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https://issues.apache.org/jira/browse/BEAM-2573?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16080867#comment-16080867
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Dmitry Demeshchuk commented on BEAM-2573:
-----------------------------------------
So, I tried to switch to the head version of the SDK and use the
{{beam_plugins}} option.
Here are the steps I performed:
1. Did {{python setup.py sdist}} in the master branch of Beam. Also, did
{{python setup.py install}}.
2. Created the following dictionary in Python (note the {{beam_plugins}} option
present):
{code}
{'job_name': 's3-wordcount-example-2', 'staging_location':
'gs://dataflow-test-gc-project-168517/s3-wordcount-example-2/staging_location',
'runner': 'dataflow', 'streaming': False, 'runtime_type_check': False,
'temp_location':
'gs://dataflow-test-gc-project-168517/s3-wordcount-example-2/temporary_location',
'setup_file': '/tmp/tmpEdRIo2/setup.py', 'dataflow_endpoint':
'https://dataflow.googleapis.com', 'sdk_location':
'/Users/dmitrydemeshchuk/beam/sdks/python/dist/apache-beam-2.2.0.dev0.tar.gz',
'save_main_session': True, 'zone': 'us-west1-a', 'region': 'us-west1',
'profile_cpu': False, 'bucket': 'gs://dataflow-test-gc-project-168517',
'profile_memory': False, 'pipeline_type_check': True, 'project':
'test-gc-project-168517', 'direct_runner_use_stacked_bundle': True,
'type_check_strictness': 'DEFAULT_TO_ANY', 'beam_plugins': ' dataflow',
'no_auth': False}
{code}
3. Created a {{PipelineOptions}} object and used it inside a pipeline:
{{options = PipelineOptions.from_dictionary(options_dict)}}
4. Ran the pipeline in Dataflow.
Now, in the Dataflow UI I'm seeing some of the pipeline options (for example,
{{sdk_location}} is correct), however I'm not seeing {{beam_plugins}} anywhere.
FWIW, the job's "Dataflow SDK version" equals to "Google Cloud Dataflow SDK for
Python 2.0.0", but {{sdk_location}} equals to
{{/Users/dmitrydemeshchuk/beam/sdks/python/dist/apache-beam-2.2.0.dev0.tar.gz}}
(note the 2.2.0 version).
Needless to say, the `beam_plugins` option doesn't seem to get applied to my
pipeline, at least it fails as if the plugin wasn't imported.
I'm almost sure this has something to do with the Dataflow SDK version, but so
far cannot find a way to make it right. Any suggestions?
> Better filesystem discovery mechanism in Python SDK
> ---------------------------------------------------
>
> Key: BEAM-2573
> URL: https://issues.apache.org/jira/browse/BEAM-2573
> Project: Beam
> Issue Type: Task
> Components: runner-dataflow, sdk-py
> Affects Versions: 2.0.0
> Reporter: Dmitry Demeshchuk
> Priority: Minor
>
> It looks like right now custom filesystem classes have to be imported
> explicitly:
> https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/filesystems.py#L30
> Seems like the current implementation doesn't allow discovering filesystems
> that come from side packages, not from apache_beam itself. Even if I put a
> custom FileSystem-inheriting class into a package and explicitly import it in
> the root __init__.py of that package, it still doesn't make the class
> discoverable.
> The problems I'm experiencing happen on Dataflow runner, while Direct runner
> works just fine. Here's an example of Dataflow output:
> {code}
> (320418708fe777d7): Traceback (most recent call last):
> File
> "/usr/local/lib/python2.7/dist-packages/dataflow_worker/batchworker.py", line
> 581, in do_work
> work_executor.execute()
> File "/usr/local/lib/python2.7/dist-packages/dataflow_worker/executor.py",
> line 166, in execute
> op.start()
> File
> "/usr/local/lib/python2.7/dist-packages/dataflow_worker/native_operations.py",
> line 54, in start
> self.output(windowed_value)
> File "dataflow_worker/operations.py", line 138, in
> dataflow_worker.operations.Operation.output
> (dataflow_worker/operations.c:5768)
> def output(self, windowed_value, output_index=0):
> File "dataflow_worker/operations.py", line 139, in
> dataflow_worker.operations.Operation.output
> (dataflow_worker/operations.c:5654)
> cython.cast(Receiver,
> self.receivers[output_index]).receive(windowed_value)
> File "dataflow_worker/operations.py", line 72, in
> dataflow_worker.operations.ConsumerSet.receive
> (dataflow_worker/operations.c:3506)
> cython.cast(Operation, consumer).process(windowed_value)
> File "dataflow_worker/operations.py", line 328, in
> dataflow_worker.operations.DoOperation.process
> (dataflow_worker/operations.c:11162)
> with self.scoped_process_state:
> File "dataflow_worker/operations.py", line 329, in
> dataflow_worker.operations.DoOperation.process
> (dataflow_worker/operations.c:11116)
> self.dofn_receiver.receive(o)
> File "apache_beam/runners/common.py", line 382, in
> apache_beam.runners.common.DoFnRunner.receive
> (apache_beam/runners/common.c:10156)
> self.process(windowed_value)
> File "apache_beam/runners/common.py", line 390, in
> apache_beam.runners.common.DoFnRunner.process
> (apache_beam/runners/common.c:10458)
> self._reraise_augmented(exn)
> File "apache_beam/runners/common.py", line 431, in
> apache_beam.runners.common.DoFnRunner._reraise_augmented
> (apache_beam/runners/common.c:11673)
> raise new_exn, None, original_traceback
> File "apache_beam/runners/common.py", line 388, in
> apache_beam.runners.common.DoFnRunner.process
> (apache_beam/runners/common.c:10371)
> self.do_fn_invoker.invoke_process(windowed_value)
> File "apache_beam/runners/common.py", line 281, in
> apache_beam.runners.common.PerWindowInvoker.invoke_process
> (apache_beam/runners/common.c:8626)
> self._invoke_per_window(windowed_value)
> File "apache_beam/runners/common.py", line 307, in
> apache_beam.runners.common.PerWindowInvoker._invoke_per_window
> (apache_beam/runners/common.c:9302)
> windowed_value, self.process_method(*args_for_process))
> File
> "/Users/dmitrydemeshchuk/miniconda2/lib/python2.7/site-packages/apache_beam/transforms/core.py",
> line 749, in <lambda>
> File
> "/Users/dmitrydemeshchuk/miniconda2/lib/python2.7/site-packages/apache_beam/io/iobase.py",
> line 891, in <lambda>
> File
> "/usr/local/lib/python2.7/dist-packages/apache_beam/options/value_provider.py",
> line 109, in _f
> return fnc(self, *args, **kwargs)
> File
> "/usr/local/lib/python2.7/dist-packages/apache_beam/io/filebasedsink.py",
> line 146, in initialize_write
> tmp_dir = self._create_temp_dir(file_path_prefix)
> File
> "/usr/local/lib/python2.7/dist-packages/apache_beam/io/filebasedsink.py",
> line 151, in _create_temp_dir
> base_path, last_component = FileSystems.split(file_path_prefix)
> File
> "/usr/local/lib/python2.7/dist-packages/apache_beam/io/filesystems.py", line
> 99, in split
> filesystem = FileSystems.get_filesystem(path)
> File
> "/usr/local/lib/python2.7/dist-packages/apache_beam/io/filesystems.py", line
> 61, in get_filesystem
> raise ValueError('Unable to get the Filesystem for path %s' % path)
> ValueError: Unable to get the Filesystem for path
> s3://my-test-bucket/test_output [while running
> 'write/Write/WriteImpl/InitializeWrite']
> {code}
> I apologize for not providing full context or codebase, because a lot of the
> code we are running is internal, and some of it is tightly coupled to our
> infrastructure. If I run out of experimenting options, I'll try to narrow my
> use case down to the simplest case possible (like, override a gcs filesystem
> with a different path prefix or something).
> I think there are several possibilities here:
> 1. I'm doing something wrong, and it should be trivial to achieve something
> like that. This probably implies figuring out the right approach and writing
> some guideline for the sources/sinks page in the docs.
> 2. The current order of imports is not optimal, and we could possibly import
> the side packages before initializing the filesystem classes. I currently
> possess too little knowledge about the way things get executed in Dataflow,
> so it's hard for me to tell how much it's worth diving that rabbithole.
> 3. There just needs to be a better way of referring to additional filesystem
> classes. One way of doing that is to just specify a class name explicitly
> inside the ReadFromText and WriteToText functions (or something along these
> lines). PipelineOptions seems like an overkill for this, but may still be an
> option. Finally, maybe there could be just a function that gets called in the
> main script that somehow tells Beam to discover a specific class?
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