1 # Copyright (c) 2018 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.
14 """Generation of Continuous Performance Trending and Analysis.
17 import multiprocessing
22 import plotly.offline as ploff
23 import plotly.graph_objs as plgo
24 import plotly.exceptions as plerr
27 from collections import OrderedDict
28 from datetime import datetime
30 from utils import split_outliers, archive_input_data, execute_command,\
31 classify_anomalies, Worker
34 # Command to build the html format of the report
35 HTML_BUILDER = 'sphinx-build -v -c conf_cpta -a ' \
38 '-D version="{date}" ' \
42 # .css file for the html format of the report
43 THEME_OVERRIDES = """/* override table width restrictions */
45 max-width: 1200px !important;
49 COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
50 "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
51 "Violet", "Blue", "Yellow"]
54 def generate_cpta(spec, data):
55 """Generate all formats and versions of the Continuous Performance Trending
58 :param spec: Specification read from the specification file.
59 :param data: Full data set.
60 :type spec: Specification
64 logging.info("Generating the Continuous Performance Trending and Analysis "
67 ret_code = _generate_all_charts(spec, data)
69 cmd = HTML_BUILDER.format(
70 date=datetime.utcnow().strftime('%m/%d/%Y %H:%M UTC'),
71 working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"],
72 build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"])
75 with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE]"], "w") as \
77 css_file.write(THEME_OVERRIDES)
79 with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE2]"], "w") as \
81 css_file.write(THEME_OVERRIDES)
83 archive_input_data(spec)
90 def _generate_trending_traces(in_data, job_name, build_info, moving_win_size=10,
91 show_trend_line=True, name="", color=""):
92 """Generate the trending traces:
94 - trimmed moving median (trending line)
95 - outliers, regress, progress
97 :param in_data: Full data set.
98 :param job_name: The name of job which generated the data.
99 :param build_info: Information about the builds.
100 :param moving_win_size: Window size.
101 :param show_trend_line: Show moving median (trending plot).
102 :param name: Name of the plot
103 :param color: Name of the color for the plot.
104 :type in_data: OrderedDict
106 :type build_info: dict
107 :type moving_win_size: int
108 :type show_trend_line: bool
111 :returns: Generated traces (list) and the evaluated result.
112 :rtype: tuple(traces, result)
115 data_x = list(in_data.keys())
116 data_y = list(in_data.values())
121 if "dpdk" in job_name:
122 hover_text.append("dpdk-ref: {0}<br>csit-ref: mrr-weekly-build-{1}".
123 format(build_info[job_name][str(idx)][1].
124 rsplit('~', 1)[0], idx))
125 elif "vpp" in job_name:
126 hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
127 format(build_info[job_name][str(idx)][1].
128 rsplit('~', 1)[0], idx))
129 date = build_info[job_name][str(idx)][0]
130 xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
131 int(date[9:11]), int(date[12:])))
133 data_pd = pd.Series(data_y, index=xaxis)
135 t_data, outliers = split_outliers(data_pd, outlier_const=1.5,
136 window=moving_win_size)
137 anomaly_classification = classify_anomalies(t_data, window=moving_win_size)
139 anomalies = pd.Series()
140 anomalies_colors = list()
147 if anomaly_classification:
148 for idx, item in enumerate(data_pd.items()):
149 if anomaly_classification[idx] in \
150 ("outlier", "regression", "progression"):
151 anomalies = anomalies.append(pd.Series([item[1], ],
153 anomalies_colors.append(
154 anomaly_color[anomaly_classification[idx]])
155 anomalies_colors.extend([0.0, 0.33, 0.66, 1.0])
159 trace_samples = plgo.Scatter(
167 name="{name}-thput".format(name=name),
174 hoverinfo="x+y+text+name"
176 traces = [trace_samples, ]
178 trace_anomalies = plgo.Scatter(
185 name="{name}-anomalies".format(name=name),
188 "symbol": "circle-open",
189 "color": anomalies_colors,
190 "colorscale": [[0.00, "grey"],
205 "title": "Circles Marking Data Classification",
206 "titleside": 'right',
211 "tickvals": [0.125, 0.375, 0.625, 0.875],
212 "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
220 traces.append(trace_anomalies)
223 data_trend = t_data.rolling(window=moving_win_size,
224 min_periods=2).median()
225 trace_trend = plgo.Scatter(
227 y=data_trend.tolist(),
235 name='{name}-trend'.format(name=name)
237 traces.append(trace_trend)
239 if anomaly_classification:
240 return traces, anomaly_classification[-1]
245 def _generate_all_charts(spec, input_data):
246 """Generate all charts specified in the specification file.
248 :param spec: Specification.
249 :param input_data: Full data set.
250 :type spec: Specification
251 :type input_data: InputData
254 def _generate_chart(_, data_q, graph):
255 """Generates the chart.
260 logging.info(" Generating the chart '{0}' ...".
261 format(graph.get("title", "")))
262 logs.append(("INFO", " Generating the chart '{0}' ...".
263 format(graph.get("title", ""))))
265 job_name = graph["data"].keys()[0]
271 logs.append(("INFO", " Creating the data set for the {0} '{1}'.".
272 format(graph.get("type", ""), graph.get("title", ""))))
273 data = input_data.filter_data(graph, continue_on_error=True)
275 logging.error("No data.")
279 for job, job_data in data.iteritems():
282 for index, bld in job_data.items():
283 for test_name, test in bld.items():
284 if chart_data.get(test_name, None) is None:
285 chart_data[test_name] = OrderedDict()
287 chart_data[test_name][int(index)] = \
288 test["result"]["throughput"]
289 except (KeyError, TypeError):
292 # Add items to the csv table:
293 for tst_name, tst_data in chart_data.items():
295 for bld in builds_dict[job_name]:
296 itm = tst_data.get(int(bld), '')
297 tst_lst.append(str(itm))
298 csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
303 for test_name, test_data in chart_data.items():
305 logs.append(("WARNING", "No data for the test '{0}'".
308 test_name = test_name.split('.')[-1]
309 trace, rslt = _generate_trending_traces(
312 build_info=build_info,
313 moving_win_size=win_size,
314 name='-'.join(test_name.split('-')[3:-1]),
321 # Generate the chart:
322 graph["layout"]["xaxis"]["title"] = \
323 graph["layout"]["xaxis"]["title"].format(job=job_name)
324 name_file = "{0}-{1}{2}".format(spec.cpta["output-file"],
325 graph["output-file-name"],
326 spec.cpta["output-file-type"])
328 logs.append(("INFO", " Writing the file '{0}' ...".
330 plpl = plgo.Figure(data=traces, layout=graph["layout"])
332 ploff.plot(plpl, show_link=False, auto_open=False,
334 except plerr.PlotlyEmptyDataError:
335 logs.append(("WARNING", "No data for the plot. Skipped."))
338 "job_name": job_name,
339 "csv_table": csv_tbl,
346 for job in spec.input["builds"].keys():
347 if builds_dict.get(job, None) is None:
348 builds_dict[job] = list()
349 for build in spec.input["builds"][job]:
350 status = build["status"]
351 if status != "failed" and status != "not found":
352 builds_dict[job].append(str(build["build"]))
354 # Create "build ID": "date" dict:
356 for job_name, job_data in builds_dict.items():
357 if build_info.get(job_name, None) is None:
358 build_info[job_name] = OrderedDict()
359 for build in job_data:
360 build_info[job_name][build] = (
361 input_data.metadata(job_name, build).get("generated", ""),
362 input_data.metadata(job_name, build).get("version", "")
365 work_queue = multiprocessing.JoinableQueue()
366 manager = multiprocessing.Manager()
367 data_queue = manager.Queue()
368 cpus = multiprocessing.cpu_count()
371 for cpu in range(cpus):
372 worker = Worker(work_queue,
377 workers.append(worker)
378 os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
379 format(cpu, worker.pid))
381 for chart in spec.cpta["plots"]:
382 work_queue.put((chart, ))
385 anomaly_classifications = list()
389 for job_name in builds_dict.keys():
390 if csv_tables.get(job_name, None) is None:
391 csv_tables[job_name] = list()
392 header = "Build Number:," + ",".join(builds_dict[job_name]) + '\n'
393 csv_tables[job_name].append(header)
394 build_dates = [x[0] for x in build_info[job_name].values()]
395 header = "Build Date:," + ",".join(build_dates) + '\n'
396 csv_tables[job_name].append(header)
397 versions = [x[1] for x in build_info[job_name].values()]
398 header = "Version:," + ",".join(versions) + '\n'
399 csv_tables[job_name].append(header)
401 while not data_queue.empty():
402 result = data_queue.get()
404 anomaly_classifications.extend(result["results"])
405 csv_tables[result["job_name"]].extend(result["csv_table"])
407 for item in result["logs"]:
408 if item[0] == "INFO":
409 logging.info(item[1])
410 elif item[0] == "ERROR":
411 logging.error(item[1])
412 elif item[0] == "DEBUG":
413 logging.debug(item[1])
414 elif item[0] == "CRITICAL":
415 logging.critical(item[1])
416 elif item[0] == "WARNING":
417 logging.warning(item[1])
421 # Terminate all workers
422 for worker in workers:
427 for job_name, csv_table in csv_tables.items():
428 file_name = spec.cpta["output-file"] + "-" + job_name + "-trending"
429 with open("{0}.csv".format(file_name), 'w') as file_handler:
430 file_handler.writelines(csv_table)
433 with open("{0}.csv".format(file_name), 'rb') as csv_file:
434 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
436 for row in csv_content:
437 if txt_table is None:
438 txt_table = prettytable.PrettyTable(row)
441 for idx, item in enumerate(row):
443 row[idx] = str(round(float(item) / 1000000, 2))
447 txt_table.add_row(row)
448 except Exception as err:
449 logging.warning("Error occurred while generating TXT "
450 "table:\n{0}".format(err))
452 txt_table.align["Build Number:"] = "l"
453 with open("{0}.txt".format(file_name), "w") as txt_file:
454 txt_file.write(str(txt_table))
457 if anomaly_classifications:
459 for classification in anomaly_classifications:
460 if classification == "regression" or classification == "outlier":
466 logging.info("Partial results: {0}".format(anomaly_classifications))
467 logging.info("Result: {0}".format(result))