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https://issues.apache.org/jira/browse/BEAM-3106?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16341912#comment-16341912
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RK commented on BEAM-3106:
--------------------------
This can result in some difficult-to-pin-down errors in the
google-cloud-platform python libraries. For example, on a clean virtualenv:
{code:java}
pip install google-cloud-storage
{code}
Now:
{code:java}
from google.cloud.storage import Client
print(Client().bucket("gcp-public-data-landsat")\
.blob("LE00/PRE/001/049/LE07_L1TP_001049_20160215_20161015_01_T1/LE07_L1TP_001049_20160215_20161015_01_T1_ANG.txt")\
.download_as_string())
{code}
Works as expected, but after installing apache_beam[gcp]
{code:java}
pip install apache_beam[gcp]
{code}
{code:java}
# same code as above
from google.cloud.storage import Client
print(Client().bucket("gcp-public-data-landsat")\
.blob("LE00/PRE/001/049/LE07_L1TP_001049_20160215_20161015_01_T1/LE07_L1TP_001049_20160215_20161015_01_T1_ANG.txt")\
.download_as_string())
# File
"/Users/karbr001/Documents/tmp_gcs_test/env/lib/python2.7/site-packages/google_auth_httplib2.py",
line 198, in request
# uri, method, body=body, headers=request_headers, **kwargs)
# TypeError: request() got an unexpected keyword argument 'data'
{code}
[~altay] what's the temporary relief you mentioned? Is it just installing beam
with a custom setup.py file that points bigquery 0.28?
> 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
> Priority: Major
>
> 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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