-# Copyright (c) 2020 Cisco and/or its affiliates.
+# Copyright (c) 2022 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
"""Generation of Continuous Performance Trending and Analysis.
"""
-
import re
import logging
import csv
from collections import OrderedDict
from datetime import datetime
from copy import deepcopy
+from os import listdir
import prettytable
import plotly.offline as ploff
# Command to build the html format of the report
-HTML_BUILDER = u'sphinx-build -v -c conf_cpta -a ' \
+HTML_BUILDER = u'sphinx-build -v -c sphinx_conf/trending -a ' \
u'-b html -E ' \
u'-t html ' \
u'-D version="{date}" ' \
css_file:
css_file.write(THEME_OVERRIDES)
- if spec.configuration.get(u"archive-inputs", True):
+ if spec.environment.get(u"archive-inputs", False):
archive_input_data(spec)
logging.info(u"Done.")
:rtype: tuple(traces, result)
"""
- if incl_tests not in (u"mrr", u"ndr", u"pdr"):
+ if incl_tests not in (u"mrr", u"ndr", u"pdr", u"pdr-lat"):
return list(), None
data_x = list(in_data.keys())
data_y_pps = list()
data_y_mpps = list()
data_y_stdev = list()
- for item in in_data.values():
- data_y_pps.append(float(item[u"receive-rate"]))
- data_y_stdev.append(float(item[u"receive-stdev"]) / 1e6)
- data_y_mpps.append(float(item[u"receive-rate"]) / 1e6)
-
+ if incl_tests == u"pdr-lat":
+ for item in in_data.values():
+ data_y_pps.append(float(item.get(u"lat_1", u"nan")) / 1e6)
+ data_y_stdev.append(float(u"nan"))
+ data_y_mpps.append(float(item.get(u"lat_1", u"nan")) / 1e6)
+ multi = 1.0
+ else:
+ for item in in_data.values():
+ data_y_pps.append(float(item[u"receive-rate"]))
+ data_y_stdev.append(float(item[u"receive-stdev"]) / 1e6)
+ data_y_mpps.append(float(item[u"receive-rate"]) / 1e6)
+ multi = 1e6
hover_text = list()
xaxis = list()
for index, key in enumerate(data_x):
str_key = str(key)
date = build_info[job_name][str_key][0]
hover_str = (u"date: {date}<br>"
- u"{property} [Mpps]: {value:.3f}<br>"
+ u"{property} [Mpps]: <val><br>"
u"<stdev>"
u"{sut}-ref: {build}<br>"
u"csit-ref: {test}-{period}-build-{build_nr}<br>"
)
else:
hover_str = hover_str.replace(u"<stdev>", u"")
+ if incl_tests == u"pdr-lat":
+ hover_str = hover_str.replace(u"<val>", u"{value:.1e}")
+ else:
+ hover_str = hover_str.replace(u"<val>", u"{value:.3f}")
if u"-cps" in name:
- hover_str = hover_str.replace(u"[Mpps]", u"[Mcps]")
- if u"dpdk" in job_name:
- hover_text.append(hover_str.format(
+ hover_str = hover_str.replace(u"[Mpps]", u"[Mcps]").\
+ replace(u"throughput", u"connection rate")
+ if u"vpp" in job_name:
+ hover_str = hover_str.format(
+ date=date,
+ property=u"average" if incl_tests == u"mrr" else u"throughput",
+ value=data_y_mpps[index],
+ sut=u"vpp",
+ build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
+ test=incl_tests,
+ period=u"daily" if incl_tests == u"mrr" else u"weekly",
+ build_nr=str_key,
+ testbed=build_info[job_name][str_key][2])
+ elif u"dpdk" in job_name:
+ hover_str = hover_str.format(
date=date,
property=u"average" if incl_tests == u"mrr" else u"throughput",
value=data_y_mpps[index],
test=incl_tests,
period=u"weekly",
build_nr=str_key,
- testbed=build_info[job_name][str_key][2]))
- elif u"vpp" in job_name:
+ testbed=build_info[job_name][str_key][2])
+ elif u"trex" in job_name:
hover_str = hover_str.format(
date=date,
property=u"average" if incl_tests == u"mrr" else u"throughput",
value=data_y_mpps[index],
- sut=u"vpp",
- build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
+ sut=u"trex",
+ build=u"",
test=incl_tests,
period=u"daily" if incl_tests == u"mrr" else u"weekly",
build_nr=str_key,
testbed=build_info[job_name][str_key][2])
- if u"-cps" in name:
- hover_str = hover_str.replace(u"throughput", u"connection rate")
- hover_text.append(hover_str)
-
+ if incl_tests == u"pdr-lat":
+ hover_str = hover_str.replace(
+ u"throughput [Mpps]", u"latency [s]"
+ )
+ hover_text.append(hover_str)
xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
int(date[9:11]), int(date[12:])))
for key, value in zip(xaxis, data_y_pps):
data_pd[key] = value
- anomaly_classification, avgs_pps, stdevs_pps = classify_anomalies(data_pd)
- avgs_mpps = [avg_pps / 1e6 for avg_pps in avgs_pps]
- stdevs_mpps = [stdev_pps / 1e6 for stdev_pps in stdevs_pps]
+ try:
+ anomaly_classification, avgs_pps, stdevs_pps = \
+ classify_anomalies(data_pd)
+ except ValueError as err:
+ logging.info(f"{err} Skipping")
+ return list(), None
+ avgs_mpps = [avg_pps / multi for avg_pps in avgs_pps]
+ stdevs_mpps = [stdev_pps / multi for stdev_pps in stdevs_pps]
anomalies = OrderedDict()
anomalies_colors = list()
if anomaly_classification:
for index, (key, value) in enumerate(data_pd.items()):
if anomaly_classification[index] in (u"regression", u"progression"):
- anomalies[key] = value / 1e6
+ anomalies[key] = value / multi
anomalies_colors.append(
anomaly_color[anomaly_classification[index]])
anomalies_avgs.append(avgs_mpps[index])
trend_hover_text = list()
for idx in range(len(data_x)):
- trend_hover_str = (
- f"trend [Mpps]: {avgs_mpps[idx]:.3f}<br>"
- f"stdev [Mpps]: {stdevs_mpps[idx]:.3f}"
- )
+ if incl_tests == u"pdr-lat":
+ trend_hover_str = (
+ f"trend [s]: {avgs_mpps[idx]:.1e}<br>"
+ )
+ else:
+ trend_hover_str = (
+ f"trend [Mpps]: {avgs_mpps[idx]:.3f}<br>"
+ f"stdev [Mpps]: {stdevs_mpps[idx]:.3f}"
+ )
trend_hover_text.append(trend_hover_str)
trace_trend = plgo.Scatter(
)
traces.append(trace_trend)
+ if incl_tests == u"pdr-lat":
+ colorscale = [
+ [0.00, u"green"],
+ [0.33, u"green"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"red"],
+ [1.00, u"red"]
+ ]
+ ticktext = [u"Progression", u"Normal", u"Regression"]
+ else:
+ colorscale = [
+ [0.00, u"red"],
+ [0.33, u"red"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"green"],
+ [1.00, u"green"]
+ ]
+ ticktext = [u"Regression", u"Normal", u"Progression"]
trace_anomalies = plgo.Scatter(
x=list(anomalies.keys()),
y=anomalies_avgs,
u"size": 15,
u"symbol": u"circle-open",
u"color": anomalies_colors,
- u"colorscale": [
- [0.00, u"red"],
- [0.33, u"red"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"green"],
- [1.00, u"green"]
- ],
+ u"colorscale": colorscale,
u"showscale": True,
u"line": {
u"width": 2
},
u"tickmode": u"array",
u"tickvals": [0.167, 0.500, 0.833],
- u"ticktext": [u"Regression", u"Normal", u"Progression"],
+ u"ticktext": ticktext,
u"ticks": u"",
u"ticklen": 0,
u"tickangle": -90,
data = input_data.filter_tests_by_name(
graph,
- params=[u"type", u"result", u"throughput", u"tags"],
+ params=[u"type", u"result", u"throughput", u"latency", u"tags"],
continue_on_error=True
)
for ttype in graph.get(u"test-type", (u"mrr", )):
for core in graph.get(u"core", tuple()):
csv_tbl = list()
+ csv_tbl_lat_1 = list()
+ csv_tbl_lat_2 = list()
res = dict()
chart_data = dict()
chart_tags = dict()
if chart_data.get(test_id, None) is None:
chart_data[test_id] = OrderedDict()
try:
+ lat_1 = u""
+ lat_2 = u""
if ttype == u"mrr":
rate = test[u"result"][u"receive-rate"]
stdev = \
rate = \
test["throughput"][u"PDR"][u"LOWER"]
stdev = float(u"nan")
+ lat_1 = test[u"latency"][u"PDR50"]\
+ [u"direction1"][u"avg"]
+ lat_2 = test[u"latency"][u"PDR50"]\
+ [u"direction2"][u"avg"]
else:
continue
chart_data[test_id][int(index)] = {
u"receive-rate": rate,
u"receive-stdev": stdev
}
+ if ttype == u"pdr":
+ chart_data[test_id][int(index)].update(
+ {
+ u"lat_1": lat_1,
+ u"lat_2": lat_2
+ }
+ )
chart_tags[test_id] = \
test.get(u"tags", None)
except (KeyError, TypeError):
# Add items to the csv table:
for tst_name, tst_data in chart_data.items():
tst_lst = list()
+ tst_lst_lat_1 = list()
+ tst_lst_lat_2 = list()
for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), dict())
# CSIT-1180: Itm will be list, compute stats.
try:
tst_lst.append(str(itm.get(u"receive-rate", u"")))
+ if ttype == u"pdr":
+ tst_lst_lat_1.append(
+ str(itm.get(u"lat_1", u""))
+ )
+ tst_lst_lat_2.append(
+ str(itm.get(u"lat_2", u""))
+ )
except AttributeError:
tst_lst.append(u"")
+ if ttype == u"pdr":
+ tst_lst_lat_1.append(u"")
+ tst_lst_lat_2.append(u"")
csv_tbl.append(f"{tst_name}," + u",".join(tst_lst) + u'\n')
+ csv_tbl_lat_1.append(
+ f"{tst_name}," + u",".join(tst_lst_lat_1) + u"\n"
+ )
+ csv_tbl_lat_2.append(
+ f"{tst_name}," + u",".join(tst_lst_lat_2) + u"\n"
+ )
# Generate traces:
traces = list()
+ traces_lat = list()
index = 0
groups = graph.get(u"groups", None)
visibility = list()
color=COLORS[index],
incl_tests=ttype
)
+ if ttype == u"pdr":
+ trace_lat, _ = _generate_trending_traces(
+ test_data,
+ job_name=job_name,
+ build_info=build_info,
+ name=u'-'.join(
+ tst_name.split(u'.')[-1].split(
+ u'-')[2:-1]),
+ color=COLORS[index],
+ incl_tests=u"pdr-lat"
+ )
+ traces_lat.extend(trace_lat)
except IndexError:
logging.error(
f"Out of colors: index: "
except plerr.PlotlyEmptyDataError:
logging.warning(u"No data for the plot. Skipped.")
+ if traces_lat:
+ try:
+ layout = deepcopy(graph[u"layout"])
+ layout[u"yaxis"][u"title"] = u"Latency [s]"
+ layout[u"yaxis"][u"tickformat"] = u".3s"
+ except KeyError as err:
+ logging.error(u"Finished with error: No layout defined")
+ logging.error(repr(err))
+ return dict()
+ name_file = (
+ f"{spec.cpta[u'output-file']}/"
+ f"{graph[u'output-file-name']}-lat.html"
+ )
+ name_file = name_file.format(core=core, test_type=ttype)
+
+ logging.info(f" Writing the file {name_file}")
+ plpl = plgo.Figure(data=traces_lat, layout=layout)
+ try:
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=name_file
+ )
+ except plerr.PlotlyEmptyDataError:
+ logging.warning(u"No data for the plot. Skipped.")
+
return_lst.append(
{
u"job_name": job_name,
u"csv_table": csv_tbl,
+ u"csv_lat_1": csv_tbl_lat_1,
+ u"csv_lat_2": csv_tbl_lat_2,
u"results": res
}
)
return return_lst
builds_dict = dict()
- for job in spec.input[u"builds"].keys():
+ for job, builds in spec.input.items():
if builds_dict.get(job, None) is None:
builds_dict[job] = list()
- for build in spec.input[u"builds"][job]:
- status = build[u"status"]
- if status not in (u"failed", u"not found", u"removed", None):
+ for build in builds:
+ if build[u"status"] not in (u"failed", u"not found", u"removed",
+ None):
builds_dict[job].append(str(build[u"build"]))
# Create "build ID": "date" dict:
# Create the table header:
csv_tables = dict()
+ csv_tables_l1 = dict()
+ csv_tables_l2 = dict()
for job_name in builds_dict:
if csv_tables.get(job_name, None) is None:
csv_tables[job_name] = list()
+ if csv_tables_l1.get(job_name, None) is None:
+ csv_tables_l1[job_name] = list()
+ if csv_tables_l2.get(job_name, None) is None:
+ csv_tables_l2[job_name] = list()
header = f"Build Number:,{u','.join(builds_dict[job_name])}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
build_dates = [x[0] for x in build_info[job_name].values()]
header = f"Build Date:,{u','.join(build_dates)}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
versions = [x[1] for x in build_info[job_name].values()]
header = f"Version:,{u','.join(versions)}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
+ testbed = [x[2] for x in build_info[job_name].values()]
+ header = f"Test bed:,{u','.join(testbed)}\n"
+ csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
for chart in spec.cpta[u"plots"]:
results = _generate_chart(chart)
for result in results:
csv_tables[result[u"job_name"]].extend(result[u"csv_table"])
+ csv_tables_l1[result[u"job_name"]].extend(result[u"csv_lat_1"])
+ csv_tables_l2[result[u"job_name"]].extend(result[u"csv_lat_2"])
if anomaly_classifications.get(result[u"job_name"], None) is None:
anomaly_classifications[result[u"job_name"]] = dict()
with open(f"{file_name}.txt", u"wt") as txt_file:
txt_file.write(str(txt_table))
+ for job_name, csv_table in csv_tables_l1.items():
+ file_name = f"{spec.cpta[u'output-file']}/{job_name}-lat-P50-50-d1"
+ with open(f"{file_name}.csv", u"wt") as file_handler:
+ file_handler.writelines(csv_table)
+ for job_name, csv_table in csv_tables_l2.items():
+ file_name = f"{spec.cpta[u'output-file']}/{job_name}-lat-P50-50-d2"
+ with open(f"{file_name}.csv", u"wt") as file_handler:
+ file_handler.writelines(csv_table)
+
# Evaluate result:
if anomaly_classifications:
+ test_reg_lst = []
+ nic_reg_lst = []
+ frmsize_reg_lst = []
+ trend_reg_lst = []
+ number_reg_lst = []
+ ltc_reg_lst = []
+ test_prog_lst = []
+ nic_prog_lst = []
+ frmsize_prog_lst = []
+ trend_prog_lst = []
+ number_prog_lst = []
+ ltc_prog_lst = []
result = u"PASS"
+
+ class MaxLens():
+ """Class to store the max lengths of strings displayed in
+ regressions and progressions.
+ """
+
+ def __init__(self, tst, nic, frmsize, trend, run, ltc):
+ """Initialisation.
+
+ :param tst: Name of the test.
+ :param nic: NIC used in the test.
+ :param frmsize: Frame size used in the test.
+ :param trend: Trend Change.
+ :param run: Number of runs for last trend.
+ :param ltc: Regression or Progression
+ """
+ self.tst = tst
+ self.nic = nic
+ self.frmsize = frmsize
+ self.trend = trend
+ self.run = run
+ self.ltc = ltc
+
+ max_len = MaxLens(0, 0, 0, 0, 0, 0)
+
for job_name, job_data in anomaly_classifications.items():
- file_name = \
- f"{spec.cpta[u'output-file']}/regressions-{job_name}.txt"
- with open(file_name, u'w') as txt_file:
- for test_name, classification in job_data.items():
- if classification == u"regression":
- txt_file.write(test_name + u'\n')
+ data = []
+ tb = u"-".join(job_name.split(u"-")[-2:])
+ for file in listdir(f"{spec.cpta[u'output-file']}"):
+ if tb in file and u"performance-trending-dashboard" in \
+ file and u"txt" in file:
+ file_to_read = f"{spec.cpta[u'output-file']}/{file}"
+ with open(f"{file_to_read}", u"rt") as f_in:
+ data = data + f_in.readlines()
+
+ for test_name, classification in job_data.items():
+ if classification != u"normal":
+ if u"2n" in test_name:
+ test_name = test_name.split("-", 2)
+ tst = test_name[2].split(".")[-1]
+ nic = test_name[1]
+ else:
+ test_name = test_name.split("-", 1)
+ tst = test_name[1].split(".")[-1]
+ nic = test_name[0].split(".")[-1]
+ frmsize = tst.split("-")[0]
+ tst = u"-".join(tst.split("-")[1:])
+ tst_name = f"{nic}-{frmsize}-{tst}"
+ if len(tst) > max_len.tst:
+ max_len.tst = len(tst)
+ if len(nic) > max_len.nic:
+ max_len.nic = len(nic)
+ if len(frmsize) > max_len.frmsize:
+ max_len.frmsize = len(frmsize)
+
+ for line in data:
+ if tst_name in line:
+ line = line.replace(" ", "")
+ trend = line.split("|")[2]
+ if len(str(trend)) > max_len.trend:
+ max_len.trend = len(str(trend))
+ number = line.split("|")[3]
+ if len(str(number)) > max_len.run:
+ max_len.run = len(str(number))
+ ltc = line.split("|")[4]
+ if len(str(ltc)) > max_len.ltc:
+ max_len.ltc = len(str(ltc))
+ if classification == u'regression':
+ test_reg_lst.append(tst)
+ nic_reg_lst.append(nic)
+ frmsize_reg_lst.append(frmsize)
+ trend_reg_lst.append(trend)
+ number_reg_lst.append(number)
+ ltc_reg_lst.append(ltc)
+ elif classification == u'progression':
+ test_prog_lst.append(tst)
+ nic_prog_lst.append(nic)
+ frmsize_prog_lst.append(frmsize)
+ trend_prog_lst.append(trend)
+ number_prog_lst.append(number)
+ ltc_prog_lst.append(ltc)
+
if classification in (u"regression", u"outlier"):
result = u"FAIL"
+
+ text = u""
+ for idx in range(len(test_reg_lst)):
+ text += (
+ f"{test_reg_lst[idx]}"
+ f"{u' ' * (max_len.tst - len(test_reg_lst[idx]))} "
+ f"{nic_reg_lst[idx]}"
+ f"{u' ' * (max_len.nic - len(nic_reg_lst[idx]))} "
+ f"{frmsize_reg_lst[idx].upper()}"
+ f"{u' ' * (max_len.frmsize - len(frmsize_reg_lst[idx]))} "
+ f"{trend_reg_lst[idx]}"
+ f"{u' ' * (max_len.trend - len(str(trend_reg_lst[idx])))} "
+ f"{number_reg_lst[idx]}"
+ f"{u' ' * (max_len.run - len(str(number_reg_lst[idx])))} "
+ f"{ltc_reg_lst[idx]}"
+ f"{u' ' * (max_len.ltc - len(str(ltc_reg_lst[idx])))} "
+ f"\n"
+ )
+
+ file_name = \
+ f"{spec.cpta[u'output-file']}/regressions-{job_name}.txt"
+
+ try:
+ with open(f"{file_name}", u'w') as txt_file:
+ txt_file.write(text)
+ except IOError:
+ logging.error(
+ f"Not possible to write the file {file_name}.")
+
+ text = u""
+ for idx in range(len(test_prog_lst)):
+ text += (
+ f"{test_prog_lst[idx]}"
+ f"{u' ' * (max_len.tst - len(test_prog_lst[idx]))} "
+ f"{nic_prog_lst[idx]}"
+ f"{u' ' * (max_len.nic - len(nic_prog_lst[idx]))} "
+ f"{frmsize_prog_lst[idx].upper()}"
+ f"{u' ' * (max_len.frmsize - len(frmsize_prog_lst[idx]))} "
+ f"{trend_prog_lst[idx]}"
+ f"{u' ' * (max_len.trend -len(str(trend_prog_lst[idx])))} "
+ f"{number_prog_lst[idx]}"
+ f"{u' ' * (max_len.run - len(str(number_prog_lst[idx])))} "
+ f"{ltc_prog_lst[idx]}"
+ f"{u' ' * (max_len.ltc - len(str(ltc_prog_lst[idx])))} "
+ f"\n"
+ )
+
file_name = \
f"{spec.cpta[u'output-file']}/progressions-{job_name}.txt"
- with open(file_name, u'w') as txt_file:
- for test_name, classification in job_data.items():
- if classification == u"progression":
- txt_file.write(test_name + u'\n')
+ try:
+ with open(f"{file_name}", u'w') as txt_file:
+ txt_file.write(text)
+ except IOError:
+ logging.error(f"Not possible to write the file {file_name}.")
+
else:
result = u"FAIL"
Code Review / csit.git / blobdiff