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From "Ahmet Altay (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (BEAM-3106) Consider not pinning all python dependencies, or moving them to requirements.txt
Date Sun, 29 Oct 2017 00:19:05 GMT

     [ https://issues.apache.org/jira/browse/BEAM-3106?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ahmet Altay reassigned BEAM-3106:
---------------------------------

    Assignee:     (was: Ahmet Altay)

> Consider not pinning all python dependencies, or moving them to requirements.txt
> --------------------------------------------------------------------------------
>
>                 Key: BEAM-3106
>                 URL: https://issues.apache.org/jira/browse/BEAM-3106
>             Project: Beam
>          Issue Type: Wish
>          Components: build-system
>    Affects Versions: 2.1.0
>         Environment: python
>            Reporter: Maximilian Roos
>
> Currently all python dependencies are [pinned or capped|https://github.com/apache/beam/blob/master/sdks/python/setup.py#L97]
> While there's a good argument for supplying a `requirements.txt` with well tested dependencies,
having them specified in `setup.py` forces them to an exact state on each install of Beam.
This makes using Beam in any environment with other libraries nigh on impossible. 
> This is particularly severe for the `gcp` dependencies, where we have libraries that
won't work with an older version (but Beam _does_ work with an newer version). We have to
do a bunch of gymnastics to get the correct versions installed because of this. Unfortunately,
airflow repeats this practice and conflicts on a number of dependencies, adding further complication
(but, again there is no real conflict).
> I haven't seen this practice outside of the Apache & Google ecosystem - for example
no libraries in numerical python do this. Here's a [discussion on SO|https://stackoverflow.com/questions/28509481/should-i-pin-my-python-dependencies-versions]



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