mirror of
https://github.com/aljazceru/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>
112 lines
3.5 KiB
R
112 lines
3.5 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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# Show system memory reduction, and hence container 'density', by analysing the
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# scaling footprint data results and the 'system free' memory.
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library(ggplot2) # ability to plot nicely.
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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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testnames=c(
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paste("footprint-busybox.*", test_name_extra, sep=""),
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paste("footprint-mysql.*", test_name_extra, sep=""),
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paste("footprint-elasticsearch.*", test_name_extra, sep="")
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)
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data=c()
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stats=c()
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rstats=c()
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rstats_names=c()
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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 (testname in testnames) {
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matchdir=paste(inputdir, currentdir, sep="")
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matchfile=paste(testname, '\\.json', sep="")
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files=list.files(matchdir, pattern=matchfile)
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if ( length(files) == 0 ) {
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#warning(paste("Pattern [", matchdir, "/", matchfile, "] matched nothing"))
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}
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for (ffound in files) {
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fname=paste(inputdir, currentdir, ffound, 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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# De-nest the test name specific data
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shortname=substr(ffound, 1, nchar(ffound)-nchar(".json"))
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fdata=fdata[[shortname]]
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payload=fdata$Config$payload
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testname=paste(datasetname, payload)
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cdata=data.frame(avail_mb=as.numeric(fdata$Results$system$avail)/(1024*1024))
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cdata=cbind(cdata, avail_decr=as.numeric(fdata$Results$system$avail_decr))
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cdata=cbind(cdata, count=seq_len(length(cdata[, "avail_mb"])))
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cdata=cbind(cdata, testname=rep(testname, length(cdata[, "avail_mb"]) ))
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cdata=cbind(cdata, payload=rep(payload, length(cdata[, "avail_mb"]) ))
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cdata=cbind(cdata, dataset=rep(datasetname, length(cdata[, "avail_mb"]) ))
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# Gather our statistics
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sdata=data.frame(num_containers=length(cdata[, "avail_mb"]))
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# Pick out the last avail_decr value - which in theory should be
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# the most we have consumed...
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sdata=cbind(sdata, mem_consumed=cdata[, "avail_decr"][length(cdata[, "avail_decr"])])
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sdata=cbind(sdata, avg_bytes_per_c=sdata$mem_consumed / sdata$num_containers)
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sdata=cbind(sdata, runtime=testname)
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# Store away as a single set
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data=rbind(data, cdata)
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stats=rbind(stats, sdata)
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s = c(
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"Test"=testname,
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"n"=sdata$num_containers,
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"size"=(sdata$mem_consumed) / 1024,
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"kb/n"=round((sdata$mem_consumed / sdata$num_containers) / 1024, digits=1),
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"n/Gb"= round((1*1024*1024*1024) / (sdata$mem_consumed / sdata$num_containers), digits=1)
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)
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rstats=rbind(rstats, s)
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count = count + 1
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}
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}
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}
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# Set up the text table headers
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colnames(rstats)=c("Test", "n", "Tot_Kb", "avg_Kb", "n_per_Gb")
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# Build us a text table of numerical results
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stats_plot = suppressWarnings(ggtexttable(data.frame(rstats),
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theme=ttheme(base_size=10),
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rows=NULL
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))
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# plot how samples varioed over 'time'
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line_plot <- ggplot() +
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geom_point( data=data, aes(count, avail_mb, group=testname, color=payload, shape=dataset)) +
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geom_line( data=data, aes(count, avail_mb, group=testname, color=payload)) +
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xlab("Containers") +
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ylab("System Avail (Mb)") +
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ggtitle("System Memory free") +
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ylim(0, NA) +
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theme(axis.text.x=element_text(angle=90))
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master_plot = grid.arrange(
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line_plot,
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stats_plot,
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nrow=2,
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ncol=1 )
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