Implementation of multi-threaded operation based on Python script under Linux platform

Implementation of multi-threaded operations based on Python script under Linux platform

Overview:
Multi-threading is a common concurrent programming method, which can improve the efficiency of the program. Execution efficiency, especially when processing IO-intensive tasks, is more prominent. As a high-level programming language, Python provides a rich thread operation library, making multi-threaded programming possible. This article will introduce how to use Python scripts for multi-threaded operations on the Linux platform and give specific code examples.

1. The difference between threads and processes
   In the operating system, threads are the basic unit for executing computer programs, and processes are the basic units for program execution. A thread is a lightweight process that shares memory space with the process and can quickly switch execution with less resource consumption. Processes have independent memory spaces and cannot directly access each other.
2. Python multi-threading module under Linux platform
   In Python, there are two main multi-threading modules: threading and multiprocessing. Among them, the threading module is a standard library used to implement multi-threaded programming. It provides the Thread class, which can create and start new threads. The multiprocessing module is process-based multi-thread programming. It provides the Process class to create and start new processes.

In this article, we mainly focus on Python’s threading module, which has the advantages of simplicity, ease of use, cross-platform, etc., and is suitable for use under the Linux platform.

3. Basic steps for implementing multi-threaded operation in Python
   (1) Import threading module
   import threading

(2) Define and create threads
class MyThread(threading.Thread):

  def __init__(self):
      threading.Thread.__init__(self)
  
  def run(self):
      # 线程执行的代码

thread1 = MyThread()
thread2 = MyThread()
...

(3) Start thread
thread1. start()
thread2.start()
...

(4) Wait for the thread to end
thread1.join()
thread2.join()
.. .

In the above steps, we first imported the threading module, and then defined a custom thread class MyThread that inherits from the Thread class. In the custom thread class, you need to implement the run method and write the code for thread execution in it.

4. Example: Using Python multi-threading for concurrent downloads
   The following uses an example of concurrent downloading of files to demonstrate how to use Python multi-threading for concurrent operations.

import threading
import urllib.request

class DownloadThread(threading.Thread):
    def __init__(self, url, filename):
        threading.Thread.__init__(self)
        self.url = url
        self.filename = filename

    def run(self):
        print("开始下载：{0}".format(self.filename))
        urllib.request.urlretrieve(self.url, self.filename)
        print("下载完成：{0}".format(self.filename))

# 定义文件列表和下载链接
files = ["file1.txt", "file2.txt", "file3.txt"]
urls = [
    "http://example.com/file1.txt",
    "http://example.com/file2.txt",
    "http://example.com/file3.txt"
]

# 创建并启动线程
threads = []
for i in range(len(files)):
    t = DownloadThread(urls[i], files[i])
    t.start()
    threads.append(t)

# 等待线程结束
for t in threads:
    t.join()

In the above example, a custom thread class DownloadThread is first defined, and its initialization method receives a download link and file name. In the run method, use the urllib.request.urlretrieve function to download the file and print relevant information when the download starts and completes.

Next, we define the list of files to be downloaded and the corresponding download link. Then, create and start multiple download threads through a loop and add them to the thread list.

Finally, use the join method to wait for all threads to complete execution to ensure that the download operation is completed.

5. Summary
   This article introduces the method of using Python scripts for multi-threaded operations under the Linux platform, and gives specific code examples. By using multi-thread programming, you can make full use of the computing power of multi-core processors and improve program execution efficiency. Although multi-threaded programming has its own challenges and considerations, with proper planning and design, multi-threading can be effectively utilized for concurrent operations.

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