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kata-containers/tests/metrics/report/report_dockerfile/lifecycle-time.R
Gabriela Cervantes fce2487971 metrics: Add metrics report R files
This PR adds the metrics report R files.

Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
2023-08-29 16:45:22 +00:00

158 lines
4.9 KiB
R

#!/usr/bin/env Rscript
# Copyright (c) 2018-2023 Intel Corporation
#
# SPDX-License-Identifier: Apache-2.0
# Display how long the various phases of a container lifecycle (run, execute, die etc.
# take.
library(ggplot2) # ability to plot nicely.
suppressMessages(suppressWarnings(library(tidyr))) # for gather().
# So we can plot multiple graphs
library(gridExtra) # together.
suppressMessages(suppressWarnings(library(ggpubr))) # for ggtexttable.
suppressMessages(library(jsonlite)) # to load the data.
testnames=c(
"boot-times"
)
data=c()
stats=c()
rstats=c()
rstats_names=c()
# For each set of results
for (currentdir in resultdirs) {
count=1
dirstats=c()
for (testname in testnames) {
fname=paste(inputdir, currentdir, testname, '.json', sep="")
if ( !file.exists(fname)) {
warning(paste("Skipping non-existent file: ", fname))
next
}
# Derive the name from the test result dirname
datasetname=basename(currentdir)
# Import the data
fdata=fromJSON(fname)
# De-nest the test specific name
fdata=fdata[[testname]]
cdata=data.frame(workload=as.numeric(fdata$Results$'to-workload'$Result))
cdata=cbind(cdata, quit=as.numeric(fdata$Results$'to-quit'$Result))
cdata=cbind(cdata, tokernel=as.numeric(fdata$Results$'to-kernel'$Result))
cdata=cbind(cdata, inkernel=as.numeric(fdata$Results$'in-kernel'$Result))
cdata=cbind(cdata, total=as.numeric(fdata$Results$'total'$Result))
cdata=cbind(cdata, count=seq_len(length(cdata[,"workload"])))
cdata=cbind(cdata, runtime=rep(datasetname, length(cdata[, "workload"]) ))
# Calculate some stats for total time
sdata=data.frame(workload_mean=mean(cdata$workload))
sdata=cbind(sdata, workload_min=min(cdata$workload))
sdata=cbind(sdata, workload_max=max(cdata$workload))
sdata=cbind(sdata, workload_sd=sd(cdata$workload))
sdata=cbind(sdata, workload_cov=((sdata$workload_sd / sdata$workload_mean) * 100))
sdata=cbind(sdata, runtime=datasetname)
sdata=cbind(sdata, quit_mean = mean(cdata$quit))
sdata=cbind(sdata, quit_min = min(cdata$quit))
sdata=cbind(sdata, quit_max = max(cdata$quit))
sdata=cbind(sdata, quit_sd = sd(cdata$quit))
sdata=cbind(sdata, quit_cov = (sdata$quit_sd / sdata$quit_mean) * 100)
sdata=cbind(sdata, tokernel_mean = mean(cdata$tokernel))
sdata=cbind(sdata, inkernel_mean = mean(cdata$inkernel))
sdata=cbind(sdata, total_mean = mean(cdata$total))
# Store away as a single set
data=rbind(data, cdata)
stats=rbind(stats, sdata)
# Store away some stats for the text table
dirstats[count]=round(sdata$tokernel_mean, digits=2)
count = count + 1
dirstats[count]=round(sdata$inkernel_mean, digits=2)
count = count + 1
dirstats[count]=round(sdata$workload_mean, digits=2)
count = count + 1
dirstats[count]=round(sdata$quit_mean, digits=2)
count = count + 1
dirstats[count]=round(sdata$total_mean, digits=2)
count = count + 1
}
rstats=rbind(rstats, dirstats)
rstats_names=rbind(rstats_names, datasetname)
}
unts=c("s", "s", "s", "s", "s")
rstats=rbind(rstats, unts)
rstats_names=rbind(rstats_names, "Units")
# If we have only 2 sets of results, then we can do some more
# stats math for the text table
if (length(resultdirs) == 2) {
# This is a touch hard wired - but we *know* we only have two
# datasets...
diff=c()
for( i in 1:5) {
difference = as.double(rstats[2,i]) - as.double(rstats[1,i])
val = 100 * (difference/as.double(rstats[1,i]))
diff[i] = paste(round(val, digits=2), "%", sep=" ")
}
rstats=rbind(rstats, diff)
rstats_names=rbind(rstats_names, "Diff")
}
rstats=cbind(rstats_names, rstats)
# Set up the text table headers
colnames(rstats)=c("Results", "2k", "ik", "2w", "2q", "tot")
# Build us a text table of numerical results
stats_plot = suppressWarnings(ggtexttable(data.frame(rstats, check.names=FALSE),
theme=ttheme(base_size=8),
rows=NULL
))
# plot how samples varioed over 'time'
line_plot <- ggplot() +
geom_line( data=data, aes(count, workload, color=runtime)) +
geom_smooth( data=data, aes(count, workload, color=runtime), se=FALSE, method="loess") +
xlab("Iteration") +
ylab("Time (s)") +
ggtitle("Boot to workload", subtitle="First container") +
ylim(0, NA) +
theme(axis.text.x=element_text(angle=90))
boot_boxplot <- ggplot() +
geom_boxplot( data=data, aes(runtime, workload, color=runtime), show.legend=FALSE) +
ylim(0, NA) +
ylab("Time (s)")
# convert the stats to a long format so we can more easily do a side-by-side barplot
longstats <- gather(stats, measure, value, workload_mean, quit_mean, inkernel_mean, tokernel_mean, total_mean)
bar_plot <- ggplot() +
geom_bar( data=longstats, aes(measure, value, fill=runtime), stat="identity", position="dodge", show.legend=FALSE) +
xlab("Phase") +
ylab("Time (s)") +
ggtitle("Lifecycle phase times", subtitle="Mean") +
ylim(0, NA) +
theme(axis.text.x=element_text(angle=90))
master_plot = grid.arrange(
bar_plot,
line_plot,
stats_plot,
boot_boxplot,
nrow=2,
ncol=2 )