| # |
| # 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. |
| # |
| |
| """A word-counting workflow.""" |
| |
| from __future__ import absolute_import |
| |
| import argparse |
| import unittest |
| |
| from nose.plugins.attrib import attr |
| |
| import apache_beam as beam |
| from apache_beam.options.pipeline_options import PipelineOptions |
| from apache_beam.options.pipeline_options import SetupOptions |
| from apache_beam.runners.dataflow import dataflow_exercise_metrics_pipeline |
| from apache_beam.testing import metric_result_matchers |
| from apache_beam.testing.test_pipeline import TestPipeline |
| |
| |
| class ExerciseMetricsPipelineTest(unittest.TestCase): |
| |
| def run_pipeline(self, **opts): |
| test_pipeline = TestPipeline(is_integration_test=True) |
| argv = test_pipeline.get_full_options_as_args(**opts) |
| parser = argparse.ArgumentParser() |
| unused_known_args, pipeline_args = parser.parse_known_args(argv) |
| |
| # We use the save_main_session option because one or more DoFn's in this |
| # workflow rely on global context (e.g., a module imported at module level). |
| pipeline_options = PipelineOptions(pipeline_args) |
| pipeline_options.view_as(SetupOptions).save_main_session = True |
| p = beam.Pipeline(options=pipeline_options) |
| return dataflow_exercise_metrics_pipeline.apply_and_run(p) |
| |
| @attr('IT') |
| def test_metrics_it(self): |
| result = self.run_pipeline() |
| errors = metric_result_matchers.verify_all( |
| result.metrics().all_metrics(), |
| dataflow_exercise_metrics_pipeline.legacy_metric_matchers()) |
| self.assertFalse(errors, str(errors)) |
| |
| @attr('IT', 'ValidatesContainer') |
| def test_metrics_fnapi_it(self): |
| result = self.run_pipeline(experiment='beam_fn_api') |
| errors = metric_result_matchers.verify_all( |
| result.metrics().all_metrics(), |
| dataflow_exercise_metrics_pipeline.fn_api_metric_matchers()) |
| self.assertFalse(errors, str(errors)) |
| |
| |
| if __name__ == '__main__': |
| unittest.main() |