def test_metrics_stats_can_add_values():
stats = MetricsStats()
min_ts = trunc_ts(datetime.utcnow(), stats.timestep)
next_ts = min_ts + stats.timestep
gap_ts = next_ts + 3 * stats.timestep
max_ts = gap_ts + stats.timestep
first_events = [ev for ev in generate_events(10, min_ts) if ev.timestamp < min_ts + stats.timestep]
next_events = [ev for ev in generate_events(10, next_ts) if ev.timestamp < next_ts + stats.timestep]
after_gap_events = [ev for ev in generate_events(10, gap_ts) if ev.timestamp < gap_ts + stats.timestep]
all_events = first_events + next_events + after_gap_events
for ev in all_events:
stats.add(ev.timestamp, ev.name, ev.value)
assert stats.min_ts == min_ts
assert stats.max_ts == max_ts
assert sorted(ts for ts, _ in stats.frames()) == [min_ts, next_ts, gap_ts]
assert stats.frame(min_ts) == _metrics_stats_frame(first_events)
assert stats.frame(next_ts) == _metrics_stats_frame(next_events)
assert stats.frame(gap_ts) == _metrics_stats_frame(after_gap_events)
assert stats.frame(max_ts) == MetricsStatsFrame()
assert stats.frame(min_ts - 0.2 * stats.timestep) == MetricsStatsFrame()
assert stats.frame(min_ts + 0.8 * stats.timestep) == _metrics_stats_frame(first_events)
assert stats.frame(next_ts + 1.1 * stats.timestep) == MetricsStatsFrame()
assert stats.frame(gap_ts - 0.1 * stats.timestep) == MetricsStatsFrame()
assert stats.frame(max_ts + 4.2 * stats.timestep) == MetricsStatsFrame()
def test_metrics_stats_merge_all_should_not_alter_source_frames():
events = generate_events(50)
stats = MetricsStats()
for ev in events:
stats.add(ev.timestamp, ev.name, ev.value)
frames = [frame for _, frame in stats.frames()]
saved_frames = deepcopy(frames)
MetricsStats.merge_all(frames)
assert frames == saved_frames
def test_metrics_stats_total_is_merge_of_all_frames():
events = generate_events(50)
stats = MetricsStats()
for ev in events:
stats.add(ev.timestamp, ev.name, ev.value)
expected_total = MetricsStatsFrame()
for _, frame in stats.frames():
expected_total.merge(frame)
assert stats.total == expected_total
def test_load_metrics_from_kv_store_can_load_all_values(storage):
events = generate_events(10)
step = timedelta(seconds=5)
ts = MockTimestamp()
metrics = KvStoreMetricsCollector(storage, ts)
expected_stats = MetricsStats(step)
for ev in events:
ts.value = ev.timestamp
metrics.store_event(ev.name, ev.value)
expected_stats.add(ev.timestamp, ev.name, ev.value)
stats = load_metrics_from_kv_store(storage, step=step)
assert stats == expected_stats
def test_load_metrics_from_kv_store_can_filter_values(storage):
events = generate_events(10)
step = timedelta(seconds=3)
ts = MockTimestamp()
metrics = KvStoreMetricsCollector(storage, ts)
expected_stats = MetricsStats(step)
timestamps = sorted(ev.timestamp for ev in events)
min_ts = timestamps[len(events) // 3]
max_ts = timestamps[2 * len(events) // 3]
for ev in events:
ts.value = ev.timestamp
metrics.store_event(ev.name, ev.value)
if min_ts <= ev.timestamp <= max_ts:
expected_stats.add(ev.timestamp, ev.name, ev.value)
stats = load_metrics_from_kv_store(storage, min_ts, max_ts, step)
assert stats == expected_stats
def test_metrics_stats_eq_has_value_semantics():
ts = datetime.utcnow()
a = MetricsStats()
b = MetricsStats()
assert a == b
a.add(ts, MetricsName.LOOPER_RUN_TIME_SPENT, 2.0)
assert a != b
b.add(ts, MetricsName.LOOPER_RUN_TIME_SPENT, 2.0)
assert a == b
a.add(datetime.utcnow(), MetricsName.BACKUP_THREE_PC_BATCH_SIZE, 1)
b.add(datetime.utcnow(), MetricsName.TRANSPORT_BATCH_SIZE, 2)
assert a != b