June 17, 2020 3 min to read

Logging like a Lumberjack

Logging like a Lumberjack

Standard Library Logging Tutorial

Logging is standard for any service based project where you would like to know what happened before an issue occurred. Theoretically of course, as most projects are scant with tools to debug or documentation.

Python’s logging is almost entirely done through it’s standard library’s logging module, unlike Java’s variety of different logging frameworks. This is due to Python’s logging simplicity and most of the functionality modified strictly through configuration.

Basic Configuration

Each language has a unique setup for logging, with Python using dictionary / file based configuration.

Logging has a default level, which decides what is written.

By default Python logs to the console, with the default level WARNING, indicating it will log anything of WARNING severity or higher. This can be modified to log more information to the console, or to files.

To Console

import logging
logging.warning('Watch out!')  # will print a message to the console
logging.info('I told you so')  # will not print anything

This logs messages of the default logging level and above to console.

To File

import logging
logging.basicConfig(filename='example.log',level=logging.DEBUG)
logging.debug('This message should go to the log file')
logging.info('So should this')
logging.warning('And this, too')

This logs these messages to the example.log file

Levels

Levels are listening in ascending order to severity, with the designated level configuring the logger to write all higher severity messages.

Advanced Configuration

There’s a lot of advanced configuration than can be covered, but I’ll only focus on the configuration file.

The rest can be found in Advanced Logging Tutorial portion of Python.

Logger

Module level loggers are useful for tracking which module wrote which output. They can be maintained as singletons allowing one time setups.

logger = logging.getLogger(__name__)

They also allow setting Handlers and Filters → useful for advanced formatting in logging.

Configuration File

Configuration files can be in several formats: conf, json, yaml, etc.

Through conf files, you modify a Logger object through using the fileConfig method. This is a specific shell-like configuration format which allows setting values.

Similarly, JSON and YAML files can be used, but must be passed in through the dictConfig method. Data is passed after parsing a JSON or YAML file, then converting into a Python dictionary. This dictionary also has a specific schema

YAML Example:

version: 1
formatters:
  simple:
    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
handlers:
  console:
    class: logging.StreamHandler
    level: DEBUG
    formatter: simple
    stream: ext://sys.stdout
loggers:
  simpleExample:
    level: DEBUG
    handlers: [console]
    propagate: no
root:
  level: DEBUG
  handlers: [console]

Overall, configuration has many ways to set it up.

Alternatives

There are a few basic alternatives to logging, for specific use cases.

print: classic Python print method, for ordinary usage

warnings: For avoidable issues