biodome

Controlled environments

Reading environment variables with os.environ is pretty easy, but after a while one gets pretty tired of having to cast variables to the right types and handling fallback to defaults.

This library provides a clean way read environment variables and fall back to defaults in a sane way.

How you were doing it:

import os

try:
    TIMEOUT = int(os.environ.get('TIMEOUT', 10))
except ValueError:
    TIMEOUT = 10

Wordy, boilerplate, DRY violation, etc.

How you will be doing it:

import biodome

TIMEOUT = biodome.environ.get('TIMEOUT', 10)

That's right, it becomes a single line. But there's a magic trick here: how does biodome know that TIMEOUT should be set to an int? It knows because it looks at the type of the default arguments. This works for a bunch of different things:

# Lists
os.environ['IGNORE_KEYS'] = '[1, 2, 3]'
biodome.environ.get('TIMEOUT', []) == [1, 2, 3]

# Dicts
os.environ['SETTINGS'] = '{"a": 1, "b": 2}'
biodome.environ.get('SETTINGS', {}) == dict(a=1, b=2)

If you look carefully at the above, you can see that we set the data via the stdlib os.environ dictionary; that's right, biodome.environ is a drop-in replacement for os.environ. You don't even have to switch out your entire codebase, you can do it piece by piece.

And while we're on the subject of setting env vars, with biodome you don't have to cast them first, it does string casting internally automatically, unlike os.environ:

# Dicts
biodome.environ['SETTINGS'] = dict(b=2, a=1)  # No cast required
biodome.environ.get('SETTINGS', {}) == dict(a=1, b=2)

Loading env files

biodome also provides a function to load a file which specifies the values of environment variables. An example of such an env file:

# myconfig.env
# This sets the log level for all the loggers in the program
LOGGER_LEVEL=info

# Hourly backups are stored at this path and named with a timestamp.
BACKUP_PATH=/data/backups/

# The number of times to retry outgoing HTTP requests if the status
# code is > 500
RETRY_TIME=5

The name of the environment variable must be on the left and the value on the right. Each variable must be on its own line. Lines starting with a # are considered comments and are ignored.

This env file can be loaded like this:

>>> import biodome
>>> biodome.load_env_file('myconfig.env')
>>> print(biodome.environ['RETRY_TIME'])
5

True and False

I don't know about you, but I use bool settings a LOT in environment variables, so handling this properly is really important to me. When calling biodome.environ.get('SETTING', default=<value>), the default value can also be a bool, i.e., True or False. In this case, any of the following values, and their upper- or mixed-case equivalents will be recognized as True:

['1', 'y', 'yes', 'on', 'active', 'activated', 'enabled', 'true',
't', 'ok', 'yeah']

Anything not in this list will be considered as False. Do you have ideas for more things that should be considered as True? I take PRs!

Callables

For explictness it is often convenient to declare and load environment variables at the top of the module in which they're used:

""" My new module """
import biodome

ENABLE_SETTING_XYZ = biodome.environ.get('ENABLE_SETTING_XYZ', True)

def blah():
    print(ENABLE_SETTING_XYZ)

You could call environ.get() inside the functions and methods where it is used, but then you would lose the convenience of documenting all the available environment variables at the top of the module. As a solution to this problem, biodome provides a way to produce a callable for a particular setting. An extra advantage of doing this is that it becomes quite easy to make use of changes in environment variables on the fly. Here's the modified example:

""" My new module """
import biodome

ENABLE_SETTING_XYZ = biodome.environ.get_callable(
    # Same as before
    'ENABLE_SETTING_XYZ', True
    )

def blah():
    print(ENABLE_SETTING_XYZ())  # Now a callable!

How it works internally

The key theme here is that the type of the default value is used to determine how to cast the input value. This works for the following types:

• int
• float
• str
• list
• dict
• set (NOTE: only supported in Python 3+ due to ast.literal_eval())
• tuple

For the containers, we use ast.literal_eval() which is much safer than using eval() because code is not evaluated. Safety first! (thanks to @nickdirienzo for the tip)