meinheld-gunicorn-flask VS uvicorn-gunicorn-fastapi 性能对比测试

本文会使用如下两个项目：meinheld-gunicorn-flask 与 uvicorn-gunicorn-fastapi

前文有测过 Flask vs FastApi 性能对比测试，可能不够有说服力，这次使用了号称最快的wsgi或asgi服务器+gunicorn来运行flask或fastapi。

上面meinheld-gunicorn-flask和uvicorn-gunicorn-fastapi项目都是由fastapi的作者发起，致力于使用flask或fastapi开发高性能后端应用，然后用docker的方式快速部署。文末会给出测试结论。

名词解释

meinheld：高性能wsgi服务器
uvicorn：高性能asgi服务器
gunicorn：用来管理meinheld或uvicorn的运行，比如用多进程/多线程的方式运行meinheld或uvicorn，以便最大化性能来运行flask或fastapi程序。

测试环境

Ubuntu 22.04系统，CPU 16核，内存32G。

一、创建 meinheld-gunicorn-flask-test 目录，目录结构为

meinheld-gunicorn-flask-test
├── Dockerfile
├── app
│   ├── main.py
│   └── prestart.sh  # 可以不要这个文件
└── requirements.txt

Dockerfile内容：

FROM tiangolo/meinheld-gunicorn-flask:python3.9COPY ./requirements.txt /app/requirements.txtRUN pip install --no-cache-dir --upgrade -r /app/requirements.txtCOPY ./app /app

requirements.txt 内容

blinker==1.8.2
click==8.1.7
Flask==3.0.3
importlib_metadata==7.1.0
itsdangerous==2.2.0
Jinja2==3.1.4
MarkupSafe==2.1.5
Werkzeug==3.0.3
zipp==3.18.2

app/main.py 内容

from flask import Flaskapp = Flask(__name__)@app.route('/', methods=['GET'])
def root():return {"Hello": "Flask"}

构建docker镜像：

cd meinheld-gunicorn-flask-test/
docker build -t meinheld-gunicorn-flask-test:1.0.0 .

运行docker容器：

docker run -d --name meinheld-gunicorn-flask-test -p 8080:80 meinheld-gunicorn-flask-test:1.0.0

二、创建 uvicorn-gunicorn-fastapi-test 目录，目录结构为：

uvicorn-gunicorn-fastapi-test
├── Dockerfile
├── app
│   ├── main.py
│   └── prestart.sh  # 可以不要这个文件
└── requirements.txt

Dockerfile内容：

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.10COPY ./requirements.txt /app/requirements.txtRUN pip install --no-cache-dir --upgrade -r /app/requirements.txtCOPY ./app /app

requirements.txt 内容

annotated-types==0.7.0
anyio==4.3.0
certifi==2024.2.2
click==8.1.7
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
idna==3.7
Jinja2==3.1.4
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
orjson==3.10.3
pydantic==2.7.1
pydantic_core==2.18.2
Pygments==2.18.0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
rich==13.7.1
shellingham==1.5.4
sniffio==1.3.1
starlette==0.37.2
typer==0.12.3
typing_extensions==4.12.0
ujson==5.10.0
uvicorn==0.29.0
uvloop==0.19.0
watchfiles==0.21.0
websockets==12.0

app/main.py 内容

from fastapi import FastAPIapp = FastAPI()@app.get("/")
async def root():return {"Hello": "FastA"}

构建docker镜像：

cd uvicorn-gunicorn-fastapi-test/
docker build -t uvicorn-gunicorn-fastapi-test:1.0.0 .

运行docker容器：

docker run -d --name uvicorn-gunicorn-fastapi-test -p 8081:80 uvicorn-gunicorn-fastapi-test:1.0.0

性能测试

这里使用ab工具测试并发。先把最大打开文件数调大，执行命令：ulimit -n 65535

# 测试flask，并发3000，连接数10万
ab -n 100000 -c 3000 http://127.0.0.1:8080/Benchmarking 127.0.0.1 (be patient)
Completed 10000 requests
Completed 20000 requests
Completed 30000 requests
Completed 40000 requests
Completed 50000 requests
Completed 60000 requests
Completed 70000 requests
Completed 80000 requests
Completed 90000 requests
Completed 100000 requests
Finished 100000 requestsServer Software:        meinheld/1.0.2
Server Hostname:        127.0.0.1
Server Port:            8080Document Path:          /
Document Length:        18 bytesConcurrency Level:      3000
Time taken for tests:   7.209 seconds
Complete requests:      100000
Failed requests:        0
Total transferred:      16900000 bytes
HTML transferred:       1800000 bytes
Requests per second:    13871.03 [#/sec] (mean)
Time per request:       216.278 [ms] (mean)
Time per request:       0.072 [ms] (mean, across all concurrent requests)
Transfer rate:          2289.26 [Kbytes/sec] receivedConnection Times (ms)min  mean[+/-sd] median   max
Connect:        0   99  12.5     99     148
Processing:    36  113  20.7    110     346
Waiting:        1   82  18.0     80     317
Total:        109  213  18.1    208     430Percentage of the requests served within a certain time (ms)50%    20866%    21275%    21880%    22590%    23495%    24298%    25299%    258100%    430 (longest request)

下面是fastapi：

# 测试fastapi，并发3000，连接数10万
ab -n 100000 -c 3000 http://127.0.0.1:8081/Benchmarking 127.0.0.1 (be patient)
Completed 10000 requests
Completed 20000 requests
Completed 30000 requests
Completed 40000 requests
Completed 50000 requests
Completed 60000 requests
Completed 70000 requests
Completed 80000 requests
Completed 90000 requests
Completed 100000 requests
Finished 100000 requestsServer Software:        uvicorn
Server Hostname:        127.0.0.1
Server Port:            8081Document Path:          /
Document Length:        17 bytesConcurrency Level:      3000
Time taken for tests:   7.198 seconds
Complete requests:      100000
Failed requests:        0
Total transferred:      14200000 bytes
HTML transferred:       1700000 bytes
Requests per second:    13892.29 [#/sec] (mean)
Time per request:       215.947 [ms] (mean)
Time per request:       0.072 [ms] (mean, across all concurrent requests)
Transfer rate:          1926.47 [Kbytes/sec] receivedConnection Times (ms)min  mean[+/-sd] median   max
Connect:        0   88  10.2     88     138
Processing:    44  124  25.8    121     223
Waiting:        1   94  27.8     82     200
Total:         99  213  24.7    208     324Percentage of the requests served within a certain time (ms)50%    20866%    22375%    22980%    23290%    24295%    25698%    27999%    287100%    324 (longest request)

结论：

fastapi（13892.29） 和 flask（13892.29）并发量几乎一样，看来 meinheld 的性能确实很高。甚至在 ab 参数 -n 100000 -c 1000 的情况下，多次测试后，fastapi和flask并发数各有胜负。

因此在开发简单快速的项目时，可以用 meinheld-gunicorn-flask 这套方案。而在需要更多异步编程，需要类型检查，或者docs（Swagger）文档时，可以用 uvicorn-gunicorn-fastapi 这套方案。