1 端口映射
举个例子来说明一下端口映射的作用。
有A、B、C三台计算机,A、B互通,B、C互通,但是A、C不通,这个时候在C上开了一个Web服务,如何让A访问C的Web服务?
最简单有效的办法就是在B上开一个端口映射服务,然后让A访问B的某个端口,B将这个端口上的所有流量全部转发到C的Web服务端口上,同时将C上Web服务返回的流量也全部转发给A。这样对A来说,以B为跳板,实现了间接访问C上Web服务的目的。
2 实现流程
端口映射的原理并不复杂,本文以TCP为例介绍一下实现过程,简单画了个时序图(如下),这里就不再用文字赘述了。
需要注意的是,由于端口映射只是单纯的流量转发,对应用层数据不进行处理,所以对于多通道协议是无法支持的(如FTP协议)。
3 代码示例
按照上面的流程,Python实现如下(建议从后向前看):
# -*- coding: utf-8 -*- # tcp mapping created by hutaow(hutaow.com) at 2014-08-31 import socket import threading # 端口映射配置信息 CFG_REMOTE_IP = '192.168.0.10' CFG_REMOTE_PORT = 22 CFG_LOCAL_IP = '0.0.0.0' CFG_LOCAL_PORT = 10022 # 接收数据缓存大小 PKT_BUFF_SIZE = 2048 # 调试日志封装 def send_log(content): print content return # 单向流数据传递 def tcp_mapping_worker(conn_receiver, conn_sender): while True: try: data = conn_receiver.recv(PKT_BUFF_SIZE) except Exception: send_log('Event: Connection closed.') break if not data: send_log('Info: No more data is received.') break try: conn_sender.sendall(data) except Exception: send_log('Error: Failed sending data.') break # send_log('Info: Mapping data > %s ' % repr(data)) send_log('Info: Mapping > %s -> %s > %d bytes.' % (conn_receiver.getpeername(), conn_sender.getpeername(), len(data))) conn_receiver.close() conn_sender.close() return # 端口映射请求处理 def tcp_mapping_request(local_conn, remote_ip, remote_port): remote_conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: remote_conn.connect((remote_ip, remote_port)) except Exception: local_conn.close() send_log('Error: Unable to connect to the remote server.') return threading.Thread(target=tcp_mapping_worker, args=(local_conn, remote_conn)).start() threading.Thread(target=tcp_mapping_worker, args=(remote_conn, local_conn)).start() return # 端口映射函数 def tcp_mapping(remote_ip, remote_port, local_ip, local_port): local_server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) local_server.bind((local_ip, local_port)) local_server.listen(5) send_log('Event: Starting mapping service on ' + local_ip + ':' + str(local_port) + ' ...') while True: try: (local_conn, local_addr) = local_server.accept() except KeyboardInterrupt, Exception: local_server.close() send_log('Event: Stop mapping service.') break threading.Thread(target=tcp_mapping_request, args=(local_conn, remote_ip, remote_port)).start() send_log('Event: Receive mapping request from %s:%d.' % local_addr) return # 主函数 if __name__ == '__main__': tcp_mapping(CFG_REMOTE_IP, CFG_REMOTE_PORT, CFG_LOCAL_IP, CFG_LOCAL_PORT)
4 运行
运行效果如下,192.168.0.20通过连接映射服务器的10022端口,成功访问192.168.0.10的SSH服务(22端口):

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.

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 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.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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