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             d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
 139             hover_itm = (
 140                 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 141                 f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
 142                 f"<stdev>"
 143                 f"<additional-info>"
 144                 f"{d_type}-ref: {row['dut_version']}<br>"
 145                 f"csit-ref: {row['job']}/{row['build']}<br>"
 146                 f"hosts: {', '.join(row['hosts'])}"
 147             )
 148             if ttype == "mrr":
 149                 stdev = (
 150                     f"stdev [{row['result_receive_rate_rate_unit']}]: "
 151                     f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
 152                 )
 153             else:
 154                 stdev = str()
 155             if ttype in ("hoststack-cps", "hoststack-rps"):
 156                 add_info = (
 157                     f"bandwidth [{row[C.UNIT['hoststack-bps']]}]: "
 158                     f"{row[C.VALUE['hoststack-bps']]:,.0f}<br>"
 159                     f"latency [{row[C.UNIT['hoststack-lat']]}]: "
 160                     f"{row[C.VALUE['hoststack-lat']]:,.0f}<br>"
 161                 )
 162             else:
 163                 add_info = str()
 164             hover_itm = hover_itm.replace(
 165                 "<prop>", "latency" if ttype == "latency" else "average"
 166             ).replace("<stdev>", stdev).replace("<additional-info>", add_info)
 167             hover.append(hover_itm)
 168             if ttype == "latency":
 169                 customdata_samples.append(get_hdrh_latencies(row, name))
 170                 customdata.append({"name": name})
 171             else:
 172                 customdata_samples.append(
 173                     {"name": name, "show_telemetry": True}
 174                 )
 175                 customdata.append({"name": name})
 176
 177         hover_trend = list()
 178         for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
 179             d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
 180             hover_itm = (
 181                 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 182                 f"trend [{row[C.UNIT[ttype]]}]: {avg:,.0f}<br>"
 183                 f"stdev [{row[C.UNIT[ttype]]}]: {stdev:,.0f}<br>"
 184                 f"{d_type}-ref: {row['dut_version']}<br>"
 185                 f"csit-ref: {row['job']}/{row['build']}<br>"
 186                 f"hosts: {', '.join(row['hosts'])}"
 187             )
 188             if ttype == "latency":
 189                 hover_itm = hover_itm.replace("[pps]", "[us]")
 190             hover_trend.append(hover_itm)
 191
 192         traces = [
 193             go.Scatter(  # Samples
 194                 x=x_axis,
 195                 y=y_data,
 196                 name=name,
 197                 mode="markers",
 198                 marker={
 199                     "size": 5,
 200                     "color": color,
 201                     "symbol": "circle",
 202                 },
 203                 text=hover,
 204                 hoverinfo="text+name",
 205                 showlegend=True,
 206                 legendgroup=name,
 207                 customdata=customdata_samples
 208             ),
 209             go.Scatter(  # Trend line
 210                 x=x_axis,
 211                 y=trend_avg,
 212                 name=name,
 213                 mode="lines",
 214                 line={
 215                     "shape": "linear",
 216                     "width": 1,
 217                     "color": color,
 218                 },
 219                 text=hover_trend,
 220                 hoverinfo="text+name",
 221                 showlegend=False,
 222                 legendgroup=name,
 223                 customdata=customdata
 224             )
 225         ]
 226
 227         if anomalies:
 228             anomaly_x = list()
 229             anomaly_y = list()
 230             anomaly_color = list()
 231             hover = list()
 232             for idx, anomaly in enumerate(anomalies):
 233                 if anomaly in ("regression", "progression"):
 234                     anomaly_x.append(x_axis[idx])
 235                     anomaly_y.append(trend_avg[idx])
 236                     anomaly_color.append(C.ANOMALY_COLOR[anomaly])
 237                     hover_itm = (
 238                         f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 239                         f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
 240                         f"classification: {anomaly}"
 241                     )
 242                     if ttype == "latency":
 243                         hover_itm = hover_itm.replace("[pps]", "[us]")
 244                     hover.append(hover_itm)
 245             anomaly_color.extend([0.0, 0.5, 1.0])
 246             traces.append(
 247                 go.Scatter(
 248                     x=anomaly_x,
 249                     y=anomaly_y,
 250                     mode="markers",
 251                     text=hover,
 252                     hoverinfo="text+name",
 253                     showlegend=False,
 254                     legendgroup=name,
 255                     name=name,
 256                     customdata=customdata,
 257                     marker={
 258                         "size": 15,
 259                         "symbol": "circle-open",
 260                         "color": anomaly_color,
 261                         "colorscale": C.COLORSCALE_LAT \
 262                             if ttype == "latency" else C.COLORSCALE_TPUT,
 263                         "showscale": True,
 264                         "line": {
 265                             "width": 2
 266                         },
 267                         "colorbar": {
 268                             "y": 0.5,
 269                             "len": 0.8,
 270                             "title": "Circles Marking Data Classification",
 271                             "titleside": "right",
 272                             "tickmode": "array",
 273                             "tickvals": [0.167, 0.500, 0.833],
 274                             "ticktext": C.TICK_TEXT_LAT \
 275                                 if ttype == "latency" else C.TICK_TEXT_TPUT,
 276                             "ticks": "",
 277                             "ticklen": 0,
 278                             "tickangle": -90,
 279                             "thickness": 10
 280                         }
 281                     }
 282                 )
 283             )
 284
 285         return traces, units
 286
 287
 288     fig_tput = None
 289     fig_lat = None
 290     y_units = set()
 291     for idx, itm in enumerate(sel):
 292         df = select_trending_data(data, itm)
 293         if df is None or df.empty:
 294             continue
 295
 296         if normalize:
 297             phy = itm["phy"].split("-")
 298             topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 299             norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 300                 if topo_arch else 1.0
 301         else:
 302             norm_factor = 1.0
 303
 304         if itm["area"] == "hoststack":
 305             ttype = f"hoststack-{itm['testtype']}"
 306         else:
 307             ttype = itm["testtype"]
 308
 309         traces, units = _generate_trending_traces(
 310             ttype,
 311             itm["id"],
 312             df,
 313             get_color(idx),
 314             norm_factor
 315         )
 316         if traces:
 317             if not fig_tput:
 318                 fig_tput = go.Figure()
 319             fig_tput.add_traces(traces)
 320
 321         if itm["testtype"] == "pdr":
 322             traces, _ = _generate_trending_traces(
 323                 "latency",
 324                 itm["id"],
 325                 df,
 326                 get_color(idx),
 327                 norm_factor
 328             )
 329             if traces:
 330                 if not fig_lat:
 331                     fig_lat = go.Figure()
 332                 fig_lat.add_traces(traces)
 333
 334         y_units.update(units)
 335
 336     if fig_tput:
 337         fig_layout = layout.get("plot-trending-tput", dict())
 338         fig_layout["yaxis"]["title"] = \
 339             f"Throughput [{'|'.join(sorted(y_units))}]"
 340         fig_tput.update_layout(fig_layout)
 341     if fig_lat:
 342         fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
 343
 344     return fig_tput, fig_lat
 345
 346
 347 def graph_tm_trending(
 348         data: pd.DataFrame,
 349         layout: dict,
 350         all_in_one: bool=False
 351     ) -> list:
 352     """Generates one trending graph per test, each graph includes all selected
 353     metrics.
 354
 355     :param data: Data frame with telemetry data.
 356     :param layout: Layout of plot.ly graph.
 357     :param all_in_one: If True, all telemetry traces are placed in one graph,
 358         otherwise they are split to separate graphs grouped by test ID.
 359     :type data: pandas.DataFrame
 360     :type layout: dict
 361     :type all_in_one: bool
 362     :returns: List of generated graphs together with test names.
 363         list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...)
 364     :rtype: list
 365     """
 366
 367     if data.empty:
 368         return list()
 369
 370     def _generate_traces(
 371             data: pd.DataFrame,
 372             test: str,
 373             all_in_one: bool,
 374             color_index: int
 375         ) -> list:
 376         """Generates a trending graph for given test with all metrics.
 377
 378         :param data: Data frame with telemetry data for the given test.
 379         :param test: The name of the test.
 380         :param all_in_one: If True, all telemetry traces are placed in one
 381             graph, otherwise they are split to separate graphs grouped by
 382             test ID.
 383         :param color_index: The index of the test used if all_in_one is True.
 384         :type data: pandas.DataFrame
 385         :type test: str
 386         :type all_in_one: bool
 387         :type color_index: int
 388         :returns: List of traces.
 389         :rtype: list
 390         """
 391         traces = list()
 392         metrics = data.tm_metric.unique().tolist()
 393         for idx, metric in enumerate(metrics):
 394             if "-pdr" in test and "='pdr'" not in metric:
 395                 continue
 396             if "-ndr" in test and "='ndr'" not in metric:
 397                 continue
 398
 399             df = data.loc[(data["tm_metric"] == metric)]
 400             x_axis = df["start_time"].tolist()
 401             y_data = [float(itm) for itm in df["tm_value"].tolist()]
 402             hover = list()
 403             for i, (_, row) in enumerate(df.iterrows()):
 404                 if row["test_type"] == "mrr":
 405                     rate = (
 406                         f"mrr avg [{row[C.UNIT['mrr']]}]: "
 407                         f"{row[C.VALUE['mrr']]:,.0f}<br>"
 408                         f"mrr stdev [{row[C.UNIT['mrr']]}]: "
 409                         f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
 410                     )
 411                 elif row["test_type"] == "ndrpdr":
 412                     if "-pdr" in test:
 413                         rate = (
 414                             f"pdr [{row[C.UNIT['pdr']]}]: "
 415                             f"{row[C.VALUE['pdr']]:,.0f}<br>"
 416                         )
 417                     elif "-ndr" in test:
 418                         rate = (
 419                             f"ndr [{row[C.UNIT['ndr']]}]: "
 420                             f"{row[C.VALUE['ndr']]:,.0f}<br>"
 421                         )
 422                     else:
 423                         rate = str()
 424                 else:
 425                     rate = str()
 426                 hover.append(
 427                     f"date: "
 428                     f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 429                     f"value: {y_data[i]:,.2f}<br>"
 430                     f"{rate}"
 431                     f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 432                     f"csit-ref: {row['job']}/{row['build']}<br>"
 433                 )
 434             if any(y_data):
 435                 anomalies, trend_avg, trend_stdev = classify_anomalies(
 436                     {k: v for k, v in zip(x_axis, y_data)}
 437                 )
 438                 hover_trend = list()
 439                 for avg, stdev, (_, row) in \
 440                         zip(trend_avg, trend_stdev, df.iterrows()):
 441                     hover_trend.append(
 442                         f"date: "
 443                         f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
 444                         f"trend: {avg:,.2f}<br>"
 445                         f"stdev: {stdev:,.2f}<br>"
 446                         f"{row['dut_type']}-ref: {row['dut_version']}<br>"
 447                         f"csit-ref: {row['job']}/{row['build']}"
 448                     )
 449             else:
 450                 anomalies = None
 451             if all_in_one:
 452                 color = get_color(color_index * len(metrics) + idx)
 453                 metric_name = f"{test}<br>{metric}"
 454             else:
 455                 color = get_color(idx)
 456                 metric_name = metric
 457
 458             traces.append(
 459                 go.Scatter(  # Samples
 460                     x=x_axis,
 461                     y=y_data,
 462                     name=metric_name,
 463                     mode="markers",
 464                     marker={
 465                         "size": 5,
 466                         "color": color,
 467                         "symbol": "circle",
 468                     },
 469                     text=hover,
 470                     hoverinfo="text+name",
 471                     showlegend=True,
 472                     legendgroup=metric_name
 473                 )
 474             )
 475             if anomalies:
 476                 traces.append(
 477                     go.Scatter(  # Trend line
 478                         x=x_axis,
 479                         y=trend_avg,
 480                         name=metric_name,
 481                         mode="lines",
 482                         line={
 483                             "shape": "linear",
 484                             "width": 1,
 485                             "color": color,
 486                         },
 487                         text=hover_trend,
 488                         hoverinfo="text+name",
 489                         showlegend=False,
 490                         legendgroup=metric_name
 491                     )
 492                 )
 493
 494                 anomaly_x = list()
 495                 anomaly_y = list()
 496                 anomaly_color = list()
 497                 hover = list()
 498                 for idx, anomaly in enumerate(anomalies):
 499                     if anomaly in ("regression", "progression"):
 500                         anomaly_x.append(x_axis[idx])
 501                         anomaly_y.append(trend_avg[idx])
 502                         anomaly_color.append(C.ANOMALY_COLOR[anomaly])
 503                         hover_itm = (
 504                             f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}"
 505                             f"<br>trend: {trend_avg[idx]:,.0f}"
 506                             f"<br>classification: {anomaly}"
 507                         )
 508                         hover.append(hover_itm)
 509                 anomaly_color.extend([0.0, 0.5, 1.0])
 510                 traces.append(
 511                     go.Scatter(
 512                         x=anomaly_x,
 513                         y=anomaly_y,
 514                         mode="markers",
 515                         text=hover,
 516                         hoverinfo="text+name",
 517                         showlegend=False,
 518                         legendgroup=metric_name,
 519                         name=metric_name,
 520                         marker={
 521                             "size": 15,
 522                             "symbol": "circle-open",
 523                             "color": anomaly_color,
 524                             "colorscale": C.COLORSCALE_TPUT,
 525                             "showscale": True,
 526                             "line": {
 527                                 "width": 2
 528                             },
 529                             "colorbar": {
 530                                 "y": 0.5,
 531                                 "len": 0.8,
 532                                 "title": "Circles Marking Data Classification",
 533                                 "titleside": "right",
 534                                 "tickmode": "array",
 535                                 "tickvals": [0.167, 0.500, 0.833],
 536                                 "ticktext": C.TICK_TEXT_TPUT,
 537                                 "ticks": "",
 538                                 "ticklen": 0,
 539                                 "tickangle": -90,
 540                                 "thickness": 10
 541                             }
 542                         }
 543                     )
 544                 )
 545
 546         unique_metrics = set()
 547         for itm in metrics:
 548             unique_metrics.add(itm.split("{", 1)[0])
 549         return traces, unique_metrics
 550
 551     tm_trending_graphs = list()
 552     graph_layout = layout.get("plot-trending-telemetry", dict())
 553
 554     if all_in_one:
 555         all_traces = list()
 556
 557     all_metrics = set()
 558     all_tests = list()
 559     for idx, test in enumerate(data.test_name.unique()):
 560         df = data.loc[(data["test_name"] == test)]
 561         traces, metrics = _generate_traces(df, test, all_in_one, idx)
 562         if traces:
 563             all_metrics.update(metrics)
 564             if all_in_one:
 565                 all_traces.extend(traces)
 566                 all_tests.append(test)
 567             else:
 568                 graph = go.Figure()
 569                 graph.add_traces(traces)
 570                 graph.update_layout(graph_layout)
 571                 tm_trending_graphs.append((graph, [test, ], ))
 572
 573     if all_in_one:
 574         graph = go.Figure()
 575         graph.add_traces(all_traces)
 576         graph.update_layout(graph_layout)
 577         tm_trending_graphs.append((graph, all_tests, ))
 578
 579     return tm_trending_graphs, all_metrics