minibone 0.7.1

Small boiler plate with tools for multithreading.

minibone

minibone is an easy to use yet powerful boiler plate for multithreading, multiprocessing and others:

• Config: To handle configuration settings
• Daemon: To run a periodical task in another thread
• HTMLBase: To render html using snippets and toml configuration file in async mode
• Logging: To setup a logger friendly with filerotation
• PARProcesses: To run parallel tasks as processes
• PARThreads: To run parallel tasks in threads
• Storing: To queue and store files periodically in a thread (queue and forget)
• Among others (I will add more later)

It will be deployed to PyPi when a new release is created

Installation

pip install minibone

Config

Handle configuration settings in memory and/or persist them into toml/yaml/json formats

from minibone.config import Config

# Create a new set of settings and persist them
cfg = Config(settings={"listen": "localhost", "port": 80}, filepath="config.toml")	
cfg.add("debug", True)	
cfg.to_toml()

# Load settings from a file. Defaults can be set. More information: help(Config.from_toml)
cfg2 = Config.from_toml("config.toml")

# also there are async counter part methods
import asyncio

cfg3 = asyncio.run(Config.aiofrom_toml("config.toml"))

Usually config files are editted externaly then loaded as read only on your code, so in such case, you may want to subclass Config for easier usage

from minibone.config import Config

class MyConfig(Config):

    def __init__(self):
        defaults = {"main": {"listen": "localhost", "port": 80}}
        settings = Config.from_toml(filepath="config.toml", defaults=defaults)
        super().__init__(settings=settings)

    def listen(self) -> str:
        return self["main"]["listen"]

    def port(self) -> int:
        return self["main"]["port"]

if __name__ == "__main__":
    cfg = MyConfig()
    print(cfg.port())
    # it will print the default port value if not port setting was defined in config.toml

Daemon

It is just another python class to run a periodical task in another thread. It can be used in two modes: subclasing and callback

Usage as SubClass mode

• Subclass Daemon
• call super().init() in yours
• Overwrite on_process method with yours
• Add logic you want to run inside on_process
• Be sure your methods are safe-thread to avoid race condition
• self.lock is available for lock.acquire / your_logic / lock.release
• call start() method to keep running on_process in a new thread
• call stop() to finish the thread

Check sample_clock.py for a sample

Usage as callback mode

• Instance Daemon by passing a callable
• Add logic to your callable method
• Be sure your callable is safe-thread to avoid race condition
• call start() method to keep running callable in a new thread
• call stop() to finish the thread

Check sample_clock_callback.py for a sample

Logging

Setup a logger using UTC time that outputs logs to stdin or to a file. It is friendly to filerotation (when setting output to a file)

import logging

from minibone.logging import setup_log

if __name__ == "__main__":

    # setup_log must be called only once in your code.
    # So you have to choice if logging to stderr or to a file when calling it

    setup_log(level="INFO")
    logging.info('This is a log to the stderr')

    # or call the next lines instead if you want to log into a file
    # setup_log(file="sample.log", level="INFO")
    # logging.info('yay!')

Contribution

• Feel free to clone this repository and send any pull requests.
• Add issues if something is not working as expected.