IT博客汇 | #转载# Flask超高并发，Flask多进程gevent ，Flask非gunicorn gevent多进程

#转载# Flask超高并发，Flask多进程gevent ，Flask非gunicorn gevent多进程

admin发表于 2020-11-24 06:06:44

Flask超高并发，Flask多进程gevent ，Flask非gunicorn gevent多进程。

Flask超高性能解决方案，Flask多进程gevent ，Flask非gunicorn gevent多进程。

Flask并发，高性能，Flask多进程gevent ，Flask非gunicorn gevent多进程。

转载一篇文章，实测效果不错。转载自：https://cpp.la/439.html

python多进程gevent，Flask gevent multiprocess WSGI，Flask gevent 多进程WSGI，Flask多进程gevent。

题外话：Flask，Instagram据说扛住了上亿日活，以前是Django。其是一个非常优秀的web service 框架，简洁灵活，可以利用大量的第三方组件和模块来快速开发。

如今，Instagram 的总注册用户达到 30 亿，月活用户超过 7 亿（作为对比，微信最新披露的月活跃用户为 9.38 亿）。而令人吃惊的是，这么高的访问量背后，竟完全是由以速度“慢”著称的 Python + Django 支撑。

时至今日，即使已经拥有超过 30 亿的注册用户。Instagram 仍然是 Python 和 Django 的重度使用者。Instagram 的工程师 Hui Ding 说到：『一直到用户 ID 已经超过了 32bit int 的限额（约为 20 亿），Django 本身仍然没有成为我们的瓶颈所在。』

flask + gevent + multiprocess + wsgi简介

常常大家都是用gunicorn来解决flask后端部署并发的问题，然而觉得自启多进程是为更优雅的高并发方式。这样就不需要gunicorn了。也没有额外的第三方部署工作，于是有了以下flask + gevent + multiprocess + wsgi的测试

flask + gevent + multiprocess + wsgi程序代码cppla.py

# coding: utf-8
# code by https://cpp.la, 2020-04-20
# flask + gevent + multiprocess + wsgi

from gevent import monkey
from gevent.pywsgi import WSGIServer
monkey.patch_all()

import datetime
import os
from multiprocessing import cpu_count, Process
from flask import Flask, jsonify


app = Flask(__name__)

@app.route("/cppla", methods=['GET'])
def function_benchmark():
    return jsonify(
        {
            "status": "ok",
            "time": datetime.datetime.now().strftime('%Y-%m-%d %H:%M'),
            "pid": os.getpid()
        }
    ), 200

def run(MULTI_PROCESS):
    if MULTI_PROCESS == False:
        WSGIServer(('0.0.0.0', 8080), app).serve_forever()
    else:
        mulserver = WSGIServer(('0.0.0.0', 8080), app)
        mulserver.start()

        def server_forever():
            mulserver.start_accepting()
            mulserver._stop_event.wait()

        for i in range(cpu_count()):
            p = Process(target=server_forever)
            p.start()

if __name__ == "__main__":
    # 单进程 + 协程
    run(False)
    # 多进程 + 协程
    # run(True)

测试环境

机器配置

4vCPU
8GB

环境配置

Centos 8
Python 3.6.8
Flask 1.1.2
Gevent 1.5.0

压测工具

WRK
4H8G机器

单进程 + 协程 + WSGI

测试结果

QPS ： 2361 r/s

该模式启动了一个进程

[root@vm5 ~]# nohup python3 cppla.py &amp;
[1] 9371
[root@vm5 ~]# nohup: ignoring input and appending output to 'nohup.out'
 
[root@vm5 ~]# ps -ef | grep cppla
root      9371  4184  4 03:33 pts/0    00:00:00 python3 cppla.py
root      9377  4184  0 03:33 pts/0    00:00:00 grep --color=auto cppla
[root@vm5 ~]#

测试详情

[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9371,"status":"ok","time":"2020-04-20 04:19"}
[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9371,"status":"ok","time":"2020-04-20 04:19"}
[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9371,"status":"ok","time":"2020-04-20 04:19"}
[root@vm4 wrk]# wrk -t12 -c400 -d30s http://10.10.10.5:8080/cppla
Running 30s test @ http://10.10.10.5:8080/cppla
  12 threads and 400 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   164.40ms   21.15ms 515.30ms   80.85%
    Req/Sec   199.61     47.81   565.00     70.38%
  71237 requests in 30.10s, 10.95MB read
Requests/sec:   2366.72
Transfer/sec:    372.44KB
[root@vm4 wrk]# wrk -t20 -c800 -d30s http://10.10.10.5:8080/cppla   
Running 30s test @ http://10.10.10.5:8080/cppla
  20 threads and 800 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   329.39ms   93.86ms   1.78s    92.80%
    Req/Sec   126.19     80.69   696.00     67.71%
  71075 requests in 30.10s, 10.92MB read
  Socket errors: connect 0, read 0, write 0, timeout 97
Requests/sec:   2361.39
Transfer/sec:    371.65KB
[root@vm4 wrk]#

多进程 + 协程 + WSGI

测试结果

QPS ： 7500 r/s

该模式启动了4+1个进程（4vCPU）

[root@vm5 ~]# nohup python3 cppla.py &
[1] 9537
[root@vm5 ~]# nohup: ignoring input and appending output to ‘nohup.out’

[root@vm5 ~]# ps -ef | grep cppla
root 9537 4184 5 04:32 pts/0 00:00:00 python3 cppla.py
root 9542 9537 0 04:32 pts/0 00:00:00 python3 cppla.py
root 9543 9537 0 04:32 pts/0 00:00:00 python3 cppla.py
root 9544 9537 0 04:32 pts/0 00:00:00 python3 cppla.py
root 9545 9537 0 04:32 pts/0 00:00:00 python3 cppla.py
root 9547 4184 0 04:32 pts/0 00:00:00 grep –color=auto cppla
[root@vm5 ~]#

测试详情

[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9543,"status":"ok","time":"2020-04-20 04:34"}
[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9542,"status":"ok","time":"2020-04-20 04:34"}
[root@vm4 wrk]# curl 10.10.10.5:8080/cppla
{"pid":9545,"status":"ok","time":"2020-04-20 04:34"}
[root@vm4 wrk]# wrk -t12 -c400 -d30s http://10.10.10.5:8080/cppla
Running 30s test @ http://10.10.10.5:8080/cppla
  12 threads and 400 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    56.10ms   15.16ms 187.30ms   85.05%
    Req/Sec   590.77     79.95   830.00     67.97%
  212138 requests in 30.08s, 32.60MB read
Requests/sec:   7051.89
Transfer/sec:      1.08MB
[root@vm4 wrk]# wrk -t20 -c800 -d30s http://10.10.10.5:8080/cppla
Running 30s test @ http://10.10.10.5:8080/cppla
  20 threads and 800 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   101.59ms   40.23ms 337.80ms   66.06%
    Req/Sec   394.20    109.48     0.97k    74.47%
  235844 requests in 30.10s, 36.25MB read
Requests/sec:   7835.77
Transfer/sec:      1.20MB
[root@vm4 wrk]#

协程并发真的很强！4核虚拟机多进程并发高达7000QPS，回头测试一下python协程和golang协程的效率对比。

补充：4核8线程3.6GHZ物理机上多进程并发高达15000QPS

也就是“理想”情况，每秒1w5 QPS，每小时5400万请求。

[root@vm4 wrk]# wrk -t30 -c1000 -d30s http://10.10.10.1:8080/cppla 
Running 30s test @ http://10.10.10.1:8080/cppla
  30 threads and 1000 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    67.67ms   18.54ms 312.76ms   81.81%
    Req/Sec   489.92     76.58   710.00     68.65%
  440105 requests in 30.09s, 68.07MB read
Requests/sec:  14626.25
Transfer/sec:      2.26MB
[root@vm4 wrk]#

转载自：https://cpp.la/439.html