def test_recent_point_threshold(self): threshold = 100 # The min is not enabled by default stats = MetricsAggregator( 'myhost', recent_point_threshold=threshold, histogram_aggregates=DEFAULT_HISTOGRAM_AGGREGATES + ['min']) timestamp_beyond_threshold = time.time() - threshold * 2 timestamp_within_threshold = time.time() - threshold / 2 # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.first.gauge', 5, 'g') stats.submit_metric('my.first.gauge', 1, 'g', timestamp=timestamp_beyond_threshold) stats.submit_metric('my.second.gauge', 20, 'g', timestamp=timestamp_beyond_threshold) metrics = self.sort_metrics(stats.flush()) assert len(metrics) == 1 first = metrics[0] nt.assert_equals(first['metric'], 'my.first.gauge') nt.assert_equals(first['points'][0][1], 5) nt.assert_equals(first['host'], 'myhost') # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.1.gauge', 5, 'g') stats.submit_metric('my.1.gauge', 1, 'g', timestamp=timestamp_within_threshold) stats.submit_metric('my.2.counter', 20, 'c', timestamp=timestamp_within_threshold) stats.submit_metric('my.3.set', 20, 's', timestamp=timestamp_within_threshold) stats.submit_metric('my.4.histogram', 20, 'h', timestamp=timestamp_within_threshold) flush_timestamp = time.time() metrics = self.sort_metrics(stats.flush()) nt.assert_equal(len(metrics), 9) first, second, third, h1, h2, h3, h4, h5, h6 = metrics nt.assert_equals(first['metric'], 'my.1.gauge') nt.assert_equals(first['points'][0][1], 1) nt.assert_equals(first['host'], 'myhost') self.assert_almost_equal(first['points'][0][0], timestamp_within_threshold, 0.1) nt.assert_equals(second['metric'], 'my.2.counter') nt.assert_equals(second['points'][0][1], 20) self.assert_almost_equal(second['points'][0][0], flush_timestamp, 0.1) nt.assert_equals(third['metric'], 'my.3.set') nt.assert_equals(third['points'][0][1], 1) self.assert_almost_equal(third['points'][0][0], flush_timestamp, 0.1) nt.assert_equals(h1['metric'], 'my.4.histogram.95percentile') nt.assert_equals(h1['points'][0][1], 20) self.assert_almost_equal(h1['points'][0][0], flush_timestamp, 0.1) nt.assert_equal(h1['points'][0][0], h2['points'][0][0]) nt.assert_equal(h1['points'][0][0], h3['points'][0][0]) nt.assert_equal(h1['points'][0][0], h4['points'][0][0]) nt.assert_equal(h1['points'][0][0], h5['points'][0][0])
def test_recent_point_threshold(self): threshold = 100 stats = MetricsAggregator('myhost', recent_point_threshold=threshold) timestamp_beyond_threshold = time.time() - threshold*2 timestamp_within_threshold = time.time() - threshold/2 # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.first.gauge', 5, 'g') stats.submit_metric('my.first.gauge', 1, 'g', timestamp=timestamp_beyond_threshold) stats.submit_metric('my.second.gauge', 20, 'g', timestamp=timestamp_beyond_threshold) metrics = self.sort_metrics(stats.flush()) assert len(metrics) == 1 first = metrics[0] nt.assert_equals(first['metric'], 'my.first.gauge') nt.assert_equals(first['points'][0][1], 5) nt.assert_equals(first['host'], 'myhost') # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.1.gauge', 5, 'g') stats.submit_metric('my.1.gauge', 1, 'g', timestamp=timestamp_within_threshold) stats.submit_metric('my.2.counter', 20, 'c', timestamp=timestamp_within_threshold) stats.submit_metric('my.3.set', 20, 's', timestamp=timestamp_within_threshold) stats.submit_metric('my.4.histogram', 20, 'h', timestamp=timestamp_within_threshold) flush_timestamp = time.time() metrics = self.sort_metrics(stats.flush()) nt.assert_equal(len(metrics), 8) first, second, third, h1, h2, h3, h4, h5 = metrics nt.assert_equals(first['metric'], 'my.1.gauge') nt.assert_equals(first['points'][0][1], 1) nt.assert_equals(first['host'], 'myhost') self.assert_almost_equal(first['points'][0][0], timestamp_within_threshold, 0.1) nt.assert_equals(second['metric'], 'my.2.counter') nt.assert_equals(second['points'][0][1], 20) self.assert_almost_equal(second['points'][0][0], flush_timestamp, 0.1) nt.assert_equals(third['metric'], 'my.3.set') nt.assert_equals(third['points'][0][1], 1) self.assert_almost_equal(third['points'][0][0], flush_timestamp, 0.1) nt.assert_equals(h1['metric'], 'my.4.histogram.95percentile') nt.assert_equals(h1['points'][0][1], 20) self.assert_almost_equal(h1['points'][0][0], flush_timestamp, 0.1) nt.assert_equal(h1['points'][0][0], h2['points'][0][0]) nt.assert_equal(h1['points'][0][0], h3['points'][0][0]) nt.assert_equal(h1['points'][0][0], h4['points'][0][0]) nt.assert_equal(h1['points'][0][0], h5['points'][0][0])
def test_recent_point_threshold(self): threshold = 100 stats = MetricsAggregator("myhost", recent_point_threshold=threshold) timestamp_beyond_threshold = time.time() - threshold * 2 timestamp_within_threshold = time.time() - threshold / 2 # Ensure that old gauges get dropped due to old timestamps stats.submit_metric("my.first.gauge", 5, "g") stats.submit_metric("my.first.gauge", 1, "g", timestamp=timestamp_beyond_threshold) stats.submit_metric("my.second.gauge", 20, "g", timestamp=timestamp_beyond_threshold) metrics = self.sort_metrics(stats.flush()) assert len(metrics) == 1 first = metrics[0] nt.assert_equals(first["metric"], "my.first.gauge") nt.assert_equals(first["points"][0][1], 5) nt.assert_equals(first["host"], "myhost") # Ensure that old gauges get dropped due to old timestamps stats.submit_metric("my.1.gauge", 5, "g") stats.submit_metric("my.1.gauge", 1, "g", timestamp=timestamp_within_threshold) stats.submit_metric("my.2.counter", 20, "c", timestamp=timestamp_within_threshold) stats.submit_metric("my.3.set", 20, "s", timestamp=timestamp_within_threshold) stats.submit_metric("my.4.histogram", 20, "h", timestamp=timestamp_within_threshold) flush_timestamp = time.time() metrics = self.sort_metrics(stats.flush()) nt.assert_equal(len(metrics), 8) first, second, third, h1, h2, h3, h4, h5 = metrics nt.assert_equals(first["metric"], "my.1.gauge") nt.assert_equals(first["points"][0][1], 1) nt.assert_equals(first["host"], "myhost") self.assert_almost_equal(first["points"][0][0], timestamp_within_threshold, 0.1) nt.assert_equals(second["metric"], "my.2.counter") nt.assert_equals(second["points"][0][1], 20) self.assert_almost_equal(second["points"][0][0], flush_timestamp, 0.1) nt.assert_equals(third["metric"], "my.3.set") nt.assert_equals(third["points"][0][1], 1) self.assert_almost_equal(third["points"][0][0], flush_timestamp, 0.1) nt.assert_equals(h1["metric"], "my.4.histogram.95percentile") nt.assert_equals(h1["points"][0][1], 20) self.assert_almost_equal(h1["points"][0][0], flush_timestamp, 0.1) nt.assert_equal(h1["points"][0][0], h2["points"][0][0]) nt.assert_equal(h1["points"][0][0], h3["points"][0][0]) nt.assert_equal(h1["points"][0][0], h4["points"][0][0]) nt.assert_equal(h1["points"][0][0], h5["points"][0][0])
def test_source_classification(self): # The min is not enabled by default myaggregator = MetricsAggregator('myhost') # Ensure that old gauges get dropped due to old timestamps myaggregator.submit_metric('my.first.gauge', 5, 'g', source='foo') myaggregator.submit_metric('my.first.gauge', 1, 'g', source='foo') myaggregator.submit_metric('my.second.gauge', 20, 'g', source='foo') # Same metric different source myaggregator.submit_metric('my.first.gauge', 10, 'g', source='bar') myaggregator.submit_metric('my.first.gauge', 2, 'g', source='bar') myaggregator.submit_metric('my.second.gauge', 40, 'g', source='bar') # Same metric different source myaggregator.submit_metric('my.first.gauge', 15, 'g', source='haz') myaggregator.submit_metric('my.first.gauge', 3, 'g', source='haz') # Same metric from unknown sources myaggregator.submit_metric('my.first.gauge', 15, 'g') myaggregator.submit_metric('my.first.gauge', 3, 'g') metrics = self.sort_metrics(myaggregator.flush()[:-1]) assert len(metrics) == 2 sources = myaggregator.stats.get_aggregator_stats() assert len(sources['stats']) == 4 assert sources['stats']['foo'] == 2 assert sources['stats']['bar'] == 2 assert sources['stats']['haz'] == 1 assert sources['stats'][UNKNOWN_SOURCE] == 1
def test_recent_point_threshold(self): threshold = 100 # The min is not enabled by default stats = MetricsAggregator( 'myhost', recent_point_threshold=threshold, histogram_aggregates=DEFAULT_HISTOGRAM_AGGREGATES+['min'] ) timestamp_beyond_threshold = time.time() - threshold*2 timestamp_within_threshold = time.time() - threshold/2 # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.first.gauge', 5, 'g') stats.submit_metric('my.first.gauge', 1, 'g', timestamp=timestamp_beyond_threshold) stats.submit_metric('my.second.gauge', 20, 'g', timestamp=timestamp_beyond_threshold) metrics = self.sort_metrics(stats.flush()) assert len(metrics) == 1 first = metrics[0] nt.assert_equals(first[0], 'my.first.gauge') nt.assert_equals(first[2], 5) nt.assert_equals(first[3]['hostname'], 'myhost') # Ensure that old gauges get dropped due to old timestamps stats.submit_metric('my.1.gauge', 5, 'g') stats.submit_metric('my.1.gauge', 1, 'g', timestamp=timestamp_within_threshold) stats.submit_metric('my.2.counter', 20, 'c', timestamp=timestamp_within_threshold) stats.submit_metric('my.3.set', 20, 's', timestamp=timestamp_within_threshold) stats.submit_metric('my.4.histogram', 20, 'h', timestamp=timestamp_within_threshold) flush_timestamp = time.time() metrics = self.sort_metrics(stats.flush()) nt.assert_equal(len(metrics), 9) first, second, third, h1, h2, h3, h4, h5, h6 = metrics nt.assert_equals(first[0], 'my.1.gauge') nt.assert_equals(first[2], 1) nt.assert_equals(first[3]['hostname'], 'myhost') self.assert_almost_equal(first[1], timestamp_within_threshold, 0.1) nt.assert_equals(second[0], 'my.2.counter') nt.assert_equals(second[2], 20) self.assert_almost_equal(second[1], flush_timestamp, 0.1) nt.assert_equals(third[0], 'my.3.set') nt.assert_equals(third[2], 1) self.assert_almost_equal(third[1], flush_timestamp, 0.1) nt.assert_equals(h1[0], 'my.4.histogram.95percentile') nt.assert_equals(h1[2], 20) self.assert_almost_equal(h1[1], flush_timestamp, 0.1) nt.assert_equal(h1[1], h2[1]) nt.assert_equal(h1[1], h3[1]) nt.assert_equal(h1[1], h4[1]) nt.assert_equal(h1[1], h5[1])