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 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,
91 show_trend_line=True, name="", color=""):
92 """Generate the trending traces:
94 - outliers, regress, progress
95 - average of normal samples (trending line)
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 show_trend_line: Show moving median (trending plot).
101 :param name: Name of the plot
102 :param color: Name of the color for the plot.
103 :type in_data: OrderedDict
105 :type build_info: dict
106 :type show_trend_line: bool
109 :returns: Generated traces (list) and the evaluated result.
110 :rtype: tuple(traces, result)
113 data_x = list(in_data.keys())
114 data_y = list(in_data.values())
119 if "dpdk" in job_name:
120 hover_text.append("dpdk-ref: {0}<br>csit-ref: mrr-weekly-build-{1}".
121 format(build_info[job_name][str(idx)][1].
122 rsplit('~', 1)[0], idx))
123 elif "vpp" in job_name:
124 hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
125 format(build_info[job_name][str(idx)][1].
126 rsplit('~', 1)[0], idx))
127 date = build_info[job_name][str(idx)][0]
128 xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
129 int(date[9:11]), int(date[12:])))
131 data_pd = pd.Series(data_y, index=xaxis)
133 anomaly_classification, avgs = classify_anomalies(data_pd)
135 anomalies = pd.Series()
136 anomalies_colors = list()
137 anomalies_avgs = list()
144 if anomaly_classification:
145 for idx, item in enumerate(data_pd.items()):
146 if anomaly_classification[idx] in \
147 ("outlier", "regression", "progression"):
148 anomalies = anomalies.append(pd.Series([item[1], ],
150 anomalies_colors.append(
151 anomaly_color[anomaly_classification[idx]])
152 anomalies_avgs.append(avgs[idx])
153 anomalies_colors.extend([0.0, 0.33, 0.66, 1.0])
157 trace_samples = plgo.Scatter(
165 name="{name}-thput".format(name=name),
172 hoverinfo="x+y+text+name"
174 traces = [trace_samples, ]
177 trace_trend = plgo.Scatter(
187 name='{name}-trend'.format(name=name)
189 traces.append(trace_trend)
191 trace_anomalies = plgo.Scatter(
198 name="{name}-anomalies".format(name=name),
201 "symbol": "circle-open",
202 "color": anomalies_colors,
203 "colorscale": [[0.00, "grey"],
218 "title": "Circles Marking Data Classification",
219 "titleside": 'right',
224 "tickvals": [0.125, 0.375, 0.625, 0.875],
225 "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
233 traces.append(trace_anomalies)
235 if anomaly_classification:
236 return traces, anomaly_classification[-1]
241 def _generate_all_charts(spec, input_data):
242 """Generate all charts specified in the specification file.
244 :param spec: Specification.
245 :param input_data: Full data set.
246 :type spec: Specification
247 :type input_data: InputData
250 def _generate_chart(_, data_q, graph):
251 """Generates the chart.
256 logging.info(" Generating the chart '{0}' ...".
257 format(graph.get("title", "")))
258 logs.append(("INFO", " Generating the chart '{0}' ...".
259 format(graph.get("title", ""))))
261 job_name = graph["data"].keys()[0]
267 logs.append(("INFO", " Creating the data set for the {0} '{1}'.".
268 format(graph.get("type", ""), graph.get("title", ""))))
269 data = input_data.filter_data(graph, continue_on_error=True)
271 logging.error("No data.")
275 for job, job_data in data.iteritems():
278 for index, bld in job_data.items():
279 for test_name, test in bld.items():
280 if chart_data.get(test_name, None) is None:
281 chart_data[test_name] = OrderedDict()
283 chart_data[test_name][int(index)] = \
284 test["result"]["throughput"]
285 except (KeyError, TypeError):
288 # Add items to the csv table:
289 for tst_name, tst_data in chart_data.items():
291 for bld in builds_dict[job_name]:
292 itm = tst_data.get(int(bld), '')
293 tst_lst.append(str(itm))
294 csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
299 for test_name, test_data in chart_data.items():
301 logs.append(("WARNING", "No data for the test '{0}'".
304 test_name = test_name.split('.')[-1]
305 trace, rslt = _generate_trending_traces(
308 build_info=build_info,
309 name='-'.join(test_name.split('-')[3:-1]),
316 # Generate the chart:
317 graph["layout"]["xaxis"]["title"] = \
318 graph["layout"]["xaxis"]["title"].format(job=job_name)
319 name_file = "{0}-{1}{2}".format(spec.cpta["output-file"],
320 graph["output-file-name"],
321 spec.cpta["output-file-type"])
323 logs.append(("INFO", " Writing the file '{0}' ...".
325 plpl = plgo.Figure(data=traces, layout=graph["layout"])
327 ploff.plot(plpl, show_link=False, auto_open=False,
329 except plerr.PlotlyEmptyDataError:
330 logs.append(("WARNING", "No data for the plot. Skipped."))
333 "job_name": job_name,
334 "csv_table": csv_tbl,
341 for job in spec.input["builds"].keys():
342 if builds_dict.get(job, None) is None:
343 builds_dict[job] = list()
344 for build in spec.input["builds"][job]:
345 status = build["status"]
346 if status != "failed" and status != "not found":
347 builds_dict[job].append(str(build["build"]))
349 # Create "build ID": "date" dict:
351 for job_name, job_data in builds_dict.items():
352 if build_info.get(job_name, None) is None:
353 build_info[job_name] = OrderedDict()
354 for build in job_data:
355 build_info[job_name][build] = (
356 input_data.metadata(job_name, build).get("generated", ""),
357 input_data.metadata(job_name, build).get("version", "")
360 work_queue = multiprocessing.JoinableQueue()
361 manager = multiprocessing.Manager()
362 data_queue = manager.Queue()
363 cpus = multiprocessing.cpu_count()
366 for cpu in range(cpus):
367 worker = Worker(work_queue,
372 workers.append(worker)
373 os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
374 format(cpu, worker.pid))
376 for chart in spec.cpta["plots"]:
377 work_queue.put((chart, ))
380 anomaly_classifications = list()
384 for job_name in builds_dict.keys():
385 if csv_tables.get(job_name, None) is None:
386 csv_tables[job_name] = list()
387 header = "Build Number:," + ",".join(builds_dict[job_name]) + '\n'
388 csv_tables[job_name].append(header)
389 build_dates = [x[0] for x in build_info[job_name].values()]
390 header = "Build Date:," + ",".join(build_dates) + '\n'
391 csv_tables[job_name].append(header)
392 versions = [x[1] for x in build_info[job_name].values()]
393 header = "Version:," + ",".join(versions) + '\n'
394 csv_tables[job_name].append(header)
396 while not data_queue.empty():
397 result = data_queue.get()
399 anomaly_classifications.extend(result["results"])
400 csv_tables[result["job_name"]].extend(result["csv_table"])
402 for item in result["logs"]:
403 if item[0] == "INFO":
404 logging.info(item[1])
405 elif item[0] == "ERROR":
406 logging.error(item[1])
407 elif item[0] == "DEBUG":
408 logging.debug(item[1])
409 elif item[0] == "CRITICAL":
410 logging.critical(item[1])
411 elif item[0] == "WARNING":
412 logging.warning(item[1])
416 # Terminate all workers
417 for worker in workers:
422 for job_name, csv_table in csv_tables.items():
423 file_name = spec.cpta["output-file"] + "-" + job_name + "-trending"
424 with open("{0}.csv".format(file_name), 'w') as file_handler:
425 file_handler.writelines(csv_table)
428 with open("{0}.csv".format(file_name), 'rb') as csv_file:
429 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
431 for row in csv_content:
432 if txt_table is None:
433 txt_table = prettytable.PrettyTable(row)
436 for idx, item in enumerate(row):
438 row[idx] = str(round(float(item) / 1000000, 2))
442 txt_table.add_row(row)
443 except Exception as err:
444 logging.warning("Error occurred while generating TXT "
445 "table:\n{0}".format(err))
447 txt_table.align["Build Number:"] = "l"
448 with open("{0}.txt".format(file_name), "w") as txt_file:
449 txt_file.write(str(txt_table))
452 if anomaly_classifications:
454 for classification in anomaly_classifications:
455 if classification == "regression" or classification == "outlier":
461 logging.info("Partial results: {0}".format(anomaly_classifications))
462 logging.info("Result: {0}".format(result))