def testAddRescale(self):
s1 = timeseries.Timeseries()
for i in range(0, 5):
s1.Append(i, i * 1000)
s2 = timeseries.Timeseries()
for i in range(0, 5):
s2.Append(2 * i, i * 1000)
s1.Add(s2)
for i in range(0, 5):
self.assertEqual(3 * i, s1.data[i][0])
s1.Rescale(1 / 3.0)
for i in range(0, 5):
self.assertEqual(i, s1.data[i][0])
def testMean(self):
s = timeseries.Timeseries()
self.assertEqual(None, s.Mean())
s = self.makeSeries()
self.assertEqual(100, len(s.data))
self.assertEqual(50, s.Mean())
def ts(self):
"""Return single timeseries.Timeseries built by this query."""
if self.time_series is None:
raise RuntimeError("Time series weren't built yet.")
if not self.time_series:
return timeseries.Timeseries()
return self.time_series[0]
def testMakeIncreasing(self):
s = timeseries.Timeseries()
for i in range(0, 5):
s.Append(i, i * 1000)
for i in range(0, 5):
s.Append(i, (i + 6) * 1000)
self.assertEqual(10, len(s.data))
self.assertEqual([4, 10000], s.data[-1])
s.MakeIncreasing()
self.assertEqual(10, len(s.data))
self.assertEqual([8, 10000], s.data[-1])
def testNormalizeFillsGapsWithNone(self):
s = timeseries.Timeseries()
for i in range(21, 51):
s.Append(i, (i + 5) * 10000)
for i in range(81, 101):
s.Append(i, (i + 5) * 10000)
s.Normalize(10 * 10000, 10 * 10000, 120 * 10000)
self.assertEqual(11, len(s.data))
self.assertEqual([None, 100000], s.data[0])
self.assertEqual([22.5, 200000], s.data[1])
self.assertEqual([None, 600000], s.data[5])
self.assertEqual([None, 1100000], s.data[-1])
def testNormalize(self):
s = self.makeSeries()
s.Normalize(10 * 10000, 100000, 600000)
self.assertEqual(5, len(s.data))
self.assertEqual([9.5, 100000], s.data[0])
self.assertEqual([49.5, 500000], s.data[-1])
s = timeseries.Timeseries()
for i in range(0, 1000):
s.Append(0.5, i * 10)
s.Normalize(200, 5000, 10000)
self.assertEqual(25, len(s.data))
self.assertListEqual(s.data[0], [0.5, 5000])
self.assertListEqual(s.data[24], [0.5, 9800])
s = timeseries.Timeseries()
for i in range(0, 1000):
s.Append(i, i * 10)
s.Normalize(200, 5000, 10000, mode=timeseries.NORMALIZE_MODE_COUNTER)
self.assertEqual(25, len(s.data))
self.assertListEqual(s.data[0], [519, 5000])
self.assertListEqual(s.data[24], [999, 9800])
def _TimeSeriesFromData(self, data, attr=None):
"""Build time series from StatsStore data."""
series = timeseries.Timeseries()
for value, timestamp in data:
if attr:
try:
series.Append(getattr(value, attr), timestamp)
except AttributeError:
raise ValueError("Can't find attribute %s in value %s." % (attr,
value))
else:
if hasattr(value, "sum") or hasattr(value, "count"):
raise ValueError("Can't treat complext type as simple value: %s" %
value)
series.Append(value, timestamp)
return series
def testToDeltas(self):
s = self.makeSeries()
self.assertEqual(100, len(s.data))
s.ToDeltas()
self.assertEqual(99, len(s.data))
self.assertEqual([1, 60000], s.data[0])
self.assertEqual([1, 1040000], s.data[-1])
s = timeseries.Timeseries()
for i in range(0, 1000):
s.Append(i, i * 1e6)
s.Normalize(20 * 1e6,
500 * 1e6,
1000 * 1e6,
mode=timeseries.NORMALIZE_MODE_COUNTER)
self.assertEqual(25, len(s.data))
self.assertListEqual(s.data[0], [519, int(500 * 1e6)])
s.ToDeltas()
self.assertEqual(24, len(s.data))
self.assertListEqual(s.data[0], [20, int(500 * 1e6)])
self.assertListEqual(s.data[23], [20, int(960 * 1e6)])
def Handle(self, args, token=None):
start_time = args.start
end_time = args.end
if not end_time:
end_time = rdfvalue.RDFDatetime.Now()
if not start_time:
start_time = end_time - rdfvalue.Duration("30m")
fd = aff4.FACTORY.Create(args.client_id.ToClientURN().Add("stats"),
aff4_type=aff4_stats.ClientStats,
mode="r",
token=token,
age=(start_time, end_time))
stat_values = list(fd.GetValuesForAttribute(fd.Schema.STATS))
points = []
for stat_value in reversed(stat_values):
if args.metric == args.Metric.CPU_PERCENT:
points.extend((s.cpu_percent, s.timestamp)
for s in stat_value.cpu_samples)
elif args.metric == args.Metric.CPU_SYSTEM:
points.extend((s.system_cpu_time, s.timestamp)
for s in stat_value.cpu_samples)
elif args.metric == args.Metric.CPU_USER:
points.extend((s.user_cpu_time, s.timestamp)
for s in stat_value.cpu_samples)
elif args.metric == args.Metric.IO_READ_BYTES:
points.extend(
(s.read_bytes, s.timestamp) for s in stat_value.io_samples)
elif args.metric == args.Metric.IO_WRITE_BYTES:
points.extend((s.write_bytes, s.timestamp)
for s in stat_value.io_samples)
elif args.metric == args.Metric.IO_READ_OPS:
points.extend(
(s.read_count, s.timestamp) for s in stat_value.io_samples)
elif args.metric == args.Metric.IO_WRITE_OPS:
points.extend((s.write_count, s.timestamp)
for s in stat_value.io_samples)
elif args.metric == args.Metric.NETWORK_BYTES_RECEIVED:
points.append((stat_value.bytes_received, stat_value.age))
elif args.metric == args.Metric.NETWORK_BYTES_SENT:
points.append((stat_value.bytes_sent, stat_value.age))
elif args.metric == args.Metric.MEMORY_PERCENT:
points.append((stat_value.memory_percent, stat_value.age))
elif args.metric == args.Metric.MEMORY_RSS_SIZE:
points.append((stat_value.RSS_size, stat_value.age))
elif args.metric == args.Metric.MEMORY_VMS_SIZE:
points.append((stat_value.VMS_size, stat_value.age))
else:
raise ValueError("Unknown metric.")
# Points collected from "cpu_samples" and "io_samples" may not be correctly
# sorted in some cases (as overlaps between different stat_values are
# possible).
points.sort(key=lambda x: x[1])
ts = timeseries.Timeseries()
ts.MultiAppend(points)
if args.metric not in self.GAUGE_METRICS:
ts.MakeIncreasing()
if len(stat_values) > self.MAX_SAMPLES:
sampling_interval = rdfvalue.Duration.FromSeconds(
((end_time - start_time).seconds / self.MAX_SAMPLES) or 1)
if args.metric in self.GAUGE_METRICS:
mode = timeseries.NORMALIZE_MODE_GAUGE
else:
mode = timeseries.NORMALIZE_MODE_COUNTER
ts.Normalize(sampling_interval, start_time, end_time, mode=mode)
result = ApiGetClientLoadStatsResult()
for value, timestamp in ts.data:
dp = api_stats.ApiStatsStoreMetricDataPoint(timestamp=timestamp,
value=value)
result.data_points.append(dp)
return result
def makeSeries(self):
s = timeseries.Timeseries()
for i in range(1, 101):
s.Append(i, (i + 5) * 10000)
return s
