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fce2487971
This PR adds the metrics report R files. Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
270 lines
9.1 KiB
R
270 lines
9.1 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 `fio` random read storage IO tests.
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library(ggplot2) # ability to plot nicely
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library(gridExtra) # So we can plot multiple graphs 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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suppressMessages(suppressWarnings(library(tidyr))) # for gather
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library(tibble)
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testnames=c(
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"fio-randread-128",
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"fio-randread-256",
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"fio-randread-512",
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"fio-randread-1k",
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"fio-randread-2k",
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"fio-randread-4k",
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"fio-randread-8k",
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"fio-randread-16k",
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"fio-randread-32k",
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"fio-randread-64k"
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)
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data2=c()
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all_ldata=c()
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all_ldata2=c()
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stats=c()
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rstats=c()
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rstats_names=c()
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# Where to store up the stats for the tables
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read_bw_stats=c()
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read_iops_stats=c()
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read_lat95_stats=c()
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read_lat99_stats=c()
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# For each set of results
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for (currentdir in resultdirs) {
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bw_dirstats=c()
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iops_dirstats=c()
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lat95_dirstats=c()
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lat99_dirstats=c()
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# Derive the name from the test result dirname
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datasetname=basename(currentdir)
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for (testname in testnames) {
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fname=paste(inputdir, currentdir, testname, '.json', 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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# Import the data
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fdata=fromJSON(fname)
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# De-ref the test named unique data
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fdata=fdata[[testname]]
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blocksize=fdata$Raw$'global options'$bs
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# Extract the latency data - it comes as a table of percentiles, so
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# we have to do a little work...
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clat=data.frame(clat_ns=fdata$Raw$jobs[[1]]$read$clat_ns$percentile)
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# Generate a clat data set with 'clean' percentile numbers so
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# we can sensibly plot it later on.
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clat2=clat
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colnames(clat2)<-sub("clat_ns.", "", colnames(clat2))
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colnames(clat2)<-sub("0000", "", colnames(clat2))
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ldata2=gather(clat2)
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colnames(ldata2)[colnames(ldata2)=="key"] <- "percentile"
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colnames(ldata2)[colnames(ldata2)=="value"] <- "ms"
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ldata2$ms=ldata2$ms/1000000 #ns->ms
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ldata2=cbind(ldata2, runtime=rep(datasetname, length(ldata2$percentile)))
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ldata2=cbind(ldata2, blocksize=rep(blocksize, length(ldata2$percentile)))
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# Pull the 95 and 99 percentile numbers for the boxplot
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# Plotting all values for all runtimes and blocksizes is just way too
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# noisy to make a meaninful picture, so we use this subset.
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# Our values fall more in the range of ms...
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pc95data=tibble(percentile=clat$clat_ns.95.000000/1000000)
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pc95data=cbind(pc95data, runtime=rep(paste(datasetname, "95pc", sep="-"), length(pc95data$percentile)))
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pc99data=tibble(percentile=clat$clat_ns.99.000000/1000000)
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pc99data=cbind(pc99data, runtime=rep(paste(datasetname, "99pc", sep="-"), length(pc95data$percentile)))
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ldata=rbind(pc95data, pc99data)
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ldata=cbind(ldata, blocksize=rep(blocksize, length(ldata$percentile)))
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# We want total bandwidth, so that is the sum of the bandwidths
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# from all the read 'jobs'.
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mdata=data.frame(read_bw_mps=as.numeric(sum(fdata$Raw$jobs[[1]]$read$bw)/1024))
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mdata=cbind(mdata, iops_tot=as.numeric(sum(fdata$Raw$jobs[[1]]$read$iops)))
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mdata=cbind(mdata, runtime=rep(datasetname, length(mdata[, "read_bw_mps"]) ))
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mdata=cbind(mdata, blocksize=rep(blocksize, length(mdata[, "read_bw_mps"]) ))
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# Extract the stats tables
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bw_dirstats=rbind(bw_dirstats, round(mdata$read_bw_mps, digits=1))
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# Rowname hack to get the blocksize recorded
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rownames(bw_dirstats)[nrow(bw_dirstats)]=blocksize
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iops_dirstats=rbind(iops_dirstats, round(mdata$iops_tot, digits=1))
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rownames(iops_dirstats)[nrow(iops_dirstats)]=blocksize
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# And do the 95 and 99 percentiles as tables as well
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lat95_dirstats=rbind(lat95_dirstats, round(mean(clat$clat_ns.95.000000)/1000000, digits=1))
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rownames(lat95_dirstats)[nrow(lat95_dirstats)]=blocksize
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lat99_dirstats=rbind(lat99_dirstats, round(mean(clat$clat_ns.99.000000)/1000000, digits=1))
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rownames(lat99_dirstats)[nrow(lat99_dirstats)]=blocksize
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# Collect up as sets across all files and runtimes.
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data2=rbind(data2, mdata)
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all_ldata=rbind(all_ldata, ldata)
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all_ldata2=rbind(all_ldata2, ldata2)
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}
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# Collect up for each dir we process into a column
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read_bw_stats=cbind(read_bw_stats, bw_dirstats)
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colnames(read_bw_stats)[ncol(read_bw_stats)]=datasetname
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read_iops_stats=cbind(read_iops_stats, iops_dirstats)
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colnames(read_iops_stats)[ncol(read_iops_stats)]=datasetname
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read_lat95_stats=cbind(read_lat95_stats, lat95_dirstats)
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colnames(read_lat95_stats)[ncol(read_lat95_stats)]=datasetname
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read_lat99_stats=cbind(read_lat99_stats, lat99_dirstats)
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colnames(read_lat99_stats)[ncol(read_lat99_stats)]=datasetname
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}
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# To get a nice looking table, we need to extract the rownames into their
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# own column
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read_bw_stats=cbind(Bandwidth=rownames(read_bw_stats), read_bw_stats)
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read_bw_stats=cbind(read_bw_stats, Units=rep("MB/s", nrow(read_bw_stats)))
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read_iops_stats=cbind(IOPS=rownames(read_iops_stats), read_iops_stats)
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read_iops_stats=cbind(read_iops_stats, Units=rep("IOP/s", nrow(read_iops_stats)))
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read_lat95_stats=cbind('lat 95pc'=rownames(read_lat95_stats), read_lat95_stats)
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read_lat95_stats=cbind(read_lat95_stats, Units=rep("ms", nrow(read_lat95_stats)))
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read_lat99_stats=cbind('lat 99pc'=rownames(read_lat99_stats), read_lat99_stats)
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read_lat99_stats=cbind(read_lat99_stats, Units=rep("ms", nrow(read_lat99_stats)))
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# Bandwidth line plot
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read_bw_line_plot <- ggplot() +
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geom_line( data=data2, aes(blocksize, read_bw_mps, group=runtime, color=runtime)) +
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ylim(0, NA) +
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ggtitle("Random Read total bandwidth") +
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xlab("Blocksize") +
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ylab("Bandwidth (MiB/s)") +
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theme(
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axis.text.x=element_text(angle=90),
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legend.position=c(0.35,0.8),
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legend.title=element_text(size=5),
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legend.text=element_text(size=5),
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legend.background = element_rect(fill=alpha('blue', 0.2))
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)
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# IOPS line plot
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read_iops_line_plot <- ggplot() +
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geom_line( data=data2, aes(blocksize, iops_tot, group=runtime, color=runtime)) +
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ylim(0, NA) +
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ggtitle("Random Read total IOPS") +
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xlab("Blocksize") +
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ylab("IOPS") +
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theme(
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axis.text.x=element_text(angle=90),
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legend.position=c(0.35,0.8),
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legend.title=element_text(size=5),
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legend.text=element_text(size=5),
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legend.background = element_rect(fill=alpha('blue', 0.2))
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)
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# 95 and 99 percentile box plot
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read_clat_box_plot <- ggplot() +
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geom_boxplot( data=all_ldata, aes(blocksize, percentile, color=runtime)) +
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stat_summary( data=all_ldata, aes(blocksize, percentile, group=runtime, color=runtime), fun.y=mean, geom="line") +
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ylim(0, NA) +
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ggtitle("Random Read completion latency", subtitle="95&99 percentiles, boxplot over jobs") +
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xlab("Blocksize") +
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ylab("Latency (ms)") +
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theme(axis.text.x=element_text(angle=90)) +
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# Use the 'paired' colour matrix as we are setting these up as pairs of
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# 95 and 99 percentiles, and it is much easier to visually group those to
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# each runtime if we use this colourmap.
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scale_colour_brewer(palette="Paired")
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# it would be nice to use the same legend theme as the other plots on this
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# page, but because of the number of entries it tends to flow off the picture.
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# theme(
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# axis.text.x=element_text(angle=90),
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# legend.position=c(0.35,0.8),
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# legend.title=element_text(size=5),
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# legend.text=element_text(size=5),
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# legend.background = element_rect(fill=alpha('blue', 0.2))
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# )
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# As the boxplot is actually quite hard to interpret, also show a linegraph
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# of all the percentiles for a single blocksize.
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which_blocksize='4k'
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clat_line_subtitle=paste("For blocksize", which_blocksize, sep=" ")
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single_blocksize=subset(all_ldata2, blocksize==which_blocksize)
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clat_line=aggregate(
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single_blocksize$ms,
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by=list(
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percentile=single_blocksize$percentile,
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blocksize=single_blocksize$blocksize,
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runtime=single_blocksize$runtime
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),
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FUN=mean
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)
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clat_line$percentile=as.numeric(clat_line$percentile)
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read_clat_line_plot <- ggplot() +
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geom_line( data=clat_line, aes(percentile, x, group=runtime, color=runtime)) +
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ylim(0, NA) +
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ggtitle("Random Read completion latency percentiles", subtitle=clat_line_subtitle) +
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xlab("Percentile") +
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ylab("Time (ms)") +
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theme(
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axis.text.x=element_text(angle=90),
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legend.position=c(0.35,0.8),
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legend.title=element_text(size=5),
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legend.text=element_text(size=5),
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legend.background = element_rect(fill=alpha('blue', 0.2))
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)
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# Output the pretty pictures
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graphics_plot = grid.arrange(
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read_bw_line_plot,
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read_iops_line_plot,
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read_clat_box_plot,
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read_clat_line_plot,
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nrow=2,
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ncol=2 )
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# A bit of an odd tweak to force a pagebreak between the pictures and
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# the tables. This only works because we have a `results='asis'` in the Rmd
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# R fragment.
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cat("\n\n\\pagebreak\n")
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read_bw_stats_plot = suppressWarnings(ggtexttable(read_bw_stats,
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theme=ttheme(base_size=10),
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rows=NULL
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))
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read_iops_stats_plot = suppressWarnings(ggtexttable(read_iops_stats,
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theme=ttheme(base_size=10),
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rows=NULL
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))
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read_lat95_stats_plot = suppressWarnings(ggtexttable(read_lat95_stats,
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theme=ttheme(base_size=10),
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rows=NULL
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))
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read_lat99_stats_plot = suppressWarnings(ggtexttable(read_lat99_stats,
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theme=ttheme(base_size=10),
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rows=NULL
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))
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# and then the statistics tables
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stats_plot = grid.arrange(
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read_bw_stats_plot,
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read_iops_stats_plot,
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read_lat95_stats_plot,
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read_lat99_stats_plot,
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nrow=4,
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ncol=1 )
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