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From "Dmitry Demeshchuk (JIRA)" <j...@apache.org>
Subject [jira] [Created] (BEAM-2572) Implement an S3 filesystem for Python SDK
Date Fri, 07 Jul 2017 21:06:00 GMT
Dmitry Demeshchuk created BEAM-2572:
---------------------------------------

             Summary: Implement an S3 filesystem for Python SDK
                 Key: BEAM-2572
                 URL: https://issues.apache.org/jira/browse/BEAM-2572
             Project: Beam
          Issue Type: Task
          Components: sdk-py
            Reporter: Dmitry Demeshchuk
            Assignee: Ahmet Altay
            Priority: Minor


There are two paths worth exploring, to my understanding:

1. Sticking to the HDFS-based approach (like it's done in Java).
2. Using boto/boto3 for accessing S3 through its common API endpoints.

I personally prefer the second approach, for a few reasons:

1. In real life, HDFS and S3 have different consistency guarantees, therefore their behaviors
may contradict each other in some edge cases (say, we write something to S3, but it's not
immediately accessible for reading from another end).

2. There are other AWS-based sources and sinks we may want to create in the future: DynamoDB,
Kinesis, SQS, etc.

3. boto3 already provides somewhat good logic for basic things like reattempting.

Whatever path we choose, there's another problem related to this: we currently cannot pass
any global settings (say, pipeline options, or just an arbitrary kwarg) to a filesystem. Because
of that, we'd have to setup the runner nodes to have AWS keys set up in the environment, which
is not trivial to achieve and doesn't look too clean either (I'd rather see one single place
for configuring the runner options).

Also, it's worth mentioning that I already have a janky S3 filesystem implementation that
only supports DirectRunner at the moment (because of the previous paragraph). I'm perfectly
fine finishing it myself, with some guidance from the maintainers.

Where should I move on from here, and whose input should I be looking for?

Thanks!



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