def get_aggregate_function(self, column_name, metric_name, service=None):
"""
Returns string with metric value column name surrounded by aggregate function
based on metric type (which tells how to interpret value - is it a counter,
rate or something else).
"""
metric = Metric.get_for(metric_name, service=service)
if not metric:
raise ValueError("Invalid metric {}".format(metric_name))
f = metric.get_aggregate_name()
return f or column_name
def set_metric_values(self, metric_name, column_name,
requests, service, **metric_values):
metric = Metric.get_for(metric_name, service=service)
q = requests
def _key(v):
return v['value']
# we need list of three items:
# * value - numeric value for given metric
# * label - label value to be used
# * samples count - number of samples for a metric
if metric.is_rate:
row = requests.aggregate(value=models.Avg(column_name))
row['samples'] = requests.count()
row['label'] = Metric.TYPE_RATE
q = [row]
elif metric.is_count:
q = []
values = requests.distinct(
column_name).values_list(column_name, flat=True)
for v in values:
rqs = requests.filter(**{column_name: v})
row = rqs.aggregate(
value=models.Sum(column_name),
samples=models.Count(column_name)
)
row['label'] = v
q.append(row)
q.sort(key=_key)
q.reverse()
elif metric.is_value:
q = []
is_user_metric = column_name == "user_identifier"
if is_user_metric:
values = requests.distinct(
column_name).values_list(column_name, "user_username")
else:
values = requests.distinct(
column_name).values_list(column_name, flat=True)
for v in values:
value = v
if is_user_metric:
value = v[0]
rqs = requests.filter(**{column_name: value})
row = rqs.aggregate(
value=models.Count(column_name),
samples=models.Count(column_name)
)
row['label'] = v
q.append(row)
q.sort(key=_key)
q.reverse()
elif metric.is_value_numeric:
q = []
row = requests.aggregate(value=models.Max(column_name),
samples=models.Count(column_name))
row['label'] = Metric.TYPE_VALUE_NUMERIC
q.append(row)
else:
raise ValueError("Unsupported metric type: {}".format(metric.type))
rows = q[:100]
metric_values.update({'metric': metric_name, 'service': service})
for row in rows:
label = row['label']
value = row['value']
samples = row['samples']
metric_values.update({'value': value or 0,
'label': label,
'samples_count': samples,
'value_raw': value or 0,
'value_num': value if isinstance(value, (int, float, long, Decimal,)) else None})
# log.debug(MetricValue.add(**metric_values))
print MetricValue.add(**metric_values)