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clean up convert_dbms_metrics; fix invalid results handling bug

This commit is contained in:
yangdsh 2020-04-28 20:30:25 +00:00 committed by Dana Van Aken
parent 285522a0f5
commit 4e290c9548
2 changed files with 24 additions and 26 deletions
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18
server/website/website/db/base/parser.py
View File

@ -187,10 +187,7 @@ class BaseParser:
return value in self.valid_true_val or value in self.valid_false_val
def convert_dbms_metrics(self, metrics, observation_time, target_objective):
metric_data = {}
# Same as metric_data except COUNTER metrics are not divided by the time
# Note: metric_data is also not divided by the time now so base_metric_data is redundant
base_metric_data = {}
numeric_metric_data = {}
numeric_metric_catalog = MetricCatalog.objects.filter(
dbms__id=self.dbms_id, metric_type__in=MetricType.numeric())
@ -210,12 +207,10 @@ class BaseParser:
if metadata.metric_type == MetricType.COUNTER:
assert isinstance(converted, float)
base_metric_data[name] = converted
metric_data[name] = converted
numeric_metric_data[name] = converted
elif metadata.metric_type == MetricType.STATISTICS:
assert isinstance(converted, float)
base_metric_data[name] = converted
metric_data[name] = converted
numeric_metric_data[name] = converted
else:
raise ValueError(
'Unknown metric type for {}: {}'.format(name, metadata.metric_type))
@ -227,10 +222,11 @@ class BaseParser:
target_objective, ', '.join(target_list.keys())))
for target_name, target_instance in target_list.items():
metric_data[target_name] = target_instance.compute(
base_metric_data, observation_time)
# wait_class is needed to calculate target_objectives, but it is not numeric
numeric_metric_data[target_name] = target_instance.compute(
metrics, observation_time)
return metric_data
return numeric_metric_data
def extract_valid_variables(self, variables, catalog, default_value=None):
valid_variables = {}

32
server/website/website/views.py
View File

@ -530,27 +530,29 @@ def handle_result_files(session, files, execution_times=None):
worst_result = Result.objects.filter(metric_data=worst_metric).first()
last_result = Result.objects.filter(session=session).order_by("-id").first()
last_conf = JSONUtil.loads(last_result.next_configuration)
last_conf = last_conf["recommendation"]
last_conf = parser.convert_dbms_knobs(last_result.dbms.pk, last_conf)
# Copy worst data and modify
knob_data = worst_result.knob_data
knob_data.pk = None
all_knobs = JSONUtil.loads(knob_data.knobs)
for knob in all_knobs.keys():
for tunable_knob in last_conf.keys():
if tunable_knob in knob:
all_knobs[knob] = last_conf[tunable_knob]
knob_data.knobs = JSONUtil.dumps(all_knobs)
if last_result.next_configuration is not None:
last_conf = JSONUtil.loads(last_result.next_configuration)
if last_conf.get("recommendation", None) is not None:
last_conf = last_conf["recommendation"]
last_conf = parser.convert_dbms_knobs(last_result.dbms.pk, last_conf)
all_knobs = JSONUtil.loads(knob_data.knobs)
for knob in all_knobs.keys():
for tunable_knob in last_conf.keys():
if tunable_knob in knob:
all_knobs[knob] = last_conf[tunable_knob]
knob_data.knobs = JSONUtil.dumps(all_knobs)
data_knobs = JSONUtil.loads(knob_data.data)
for knob in data_knobs.keys():
for tunable_knob in last_conf.keys():
if tunable_knob in knob:
data_knobs[knob] = last_conf[tunable_knob]
data_knobs = JSONUtil.loads(knob_data.data)
for knob in data_knobs.keys():
for tunable_knob in last_conf.keys():
if tunable_knob in knob:
data_knobs[knob] = last_conf[tunable_knob]
knob_data.data = JSONUtil.dumps(data_knobs)
knob_data.data = JSONUtil.dumps(data_knobs)
knob_name_parts = last_result.knob_data.name.split('*')[0].split('#')
knob_name_parts[-1] = str(int(knob_name_parts[-1]) + 1) + '*'
knob_data.name = '#'.join(knob_name_parts)