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_r = tbl_dict[tst_name]["ref-data"]
613 data_r_mean = mean(data_r)
614 item.append(round(data_r_mean / 1000000, 2))
615 item.append(round(stdev(data_r) / 1000000, 2))
618 item.extend([None, None])
619 data_c = tbl_dict[tst_name]["cmp-data"]
621 data_c_mean = mean(data_c)
622 item.append(round(data_c_mean / 1000000, 2))
623 item.append(round(stdev(data_c) / 1000000, 2))
626 item.extend([None, None])
627 if data_r_mean and data_c_mean is not None:
628 item.append(round(relative_change(data_r_mean, data_c_mean), 2))
631 # Sort the table according to the relative change
632 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
634 # Generate csv tables:
635 csv_file = "{0}.csv".format(table["output-file"])
636 with open(csv_file, "w") as file_handler:
637 file_handler.write(header_str)
639 file_handler.write(",".join([str(item) for item in test]) + "\n")
641 convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
644 def table_performance_trending_dashboard(table, input_data):
645 """Generate the table(s) with algorithm:
646 table_performance_trending_dashboard
647 specified in the specification file.
649 :param table: Table to generate.
650 :param input_data: Data to process.
651 :type table: pandas.Series
652 :type input_data: InputData
655 logging.info(" Generating the table {0} ...".
656 format(table.get("title", "")))
659 logging.info(" Creating the data set for the {0} '{1}'.".
660 format(table.get("type", ""), table.get("title", "")))
661 data = input_data.filter_data(table, continue_on_error=True)
663 # Prepare the header of the tables
664 header = ["Test Case",
666 "Short-Term Change [%]",
667 "Long-Term Change [%]",
671 header_str = ",".join(header) + "\n"
673 # Prepare data to the table:
675 for job, builds in table["data"].items():
677 for tst_name, tst_data in data[job][str(build)].iteritems():
678 if tst_name.lower() in table["ignore-list"]:
680 if tbl_dict.get(tst_name, None) is None:
681 groups = re.search(REGEX_NIC, tst_data["parent"])
684 nic = groups.group(0)
685 tbl_dict[tst_name] = {
686 "name": "{0}-{1}".format(nic, tst_data["name"]),
687 "data": OrderedDict()}
689 tbl_dict[tst_name]["data"][str(build)] = \
690 tst_data["result"]["receive-rate"]
691 except (TypeError, KeyError):
692 pass # No data in output.xml for this test
695 for tst_name in tbl_dict.keys():
696 data_t = tbl_dict[tst_name]["data"]
700 classification_lst, avgs = classify_anomalies(data_t)
702 win_size = min(len(data_t), table["window"])
703 long_win_size = min(len(data_t), table["long-trend-window"])
707 [x for x in avgs[-long_win_size:-win_size]
712 avg_week_ago = avgs[max(-win_size, -len(avgs))]
714 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
715 rel_change_last = nan
717 rel_change_last = round(
718 ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
720 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
721 rel_change_long = nan
723 rel_change_long = round(
724 ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
726 if classification_lst:
727 if isnan(rel_change_last) and isnan(rel_change_long):
729 if (isnan(last_avg) or
730 isnan(rel_change_last) or
731 isnan(rel_change_long)):
734 [tbl_dict[tst_name]["name"],
735 round(last_avg / 1000000, 2),
738 classification_lst[-win_size:].count("regression"),
739 classification_lst[-win_size:].count("progression")])
741 tbl_lst.sort(key=lambda rel: rel[0])
744 for nrr in range(table["window"], -1, -1):
745 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
746 for nrp in range(table["window"], -1, -1):
747 tbl_out = [item for item in tbl_reg if item[5] == nrp]
748 tbl_out.sort(key=lambda rel: rel[2])
749 tbl_sorted.extend(tbl_out)
751 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
753 logging.info(" Writing file: '{0}'".format(file_name))
754 with open(file_name, "w") as file_handler:
755 file_handler.write(header_str)
756 for test in tbl_sorted:
757 file_handler.write(",".join([str(item) for item in test]) + '\n')
759 txt_file_name = "{0}.txt".format(table["output-file"])
760 logging.info(" Writing file: '{0}'".format(txt_file_name))
761 convert_csv_to_pretty_txt(file_name, txt_file_name)
764 def _generate_url(base, testbed, test_name):
765 """Generate URL to a trending plot from the name of the test case.
767 :param base: The base part of URL common to all test cases.
768 :param testbed: The testbed used for testing.
769 :param test_name: The name of the test case.
773 :returns: The URL to the plot with the trending data for the given test
783 if "lbdpdk" in test_name or "lbvpp" in test_name:
784 file_name = "link_bonding"
786 elif "114b" in test_name and "vhost" in test_name:
789 elif "testpmd" in test_name or "l3fwd" in test_name:
792 elif "memif" in test_name:
793 file_name = "container_memif"
796 elif "srv6" in test_name:
799 elif "vhost" in test_name:
800 if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
801 file_name = "vm_vhost_l2"
802 if "114b" in test_name:
804 elif "l2xcbase" in test_name and "x520" in test_name:
805 feature = "-base-l2xc"
806 elif "l2bdbasemaclrn" in test_name and "x520" in test_name:
807 feature = "-base-l2bd"
810 elif "ip4base" in test_name:
811 file_name = "vm_vhost_ip4"
814 elif "ipsec" in test_name:
816 feature = "-base-scale"
818 elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
819 file_name = "ip4_tunnels"
822 elif "ip4base" in test_name or "ip4scale" in test_name:
824 if "xl710" in test_name:
825 feature = "-base-scale-features"
826 elif "iacl" in test_name:
827 feature = "-features-iacl"
828 elif "oacl" in test_name:
829 feature = "-features-oacl"
830 elif "snat" in test_name or "cop" in test_name:
831 feature = "-features"
833 feature = "-base-scale"
835 elif "ip6base" in test_name or "ip6scale" in test_name:
837 feature = "-base-scale"
839 elif "l2xcbase" in test_name or "l2xcscale" in test_name \
840 or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
841 or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
843 if "macip" in test_name:
844 feature = "-features-macip"
845 elif "iacl" in test_name:
846 feature = "-features-iacl"
847 elif "oacl" in test_name:
848 feature = "-features-oacl"
850 feature = "-base-scale"
852 if "x520" in test_name:
854 elif "x710" in test_name:
856 elif "xl710" in test_name:
858 elif "xxv710" in test_name:
860 elif "vic1227" in test_name:
862 elif "vic1385" in test_name:
868 if "64b" in test_name:
870 elif "78b" in test_name:
872 elif "imix" in test_name:
874 elif "9000b" in test_name:
876 elif "1518b" in test_name:
878 elif "114b" in test_name:
882 anchor += framesize + '-'
884 if "1t1c" in test_name:
886 elif "2t2c" in test_name:
888 elif "4t4c" in test_name:
890 elif "2t1c" in test_name:
892 elif "4t2c" in test_name:
894 elif "8t4c" in test_name:
897 return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \
901 def table_performance_trending_dashboard_html(table, input_data):
902 """Generate the table(s) with algorithm:
903 table_performance_trending_dashboard_html specified in the specification
906 :param table: Table to generate.
907 :param input_data: Data to process.
909 :type input_data: InputData
912 testbed = table.get("testbed", None)
914 logging.error("The testbed is not defined for the table '{0}'.".
915 format(table.get("title", "")))
918 logging.info(" Generating the table {0} ...".
919 format(table.get("title", "")))
922 with open(table["input-file"], 'rb') as csv_file:
923 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
924 csv_lst = [item for item in csv_content]
926 logging.warning("The input file is not defined.")
928 except csv.Error as err:
929 logging.warning("Not possible to process the file '{0}'.\n{1}".
930 format(table["input-file"], err))
934 dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
937 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
938 for idx, item in enumerate(csv_lst[0]):
939 alignment = "left" if idx == 0 else "center"
940 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
944 colors = {"regression": ("#ffcccc", "#ff9999"),
945 "progression": ("#c6ecc6", "#9fdf9f"),
946 "normal": ("#e9f1fb", "#d4e4f7")}
947 for r_idx, row in enumerate(csv_lst[1:]):
951 color = "progression"
954 background = colors[color][r_idx % 2]
955 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
958 for c_idx, item in enumerate(row):
959 alignment = "left" if c_idx == 0 else "center"
960 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
963 url = _generate_url("../trending/", testbed, item)
964 ref = ET.SubElement(td, "a", attrib=dict(href=url))
969 with open(table["output-file"], 'w') as html_file:
970 logging.info(" Writing file: '{0}'".format(table["output-file"]))
971 html_file.write(".. raw:: html\n\n\t")
972 html_file.write(ET.tostring(dashboard))
973 html_file.write("\n\t<p><br><br></p>\n")
975 logging.warning("The output file is not defined.")
979 def table_last_failed_tests(table, input_data):
980 """Generate the table(s) with algorithm: table_last_failed_tests
981 specified in the specification file.
983 :param table: Table to generate.
984 :param input_data: Data to process.
985 :type table: pandas.Series
986 :type input_data: InputData
989 logging.info(" Generating the table {0} ...".
990 format(table.get("title", "")))
993 logging.info(" Creating the data set for the {0} '{1}'.".
994 format(table.get("type", ""), table.get("title", "")))
995 data = input_data.filter_data(table, continue_on_error=True)
997 if data is None or data.empty:
998 logging.warn(" No data for the {0} '{1}'.".
999 format(table.get("type", ""), table.get("title", "")))
1003 for job, builds in table["data"].items():
1004 for build in builds:
1007 version = input_data.metadata(job, build).get("version", "")
1009 logging.error("Data for {job}: {build} is not present.".
1010 format(job=job, build=build))
1012 tbl_list.append(build)
1013 tbl_list.append(version)
1014 for tst_name, tst_data in data[job][build].iteritems():
1015 if tst_data["status"] != "FAIL":
1017 groups = re.search(REGEX_NIC, tst_data["parent"])
1020 nic = groups.group(0)
1021 tbl_list.append("{0}-{1}".format(nic, tst_data["name"]))
1023 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1024 logging.info(" Writing file: '{0}'".format(file_name))
1025 with open(file_name, "w") as file_handler:
1026 for test in tbl_list:
1027 file_handler.write(test + '\n')
1030 def table_failed_tests(table, input_data):
1031 """Generate the table(s) with algorithm: table_failed_tests
1032 specified in the specification file.
1034 :param table: Table to generate.
1035 :param input_data: Data to process.
1036 :type table: pandas.Series
1037 :type input_data: InputData
1040 logging.info(" Generating the table {0} ...".
1041 format(table.get("title", "")))
1043 # Transform the data
1044 logging.info(" Creating the data set for the {0} '{1}'.".
1045 format(table.get("type", ""), table.get("title", "")))
1046 data = input_data.filter_data(table, continue_on_error=True)
1048 # Prepare the header of the tables
1049 header = ["Test Case",
1051 "Last Failure [Time]",
1052 "Last Failure [VPP-Build-Id]",
1053 "Last Failure [CSIT-Job-Build-Id]"]
1055 # Generate the data for the table according to the model in the table
1059 timeperiod = timedelta(int(table.get("window", 7)))
1062 for job, builds in table["data"].items():
1063 for build in builds:
1065 for tst_name, tst_data in data[job][build].iteritems():
1066 if tst_name.lower() in table["ignore-list"]:
1068 if tbl_dict.get(tst_name, None) is None:
1069 groups = re.search(REGEX_NIC, tst_data["parent"])
1072 nic = groups.group(0)
1073 tbl_dict[tst_name] = {
1074 "name": "{0}-{1}".format(nic, tst_data["name"]),
1075 "data": OrderedDict()}
1077 generated = input_data.metadata(job, build).\
1078 get("generated", "")
1081 then = dt.strptime(generated, "%Y%m%d %H:%M")
1082 if (now - then) <= timeperiod:
1083 tbl_dict[tst_name]["data"][build] = (
1086 input_data.metadata(job, build).get("version", ""),
1088 except (TypeError, KeyError) as err:
1089 logging.warning("tst_name: {} - err: {}".
1090 format(tst_name, repr(err)))
1094 for tst_data in tbl_dict.values():
1096 for val in tst_data["data"].values():
1097 if val[0] == "FAIL":
1099 fails_last_date = val[1]
1100 fails_last_vpp = val[2]
1101 fails_last_csit = val[3]
1103 max_fails = fails_nr if fails_nr > max_fails else max_fails
1104 tbl_lst.append([tst_data["name"],
1108 "mrr-daily-build-{0}".format(fails_last_csit)])
1110 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1112 for nrf in range(max_fails, -1, -1):
1113 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1114 tbl_sorted.extend(tbl_fails)
1115 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1117 logging.info(" Writing file: '{0}'".format(file_name))
1118 with open(file_name, "w") as file_handler:
1119 file_handler.write(",".join(header) + "\n")
1120 for test in tbl_sorted:
1121 file_handler.write(",".join([str(item) for item in test]) + '\n')
1123 txt_file_name = "{0}.txt".format(table["output-file"])
1124 logging.info(" Writing file: '{0}'".format(txt_file_name))
1125 convert_csv_to_pretty_txt(file_name, txt_file_name)
1128 def table_failed_tests_html(table, input_data):
1129 """Generate the table(s) with algorithm: table_failed_tests_html
1130 specified in the specification file.
1132 :param table: Table to generate.
1133 :param input_data: Data to process.
1134 :type table: pandas.Series
1135 :type input_data: InputData
1138 testbed = table.get("testbed", None)
1140 logging.error("The testbed is not defined for the table '{0}'.".
1141 format(table.get("title", "")))
1144 logging.info(" Generating the table {0} ...".
1145 format(table.get("title", "")))
1148 with open(table["input-file"], 'rb') as csv_file:
1149 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
1150 csv_lst = [item for item in csv_content]
1152 logging.warning("The input file is not defined.")
1154 except csv.Error as err:
1155 logging.warning("Not possible to process the file '{0}'.\n{1}".
1156 format(table["input-file"], err))
1160 failed_tests = ET.Element("table", attrib=dict(width="100%", border='0'))
1163 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#7eade7"))
1164 for idx, item in enumerate(csv_lst[0]):
1165 alignment = "left" if idx == 0 else "center"
1166 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
1170 colors = ("#e9f1fb", "#d4e4f7")
1171 for r_idx, row in enumerate(csv_lst[1:]):
1172 background = colors[r_idx % 2]
1173 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor=background))
1176 for c_idx, item in enumerate(row):
1177 alignment = "left" if c_idx == 0 else "center"
1178 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
1181 url = _generate_url("../trending/", testbed, item)
1182 ref = ET.SubElement(td, "a", attrib=dict(href=url))
1187 with open(table["output-file"], 'w') as html_file:
1188 logging.info(" Writing file: '{0}'".format(table["output-file"]))
1189 html_file.write(".. raw:: html\n\n\t")
1190 html_file.write(ET.tostring(failed_tests))
1191 html_file.write("\n\t<p><br><br></p>\n")
1193 logging.warning("The output file is not defined.")