def CompoundEvaluatorForTesting(self, fake_evaluator):
return evaluators.SequenceEvaluator([
evaluators.FilteringEvaluator(
predicate=evaluators.All(evaluators.TaskTypeEq('read_value'),
evaluators.TaskStatusIn({'pending'})),
delegate=evaluators.SequenceEvaluator(
[fake_evaluator,
evaluators.TaskPayloadLiftingEvaluator()])),
evaluators.SequenceEvaluator([
performance_bisection.Evaluator(self.job),
evaluators.TaskPayloadLiftingEvaluator(
exclude_keys={'commits'})
]),
])
def __init__(self, job):
# We gather all the evaluators from the modules we know.
super(ExecutionEngine, self).__init__(evaluators=[
evaluators.DispatchByTaskType(
{
'find_isolate': find_isolate.Evaluator(job),
'find_culprit': performance_bisection.Evaluator(job),
'read_value': read_value.Evaluator(job),
'run_test': run_test.Evaluator(job),
}),
# We then always lift the task payload up, skipping some of the
# larger objects that we know we are not going to need when deciding
# what the end result is.
evaluators.TaskPayloadLiftingEvaluator(
exclude_keys=EXCLUDED_PAYLOAD_KEYS)
])
def BisectionEvaluatorForTesting(self, *seeded_evaluators):
"""Creates an evaluator for bisection with the provided evaluators.
This is a utility function for creating a bisection evaluator which has the
list of evaluators nested within the sequence of evaluators.
"""
return evaluators.SequenceEvaluator([
evaluators.FilteringEvaluator(
predicate=evaluators.All(evaluators.TaskStatusIn({'pending'})),
delegate=evaluators.SequenceEvaluator(
list(seeded_evaluators) +
[evaluators.TaskPayloadLiftingEvaluator()])),
evaluators.SequenceEvaluator([
performance_bisection.Evaluator(self.job),
evaluators.TaskPayloadLiftingEvaluator(exclude_keys={'commits'})
]),
])
def testSerializeJob(self):
self.PopulateSimpleBisectionGraph(self.job)
task_module.Evaluate(
self.job, bisection_test_util.SelectEvent(),
evaluators.SequenceEvaluator([
evaluators.DispatchByTaskType({
'find_isolate':
bisection_test_util.FakeFoundIsolate(self.job),
'run_test':
bisection_test_util.FakeSuccessfulRunTest(self.job),
'read_value':
bisection_test_util.FakeReadValueSameResult(self.job, 1.0),
'find_culprit':
performance_bisection.Evaluator(self.job),
}),
evaluators.TaskPayloadLiftingEvaluator()
]))
logging.debug('Finished evaluating job state.')
job_dict = self.job.AsDict(options=[job_module.OPTION_STATE])
logging.debug('Job = %s', pprint.pformat(job_dict))
self.assertTrue(self.job.use_execution_engine)
self.assertEqual(
{
'arguments':
mock.ANY,
'bug_id':
None,
'cancel_reason':
None,
'comparison_mode':
'performance',
'configuration':
'some_configuration',
'created':
mock.ANY,
'difference_count':
0,
'exception':
None,
'job_id':
mock.ANY,
'metric':
'some_benchmark',
'name':
mock.ANY,
'quests': ['Build', 'Test', 'Get results'],
'results_url':
mock.ANY,
'status':
mock.ANY,
'updated':
mock.ANY,
'user':
None,
# NOTE: Here we're asseessing the structure of the results, not the
# actual contents. We'll reserve more specific content form testing
# in other test cases, but for now we're ensuring that we're able to
# get the shape of the data in a certain way.
'state': [{
'attempts': [{
'executions': [mock.ANY] * 3
}] + [{
'executions': [None, mock.ANY, mock.ANY]
}] * 9,
'change':
self.start_change.AsDict(),
'comparisons': {
'prev': None,
'next': 'same',
},
'result_values': [mock.ANY] * 10,
}, {
'attempts': [{
'executions': [mock.ANY] * 3
}] + [{
'executions': [None, mock.ANY, mock.ANY]
}] * 9,
'change':
self.end_change.AsDict(),
'comparisons': {
'prev': 'same',
'next': None,
},
'result_values': [mock.ANY] * 10,
}]
},
job_dict)