def test_from_json(self):
item = {
'usage_perc_all_services': 1.22,
'all_services_time': 4360,
'time_perc_all_services': 17.64,
'mean_trend':
'0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,769,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0',
'min_resp_time': 769.0,
'service_name': 'zato.stats.summary.create-summary-by-year',
'max_resp_time': 769.0,
'rate': 0.0,
'mean_all_services': '63',
'all_services_usage': 82,
'time': 769.0,
'usage': 1,
'usage_trend':
'0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0',
'mean': 12.61
}
stats_elem = StatsElem.from_json(item)
for k, v in item.items():
value = getattr(stats_elem, k)
eq_(v, value)
def test_from_json(self):
item = {'usage_perc_all_services': 1.22, 'all_services_time': 4360,
'time_perc_all_services': 17.64,
'mean_trend': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,769,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0',
'min_resp_time': 769.0, 'service_name': 'zato.stats.summary.create-summary-by-year',
'max_resp_time': 769.0, 'rate': 0.0, 'mean_all_services': '63',
'all_services_usage': 82, 'time': 769.0, 'usage': 1,
'usage_trend': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0',
'mean': 12.61}
stats_elem = StatsElem.from_json(item)
for k, v in item.items():
value = getattr(stats_elem, k)
eq_(v, value)
def _get_stats(client, start, stop, n, n_type, stats_type=None):
""" Returns at most n statistics elements of a given n_type for the period
between start and stop.
"""
out = []
input_dict = {'start':start, 'n':n, 'n_type':n_type}
if stop:
input_dict['stop'] = stop
if stats_type == 'trends':
service_name = 'zato.stats.trends.get-trends'
else:
service_name = 'zato.stats.summary.get-summary-by-range'
response = client.invoke(service_name, input_dict)
if response.has_data:
for item in response.data:
out.append(StatsElem.from_json(item))
return out
def _get_stats(client, start, stop, n, n_type, stats_type=None):
""" Returns at most n statistics elements of a given n_type for the period
between start and stop.
"""
out = []
input_dict = {'start': start, 'n': n, 'n_type': n_type}
if stop:
input_dict['stop'] = stop
if stats_type == 'trends':
service_name = 'zato.stats.trends.get-trends'
else:
service_name = 'zato.stats.summary.get-summary-by-range'
response = client.invoke(service_name, input_dict)
if response.has_data:
for item in response.data:
out.append(StatsElem.from_json(item))
return out
def _get_stats(cluster, start, stop, n, n_type, stats_type=None):
""" Returns at most n statistics elements of a given n_type for the period
between start and stop.
"""
out = []
input_dict = {'start':start, 'n':n, 'n_type':n_type}
if stop:
input_dict['stop'] = stop
if stats_type == 'trends':
service_name = 'zato:stats.get-trends'
else:
service_name = 'zato:stats.get-summary-by-range'
zato_message, _ = invoke_admin_service(cluster, service_name, input_dict)
if zato_path('response.item_list.item').get_from(zato_message) is not None:
for msg_item in zato_message.response.item_list.item:
out.append(StatsElem.from_xml(msg_item))
return out
def get_stats(self,
start,
stop,
service='*',
n=None,
n_type=None,
needs_trends=True,
stats_key_prefix=None,
suffixes=None):
""" Returns statistics for a given interval, as defined by 'start' and 'stop'.
service default to '*' for all services in that period and may be set to return
a one-element list of information regarding that particular service. Setting 'n'
to a positive integer will make it return only top n services.
"""
if not stats_key_prefix:
stats_key_prefix = self.stats_key_prefix
stats_elems = {}
all_services_stats = Bunch({'usage': 0, 'time': 0})
# All mean values
mean_all_services_list = []
# A mean value of all the mean values (mean_all_services_list)
mean_all_services = 0
start = parse(start)
stop = parse(stop)
delta = (stop - start)
if hasattr(delta, 'total_seconds'):
delta_seconds = delta.total_seconds()
else:
delta_seconds = delta.seconds
if not suffixes:
suffixes = self.get_suffixes(start, stop)
# We make several passes. First two passes are made over Redis keys, one gathers the services, if any at all,
# and another one actually collects statistics for each service found. Next pass, a partly optional one,
# computes trends for mean response time and service usage. Another one computes each of the service's
# average rate and updates other attributes basing on values collected in the previous step.
# Optionally, the last one will pick only top n elements of a given type (top mean response time
# or top usage).
# 1st pass
for suffix in suffixes:
keys = self.server.kvdb.conn.keys('{}{}:{}'.format(
stats_key_prefix, service, suffix))
for key in keys:
service_name = key.replace(stats_key_prefix,
'').replace(':{}'.format(suffix),
'')
stats_elem = StatsElem(service_name)
stats_elems[service_name] = stats_elem
# When building statistics, we can't expect there will be data for all the time
# elems built above so to guard against it, this is a dictionary whose keys are the
# said elems and values are mean/usage for each elem. The values will remain
# 0/0.0 if there is no data for the time elem, which may mean that in this
# particular time slice the service wasn't invoked at all.
stats_elem.expected_time_elems = OrderedDict(
(elem, Bunch({
'mean': 0,
'usage': 0.0
})) for elem in suffixes)
# 2nd pass
for service, stats_elem in stats_elems.items():
for suffix in suffixes:
key = '{}{}:{}'.format(stats_key_prefix, service, suffix)
# We can convert all the values to floats here to ease with computing
# all the stuff and convert them still to integers later on, when necessary.
key_values = Bunch((
(name, float(value))
for (name,
value) in self.server.kvdb.conn.hgetall(key).items()))
if key_values:
time = (key_values.usage * key_values.mean)
stats_elem.time += time
mean_all_services_list.append(key_values.mean)
all_services_stats.time += time
all_services_stats.usage += key_values.usage
stats_elem.min_resp_time = min(stats_elem.min_resp_time,
key_values.min)
stats_elem.max_resp_time = max(stats_elem.max_resp_time,
key_values.max)
for attr in ('mean', 'usage'):
stats_elem.expected_time_elems[suffix][
attr] = key_values[attr]
mean_all_services = '{:.0f}'.format(
sp_stats.tmean(
mean_all_services_list)) if mean_all_services_list else 0
# 3rd pass (partly optional)
for stats_elem in stats_elems.values():
stats_elem.mean_all_services = mean_all_services
stats_elem.all_services_time = int(all_services_stats.time)
stats_elem.all_services_usage = int(all_services_stats.usage)
values = stats_elem.expected_time_elems.values()
stats_elem.mean_trend_int = [int(elem.mean) for elem in values]
stats_elem.usage_trend_int = [int(elem.usage) for elem in values]
stats_elem.mean = float('{:.2f}'.format(
sp_stats.tmean(stats_elem.mean_trend_int)))
stats_elem.usage = sum(stats_elem.usage_trend_int)
stats_elem.rate = float('{:.2f}'.format(
sum(stats_elem.usage_trend_int) / delta_seconds))
self.set_percent_of_all_services(all_services_stats, stats_elem)
if needs_trends:
stats_elem.mean_trend = ','.join(
str(elem) for elem in stats_elem.mean_trend_int)
stats_elem.usage_trend = ','.join(
str(elem) for elem in stats_elem.usage_trend_int)
# 4th pass (optional)
if n:
for stats_elem in self.yield_top_n(n, n_type, stats_elems):
yield stats_elem
else:
for stats_elem in stats_elems.values():
yield stats_elem
def merge_slices(self, slices, n=None, n_type=None):
""" Merges a list of stats slices into a single aggregated elem.
"""
all_services_stats = Bunch({'usage': 0, 'time': 0, 'mean': 0})
total_seconds = 0.0
merged_stats_elems = {}
merged_template = {
'usage_perc_all_services': 0.0,
'all_services_time': 0,
'time_perc_all_services': 0.0,
'min_resp_time': 0.0,
'service_name': None,
'max_resp_time': 0.0,
'rate': 0.0,
'mean_all_services': 0.0,
'all_services_usage': 0,
'time': 0,
'usage': 0,
'mean': 0.0
}
for slice in slices:
seen_repeated_stats = False
total_seconds += slice.total_seconds
if slice.stats:
for stats_elem in slice.stats:
# Each slice has a list of per-service stats. Each of the statistics
# has certain data repeated hence it's required we pick this data
# once only.
if not seen_repeated_stats:
all_services_stats.time += stats_elem.all_services_time
all_services_stats.usage += stats_elem.all_services_usage
seen_repeated_stats = True
# Fetch an existing elem or assign a new one
merged_stats_elem = merged_stats_elems.setdefault(
stats_elem.service_name,
StatsElem(stats_elem.service_name))
# Total time spent by this service and its total usage
merged_stats_elem.time += stats_elem.time
merged_stats_elem.usage += stats_elem.usage
all_services_stats.mean += stats_elem.mean
# Minimum and maximum execution time
merged_stats_elem.min_resp_time = min(
merged_stats_elem.min_resp_time,
stats_elem.min_resp_time)
merged_stats_elem.max_resp_time = max(
merged_stats_elem.max_resp_time,
stats_elem.max_resp_time)
# Temporary data, will be divided by total_seconds later on,
# after collecting all the stats.
merged_stats_elem.temp_rate += slice.total_seconds * stats_elem.rate
# Temporary data, aggregated later on
merged_stats_elem.temp_mean += stats_elem.mean
merged_stats_elem.temp_mean_count += 1
if merged_stats_elems:
mean_all_services = all_services_stats.mean / len(
merged_stats_elems)
else:
mean_all_services = 0
for value in merged_stats_elems.values():
value.all_services_time = all_services_stats.time
value.all_services_usage = all_services_stats.usage
value.time = round(value.time, 1)
if total_seconds:
value.rate = round(value.temp_rate / total_seconds, 1)
value.rate = round(value.temp_rate / total_seconds, 1)
value.mean_all_services = mean_all_services
if value.temp_mean:
value.mean = round(value.temp_mean / value.temp_mean_count)
self.set_percent_of_all_services(all_services_stats, value)
if n:
for stats_elem in self.yield_top_n(int(n), n_type,
merged_stats_elems):
yield stats_elem
else:
for stats_elem in merged_stats_elems.values():
yield stats_elem
def get_stats(self, start, stop, service='*', n=None, n_type=None, needs_trends=True,
stats_key_prefix=None, suffixes=None):
""" Returns statistics for a given interval, as defined by 'start' and 'stop'.
service default to '*' for all services in that period and may be set to return
a one-element list of information regarding that particular service. Setting 'n'
to a positive integer will make it return only top n services.
"""
if not stats_key_prefix:
stats_key_prefix = self.stats_key_prefix
stats_elems = {}
all_services_stats = Bunch({'usage':0, 'time':0})
# All mean values
mean_all_services_list = []
# A mean value of all the mean values (mean_all_services_list)
mean_all_services = 0
start = parse(start)
stop = parse(stop)
delta = (stop - start)
if hasattr(delta, 'total_seconds'):
delta_seconds = delta.total_seconds()
else:
delta_seconds = delta.seconds
if not suffixes:
suffixes = self.get_suffixes(start, stop)
# We make several passes. First two passes are made over Redis keys, one gathers the services, if any at all,
# and another one actually collects statistics for each service found. Next pass, a partly optional one,
# computes trends for mean response time and service usage. Another one computes each of the service's
# average rate and updates other attributes basing on values collected in the previous step.
# Optionally, the last one will pick only top n elements of a given type (top mean response time
# or top usage).
# 1st pass
for suffix in suffixes:
keys = self.server.kvdb.conn.keys('{}{}:{}'.format(stats_key_prefix, service, suffix))
for key in keys:
service_name = key.replace(stats_key_prefix, '').replace(':{}'.format(suffix), '')
stats_elem = StatsElem(service_name)
stats_elems[service_name] = stats_elem
# When building statistics, we can't expect there will be data for all the time
# elems built above so to guard against it, this is a dictionary whose keys are the
# said elems and values are mean/usage for each elem. The values will remain
# 0/0.0 if there is no data for the time elem, which may mean that in this
# particular time slice the service wasn't invoked at all.
stats_elem.expected_time_elems = OrderedDict(
(elem, Bunch({'mean':0, 'usage':0.0})) for elem in suffixes)
# 2nd pass
for service, stats_elem in stats_elems.items():
for suffix in suffixes:
key = '{}{}:{}'.format(stats_key_prefix, service, suffix)
# We can convert all the values to floats here to ease with computing
# all the stuff and convert them still to integers later on, when necessary.
key_values = Bunch(
((name, float(value)) for (name, value) in self.server.kvdb.conn.hgetall(key).items()))
if key_values:
time = (key_values.usage * key_values.mean)
stats_elem.time += time
mean_all_services_list.append(key_values.mean)
all_services_stats.time += time
all_services_stats.usage += key_values.usage
stats_elem.min_resp_time = min(stats_elem.min_resp_time, key_values.min)
stats_elem.max_resp_time = max(stats_elem.max_resp_time, key_values.max)
for attr in('mean', 'usage'):
stats_elem.expected_time_elems[suffix][attr] = key_values[attr]
mean_all_services = '{:.0f}'.format(sp_stats.tmean(mean_all_services_list)) if mean_all_services_list else 0
# 3rd pass (partly optional)
for stats_elem in stats_elems.values():
stats_elem.mean_all_services = mean_all_services
stats_elem.all_services_time = int(all_services_stats.time)
stats_elem.all_services_usage = int(all_services_stats.usage)
values = stats_elem.expected_time_elems.values()
stats_elem.mean_trend_int = [int(elem.mean) for elem in values]
stats_elem.usage_trend_int = [int(elem.usage) for elem in values]
stats_elem.mean = float('{:.2f}'.format(sp_stats.tmean(stats_elem.mean_trend_int)))
stats_elem.usage = sum(stats_elem.usage_trend_int)
stats_elem.rate = float('{:.2f}'.format(sum(stats_elem.usage_trend_int) / delta_seconds))
self.set_percent_of_all_services(all_services_stats, stats_elem)
if needs_trends:
stats_elem.mean_trend = ','.join(str(elem) for elem in stats_elem.mean_trend_int)
stats_elem.usage_trend = ','.join(str(elem) for elem in stats_elem.usage_trend_int)
# 4th pass (optional)
if n:
for stats_elem in self.yield_top_n(n, n_type, stats_elems):
yield stats_elem
else:
for stats_elem in stats_elems.values():
yield stats_elem
