def HandleGet(self):
"""Responses the deployed version of this app."""
return {
'data': {
'version': appengine_util.GetCurrentVersion(),
}
}
def _ResetAnalysis(self, master_name, builder_name, build_number):
analysis = WfAnalysis.Get(master_name, builder_name, build_number)
analysis.pipeline_status_path = self.pipeline_status_path()
analysis.status = analysis_status.RUNNING
analysis.result_status = None
analysis.start_time = time_util.GetUTCNow()
analysis.version = appengine_util.GetCurrentVersion()
analysis.end_time = None
analysis.put()
def UpdateTriageResult(self,
triage_result,
suspect_info,
user_name,
version_number=None):
result = TriageResult()
result.user_name = user_name
result.triage_result = triage_result
result.findit_version = appengine_util.GetCurrentVersion()
result.version_number = version_number
result.suspect_info = suspect_info
self.triage_history.append(result)
self.triage_email_obscured = False
self.triage_record_last_add = time_util.GetUTCNow()
def run(self, *_args, **_kwargs):
"""Call predator to do the analysis of the given crash.
N.B., due to the structure of AppEngine pipelines, this method must
accept the same arguments as are passed to ``__init__``; however,
because they were already passed to ``__init__`` there's no use in
recieving them here. Thus, we discard all the arguments to this method
(except for ``self``, naturally).
"""
# TODO(wrengr): shouldn't this method somehow call _NeedsNewAnalysis
# to guard against race conditions?
analysis = self._findit.GetAnalysis(self._crash_identifiers)
# Update the model's status to say we're in the process of doing analysis.
analysis.pipeline_status_path = self.pipeline_status_path()
analysis.status = analysis_status.RUNNING
analysis.started_time = time_util.GetUTCNow()
analysis.findit_version = appengine_util.GetCurrentVersion()
analysis.put()
# Actually do the analysis.
culprit = self._findit.FindCulprit(analysis.ToCrashReport())
if culprit is not None:
result, tags = culprit.ToDicts()
else:
result = {'found': False}
tags = {
'found_suspects': False,
'found_project': False,
'found_components': False,
'has_regression_range': False,
'solution': None,
}
# Update model's status to say we're done, and save the results.
analysis.completed_time = time_util.GetUTCNow()
analysis.result = result
for tag_name, tag_value in tags.iteritems():
# TODO(http://crbug.com/602702): make it possible to add arbitrary tags.
# TODO(http://crbug.com/659346): we misplaced the coverage test; find it!
if hasattr(analysis, tag_name): # pragma: no cover
setattr(analysis, tag_name, tag_value)
if hasattr(monitoring, tag_name):
metric = getattr(monitoring, tag_name)
metric.increment({tag_name: tag_value,
'client_id': self.client_id})
analysis.status = analysis_status.COMPLETED
analysis.put()
def PopulateAnalysisInfo(analysis):
analysis.Reset()
analysis.request_time = time_util.GetUTCNow()
analysis.status = analysis_status.PENDING
analysis.version = appengine_util.GetCurrentVersion()
analysis.triggering_user_email = user_email
analysis.triggering_user_email_obscured = False
analysis.triggering_source = triggering_source
analysis.original_master_name = original_test.master_name
analysis.original_builder_name = original_test.builder_name
analysis.original_build_number = original_test.build_number
analysis.original_step_name = original_test.step_name
analysis.original_test_name = original_test.test_name
analysis.bug_id = bug_id
analysis.flake_key = flake_key
def RunImpl(self, pipeline_input):
master_name, builder_name, build_number = (
pipeline_input.build_key.GetParts())
build_failure_analysis.ResetAnalysisForANewAnalysis(
master_name,
builder_name,
build_number,
build_completed=pipeline_input.build_completed,
pipeline_status_path=self.pipeline_status_path,
current_version=appengine_util.GetCurrentVersion())
# TODO(crbug/869684): Use a gauge metric to track intermittent statuses.
# The yield statements below return PipelineFutures, which allow subsequent
# pipelines to refer to previous output values.
# https://github.com/GoogleCloudPlatform/appengine-pipelines/wiki/Python
# Heuristic Approach.
heuristic_params = CompileHeuristicAnalysisParameters(
failure_info=pipeline_input.current_failure_info,
build_completed=pipeline_input.build_completed)
heuristic_result = yield HeuristicAnalysisForCompilePipeline(
heuristic_params)
# Try job approach.
# Checks if first time failures happen and starts a try job if yes.
with pipelines.pipeline.InOrder():
start_compile_try_job_input = self.CreateInputObjectInstance(
StartCompileTryJobInput,
build_key=BuildKey(master_name=master_name,
builder_name=builder_name,
build_number=build_number),
heuristic_result=heuristic_result,
build_completed=pipeline_input.build_completed,
force=pipeline_input.force)
yield StartCompileTryJobPipeline(start_compile_try_job_input)
# Report event to BQ.
report_event_input = self.CreateInputObjectInstance(
report_event_pipeline.ReportEventInput,
analysis_urlsafe_key=WfAnalysis.Get(
master_name, builder_name, build_number).key.urlsafe())
if not pipeline_input.force:
yield report_event_pipeline.ReportAnalysisEventPipeline(
report_event_input)
def run(self, *_args, **_kwargs):
"""Call predator to do the analysis of the given crash.
N.B., due to the structure of AppEngine pipelines, this method must
accept the same arguments as are passed to ``__init__``; however,
because they were already passed to ``__init__`` there's no use in
recieving them here. Thus, we discard all the arguments to this method
(except for ``self``, naturally).
"""
logging.info('Start analysis of crash_pipeline. %s',
json.dumps(self._crash_identifiers))
# TODO(wrengr): shouldn't this method somehow call _NeedsNewAnalysis
# to guard against race conditions?
analysis = self._predator.GetAnalysis(self._crash_identifiers)
# Update the model's status to say we're in the process of doing analysis.
analysis.pipeline_status_path = self.pipeline_status_path()
analysis.status = analysis_status.RUNNING
analysis.started_time = time_util.GetUTCNow()
analysis.predator_version = appengine_util.GetCurrentVersion()
analysis.put()
# Actually do the analysis.
culprit = self._predator.FindCulprit(analysis)
result, tags = culprit.ToDicts()
analysis.status = (analysis_status.COMPLETED
if tags['success'] else analysis_status.ERROR)
analysis.completed_time = time_util.GetUTCNow()
# Update model's status to say we're done, and save the results.
analysis.result = result
for tag_name, tag_value in tags.iteritems():
# TODO(http://crbug.com/602702): make it possible to add arbitrary tags.
# TODO(http://crbug.com/659346): we misplaced the coverage test;
# find it!
if hasattr(analysis, tag_name): # pragma: no cover
setattr(analysis, tag_name, tag_value)
analysis.put()
self._predator.UpdateMetrics(analysis)
logging.info('Found %s analysis result for %s: \n%s', self.client_id,
repr(self._crash_identifiers),
json.dumps(analysis.result, indent=2, sort_keys=True))
def RunImpl(self, pipeline_input):
master_name, builder_name, build_number = (
pipeline_input.build_key.GetParts())
build_failure_analysis.ResetAnalysisForANewAnalysis(
master_name,
builder_name,
build_number,
build_completed=pipeline_input.build_completed,
pipeline_status_path=self.pipeline_status_path,
current_version=appengine_util.GetCurrentVersion())
# TODO(crbug/869684): Use a gauge metric to track intermittent statuses.
# The yield statements below return PipelineFutures, which allow subsequent
# pipelines to refer to previous output values.
# https://github.com/GoogleCloudPlatform/appengine-pipelines/wiki/Python
# Heuristic Approach.
heuristic_params = TestHeuristicAnalysisParameters(
failure_info=pipeline_input.current_failure_info,
build_completed=pipeline_input.build_completed)
heuristic_result = yield HeuristicAnalysisForTestPipeline(
heuristic_params)
# Try job approach.
with pipeline.InOrder():
run_tasks_inputs = self.CreateInputObjectInstance(
RunSwarmingTasksInput,
build_key=pipeline_input.build_key,
heuristic_result=heuristic_result,
force=pipeline_input.force)
# Swarming rerun.
# Triggers swarming tasks when first time test failure happens.
# This pipeline will run before build completes.
yield RunSwarmingTasksPipeline(run_tasks_inputs)
collect_task_results_inputs = self.CreateInputObjectInstance(
CollectSwarmingTaskResultsInputs,
build_key=pipeline_input.build_key,
build_completed=pipeline_input.build_completed)
# An async pipeline that queries swarming tasks periodically until all
# swarming tasks completes and return consistent failures.
consistent_failures = yield CollectSwarmingTaskResultsPipeline(
collect_task_results_inputs)
start_waterfall_try_job_inputs = self.CreateInputObjectInstance(
StartTestTryJobInputs,
build_key=pipeline_input.build_key,
build_completed=pipeline_input.build_completed,
force=pipeline_input.force,
heuristic_result=heuristic_result,
consistent_failures=consistent_failures)
yield StartTestTryJobPipeline(start_waterfall_try_job_inputs)
if not pipeline_input.force:
# Report event to BQ.
report_event_input = self.CreateInputObjectInstance(
report_event_pipeline.ReportEventInput,
analysis_urlsafe_key=WfAnalysis.Get(
master_name, builder_name, build_number).key.urlsafe())
yield report_event_pipeline.ReportAnalysisEventPipeline(
report_event_input)
# Trigger flake analysis on flaky tests, if any.
yield TriggerFlakeAnalysesPipeline(pipeline_input.build_key)