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https://github.com/kata-containers/kata-containers.git
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fce2487971
This PR adds the metrics report R files. Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
123 lines
4.1 KiB
R
123 lines
4.1 KiB
R
#!/usr/bin/env Rscript
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# Copyright (c) 2018-2023 Intel Corporation
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#
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# SPDX-License-Identifier: Apache-2.0
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# Display details for the 'Device Under Test', for all data sets being processed.
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suppressMessages(suppressWarnings(library(tidyr))) # for gather().
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library(tibble)
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suppressMessages(suppressWarnings(library(plyr))) # rbind.fill
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# So we can plot multiple graphs
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library(gridExtra) # together.
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suppressMessages(suppressWarnings(library(ggpubr))) # for ggtexttable.
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suppressMessages(library(jsonlite)) # to load the data.
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# A list of all the known results files we might find the information inside.
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resultsfiles=c(
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"boot-times.json",
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"memory-footprint.json",
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"memory-footprint-ksm.json",
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"memory-footprint-inside-container.json"
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)
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data=c()
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stats=c()
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stats_names=c()
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# For each set of results
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for (currentdir in resultdirs) {
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count=1
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dirstats=c()
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for (resultsfile in resultsfiles) {
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fname=paste(inputdir, currentdir, resultsfile, sep="/")
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if ( !file.exists(fname)) {
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#warning(paste("Skipping non-existent file: ", fname))
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next
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}
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# Derive the name from the test result dirname
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datasetname=basename(currentdir)
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# Import the data
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fdata=fromJSON(fname)
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if (length(fdata$'kata-env') != 0 ) {
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# We have kata-runtime data
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dirstats=tibble("Run Ver"=as.character(fdata$'kata-env'$Runtime$Version$Semver))
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dirstats=cbind(dirstats, "Run SHA"=as.character(fdata$'kata-env'$Runtime$Version$Commit))
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pver=as.character(fdata$'kata-env'$Proxy$Version)
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pver=sub("^[[:alpha:][:blank:]-]*", "", pver)
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# uncomment if you want to drop the commit sha as well
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#pver=sub("([[:digit:].]*).*", "\\1", pver)
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dirstats=cbind(dirstats, "Proxy Ver"=pver)
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# Trim the shim string
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sver=as.character(fdata$'kata-env'$Shim$Version)
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sver=sub("^[[:alpha:][:blank:]-]*", "", sver)
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# uncomment if you want to drop the commit sha as well
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#sver=sub("([[:digit:].]*).*", "\\1", sver)
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dirstats=cbind(dirstats, "Shim Ver"=sver)
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# Default QEMU ver string is far too long and noisy - trim.
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hver=as.character(fdata$'kata-env'$Hypervisor$Version)
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hver=sub("^[[:alpha:][:blank:]]*", "", hver)
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hver=sub("([[:digit:].]*).*", "\\1", hver)
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dirstats=cbind(dirstats, "Hyper Ver"=hver)
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iver=as.character(fdata$'kata-env'$Image$Path)
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iver=sub("^[[:alpha:]/-]*", "", iver)
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dirstats=cbind(dirstats, "Image Ver"=iver)
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kver=as.character(fdata$'kata-env'$Kernel$Path)
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kver=sub("^[[:alpha:]/-]*", "", kver)
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dirstats=cbind(dirstats, "Guest Krnl"=kver)
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dirstats=cbind(dirstats, "Host arch"=as.character(fdata$'kata-env'$Host$Architecture))
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dirstats=cbind(dirstats, "Host Distro"=as.character(fdata$'kata-env'$Host$Distro$Name))
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dirstats=cbind(dirstats, "Host DistVer"=as.character(fdata$'kata-env'$Host$Distro$Version))
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dirstats=cbind(dirstats, "Host Model"=as.character(fdata$'kata-env'$Host$CPU$Model))
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dirstats=cbind(dirstats, "Host Krnl"=as.character(fdata$'kata-env'$Host$Kernel))
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dirstats=cbind(dirstats, "runtime"=as.character(fdata$test$runtime))
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break
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} else {
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if (length(fdata$'runc-env') != 0 ) {
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dirstats=tibble("Run Ver"=as.character(fdata$'runc-env'$Version$Semver))
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dirstats=cbind(dirstats, "Run SHA"=as.character(fdata$'runc-env'$Version$Commit))
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dirstats=cbind(dirstats, "runtime"=as.character(fdata$test$runtime))
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} else {
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dirstats=tibble("runtime"="Unknown")
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}
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break
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}
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}
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if ( length(dirstats) == 0 ) {
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warning(paste("No valid data found for directory ", currentdir))
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}
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# use plyr rbind.fill so we can combine disparate version info frames
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stats=rbind.fill(stats, dirstats)
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stats_names=rbind(stats_names, datasetname)
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}
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rownames(stats) = stats_names
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# Rotate the tibble so we get data dirs as the columns
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spun_stats = as_tibble(cbind(What=names(stats), t(stats)))
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# Build us a text table of numerical results
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stats_plot = suppressWarnings(ggtexttable(data.frame(spun_stats, check.names=FALSE),
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theme=ttheme(base_size=6),
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rows=NULL
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))
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# It may seem odd doing a grid of 1x1, but it should ensure we get a uniform format and
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# layout to match the other charts and tables in the report.
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master_plot = grid.arrange(
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stats_plot,
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nrow=1,
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ncol=1 )
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