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"]),
554 "Delta [%]", "Stdev of delta [%]"]
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 data_r_stdev = stdev(data_r)
616 item.append(round(data_r_stdev / 1000000, 2))
619 item.extend([None, None])
620 data_c = tbl_dict[tst_name]["cmp-data"]
622 data_c_mean = mean(data_c)
623 item.append(round(data_c_mean / 1000000, 2))
624 data_c_stdev = stdev(data_c)
625 item.append(round(data_c_stdev / 1000000, 2))
628 item.extend([None, None])
629 if data_r_mean and data_c_mean is not None:
630 item.append(round(relative_change(data_r_mean, data_c_mean), 2))
631 delta, d_stdev = relative_change_stdev(
632 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
633 item.append(round(delta, 2))
634 item.append(round(d_stdev, 2))
637 # Sort the table according to the relative change
638 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
640 # Generate csv tables:
641 csv_file = "{0}.csv".format(table["output-file"])
642 with open(csv_file, "w") as file_handler:
643 file_handler.write(header_str)
645 file_handler.write(",".join([str(item) for item in test]) + "\n")
647 convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
650 def table_performance_trending_dashboard(table, input_data):
651 """Generate the table(s) with algorithm:
652 table_performance_trending_dashboard
653 specified in the specification file.
655 :param table: Table to generate.
656 :param input_data: Data to process.
657 :type table: pandas.Series
658 :type input_data: InputData
661 logging.info(" Generating the table {0} ...".
662 format(table.get("title", "")))
665 logging.info(" Creating the data set for the {0} '{1}'.".
666 format(table.get("type", ""), table.get("title", "")))
667 data = input_data.filter_data(table, continue_on_error=True)
669 # Prepare the header of the tables
670 header = ["Test Case",
672 "Short-Term Change [%]",
673 "Long-Term Change [%]",
677 header_str = ",".join(header) + "\n"
679 # Prepare data to the table:
681 for job, builds in table["data"].items():
683 for tst_name, tst_data in data[job][str(build)].iteritems():
684 if tst_name.lower() in table["ignore-list"]:
686 if tbl_dict.get(tst_name, None) is None:
687 groups = re.search(REGEX_NIC, tst_data["parent"])
690 nic = groups.group(0)
691 tbl_dict[tst_name] = {
692 "name": "{0}-{1}".format(nic, tst_data["name"]),
693 "data": OrderedDict()}
695 tbl_dict[tst_name]["data"][str(build)] = \
696 tst_data["result"]["receive-rate"]
697 except (TypeError, KeyError):
698 pass # No data in output.xml for this test
701 for tst_name in tbl_dict.keys():
702 data_t = tbl_dict[tst_name]["data"]
706 classification_lst, avgs = classify_anomalies(data_t)
708 win_size = min(len(data_t), table["window"])
709 long_win_size = min(len(data_t), table["long-trend-window"])
713 [x for x in avgs[-long_win_size:-win_size]
718 avg_week_ago = avgs[max(-win_size, -len(avgs))]
720 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
721 rel_change_last = nan
723 rel_change_last = round(
724 ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
726 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
727 rel_change_long = nan
729 rel_change_long = round(
730 ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
732 if classification_lst:
733 if isnan(rel_change_last) and isnan(rel_change_long):
735 if (isnan(last_avg) or
736 isnan(rel_change_last) or
737 isnan(rel_change_long)):
740 [tbl_dict[tst_name]["name"],
741 round(last_avg / 1000000, 2),
744 classification_lst[-win_size:].count("regression"),
745 classification_lst[-win_size:].count("progression")])
747 tbl_lst.sort(key=lambda rel: rel[0])
750 for nrr in range(table["window"], -1, -1):
751 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
752 for nrp in range(table["window"], -1, -1):
753 tbl_out = [item for item in tbl_reg if item[5] == nrp]
754 tbl_out.sort(key=lambda rel: rel[2])
755 tbl_sorted.extend(tbl_out)
757 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
759 logging.info(" Writing file: '{0}'".format(file_name))
760 with open(file_name, "w") as file_handler:
761 file_handler.write(header_str)
762 for test in tbl_sorted:
763 file_handler.write(",".join([str(item) for item in test]) + '\n')
765 txt_file_name = "{0}.txt".format(table["output-file"])
766 logging.info(" Writing file: '{0}'".format(txt_file_name))
767 convert_csv_to_pretty_txt(file_name, txt_file_name)
770 def _generate_url(base, testbed, test_name):
771 """Generate URL to a trending plot from the name of the test case.
773 :param base: The base part of URL common to all test cases.
774 :param testbed: The testbed used for testing.
775 :param test_name: The name of the test case.
779 :returns: The URL to the plot with the trending data for the given test
789 if "lbdpdk" in test_name or "lbvpp" in test_name:
790 file_name = "link_bonding"
792 elif "114b" in test_name and "vhost" in test_name:
795 elif "testpmd" in test_name or "l3fwd" in test_name:
798 elif "memif" in test_name:
799 file_name = "container_memif"
802 elif "srv6" in test_name:
805 elif "vhost" in test_name:
806 if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
807 file_name = "vm_vhost_l2"
808 if "114b" in test_name:
810 elif "l2xcbase" in test_name and "x520" in test_name:
811 feature = "-base-l2xc"
812 elif "l2bdbasemaclrn" in test_name and "x520" in test_name:
813 feature = "-base-l2bd"
816 elif "ip4base" in test_name:
817 file_name = "vm_vhost_ip4"
820 elif "ipsecbasetnlsw" in test_name:
821 file_name = "ipsecsw"
822 feature = "-base-scale"
824 elif "ipsec" in test_name:
826 feature = "-base-scale"
828 elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
829 file_name = "ip4_tunnels"
832 elif "ip4base" in test_name or "ip4scale" in test_name:
834 if "xl710" in test_name:
835 feature = "-base-scale-features"
836 elif "iacl" in test_name:
837 feature = "-features-iacl"
838 elif "oacl" in test_name:
839 feature = "-features-oacl"
840 elif "snat" in test_name or "cop" in test_name:
841 feature = "-features"
843 feature = "-base-scale"
845 elif "ip6base" in test_name or "ip6scale" in test_name:
847 feature = "-base-scale"
849 elif "l2xcbase" in test_name or "l2xcscale" in test_name \
850 or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
851 or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
853 if "macip" in test_name:
854 feature = "-features-macip"
855 elif "iacl" in test_name:
856 feature = "-features-iacl"
857 elif "oacl" in test_name:
858 feature = "-features-oacl"
860 feature = "-base-scale"
862 if "x520" in test_name:
864 elif "x710" in test_name:
866 elif "xl710" in test_name:
868 elif "xxv710" in test_name:
870 elif "vic1227" in test_name:
872 elif "vic1385" in test_name:
878 if "64b" in test_name:
880 elif "78b" in test_name:
882 elif "imix" in test_name:
884 elif "9000b" in test_name:
886 elif "1518b" in test_name:
888 elif "114b" in test_name:
892 anchor += framesize + '-'
894 if "1t1c" in test_name:
896 elif "2t2c" in test_name:
898 elif "4t4c" in test_name:
900 elif "2t1c" in test_name:
902 elif "4t2c" in test_name:
904 elif "8t4c" in test_name:
907 return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \
911 def table_performance_trending_dashboard_html(table, input_data):
912 """Generate the table(s) with algorithm:
913 table_performance_trending_dashboard_html specified in the specification
916 :param table: Table to generate.
917 :param input_data: Data to process.
919 :type input_data: InputData
922 testbed = table.get("testbed", None)
924 logging.error("The testbed is not defined for the table '{0}'.".
925 format(table.get("title", "")))
928 logging.info(" Generating the table {0} ...".
929 format(table.get("title", "")))
932 with open(table["input-file"], 'rb') as csv_file:
933 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
934 csv_lst = [item for item in csv_content]
936 logging.warning("The input file is not defined.")
938 except csv.Error as err:
939 logging.warning("Not possible to process the file '{0}'.\n{1}".
940 format(table["input-file"], err))
944 dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
947 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
948 for idx, item in enumerate(csv_lst[0]):
949 alignment = "left" if idx == 0 else "center"
950 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
954 colors = {"regression": ("#ffcccc", "#ff9999"),
955 "progression": ("#c6ecc6", "#9fdf9f"),
956 "normal": ("#e9f1fb", "#d4e4f7")}
957 for r_idx, row in enumerate(csv_lst[1:]):
961 color = "progression"
964 background = colors[color][r_idx % 2]
965 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
968 for c_idx, item in enumerate(row):
969 alignment = "left" if c_idx == 0 else "center"
970 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
973 url = _generate_url("../trending/", testbed, item)
974 ref = ET.SubElement(td, "a", attrib=dict(href=url))
979 with open(table["output-file"], 'w') as html_file:
980 logging.info(" Writing file: '{0}'".format(table["output-file"]))
981 html_file.write(".. raw:: html\n\n\t")
982 html_file.write(ET.tostring(dashboard))
983 html_file.write("\n\t<p><br><br></p>\n")
985 logging.warning("The output file is not defined.")
989 def table_last_failed_tests(table, input_data):
990 """Generate the table(s) with algorithm: table_last_failed_tests
991 specified in the specification file.
993 :param table: Table to generate.
994 :param input_data: Data to process.
995 :type table: pandas.Series
996 :type input_data: InputData
999 logging.info(" Generating the table {0} ...".
1000 format(table.get("title", "")))
1002 # Transform the data
1003 logging.info(" Creating the data set for the {0} '{1}'.".
1004 format(table.get("type", ""), table.get("title", "")))
1005 data = input_data.filter_data(table, continue_on_error=True)
1007 if data is None or data.empty:
1008 logging.warn(" No data for the {0} '{1}'.".
1009 format(table.get("type", ""), table.get("title", "")))
1013 for job, builds in table["data"].items():
1014 for build in builds:
1017 version = input_data.metadata(job, build).get("version", "")
1019 logging.error("Data for {job}: {build} is not present.".
1020 format(job=job, build=build))
1022 tbl_list.append(build)
1023 tbl_list.append(version)
1024 for tst_name, tst_data in data[job][build].iteritems():
1025 if tst_data["status"] != "FAIL":
1027 groups = re.search(REGEX_NIC, tst_data["parent"])
1030 nic = groups.group(0)
1031 tbl_list.append("{0}-{1}".format(nic, tst_data["name"]))
1033 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1034 logging.info(" Writing file: '{0}'".format(file_name))
1035 with open(file_name, "w") as file_handler:
1036 for test in tbl_list:
1037 file_handler.write(test + '\n')
1040 def table_failed_tests(table, input_data):
1041 """Generate the table(s) with algorithm: table_failed_tests
1042 specified in the specification file.
1044 :param table: Table to generate.
1045 :param input_data: Data to process.
1046 :type table: pandas.Series
1047 :type input_data: InputData
1050 logging.info(" Generating the table {0} ...".
1051 format(table.get("title", "")))
1053 # Transform the data
1054 logging.info(" Creating the data set for the {0} '{1}'.".
1055 format(table.get("type", ""), table.get("title", "")))
1056 data = input_data.filter_data(table, continue_on_error=True)
1058 # Prepare the header of the tables
1059 header = ["Test Case",
1061 "Last Failure [Time]",
1062 "Last Failure [VPP-Build-Id]",
1063 "Last Failure [CSIT-Job-Build-Id]"]
1065 # Generate the data for the table according to the model in the table
1069 timeperiod = timedelta(int(table.get("window", 7)))
1072 for job, builds in table["data"].items():
1073 for build in builds:
1075 for tst_name, tst_data in data[job][build].iteritems():
1076 if tst_name.lower() in table["ignore-list"]:
1078 if tbl_dict.get(tst_name, None) is None:
1079 groups = re.search(REGEX_NIC, tst_data["parent"])
1082 nic = groups.group(0)
1083 tbl_dict[tst_name] = {
1084 "name": "{0}-{1}".format(nic, tst_data["name"]),
1085 "data": OrderedDict()}
1087 generated = input_data.metadata(job, build).\
1088 get("generated", "")
1091 then = dt.strptime(generated, "%Y%m%d %H:%M")
1092 if (now - then) <= timeperiod:
1093 tbl_dict[tst_name]["data"][build] = (
1096 input_data.metadata(job, build).get("version", ""),
1098 except (TypeError, KeyError) as err:
1099 logging.warning("tst_name: {} - err: {}".
1100 format(tst_name, repr(err)))
1104 for tst_data in tbl_dict.values():
1106 for val in tst_data["data"].values():
1107 if val[0] == "FAIL":
1109 fails_last_date = val[1]
1110 fails_last_vpp = val[2]
1111 fails_last_csit = val[3]
1113 max_fails = fails_nr if fails_nr > max_fails else max_fails
1114 tbl_lst.append([tst_data["name"],
1118 "mrr-daily-build-{0}".format(fails_last_csit)])
1120 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1122 for nrf in range(max_fails, -1, -1):
1123 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1124 tbl_sorted.extend(tbl_fails)
1125 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
1127 logging.info(" Writing file: '{0}'".format(file_name))
1128 with open(file_name, "w") as file_handler:
1129 file_handler.write(",".join(header) + "\n")
1130 for test in tbl_sorted:
1131 file_handler.write(",".join([str(item) for item in test]) + '\n')
1133 txt_file_name = "{0}.txt".format(table["output-file"])
1134 logging.info(" Writing file: '{0}'".format(txt_file_name))
1135 convert_csv_to_pretty_txt(file_name, txt_file_name)
1138 def table_failed_tests_html(table, input_data):
1139 """Generate the table(s) with algorithm: table_failed_tests_html
1140 specified in the specification file.
1142 :param table: Table to generate.
1143 :param input_data: Data to process.
1144 :type table: pandas.Series
1145 :type input_data: InputData
1148 testbed = table.get("testbed", None)
1150 logging.error("The testbed is not defined for the table '{0}'.".
1151 format(table.get("title", "")))
1154 logging.info(" Generating the table {0} ...".
1155 format(table.get("title", "")))
1158 with open(table["input-file"], 'rb') as csv_file:
1159 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
1160 csv_lst = [item for item in csv_content]
1162 logging.warning("The input file is not defined.")
1164 except csv.Error as err:
1165 logging.warning("Not possible to process the file '{0}'.\n{1}".
1166 format(table["input-file"], err))
1170 failed_tests = ET.Element("table", attrib=dict(width="100%", border='0'))
1173 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#7eade7"))
1174 for idx, item in enumerate(csv_lst[0]):
1175 alignment = "left" if idx == 0 else "center"
1176 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
1180 colors = ("#e9f1fb", "#d4e4f7")
1181 for r_idx, row in enumerate(csv_lst[1:]):
1182 background = colors[r_idx % 2]
1183 tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor=background))
1186 for c_idx, item in enumerate(row):
1187 alignment = "left" if c_idx == 0 else "center"
1188 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
1191 url = _generate_url("../trending/", testbed, item)
1192 ref = ET.SubElement(td, "a", attrib=dict(href=url))
1197 with open(table["output-file"], 'w') as html_file:
1198 logging.info(" Writing file: '{0}'".format(table["output-file"]))
1199 html_file.write(".. raw:: html\n\n\t")
1200 html_file.write(ET.tostring(failed_tests))
1201 html_file.write("\n\t<p><br><br></p>\n")
1203 logging.warning("The output file is not defined.")