Constantly print Subprocess output while process is running

Efficient real-time monitoring with iter and subprocess.Popen

iter and subprocess.Popen embark on this readline adventure hand-in-hand - their combined efforts spell an efficient and buffered approach aimed at capturing the output instantly. A bufsize=1 command launches the line-buffering magic, making your output available line by line. No, it's not procrastinating until the entire output is ready.

Now, not to knock print(), but sys.stdout.write() gives us weirdly specific control over our output formatting, and dare I say, it's pretty cool. flush() makes sure we give stdout a hearty hi-five with sys.stdout.flush(), making the output side-show start as soon as it's written. Line buffering, anyone?

We are no fans of silence, and we sure don't want to ignore failed subprocesses, so we best raise a subprocess.CalledProcessError for non-zero return codes. One more thing, respect your resources, and clean up post-process execution. Or, you risk tripping over memory leaks and resource issues.

The beauty of advanced line-by-line output processing

Capturing stdout is an art - if the basic line-by-line methodology seems too dull, task-specific logic might add color to your script. From parsing and logging to custom formatting your output - handling each line as it's read equates to a bespoke approach for your unique process.

Rolling with long processes? How about introducing threads to your script? The matchless threading Python library gifts your subprocess output processing to another thread, so your main program can take a breather. It's all about balance.

So the subprocess is completed, what about the lingering output? communicate() is your friend - it will catch any remaining output and flush buffers. Also, closing file descriptors is as cool as a sea breeze on a hot day. And if you crave a succinct tag team of standard output and error - stderr=subprocess.STDOUT will serve you a combined output treat.

Disclaimer! shell=True might look inviting, but it's a shady alley better left unexplored. Instead, present your command and arguments as a list to subprocess.Popen. For the love of simple use cases or if you ride the Python 3.5 wave, subprocess.run or subprocess.check_call might just be your cup of Python.

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Now apply that same principle to your Python **subprocess output**:

```python
process = subprocess.Popen(['your_command'], stdout=subprocess.PIPE, text=True)

for line in iter(process.stdout.readline, ''):
    print("🏃‍♂️💨 ->", line, end='')

Live Feedback: You're not waiting for the race to end; you're tracking their progress as it happens!

Avoiding command blunders

Sometimes things don't go according to the script. Here's how to face some common hurdles:

• Has your subprocess fallen into a black hole? Use timeouts and process.kill() or process.terminate() to retrieve it.
• Orphaned processes remind us of sad puppy eyes. Don't be cruel, clean up using atexit or signal handlers.
• Buffering issues messing with your vibe? sys.stdout to the rescue - write directly and flush regularly.
• Lost in translation? Set errors='replace' or tackle them in your iter loop to address unexpected characters.

Best practices for subprocess management

Applying use case scenarios

The subprocess journey varies as per your needs:

• Asynchronous tasks? asyncio with subprocess makes for a delightful dance.
• Unpredictable output? An output loop parser ensures no data catches you off guard.
• Expecting resource-heavy processes? Set limitations for a smooth run using a resource library.

Seamless integration with applications

Integration isn't just a math term. It can help align your subprocess output monitoring with your Python ecosystem:

• Logging frameworks: Use the power of your application's logging system to record subprocess outputs.
• Data streaming: Turn the output into a data stream fit for other application parts.
• Output redirection: Voyeur much? Send your output to a file or network socket for a bird's-eye view.