本文实例讲述了Python多进程机制。分享给大家供大家参考。具体如下:
在以前只是接触过PYTHON的多线程机制,今天搜了一下多进程,相关文章好像不是特别多。看了几篇,小试了一把。程序如下,主要内容就是通过PRODUCER读一个本地文件,一行一行的放到队列中去。然后会有相应的WORKER从队列中取出这些行。
import multiprocessing import os import sys import Queue import time def writeQ(q,obj): q.put(obj,True,None) print "put size: ",q.qsize() def readQ(q): ret = q.get(True,1) print "get size: ",q.qsize() return ret def producer(q): time.sleep(5) #让进行休息几秒 方便ps命令看到相关内容 pid = os.getpid() handle_file = '/home/dwapp/joe.wangh/test/multiprocess/datafile' with open(handle_file,'r') as f: #with...as... 这个用法今天也是第一次看到的 for line in f: print "producer <" ,pid , "> is doing: ",line writeQ(q,line.strip()) q.close() def worker(q): time.sleep(5) #让进行休息几秒 方便ps命令看到相关内容 pid = os.getpid() empty_count = 0 while True: try: task = readQ(q) print "worker <" , pid , "> is doing: " ,task ''' 如果这里不休眠的话 一般情况下所有行都会被同一个子进程读取到 为了使实验效果更加清楚 在这里让每个进程读取完 一行内容时候休眠5s 这样就可以让其他的进程到队列中进行读取 ''' time.sleep(5) except Queue.Empty: empty_count += 1 if empty_count == 3: print "queue is empty, quit" q.close() sys.exit(0) def main(): concurrence = 3 q = multiprocessing.Queue(10) funcs = [producer , worker] for i in range(concurrence-1): funcs.append(worker) for item in funcs: print str(item) nfuncs = range( len(funcs) ) processes = [] for i in nfuncs: p = multiprocessing.Process(target=funcs[i] , args=(q,)) processes.append(p) print "concurrence worker is : ",concurrence," working start" for i in nfuncs: processes[i].start() for i in nfuncs: processes[i].join() print "all DONE" if __name__ == '__main__': main()
实验结果如下:
dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>python 1.py <function producer at 0xb7b9141c> <function worker at 0xb7b91454> <function worker at 0xb7b91454> <function worker at 0xb7b91454> concurrence worker is : 3 working start producer < 28320 > is doing: line 1 put size: 1 producer < 28320 > is doing: line 2 put size: 2 producer < 28320 > is doing: line 3 put size: 3 producer < 28320 > is doing: line 4 put size: 3 producer < 28320 > is doing: line 5 get size: 3 put size: 4 worker < 28321 > is doing: line 1 get size: 3 worker < 28322 > is doing: line 2 get size: 2 worker < 28323 > is doing: line 3 get size: 1 worker < 28321 > is doing: line 4 get size: 0 worker < 28322 > is doing: line 5 queue is empty, quit queue is empty, quit queue is empty, quit all DONE
程序运行期间在另外一个窗口进行ps命令 可以观测到一些进程的信息
dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python dwapp 28319 27481 8 14:04 pts/0 00:00:00 python 1.py dwapp 28320 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28323 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28325 27849 0 14:04 pts/13 00:00:00 grep python dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python #此时28320进程 也就是PRODUCER进程已经结束 dwapp 28319 27481 1 14:04 pts/0 00:00:00 python 1.py dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28323 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28328 27849 0 14:04 pts/13 00:00:00 grep python dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python dwapp 28319 27481 0 14:04 pts/0 00:00:00 python 1.py dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py dwapp 28323 28319 0 14:04 pts/0 00:00:00 [python] <defunct> #这里应该是代表28323进程(WORKER)已经运行结束了 dwapp 28331 27849 0 14:04 pts/13 00:00:00 grep python dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python dwapp 28337 27849 0 14:05 pts/13 00:00:00 grep python
希望本文所述对大家的Python程序设计有所帮助。

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.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SublimeText3 Chinese version
Chinese version, very easy to use