def collect_statistics_sites_loaded(cls):
now = round_time(datetime.datetime.now(), round_to=60)
sites_loaded = []
avg_time_taken = []
last_5_min_time_taken = 0
r = redis.Redis(connection_pool=settings.REDIS_STATISTICS_POOL)
for hour in range(24):
start_hours_ago = now - datetime.timedelta(hours=hour + 1)
pipe = r.pipeline()
for m in range(60):
minute = start_hours_ago + datetime.timedelta(minutes=m)
key = "%s:%s" % (RStats.stats_type('page_load'),
minute.strftime('%s'))
pipe.get("%s:s" % key)
pipe.get("%s:a" % key)
times = pipe.execute()
counts = [int(c) for c in times[::2] if c]
avgs = [float(a) for a in times[1::2] if a]
if hour == 0:
last_5_min_time_taken = round(
sum(avgs[:1]) / max(1, sum(counts[:1])), 2)
if counts and avgs:
count = max(1, sum(counts))
avg = round(sum(avgs) / count, 3)
else:
count = 0
avg = 0
sites_loaded.append(count)
avg_time_taken.append(avg)
sites_loaded.reverse()
avg_time_taken.reverse()
values = (
('sites_loaded', json.encode(sites_loaded)),
('avg_time_taken', json.encode(avg_time_taken)),
('latest_sites_loaded', sites_loaded[-1]),
('latest_avg_time_taken', avg_time_taken[-1]),
('max_sites_loaded', max(sites_loaded)),
('max_avg_time_taken', max(1, max(avg_time_taken))),
('last_5_min_time_taken', last_5_min_time_taken),
)
for key, value in values:
cls.objects(key=key).update_one(upsert=True,
set__key=key,
set__value=value)
def collect_statistics_sites_loaded(cls):
now = round_time(datetime.datetime.now(), round_to=60)
sites_loaded = []
avg_time_taken = []
last_5_min_time_taken = 0
r = redis.Redis(connection_pool=settings.REDIS_STATISTICS_POOL)
for hour in range(24):
start_hours_ago = now - datetime.timedelta(hours=hour+1)
pipe = r.pipeline()
for m in range(60):
minute = start_hours_ago + datetime.timedelta(minutes=m)
key = "%s:%s" % (RStats.stats_type('page_load'), minute.strftime('%s'))
pipe.get("%s:s" % key)
pipe.get("%s:a" % key)
times = pipe.execute()
counts = [int(c) for c in times[::2] if c]
avgs = [float(a) for a in times[1::2] if a]
if hour == 0:
last_5_min_time_taken = round(sum(avgs[:1]) / max(1, sum(counts[:1])), 2)
if counts and avgs:
count = max(1, sum(counts))
avg = round(sum(avgs) / count, 3)
else:
count = 0
avg = 0
sites_loaded.append(count)
avg_time_taken.append(avg)
sites_loaded.reverse()
avg_time_taken.reverse()
values = (
('sites_loaded', json.encode(sites_loaded)),
('avg_time_taken', json.encode(avg_time_taken)),
('latest_sites_loaded', sites_loaded[-1]),
('latest_avg_time_taken', avg_time_taken[-1]),
('max_sites_loaded', max(sites_loaded)),
('max_avg_time_taken', max(1, max(avg_time_taken))),
('last_5_min_time_taken', last_5_min_time_taken),
)
for key, value in values:
cls.objects(key=key).update_one(upsert=True, set__key=key, set__value=value)
