text: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  """End-to-end test for Bigquery tornadoes example."""
  
  import logging
  import time
  import unittest
  
  from hamcrest.core.core.allof import all_of
  from nose.plugins.attrib import attr
  
  from apache_beam.examples.cookbook import bigquery_tornadoes
  from apache_beam.io.gcp.tests import utils
  from apache_beam.io.gcp.tests.bigquery_matcher import BigqueryMatcher
  from apache_beam.testing.pipeline_verifiers import PipelineStateMatcher
  from apache_beam.testing.test_pipeline import TestPipeline
  
  
  class BigqueryTornadoesIT(unittest.TestCase):
  
    # Enable nose tests running in parallel
    _multiprocess_can_split_ = True
  
    # The default checksum is a SHA-1 hash generated from sorted rows reading
    # from expected Bigquery table.
    DEFAULT_CHECKSUM = '83789a7c1bca7959dcf23d3bc37e9204e594330f'
  
    @attr('IT')
    def test_bigquery_tornadoes_it(self):
      test_pipeline = TestPipeline(is_integration_test=True)
  
      # Set extra options to the pipeline for test purpose
      project = test_pipeline.get_option('project')
  
      dataset = 'BigQueryTornadoesIT'
      table = 'monthly_tornadoes_%s' % int(round(time.time() * 1000))
      output_table = '.'.join([dataset, table])
      query = 'SELECT month, tornado_count FROM [%s]' % output_table
  
      pipeline_verifiers = [PipelineStateMatcher(),
                            BigqueryMatcher(
                                project=project,
                                query=query,
                                checksum=self.DEFAULT_CHECKSUM)]
      extra_opts = {'output': output_table,
                    'on_success_matcher': all_of(*pipeline_verifiers)}
  
      # Register cleanup before pipeline execution.
      self.addCleanup(utils.delete_bq_table, project, dataset, table)
  
      # Get pipeline options from command argument: --test-pipeline-options,
      # and start pipeline job by calling pipeline main function.
      bigquery_tornadoes.run(
          test_pipeline.get_full_options_as_args(**extra_opts))
  
  
  if __name__ == '__main__':
    logging.getLogger().setLevel(logging.INFO)
    unittest.main()
  