blob: d1afbcf6b189517abfd2af534b23d984a2b08a58 [file] [log] [blame]
#
# 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.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)
pipeline_options = PipelineOptions(pipeline_args)
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.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.metric_matchers())
self.assertFalse(errors, str(errors))
if __name__ == '__main__':
unittest.main()