sdks/python/apache_beam/runners/runner_test.py

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# The ASF licenses this file to You under the Apache License, Version 2.0
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""Unit tests for the PipelineRunner and DirectRunner classes.

Note that PipelineRunner and DirectRunner functionality is tested in all
the other unit tests. In this file we choose to test only aspects related to
caching and clearing values that are not tested elsewhere.
"""

# pytype: skip-file

import unittest

import apache_beam as beam
from apache_beam.metrics.metric import Metrics
from apache_beam.runners import DirectRunner
from apache_beam.runners import create_runner
from apache_beam.runners.portability.fn_api_runner import FnApiRunner


class RunnerTest(unittest.TestCase):
  default_properties = [
      '--dataflow_endpoint=ignored',
      '--job_name=test-job',
      '--project=test-project',
      '--staging_location=ignored',
      '--temp_location=/dev/null',
      '--no_auth'
  ]

  def test_create_runner(self):
    self.assertTrue(isinstance(create_runner('DirectRunner'), DirectRunner))
    self.assertRaises(ValueError, create_runner, 'xyz')

  def test_create_runner_shorthand(self):
    self.assertTrue(isinstance(create_runner('DiReCtRuNnEr'), DirectRunner))
    self.assertTrue(isinstance(create_runner('directrunner'), DirectRunner))
    self.assertTrue(isinstance(create_runner('direct'), DirectRunner))
    self.assertTrue(isinstance(create_runner('DiReCt'), DirectRunner))
    self.assertTrue(isinstance(create_runner('Direct'), DirectRunner))

  def test_run_api(self):
    my_metric = Metrics.counter('namespace', 'my_metric')
    runner = FnApiRunner()
    result = runner.run(
        beam.Create([1, 10, 100]) | beam.Map(lambda x: my_metric.inc(x)))
    result.wait_until_finish()
    # Use counters to assert the pipeline actually ran.
    my_metric_value = result.metrics().query()['counters'][0].committed
    self.assertEqual(my_metric_value, 111)

  def test_run_api_with_callable(self):
    my_metric = Metrics.counter('namespace', 'my_metric')

    def fn(start):
      return (
          start
          | beam.Create([1, 10, 100])
          | beam.Map(lambda x: my_metric.inc(x)))

    runner = FnApiRunner()
    result = runner.run(fn)
    result.wait_until_finish()
    # Use counters to assert the pipeline actually ran.
    my_metric_value = result.metrics().query()['counters'][0].committed
    self.assertEqual(my_metric_value, 111)


if __name__ == '__main__':
  unittest.main()