1 # Copyright (c) 2022 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
17 import plotly.graph_objects as go
24 from datetime import datetime
25 from numpy import isnan
27 from ..jumpavg import classify
31 u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
32 u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
33 u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
34 u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
49 _TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
58 _TICK_TEXT_LAT = [u"Progression", u"Normal", u"Regression"]
60 "mrr": "result_receive_rate_rate_avg",
61 "ndr": "result_ndr_lower_rate_value",
62 "pdr": "result_pdr_lower_rate_value",
63 "pdr-lat": "result_latency_forward_pdr_50_avg"
66 "mrr": "result_receive_rate_rate_unit",
67 "ndr": "result_ndr_lower_rate_unit",
68 "pdr": "result_pdr_lower_rate_unit",
69 "pdr-lat": "result_latency_forward_pdr_50_unit"
71 _LAT_HDRH = ( # Do not change the order
72 "result_latency_forward_pdr_0_hdrh",
73 "result_latency_reverse_pdr_0_hdrh",
74 "result_latency_forward_pdr_10_hdrh",
75 "result_latency_reverse_pdr_10_hdrh",
76 "result_latency_forward_pdr_50_hdrh",
77 "result_latency_reverse_pdr_50_hdrh",
78 "result_latency_forward_pdr_90_hdrh",
79 "result_latency_reverse_pdr_90_hdrh",
81 # This value depends on latency stream rate (9001 pps) and duration (5s).
82 # Keep it slightly higher to ensure rounding errors to not remove tick mark.
83 PERCENTILE_MAX = 99.999501
85 _GRAPH_LAT_HDRH_DESC = {
86 u"result_latency_forward_pdr_0_hdrh": u"No-load.",
87 u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
88 u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
89 u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
90 u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
91 u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
92 u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
93 u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
97 def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
101 latencies = {"name": name}
102 for key in _LAT_HDRH:
104 latencies[key] = row[key]
111 def _classify_anomalies(data):
112 """Process the data and return anomalies and trending values.
114 Gather data into groups with average as trend value.
115 Decorate values within groups to be normal,
116 the first value of changed average as a regression, or a progression.
118 :param data: Full data set with unavailable samples replaced by nan.
119 :type data: OrderedDict
120 :returns: Classification and trend values
121 :rtype: 3-tuple, list of strings, list of floats and list of floats
123 # NaN means something went wrong.
124 # Use 0.0 to cause that being reported as a severe regression.
125 bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
126 # TODO: Make BitCountingGroupList a subclass of list again?
127 group_list = classify(bare_data).group_list
128 group_list.reverse() # Just to use .pop() for FIFO.
129 classification = list()
136 for sample in data.values():
138 classification.append(u"outlier")
140 stdevs.append(sample)
142 if values_left < 1 or active_group is None:
144 while values_left < 1: # Ignore empty groups (should not happen).
145 active_group = group_list.pop()
146 values_left = len(active_group.run_list)
147 avg = active_group.stats.avg
148 stdv = active_group.stats.stdev
149 classification.append(active_group.comment)
154 classification.append(u"normal")
158 return classification, avgs, stdevs
161 def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
165 phy = itm["phy"].split("-")
167 topo, arch, nic, drv = phy
168 if drv in ("dpdk", "ixgbe"):
172 drv = drv.replace("_", "-")
176 "weekly" if (arch == "aws" or itm["testtype"] != "mrr") else "daily"
179 f"{itm['testtype'] if itm['testtype'] == 'mrr' else 'ndrpdr'}-"
180 f"{cadence}-master-{topo}-{arch}"
182 df_sel = data.loc[(data["job"] == sel_topo_arch)]
184 f"^.*{nic}.*\.{itm['framesize']}-{itm['core']}-{drv}{itm['test']}-"
185 f"{'mrr' if itm['testtype'] == 'mrr' else 'ndrpdr'}$"
188 df_sel["test_id"].apply(
189 lambda x: True if re.search(regex, x) else False
191 ].sort_values(by="start_time", ignore_index=True)
196 def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
197 start: datetime, end: datetime, color: str) -> list:
201 df = df.dropna(subset=[_VALUE[ttype], ])
205 x_axis = [d for d in df["start_time"] if d >= start and d <= end]
209 anomalies, trend_avg, trend_stdev = _classify_anomalies(
210 {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
215 for _, row in df.iterrows():
217 f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}<br>"
218 f"<prop> [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}<br>"
220 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
221 f"csit-ref: {row['job']}/{row['build']}<br>"
222 f"hosts: {', '.join(row['hosts'])}"
226 f"stdev [{row['result_receive_rate_rate_unit']}]: "
227 f"{row['result_receive_rate_rate_stdev']}<br>"
231 hover_itm = hover_itm.replace(
232 "<prop>", "latency" if ttype == "pdr-lat" else "average"
233 ).replace("<stdev>", stdev)
234 hover.append(hover_itm)
235 if ttype == "pdr-lat":
236 customdata.append(_get_hdrh_latencies(row, name))
239 for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
241 f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}<br>"
242 f"trend [pps]: {avg}<br>"
243 f"stdev [pps]: {stdev}<br>"
244 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
245 f"csit-ref: {row['job']}/{row['build']}<br>"
246 f"hosts: {', '.join(row['hosts'])}"
248 if ttype == "pdr-lat":
249 hover_itm = hover_itm.replace("[pps]", "[us]")
250 hover_trend.append(hover_itm)
253 go.Scatter( # Samples
261 u"symbol": u"circle",
264 hoverinfo=u"text+name",
267 customdata=customdata
269 go.Scatter( # Trend line
280 hoverinfo=u"text+name",
289 anomaly_color = list()
291 for idx, anomaly in enumerate(anomalies):
292 if anomaly in (u"regression", u"progression"):
293 anomaly_x.append(x_axis[idx])
294 anomaly_y.append(trend_avg[idx])
295 anomaly_color.append(_ANOMALY_COLOR[anomaly])
297 f"date: {x_axis[idx].strftime('%d-%m-%Y %H:%M:%S')}<br>"
298 f"trend [pps]: {trend_avg[idx]}<br>"
299 f"classification: {anomaly}"
301 if ttype == "pdr-lat":
302 hover_itm = hover_itm.replace("[pps]", "[us]")
303 hover.append(hover_itm)
304 anomaly_color.extend([0.0, 0.5, 1.0])
311 hoverinfo=u"text+name",
317 u"symbol": u"circle-open",
318 u"color": anomaly_color,
319 u"colorscale": _COLORSCALE_LAT \
320 if ttype == "pdr-lat" else _COLORSCALE_TPUT,
328 u"title": u"Circles Marking Data Classification",
329 u"titleside": u"right",
333 u"tickmode": u"array",
334 u"tickvals": [0.167, 0.500, 0.833],
335 u"ticktext": _TICK_TEXT_LAT \
336 if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
349 def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
350 start: datetime, end: datetime) -> tuple:
359 for idx, itm in enumerate(sel):
361 df = select_trending_data(data, itm)
366 f"{itm['phy']}-{itm['framesize']}-{itm['core']}-"
367 f"{itm['test']}-{itm['testtype']}"
370 traces = _generate_trending_traces(
371 itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
375 fig_tput = go.Figure()
376 fig_tput.add_traces(traces)
378 if itm["testtype"] == "pdr":
379 traces = _generate_trending_traces(
380 "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
384 fig_lat = go.Figure()
385 fig_lat.add_traces(traces)
388 fig_tput.update_layout(layout.get("plot-trending-tput", dict()))
390 fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
392 return fig_tput, fig_lat
395 def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
402 for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
404 decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
405 except (hdrh.codec.HdrLengthException, TypeError) as err:
412 for item in decoded.get_recorded_iterator():
413 # The real value is "percentile".
414 # For 100%, we cut that down to "x_perc" to avoid
416 percentile = item.percentile_level_iterated_to
417 x_perc = min(percentile, PERCENTILE_MAX)
418 xaxis.append(previous_x)
419 yaxis.append(item.value_iterated_to)
421 f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
422 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
423 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
424 f"Latency: {item.value_iterated_to}uSec"
426 next_x = 100.0 / (100.0 - x_perc)
428 yaxis.append(item.value_iterated_to)
430 f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
431 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
432 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
433 f"Latency: {item.value_iterated_to}uSec"
436 prev_perc = percentile
442 name=_GRAPH_LAT_HDRH_DESC[lat_name],
444 legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
445 showlegend=bool(idx % 2),
447 color=_COLORS[int(idx/2)],
449 width=1 if idx % 2 else 2
457 fig.add_traces(traces)
458 layout_hdrh = layout.get("plot-hdrh-latency", None)
460 fig.update_layout(layout_hdrh)