1 # Copyright (c) 2019 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 """Algorithms to generate tables.
22 from string import replace
23 from collections import OrderedDict
24 from numpy import nan, isnan
25 from xml.etree import ElementTree as ET
26 from datetime import datetime as dt
27 from datetime import timedelta
29 from utils import mean, stdev, relative_change, classify_anomalies, \
30 convert_csv_to_pretty_txt
33 REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
36 def generate_tables(spec, data):
37 """Generate all tables specified in the specification file.
39 :param spec: Specification read from the specification file.
40 :param data: Data to process.
41 :type spec: Specification
45 logging.info("Generating the tables ...")
46 for table in spec.tables:
48 eval(table["algorithm"])(table, data)
49 except NameError as err:
50 logging.error("Probably algorithm '{alg}' is not defined: {err}".
51 format(alg=table["algorithm"], err=repr(err)))
55 def table_details(table, input_data):
56 """Generate the table(s) with algorithm: table_detailed_test_results
57 specified in the specification file.
59 :param table: Table to generate.
60 :param input_data: Data to process.
61 :type table: pandas.Series
62 :type input_data: InputData
65 logging.info(" Generating the table {0} ...".
66 format(table.get("title", "")))
69 logging.info(" Creating the data set for the {0} '{1}'.".
70 format(table.get("type", ""), table.get("title", "")))
71 data = input_data.filter_data(table)
73 # Prepare the header of the tables
75 for column in table["columns"]:
76 header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
78 # Generate the data for the table according to the model in the table
80 job = table["data"].keys()[0]
81 build = str(table["data"][job][0])
83 suites = input_data.suites(job, build)
85 logging.error(" No data available. The table will not be generated.")
88 for suite_longname, suite in suites.iteritems():
90 suite_name = suite["name"]
92 for test in data[job][build].keys():
93 if data[job][build][test]["parent"] in suite_name:
95 for column in table["columns"]:
97 col_data = str(data[job][build][test][column["data"].
98 split(" ")[1]]).replace('"', '""')
99 if column["data"].split(" ")[1] in ("conf-history",
101 col_data = replace(col_data, " |br| ", "",
103 col_data = " |prein| {0} |preout| ".\
104 format(col_data[:-5])
105 row_lst.append('"{0}"'.format(col_data))
107 row_lst.append("No data")
108 table_lst.append(row_lst)
110 # Write the data to file
112 file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
113 table["output-file-ext"])
114 logging.info(" Writing file: '{}'".format(file_name))
115 with open(file_name, "w") as file_handler:
116 file_handler.write(",".join(header) + "\n")
117 for item in table_lst:
118 file_handler.write(",".join(item) + "\n")
120 logging.info(" Done.")
123 def table_merged_details(table, input_data):
124 """Generate the table(s) with algorithm: table_merged_details
125 specified in the specification file.
127 :param table: Table to generate.
128 :param input_data: Data to process.
129 :type table: pandas.Series
130 :type input_data: InputData
133 logging.info(" Generating the table {0} ...".
134 format(table.get("title", "")))
137 logging.info(" Creating the data set for the {0} '{1}'.".
138 format(table.get("type", ""), table.get("title", "")))
139 data = input_data.filter_data(table)
140 data = input_data.merge_data(data)
141 data.sort_index(inplace=True)
143 logging.info(" Creating the data set for the {0} '{1}'.".
144 format(table.get("type", ""), table.get("title", "")))
145 suites = input_data.filter_data(table, data_set="suites")
146 suites = input_data.merge_data(suites)
148 # Prepare the header of the tables
150 for column in table["columns"]:
151 header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
153 for _, suite in suites.iteritems():
155 suite_name = suite["name"]
157 for test in data.keys():
158 if data[test]["parent"] in suite_name:
160 for column in table["columns"]:
162 col_data = str(data[test][column["data"].
163 split(" ")[1]]).replace('"', '""')
164 col_data = replace(col_data, "No Data",
166 if column["data"].split(" ")[1] in ("conf-history",
168 col_data = replace(col_data, " |br| ", "",
170 col_data = " |prein| {0} |preout| ".\
171 format(col_data[:-5])
172 row_lst.append('"{0}"'.format(col_data))
174 row_lst.append('"Not captured"')
175 table_lst.append(row_lst)
177 # Write the data to file
179 file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
180 table["output-file-ext"])
181 logging.info(" Writing file: '{}'".format(file_name))
182 with open(file_name, "w") as file_handler:
183 file_handler.write(",".join(header) + "\n")
184 for item in table_lst:
185 file_handler.write(",".join(item) + "\n")
187 logging.info(" Done.")
190 def table_performance_comparison(table, input_data):
191 """Generate the table(s) with algorithm: table_performance_comparison
192 specified in the specification file.
194 :param table: Table to generate.
195 :param input_data: Data to process.
196 :type table: pandas.Series
197 :type input_data: InputData
200 logging.info(" Generating the table {0} ...".
201 format(table.get("title", "")))
204 logging.info(" Creating the data set for the {0} '{1}'.".
205 format(table.get("type", ""), table.get("title", "")))
206 data = input_data.filter_data(table, continue_on_error=True)
208 # Prepare the header of the tables
210 header = ["Test case", ]
212 if table["include-tests"] == "MRR":
213 hdr_param = "Receive Rate"
215 hdr_param = "Throughput"
217 history = table.get("history", None)
221 ["{0} {1} [Mpps]".format(item["title"], hdr_param),
222 "{0} Stdev [Mpps]".format(item["title"])])
224 ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
225 "{0} Stdev [Mpps]".format(table["reference"]["title"]),
226 "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
227 "{0} Stdev [Mpps]".format(table["compare"]["title"]),
229 header_str = ",".join(header) + "\n"
230 except (AttributeError, KeyError) as err:
231 logging.error("The model is invalid, missing parameter: {0}".
235 # Prepare data to the table:
237 for job, builds in table["reference"]["data"].items():
239 for tst_name, tst_data in data[job][str(build)].iteritems():
240 tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\
241 replace("-ndrpdr", "").replace("-pdrdisc", "").\
242 replace("-ndrdisc", "").replace("-pdr", "").\
243 replace("-ndr", "").\
244 replace("1t1c", "1c").replace("2t1c", "1c").\
245 replace("2t2c", "2c").replace("4t2c", "2c").\
246 replace("4t4c", "4c").replace("8t4c", "4c")
247 if "across topologies" in table["title"].lower():
248 tst_name_mod = tst_name_mod.replace("2n1l-", "")
249 if tbl_dict.get(tst_name_mod, None) is None:
250 groups = re.search(REGEX_NIC, tst_data["parent"])
251 nic = groups.group(0) if groups else ""
252 name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
254 if "across testbeds" in table["title"].lower() or \
255 "across topologies" in table["title"].lower():
257 replace("1t1c", "1c").replace("2t1c", "1c").\
258 replace("2t2c", "2c").replace("4t2c", "2c").\
259 replace("4t4c", "4c").replace("8t4c", "4c")
260 tbl_dict[tst_name_mod] = {"name": name,
264 # TODO: Re-work when NDRPDRDISC tests are not used
265 if table["include-tests"] == "MRR":
266 tbl_dict[tst_name_mod]["ref-data"]. \
267 append(tst_data["result"]["receive-rate"].avg)
268 elif table["include-tests"] == "PDR":
269 if tst_data["type"] == "PDR":
270 tbl_dict[tst_name_mod]["ref-data"]. \
271 append(tst_data["throughput"]["value"])
272 elif tst_data["type"] == "NDRPDR":
273 tbl_dict[tst_name_mod]["ref-data"].append(
274 tst_data["throughput"]["PDR"]["LOWER"])
275 elif table["include-tests"] == "NDR":
276 if tst_data["type"] == "NDR":
277 tbl_dict[tst_name_mod]["ref-data"]. \
278 append(tst_data["throughput"]["value"])
279 elif tst_data["type"] == "NDRPDR":
280 tbl_dict[tst_name_mod]["ref-data"].append(
281 tst_data["throughput"]["NDR"]["LOWER"])
285 pass # No data in output.xml for this test
287 for job, builds in table["compare"]["data"].items():
289 for tst_name, tst_data in data[job][str(build)].iteritems():
290 tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
291 replace("-ndrpdr", "").replace("-pdrdisc", ""). \
292 replace("-ndrdisc", "").replace("-pdr", ""). \
293 replace("-ndr", "").\
294 replace("1t1c", "1c").replace("2t1c", "1c").\
295 replace("2t2c", "2c").replace("4t2c", "2c").\
296 replace("4t4c", "4c").replace("8t4c", "4c")
297 if "across topologies" in table["title"].lower():
298 tst_name_mod = tst_name_mod.replace("2n1l-", "")
300 # TODO: Re-work when NDRPDRDISC tests are not used
301 if table["include-tests"] == "MRR":
302 tbl_dict[tst_name_mod]["cmp-data"]. \
303 append(tst_data["result"]["receive-rate"].avg)
304 elif table["include-tests"] == "PDR":
305 if tst_data["type"] == "PDR":
306 tbl_dict[tst_name_mod]["cmp-data"]. \
307 append(tst_data["throughput"]["value"])
308 elif tst_data["type"] == "NDRPDR":
309 tbl_dict[tst_name_mod]["cmp-data"].append(
310 tst_data["throughput"]["PDR"]["LOWER"])
311 elif table["include-tests"] == "NDR":
312 if tst_data["type"] == "NDR":
313 tbl_dict[tst_name_mod]["cmp-data"]. \
314 append(tst_data["throughput"]["value"])
315 elif tst_data["type"] == "NDRPDR":
316 tbl_dict[tst_name_mod]["cmp-data"].append(
317 tst_data["throughput"]["NDR"]["LOWER"])
323 tbl_dict.pop(tst_name_mod, None)
326 for job, builds in item["data"].items():
328 for tst_name, tst_data in data[job][str(build)].iteritems():
329 tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
330 replace("-ndrpdr", "").replace("-pdrdisc", ""). \
331 replace("-ndrdisc", "").replace("-pdr", ""). \
332 replace("-ndr", "").\
333 replace("1t1c", "1c").replace("2t1c", "1c").\
334 replace("2t2c", "2c").replace("4t2c", "2c").\
335 replace("4t4c", "4c").replace("8t4c", "4c")
336 if "across topologies" in table["title"].lower():
337 tst_name_mod = tst_name_mod.replace("2n1l-", "")
338 if tbl_dict.get(tst_name_mod, None) is None:
340 if tbl_dict[tst_name_mod].get("history", None) is None:
341 tbl_dict[tst_name_mod]["history"] = OrderedDict()
342 if tbl_dict[tst_name_mod]["history"].get(item["title"],
344 tbl_dict[tst_name_mod]["history"][item["title"]] = \
347 # TODO: Re-work when NDRPDRDISC tests are not used
348 if table["include-tests"] == "MRR":
349 tbl_dict[tst_name_mod]["history"][item["title"
350 ]].append(tst_data["result"]["receive-rate"].
352 elif table["include-tests"] == "PDR":
353 if tst_data["type"] == "PDR":
354 tbl_dict[tst_name_mod]["history"][
356 append(tst_data["throughput"]["value"])
357 elif tst_data["type"] == "NDRPDR":
358 tbl_dict[tst_name_mod]["history"][item[
359 "title"]].append(tst_data["throughput"][
361 elif table["include-tests"] == "NDR":
362 if tst_data["type"] == "NDR":
363 tbl_dict[tst_name_mod]["history"][
365 append(tst_data["throughput"]["value"])
366 elif tst_data["type"] == "NDRPDR":
367 tbl_dict[tst_name_mod]["history"][item[
368 "title"]].append(tst_data["throughput"][
372 except (TypeError, KeyError):
376 for tst_name in tbl_dict.keys():
377 item = [tbl_dict[tst_name]["name"], ]
379 if tbl_dict[tst_name].get("history", None) is not None:
380 for hist_data in tbl_dict[tst_name]["history"].values():
382 item.append(round(mean(hist_data) / 1000000, 2))
383 item.append(round(stdev(hist_data) / 1000000, 2))
385 item.extend([None, None])
387 item.extend([None, None])
388 data_t = tbl_dict[tst_name]["ref-data"]
390 item.append(round(mean(data_t) / 1000000, 2))
391 item.append(round(stdev(data_t) / 1000000, 2))
393 item.extend([None, None])
394 data_t = tbl_dict[tst_name]["cmp-data"]
396 item.append(round(mean(data_t) / 1000000, 2))
397 item.append(round(stdev(data_t) / 1000000, 2))
399 item.extend([None, None])
400 if item[-4] is not None and item[-2] is not None and item[-4] != 0:
401 item.append(int(relative_change(float(item[-4]), float(item[-2]))))
402 if len(item) == len(header):
405 # Sort the table according to the relative change
406 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
408 # Generate csv tables:
409 csv_file = "{0}.csv".format(table["output-file"])
410 with open(csv_file, "w") as file_handler:
411 file_handler.write(header_str)
413 file_handler.write(",".join([str(item) for item in test]) + "\n")
415 convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
418 def table_nics_comparison(table, input_data):
419 """Generate the table(s) with algorithm: table_nics_comparison
420 specified in the specification file.
422 :param table: Table to generate.
423 :param input_data: Data to process.
424 :type table: pandas.Series
425 :type input_data: InputData
428 logging.info(" Generating the table {0} ...".
429 format(table.get("title", "")))
432 logging.info(" Creating the data set for the {0} '{1}'.".
433 format(table.get("type", ""), table.get("title", "")))
434 data = input_data.filter_data(table, continue_on_error=True)
436 # Prepare the header of the tables
438 header = ["Test case", ]
440 if table["include-tests"] == "MRR":
441 hdr_param = "Receive Rate"
443 hdr_param = "Throughput"
446 ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
447 "{0} Stdev [Mpps]".format(table["reference"]["title"]),
448 "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
449 "{0} Stdev [Mpps]".format(table["compare"]["title"]),
451 header_str = ",".join(header) + "\n"
452 except (AttributeError, KeyError) as err:
453 logging.error("The model is invalid, missing parameter: {0}".
457 # Prepare data to the table:
459 for job, builds in table["data"].items():
461 for tst_name, tst_data in data[job][str(build)].iteritems():
462 tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\
463 replace("-ndrpdr", "").replace("-pdrdisc", "").\
464 replace("-ndrdisc", "").replace("-pdr", "").\
465 replace("-ndr", "").\
466 replace("1t1c", "1c").replace("2t1c", "1c").\
467 replace("2t2c", "2c").replace("4t2c", "2c").\
468 replace("4t4c", "4c").replace("8t4c", "4c")
469 tst_name_mod = re.sub(REGEX_NIC, "", tst_name_mod)
470 if tbl_dict.get(tst_name_mod, None) is None:
471 name = "-".join(tst_data["name"].split("-")[:-1])
472 tbl_dict[tst_name_mod] = {"name": name,
476 if table["include-tests"] == "MRR":
477 result = tst_data["result"]["receive-rate"].avg
478 elif table["include-tests"] == "PDR":
479 result = tst_data["throughput"]["PDR"]["LOWER"]
480 elif table["include-tests"] == "NDR":
481 result = tst_data["throughput"]["NDR"]["LOWER"]
486 if table["reference"]["nic"] in tst_data["tags"]:
487 tbl_dict[tst_name_mod]["ref-data"].append(result)
488 elif table["compare"]["nic"] in tst_data["tags"]:
489 tbl_dict[tst_name_mod]["cmp-data"].append(result)
490 except (TypeError, KeyError) as err:
491 logging.debug("No data for {0}".format(tst_name))
492 logging.debug(repr(err))
493 # No data in output.xml for this test
496 for tst_name in tbl_dict.keys():
497 item = [tbl_dict[tst_name]["name"], ]
498 data_t = tbl_dict[tst_name]["ref-data"]
500 item.append(round(mean(data_t) / 1000000, 2))
501 item.append(round(stdev(data_t) / 1000000, 2))
503 item.extend([None, None])
504 data_t = tbl_dict[tst_name]["cmp-data"]
506 item.append(round(mean(data_t) / 1000000, 2))
507 item.append(round(stdev(data_t) / 1000000, 2))
509 item.extend([None, None])
510 if item[-4] is not None and item[-2] is not None and item[-4] != 0:
511 item.append(int(relative_change(float(item[-4]), float(item[-2]))))
512 if len(item) == len(header):
515 # Sort the table according to the relative change
516 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
518 # Generate csv tables:
519 csv_file = "{0}.csv".format(table["output-file"])
520 with open(csv_file, "w") as file_handler:
521 file_handler.write(header_str)
523 file_handler.write(",".join([str(item) for item in test]) + "\n")
525 convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
528 def table_soak_vs_ndr(table, input_data):
529 """Generate the table(s) with algorithm: table_soak_vs_ndr
530 specified in the specification file.
532 :param table: Table to generate.
533 :param input_data: Data to process.
534 :type table: pandas.Series
535 :type input_data: InputData
538 logging.info(" Generating the table {0} ...".
539 format(table.get("title", "")))
542 logging.info(" Creating the data set for the {0} '{1}'.".
543 format(table.get("type", ""), table.get("title", "")))
544 data = input_data.filter_data(table, continue_on_error=True)
546 # Prepare the header of the table
550 "{0} Throughput [Mpps]".format(table["reference"]["title"]),
551 "{0} Stdev [Mpps]".format(table["reference"]["title"]),
552 "{0} Throughput [Mpps]".format(table["compare"]["title"]),
553 "{0} Stdev [Mpps]".format(table["compare"]["title"]),
555 header_str = ",".join(header) + "\n"
556 except (AttributeError, KeyError) as err:
557 logging.error("The model is invalid, missing parameter: {0}".
561 # Create a list of available SOAK test results:
563 for job, builds in table["compare"]["data"].items():
565 for tst_name, tst_data in data[job][str(build)].iteritems():
566 if tst_data["type"] == "SOAK":
567 tst_name_mod = tst_name.replace("-soak", "")
568 if tbl_dict.get(tst_name_mod, None) is None:
569 groups = re.search(REGEX_NIC, tst_data["parent"])
570 nic = groups.group(0) if groups else ""
571 name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
573 tbl_dict[tst_name_mod] = {
579 tbl_dict[tst_name_mod]["cmp-data"].append(
580 tst_data["throughput"]["LOWER"])
581 except (KeyError, TypeError):
583 tests_lst = tbl_dict.keys()
585 # Add corresponding NDR test results:
586 for job, builds in table["reference"]["data"].items():
588 for tst_name, tst_data in data[job][str(build)].iteritems():
589 tst_name_mod = tst_name.replace("-ndrpdr", "").\
591 if tst_name_mod in tests_lst:
593 if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"):
594 if table["include-tests"] == "MRR":
595 result = tst_data["result"]["receive-rate"].avg
596 elif table["include-tests"] == "PDR":
597 result = tst_data["throughput"]["PDR"]["LOWER"]
598 elif table["include-tests"] == "NDR":
599 result = tst_data["throughput"]["NDR"]["LOWER"]
602 if result is not None:
603 tbl_dict[tst_name_mod]["ref-data"].append(
605 except (KeyError, TypeError):
609 for tst_name in tbl_dict.keys():
610 item = [tbl_dict[tst_name]["name"], ]
611 data_t = tbl_dict[tst_name]["ref-data"]
613 item.append(round(mean(data_t) / 1000000, 2))
614 item.append(round(stdev(data_t) / 1000000, 2))
616 item.extend([None, None])
617 data_t = tbl_dict[tst_name]["cmp-data"]
619 item.append(round(mean(data_t) / 1000000, 2))
620 item.append(round(stdev(data_t) / 1000000, 2))
622 item.extend([None, None])
623 if item[-4] is not None and item[-2] is not None and item[-4] != 0:
624 item.append(int(relative_change(float(item[-4]), float(item[-2]))))
625 if len(item) == len(header):
628 # Sort the table according to the relative change
629 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
631 # Generate csv tables:
632 csv_file = "{0}.csv".format(table["output-file"])
633 with open(csv_file, "w") as file_handler:
634 file_handler.write(header_str)
636 file_handler.write(",".join([str(item) for item in test]) + "\n")
638 convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
641 def table_performance_trending_dashboard(table, input_data):
642 """Generate the table(s) with algorithm:
643 table_performance_trending_dashboard
644 specified in the specification file.
646 :param table: Table to generate.
647 :param input_data: Data to process.
648 :type table: pandas.Series
649 :type input_data: InputData
652 logging.info(" Generating the table {0} ...".
653 format(table.get("title", "")))
656 logging.info(" Creating the data set for the {0} '{1}'.".
657 format(table.get("type", ""), table.get("title", "")))
658 data = input_data.filter_data(table, continue_on_error=True)
660 # Prepare the header of the tables
661 header = ["Test Case",
663 "Short-Term Change [%]",
664 "Long-Term Change [%]",
668 header_str = ",".join(header) + "\n"
670 # Prepare data to the table:
672 for job, builds in table["data"].items():
674 for tst_name, tst_data in data[job][str(build)].iteritems():
675 if tst_name.lower() in table["ignore-list"]:
677 if tbl_dict.get(tst_name, None) is None:
678 groups = re.search(REGEX_NIC, tst_data["parent"])
681 nic = groups.group(0)
682 tbl_dict[tst_name] = {
683 "name": "{0}-{1}".format(nic, tst_data["name"]),
684 "data": OrderedDict()}
686 tbl_dict[tst_name]["data"][str(build)] = \
687 tst_data["result"]["receive-rate"]
688 except (TypeError, KeyError):
689 pass # No data in output.xml for this test
692 for tst_name in tbl_dict.keys():
693 data_t = tbl_dict[tst_name]["data"]
697 classification_lst, avgs = classify_anomalies(data_t)
699 win_size = min(len(data_t), table["window"])
700 long_win_size = min(len(data_t), table["long-trend-window"])
704 [x for x in avgs[-long_win_size:-win_size]
709 avg_week_ago = avgs[max(-win_size, -len(avgs))]
711 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
712 rel_change_last = nan
714 rel_change_last = round(
715 ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
717 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
718 rel_change_long = nan
720 rel_change_long = round(
721 ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
723 if classification_lst:
724 if isnan(rel_change_last) and isnan(rel_change_long):
726 if (isnan(last_avg) or
727 isnan(rel_change_last) or
728 isnan(rel_change_long)):
731 [tbl_dict[tst_name]["name"],
732 round(last_avg / 1000000, 2),
735 classification_lst[-win_size:].count("regression"),
736 classification_lst[-win_size:].count("progression")])
738 tbl_lst.sort(key=lambda rel: rel[0])
741 for nrr in range(table["window"], -1, -1):
742 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
743 for nrp in range(table["window"], -1, -1):
744 tbl_out = [item for item in tbl_reg if item[5] == nrp]
745 tbl_out.sort(key=lambda rel: rel[2])
746 tbl_sorted.extend(tbl_out)
748 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
750 logging.info(" Writing file: '{0}'".format(file_name))
751 with open(file_name, "w") as file_handler:
752 file_handler.write(header_str)
753 for test in tbl_sorted:
754 file_handler.write(",".join([str(item) for item in test]) + '\n')
756 txt_file_name = "{0}.txt".format(table["output-file"])
757 logging.info(" Writing file: '{0}'".format(txt_file_name))
758 convert_csv_to_pretty_txt(file_name, txt_file_name)
761 def _generate_url(base, testbed, test_name):
762 """Generate URL to a trending plot from the name of the test case.
764 :param base: The base part of URL common to all test cases.
765 :param testbed: The testbed used for testing.
766 :param test_name: The name of the test case.
770 :returns: The URL to the plot with the trending data for the given test
780 if "lbdpdk" in test_name or "lbvpp" in test_name:
781 file_name = "link_bonding"
783 elif "114b" in test_name and "vhost" in test_name:
786 elif "testpmd" in test_name or "l3fwd" in test_name:
789 elif "memif" in test_name:
790 file_name = "container_memif"
793 elif "srv6" in test_name:
796 elif "vhost" in test_name:
797 if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
798 file_name = "vm_vhost_l2"
799 if "114b" in test_name:
801 elif "l2xcbase" in test_name and "x520" in test_name:
802 feature = "-base-l2xc"
803 elif "l2bdbasemaclrn" in test_name and "x520" in test_name:
804 feature = "-base-l2bd"
807 elif "ip4base" in test_name:
808 file_name = "vm_vhost_ip4"
811 elif "ipsec" in test_name:
813 feature = "-base-scale"
815 elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
816 file_name = "ip4_tunnels"
819 elif "ip4base" in test_name or "ip4scale" in test_name:
821 if "xl710" in test_name:
822 feature = "-base-scale-features"
823 elif "iacl" in test_name:
824 feature = "-features-iacl"
825 elif "oacl" in test_name:
826 feature = "-features-oacl"
827 elif "snat" in test_name or "cop" in test_name:
828 feature = "-features"
830 feature = "-base-scale"
832 elif "ip6base" in test_name or "ip6scale" in test_name:
834 feature = "-base-scale"
836 elif "l2xcbase" in test_name or "l2xcscale" in test_name \
837 or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
838 or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
840 if "macip" in test_name:
841 feature = "-features-macip"
842 elif "iacl" in test_name:
843 feature = "-features-iacl"
844 elif "oacl" in test_name:
845 feature = "-features-oacl"
847 feature = "-base-scale"
849 if "x520" in test_name:
851 elif "x710" in test_name:
853 elif "xl710" in test_name:
855 elif "xxv710" in test_name:
857 elif "vic1227" in test_name:
859 elif "vic1385" in test_name:
865 if "64b" in test_name:
867 elif "78b" in test_name:
869 elif "imix" in test_name:
871 elif "9000b" in test_name:
873 elif "1518b" in test_name:
875 elif "114b" in test_name:
879 anchor += framesize + '-'
881 if "1t1c" in test_name:
883 elif "2t2c" in test_name:
885 elif "4t4c" in test_name:
887 elif "2t1c" in test_name:
889 elif "4t2c" in test_name:
891 elif "8t4c" in test_name:
894 return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \
898 def table_performance_trending_dashboard_html(table, input_data):
899 """Generate the table(s) with algorithm:
900 table_performance_trending_dashboard_html specified in the specification
903 :param table: Table to generate.
904 :param input_data: Data to process.
906 :type input_data: InputData
909 testbed = table.get("testbed", None)
911 logging.error("The testbed is not defined for the table '{0}'.".
912 format(table.get("title", "")))
915 logging.info(" Generating the table {0} ...".
916 format(table.get("title", "")))
919 with open(table["input-file"], 'rb') as csv_file:
920 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
921 csv_lst = [item for item in csv_content]
923 logging.warning("The input file is not defined.")
925 except csv.Error as err:
926 logging.warning("Not possible to process the file '{0}'.\n{1}".
927 format(table["input-file"], err))
931 dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
934 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
935 for idx, item in enumerate(csv_lst[0]):
936 alignment = "left" if idx == 0 else "center"
937 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
941 colors = {"regression": ("#ffcccc", "#ff9999"),
942 "progression": ("#c6ecc6", "#9fdf9f"),
943 "normal": ("#e9f1fb", "#d4e4f7")}
944 for r_idx, row in enumerate(csv_lst[1:]):
948 color = "progression"
951 background = colors[color][r_idx % 2]
952 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
955 for c_idx, item in enumerate(row):
956 alignment = "left" if c_idx == 0 else "center"
957 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
960 url = _generate_url("../trending/", testbed, item)
961 ref = ET.SubElement(td, "a", attrib=dict(href=url))
966 with open(table["output-file"], 'w') as html_file:
967 logging.info(" Writing file: '{0}'".format(table["output-file"]))
968 html_file.write(".. raw:: html\n\n\t")
969 html_file.write(ET.tostring(dashboard))
970 html_file.write("\n\t<p><br><br></p>\n")
972 logging.warning("The output file is not defined.")
976 def table_last_failed_tests(table, input_data):
977 """Generate the table(s) with algorithm: table_last_failed_tests
978 specified in the specification file.
980 :param table: Table to generate.
981 :param input_data: Data to process.
982 :type table: pandas.Series
983 :type input_data: InputData
986 logging.info(" Generating the table {0} ...".
987 format(table.get("title", "")))
990 logging.info(" Creating the data set for the {0} '{1}'.".
991 format(table.get("type", ""), table.get("title", "")))
992 data = input_data.filter_data(table, continue_on_error=True)
994 if data is None or data.empty:
995 logging.warn(" No data for the {0} '{1}'.".
996 format(table.get("type", ""), table.get("title", "")))
1000 for job, builds in table["data"].items():
1001 for build in builds:
1004 version = input_data.metadata(job, build).get("version", "")
1006 logging.error("Data for {job}: {build} is not present.".
1007 format(job=job, build=build))
1009 tbl_list.append(build)
1010 tbl_list.append(version)
1011 for tst_name, tst_data in data[job][build].iteritems():
1012 if tst_data["status"] != "FAIL":
1014 groups = re.search(REGEX_NIC, tst_data["parent"])
1017 nic = groups.group(0)
1018 tbl_list.append("{0}-{1}".format(nic, tst_data["name"]))
1020 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1021 logging.info(" Writing file: '{0}'".format(file_name))
1022 with open(file_name, "w") as file_handler:
1023 for test in tbl_list:
1024 file_handler.write(test + '\n')
1027 def table_failed_tests(table, input_data):
1028 """Generate the table(s) with algorithm: table_failed_tests
1029 specified in the specification file.
1031 :param table: Table to generate.
1032 :param input_data: Data to process.
1033 :type table: pandas.Series
1034 :type input_data: InputData
1037 logging.info(" Generating the table {0} ...".
1038 format(table.get("title", "")))
1040 # Transform the data
1041 logging.info(" Creating the data set for the {0} '{1}'.".
1042 format(table.get("type", ""), table.get("title", "")))
1043 data = input_data.filter_data(table, continue_on_error=True)
1045 # Prepare the header of the tables
1046 header = ["Test Case",
1048 "Last Failure [Time]",
1049 "Last Failure [VPP-Build-Id]",
1050 "Last Failure [CSIT-Job-Build-Id]"]
1052 # Generate the data for the table according to the model in the table
1056 timeperiod = timedelta(int(table.get("window", 7)))
1059 for job, builds in table["data"].items():
1060 for build in builds:
1062 for tst_name, tst_data in data[job][build].iteritems():
1063 if tst_name.lower() in table["ignore-list"]:
1065 if tbl_dict.get(tst_name, None) is None:
1066 groups = re.search(REGEX_NIC, tst_data["parent"])
1069 nic = groups.group(0)
1070 tbl_dict[tst_name] = {
1071 "name": "{0}-{1}".format(nic, tst_data["name"]),
1072 "data": OrderedDict()}
1074 generated = input_data.metadata(job, build).\
1075 get("generated", "")
1078 then = dt.strptime(generated, "%Y%m%d %H:%M")
1079 if (now - then) <= timeperiod:
1080 tbl_dict[tst_name]["data"][build] = (
1083 input_data.metadata(job, build).get("version", ""),
1085 except (TypeError, KeyError) as err:
1086 logging.warning("tst_name: {} - err: {}".
1087 format(tst_name, repr(err)))
1091 for tst_data in tbl_dict.values():
1093 for val in tst_data["data"].values():
1094 if val[0] == "FAIL":
1096 fails_last_date = val[1]
1097 fails_last_vpp = val[2]
1098 fails_last_csit = val[3]
1100 max_fails = fails_nr if fails_nr > max_fails else max_fails
1101 tbl_lst.append([tst_data["name"],
1105 "mrr-daily-build-{0}".format(fails_last_csit)])
1107 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1109 for nrf in range(max_fails, -1, -1):
1110 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1111 tbl_sorted.extend(tbl_fails)
1112 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1114 logging.info(" Writing file: '{0}'".format(file_name))
1115 with open(file_name, "w") as file_handler:
1116 file_handler.write(",".join(header) + "\n")
1117 for test in tbl_sorted:
1118 file_handler.write(",".join([str(item) for item in test]) + '\n')
1120 txt_file_name = "{0}.txt".format(table["output-file"])
1121 logging.info(" Writing file: '{0}'".format(txt_file_name))
1122 convert_csv_to_pretty_txt(file_name, txt_file_name)
1125 def table_failed_tests_html(table, input_data):
1126 """Generate the table(s) with algorithm: table_failed_tests_html
1127 specified in the specification file.
1129 :param table: Table to generate.
1130 :param input_data: Data to process.
1131 :type table: pandas.Series
1132 :type input_data: InputData
1135 testbed = table.get("testbed", None)
1137 logging.error("The testbed is not defined for the table '{0}'.".
1138 format(table.get("title", "")))
1141 logging.info(" Generating the table {0} ...".
1142 format(table.get("title", "")))
1145 with open(table["input-file"], 'rb') as csv_file:
1146 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
1147 csv_lst = [item for item in csv_content]
1149 logging.warning("The input file is not defined.")
1151 except csv.Error as err:
1152 logging.warning("Not possible to process the file '{0}'.\n{1}".
1153 format(table["input-file"], err))
1157 failed_tests = ET.Element("table", attrib=dict(width="100%", border='0'))
1160 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#7eade7"))
1161 for idx, item in enumerate(csv_lst[0]):
1162 alignment = "left" if idx == 0 else "center"
1163 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
1167 colors = ("#e9f1fb", "#d4e4f7")
1168 for r_idx, row in enumerate(csv_lst[1:]):
1169 background = colors[r_idx % 2]
1170 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor=background))
1173 for c_idx, item in enumerate(row):
1174 alignment = "left" if c_idx == 0 else "center"
1175 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
1178 url = _generate_url("../trending/", testbed, item)
1179 ref = ET.SubElement(td, "a", attrib=dict(href=url))
1184 with open(table["output-file"], 'w') as html_file:
1185 logging.info(" Writing file: '{0}'".format(table["output-file"]))
1186 html_file.write(".. raw:: html\n\n\t")
1187 html_file.write(ET.tostring(failed_tests))
1188 html_file.write("\n\t<p><br><br></p>\n")
1190 logging.warning("The output file is not defined.")