https://img.shields.io/pypi/v/logquacious.svg https://travis-ci.com/tonysyu/logquacious.svg?branch=master Documentation Status https://codecov.io/gh/tonysyu/logquacious/branch/master/graph/badge.svg

Logquacious is a set of simple logging utilities to help you over-communicate. (Logorrhea would’ve been a good name, if it didn’t sound so terrible.)

Good application logging is easy to overlook, until you have to debug an error in production. Logquacious aims to make logging as easy as possible. You can find read more at the official ReadTheDocs documentation.

Quick start

To get started, first make sure logquacious is installed:

$ pip install logquacious

You’ll also need to set up logging for your application. For this example, we’ll use a really simple configuration:

import logging

logging.basicConfig(format='%(levelname)s: %(message)s',

Note that this simple configuration is used for demonstration purposes, only. See the Logging Cookbook in the official Python docs for examples of options used for real logging configuration.

The main interface to logquacious is the LogManager, which can be used for normal logging:

import logquacious

log = logquacious.LogManager(__name__)
log.debug('Nothing to see here.')

Due to our simplified logging format defined earlier, that would output:

DEBUG: Nothing to see here.

That isn’t a very interesting example. In addition to basic logging, LogManager has a context attribute for use as a context manager:

>>> with log.context.debug('greetings'):
...    print('Hello!')
DEBUG: Enter greetings
DEBUG: Exit greetings

The same attribute can be used as a decorator, as well:

def divide(numerator, denominator):
    if denominator == 0:
        log.warning('Attempted division by zero. Returning None')
        return None
    return numerator / denominator

>>> divide(1, 0)
INFO: Call `divide()`
WARNING: Attempted division by zero. Returning None
INFO: Return from `divide`

Even better, you can log input arguments as well:

@log.context.info(show_args=True, show_kwargs=True)
def greet(name, char='-'):
    msg = 'Hello, {name}'.format(name=name)
    print(char * len(msg))

>>> greet('Tony', char='~')
INFO: Call `greet('Tony', char='~')`
Hello, Tony
INFO: Return from `greet`

There’s also a special context manager for suppressing errors and logging:

with log.and_suppress(ValueError, msg="It's ok, mistakes happen"):
    raise ValueError('Test error')
[ERROR] It's ok, mistakes happen
Traceback (most recent call last):
  File "/Users/tyu/code/logquacious/logquacious/log_manager.py", line 103, in and_suppress
  File "scripts/example.py", line 26, in <module>
    raise ValueError('Test error')
ValueError: Test error

Note the traceback above is logged, not streamed to stderr.


The message templates used by LogManager.context can be configured to your liking by passing a context_templates argument to LogManager:

log = logquacious.LogManager(__name__, context_templates={
    'context.start': '=============== Enter {label} ===============',
    'context.finish': '=============== Exit {label} ===============',

with log.context.debug('greetings'):
[DEBUG] =============== Enter greetings ===============
[DEBUG] =============== Exit greetings ===============

The general format for context_templates keys is:


where square-brackets marks optional fields.

CONTEXT_TYPE can be any of the following:

  • function: Template used when called as a decorator.

  • context: Template used when called as a context manager.

LOG_LEVEL_NAME can be any of the following logging levels:


  • INFO




For example, consider the cascade graph for function.start.DEBUG, which looks like:

          /       \
start.DEBUG       function.start
          \       /

The cascade is performed using a breadth-first search. If function.start.DEBUG is not defined, check start.DEBUG then check function.start BEFORE checking start.

The default configuration is:

    'start': 'Enter {label}',
    'finish': 'Exit {label}',
    'function.start': 'Call `{label}({arguments})`',
    'function.finish': 'Return from `{label}`',

Note that custom configuration updates these defaults. For example, if you want to if you want to skip logging on exit for all context managers and decorators, you’ll have set both 'finish' and 'function.finish' to None or an empty string.

As you can see above, two template variables may be passed to the template string: label and arguments. When called as a context manager, the label is the first argument to the context manager and arguments is always empty. When called as a decorator, label is the function’s __name__ and arguments a string representing input arguments, if show_args or show_kwargs parameters are True.


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.