search
HomeBackend DevelopmentPython Tutorial在Mac OS上部署Nginx和FastCGI以及Flask框架的教程

最近在学习Flask,本文介绍一下如何部署Flask开发的应用,同时也学习一下Nginx的使用,这只是在Mac上的一个实验。
应用

这里使用的应用就是官方的文档中给出的Flaskr。
安装Nginx

使用HomeBrew安装Nginx:

$ brew install nginx

HomeBrew会自动安装Nginx及其依赖的程序。在我的电脑上安装的是Nginx 1.6.2,配置文件的路径是/usr/local/etc/nginx/nginx.conf。

启动Nginx的命令:

$ nginx

Nginx的默认端口是8080,用浏览器打开localhost:8080,显示如下所示的页面说明Nginx已经工作了。

201552143836668.jpg (600×358)

配置Nginx

修改Nginx的配置文件:

server {
  listen 80;
  server_name localhost;
  charset utf-8;

  location / { try_files $uri @flaskr; }
  location @flaskr {
    include fastcgi_params;
    fastcgi_param PATH_INFO $fastcgi_script_name;
    fastcgi_param SCRIPT_NAME "";
    fastcgi_pass unix:/tmp/flaskr-fcgi.sock;
  }
}

重新启动Nginx:

$ nginx -s quit
$ sudo nginx

因为使用了80端口,启动Nginx时需要加上sudo。

启动完成后,访问localhost:

201552143905542.jpg (600×322)

访问时出现了错误,这是因为我们的应用还没有启动。
FastCGI Server

Nginx是一个静态WEB服务器,不能直接运行我们的Python应用,当Nginx接受到请求时,会通过FastCGI转发给我们的应用,应用是运行在FastCGI Server上的,这个server接收Nginx的请求并调用我们的程序,将结果返回给Nginx,Nginx再将结果返回给用户。

我们要使用的FastCGI Server是flup,安装方法:

$ pip install flup

在应用目录下创建一个fcgi文件,例如flaskr.fcgi:

#!/usr/bin/python
from flup.server.fcgi import WSGIServer
from flaskr import app

if __name__ == '__main__':
  WSGIServer(app, bindAddress='/tmp/flaskr-fcgi.sock').run()

同时给fcgi文件可执行的权限:

$ chmod +x flaskr.fcgi

手动启动server:

$ screen
$ ./flaskr.fcgi

使用screen使server在后台运行,或者:

$ nohup ./flaskr.fcgi &

再次访问localhost就可以看到我们的应用了。
遇到的问题

第一次运行FastCGI server后,任然无法访问,查看Nginx的日志后发现Nginx服务器没有权限访问socket文件,修改nginx.conf添加user配置:

复制代码 代码如下:
user wzy;

启动的时候Nginx报错:

nginx: [emerg] getgrnam("wzy") failed in /usr/local/etc/nginx/nginx.conf:2

Google一下后发现要加上用户组才行,改成这样:

复制代码 代码如下:
user wzy wheel;

再次启动Nginx后一切正常了。

Nginx配置项user的使用方法:

Syntax: user user [group];
Default: user nobody nobody;

如果忽略group,Nginx会使用和user名称一样的用户组,例如我设置user wzy,那么Nginx启动的时候会去查找用户组wzy,我的电脑上没有这个用户组,所以Nginx会报错。

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment