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_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_metrics_stats_frame_can_add_values(): events_transport_batch_size = [10, 2, 54] events_looper_run_time_spent = [0.1, 3.4, 0.01, 0.5] events = [] for v in events_transport_batch_size: events.append((MetricsName.TRANSPORT_BATCH_SIZE, v)) for v in events_looper_run_time_spent: events.append((MetricsName.LOOPER_RUN_TIME_SPENT, v)) shuffle(events) frame = MetricsStatsFrame() for id, value in events: frame.add(id, value) assert frame.get(MetricsName.TRANSPORT_BATCH_SIZE) == ValueAccumulator(events_transport_batch_size) assert frame.get(MetricsName.LOOPER_RUN_TIME_SPENT) == ValueAccumulator(events_looper_run_time_spent) assert frame.get(MetricsName.BACKUP_THREE_PC_BATCH_SIZE) == ValueAccumulator()
def test_metrics_stats_frame_eq_has_value_semantics(): a = MetricsStatsFrame() b = MetricsStatsFrame() assert a == b a.add(MetricsName.LOOPER_RUN_TIME_SPENT, 2.0) assert a != b b.add(MetricsName.LOOPER_RUN_TIME_SPENT, 2.0) assert a == b a.add(MetricsName.BACKUP_THREE_PC_BATCH_SIZE, 1) b.add(MetricsName.TRANSPORT_BATCH_SIZE, 2) assert a != b
def _metrics_stats_frame(events): frame = MetricsStatsFrame() for ev in events: frame.add(ev.name, ev.value) return frame