使用 Simple Protocol
asyncio.BaseProtocol 类是asyncio模块中协议接口(protocol interface)的一个常见的基类。asyncio.Protocolclass 继承自asyncio.BaseProtocol 并为stream protocols提供了一个接口。下面的代码演示了asyncio.Protocol 接口的一个简单实现,它的行为1就像一个echo server,同时,它还会在Python的控制台中输出一些信息。SimpleEchoProtocol 继承自asyncio.Protocol,并且实现了3个方法:connection_made, data_received 以及 andconnection_lost:
import asyncio class SimpleEchoProtocol(asyncio.Protocol): def connection_made(self, transport): """ Called when a connection is made. The argument is the transport representing the pipe connection. To receive data, wait for data_received() calls. When the connection is closed, connection_lost() is called. """ print("Connection received!") self.transport = transport def data_received(self, data): """ Called when some data is received. The argument is a bytes object. """ print(data) self.transport.write(b'echo:') self.transport.write(data) def connection_lost(self, exc): """ Called when the connection is lost or closed. The argument is an exception object or None (the latter meaning a regular EOF is received or the connection was aborted or closed). """ print("Connection lost! Closing server...") server.close() loop = asyncio.get_event_loop() server = loop.run_until_complete(loop.create_server(SimpleEchoProtocol, 'localhost', 2222)) loop.run_until_complete(server.wait_closed())
你可以通过运行一个telnet客户端程序,并且连接到localhost的2222端口来测试这个echo server。如果你正在使用这个端口,你可以将这个端口号修改为任何其他可以使用的端口。如果你使用默认的值,你可以在Python的控制台中运行上面的代码,之后在命令提示符或终端中运行 telnet localhost 2222。你将会看到 Connection received! 的信息显示在Python的控制台中。接下来,你在telnet的控制台中输入的任何字符都会以echo:跟上输入的字符的形式展示出来,同时,在Python的控制台中会显示出刚才新输入的字符。当你退出telnet控制台时,你会看到Connection lost! Closing server... 的信息展示在Python的控制台中。
举个例子,如果你在开启telnet之后输入 abc,你将会在telnet的窗口中看到下面的消息:
echo:abecho:bcecho:c
此外,在Python的控制台中会显示下面的消息:
Connection received! b'a' b'b' b'c' Connection lost! Closing server...
在创建了一个名为loop的事件循环之后,代码将会调用loop.run_until_complete来运行loop.create_server这个协程(coroutine)。这个协程创建了一个TCP服务器并使用protocol的工厂类绑定到指定主机的指定端口(在这个例子中是localhost上的2222端口,使用的工厂类是SimpleEchoProtocol)并返回一个Server的对象,以便用来停止服务。代码将这个实例赋值给server变量。用这种方式,当建立一个客户端连接时,会创建一个新的SimpleEchoProtocol的实例并且该类中的方法会被执行。
当成功的创建了一个连接之后,connection_made 方法里面的代码输出了一条消息,并将收到的内容作为一个参数赋值给transport成员变量,以便稍后在另一个方法中使用。
当收到了传来的数据时,data_received方面里面的代码会将收到的数据字节输出,并且通过调用两次self.transport.write 方法将echo: 和收到数据发送给客户端。当然了,也可以只调用一次self.transport.write将所有的数据返回,但是我想更清楚的将发送echo:的代码和发送收到的数据的代码区分开来。
当连接关掉或者断开时,connection_lost方法中的代码将会输出一条消息,并且调用server.close();此时,那个在服务器关闭前一直运行的循环停止了运行。
使用 Clients and Servers
在上面的例子中,telnet是一个客户端。asyncio模块提供了一个协程方便你很容易的使用stream reader 和 writer来编写服务端和客户端。下面的代码演示了一个简单的echo server,该server监听localhost上的2222端口。你可以在Python的控制台中运行下面的代码,之后在另一个Python的控制台中运行客户端的代码作为客户端。
import asyncio @asyncio.coroutine def simple_echo_server(): # Start a socket server, call back for each client connected. # The client_connected_handler coroutine will be automatically converted to a Task yield from asyncio.start_server(client_connected_handler, 'localhost', 2222) @asyncio.coroutine def client_connected_handler(client_reader, client_writer): # Runs for each client connected # client_reader is a StreamReader object # client_writer is a StreamWriter object print("Connection received!") while True: data = yield from client_reader.read(8192) if not data: break print(data) client_writer.write(data) loop = asyncio.get_event_loop() loop.run_until_complete(simple_echo_server()) try: loop.run_forever() finally: loop.close()
下面的代码演示了一个客户端程序连接了localhost上的2222端口,并且使用asyncio.StreamWriter对象写了几行数据,之后使用asyncio.StreamWriter对象读取服务端返回的数据。
import asyncio LASTLINE = b'Last line.\n' @asyncio.coroutine def simple_echo_client(): # Open a connection and write a few lines by using the StreamWriter object reader, writer = yield from asyncio.open_connection('localhost', 2222) # reader is a StreamReader object # writer is a StreamWriter object writer.write(b'First line.\n') writer.write(b'Second line.\n') writer.write(b'Third line.\n') writer.write(LASTLINE) # Now, read a few lines by using the StreamReader object print("Lines received") while True: line = yield from reader.readline() print(line) if line == LASTLINE or not line: break writer.close() loop = asyncio.get_event_loop() loop.run_until_complete(simple_echo_client())
你可以在不同的Python控制台中执行客户端的代码。如果服务端正在运行,控制台中会输出下面的内容:
Lines received b'First line.\n' b'Second line.\n' b'Third line.\n' b'Last line.\n'
执行服务端代码的Python控制台会显示下面的内容:
Connection received! b'First line.\nSecond line.\nThird line.\nLast line.\n'
首先,让我们关注一下服务端的代码。在创建完一个叫loop的事件循环之后,代码会调用loop.run_until_complete来运行这个simple_echo_server协程。该协程调用asyncio.start_server协程来开启一个socket服务器,绑定到指定的主机和端口号,之后,对每一个客户端连接执行作为参数传入的回调函数——client_connected_handler。在这个例子中,client_connected_handler是另一个协程,并且不会被自动的转换为一个Task。除了协程(coroutine)之外,你可以指定一个普通的回调函数。

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 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'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.

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 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.

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.


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