k 544.20 4.64k 68.67% 727517 requests in 1.00m, 93.73MB read Requests/sec: 12123.17 Transfer/sec: 1.56MB gunicorn也支持uvicorn，看看fastapi在gunicorn的加持下会有怎样的性能表现。
gunicorn demo2:app -b 127.0.0.1:8080 -w 4 -k uvicorn.workers.UvicornWorker --worker-connections 2000 wrk测试结果，相较于unicorn运行方式，性能提升并不多。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 146.43ms 21.21ms 263.13ms 69.52% Req/Sec 3.43k 508.71 4.88k 71.40% 818281 requests in 1.00m, 105.35MB read Requests/sec: 13620.16 Transfer/sec: 1.75MB Sanic
之前用过一段时间Sanic，也是个python异步框架，版本：24.6.0
from sanic import Sanic from sanic.response import text import asyncio app = Sanic("HelloWorld") @app.get("/health") async def hello_world(request): await asyncio.sleep(0.1) return text("ok") if __name__ == "__main__": app.run(host="127.0.0.1", port=8080, fast=True, debug=False, access_log=False) wrk测试结果。虽然QPS比FastAPI高，但是有timeout的情况，不是很稳定。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 104.05ms 45.30ms 1.82s 99.59% Req/Sec 4.84k 538.65 5.07k 95.98% 1154792 requests in 1.00m, 115.64MB read Socket errors: connect 0, read 0, write 0, timeout 88 Requests/sec: 19218.08 Transfer/sec: 1.92MB Openresty
openresty基于nginx，通过集成lua，也可以用来写api。配置如下，只是增加了一个location，稍微调整下nginx的参数
worker_processes auto; worker_cpu_affinity auto; events { worker_connections 65535; } http { include mime.types; default_type application/octet-stream; access_log off; sendfile on; keepalive_timeout 65; server { listen 8080 deferred; server_name localhost; location /health { content_by_lua_block { ngx.sleep(0.1) ngx.print("ok") } } location / { root html; index index.html index.htm; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } } } wrk测试结果，和Go语言相当。
$ wrk -s bm.lua -t 4 -c2000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://192.168.0.201:8080/health 4 threads and 2000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 101.43ms 1.62ms 136.59ms 88.97% Req/Sec 4.94k 213.51 5.33k 92.65% 1178854 requests in 1.00m, 211.36MB read Requests/sec: 19619.86 Transfer/sec: 3.52MB top观察openresty的cpu占用并不高，加大连接再试试。连接数达到25000后，系统平均负载已经基本满了，而且wrk也占用了不少CPU资源。和Go差不多，并没有到服务端的性能瓶颈，而是受到系统资源限制。
$ wrk -s bm.lua -t 4 -c25000 -d60s http://127.0.0.1:8080/health Running 1m test @ http://127.0.0.1:8080/health 4 threads and 25000 connections Thread Stats Avg Stdev Max +/- Stdev Latency 149.17ms 36.61ms 659.40ms 71.22% Req/Sec 41.00k 6.64k 59.03k 65.95% 9330399 requests in 1.00m, 1.63GB read Requests/sec: 155277.58 Transfer/sec: 27.84MB 小结
整理下测试数据汇总成表格如下
| 项目 | 总请求量 | 每秒请求量 | 平均响应时间 | 最大响应时间 | 备注 |
|---|---|---|---|---|---|
| Flask-no sleep | 137538 | 2288.49 | 65.47ms | 1.99s | 有响应超时情况 |
| Flask-同步方式 | 114440 | 1904.45 | 201.90ms | 2.00s | 有响应超时情况 |
| Flask-异步函数 | 57896 | 964.31 | 275.49ms | 2.00s | 有响应超时情况 |
| Flask+gevent | 592 | 9.85 | 185.36ms | 1.93s | 有响应超时情况 |
| Flask+gevent(monkeypatch) | 592 | 9.85 | 182.89ms | 1.82s | 有响应超时情况 |
| Flask+gevent+unicorn | 948506 | 15799.31 | 126.18ms | 310.48ms | |
| Golang | 1180108 | 19643.94 | 101.56ms | 121.62ms | |
| Golang | 6867733 | 114258.70 | 167.56ms | 397.95ms | wrk的配置为t4c20000 |
| FastAPI-同步函数 | 23494 | 390.92 | 1.22s | 1.95s | 有响应超时情况 |
| FastAPI-异步函数 | 246744 | 4106.80 | 484.53ms | 654.48ms | |
| FastAPI-多worker | 727517 | 12123.17 | 164.52ms | 273.27ms | |
| FastAPI+Gunicorn | 818281 | 13620.16 | 146.43ms | 273.27ms | |
| Sanic | 1154792 | 19218.08 | 104.05ms | 1.82s | 有响应超时情况，不是很稳定 |
| OpenResty | 1178854 | 19619.86 | 101.43ms | 136.59ms | |
| OpenResty | 9330399 | 155277.58 | 149.17ms | 659.40ms | wrk的配置为t4c25000 |
根据测试结果，测试的三个Python Web框架中，Flask+gevent+unicorn综合最佳，不低的QPS，而且没有请求超时的情况，也不需要将代码修改成异步方式。Sanic的QPS虽高，但是有响应超时的情况，说明并不稳定，而且代码需要是异步的。FastAPI+Gunicorn的表现也不差，在不使用Gunicorn的情况下也能提供不错的性能，但代码同样需要改成异步方式。对于Sanic和FastAPI，Gunicorn的加持并不必要，而Gunicorn对Flask的性能提升至少7倍，而且能避免请求超时的情况，生产环境下应该尽量使用Gunicorn来运行Flask。
Go比各个Python框架的性能都更好，资源占用也更低，运行方式还更简单，不需要依赖编程语言环境和其他组件，非要说缺点的话就是开发没有Python快。
OpenResty的性能在测试中是最高的，主要是nginx本身性能良好。缺点是开发更麻烦。虽然是用lua开发，但lua作为动态语言，既不如Python极其灵活，还有动态语言本身代码不够清晰的缺点。以前尝试过用openresty实现一个crud服务，后来连自己都懒得维护就放弃了，干脆只用来当网关。
鱼与熊掌不可兼得，开发速度跟运行速度往往相斥，除非代码以后都是AI来写。就公司目前这服务的使用情况来说，Flask+Gunicorn的性能已经足够，还不需要改代码，实乃社畜良伴。而且现在啥都上k8s了，服务扩展也简单，性能不够就加实例嘛 😃
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