def run(self, result_uri, raw_result_uri, test_run_uri, task_uri,
results_uri, **params):
logger.info('Starting processing results...')
self.position = params.get('task_position', self.MIDDLE)
self.on_start(task_uri)
generators = {
'phantomas': [PhantomasMetricGenerator()],
'mw_profiler': [ProfilerMetricGenerator()],
'python.requests': [RequestsMetricGenerator()],
'selenium': [SeleniumMetricGenerator()]
}
metrics = Collection()
result = ApiClient.get(result_uri)
for raw_result_uri in result['raw_results']:
raw_result = ApiClient.get(raw_result_uri)
for item in raw_result['data']:
for generator in generators[raw_result['generator']]:
generator(metrics, raw_result)
ApiClient.put(
result_uri, {
'test_run': test_run_uri,
'task': task_uri,
'results': metrics.serialize(),
})
def extract(self, context, data):
context['origin'] = 'mw_profiler'
metric_defs = {
'response_time': MetricType.TIME,
'database.queries.list': MetricType.QUERY_LIST,
'database.queries.time': MetricType.TIME,
'database.queries.master_count': MetricType.COUNT,
'database.queries.slave_count': MetricType.COUNT,
'memcached.time': MetricType.TIME,
'memcached.miss_count': MetricType.COUNT,
'memcached.hit_count': MetricType.COUNT,
'memcached.dupe_count': MetricType.COUNT,
}
name_template = 'server.app.{}'
metrics = {
name: Metric(name_template.format(name), context, type)
for name, type in metric_defs.items()
}
for single_run in data:
data = self.parse_data(single_run['content'])
for name, raw_value in data.items():
metrics[name].add_value(raw_value, None)
return Collection(metrics.values())
def handle(self, *args, **options):
with open(options['datafile'], 'r') as f:
data = ujson.load(f)
all_metrics = Collection.unserialize(data)
query = Query().where_eq('id', 'browser.dom.event.interactive')
result_set = query.execute(all_metrics)
dump_result_set(result_set)
def handle(self, *args, **options):
with open(options['datafile'], 'r') as f:
data = ujson.load(f)
all_metrics = Collection.unserialize(data)
query = Query().where_eq('id', 'browser.dom.event.interactive')
result_set = query.execute(all_metrics)
dump_result_set(result_set)
def extract(self, context, data):
context['origin'] = 'selenium'
noexternals_context = merge_context(context, {'mode': 'noexternals'})
noads_context = merge_context(context, {'mode': 'noads'})
anon_search_context = merge_context(context, {
'scenario': 'anon_visit',
})
anon_search_context.pop('url', None)
user_search_context = merge_context(context, {
'scenario': 'user_visit',
})
user_search_context.pop('url', None)
context['mode'] = 'default'
metrics = Collection()
types = [
# (noexternals_context, 'oasis_perftest_medium_article_no_externals'),
# (noads_context, 'oasis_perftest_medium_article_no_ads'),
(context, 'load_provided_url'),
]
metric_defs = [
('browser.transaction.time', MetricType.TIME, 'total_load_time'),
('browser.dom.event.interactive', MetricType.TIME,
'interactive_time'),
('browser.dom.event.content_loaded', MetricType.TIME,
'dom_content_loaded_time'),
('browser.dom.event.complete', MetricType.TIME,
'dom_complete_time'),
]
for test_context, test_name in types:
for out_name, data_type, in_name in metric_defs:
self.fan_out_by_url_and_push_metrics(
metrics,
out_name,
context,
data_type,
values=self.metric_values_single_step_with_urls(
data, test_name, in_name))
# total time
# metrics.add(Metric(
# 'browser.transaction.time', anon_search_context, MetricType.TIME,
# values=self.total_load_time_all_steps(data, 'perftest_oasis_anon_search_pageviews')))
# metrics.add(Metric(
# 'browser.transaction.time', user_search_context, MetricType.TIME,
# values=self.total_load_time_all_steps(data, 'perftest_oasis_user_search_pageviews')))
return metrics
def extract(self, context, data):
context['origin'] = 'requests'
metrics = Collection()
metrics.add(
Metric('server.app.response_time',
context,
MetricType.TIME,
values=[(float(single_run['time']), None)
for single_run in data]))
metrics.add(
Metric('server.app.response_size',
context,
MetricType.BYTES,
values=[(single_run['content_length'], None)
for single_run in data]))
return metrics
def handle(self, *args, **options):
if options['datafile']:
with open(options['datafile'], 'r') as f:
data = ujson.load(f)
elif options['resultid']:
url = build_absolute_uri('/api/v1/results/{}/'.format(options['resultid']))
response = requests.get(url)
data = response.json()['results']
else:
raise RuntimeError('You need to specify either --resultid or --datafile')
all_metrics = Collection.unserialize(data)
basic_metric_set = BasicMetricSet(all_metrics)
basic_metrics = basic_metric_set.items
for k, v in basic_metrics.items():
print('METRIC: {}'.format(k))
dump_stats(v)
def extract(self, context, data):
context['origin'] = 'phantomas'
metrics = Collection()
if len(data) == 0:
return metrics
metric_names = set()
for run in data:
metric_names.update(run['metrics'].keys())
for metric_name in metric_names:
raw_id = 'raw.phantomas.' + metric_name
id = raw_id
type = MetricType.UNKNOWN
if metric_name in self.KNOWN_METRICS:
metric_def = self.KNOWN_METRICS[metric_name]
if ':' in metric_def:
type, id = metric_def.split(':')
else:
id = metric_def
raw_values_and_infos = [
(self.normalize_phantomas_value(run['metrics'][metric_name],
type),
run['offenders'][metric_name]
if metric_name in run['offenders'] else None) for run in data
]
metrics.add(Metric(id, context, type, None, raw_values_and_infos))
if id != raw_id:
metrics.add(
Metric(raw_id, context, type, None, raw_values_and_infos))
return metrics
def get_metrics(self):
results = self.results.all()[0:1]
if len(results) == 0:
return None
return Collection.unserialize(results[0].results)