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Allows to execute subprocess commands and get their output in real-time.

Project description

B.ContinuousSubprocess

A helper package that lets you execute long running processes and continuously receive their output.

Description

Imagine you want to programmatically run a command that lists objects in a given directory. The command subprocess.call('ls') will do the trick quite conveniently. However, the same approach to long running commands like cdk deploy * or pytest -s or ls / -R is not very convenient as you get the whole output only when the process finishes. This package solves this inconvenience by outputting data in real-time as the process runs.

Remarks

Biomapas aims to modernise life-science industry by sharing its IT knowledge with other companies and the community. This is an open source library intended to be used by anyone. Improvements and pull requests are welcome.

Related technology

  • Python 3
  • Python subprocess Popen
  • OS processes

Assumptions

The project assumes the following:

  • You have basic knowledge in python programming.
  • You have basic knowledge in OS processes.

Useful sources

Install

The project is built and uploaded to PyPi. Install it by using pip.

pip install b-continuous-subprocess

Or directly install it through source.

pip install .

Usage & Examples

The library is extremely easy to use. Simply create an instance of ContinuousSubprocess and execute it to get a generator. Then iterate through the generator to receive your process output in real time.

from b_continuous_subprocess.continuous_subprocess import ContinuousSubprocess

command = 'cdk deploy *'
generator = ContinuousSubprocess(command).execute()

for data in generator:
    print(data, end='')

Example how to handle errors:

import json
import subprocess

from b_continuous_subprocess.continuous_subprocess import ContinuousSubprocess

continuous_process = ContinuousSubprocess('cdk deploy *')
generator = continuous_process.execute()

try:
    for line in generator:
        print(line, end='')
except subprocess.CalledProcessError as ex:
    error_output = json.loads(ex.output)
    
    # Error message.
    message = error_output['message']
    # Stack trace.
    trace = error_output['trace']
    # The length of a stack trace (in lines).
    trace_size = error_output['trace_size']
    # The maximum possible (allowed) length of a stack trace.
    max_trace_size = error_output['max_trace_size']
    
    print(message)
    
    for line in trace:
        print(line, end='')

Testing

The project has tests that can be run. These are mostly simple tests that can be run out-of-the-box.

Run tests from a root directory with pytest python testing library:

pytest

The tests usually take less than a few seconds to complete.

Contribution

Found a bug? Want to add or suggest a new feature?
Contributions of any kind are gladly welcome. You may contact us directly, create a pull-request or an issue in github platform. Lets modernize the world together.

Release history

0.4.0

  • Remove unnecessary testing dependencies.
  • Improve CI/CD pipelines.

0.3.2

  • More logging.
  • More debugging.
  • Fix an actual bug where the std streams que might be empty.

0.3.1

  • Cleanup process.

0.3.0

  • Add ability to control stack trace size.

0.2.1

  • Deleted all .idea files from the project.

0.2.0

  • Add stack trace when the error has happened.

0.1.2

  • Rethink the way stdout and stderr are yielded.

0.1.1

  • Pipe errors too.

0.1.0

  • Add ability to kill a process.

0.0.3

  • Start using Pylint.
  • Start using Black.

0.0.2

  • Finalize pipelines.
  • Finalize project structure and naming.

0.0.1

  • Initial build.

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