search
HomeBackend DevelopmentPython TutorialWindows系统下使用flup搭建Nginx和Python环境的方法

首先确保你的电脑里已经安装了Python和Django,接下来我们还需要两个组件,nginx服务器和flup(Python的FastCGI组件)
nginx下载地址:http://nginx.org/en/download.html
flup下载地址:http://www.saddi.com/software/flup/dist/flup-1.0.2.tar.gz
与Linux下不同的是,nginx在windows下是以一个应用程序的方式运行,而不是以一个服务运行(难怪没人在windows服务器上用nginx)
把刚刚下载好的两个压缩包都解压到C:\nginx\, C:\flup\(目录可自己选择,这里只做个演示)然后用python setup.py install 命令

安装flup,接着就要配置nginx了,打开C:\nginx\conf\nginx.conf,我的配置文件如下,大家可根据需要自行修改:

#user nobody; 
worker_processes 1; 
 
#error_log logs/error.log; 
#error_log logs/error.log notice; 
#error_log logs/error.log info; 
 
#pid    logs/nginx.pid; 
 
 
events { 
  worker_connections 1024; 
} 
 
 
http { 
  include    mime.types; 
  default_type application/octet-stream; 
 
  #log_format main '$remote_addr - $remote_user [$time_local] "$request" ' 
  #         '$status $body_bytes_sent "$http_referer" ' 
  #         '"$http_user_agent" "$http_x_forwarded_for"'; 
 
  #access_log logs/access.log main; 
 
  sendfile    on; 
  #tcp_nopush   on; 
 
  #keepalive_timeout 0; 
  keepalive_timeout 65; 
 
  #gzip on; 
 
  server { 
    listen    80; 
    server_name localhost; 
 
    #charset koi8-r; 
 
    #access_log logs/host.access.log main; 
 
    location / { 
      root  html; 
      index index.html index.htm; 
    } 
 
    #error_page 404       /404.html; 
 
    # redirect server error pages to the static page /50x.html 
    # 
    error_page  500 502 503 504 /50x.html; 
    location = /50x.html { 
      root  html; 
    } 
 
    # proxy the PHP scripts to Apache listening on 127.0.0.1:80 
    # 
    #location ~ \.php$ { 
    #  proxy_pass  http://127.0.0.1; 
    #} 
 
    # pass the PHP scripts to FastCGI server listening on 127.0.0.1:9000 
    # 
    #location ~ \.php$ { 
    #  root      html; 
    #  fastcgi_pass  127.0.0.1:9000; 
    #  fastcgi_index index.php; 
    #  fastcgi_param SCRIPT_FILENAME /scripts$fastcgi_script_name; 
    #  include    fastcgi_params; 
    #} 
 
    # deny access to .htaccess files, if Apache's document root 
    # concurs with nginx's one 
    # 
    #location ~ /\.ht { 
    #  deny all; 
    #} 
     
    # 静态资源 
    location ~* ^.+\.(html|jpg|jpeg|gif|png|ico|css|js)$ 
    { 
      root e:/gin/gin/; 
      expires 30d; 
      break; 
    } 
 
    location ~ ^/static/ { 
      root e:/gin/gin/; 
      expires 30d; 
      break; 
    }  
 
    location ~ ^/ { 
      # 指定 fastcgi 的主机和端口 
      fastcgi_pass 127.0.0.1:8051; 
      fastcgi_param PATH_INFO $fastcgi_script_name; 
      fastcgi_param REQUEST_METHOD $request_method; 
      fastcgi_param QUERY_STRING $query_string; 
      fastcgi_param CONTENT_TYPE $content_type; 
      fastcgi_param CONTENT_LENGTH $content_length; 
      fastcgi_param SERVER_PROTOCOL $server_protocol; 
      fastcgi_param SERVER_PORT $server_port; 
      fastcgi_param SERVER_NAME $server_name; 
      fastcgi_pass_header Authorization; 
      fastcgi_intercept_errors off; 
    } 
  } 
 
  # another virtual host using mix of IP-, name-, and port-based configuration 
  # 
  #server { 
  #  listen    8000; 
  #  listen    somename:8080; 
  #  server_name somename alias another.alias; 
 
  #  location / { 
  #    root  html; 
  #    index index.html index.htm; 
  #  } 
  #} 
 
 
  # HTTPS server 
  # 
  #server { 
  #  listen    443; 
  #  server_name localhost; 
 
  #  ssl         on; 
  #  ssl_certificate   cert.pem; 
  #  ssl_certificate_key cert.key; 
 
  #  ssl_session_timeout 5m; 
 
  #  ssl_protocols SSLv2 SSLv3 TLSv1; 
  #  ssl_ciphers HIGH:!aNULL:!MD5; 
  #  ssl_prefer_server_ciphers  on; 
 
  #  location / { 
  #    root  html; 
  #    index index.html index.htm; 
  #  } 
  #} 
 
} 

需要注意的是,对于不需要url rewrite的目录,比如存放css和图片的目录,需要在配置文件里指明,否则将无法访问这些文件

    location ~ ^/static/ {
      root e:/gin/gin/;
      expires 30d;
      break;
    }

最后一步就是运行nginx服务器,并且用FastCGI运行你的Django项目了
进入nginx的目录:

  cd c:\nginx\
  start nginx

然后在浏览器里访问http://loaclhost/ 就应该可以看到nginx的欢迎界面了。最后进入你的Django项目的根目录,然后用一下命令来运行服务器:

  python manage.py runfcgi method=threaded host=127.0.0.1 port=8051

刷新localhost页面,你就能看到你的项目主页啦~~
补充一点windwos下nginx操作的命令(来自官方文档)

nginx -s stop quick exit
nginx -s quit graceful quit
nginx -s reload changing configuration, starting a new worker, quitting an old worker gracefully
nginx -s reopen reopening log files

大功告成,开始django之旅,ohye!!!

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

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

VSCode Windows 64-bit Download

VSCode Windows 64-bit Download

A free and powerful IDE editor launched by Microsoft

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development 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.