csit.infra.dash/app/cdash/trending/graphs.py

   1 # Copyright (c) 2023 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:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   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.
  13
  14 """Implementation of graphs for trending data.
  15 """
  16
  17 import plotly.graph_objects as go
  18 import pandas as pd
  19
  20 from ..utils.constants import Constants as C
  21 from ..utils.utils import get_color, get_hdrh_latencies
  22 from ..utils.anomalies import classify_anomalies
  23
  24
  25 def select_trending_data(data: pd.DataFrame, itm: dict) -> pd.DataFrame:
  26     """Select the data for graphs from the provided data frame.
  27
  28     :param data: Data frame with data for graphs.
  29     :param itm: Item (in this case job name) which data will be selected from
  30         the input data frame.
  31     :type data: pandas.DataFrame
  32     :type itm: dict
  33     :returns: A data frame with selected data.
  34     :rtype: pandas.DataFrame
  35     """
  36
  37     phy = itm["phy"].split("-")
  38     if len(phy) == 4:
  39         topo, arch, nic, drv = phy
  40         if drv == "dpdk":
  41             drv = ""
  42         else:
  43             drv += "-"
  44             drv = drv.replace("_", "-")
  45     else:
  46         return None
  47
  48     if itm["testtype"] in ("ndr", "pdr"):
  49         test_type = "ndrpdr"
  50     elif itm["testtype"] == "mrr":
  51         test_type = "mrr"
  52     elif itm["area"] == "hoststack":
  53         test_type = "hoststack"
  54     df = data.loc[(
  55         (data["test_type"] == test_type) &
  56         (data["passed"] == True)
  57     )]
  58     df = df[df.job.str.endswith(f"{topo}-{arch}")]
  59     core = str() if itm["dut"] == "trex" else f"{itm['core']}"
  60     ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
  61     df = df[df.test_id.str.contains(
  62         f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
  63         regex=True
  64     )].sort_values(by="start_time", ignore_index=True)
  65
  66     return df
  67
  68
  69 def graph_trending(
  70         data: pd.DataFrame,
  71         sel: dict,
  72         layout: dict,
  73         normalize: bool
  74     ) -> tuple:
  75     """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
  76     (result_latency_forward_pdr_50_avg).
  77
  78     :param data: Data frame with test results.
  79     :param sel: Selected tests.
  80     :param layout: Layout of plot.ly graph.
  81     :param normalize: If True, the data is normalized to CPU frquency
  82         Constants.NORM_FREQUENCY.
  83     :type data: pandas.DataFrame
  84     :type sel: dict
  85     :type layout: dict
  86     :type normalize: bool
  87     :returns: Trending graph(s)
  88     :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
  89     """
  90
  91     if not sel:
  92         return None, None
  93
  94
  95     def _generate_trending_traces(
  96             ttype: str,
  97             name: str,
  98             df: pd.DataFrame,
  99             color: str,
 100             norm_factor: float
 101         ) -> list:
 102         """Generate the trending traces for the trending graph.
 103
 104         :param ttype: Test type (MRR, NDR, PDR).
 105         :param name: The test name to be displayed as the graph title.
 106         :param df: Data frame with test data.
 107         :param color: The color of the trace (samples and trend line).
 108         :param norm_factor: The factor used for normalization of the results to
 109             CPU frequency set to Constants.NORM_FREQUENCY.
 110         :type ttype: str
 111         :type name: str
 112         :type df: pandas.DataFrame
 113         :type color: str
 114         :type norm_factor: float
 115         :returns: Traces (samples, trending line, anomalies)
 116         :rtype: list
 117         """
 118
 119         df = df.dropna(subset=[C.VALUE[ttype], ])
 120         if df.empty:
 121             return list(), list()
 122
 123         x_axis = df["start_time"].tolist()
 124         if ttype == "latency":
 125             y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()]
 126         else:
 127             y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()]
 128         units = df[C.UNIT[ttype]].unique().tolist()
 129
 130         anomalies, trend_avg, trend_stdev = classify_anomalies(
 131             {k: v for k, v in zip(x_axis, y_data)}
 132         )
 133
 134         hover = list()
 135         customdata = list()
 136         customdata_samples = list()
 137         for idx, (_, row) in enumerate(df.iterrows()):
 138             hover_itm = (
 139                 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 140                 f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
 141                 f"<stdev>"
 142                 f"<additional-info>"
 143                 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 144                 f"csit-ref: {row['job']}/{row['build']}<br>"
 145                 f"hosts: {', '.join(row['hosts'])}"
 146             )
 147             if ttype == "mrr":
 148                 stdev = (
 149                     f"stdev [{row['result_receive_rate_rate_unit']}]: "
 150                     f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
 151                 )
 152             else:
 153                 stdev = str()
 154             if ttype in ("hoststack-cps", "hoststack-rps"):
 155                 add_info = (
 156                     f"bandwidth [{row[C.UNIT['hoststack-bps']]}]: "
 157                     f"{row[C.VALUE['hoststack-bps']]:,.0f}<br>"
 158                     f"latency [{row[C.UNIT['hoststack-lat']]}]: "
 159                     f"{row[C.VALUE['hoststack-lat']]:,.0f}<br>"
 160                 )
 161             else:
 162                 add_info = str()
 163             hover_itm = hover_itm.replace(
 164                 "<prop>", "latency" if ttype == "latency" else "average"
 165             ).replace("<stdev>", stdev).replace("<additional-info>", add_info)
 166             hover.append(hover_itm)
 167             if ttype == "latency":
 168                 customdata_samples.append(get_hdrh_latencies(row, name))
 169                 customdata.append({"name": name})
 170             else:
 171                 customdata_samples.append(
 172                     {"name": name, "show_telemetry": True}
 173                 )
 174                 customdata.append({"name": name})
 175
 176         hover_trend = list()
 177         for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
 178             hover_itm = (
 179                 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 180                 f"trend [{row[C.UNIT[ttype]]}]: {avg:,.0f}<br>"
 181                 f"stdev [{row[C.UNIT[ttype]]}]: {stdev:,.0f}<br>"
 182                 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 183                 f"csit-ref: {row['job']}/{row['build']}<br>"
 184                 f"hosts: {', '.join(row['hosts'])}"
 185             )
 186             if ttype == "latency":
 187                 hover_itm = hover_itm.replace("[pps]", "[us]")
 188             hover_trend.append(hover_itm)
 189
 190         traces = [
 191             go.Scatter(  # Samples
 192                 x=x_axis,
 193                 y=y_data,
 194                 name=name,
 195                 mode="markers",
 196                 marker={
 197                     "size": 5,
 198                     "color": color,
 199                     "symbol": "circle",
 200                 },
 201                 text=hover,
 202                 hoverinfo="text+name",
 203                 showlegend=True,
 204                 legendgroup=name,
 205                 customdata=customdata_samples
 206             ),
 207             go.Scatter(  # Trend line
 208                 x=x_axis,
 209                 y=trend_avg,
 210                 name=name,
 211                 mode="lines",
 212                 line={
 213                     "shape": "linear",
 214                     "width": 1,
 215                     "color": color,
 216                 },
 217                 text=hover_trend,
 218                 hoverinfo="text+name",
 219                 showlegend=False,
 220                 legendgroup=name,
 221                 customdata=customdata
 222             )
 223         ]
 224
 225         if anomalies:
 226             anomaly_x = list()
 227             anomaly_y = list()
 228             anomaly_color = list()
 229             hover = list()
 230             for idx, anomaly in enumerate(anomalies):
 231                 if anomaly in ("regression", "progression"):
 232                     anomaly_x.append(x_axis[idx])
 233                     anomaly_y.append(trend_avg[idx])
 234                     anomaly_color.append(C.ANOMALY_COLOR[anomaly])
 235                     hover_itm = (
 236                         f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 237                         f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
 238                         f"classification: {anomaly}"
 239                     )
 240                     if ttype == "latency":
 241                         hover_itm = hover_itm.replace("[pps]", "[us]")
 242                     hover.append(hover_itm)
 243             anomaly_color.extend([0.0, 0.5, 1.0])
 244             traces.append(
 245                 go.Scatter(
 246                     x=anomaly_x,
 247                     y=anomaly_y,
 248                     mode="markers",
 249                     text=hover,
 250                     hoverinfo="text+name",
 251                     showlegend=False,
 252                     legendgroup=name,
 253                     name=name,
 254                     customdata=customdata,
 255                     marker={
 256                         "size": 15,
 257                         "symbol": "circle-open",
 258                         "color": anomaly_color,
 259                         "colorscale": C.COLORSCALE_LAT \
 260                             if ttype == "latency" else C.COLORSCALE_TPUT,
 261                         "showscale": True,
 262                         "line": {
 263                             "width": 2
 264                         },
 265                         "colorbar": {
 266                             "y": 0.5,
 267                             "len": 0.8,
 268                             "title": "Circles Marking Data Classification",
 269                             "titleside": "right",
 270                             "tickmode": "array",
 271                             "tickvals": [0.167, 0.500, 0.833],
 272                             "ticktext": C.TICK_TEXT_LAT \
 273                                 if ttype == "latency" else C.TICK_TEXT_TPUT,
 274                             "ticks": "",
 275                             "ticklen": 0,
 276                             "tickangle": -90,
 277                             "thickness": 10
 278                         }
 279                     }
 280                 )
 281             )
 282
 283         return traces, units
 284
 285
 286     fig_tput = None
 287     fig_lat = None
 288     y_units = set()
 289     for idx, itm in enumerate(sel):
 290         df = select_trending_data(data, itm)
 291         if df is None or df.empty:
 292             continue
 293
 294         if normalize:
 295             phy = itm["phy"].split("-")
 296             topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 297             norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 298                 if topo_arch else 1.0
 299         else:
 300             norm_factor = 1.0
 301
 302         if itm["area"] == "hoststack":
 303             ttype = f"hoststack-{itm['testtype']}"
 304         else:
 305             ttype = itm["testtype"]
 306
 307         traces, units = _generate_trending_traces(
 308             ttype,
 309             itm["id"],
 310             df,
 311             get_color(idx),
 312             norm_factor
 313         )
 314         if traces:
 315             if not fig_tput:
 316                 fig_tput = go.Figure()
 317             fig_tput.add_traces(traces)
 318
 319         if itm["testtype"] == "pdr":
 320             traces, _ = _generate_trending_traces(
 321                 "latency",
 322                 itm["id"],
 323                 df,
 324                 get_color(idx),
 325                 norm_factor
 326             )
 327             if traces:
 328                 if not fig_lat:
 329                     fig_lat = go.Figure()
 330                 fig_lat.add_traces(traces)
 331
 332         y_units.update(units)
 333
 334     if fig_tput:
 335         fig_layout = layout.get("plot-trending-tput", dict())
 336         fig_layout["yaxis"]["title"] = \
 337             f"Throughput [{'|'.join(sorted(y_units))}]"
 338         fig_tput.update_layout(fig_layout)
 339     if fig_lat:
 340         fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
 341
 342     return fig_tput, fig_lat
 343
 344
 345 def graph_tm_trending(
 346         data: pd.DataFrame,
 347         layout: dict,
 348         all_in_one: bool=False
 349     ) -> list:
 350     """Generates one trending graph per test, each graph includes all selected
 351     metrics.
 352
 353     :param data: Data frame with telemetry data.
 354     :param layout: Layout of plot.ly graph.
 355     :param all_in_one: If True, all telemetry traces are placed in one graph,
 356         otherwise they are split to separate graphs grouped by test ID.
 357     :type data: pandas.DataFrame
 358     :type layout: dict
 359     :type all_in_one: bool
 360     :returns: List of generated graphs together with test names.
 361         list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...)
 362     :rtype: list
 363     """
 364
 365     if data.empty:
 366         return list()
 367
 368     def _generate_traces(
 369             data: pd.DataFrame,
 370             test: str,
 371             all_in_one: bool,
 372             color_index: int
 373         ) -> list:
 374         """Generates a trending graph for given test with all metrics.
 375
 376         :param data: Data frame with telemetry data for the given test.
 377         :param test: The name of the test.
 378         :param all_in_one: If True, all telemetry traces are placed in one
 379             graph, otherwise they are split to separate graphs grouped by
 380             test ID.
 381         :param color_index: The index of the test used if all_in_one is True.
 382         :type data: pandas.DataFrame
 383         :type test: str
 384         :type all_in_one: bool
 385         :type color_index: int
 386         :returns: List of traces.
 387         :rtype: list
 388         """
 389         traces = list()
 390         metrics = data.tm_metric.unique().tolist()
 391         for idx, metric in enumerate(metrics):
 392             if "-pdr" in test and "='pdr'" not in metric:
 393                 continue
 394             if "-ndr" in test and "='ndr'" not in metric:
 395                 continue
 396
 397             df = data.loc[(data["tm_metric"] == metric)]
 398             x_axis = df["start_time"].tolist()
 399             y_data = [float(itm) for itm in df["tm_value"].tolist()]
 400             hover = list()
 401             for i, (_, row) in enumerate(df.iterrows()):
 402                 if row["test_type"] == "mrr":
 403                     rate = (
 404                         f"mrr avg [{row[C.UNIT['mrr']]}]: "
 405                         f"{row[C.VALUE['mrr']]:,.0f}<br>"
 406                         f"mrr stdev [{row[C.UNIT['mrr']]}]: "
 407                         f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
 408                     )
 409                 elif row["test_type"] == "ndrpdr":
 410                     if "-pdr" in test:
 411                         rate = (
 412                             f"pdr [{row[C.UNIT['pdr']]}]: "
 413                             f"{row[C.VALUE['pdr']]:,.0f}<br>"
 414                         )
 415                     elif "-ndr" in test:
 416                         rate = (
 417                             f"ndr [{row[C.UNIT['ndr']]}]: "
 418                             f"{row[C.VALUE['ndr']]:,.0f}<br>"
 419                         )
 420                     else:
 421                         rate = str()
 422                 else:
 423                     rate = str()
 424                 hover.append(
 425                     f"date: "
 426                     f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 427                     f"value: {y_data[i]:,.2f}<br>"
 428                     f"{rate}"
 429                     f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 430                     f"csit-ref: {row['job']}/{row['build']}<br>"
 431                 )
 432             if any(y_data):
 433                 anomalies, trend_avg, trend_stdev = classify_anomalies(
 434                     {k: v for k, v in zip(x_axis, y_data)}
 435                 )
 436                 hover_trend = list()
 437                 for avg, stdev, (_, row) in \
 438                         zip(trend_avg, trend_stdev, df.iterrows()):
 439                     hover_trend.append(
 440                         f"date: "
 441                         f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 442                         f"trend: {avg:,.2f}<br>"
 443                         f"stdev: {stdev:,.2f}<br>"
 444                         f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 445                         f"csit-ref: {row['job']}/{row['build']}"
 446                     )
 447             else:
 448                 anomalies = None
 449             if all_in_one:
 450                 color = get_color(color_index * len(metrics) + idx)
 451                 metric_name = f"{test}<br>{metric}"
 452             else:
 453                 color = get_color(idx)
 454                 metric_name = metric
 455
 456             traces.append(
 457                 go.Scatter(  # Samples
 458                     x=x_axis,
 459                     y=y_data,
 460                     name=metric_name,
 461                     mode="markers",
 462                     marker={
 463                         "size": 5,
 464                         "color": color,
 465                         "symbol": "circle",
 466                     },
 467                     text=hover,
 468                     hoverinfo="text+name",
 469                     showlegend=True,
 470                     legendgroup=metric_name
 471                 )
 472             )
 473             if anomalies:
 474                 traces.append(
 475                     go.Scatter(  # Trend line
 476                         x=x_axis,
 477                         y=trend_avg,
 478                         name=metric_name,
 479                         mode="lines",
 480                         line={
 481                             "shape": "linear",
 482                             "width": 1,
 483                             "color": color,
 484                         },
 485                         text=hover_trend,
 486                         hoverinfo="text+name",
 487                         showlegend=False,
 488                         legendgroup=metric_name
 489                     )
 490                 )
 491
 492                 anomaly_x = list()
 493                 anomaly_y = list()
 494                 anomaly_color = list()
 495                 hover = list()
 496                 for idx, anomaly in enumerate(anomalies):
 497                     if anomaly in ("regression", "progression"):
 498                         anomaly_x.append(x_axis[idx])
 499                         anomaly_y.append(trend_avg[idx])
 500                         anomaly_color.append(C.ANOMALY_COLOR[anomaly])
 501                         hover_itm = (
 502                             f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}"
 503                             f"<br>trend: {trend_avg[idx]:,.2f}"
 504                             f"<br>classification: {anomaly}"
 505                         )
 506                         hover.append(hover_itm)
 507                 anomaly_color.extend([0.0, 0.5, 1.0])
 508                 traces.append(
 509                     go.Scatter(
 510                         x=anomaly_x,
 511                         y=anomaly_y,
 512                         mode="markers",
 513                         text=hover,
 514                         hoverinfo="text+name",
 515                         showlegend=False,
 516                         legendgroup=metric_name,
 517                         name=metric_name,
 518                         marker={
 519                             "size": 15,
 520                             "symbol": "circle-open",
 521                             "color": anomaly_color,
 522                             "colorscale": C.COLORSCALE_TPUT,
 523                             "showscale": True,
 524                             "line": {
 525                                 "width": 2
 526                             },
 527                             "colorbar": {
 528                                 "y": 0.5,
 529                                 "len": 0.8,
 530                                 "title": "Circles Marking Data Classification",
 531                                 "titleside": "right",
 532                                 "tickmode": "array",
 533                                 "tickvals": [0.167, 0.500, 0.833],
 534                                 "ticktext": C.TICK_TEXT_TPUT,
 535                                 "ticks": "",
 536                                 "ticklen": 0,
 537                                 "tickangle": -90,
 538                                 "thickness": 10
 539                             }
 540                         }
 541                     )
 542                 )
 543
 544         unique_metrics = set()
 545         for itm in metrics:
 546             unique_metrics.add(itm.split("{", 1)[0])
 547         return traces, unique_metrics
 548
 549     tm_trending_graphs = list()
 550     graph_layout = layout.get("plot-trending-telemetry", dict())
 551
 552     if all_in_one:
 553         all_traces = list()
 554
 555     all_metrics = set()
 556     all_tests = list()
 557     for idx, test in enumerate(data.test_name.unique()):
 558         df = data.loc[(data["test_name"] == test)]
 559         traces, metrics = _generate_traces(df, test, all_in_one, idx)
 560         if traces:
 561             all_metrics.update(metrics)
 562             if all_in_one:
 563                 all_traces.extend(traces)
 564                 all_tests.append(test)
 565             else:
 566                 graph = go.Figure()
 567                 graph.add_traces(traces)
 568                 graph.update_layout(graph_layout)
 569                 tm_trending_graphs.append((graph, [test, ], ))
 570
 571     if all_in_one:
 572         graph = go.Figure()
 573         graph.add_traces(all_traces)
 574         graph.update_layout(graph_layout)
 575         tm_trending_graphs.append((graph, all_tests, ))
 576
 577     return tm_trending_graphs, list(all_metrics)