from noc.pm.storage.base import TimeSeriesDatabase
from noc.pm.apps.render.data import fetchData, db
from noc.pm.apps.render.graphite.attime import parseATTime
import time
In [2]:

t = time.time()
db.fetch("system.loadavg_1min", t - 300, t)
Out[2]:
((1408085160, 1408085460, 60), [2.29, 2.0, 1.6, 2.32, 2.21])
In [3]:

db.match_entries(["test", "test1", "test1x", "test2x"], "test{1,2,3}*")
Out[3]:
['test1', 'test1x', 'test2x']
In [7]:

db.find("system.loadavg*")
Out[7]:
['system.loadavg_15min', 'system.loadavg_1min', 'system.loadavg_5min']
In [11]:

r = fetchData({"startTime": parseATTime("-1d"), "endTime": parseATTime("now")}, "system.loadavg*")
r
Out[11]:
[TimeSeries(name=system.loadavg_15min, start=1407999120, end=1408085520, step=60),
 TimeSeries(name=system.loadavg_1min, start=1407999120, end=1408085520, step=60),
 TimeSeries(name=system.loadavg_5min, start=1407999120, end=1408085520, step=60)]
In [14]:

list(r[0])[-20:]
Out[14]:
[1.64,
 1.64,
 1.58,
 1.58,
 1.58,
 1.56,
 1.52,
 1.5,
 1.48,
 1.46,
 1.43,
 1.48,
 1.49,
 1.56,
 1.61,
 1.61,
 1.6,
 1.65,
 1.64,
 1.6]