suby 0.0.3

Slightly simplified subprocesses

Here is a small wrapper around the subprocesses. You can find many similar wrappers, but this particular one differs from the others in the following parameters:

• Beautiful minimalistic call syntax.
• Ability to specify your callbacks to catch stdout and stderr.
• Support for cancellation tokens.
• You can set timeouts for subprocesses.
• Logging of command execution.

Table of contents

• Quick start
• Run subprocess and look at the result
• Output
• Logging
• Exceptions
• Working with Cancellation Tokens
• Timeouts

Quick start

Install it:

pip install suby

And use:

import suby

suby('python', '-c', 'print("hello, world!")')
# > hello, world!

Run subprocess and look at the result

The suby module is a callable object and can be imported like this:

import suby

If you use static type checking and get an error that it is impossible to call the module, use a more detailed import form - functionally, these two import ways are identical:

from suby import suby

Let's try to call suby. You can use strings or pathlib.Path objects as positional arguments, but now we call it with only simple strings:

result = suby('python', '-c', 'print("hello, world!")')
print(result)
# > SubprocessResult(id='e9f2d29acb4011ee8957320319d7541c', stdout='hello, world!\n', stderr='', returncode=0, killed_by_token=False)

We can see that it returns an object of the SubprocessResult class. It contains the following required fields:

• id - a unique string that allows you to distinguish one result of calling the same command from another.
• stdout - a string containing the entire buffered output of the command being run.
• stderr - a string containing the entire buffered stderr of the command being run.
• returncode - an integer indicating the return code of the subprocess. 0 means that the process was completed successfully, the other options usually indicate something bad.
• killed_by_token - a boolean flag indicating whether the subprocess was killed due to token cancellation.

Output

By default, the stdout and stderr of the subprocess are forwarded to the stdout and stderr of the current process. The reading from the subprocess is continuous, and the output is every time a full line is read. For continuous reading from stderr, a separate thread is created in the main process, so that stdout and stderr are read independently.

You can override the output functions for stdout and stderr. To do this, you need to pass as arguments stdout_callback and stderr_callback, respectively, some functions that accept a string as an argument. For example, you can color the output (the code example uses the termcolor library):

import suby
from termcolor import colored

def my_new_stdout(string: str) -> None:
    print(colored(string, 'red'), end='')

suby('python', '-c', 'print("hello, world!")', stdout_callback=my_new_stdout)
# > hello, world!
# You can't see it here, but believe me, if you repeat the code at home, the output in the console will be red!

You can also completely disable the output by passing True as the catch_output parameter:

suby('python', '-c', 'print("hello, world!")', catch_output=True)
# There's nothing here.

If you specify catch_output=True, and at the same time redefine your functions for output, your functions will not be called either. In addition, suby always returns the result of executing the command, containing the full output. The catch_output argument can stop exactly the output, but it does not prevent the collection and buffering of the output.

Logging

By default, suby does not log command execution. However, you can pass a logger object to the function, and in this case logs will be recorded at the start of the command execution and at the end of the execution:

import logging
import suby

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.StreamHandler(),
    ]
)

suby('python', '-c', 'pass', logger=logging.getLogger('logger_name'))
# > 2024-02-22 02:15:08,155 [INFO] The beginning of the execution of the command "python -c pass".
# > 2024-02-22 02:15:08,190 [INFO] The command "python -c pass" has been successfully executed.

The message about the start of the command execution is always done with the INFO level. If the command is completed successfully, the end message will also be with the INFO level. And if not - ERROR:

suby('python', '-c', 'raise ValueError', logger=logging.getLogger('logger_name'), catch_exceptions=True, catch_output=True)
# > 2024-02-22 02:20:25,549 [INFO] The beginning of the execution of the command "python -c "raise ValueError"".
# > 2024-02-22 02:20:25,590 [ERROR] Error when executing the command "python -c "raise ValueError"".

If you don't need these details, just don't pass the logger object.

If you still prefer logging, you can use any object that implements the logger protocol from the emptylog library, including ones from third-party libraries.

Exceptions

By default, suby raises exceptions in three cases:

1. If the command you are calling ended with a return code not equal to 0. In this case, you will see an exception suby.RunningCommandError:

import suby

try:
    suby('python', '-c', '1/0')
except suby.RunningCommandError as e:
    print(e)
    # > Error when executing the command "python -c 1/0".

2. If you passed a cancellation token when calling the command, and the token was canceled, an exception will be raised corresponding to the type of canceled token. This part of the functionality is integrated with the cantok library, so we recommend that you familiarize yourself with it beforehand.

3. You have set a timeout for the operation and it has expired.

You can prevent suby from raising any exceptions. To do this, set the catch_exceptions parameter to True:

result = suby('python', '-c', 'import time; time.sleep(10_000)', timeout=1, catch_exceptions=True)
print(result)
# > SubprocessResult(id='c9125b90d03111ee9660320319d7541c', stdout='', stderr='', returncode=-9, killed_by_token=True)

Keep in mind that the full result of the subprocess call can also be found through the result attribute of any raised exception:

try:
    suby('python', '-c', 'import time; time.sleep(10_000)', timeout=1)
except suby.TimeoutCancellationError as e:
    print(e.result)
    # > SubprocessResult(id='a80dc26cd03211eea347320319d7541c', stdout='', stderr='', returncode=-9, killed_by_token=True)

Working with Cancellation Tokens

suby is fully compatible with the cancellation token pattern and supports any token objects from the cantok library.

The essence of the pattern is that you can pass an object to suby, from which it can find out whether the operation still needs to be continued or not. If not, it kills the subprocess. This pattern can be especially useful in the case of commands that are executed for a long time or for an unpredictably long time. When the result becomes unnecessary, there is no point in sitting and waiting for the command to work out.

So, you can pass your cancellation tokens to suby. By default, canceling a token causes an exception to be raised:

from random import randint
import suby
from cantok import ConditionToken

token = ConditionToken(lambda: randint(1, 1000) == 7)  # This token will be cancelled when a random unlikely event occurs.
suby('python', '-c', 'import time; time.sleep(10_000)', token=token)
# > cantok.errors.ConditionCancellationError: The cancellation condition was satisfied.

However, if you pass the catch_exceptions=True argument, the exception will not be raised. Instead, you will get the usual result of calling suby with the killed_by_token=True flag:

token = ConditionToken(lambda: randint(1, 1000) == 7)
print(suby('python', '-c', 'import time; time.sleep(10_000)', token=token, catch_exceptions=True))
# > SubprocessResult(id='e92ccd54d24b11ee8376320319d7541c', stdout='', stderr='', returncode=-9, killed_by_token=True)

"Under the hood" a separate thread is created to track the status of the token. When the token is canceled, the thread kills the subprocess.

Timeouts

You can set a timeout for suby. It must be an integer greater than zero, which indicates the number of seconds that the subprocess can continue to run. If the timeout expires before the subprocess completes, an exception will be raised:

import suby

suby('python', '-c', 'import time; time.sleep(10_000)', timeout=1)
# > cantok.errors.TimeoutCancellationError: The timeout of 1 seconds has expired.

To count the timeout, "under the hood" suby uses TimeoutToken from the cantok library.

The exception corresponding to this token was be reimported to suby:

try:
    suby('python', '-c', 'import time; time.sleep(10_000)', timeout=1)
except suby.TimeoutCancellationError as e:  # As you can see, TimeoutCancellationError is available in the suby module.
    print(e)
    # > The timeout of 1 seconds has expired.

Just as with regular cancellation tokens, you can prevent exceptions from being raised using the catch_exceptions=True argument:

print(suby('python', '-c', 'import time; time.sleep(10_000)', timeout=1, catch_exceptions=True))
# > SubprocessResult(id='ea88c518d25011eeb25e320319d7541c', stdout='', stderr='', returncode=-9, killed_by_token=True)