Python concurrent example

Emerald 157 Published: 12/14/2024

Python concurrent example

I'd be happy to provide a Python concurrency example for you.

Concurrency is the ability of a program to perform multiple tasks simultaneously, improving its responsiveness and overall performance. Python provides several ways to achieve concurrency, including:

Multiple processes: Use the os module's fork() function to create child processes, each running in parallel. Threading: Utilize Python's built-in threading module to manage threads within a program.

Let's explore a simple example that demonstrates concurrency using both processes and threads:

import os

import threading


def worker(process_id):


print(f"Process {process_id} is running...")


for i in range(5):


print(f"Process {process_id}: iteration {i+1}")


print(f"Process {process_id} finished.")


def thread_worker(thread_id):


print(f"Thread {thread_id} is running...")


for i in range(3):


print(f"Thread {thread_id}: iteration {i+1}")


print(f"Thread {thread_id} finished.")


if name == "main":

Create processes

process_ids = []


for i in range(os.cpu_count()):


p = os.fork()


if p:  # parent process


process_ids.append(i)


else:  # child process (worker)


worker(len(process_ids))


exit()  # terminate the child process

Create threads

thread_ids = []


for i in range(5):


t = threading.Thread(target=thread_worker, args=(i,))


t.start()


thread_ids.append(i)


for pid in process_ids:


os.kill(pid, 0)  # signal each process to exit


for tid in thread_ids:


t = threading.Thread(name=f"Thread {tid}", target=lambda: None)


t.start()  # wait for threads to finish before exiting

This example demonstrates the following:

Multiple processes: The program creates multiple child processes, which run concurrently with the parent process. Each child process runs a separate instance of the worker() function, performing five iterations. Threading: The program also creates multiple threads within the main process. Each thread runs the thread_worker() function, performing three iterations.

In this example, both processes and threads are executing concurrently, showcasing Python's capabilities in handling concurrency.

Keep in mind that while this example illustrates the basics of concurrency, it is just a starting point for your own applications. To effectively utilize concurrency in real-world scenarios, consider factors such as:

Synchronization: How will you ensure that concurrent tasks do not interfere with each other? Resource sharing: Will multiple tasks share resources like file handles or network connections? If so, how will you manage these shared resources? Error handling: What happens when errors occur within a concurrent task? How will you handle and recover from these errors?

Python provides various libraries and tools to help you address these concerns, such as queue, mutex, and Condition. With practice and experience, you'll develop the skills necessary to effectively utilize concurrency in your own applications.

Run multiple python scripts simultaneously windows

To run multiple Python scripts simultaneously on Windows, you can use the subprocess module to create new processes for each script. Here's an example of how you can do this:

Let's say you have three Python scripts: script1.py, script2.py, and script3.py. You want to run all three scripts concurrently.

First, make sure you have the correct Python interpreter version installed on your system. For example, if you're using Python 3.x, make sure it's installed as a standalone executable (e.g., C:Python39python.exe).

Next, create a new batch file (e.g., run_scripts.bat) containing the following code:

@echo off

:: Run script1.py in new process


start "" C:Python39python.exe C:Scriptsscript1.py


:: Run script2.py in new process


start "" C:Python39python.exe C:Scriptsscript2.py


:: Run script3.py in new process


start "" C:Python39python.exe C:Scriptsscript3.py

In this example, C:Scripts is the directory containing your Python scripts. Adjust the path as needed.

Save the batch file and then execute it (e.g., double-click the file or run it from the command prompt).

The start command runs each script in a new process, allowing them to run concurrently. You'll see three separate Command Prompt windows opening, each running one of your Python scripts.

Alternatively, you can use the multiprocessing module within your Python code to create and manage multiple processes. Here's an example:

import multiprocessing

def run_script(script_name):

Run the script in a new process

p = multiprocessing.Process(target=exec(open(script_name).read()))


p.start()

Run scripts concurrently

scripts_to_run = ['script1.py', 'script2.py', 'script3.py']


for script in scripts_to_run:


run_script(script)

This code defines a run_script function that takes the name of a Python script as input, runs it in a new process using the Process class from the multiprocessing module, and starts the process.

You can then call this function for each script you want to run concurrently. This approach allows you to manage multiple processes within your Python code, which can be useful if you need to perform some sort of synchronization or coordination between processes.

Remember that when running multiple Python scripts simultaneously, there's always a risk of resource competition and potential conflicts. Make sure you understand the implications of running multiple scripts at once and take steps to mitigate any potential issues.

For example, if your scripts are reading from or writing to shared files or databases, you'll need to ensure that they don't interfere with each other. You may also want to consider implementing some form of process synchronization to prevent conflicts or deadlocks.

Overall, running multiple Python scripts simultaneously on Windows can be a powerful tool for automating tasks and streamlining your workflow. Just remember to plan carefully and anticipate potential issues to get the most out of this approach.

Related Articles