How to Execute Non-Blocking Subprocesses in Python?

Non-Blocking Subprocess Invocation

When executing external scripts using subprocess.call, maintaining a non-blocking workflow is essential to avoid stalling the main program. This article presents a comprehensive solution to achieve this objective.

Approach using subprocess.Popen

The primary method to execute a non-blocking subprocess is to employ subprocess.Popen instead of subprocess.call. This alternative doesn't block the main program, allowing it to continue its operations while the subprocess runs independently. Here's an example:

<code class="python">subprocess.Popen(["python", "slave.py"] + sys.argv[1:])</code>

Comprehensive Example

For a complete demonstration of non-blocking subprocess calls, consider the following code:

<code class="python">import subprocess
import time

p = subprocess.Popen(['sleep', '5'])

while p.poll() is None:
    print('Still sleeping')
    time.sleep(1)

print('Not sleeping any longer.  Exited with returncode %d' % p.returncode)</code>

This code executes the 'sleep' command asynchronously, periodically checking its status until it completes.

Alternative Asynchronous Approach

For Python versions 3.5 and above, a more modern and efficient approach involves using asyncio. It allows for true concurrency, enabling multiple tasks to execute simultaneously. Here's an example:

<code class="python">import asyncio

async def do_subprocess():
    print('Subprocess sleeping')
    proc = await asyncio.create_subprocess_exec('sleep', '5')
    returncode = await proc.wait()
    print('Subprocess done sleeping.  Return code = %d' % returncode)

async def sleep_report(number):
    for i in range(number + 1):
        print('Slept for %d seconds' % i)
        await asyncio.sleep(1)

loop = asyncio.get_event_loop()

tasks = [
    asyncio.ensure_future(do_subprocess()),
    asyncio.ensure_future(sleep_report(5)),
]

loop.run_until_complete(asyncio.gather(*tasks))
loop.close()</code>

This approach ensures that both the subprocess and the main program run concurrently, maximizing performance and responsiveness.

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