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calcTrend2.py3
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calcTrend2.py3
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#!/usr/bin/env python3
#
# Execute on perfstat-<cluster>-<date>.csv file generated by QueryStat2.py
#
from __future__ import absolute_import
import glob
import copy
import os
from optparse import OptionParser
import datetime as dt
import numpy
from shutil import copyfile
import sys
import inspect
import traceback
import linecache
import re
import configparser
try:
import matplotlib as mpl
mpl.use('Agg') # use a non-interactive backend
import matplotlib.pyplot as plt
except ImportError as e:
print(("Error in import matplotlib: %s" % (repr(e)) ))
plt = None
finally:
pass
try:
from ReportedTestCasesHistory3 import ReportedTestCasesHistory
except ImportError as e:
print("Import error %s" % (repr(e)))
ReportedTestCasesHistory = None
debug = False
smallDataSet = False
# To enable diagram generation for "only good" cases as well
enableGood = True
def PrintException(msg = ''):
exc_type, exc_obj, tb = sys.exc_info()
f = tb.tb_frame
lineno = tb.tb_lineno
filename = f.f_code.co_filename
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, f.f_globals)
print(('EXCEPTION IN (%s, LINE %s CODE:"%s"): %s' % ( filename, lineno, line.strip(), msg)))
class TrendReport(object):
# To decide time consumption is increased (>threshold), unaltered (-threshold< <threshold)
# or decreased (<-threshold)
threshold = 5.0 # %
results2 = {}
maxDatapoints = 180
maxDataPointsOverhead = 5
def myPrint(self, Msg, *Args):
if self.verbose:
format=''.join(['%s']*(len(Args)+1))
print(( format % tuple([Msg]+list(map(str,Args))) ))
def __init__(self, options):
if options.dataPath.startswith('~/'):
self.dataPath = options.dataPath.replace('~', os.environ['HOME'])
else:
self.dataPath = os.path.abspath(options.dataPath)
if not self.dataPath.endswith('/'):
self.dataPath += '/'
if not os.path.exists(self.dataPath):
print(("Fatal error: %s report path doesn't exist." % (self.dataPath)))
exit()
if options.reportPath.startswith('~/'):
self.reportPath = options.reportPath.replace('~', os.environ['HOME'])
else:
self.reportPath = os.path.abspath(options.reportPath)
if not self.reportPath.endswith('/'):
self.reportPath += '/'
if not os.path.exists(self.reportPath):
print(("Fatal error: %s report path doesn't exist." % (self.reportPath)))
exit()
self.enablePdfReport = True
self.enablePdfReport = options.pdfReport
self.badThreshold = options.threshold
self.goodThreshold = -1 * options.threshold
self.movingAverageWindow1 = options.movingAverageWindow1
self.disableMovingAverage1 = options.disableMovingAverage1
self.movingAverageWindow2 = options.movingAverageWindow2
self.disableMovingAverage2 = options.disableMovingAverage2
self.enableSigma = options.enableSigma
self.enableMin = options.enableMin
self.enableMax = options.enableMax
self.enableTrend = True
self.enableMean = True
self.verbose = options.verbose
self.diagramWidth = 16 # Original width
self.diagramHeight = 10 # Original height
self.enableReportGood = True
self.enableTestPlotGeneration = options.enableTestPlotGeneration
if smallDataSet:
self.maxDatapoints = 4
self.numberOfTestDays = 0
# These two should be controlled by CLI parameter
self.BuildFilter = "RelWithDebInfo"
self.useAllData = True
if self.verbose:
print(("self.dataPath :%s" % (self.dataPath)))
print(("self.reportPath :%s" % (self.reportPath)))
print(("self.enablePdfReport:%s" % (str(self.enablePdfReport))))
print(("self.badThreshold :%3.0f %%" % (self.badThreshold)))
print(("self.goodThreshold :%3.0f %%" % (self.goodThreshold)))
print(("self.maxDatapoints :%3d " % (self.maxDatapoints)))
self.defaultHeaderData = [
['Test case','Last two run', '', '', 'Last five run', '', '', 'All runs', '' , '' ],
['','Trend\nsec/run','Trend','%','Trend\nsec/run','Trend','%','Trend\nsec/run','Trend','%'],
]
self.badTag = "Bad"
self.uglyTag = "Ugly"
self.uglyAndBadTag = "Ugly and Bad"
self.goodTag = "Good"
self.neutralTag = "Neutral"
self.badTags = [ self.badTag, self.uglyTag, self.uglyAndBadTag ]
self.markTags = self.badTags + [self.goodTag]
self.neutralTags = [self.neutralTag]
# To create dictionary from list
# d={ badTags[i]:x for i,x in enumerate(badTags)}
# result is:
# {'Ugly': 'Ugly', 'Bad': 'Bad', 'Bad and Ugly': 'Bad and Ugly'}
self.numOfRuns = {}
# Pro cluster pro day (commulated result of all run on a day)
self.clusterTrends = {}
self.testDays = set()
today = dt.datetime.today()
self.dateStr = today.strftime("%y%m%d")
self.hpccVersion = ''
self.currentIssues = {}
self.newIssues = {}
# Read PerformanceIssues.csv into self.currentIssues
self.issueFileName = 'PerformanceIssues.csv'
if os.path.exists(self.issueFileName):
file = open(self.issueFileName, "r")
for line in file:
items = line.replace('\n', '').split(',')
self.myPrint("Items:", items)
if items[0] not in self.currentIssues:
self.currentIssues[items[0]] ={} # test name
if items[1] not in self.currentIssues[items[0]]:
self.currentIssues[items[0]][items[1]] = {'tag': items[2], 'known' : False}
if items[3] == 'True':
self.currentIssues[items[0]][items[1]]['known'] = True
file.close()
pass
def filterOutOldDatafiles(self, files):
today = dt.date.today()
borderDate = today - dt.timedelta(self.maxDatapoints + self.maxDataPointsOverhead)
numOfRuns = {}
numOfCheckedFiles = {}
retFiles = []
try:
for fileName in files:
nameItems = fileName.replace('./', '').replace('OBT-', 'OBT_').replace('.csv', '').split('-')
if len(nameItems) < 3:
print("Wrong file name!")
continue
cluster = nameItems[1]
resultDate = dt.date(2000 + int(nameItems[2][0:2]), int(nameItems[2][2:4]),int(nameItems[2][4:6]))
if os.stat(fileName).st_size == 0:
continue
if cluster not in numOfRuns:
numOfRuns[cluster] = 0
numOfCheckedFiles[cluster] = 0
numOfCheckedFiles[cluster] += 1
# Read and process self.maxDatapoints files pro cluster only
if (numOfRuns[cluster] < self.maxDatapoints) and (numOfCheckedFiles[cluster] <= self.maxDatapoints + self.maxDataPointsOverhead) and (borderDate <= resultDate):
retFiles.append(fileName)
numOfRuns[cluster] += 1
except:
pass
pass
retFiles.sort()
return retFiles
def readData(self):
def processLine(line):
retCode = 0
testName = ''
loop = 0
testDay = ''
value = 0.0
# Regression test based extra parameter, can cause problem to create diagram. Remove
line = line.replace("-hpccbasedir('/opt/HPCCSystems/')", "")
items = line.split(',')
testNameItems = items[0].split('-')
testNameLen = len(testNameItems)
if testNameLen < 3:
retCode = -1
else:
if testNameLen == 3:
testName = testNameItems[0]
else:
testName = testNameItems[0]
p = re.compile('.*rteloop([0-9]+).*$')
for testNameItem in testNameItems[1:testNameLen-2]:
if testNameItem.startswith('#'):
# Suffix, can contain loop count
m = p.match(testNameItem)
if m:
loop = int(m.group(1))
else:
# other suffix, don't add it to test name
pass
else:
# Version tag, add it to test name
testName += '-' + testNameItem
testDay = '20' + testNameItems[testNameLen-2][0:2] + '.' + testNameItems[testNameLen-2][2:4] + '.' + testNameItems[testNameLen-2][4:6]
value = float(items[1])
return (retCode, testName, loop, testDay, value)
config = configparser.ConfigParser()
allFiles = glob.glob(self.dataPath+'perfstat-*.csv')
allFiles.sort(reverse = True)
files = self.filterOutOldDatafiles(allFiles)
for fileName in files:
print(("File name: " + fileName))
nameItems = fileName.replace('./', '').replace('OBT-', 'OBT_').replace('.csv', '').split('-')
if len(nameItems) < 3:
print("Wrong file name!")
continue
cluster = nameItems[1]
date = '20' + nameItems[2][0:2] + '.' + nameItems[2][2:4] + '.' + nameItems[2][4:6]
if len(nameItems) > 3:
# It is the last element
self.hpccVersion = nameItems[-1]
if cluster not in self.results2:
self.results2[cluster] = {}
self.numOfRuns[cluster] = 0
self.clusterTrends[cluster] = {'Dates' : [], 'Totals' :[], 'ConfigName':''}
# Check build type and skip this file if different build
# Te default build type is RelWitDebInfo and it filters out the Debug builds
self.clusterTrends[cluster]['ConfigName'] = fileName.replace('.csv', '.cfg')
config.read(self.clusterTrends[cluster]['ConfigName'])
buildType = config.get("Build", "BuildType")
if buildType != self.BuildFilter:
continue
if date not in self.clusterTrends[cluster]['Dates']:
self.clusterTrends[cluster]['Dates'].append(date)
file = open(fileName, "r")
for line in file:
line = line.strip().replace('\n', '')
(retCode, testname, loop, testDay, value) = processLine(line)
if retCode == -1:
print("Mailformed test name!")
continue
if testname not in self.results2[cluster] :
self.results2[cluster][testname] = { 'Days' : {}, 'Dates': [], 'Values2' : numpy.array([])}
if testDay not in self.results2[cluster][testname]['Days']:
self.results2[cluster][testname]['Days'][testDay] = { 'values' : [], 'totalValue' : 0, 'runsPerDay' : 0, 'max' : value, 'min' : value, 'loops' : 0 }
self.results2[cluster][testname]['Days'][testDay]['values'].append(value)
self.results2[cluster][testname]['Days'][testDay]['totalValue'] += value
self.results2[cluster][testname]['Days'][testDay]['runsPerDay'] += 1
if self.results2[cluster][testname]['Days'][testDay]['max'] < value :
self.results2[cluster][testname]['Days'][testDay]['max'] = value
if self.results2[cluster][testname]['Days'][testDay]['min'] > value :
self.results2[cluster][testname]['Days'][testDay]['min'] = value
self.results2[cluster][testname]['Days'][testDay]['loops'] += 1
# Get the largest number of test day
if self.numOfRuns[cluster] < len(self.results2[cluster][testname]['Days']):
self.numOfRuns[cluster] = len(self.results2[cluster][testname]['Days'])
pass
# More than one result pro day adjust the dates in clusterTrends
# (not the best way to use dates of each cluster first test case)
print ("Read done")
print("Read the last test config")
for cluster in sorted(self.clusterTrends):
try:
self.clusterTrends[cluster]['testConfig'] = configparser.ConfigParser()
self.clusterTrends[cluster]['testConfig'].read(self.clusterTrends[cluster]['ConfigName'])
print(("%s FlushDiskCache :'%s'" % (cluster, self.clusterTrends[cluster]['testConfig'].get("Performance", "FlushDiskCache") )))
pass
except:
print(("Unexpected error:" + str(sys.exc_info()[0]) + " (line: " + str(inspect.stack()[0][2]) + ")" ))
traceback.print_stack()
pass
# Collect run counts to determine how many performance test has been executed
loopCounts = {}
for cluster in sorted(self.clusterTrends):
loopCounts[cluster] = { 'Days':{} , 'Loops': {}}
for testname in sorted(self.results2[cluster]):
for day in sorted(self.results2[cluster][testname]['Days']):
if day not in loopCounts[cluster]['Days']:
loopCounts[cluster]['Days'][day] = {}
runCount = self.results2[cluster][testname]['Days'][day]['loops']
if runCount not in loopCounts[cluster]['Days'][day]:
loopCounts[cluster]['Days'][day][runCount] = 1
else:
loopCounts[cluster]['Days'][day][runCount] += 1
for day in loopCounts[cluster]['Days']:
loopCounts[cluster]['Loops'][day] = max(loopCounts[cluster]['Days'][day], key=loopCounts[cluster]['Days'][day].get)
for cluster in sorted(self.clusterTrends):
#clusterTotalTimes = self.numOfRuns[cluster] * [0]
clusterTotalTimes = len(self.clusterTrends[cluster]['Dates']) * [0]
clusterTotalTimesPerDay = {}
for testname in sorted(self.results2[cluster]):
# if not testname.startswith('80ab_scalesort-scale(16)'):
# continue
days = sorted(self.clusterTrends[cluster]['Dates'])
for day in days:
dayIndex = days.index(day)
date = day #self.results[cluster][testname]['Dates'][dayIndex]
# Calculate average daily value if there were more than one run on that day
# Cases:
# testLoopCount == 1 and dailyLoopCount == 1
# Fine (time) value is the exacte execution time of test case
#
# testLoopCount == 1 and dailyLoopCount > 1
# Some execution missing, the approximated (time) value is the value
#
# testLoopCount > 1 and dailyLoopCount == 1
# Fine (time) value is the summarised execution time of test case in multiple times
#
# testLoopCount > 1 and dailyLoopCount > 1
# Subcases:
# testLoopCount < dailyLoopCount
# Some execution missing, use some approximation: test is invalid, keep original value
#
# testLoopCount == testCaseInLoop * dailyLoopCount
# Fine (time) value is the approximated (time) value is value / dailyLoopCount
#
# testLoopCount != testCaseInLoop * dailyLoopCount
# Some execution missing, use some approximation: test is invalid , keep original value
#
try:
value = self.results2[cluster][testname]['Days'][day]['totalValue']
testLoopCount = self.results2[cluster][testname]['Days'][day]['loops']
if testLoopCount > 1:
value = value / testLoopCount
value2 = value
except KeyError as e:
PrintException(repr(e) + " Missing test result on '%s' with test '%s' in engine '%s'." % (day, testname, cluster))
value = 0
value2 = numpy.nan
if day not in self.results2[cluster][testname]['Days']:
self.results2[cluster][testname]['Days'][day] = { 'values' : [], 'totalValue' : 0, 'runsPerDay' : 0, 'max' : value, 'min' : value, 'loops' : 0 }
self.results2[cluster][testname]['Days'][day]['average'] = value
self.results2[cluster][testname]['Dates'].append(day)
self.results2[cluster][testname]['Values2'] = numpy.append(self.results2[cluster][testname]['Values2'], value2)
if day not in clusterTotalTimesPerDay:
clusterTotalTimesPerDay[day] = 0
clusterTotalTimesPerDay[day] += value
clusterTotalTimes[dayIndex] += value
self.clusterTrends[cluster]['Totals'] = clusterTotalTimes
self.clusterTrends[cluster]['DailyTotals'] = clusterTotalTimesPerDay
pass
# Dump Results pro cluster
for cluster in sorted(self.results2):
resultFileName= self.reportPath+ "results-" + cluster + "-" + self.hpccVersion +".csv"
print(("resultFileName:" + resultFileName))
resultFile = open(resultFileName, "w")
resultFile.write("Testcase,avg,sigma,alpha,beta,numOfTests\n")
for test in sorted(self.results2[cluster]):
try:
self.results2[cluster][test]['avg'] = numpy.nanmean(self.results2[cluster][test]['Values2'])
self.results2[cluster][test]['sigma'] = numpy.nanstd(self.results2[cluster][test]['Values2'])
except AttributeError as e:
if self.verbose:
PrintException(repr(e) + " Problem with an older numpy.")
# A hack for an older numpy ehre nonmean() and nanstd() doesn't exists
v2 = self.results2[cluster][test]['Values2'][~numpy.isnan(self.results2[cluster][test]['Values2'])]
self.results2[cluster][test]['avg'] = numpy.mean(v2)
self.results2[cluster][test]['sigma'] = numpy.std(v2)
self.results2[cluster][test]['all'] = self.CalcTrend(self.results2[cluster][test]['Values2'])
dataPoints = len(self.results2[cluster][test]['Days'])
resultFile.write(test+','+ str(self.results2[cluster][test]['avg']) + ',' + str(self.results2[cluster][test]['sigma']) + ',' + str(self.results2[cluster][test]['all']['alpha']) + ',' + str(self.results2[cluster][test]['all']['beta']) + ',' + str(dataPoints))
for day in sorted(days):
if day in self.results2[cluster][test]['Days']:
resultFile.write(',' + day + ',' + str(self.results2[cluster][test]['Days'][day]['average']))
else:
resultFile.write(',' + day + ',0')
resultFile.write('\n')
resultFile.close()
pass
# Calculate trendline for an iput data array and give back the trend
def CalcTrend(self, _data):
self.myPrint("\t\t", _data)
# Remove 'nan' values
#data = numpy.unique(_data[~numpy.isnan(_data)])
try:
data = _data[~numpy.isnan(_data)]
except TypeError as e:
# It is possible the _data is not a numpy array, so it can't convert with numpy functions
PrintException(repr(e) + " The _data is %s not a numpy array" % (repr(type(_data))) )
data = _data
n = len(data)
if n <= 1:
print("Not enough data!")
return {'alpha':0.0, 'beta': 0.0, 'direction':'unaltered', 'percentage': 0.0}
sumxy = 0
sumx = 0
sumy = 0
sumx2 = 0
for i in range(0, n): # x = 1.. n
sumxy += (i+1) * data[i] # = sum(x*y)
sumx += (i+1) # = sum(x)
sumy += data[i] # = sum(y)
sumx2 += (i+1) * (i+1) # = sum(x^2)
sum5 = sumx * sumy
sum6 = sumx * sumx # = sum(x)^2
#print ("sumxy: "+str(sumxy))
#print ("sum5: "+str(sum5))
#print ("sumx*sumy: "+str(sumx*sumy))
##print ("sumy: "+str(sumy))
#print ("sumx2: "+str(sumx2))
#print ("sum6: "+str(sum6))
alpha = (n * sumxy - sum5) / (n * sumx2 - sum6) # =(n * sumxy-L3)/(J3*M3-N3)
beta = (sumy - alpha * sumx) / n
percentage = 0.0
if data[0] != 0.0:
#percentage = (data[1] - data[0]) / data[0] * 100
percentage = alpha * 100
if percentage >= self.badThreshold:
direction = "increased"
elif percentage >= self.goodThreshold:
direction = "unaltered"
else:
direction = "decreased"
if self.verbose:
print(("\t\ty = %.4f * x %+.4f --> %s (%.4f %%)") % (alpha, beta, direction, percentage))
return {'alpha':alpha, 'beta': beta, 'direction':direction, 'percentage': percentage}
def calcTrendTest(self):
data1 = [32.53, 31.74, 32.45, 32.38, 31.75, 32.18, 31.71, 31.9]
self.CalcTrend(data1)
def calcMovingAverage(self, numberOfDays, data):
self.myPrint("\t\t number of days:%d" % (numberOfDays))
self.myPrint("\t\t", data)
n = len(data)
if n == 0:
print("Not enough data!")
return []
retArray = []
for i in range(0, n):
endIndex = i + 1
startIndex = max (endIndex - numberOfDays, 0)
dataSlice = numpy.array([])
dataSlice = numpy.append(dataSlice, data[startIndex:endIndex])
movingMean = dataSlice.mean()
retArray.append(movingMean)
pass
return retArray
def calcMovingAverageTest(self):
#data1 = [32.53, 31.74, 32.45, 32.38, 31.75, 32.18, 31.71, 31.9]
data1 = [1.0, 2.0, 20.0, 3.0, 1.0, 4.0, 5.0]
movingAverages = self.calcMovingAverage(4, data1)
self.myPrint("\t\t", movingAverages)
def separateAndWrapTestName(self, testName):
testName.replace('-', ' '),
testNameItems = testName.split('-')
testName = testNameItems[0] + '\n '
params = ''
part = ''
for item in testNameItems[1:]:
if len(part + item) > 60:
params += part + '\n '
part = ''
part += item + ', '
testName += params + part
return testName
# add test dates to the X axis
def createDateSeries(self, dateStrings, dataPoints):
# TO-DO Avoid to generate large gap(s) in the date series like
# 2018.09.11, 2018.11.27, 2018.11.28, 2018.11.29 etc.
today = dt.date.today()
# Determine the starting border date and allow a couple of missing date
borderDate = today - dt.timedelta(dataPoints + self.maxDataPointsOverhead)
isBorderDateUpdated = False
dateSeries = []
for dateString in dateStrings:
# Convert datetime object
dateItems = dateString.split('.')
if len(dateItems) > 3:
date1 = dt.datetime(int(dateItems[0]), int(dateItems[1]),int(dateItems[2]), int(dateItems[3]), int(dateItems[4]), int(dateItems[5]))
else:
date1 = dt.datetime(int(dateItems[0]), int(dateItems[1]),int(dateItems[2]))
if not isBorderDateUpdated:
# If there is larger gap between two datap oint than self.maxDataPointsOverhead (a couple of weeks data missing)
if borderDate > date1.date():
borderDate = date1.date()
isBorderDateUpdated = True
if borderDate <= date1.date():
# Convert floating point number
dateSeries.append(mpl.dates.date2num(date1))
return dateSeries
def getTestInfo(self, cluster):
retVal = ''
paramString = self.clusterTrends[cluster]['testConfig'].get("Performance", "FlushDiskCache") + self.clusterTrends[cluster]['testConfig'].get("Performance", "RunCount")
params = paramString.strip().strip('"').split('--')
for param in params:
param = param.strip()
paramItems = param.split()
if len(paramItems) > 1:
if 'flushDiskCachePolicy' in param:
if paramItems[1] == '1':
retVal = retVal.strip().strip(',')
retVal += " before each test, "
if 'runcount' in param:
retVal += "execute each test in " + paramItems[1] + " times, "
else:
if 'flushDiskCache' in param:
retVal += "clear disk cache, "
retVal = retVal.strip().strip(',')
if len(retVal) > 0:
retVal = '\n(Parameters: ' + retVal + ')'
return retVal
def manageTestCase(self, cluster, test, tag):
if tag in self.markTags:
print(("\tIt is %s so marked marked now." % (tag)))
if test not in self.newIssues:
self.newIssues[test] = {}
self.newIssues[test][cluster] = {'tag': tag, 'known': False}
else:
print(("\tIt is %s." % (tag)))
if self.enableTestPlotGeneration:
self.createPlot(cluster, test, tag)
def createPlot(self, cluster, test, tag):
self.myPrint("cluster:%s, test:%s, tag:%s" % (cluster, test, tag))
diagramFileName =''
try:
# print("cluster:%s, test: %s" % (cluster, test))
fig = plt.figure(figsize=(self.diagramWidth, self.diagramHeight), dpi=100)
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
diagramColors = { 'value': 'blue', 'mean': 'red', 'trend': 'black', 'movingAverage1' : 'green', 'movingAverage2' : 'purple', 'sigma':'yellow', 'min': 'lime', 'max' : 'red'}
self.myPrint("\t\t", self.results2[cluster][test])
dataPoints = min(len(self.results2[cluster][test]['Days']), self.maxDatapoints)
dates = self.results2[cluster][test]['Dates'][-dataPoints:]
for index in range(len(dates)):
dateLen = len(dates[index])
if dateLen== 6:
dates[index] = '20' + dates[index][0:2] + '.' + dates[index][2:4] + '.' + dates[index][4:6]
elif dateLen == 8:
dates[index] = '20' + dates[index][0:2] + '.' + dates[index][2:4] + '.' + dates[index][4:6] + '.' + dates[index][7:9] + '.' + dates[index][9:11] + '.' + dates[index][11:13]
else:
pass
dates2 = self.createDateSeries(dates, dataPoints)
dataPoints = len(dates2)
days = dates2[-1] - dates2[0] + 1
try:
self.results2[cluster][test]['avg'] = numpy.nanmean(self.results2[cluster][test]['Values2'][-dataPoints:])
self.results2[cluster][test]['sigma'] = numpy.nanstd(self.results2[cluster][test]['Values2'][-dataPoints:])
except AttributeError as e:
if self.verbose:
PrintException(repr(e) + " Problem with an older numpy.")
# A hack for an older numpy ehre nonmean() and nanstd() doesn't exists
v2 = self.results2[cluster][test]['Values2'][~numpy.isnan(self.results2[cluster][test]['Values2'])][-dataPoints:]
self.results2[cluster][test]['avg'] = numpy.mean(v2)
self.results2[cluster][test]['sigma'] = numpy.std(v2)
self.results2[cluster][test]['maxDataPoints'] = self.CalcTrend(self.results2[cluster][test]['Values2'][-dataPoints:])
# Plot the data
ax.plot_date(dates2, self.results2[cluster][test]['Values2'][-dataPoints:], label=cluster, linestyle = '--', color=diagramColors['value'])
#ax.plot_date(dates2, self.results[cluster][test]['Values'][-dataPoints:], linestyle = '--', color=diagramColors['value'])
# plot the mean
if self.enableMean:
clusterTrendLabel = '%s (mean:%0.2f, sigma:%02f)' % ( cluster, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'])
mx = [dates2[0], dates2[-1]]
my = [self.results2[cluster][test]['avg'] , self.results2[cluster][test]['avg'] ]
ax.plot(mx, my, label=clusterTrendLabel, marker ='.', linestyle = '-', color=diagramColors['mean'] )
# plot the sigma
if self.enableSigma:
mx = [dates2[0], dates2[-1]]
my = [self.results2[cluster][test]['avg']+self.results2[cluster][test]['sigma'], self.results2[cluster][test]['avg']+self.results2[cluster][test]['sigma']]
ax.plot(mx, my, marker ='.', linestyle = '-', color=diagramColors['sigma'] )
mx = [dates2[0], dates2[-1]]
my = [self.results2[cluster][test]['avg']-self.results2[cluster][test]['sigma'], self.results2[cluster][test]['avg']-self.results2[cluster][test]['sigma']]
ax.plot(mx, my, marker ='.', linestyle = '-', color=diagramColors['sigma'] )
# plot the trend
if self.enableTrend:
clusterTrendLabel = '%s-trend (%0.2f sec/day, alpha:%0.3f, beta:%0.3f)' % ( cluster, self.results2[cluster][test]['maxDataPoints']['alpha'], self.results2[cluster][test]['maxDataPoints']['alpha'], self.results2[cluster][test]['maxDataPoints']['beta'])
x1 = [dates2[0], dates2[-1]]
y1 = [self.results2[cluster][test]['maxDataPoints']['beta'], self.results2[cluster][test]['maxDataPoints']['alpha'] * (len(dates2) - 1) + self.results2[cluster][test]['maxDataPoints']['beta'] ]
ax.plot(x1, y1, label=clusterTrendLabel, marker ='.', linestyle = '-', color=diagramColors['trend'] )
# plot the moving average 1 (default: on 7 days)
if not self.disableMovingAverage1:
movingAverage1Label = 'Moving average with %d days window' % (self.movingAverageWindow1)
movingAverages = self.calcMovingAverage(self.movingAverageWindow1, self.results2[cluster][test]['Values2'][-dataPoints:])
ax.plot_date(dates2, movingAverages, label=movingAverage1Label, marker ='.', linestyle = '-', color=diagramColors['movingAverage1'])
# plot the moving average 2 (default: on 30 days)
if not self.disableMovingAverage2:
movingAverage2Label = 'Moving average with %d days window' % (self.movingAverageWindow2)
movingAverages = self.calcMovingAverage(self.movingAverageWindow2, self.results2[cluster][test]['Values2'][-dataPoints:])
ax.plot_date(dates2, movingAverages, label=movingAverage2Label, marker ='.', linestyle = '-', color=diagramColors['movingAverage2'])
# Plot the min values
if self.enableMin:
ax.plot_date(dates2, self.results2[cluster][test]['min'][-dataPoints:], label='Min', marker ='2', linestyle = '--', color=diagramColors['min'])
# Plot the max values
if self.enableMax:
ax.plot_date(dates2, self.results2[cluster][test]['max'][-dataPoints:], label='Max', marker ='1', linestyle = '--', color=diagramColors['max'])
# Plot the loop values
# Experimental
self.enableLoopValues = False
if self.enableLoopValues:
markers = { 0:'s', 1:'X', 2:'P'}
colours = { 0:diagramColors['value'], 1: diagramColors['min'], 2:diagramColors['max']}
for loop in (1, 2):
values = [ d[loop] if loop in d else 0 for d in self.results2[cluster][test]['loops'][-dataPoints:] ]
loopLabel = "Loop-%d" % (loop)
ax.plot_date(dates2, values, label=loopLabel, linestyle = (markers[loop], (2, (loop +1), (loop +1), (loop +1) )), color=colours[loop])
# Format X labels ans ticks
dateFmt = mpl.dates.DateFormatter('%Y-%m-%d')
ax.xaxis.set_major_formatter(dateFmt)
daysLoc = mpl.dates.DayLocator(interval=max( 1, int(dataPoints/30)))
hoursLoc = mpl.dates.HourLocator(interval=6)
ax.xaxis.set_major_locator(daysLoc)
ax.xaxis.set_minor_locator(hoursLoc)
fig.autofmt_xdate(bottom=0.18) # adjust for date labels display
fig.subplots_adjust(left=0.18)
# Y axis ticks
ml = mpl.ticker.AutoMinorLocator()
ax.yaxis.set_minor_locator(ml)
#ax.grid(True, which='both')
ax.grid(True)
wrapTestName = test
if len(test) > 100:
testNameItems = test.split('-')
wrapTestName = ''
partSize = 0
for index in range (len(testNameItems)):
partSize += len(testNameItems[index])
wrapTestName += testNameItems[index] + '-'
if partSize > 100:
partSize = 0
wrapTestName += '\n'
myTitle = tag + ' ' + wrapTestName + ' on ' + cluster + ' in last ' + "%d" % (days) +' days'
myTitle += self.getTestInfo(cluster)
ax.set_title(myTitle)
ax.set_xlabel('Date')
testShortName = test.split('-')[0]
ax.set_ylabel(testShortName + ' execution time (Seconds)')
try:
ax.legend(loc = 'best', framealpha=0.5)
except Exception as e:
if self.verbose:
PrintException(repr(e) + " There is an old matplotlib.")
ax.legend(loc = 'best')
#plt.show()
diagramFileName = self.reportPath + test +"-" + cluster + '-' + self.dateStr + ".png"
plt.savefig(diagramFileName)
fig.clear()
plt.close(fig)
print(("\t %s created." % (diagramFileName)))
pass
except Exception as e:
PrintException(repr(e) + " No diagram generated for %s" %(diagramFileName))
pass
pass
def processResults(self):
if self.enablePdfReport:
from .pdfPerfReportGen import PdfPerfReportGen
pdfReport = PdfPerfReportGen()
pdfShortReport = PdfPerfReportGen()
twoDays = 2
fiveDays = 5
thirtyDays = 30
today = dt.datetime.today()
dateStr = today.strftime("%y%m%d")
pageBreak = False
for cluster in sorted(self.results2):
print(("Cluster:" + cluster + ' (' +str(self.numOfRuns[cluster]) + ' datasets )'))
if self.enablePdfReport:
pdfReport.startNewSection("Performance test result on cluster " + cluster + " on " + today.strftime("%d/%m/%y"), pageBreak)
pdfShortReport.startNewSection("Performance test short result on cluster " + cluster + " on " + today.strftime("%d/%m/%y"), pageBreak)
if not pageBreak:
pageBreak = True
pdfReport.newTable()
headerData = copy.deepcopy(self.defaultHeaderData)
#headerData.extend(self.defaultHeaderData)
headerData[0][0] = headerData[0][0] + ' ( executed on ' + cluster + ' )'
headerData[0][7] = headerData[0][7] + ' (%d datasets )' % (self.numOfRuns[cluster])
pdfReport.setTableHeader(headerData)
pdfShortReport.newTable()
pdfShortReport.setTableHeader(headerData)
numOfFluctTests = 0
averageTimeOfFlucTests = 0.0
numOfBadTests = 0
averageTimeOfBadTests = 0.0
numOfUglyBadTests = 0
averageTimeOfUglyBadTests = 0.0
numberOfTests = len(self.results2[cluster])
testIndex = 0
reportFileName = self.reportPath+ "perfreport-" + cluster + "-" + self.hpccVersion + "-" + dateStr + ".csv"
print(("reportFileName:" + reportFileName))
reportFile = open(reportFileName, "w")
reportFile.write("Testcase,twoDaysTredValue,twoDaysTred,twoDaysTredPercentage,fiveDaysTredValue,fiveDaysTred,fiveDaysTredPercentage,TredValue,Tred,TredPercentage,avg,sigma,fluctuation\n")
for test in sorted(self.results2[cluster]):
testIndex += 1
if debug:
# Only for generate an example diagram
if (not test.startswith('04ad_')) and (not test.startswith('07dc_')) and (not test.startswith('80ab_scalesort-scale(16)')):
continue
isShortlisted = False
dataPoints = len(self.results2[cluster][test]['Values2'])
lastDataDateStr = self.results2[cluster][test]['Dates'][-1]
lastDataDate = dt.date(2000+int(lastDataDateStr[2:4]), int(lastDataDateStr[5:7]), int(lastDataDateStr[8:10]))
lastDataAgeDays = (dt.date.today() - lastDataDate).days
self.myPrint("\ttest:"+ test + "(data points:"+ str(dataPoints) + ")")
if lastDataAgeDays > thirtyDays:
# Last result is too old, skip it.
self.myPrint("\t\t last result date is: %s and it is %s days old. Skip this test." % (lastDataDate.strftime("%y.%m.%d"), str(lastDataAgeDays)))
continue
if dataPoints > 1:
self.results2[cluster][test]['twoDays'] = {'alpha':0.0, 'beta': 0.0, 'direction':'unaltered', 'percentage': 0.0}
self.results2[cluster][test]['fiveDays'] = {'alpha':0.0, 'beta': 0.0, 'direction':'unaltered', 'percentage': 0.0}
self.results2[cluster][test]['thirtyDays'] = {'alpha':0.0, 'beta': 0.0, 'direction':'unaltered', 'percentage': 0.0}
if dataPoints >= twoDays:
self.results2[cluster][test]['twoDays'] = self.CalcTrend(self.results2[cluster][test]['Values2'][dataPoints-twoDays:])
if 'unaltered' != self.results2[cluster][test]['twoDays']['direction']:
isShortlisted = True
if dataPoints >= fiveDays:
self.results2[cluster][test]['fiveDays'] = self.CalcTrend(self.results2[cluster][test]['Values2'][dataPoints-fiveDays:])
if 'unaltered' != self.results2[cluster][test]['fiveDays']['direction']:
isShortlisted = True
if dataPoints >= thirtyDays:
self.results2[cluster][test]['thirtyDays'] = self.CalcTrend(self.results2[cluster][test]['Values2'][dataPoints-thirtyDays:])
pass
if 'unaltered' != self.results2[cluster][test]['thirtyDays']['direction']:
isShortlisted = True
reportFile.write("%s" % (test ))
reportFile.write(",%f,%s,%f" % (self.results2[cluster][test]['twoDays']['alpha'], self.results2[cluster][test]['twoDays']['direction'], self.results2[cluster][test]['twoDays']['percentage'] ))
reportFile.write(",%f,%s,%f" % (self.results2[cluster][test]['fiveDays']['alpha'], self.results2[cluster][test]['fiveDays']['direction'], self.results2[cluster][test]['fiveDays']['percentage'] ))
reportFile.write(",%f,%s,%f" % (self.results2[cluster][test]['thirtyDays']['alpha'], self.results2[cluster][test]['thirtyDays']['direction'], self.results2[cluster][test]['thirtyDays']['percentage'] ))
fluctuation = 0.0
if self.results2[cluster][test]['avg'] != 0.0:
fluctuation = self.results2[cluster][test]['sigma'] / self.results2[cluster][test]['avg']
#print("%3d/%3d: cluster:%s, test:%s, mean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (testIndex, numberOfTests, cluster, test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha']))
print(("%3d/%3d: cluster:%s " % (testIndex, numberOfTests, cluster)), end=' ')
# # Only for generate example diagram
# if test.startswith('01da'):
# self.enableTrend = False
# self.enableMean = False
# self.enableSigma = False
# self.disableMovingAverage1 = True
# self.disableMovingAverage2 = True
# if plt:
# self.manageTestCase(cluster, test, 'Result of ')
# break
if (self.results2[cluster][test]['all']['alpha'] > self.badThreshold / 100.0) and (fluctuation >= 1.0):
print(("test:%s\n\t\tmean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha'])))
numOfUglyBadTests += 1
averageTimeOfUglyBadTests += self.results2[cluster][test]['avg']
if plt:
self.manageTestCase(cluster, test, self.uglyAndBadTag)
elif self.results2[cluster][test]['all']['alpha'] > self.badThreshold / 100.0:
print(("test:%s\n\t\tmean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha'])))
numOfBadTests += 1
averageTimeOfBadTests += self.results2[cluster][test]['avg']
if plt:
self.manageTestCase(cluster, test, self.badTag)
elif fluctuation >= 1.0:
print(("test:%s\n\t\tmean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha'])))
numOfFluctTests += 1
averageTimeOfFlucTests += self.results2[cluster][test]['avg']
if plt:
self.manageTestCase(cluster, test, self.uglyTag)
# elif test.startswith('02cd') or test.startswith('02ea') or test.startswith('02eb') or \
# test.startswith('04ae') or test.startswith('04cd') or test.startswith('04cf') or \
# elif test.startswith('05bc') or test.startswith('06bc'):
# pass
elif self.results2[cluster][test]['all']['alpha'] < self.goodThreshold / 100.0:
print(("test:%s\n\t\tmean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha'])))
if plt and self.enableReportGood:
self.manageTestCase(cluster, test, self.goodTag)
elif enableGood:
print(("test:%s\n\t\tmean:%f sec, sigma:%f sec, fluctuation:%f, alpha:%f" % (test, self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation, self.results2[cluster][test]['all']['alpha'])))
if plt and self.enableReportGood:
self.manageTestCase(cluster, test, self.neutralTag)
reportFile.write(",%f,%f,%f\n" % (self.results2[cluster][test]['avg'], self.results2[cluster][test]['sigma'], fluctuation))
if self.enablePdfReport:
rowData = [ self.separateAndWrapTestName(test),
"%0.2f" % self.results2[cluster][test]['twoDays']['alpha'], self.results2[cluster][test]['twoDays']['direction'], "%0.2f" % self.results2[cluster][test]['twoDays']['percentage'],
"%0.2f" % self.results2[cluster][test]['fiveDays']['alpha'], self.results2[cluster][test]['fiveDays']['direction'], "%0.2f" % self.results2[cluster][test]['fiveDays']['percentage'],
"%0.2f" % self.results2[cluster][test]['all']['alpha'], self.results2[cluster][test]['all']['direction'], "%0.2f" % self.results2[cluster][test]['all']['percentage']
]
pdfReport.addTableRow(rowData)
if isShortlisted:
pdfShortReport.addTableRow(rowData)
if self.enablePdfReport:
pdfReport.addTableToStory()
pdfShortReport.addTableToStory()
reportFile.close()
# Calc cluster total trends
dataPoints = self.numOfRuns[cluster]
if dataPoints >= twoDays:
self.clusterTrends[cluster]['twoDays'] = self.CalcTrend(self.clusterTrends[cluster]['Totals'][dataPoints-twoDays:])
if dataPoints >= fiveDays:
self.clusterTrends[cluster]['fiveDays'] = self.CalcTrend(self.clusterTrends[cluster]['Totals'][dataPoints-fiveDays:])
if dataPoints > 30:
self.clusterTrends[cluster]['thirtyDays'] = self.CalcTrend(self.clusterTrends[cluster]['Totals'][dataPoints-thirtyDays:])
else:
self.clusterTrends[cluster]['thirtyDays'] = self.CalcTrend(self.clusterTrends[cluster]['Totals'])
pass
if numOfUglyBadTests > 0:
print(("num of ugly (fluctuating) and bad (increasing) tests:%d, average time of them:%f\n" % (numOfUglyBadTests, averageTimeOfFlucTests)))
if numOfFluctTests > 0:
print(("num of ugly (fluctuating) tests:%d, average time of them:%f\n" % (numOfFluctTests, averageTimeOfFlucTests)))
if numOfBadTests > 0:
print(("num of bad (increasing) tests:%d, average time of them:%f\n" % (numOfBadTests, averageTimeOfBadTests)))
if self.enablePdfReport:
pdfReport.create_pdfdoc(self.reportPath + 'PerformanceTestReport-' + self.hpccVersion + "-" + today.strftime("%y-%m-%d") +'.pdf')
pdfShortReport.create_pdfdoc(self.reportPath+'PerformanceTestShortReport-' + self.hpccVersion + "-" + today.strftime("%y-%m-%d") +'.pdf')
# Write out Performance Test summary file
summaryFileName = self.reportPath + "perftest-" + self.hpccVersion + "-" + dateStr + ".summary"
print(("summaryFileName:" + summaryFileName))
summaryFile = open(summaryFileName, "w")
summaryFile.write("# cluster, time, twoDaysTredValue, twoDaysTred, twoDaysTredPercentage, fiveDaysTredValue, fiveDaysTred, fiveDaysTredPercentage, TredValue, Tred, TredPercentage\n")
for cluster in sorted(self.clusterTrends):
summaryFile.write("%s" % (cluster ))
summaryFile.write(",%0.2f" % (self.clusterTrends[cluster]['Totals'][-1] ))
if 'twoDays' in self.clusterTrends[cluster]:
summaryFile.write(",%0.2f,%s,%0.2f" % (self.clusterTrends[cluster]['twoDays']['alpha'], self.clusterTrends[cluster]['twoDays']['direction'], self.clusterTrends[cluster]['twoDays']['percentage'] ))
if 'fiveDays' in self.clusterTrends[cluster]:
summaryFile.write(",%0.2f,%s,%0.2f" % (self.clusterTrends[cluster]['fiveDays']['alpha'], self.clusterTrends[cluster]['fiveDays']['direction'], self.clusterTrends[cluster]['fiveDays']['percentage'] ))
summaryFile.write(",%0.2f,%s,%0.2f\n" % (self.clusterTrends[cluster]['thirtyDays']['alpha'], self.clusterTrends[cluster]['thirtyDays']['direction'], self.clusterTrends[cluster]['thirtyDays']['percentage'] ))
summaryFile.close()
# Write out Performance Test totals
try:
totalsFileName = self.reportPath + "perftest-totals" + "-" + self.hpccVersion + "-" + dateStr + ".csv"
print(("totalsFileName:" + totalsFileName))
totalsFile = open(totalsFileName, "w")
totalsFile.write("# cluster, date, time\n")
for cluster in sorted(self.clusterTrends):
for index in range(self.numOfRuns[cluster]):
totalsFile.write("%s" % (cluster ))
totalsFile.write(",%s" % (self.clusterTrends[cluster]['Dates'][index]))
totalsFile.write(",%0.2f" % (self.clusterTrends[cluster]['Totals'][index] ))
totalsFile.write("\n")
totalsFile.close()
except Exception as e:
PrintException(repr(e) + " Unexpected error")
#print("Unexpected error:" + str(sys.exc_info()[0]) + " (line: " + str(inspect.stack()[0][2]) + ")" )
traceback.print_stack()
pass
if plt:
fig = plt.figure(figsize=(self.diagramWidth, self.diagramHeight), dpi=100)
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
clusterColors = { 'hthor': 'blue', 'thor': 'red', 'roxie': 'magenta'}
dataPoints = 0
for cluster in sorted(self.clusterTrends):
# Plot the data
dataPoints = min(self.numOfRuns[cluster], thirtyDays)
if self.useAllData == True:
dataPoints = self.numOfRuns[cluster]
dates2 = self.createDateSeries(self.clusterTrends[cluster]['Dates'][-dataPoints:], dataPoints)