def testApplyInParallelOnFailure(self):
work_list = [[1], [2], [3]]
def fun(x):
if x == [3]:
raise RuntimeError()
util.ApplyInParallel(fun, work_list, on_failure=lambda x: x.pop())
self.assertEqual(work_list, [[1], [2], []])
def AggregateTraces(intermediate_results):
"""Replace individual traces with an aggregate one for each test result.
For each test run with traces, generates an aggregate HTML trace. Removes
all entries for individual traces and adds one entry for aggregate one.
"""
work_list = []
for result in intermediate_results['testResults']:
artifacts = result.get('outputArtifacts', {})
# TODO(crbug.com/981349): Stop checking for HTML_TRACE_NAME after
# Telemetry does not aggregate traces anymore.
if (any(name.startswith('trace/') for name in artifacts) and
compute_metrics.HTML_TRACE_NAME not in artifacts):
work_list.append(artifacts)
if work_list:
for _ in util.ApplyInParallel(_AggregateTraceWorker, work_list):
pass
# TODO(crbug.com/981349): This is to clean up traces that have been
# aggregated by Telemetry. Remove this after Telemetry no longer does this.
for result in intermediate_results['testResults']:
artifacts = result.get('outputArtifacts', {})
for name in artifacts.keys():
if name.startswith('trace/'):
del artifacts[name]
def ProcessResults(options):
"""Process intermediate results and produce the requested outputs.
This function takes the intermediate results generated by Telemetry after
running benchmarks (including artifacts such as traces, etc.), and processes
them as requested by the result processing options.
Args:
options: An options object with values parsed from the command line and
after any adjustments from ProcessOptions were applied.
"""
if not getattr(options, 'output_formats', None):
return 0
test_results = _LoadTestResults(options.intermediate_dir)
if not test_results:
# TODO(crbug.com/981349): Make sure that no one is expecting Results
# Processor to output results in the case of empty input
# and make this an error.
logging.warning('No test results to process.')
upload_bucket = options.upload_bucket
results_label = options.results_label
max_num_values = options.max_values_per_test_case
test_path_format = options.test_path_format
trace_processor_path = options.trace_processor_path
test_suite_start = (test_results[0]['startTime']
if test_results and 'startTime' in test_results[0] else
datetime.datetime.utcnow().isoformat() + 'Z')
run_identifier = RunIdentifier(results_label, test_suite_start)
should_compute_metrics = any(fmt in FORMATS_WITH_METRICS
for fmt in options.output_formats)
util.ApplyInParallel(
lambda result: ProcessTestResult(
result, upload_bucket, results_label, run_identifier,
test_suite_start, should_compute_metrics, max_num_values,
test_path_format, trace_processor_path),
test_results,
on_failure=util.SetUnexpectedFailure,
)
if should_compute_metrics:
histogram_dicts = ExtractHistograms(test_results)
for output_format in options.output_formats:
logging.info('Processing format: %s', output_format)
formatter = formatters.FORMATTERS[output_format]
if output_format in FORMATS_WITH_METRICS:
output_file = formatter.ProcessHistogramDicts(
histogram_dicts, options)
else:
output_file = formatter.ProcessIntermediateResults(
test_results, options)
print('View results at file://', output_file, sep='')
return GenerateExitCode(test_results)
def testApplyInParallelExceptionRaised(self):
work_list = [1, 2, 3]
def fun(x):
if x == 3:
raise RuntimeError()
with self.assertRaises(RuntimeError):
list(util.ApplyInParallel(fun, work_list))
def ComputeTBMv2Metrics(intermediate_results):
"""Compute metrics on aggregated traces in parallel.
For each test run that has an aggregate trace and some TBMv2 metrics listed
in its tags, compute the metrics and return the list of all resulting
histograms. Note: the order of histograms in the results may be different
from the order of tests in intermediate_results.
"""
histogram_dicts = []
work_list = []
for test_result in intermediate_results['testResults']:
artifacts = test_result.get('outputArtifacts', {})
# TODO(crbug.com/981349): If metrics have already been computed in
# Telemetry, we read it from the file. Remove this branch after Telemetry
# does not compute metrics anymore.
if HISTOGRAM_DICTS_FILE in artifacts:
with open(artifacts[HISTOGRAM_DICTS_FILE]['filePath']) as f:
histogram_dicts += json.load(f)
del artifacts[HISTOGRAM_DICTS_FILE]
continue
if test_result['status'] == 'SKIP':
continue
if (HTML_TRACE_NAME not in artifacts
or not any(tag['key'] == 'tbmv2'
for tag in test_result.get('tags', []))):
continue
trace_size_in_mib = (
os.path.getsize(artifacts[HTML_TRACE_NAME]['filePath']) / (2**20))
# Bails out on traces that are too big. See crbug.com/812631 for more
# details.
# TODO(crbug.com/1010041): Return a non-zero exit code in this case.
if trace_size_in_mib > 400:
test_result['status'] = 'FAIL'
logging.error('%s: Trace size is too big: %s MiB',
test_result['testPath'], trace_size_in_mib)
continue
work_list.append(test_result)
for dicts in util.ApplyInParallel(_PoolWorker, work_list):
histogram_dicts += dicts
return histogram_dicts
def UploadArtifacts(intermediate_results, upload_bucket, results_label):
"""Upload all artifacts to cloud.
For each test run, uploads all its artifacts to cloud and sets remoteUrl
fields in intermediate_results.
"""
if upload_bucket is None:
return
run_identifier = _RunIdentifier(
results_label, intermediate_results['benchmarkRun']['startTime'])
work_list = []
for result in intermediate_results['testResults']:
artifacts = result.get('outputArtifacts', {})
for name, artifact in artifacts.iteritems():
if 'remoteUrl' in artifact:
continue
# TODO(crbug.com/981349): Remove this check after Telemetry does not
# save histograms as an artifact anymore.
if name == compute_metrics.HISTOGRAM_DICTS_FILE:
continue
remote_name = '/'.join([run_identifier, result['testPath'], name])
work_list.append((artifact, remote_name))
def PoolUploader(work_item):
artifact, remote_name = work_item
artifact['remoteUrl'] = cloud_storage.Insert(
upload_bucket, remote_name, artifact['filePath'])
for _ in util.ApplyInParallel(PoolUploader, work_list):
pass
for result in intermediate_results['testResults']:
artifacts = result.get('outputArtifacts', {})
for name, artifact in artifacts.iteritems():
logging.info('Uploaded %s of %s to %s', name, result['testPath'],
artifact['remoteUrl'])
def testApplyInParallel(self):
work_list = [[1], [2], [3]]
def fun(x):
x.extend(x)
util.ApplyInParallel(fun, work_list)
self.assertEqual(work_list, [[1, 1], [2, 2], [3, 3]])
def testApplyInParallel(self):
work_list = [1, 2, 3]
fun = lambda x: x * x
result = set(util.ApplyInParallel(fun, work_list))
self.assertEqual(result, set([1, 4, 9]))
