守护进程(daemon)是指在UNIX或其他多任务操作系统中在后台执行的电脑程序,并不会接受电脑用户的直接操控。此类程序会被以进程的形式初始化。通常,守护进程没有任何存在的父进程(即PPID=1),且在UNIX系统进程层级中直接位于init之下。守护进程程序通常通过如下方法使自己成为守护进程:对一个子进程调用fork,然后使其父进程立即终止,使得这个子进程能在init下运行。–维基百科
守护进程区别于普通用户登陆系统后运行的进程,它是直接由系统初始化,和系统用户没有关系,而用户开启的进程依存与用户连接的终端,当终端退出或断开,进程也会随着终止。
来看一下我Linux试验机的进程状态:
[root@home tmp]# ping www.baidu.com > /dev/null & [1] 2759 [root@home tmp]# pstree -p systemd(1)-+-agetty(157) |-agetty(163) |-avahi-daemon(129)---avahi-daemon(134) |-avahi-dnsconfd(125) |-crond(121) |-dbus-daemon(130) |-haveged(128) |-ifplugd(126) |-nginx(226)---nginx(227) |-ntpd(223) |-python(2727) |-rngd(124) |-sshd(216)---sshd(2683)---bash(2690)-+-ping(2759) | `-pstree(2760) |-systemd(2687)---(sd-pam)(2688) |-systemd-journal(76) |-systemd-logind(127) |-systemd-udevd(89) `-wpa_supplicant(153)
可以看到,当前有一个ping程序在后台运行,如果如断开连接,再次去登陆,ping程序是已经终止了的。也就是说,普通进程,和用户会话相关,那么,如何去编写一个和用户会话无关,一直运行在后台的进程呢?大家可能注意到了上面pid为2727的python,如果只是正常打开python,它应该是运行在bash下的,而这里却直接运行在systemd下,实际上,它是一个守护进程,来看一下python编写linux守护进程的简单实现:
#!/usr/bin/env python import os import signal import time logfile="/tmp/daemon.log" pid=os.fork() #exit parent process if pid: exit() #get the pid of subprocess daeid=os.getpid() os.setsid() os.umask(0) os.chdir("/") #Redirection file descriptor fd=open("/dev/null","a+") os.dup2(fd.fileno(),0) os.dup2(fd.fileno(),1) os.dup2(fd.fileno(),2) fd.close() log=open(logfile,'a') log.write('Daemon start up at %s\n'%(time.strftime('%Y:%m:%d',time.localtime(time.time())))) log.close() def reload(a,b): log=open(logfile,'a') log.write('Daemon reload at %s\n'%(time.strftime('%Y:%m:%d',time.localtime(time.time())))) log.close() while True: signal.signal(signal.SIGHUP,reload) time.sleep(2)
要点是利用linux中,当一个进程的父进程终止是,系统会接管这个进程,让init成为这个进程的父进程,这时候这个进程就成为了一个守护进程。需要注意的是,通过setsid,umask和chdir做工作目录设置、关闭文件描述符、设置文件创建掩码等操作。把上面的代码保存起来,给于运行权限,并用python打开,就会看到有一个新的守护进程在运行,并且能够处理系统发送的SIGHUP信号。
以上程序仅用来测试,仅能够处理系统SIGHUP信号,请使用kill pid结束进程。

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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