def test_fold_sum(self):
"""
Tests that folding generates a sum of the timers.
"""
now = 10
metrics = [Timer("k", 10),
Timer("k", 15),
Timer("j", 7.4),
Timer("j", 8.6)]
result = Timer.fold(metrics, now)
assert ("timers.k.sum", 25, now) == self._get_metric("timers.k.sum", result)
assert ("timers.j.sum", 16.0, now) == self._get_metric("timers.j.sum", result)
def test_fold_count(self):
"""
Tests the counter of timers is properly computed.
"""
now = 10
metrics = [Timer("k", 10),
Timer("k", 15),
Timer("j", 7.9),
Timer("j", 8)]
result = Timer.fold(metrics, now)
assert ("timers.k.count", 2, now) == self._get_metric("timers.k.count", result)
assert ("timers.j.count", 2, now) == self._get_metric("timers.j.count", result)
def test_fold_upper(self):
"""
Tests that the upper bound for the timers is properly computed.
"""
now = 10
metrics = [Timer("k", 10),
Timer("k", 15),
Timer("j", 7.9),
Timer("j", 8)]
result = Timer.fold(metrics, now)
assert ("timers.k.upper", 15, now) == self._get_metric("timers.k.upper", result)
assert ("timers.j.upper", 8, now) == self._get_metric("timers.j.upper", result)
def test_fold_mean(self):
"""
Tests that the mean is properly generated for the
timers.
"""
now = 10
metrics = [Timer("k", 10),
Timer("k", 15),
Timer("j", 7),
Timer("j", 8)]
result = Timer.fold(metrics, now)
assert ("timers.k.mean", 12.5, now) == self._get_metric("timers.k.mean", result)
assert ("timers.j.mean", 7.5, now) == self._get_metric("timers.j.mean", result)
def test_sum_percentile(self):
"""
Tests the percentile sum is properly counted.
"""
now = 10
result = Timer.fold(self._100_timers, now)
assert ("timers.k.sum_90", 4545, now) == self._get_metric("timers.k.sum_90", result)
def test_stdev(self):
"""
Tests that the standard deviation is properly computed.
"""
numbers = [0.331002, 0.591082, 0.668996, 0.422566, 0.458904,
0.868717, 0.30459, 0.513035, 0.900689, 0.655826]
average = sum(numbers) / len(numbers)
assert int(0.205767 * 10000) == int(Timer._stdev(numbers, average) * 10000)
def test_parse_metrics_keeps_good_metrics(self, aggregator):
"""
Tests that parse_metrics will keep the good metrics in the face
of an error.
"""
message = "\n".join(["k::1|c",
"j:2|nope",
"k:2|ms"])
results = Collector(aggregator)._parse_metrics(message)
assert [Timer("k", 2)] == results
def test_fold_metrics_passes_metric_settings(self, monkeypatch):
"""
Tests that aggregators pass the proper metric settings when
folding over.
"""
now = 12
settings = {"ms": {"percentile": 80}}
metrics = [Timer("k", 20, now)]
monkeypatch.setattr(time, 'time', lambda: now)
result = Aggregator(None,
metrics_settings=settings)._fold_metrics(metrics)
print repr(result)
assert 1 == result.count(("timers.k.sum_80", 20, now))
def test_fold_stdev(self):
"""
Tests the standard deviations of counters is properly computed.
"""
now = 10
metrics = [Timer("k", 10),
Timer("k", 15),
Timer("j", 7.9),
Timer("j", 8)]
result = Timer.fold(metrics, now)
assert ("timers.k.stdev", Timer._stdev([10, 15], 12.5), now) == self._get_metric("timers.k.stdev", result)
assert ("timers.j.stdev", Timer._stdev([7.9, 8], 7.95), now) == self._get_metric("timers.j.stdev", result)
def test_parse_metrics_ignores_blank_lines(self, aggregator):
"""
Tests that parse_metrics will properly ignore blank lines.
"""
message = "\n".join(["", "k:2|ms"])
assert [Timer("k", 2)] == Collector(aggregator)._parse_metrics(message)
def _100_timers(self):
result = []
for i in xrange(1, 101):
result.append(Timer("k", i))
return result
