theskumar / theskumar/python-dotenv

Feature request: custom merge logic or variables

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Beschreibung

Hello, this is a feature request for enabling custom merge logic when the environment variables are updated with values read from .env file.

My use case is that I would like to append an element to a colon-separated list of paths. Typical colon-separated environment variables are PATH or LD_LIBRARY_PATH. So the reading of the .env file should neither override, nor leave the original environment variables, instead it should extend the list.

Currently I use the following pseudo-code for this:

cfg = dotenv_values()
path_elems = []
if 'LD_LIBRARY_PATH' in os.environ:
    path_elems.extend(os.environ['LD_LIBRARY_PATH'].split(':'))
if 'LD_LIBRARY_PATH' in cfg:
    path_elems.append(cfg['LD_LIBRARY_PATH'])
path_elems = list(OrderedDict.fromkeys(path_elems)) # remove duplicates
path = ':'.join(path_elems)
path = path.replace('::', ':') # remove empty elements
os.environ['LD_LIBRARY_PATH'] = path

I think such logic could be integrated into the dotenv library. This would require changing the API: instead of / apart the override parameter the dotenv methods could take a variable name -> callable dictionary, where the callable would take as parameters the current and the new value and handle the merge logic for the specified variable. Senseful default behavior could be configured to simulate the current override/not override functionality.

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Rechercherichtung

Start with the dotenv methods that accept the existing override parameter and trace how values from .env are applied to the environment. Define how a variable-to-callable API would coexist with current override and non-override behavior, using the LD_LIBRARY_PATH example as the target case. Done means custom merge logic can extend and normalize a colon-separated value without changing existing defaults.

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Bewertung

Tech-Stack
python
Bereich
devops, tooling
Issue-Typ
Feature
Schwierigkeit
5/5
Geschätzter Aufwand
Über eine Woche
Aktivitätsstatus
Veraltet
Klarheit
Größtenteils klar
Anfängerfreundlichkeit
35/100

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