In the previous chapter, we learned some basic methods of Python multi-process programming: using the Process, Pool, Queue, Lock, Pipe and other classes provided by the cross-platform multi-process module multiprocessing to implement sub-process creation, process pool (batch) Create child processes and manage the maximum number of child processes) and inter-process communication. In this chapter, you will learn about multi-threaded programming methods in Python. 1. Threading A thread is the smallest unit for the operating system to perform tasks. The threading module is provided in the Python standard library, which provides very convenient support for multi-threaded programming. The following is the code to implement multi-threading using threading: 1 #!/usr/bin/python 2 # -*- coding: utf-8 -* 3 author = 'zni.feng' 4 import&
1. Summary of multi-process module examples
##Introduction: In the previous chapter, we learned Some basic methods of Python multi-process programming: use the Process, Pool, Queue, Lock, Pipe and other classes provided by the cross-platform multi-process module multiprocessing to implement sub-process creation, process pool (create sub-processes in batches and manage the upper limit of the number of sub-processes) and Inter-process communication. In this chapter, you will learn about multi-threaded programming methods in Python. 1. Threading A thread is the smallest unit for the operating system to perform tasks. The Python standard library provides the threading module...
2. Multi-process and multi-threading examples in Python (2) Programming methods
Introduction: In the previous chapter, we learned some basic methods of Python multi-process programming: using the Process provided by the cross-platform multi-process module multiprocessing , Pool, Queue, Lock, Pipe and other classes to implement child process creation, process pool (create child processes in batches and manage the upper limit of the number of child processes) and inter-process communication. In this chapter, you will learn about multi-threaded programming methods in Python.
3. Python concurrent programming thread pool/process pool
##Introduction: Introduction The Python standard library provides us with the threading and multiprocessing modules to write corresponding multi-threading/multi-process code. However, when the project reaches a certain scale, frequent creation/destruction of processes or threads is very resource intensive. , at this time we have to write our own thread pool/process pool to trade space for time. But starting from Python3.2, the standard library provides us with the concurrent.futures module
4.
Python concurrent programming thread pool/process pool
Introduction: Introduction The Python standard library provides us with the threading and multiprocessing modules to write corresponding multi-threading/multi-process code, but when the project When reaching a certain scale, frequent creation/destruction of processes or threads consumes a lot of resources. At this time, we have to write our own thread pool/process pool to trade space for time. But starting from Python 3.2, the standard library provides us with the concurrent.futures module, which provides two classes: ThreadPoolExecutor and ProcessPoolExecutor, which implements thr..
##5.
Detailed introduction to thread pool/process pool of Python concurrent programming## Introduction: Introduction: The Python standard library is We provide threading and multiprocessing modules to write corresponding multi-thread/multi-process code. However, when the project reaches a certain scale, frequent creation/destruction of processes or threads is very resource-consuming. At this time, we have to write our own thread pool/process. Pool, trading space for time. But starting from Python 3.2, the standard library provides us with the concurrent.futures module, which provides two classes: ThreadPoolExecutor and ProcessPoolExecutor, which implements thr..
##6. Python Custom Process Pool Instance Analysis [Producer and Consumer Model Issues]
Introduction: This article mainly introduces the Python custom process pool, and analyzes the producers and producers implemented by Python using the custom process pool with examples. For consumer model issues, friends in need can refer to
##7. nginx - How to configure multiple php-cgi process pools
Introduction: Assuming there are 6 subdomains under nginx, does it mean that I need to open 6 phpcgi process pools? For example, 127.0.0.1:9005
8. PHP Concurrent IO Programming Road
Introduction:: PHP Concurrent IO The Road to Programming: Original Link: http://rango.swoole.com/archives/508 1. Introduction Concurrent IO issues have always been a technical problem in server-side programming, from the earliest synchronous blocking of direct Fork processes to Worker process pools/ Thread pool, to current asynchronous IO and coroutines. Because PHP programmers have a powerful LAMP framework, they know very little about this kind of underlying knowledge. The purpose of this article is to introduce in detail various attempts at concurrent IO programming in PHP, and finally introduce the use of Swoole to comprehensively analyze concurrent IO issues in simple terms.
9. PHP Optimization Hodgepodge
##Introduction : PHP Optimization Hodgepodge Articles about PHP optimization often teach you how to write efficient code. This article intends to discuss the problem from another angle and teach you how to configure an efficient environment, so that the purpose of optimization can also be achieved. pool The depressing news is that the vast majority of PHP programmers ignore the value of pools. The pool mentioned here does not refer to things like database connection pools, but to process pools. PHP allows multiple pools to be started at the same time, and each pool uses different configurations
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Introduction to the multi-process (multiprocessing package) of the Python standard library
Introduction: This article mainly introduces the introduction of the multi-process (multiprocessing package) of the Python standard library. This article It explains the process pool, shared resources, shared memory, Manager, etc. Friends who need it can refer to[Related Q&A recommendations]:
python multiprocessingThe problem of opening up multiple threads in multiple processespython - How to run class functions in multiple processesmongodb - A crawler written in Python appears With regular pausesIf Python has concurrent, do mutiprocessing and threading still have meaning? pool+map - Regarding the efficiency of python pool.map
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