#
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# 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.
#
"""Wrapper of Beam runners that's built for running and verifying e2e tests."""
from __future__ import absolute_import
from __future__ import print_function
import logging
import time
from apache_beam.internal import pickler
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.pipeline_options import TestOptions
from apache_beam.runners.dataflow.dataflow_runner import DataflowRunner
from apache_beam.runners.runner import PipelineState
__all__ = ['TestDataflowRunner']
# Dataflow take up to 10mins for the long tail of starting/stopping worker
# pool.
WAIT_IN_STATE_TIMEOUT = 10 * 60
_LOGGER = logging.getLogger()
[docs]class TestDataflowRunner(DataflowRunner):
[docs] def run_pipeline(self, pipeline, options):
"""Execute test pipeline and verify test matcher"""
test_options = options.view_as(TestOptions)
on_success_matcher = test_options.on_success_matcher
wait_duration = test_options.wait_until_finish_duration
is_streaming = options.view_as(StandardOptions).streaming
# [BEAM-1889] Do not send this to remote workers also, there is no need to
# send this option to remote executors.
test_options.on_success_matcher = None
self.result = super(TestDataflowRunner, self).run_pipeline(
pipeline, options)
if self.result.has_job:
# TODO(markflyhigh)(BEAM-1890): Use print since Nose dosen't show logs
# in some cases.
print('Worker logs: %s' % self.build_console_url(options))
try:
self.wait_until_in_state(PipelineState.RUNNING)
if is_streaming and not wait_duration:
_LOGGER.warning('Waiting indefinitely for streaming job.')
self.result.wait_until_finish(duration=wait_duration)
if on_success_matcher:
from hamcrest import assert_that as hc_assert_that
hc_assert_that(self.result, pickler.loads(on_success_matcher))
finally:
if not self.result.is_in_terminal_state():
self.result.cancel()
self.wait_until_in_state(PipelineState.CANCELLED)
return self.result
[docs] def build_console_url(self, options):
"""Build a console url of Dataflow job."""
project = options.view_as(GoogleCloudOptions).project
region_id = options.view_as(GoogleCloudOptions).region
job_id = self.result.job_id()
return (
'https://console.cloud.google.com/dataflow/jobsDetail/locations'
'/%s/jobs/%s?project=%s' % (region_id, job_id, project))
[docs] def wait_until_in_state(self, expected_state, timeout=WAIT_IN_STATE_TIMEOUT):
"""Wait until Dataflow pipeline enters a certain state."""
if not self.result.has_job:
raise IOError('Failed to get the Dataflow job id.')
start_time = time.time()
while time.time() - start_time <= timeout:
job_state = self.result.state
if self.result.is_in_terminal_state() or job_state == expected_state:
return job_state
time.sleep(5)
raise RuntimeError('Timeout after %d seconds while waiting for job %s '
'enters expected state %s. Current state is %s.' %
(timeout, self.result.job_id(),
expected_state, self.result.state))