CODEWITHSUNDEEP
integration-testinggoogle-cloud-dataflowapache-beam
Idhem
Idhem

Reputation: 964

Google Cloud Dataflow: ModuleNotFoundError: No module named 'main' when running integration test

I have an apache beam pipeline that works fine in both local and cloud modes. However, I have an end to end integration tests that I'm running in every MR, and the IT is submitted to Dataflow.

This time, the IT is throwing the following error:

_import_module return __import__(import_name) ModuleNotFoundError: No module named 'main'

The stacktrace is not pointing at all to the place where the module is not recognised. Just the follwing:

job-v2-test-20-08160911-vs73-harness-drt8
      Root cause: Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/apache_beam/internal/dill_pickler.py", line 285, in loads
    return dill.loads(s)
  File "/usr/local/lib/python3.8/site-packages/dill/_dill.py", line 275, in loads
    return load(file, ignore, **kwds)
  File "/usr/local/lib/python3.8/site-packages/dill/_dill.py", line 270, in load
    return Unpickler(file, ignore=ignore, **kwds).load()
  File "/usr/local/lib/python3.8/site-packages/dill/_dill.py", line 472, in load
    obj = StockUnpickler.load(self)
  File "/usr/local/lib/python3.8/site-packages/dill/_dill.py", line 826, in _import_module
    return __import__(import_name)
ModuleNotFoundError: No module named 'main'

The main module I use only in the IT file, and it doesn't exist in any transformation of the pipeline. Also, when I run the IT, half of the pipeline transformation runs successfully until it hangs with the provided error

The IT code:

from main import run
import argparse

import unittest
import logging


class PipelineIT(unittest.TestCase):

    def setUp(self):

        self.test_pipeline = TestPipeline(is_integration_test=True)

        parser = argparse.ArgumentParser()
        self.args, self.beam_args = parser.parse_known_args()
        self.pipeline_options = PipelineOptions(self.beam_args)
        self.client = get_bq_instance()
        self.tables_timestamp = datetime.now().strftime("%Y%m%d%H%M")

    def test_mc_end_to_end(self):

        state_verifier = PipelineStateMatcher(PipelineState.DONE)
        extra_opts = {
            'input': IT_BUCKET,
            'output_dataset': IT_DATASET,
            'output': IT_OUTPUT,
            'bq_timestamp': self.tables_timestamp,
            'on_success_matcher':
                all_of(state_verifier)
        }

        run(self.test_pipeline.get_full_options_as_args(**extra_opts), save_main_session=True)

# buch of asserts

THe command I'm using to run the IT

coverage run -m  pytest --log-cli-level=INFO integration_tests/end_to_end_it_test.py --job_name "end_to_end_it" --test-pipeline-options=" --run_mode=cloud --mode=test --setup_file=path_to_setup.py"

The pipeline works fine in the production mode, but in the testing mode it shows that error. I'm just wondering if the main is used only to trigger the integration test from local, how can it breaks the pipeline with the error

Upvotes: 0

Views: 648

Answers (1)

Idhem
Idhem

Reputation: 964

After deep investigation, in my pipeline, I was using beam.Filter in the following way:

dropped_and_missing = all_recs | 'Filter Dropped and Missing recs' >> beam.Filter(lambda rec: rec['existing_status'] == 'Dropped' or rec['existing_status'] == 'Missing')

Replacing the code block with a PTransformation that is based on if conditions solved the issue.

I don't know where the issue is, I tried to dig into the source code, checking if there is in main module in the Filter function, but it doesn't exist.

Also what's suspicious is the error is occurred only when running the integration test from the command line. Pipeline works fine with LocalRunner and DataflowRunner

Upvotes: 1

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