


How to Avoid Readline Hangs When Stopping Process Output in Python?
Avoiding Readline Hangs When Stopping Process Output in Python
Problem Description:
In a Python program using os.popen() or subprocess.Popen() to read the output of a continuously updating process (such as top), the program may hang when attempting to read all lines using readlines().
Solution:
Using a Temporary File and Child Process:
<code class="python">import subprocess import tempfile import time def main(): # Open a temporary file for process output with tempfile.TemporaryFile() as f: # Start the process and redirect its stdout to the file process = subprocess.Popen(["top"], stdout=f) # Wait for a specified amount of time time.sleep(2) # Kill the process process.terminate() process.wait() # Wait for the process to terminate to ensure complete output # Seek to the beginning of the file and print its contents f.seek(0) print(f.read()) if __name__ == "__main__": main()</code>
This approach uses a temporary file to store the process output, allowing the program to avoid blocking on readlines().
Alternative Solutions:
Using a Queue with Another Thread:
<code class="python">import collections import subprocess import threading def main(): # Create a queue to store process output q = collections.deque() # Start the process and redirect its stdout to a thread process = subprocess.Popen(["top"], stdout=subprocess.PIPE) t = threading.Thread(target=process.stdout.readline, args=(q.append,)) t.daemon = True t.start() # Wait for a specified amount of time time.sleep(2) # Terminate the process process.terminate() t.join() # Wait for the thread to finish # Print the stored output print(''.join(q)) if __name__ == "__main__": main()</code>
Using signal.alarm():
<code class="python">import collections import signal import subprocess class Alarm(Exception): pass def alarm_handler(signum, frame): raise Alarm def main(): # Create a queue to store process output q = collections.deque() # Register a signal handler to handle alarm signal.signal(signal.SIGALRM, alarm_handler) # Start the process and redirect its stdout process = subprocess.Popen(["top"], stdout=subprocess.PIPE) # Set an alarm to terminate the process after a specified amount of time signal.alarm(2) # Read lines until the alarm is raised or the process terminates try: while True: line = process.stdout.readline() if not line: break q.append(line) except Alarm: process.terminate() # Cancel the alarm if it hasn't already fired signal.alarm(0) # Wait for the process to finish process.wait() # Print the stored output print(''.join(q)) if __name__ == "__main__": main()</code>
These alternatives allow the program to continue running while saving the process output. They may be more appropriate for cases where you need to continuously monitor the process output.
The above is the detailed content of How to Avoid Readline Hangs When Stopping Process Output in Python?. For more information, please follow other related articles on the PHP Chinese website!

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

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

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 is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.
