from datetime import timedelta
from utils import mean, stdev, relative_change, classify_anomalies, \
- convert_csv_to_pretty_txt
+ convert_csv_to_pretty_txt, relative_change_stdev
REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
try:
col_data = str(data[job][build][test][column["data"].
split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
+ if column["data"].split(" ")[1] in ("conf-history",
"show-run"):
col_data = replace(col_data, " |br| ", "",
maxreplace=1)
try:
col_data = str(data[test][column["data"].
split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
+ col_data = replace(col_data, "No Data",
+ "Not Captured ")
+ if column["data"].split(" ")[1] in ("conf-history",
"show-run"):
col_data = replace(col_data, " |br| ", "",
maxreplace=1)
format(col_data[:-5])
row_lst.append('"{0}"'.format(col_data))
except KeyError:
- row_lst.append("No data")
+ row_lst.append('"Not captured"')
table_lst.append(row_lst)
# Write the data to file
elif table["compare"]["nic"] in tst_data["tags"]:
tbl_dict[tst_name_mod]["cmp-data"].append(result)
except (TypeError, KeyError) as err:
- logging.warning("No data for {0}".format(tst_name))
- logging.warning(repr(err))
+ logging.debug("No data for {0}".format(tst_name))
+ logging.debug(repr(err))
# No data in output.xml for this test
tbl_lst = list()
convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+def table_soak_vs_ndr(table, input_data):
+ """Generate the table(s) with algorithm: table_soak_vs_ndr
+ specified in the specification file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ # Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
+ data = input_data.filter_data(table, continue_on_error=True)
+
+ # Prepare the header of the table
+ try:
+ header = [
+ "Test case",
+ "{0} Throughput [Mpps]".format(table["reference"]["title"]),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Delta [%]", "Stdev of delta [%]"]
+ header_str = ",".join(header) + "\n"
+ except (AttributeError, KeyError) as err:
+ logging.error("The model is invalid, missing parameter: {0}".
+ format(err))
+ return
+
+ # Create a list of available SOAK test results:
+ tbl_dict = dict()
+ for job, builds in table["compare"]["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tst_data["type"] == "SOAK":
+ tst_name_mod = tst_name.replace("-soak", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ nic = groups.group(0) if groups else ""
+ name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
+ split("-")[:-1]))
+ tbl_dict[tst_name_mod] = {
+ "name": name,
+ "ref-data": list(),
+ "cmp-data": list()
+ }
+ try:
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["LOWER"])
+ except (KeyError, TypeError):
+ pass
+ tests_lst = tbl_dict.keys()
+
+ # Add corresponding NDR test results:
+ for job, builds in table["reference"]["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ tst_name_mod = tst_name.replace("-ndrpdr", "").\
+ replace("-mrr", "")
+ if tst_name_mod in tests_lst:
+ try:
+ if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"):
+ if table["include-tests"] == "MRR":
+ result = tst_data["result"]["receive-rate"].avg
+ elif table["include-tests"] == "PDR":
+ result = tst_data["throughput"]["PDR"]["LOWER"]
+ elif table["include-tests"] == "NDR":
+ result = tst_data["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+ if result is not None:
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ result)
+ except (KeyError, TypeError):
+ continue
+
+ tbl_lst = list()
+ for tst_name in tbl_dict.keys():
+ item = [tbl_dict[tst_name]["name"], ]
+ data_r = tbl_dict[tst_name]["ref-data"]
+ if data_r:
+ data_r_mean = mean(data_r)
+ item.append(round(data_r_mean / 1000000, 2))
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_stdev / 1000000, 2))
+ else:
+ data_r_mean = None
+ data_r_stdev = None
+ item.extend([None, None])
+ data_c = tbl_dict[tst_name]["cmp-data"]
+ if data_c:
+ data_c_mean = mean(data_c)
+ item.append(round(data_c_mean / 1000000, 2))
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_stdev / 1000000, 2))
+ else:
+ data_c_mean = None
+ data_c_stdev = None
+ item.extend([None, None])
+ if data_r_mean and data_c_mean:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
+ item.append(round(delta, 2))
+ item.append(round(d_stdev, 2))
+ tbl_lst.append(item)
+
+ # Sort the table according to the relative change
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+ # Generate csv tables:
+ csv_file = "{0}.csv".format(table["output-file"])
+ with open(csv_file, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ file_handler.write(",".join([str(item) for item in test]) + "\n")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+
+
def table_performance_trending_dashboard(table, input_data):
"""Generate the table(s) with algorithm:
table_performance_trending_dashboard
file_name = "vm_vhost_l2"
if "114b" in test_name:
feature = ""
- elif "l2xcbase" in test_name:
+ elif "l2xcbase" in test_name and "x520" in test_name:
feature = "-base-l2xc"
- elif "l2bdbasemaclrn" in test_name:
+ elif "l2bdbasemaclrn" in test_name and "x520" in test_name:
feature = "-base-l2bd"
else:
feature = "-base"
file_name = "vm_vhost_ip4"
feature = "-base"
+ elif "ipsecbasetnlsw" in test_name:
+ file_name = "ipsecsw"
+ feature = "-base-scale"
+
elif "ipsec" in test_name:
file_name = "ipsec"
feature = "-base-scale"
return
+def table_last_failed_tests(table, input_data):
+ """Generate the table(s) with algorithm: table_last_failed_tests
+ specified in the specification file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ # Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
+ data = input_data.filter_data(table, continue_on_error=True)
+
+ if data is None or data.empty:
+ logging.warn(" No data for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
+ return
+
+ tbl_list = list()
+ for job, builds in table["data"].items():
+ for build in builds:
+ build = str(build)
+ try:
+ version = input_data.metadata(job, build).get("version", "")
+ except KeyError:
+ logging.error("Data for {job}: {build} is not present.".
+ format(job=job, build=build))
+ return
+ tbl_list.append(build)
+ tbl_list.append(version)
+ for tst_name, tst_data in data[job][build].iteritems():
+ if tst_data["status"] != "FAIL":
+ continue
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_list.append("{0}-{1}".format(nic, tst_data["name"]))
+
+ file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
+ logging.info(" Writing file: '{0}'".format(file_name))
+ with open(file_name, "w") as file_handler:
+ for test in tbl_list:
+ file_handler.write(test + '\n')
+
+
def table_failed_tests(table, input_data):
"""Generate the table(s) with algorithm: table_failed_tests
specified in the specification file.