先说说线程
在多线程中,为了保证共享资源的正确性,我们常常会用到线程同步技术.
将一些敏感操作变成原子操作,保证同一时刻多个线程中只有一个线程在执行这个原子操作。
我最常用的是互斥锁,也称独占锁。其次还有读写锁,信号量,条件变量等。
除此之外,我们在进程间通信时会用到信号,向某一个进程发送信号,该进程中设置信号处理函数,然后当该进程收到信号时,执行某些操作。
其实在线程中,也可以接受信号,利用这种机制,我们也可以用来实现线程同步。更多信息见 http://www.jb51.net/article/64977.htm
再说说进程
进程里我们通过一些进程间通信方式,可以实现进程间的同步。
最近我遇到的一个情况是,某采集系统进程池中很多进程会向同一个日志文件中打印日志,如果通过进程间通信实现,比较麻烦。
还有一种办法,如果采用共享内存的方式,不同的进程分别将日志消息通过共享内存放入一个线程安全的队列中,再建立一个进程负责专门打印日志,这样也可以保证不被大乱,
保证日志的正确性,但代码量也很多阿。
还有一种办法,在共享内存中设置一个互斥锁,所有进程共享。
如果能像线程一样,有一个简单的互斥锁,用的时候只要加锁,就能实现进程间的互斥就好了。之前对文件加锁,也有些印象,于是我用它实现了一个进程间的互斥锁
#coding=utf-8 """ Process mutex lock. Actually it is implemented by file lock. """ import fcntl class ProcessLock(object): __lockfd = None @staticmethod def lock(): ProcessLock.__lockfd = open(__file__, 'a+') fcntl.flock(ProcessLock.__lockfd, fcntl.LOCK_EX) @staticmethod def unlock(): fcntl.flock(ProcessLock.__lockfd, fcntl.LOCK_UN)
加锁 ProcessLock.lock()
释放 ProcessLock.unlock()
非常简单使用,有兴趣的朋友可以试一试。

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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