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(
168 name="{name}".format(name=name),
175 hoverinfo="x+y+text+name"
177 traces = [trace_samples, ]
179 trace_anomalies = plgo.Scatter(
186 name="{name}-anomalies".format(name=name),
189 "symbol": "circle-open",
190 "color": anomalies_colors,
191 "colorscale": [[0.00, "grey"],
206 "title": "Circles Marking Data Classification",
207 "titleside": 'right',
212 "tickvals": [0.125, 0.375, 0.625, 0.875],
213 "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
221 traces.append(trace_anomalies)
224 data_trend = t_data.rolling(window=moving_win_size,
225 min_periods=2).median()
226 trace_trend = plgo.Scatter(
228 y=data_trend.tolist(),
237 name='{name}-trend'.format(name=name)
239 traces.append(trace_trend)
241 if anomaly_classification:
242 return traces, anomaly_classification[-1]
247 def _generate_all_charts(spec, input_data):
248 """Generate all charts specified in the specification file.
250 :param spec: Specification.
251 :param input_data: Full data set.
252 :type spec: Specification
253 :type input_data: InputData
256 def _generate_chart(_, data_q, graph):
257 """Generates the chart.
262 logging.info(" Generating the chart '{0}' ...".
263 format(graph.get("title", "")))
264 logs.append(("INFO", " Generating the chart '{0}' ...".
265 format(graph.get("title", ""))))
267 job_name = graph["data"].keys()[0]
273 logs.append(("INFO", " Creating the data set for the {0} '{1}'.".
274 format(graph.get("type", ""), graph.get("title", ""))))
275 data = input_data.filter_data(graph, continue_on_error=True)
277 logging.error("No data.")
281 for job, job_data in data.iteritems():
284 for index, bld in job_data.items():
285 for test_name, test in bld.items():
286 if chart_data.get(test_name, None) is None:
287 chart_data[test_name] = OrderedDict()
289 chart_data[test_name][int(index)] = \
290 test["result"]["throughput"]
291 except (KeyError, TypeError):
294 # Add items to the csv table:
295 for tst_name, tst_data in chart_data.items():
297 for bld in builds_dict[job_name]:
298 itm = tst_data.get(int(bld), '')
299 tst_lst.append(str(itm))
300 csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
305 for test_name, test_data in chart_data.items():
307 logs.append(("WARNING", "No data for the test '{0}'".
310 test_name = test_name.split('.')[-1]
311 trace, rslt = _generate_trending_traces(
314 build_info=build_info,
315 moving_win_size=win_size,
316 name='-'.join(test_name.split('-')[3:-1]),
323 # Generate the chart:
324 graph["layout"]["xaxis"]["title"] = \
325 graph["layout"]["xaxis"]["title"].format(job=job_name)
326 name_file = "{0}-{1}{2}".format(spec.cpta["output-file"],
327 graph["output-file-name"],
328 spec.cpta["output-file-type"])
330 logs.append(("INFO", " Writing the file '{0}' ...".
332 plpl = plgo.Figure(data=traces, layout=graph["layout"])
334 ploff.plot(plpl, show_link=False, auto_open=False,
336 except plerr.PlotlyEmptyDataError:
337 logs.append(("WARNING", "No data for the plot. Skipped."))
340 "job_name": job_name,
341 "csv_table": csv_tbl,
348 for job in spec.input["builds"].keys():
349 if builds_dict.get(job, None) is None:
350 builds_dict[job] = list()
351 for build in spec.input["builds"][job]:
352 status = build["status"]
353 if status != "failed" and status != "not found":
354 builds_dict[job].append(str(build["build"]))
356 # Create "build ID": "date" dict:
358 for job_name, job_data in builds_dict.items():
359 if build_info.get(job_name, None) is None:
360 build_info[job_name] = OrderedDict()
361 for build in job_data:
362 build_info[job_name][build] = (
363 input_data.metadata(job_name, build).get("generated", ""),
364 input_data.metadata(job_name, build).get("version", "")
367 work_queue = multiprocessing.JoinableQueue()
368 manager = multiprocessing.Manager()
369 data_queue = manager.Queue()
370 cpus = multiprocessing.cpu_count()
373 for cpu in range(cpus):
374 worker = Worker(work_queue,
379 workers.append(worker)
380 os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
381 format(cpu, worker.pid))
383 for chart in spec.cpta["plots"]:
384 work_queue.put((chart, ))
387 anomaly_classifications = list()
391 for job_name in builds_dict.keys():
392 if csv_tables.get(job_name, None) is None:
393 csv_tables[job_name] = list()
394 header = "Build Number:," + ",".join(builds_dict[job_name]) + '\n'
395 csv_tables[job_name].append(header)
396 build_dates = [x[0] for x in build_info[job_name].values()]
397 header = "Build Date:," + ",".join(build_dates) + '\n'
398 csv_tables[job_name].append(header)
399 versions = [x[1] for x in build_info[job_name].values()]
400 header = "Version:," + ",".join(versions) + '\n'
401 csv_tables[job_name].append(header)
403 while not data_queue.empty():
404 result = data_queue.get()
406 anomaly_classifications.extend(result["results"])
407 csv_tables[result["job_name"]].extend(result["csv_table"])
409 for item in result["logs"]:
410 if item[0] == "INFO":
411 logging.info(item[1])
412 elif item[0] == "ERROR":
413 logging.error(item[1])
414 elif item[0] == "DEBUG":
415 logging.debug(item[1])
416 elif item[0] == "CRITICAL":
417 logging.critical(item[1])
418 elif item[0] == "WARNING":
419 logging.warning(item[1])
423 # Terminate all workers
424 for worker in workers:
429 for job_name, csv_table in csv_tables.items():
430 file_name = spec.cpta["output-file"] + "-" + job_name + "-trending"
431 with open("{0}.csv".format(file_name), 'w') as file_handler:
432 file_handler.writelines(csv_table)
435 with open("{0}.csv".format(file_name), 'rb') as csv_file:
436 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
438 for row in csv_content:
439 if txt_table is None:
440 txt_table = prettytable.PrettyTable(row)
443 for idx, item in enumerate(row):
445 row[idx] = str(round(float(item) / 1000000, 2))
449 txt_table.add_row(row)
450 except Exception as err:
451 logging.warning("Error occurred while generating TXT "
452 "table:\n{0}".format(err))
454 txt_table.align["Build Number:"] = "l"
455 with open("{0}.txt".format(file_name), "w") as txt_file:
456 txt_file.write(str(txt_table))
459 if anomaly_classifications:
461 for classification in anomaly_classifications:
462 if classification == "regression" or classification == "outlier":
468 logging.info("Partial results: {0}".format(anomaly_classifications))
469 logging.info("Result: {0}".format(result))