metrics: report pod communication latency percentiles in pdf

- Add R routine to load and plot percentiles from JSON.
- Support any number of percentiles 1..n.
- Store percentile configuration in JSON.

Signed-off-by: Antti Kervinen <[email protected]>

metrics/lib/common.bash

@@ -84,7 +84,7 @@ framework_init() {
 	k8s_api_init
 
 	# Launch our stats gathering pod
-	if [ -n "$SMF_USE_COLLECTD" ]; then
+	if [ "$SMF_USE_COLLECTD" == "true" ]; then
 		info "Setting up collectd"
 		init_stats $wait_time
 	fi
@@ -104,7 +104,7 @@ framework_shutdown() {
 	k8s_api_shutdown
 	cpu_load_shutdown
 
-	if [ -n "$SMF_USE_COLLECTD" ]; then
+	if [ "$SMF_USE_COLLECTD" == "true" ]; then
 		cleanup_stats
 	fi
 

metrics/report/report_dockerfile/metrics_report.Rmd

@@ -51,6 +51,18 @@ source('collectd_scaling.R')
 
 \pagebreak
 
+# Pod communication latency
+This [test](https://github.com/clearlinux/cloud-native-setup/metrics/scaling/k8s_scale_rapid_nc.sh)
+measures pod query--response latency when scaling up. The
+time is measured from sending a message directly to a socket, that `nc`
+listens to inside each pod, to reading the response from the pod.
+
+```{r scaling_nc, echo=FALSE, fig.cap="K8S pod communication latency", results='asis'}
+source('tidy_scaling_nc.R')
+```
+
+\pagebreak
+
 # Test setup details
 
 This table describes the test system details, as derived from the information contained

metrics/report/report_dockerfile/tidy_scaling_nc.R

@@ -0,0 +1,155 @@
+#!/usr/bin/env Rscript
+# Copyright (c) 2019 Intel Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+
+# Show pod communication latency
+
+suppressMessages(suppressWarnings(library(ggplot2)))	# ability to plot nicely.
+suppressWarnings(suppressWarnings(library(ggpubr)))	# ggtexttable
+suppressMessages(library(jsonlite))			# to load the data.
+suppressMessages(library(scales))			# For de-science notation of axis
+library(tibble)						# tibbles for tidy data
+
+testnames=c(
+	"k8s-rapid-nc"
+)
+
+### For developers: uncomment following variables to run this as is in R
+# resultdirs=c("PATH/TO/RES1/", ...) # keep the ending slash on result paths
+# inputdir=""
+
+latencydata=c()
+
+# iterate over every set of results (test run)
+for (currentdir in resultdirs) {
+	# For every results file we are interested in evaluating
+	for (testname in testnames) {
+		matchdir=paste(inputdir, currentdir, sep="")
+		matchfile=paste(testname, '\\.json', sep="")
+		files=list.files(matchdir, pattern=matchfile)
+
+		# For every matching results file
+		for (ffound in files) {
+			fname=paste(inputdir, currentdir, ffound, 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 name specific data
+			shortname=substr(ffound, 1, nchar(ffound)-nchar(".json"))
+			fdata=fdata[[shortname]]
+			testname=datasetname
+
+			# All the data we are looking for comes in BootResults,
+			# so pick it out to make referencing easier
+			br=fdata$BootResults
+
+			########################################################
+			#### Now extract latency time percentiles (ltp) ########
+			########################################################
+			ltp=br$latency_time$Percentiles
+			# Percentile thresholds, for example [5, 25, 50, 75, 95]
+			ltp_perc=fdata$Config$nc_percentiles[[1]]
+			perc_count = length(ltp_perc)
+			# Measured times
+			ltp_meas=matrix(unlist(ltp), nrow=perc_count)
+			# Build latency percentiles tibble with nice headings
+			ltpt=tibble(n_pods=br$n_pods$Result)
+			for (n in seq(perc_count)) {
+				p_title = paste0("p", ltp_perc[n])
+				ltpt[p_title] = ltp_meas[n,]
+			}
+			# ltpt example: with percentiles [5, 50, 95]:
+			# n_pods  p5  p50  p95
+			#    100   4	8   10
+			#    200   5   11   15
+			#    300   6   14   19
+			ltpt$testname=testname
+			latencydata=rbind(latencydata, ltpt)
+		}
+	}
+}
+
+# Visualize data.
+if (length(latencydata[[1]]) <= 5 || length(unique(latencydata$testname)) > 1) {
+	# If there are many tests to compare or only few data points, use boxplot with extra percentile points.
+	latp = ggplot(data=latencydata, aes(x=n_pods)) + ylab("Latency (us)") + xlab("pods") + scale_y_continuous(labels=comma)
+	perc_mid = floor((perc_count)/2)
+	# Create boxplot around the middle percentile
+	if (perc_count >= 3) {
+		box_bottom=names(ltpt)[perc_mid+1]
+		box_mid=names(ltpt)[perc_mid+2]
+		box_top=names(ltpt)[perc_mid+3]
+		if (perc_count >= 5) {
+			whis_low=names(ltpt)[perc_mid]
+			whis_high=names(ltpt)[perc_mid+4]
+			latp = latp + geom_boxplot(aes_string(group="interaction(testname,n_pods)",ymin=whis_low,lower=box_bottom,middle=box_mid,upper=box_top,ymax=whis_high,fill="testname"),stat="identity")
+		} else {
+			latp = latp + geom_boxplot(aes_string(group="interaction(testname,n_pods)",lower=box_bottom,middle=box_mid,upper=box_top,fill="testname"),stat="identity")
+		}
+	}
+	# Boxplot (above) covers at most 5 percentiles around the center (median).
+	# Visualize the rest using a point for each percentile.
+	if (perc_count > 5) {
+		for (n in seq(1, (perc_count-5)/2)) {
+			lower_name=names(ltpt)[n+1]
+			upper_name=names(ltpt)[perc_count-n+2]
+			latp = latp + geom_point(aes_string(group="interaction(testname,n_pods)",y=lower_name, color="testname"))
+			latp = latp + geom_point(aes_string(group="interaction(testname,n_pods)",y=upper_name, color="testname"))
+		}
+	}
+} else {
+	# Use colored areas and median lines when there are many ticks on X axis
+	latp = ggplot(data=latencydata, aes(x=n_pods)) + ylab("Latency (us)") + xlab("pods") + scale_y_continuous(labels=comma)
+	perc_mid = floor((perc_count)/2)
+	perc_maxdist = perc_mid
+	plot_number = 0
+	for (plot_test in unique(latencydata$testname)) {
+		plot_number = plot_number + 1
+		for (n in seq(perc_mid)) {
+			# First fill outmost areas, like p5..p25 and p75..p95,
+			# then areas closer to the middle, like p25..p50 and p50..p75
+			lower_name = names(ltpt)[n+1]
+			lower_next_name = names(ltpt)[n+2]
+			upper_name = names(ltpt)[perc_count-n+2]
+			upper_prev_name = names(ltpt)[perc_count-n+1]
+			alpha = 0.7 * ((n+1) / (perc_mid+1))**2
+			latp = latp + geom_ribbon(data=latencydata[latencydata$testname==plot_test,],aes_string(x="n_pods",ymin=lower_name,ymax=lower_next_name,fill="testname"),alpha=alpha)
+			latp = latp + geom_ribbon(data=latencydata[latencydata$testname==plot_test,],aes_string(x="n_pods",ymin=upper_prev_name,ymax=upper_name,fill="testname"),alpha=alpha)
+		}
+		median_index = match("p50", names(ltpt))
+		if (!is.na(median_index)) {
+			# Draw median line
+			latp = latp + geom_line(data=latencydata[latencydata$testname==plot_test,],aes_string(x="n_pods",y=names(ltpt)[median_index],color="testname"))
+		}
+	}
+}
+
+# Table presentation.
+lat_table=c()
+for (testname in unique(latencydata$testname)) {
+	testlines=latencydata[latencydata$testname==testname,]
+	lat_table=rbind(lat_table,testlines[1,])
+	if (length(testlines) > 3) {
+		# middle pod count
+		lat_table=rbind(lat_table,testlines[(length(testlines)-1)/2,])
+	}
+	if (length(testlines) > 2) {
+		# max pod count
+		lat_table=rbind(lat_table,testlines[length(testlines)-1,])
+	}
+}
+latt=ggtexttable(lat_table,rows=NULL)
+
+cat("\n\nLatency percentiles illustrated in the Figure below: ", paste0(ltp_perc, "\\%"), "\n\n")
+
+page1 = grid.arrange(latp, latt, ncol=1)
+
+# pagebreak, as the graphs overflow the page otherwise
+cat("\n\n\\pagebreak\n")