Introduction

A log is a log regardless of what kind of prefixes each log message contains and what format it ended up as, as long as it is human readable and provides useful information to the user.

Python logging module’s native ability to handle and process log messages is more than sufficient for any logging needs, and has always been suggested as the de-facto logging module to use.

Therefore, for all intends and purposes, users of pyATS infrastructure should always use just the native Python logging module as-is in their scripts and testcases.

# Example
# -------
#
#   import the logging module at the top of your script
#   setup the logger

import logging

# always use your module name as the logger name.
# this enables logger hierarchy
logger = logging.getLogger(__name__)

# use logger:
logger.info('an info message')
logger.error('an error message')
logger.debug('a debug message')

Users are expected to have a good understanding of how Python logging works. Documentation for Python logging module can be found at:

• https://docs.python.org/3.6/howto/logging.html#logging-advanced-tutorial

Important

do not attempt to read and understand the rest of the logging documentation without first reading and learning how python logging works.

Delving Deeper

Why should everyone use the Python logging module as is? Simple: it is highly configurable. Behind the scenes, pyATS infrastructure configures the logging behavior for the end-user, so that all scripts & libraries output logs in the exact same format: Cisco Log Format.

The remainder of this logging documention digs deeper into the details of how and where pyATS uses and configures python logging, what the actual Log Module offers, and how advanced power users can leverage them.