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https://github.com/clearlinux/cloud-native-setup.git
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Adding charting support for multinode
This commit is contained in:
David Lyle
committed by
Graham Whaley
Graham Whaley
parent
510dd83b09
commit
d8a8d77d1b
@@ -18,6 +18,7 @@ testnames=c(
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)
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data=c()
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fndata=c()
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stats=c()
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rstats=c()
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rstats_names=c()
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@@ -56,18 +57,66 @@ for (currentdir in resultdirs) {
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testname=datasetname
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cdata=data.frame(avail_gb=as.numeric(fdata$BootResults$node_util$mem_free$Result)/(1024*1024))
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cdata=cbind(cdata, cpu_idle=as.numeric(fdata$BootResults$node_util$cpu_idle$Result))
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cdata=data.frame(boot_time=as.numeric(fdata$BootResults$launch_time$Result)/1000)
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# format the utilization data
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udata=data.frame(nodename=fdata$BootResults$node_util)
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for (i in seq(length(cdata[, "boot_time"]))) {
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if (i == 1) {
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# first iteration provide column name for c1
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c1=cbind(node=udata$nodename.node)
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c2=cbind(udata$nodename.cpu_idle$Result)
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c3=cbind(udata$nodename.mem_free$Result)/(1024*1024)
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c4=cbind(rep(i, length(udata$nodename.node)))
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c5=cbind(rep(testname, length(udata$nodename.node)))
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# declare formated utility data
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fudata=cbind(c1,c2,c3,c4,c5)
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}
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else {
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# shift to 0 based indexing
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index=i-1
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sindex=(index*3)+1
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eindex=sindex+2
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# grab 3 columns for next row bind
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row=cbind(udata[,sindex:eindex])
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c1=cbind(node=row[,1])
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c2=cbind(row[,2]$Result)
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c3=cbind(row[,3]$Result)/(1024*1024)
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c4=cbind(rep(i, length(udata$nodename.node)))
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c5=cbind(rep(testname, length(udata$nodename.node)))
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# create the new row (which is actually the number of nodes of rows)
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frow=cbind(c1,c2,c3,c4,c5)
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fudata=rbind(fudata,frow)
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}
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}
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colnames(fudata)=c("node", "cpu_idle", "mem_free", "pod", "testname")
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# fudata is considered a vector for some reason so converting it to a data.frame
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fudata=as.data.frame(fudata)
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# get unique node names
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nodes=unique(fudata$node)
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for (nodename in nodes) {
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c1=cbind(subset(fudata,node==nodename)["mem_free"])
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# extra work to name the column from a variable
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colnames(c1)=paste(nodename,"_avail_gb", sep="")
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cdata=cbind(cdata, c1)
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c2=cbind(subset(fudata,node==nodename)["cpu_idle"])
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colnames(c2)=paste(nodename,"_cpu_idle", sep="")
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cdata=cbind(cdata, c2)
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}
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# convert ms to seconds
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cdata=cbind(cdata, boot_time=as.numeric(fdata$BootResults$launch_time$Result)/1000)
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# FIXME - we should seq from 0 index
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if (length(cdata[, "avail_gb"]) > 20) {
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if (length(cdata[, "boot_time"]) > 20) {
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skip_points=1
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}
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cdata=cbind(cdata, count=seq_len(length(cdata[, "avail_gb"])))
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cdata=cbind(cdata, testname=rep(testname, length(cdata[, "avail_gb"]) ))
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cdata=cbind(cdata, dataset=rep(datasetname, length(cdata[, "avail_gb"]) ))
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cdata=cbind(cdata, count=seq_len(length(cdata[, "boot_time"])))
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cdata=cbind(cdata, testname=rep(testname, length(cdata[, "boot_time"]) ))
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cdata=cbind(cdata, dataset=rep(datasetname, length(cdata[, "boot_time"]) ))
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# Gather our statistics
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# '-1' containers, as the first entry should be a data capture of before
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@@ -75,19 +124,30 @@ for (currentdir in resultdirs) {
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# FIXME - once the test starts to store a stats baseline in slot 0, then
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# we should re-enable the '-1'
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#sdata=data.frame(num_containers=length(cdata[, "avail_gb"])-1)
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sdata=data.frame(num_containers=length(cdata[, "avail_gb"]))
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# Work out memory reduction by subtracting last (most consumed) from
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sdata=data.frame(num_containers=length(cdata[, "boot_time"]))
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sudata=c()
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# first (which should be 0-containers)
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sdata=cbind(sdata, mem_consumed= cdata[, "avail_gb"][1] -
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cdata[, "avail_gb"][length(cdata[, "avail_gb"])])
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sdata=cbind(sdata, cpu_consumed= cdata[, "cpu_idle"][1] -
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cdata[, "cpu_idle"][length(cdata[, "cpu_idle"])])
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# FIXME - don't calculate nodes with NoSchedule effect on node-role.kubernetes.io/master taint
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for (nodename in nodes) {
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node_avail_gb=paste(nodename, "_avail_gb", sep="")
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# Work out memory reduction by subtracting last (most consumed) from
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srdata=cbind(mem_consumed=as.numeric(as.character(cdata[, node_avail_gb][1])) -
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as.numeric(as.character(cdata[, node_avail_gb][length(cdata[, node_avail_gb])])))
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node_cpu_idle=paste(nodename, "_cpu_idle", sep="")
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srdata=cbind(srdata, cpu_consumed=as.numeric(as.character(cdata[, node_cpu_idle][1])) -
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as.numeric(as.character(cdata[, node_cpu_idle][length(cdata[, node_cpu_idle])])))
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sudata=rbind(sudata, srdata)
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}
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sdata=cbind(sdata, mem_consumed=sum(sudata[, "mem_consumed"]))
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sdata=cbind(sdata, cpu_consumed=sum(sudata[, "cpu_consumed"]))
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sdata=cbind(sdata, boot_time=cdata[, "boot_time"][length(cdata[, "boot_time"])])
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sdata=cbind(sdata, avg_gb_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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fndata=rbind(fndata, fudata)
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stats=rbind(stats, sdata)
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ms = c(
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@@ -126,9 +186,11 @@ mem_stats_plot = suppressWarnings(ggtexttable(data.frame(rstats),
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))
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# plot how samples varied over 'time'
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mem_line_plot <- ggplot() +
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geom_line( data=data, aes(count, avail_gb, colour=testname, group=dataset), alpha=0.2) +
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geom_smooth( data=data, aes(count, avail_gb, colour=testname, group=dataset), se=FALSE, method="loess", size=0.3) +
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mem_line_plot <- ggplot(data=fndata, aes(as.numeric(as.character(pod)),
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as.numeric(as.character(mem_free)),
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colour=interaction(testname, node), group=interaction(testname, node))) +
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geom_line(alpha=0.2) +
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geom_smooth(se=FALSE, method="loess", size=0.3) +
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xlab("Pods") +
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ylab("System Avail (Gb)") +
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scale_y_continuous(labels=comma) +
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@@ -139,7 +201,7 @@ mem_line_plot <- ggplot() +
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# If we only have relatively few samples, add points to the plot. Otherwise, skip as
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# the plot becomes far too noisy
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if ( skip_points == 0 ) {
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mem_line_plot = mem_line_plot + geom_point( data=data, aes(count, avail_gb, colour=testname, group=dataset), alpha=0.3)
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mem_line_plot = mem_line_plot + geom_point(alpha=0.3)
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}
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cpu_stats_plot = suppressWarnings(ggtexttable(data.frame(cstats),
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@@ -147,10 +209,12 @@ cpu_stats_plot = suppressWarnings(ggtexttable(data.frame(cstats),
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rows=NULL
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))
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# plot how samples varioed over 'time'
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cpu_line_plot <- ggplot() +
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geom_line( data=data, aes(count, cpu_idle, colour=testname, group=dataset), alpha=0.2) +
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geom_smooth( data=data, aes(count, cpu_idle, colour=testname, group=dataset), se=FALSE, method="loess", size=0.3) +
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# plot how samples varied over 'time'
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cpu_line_plot <- ggplot(data=fndata, aes(as.numeric(as.character(pod)),
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as.numeric(as.character(cpu_idle)),
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colour=interaction(testname, node), group=interaction(testname, node))) +
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geom_line(alpha=0.2) +
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geom_smooth(se=FALSE, method="loess", size=0.3) +
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xlab("Pods") +
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ylab("System CPU Idle (%)") +
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ggtitle("System CPU usage") +
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@@ -158,7 +222,7 @@ cpu_line_plot <- ggplot() +
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theme(axis.text.x=element_text(angle=90))
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if ( skip_points == 0 ) {
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cpu_line_plot = cpu_line_plot + geom_point( data=data, aes(count, cpu_idle, colour=testname, group=dataset), alpha=0.3)
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cpu_line_plot = cpu_line_plot + geom_point(alpha=0.3)
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}
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# Show how boot time changed
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@@ -187,12 +251,10 @@ master_plot = grid.arrange(
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mem_line_plot,
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cpu_line_plot,
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mem_stats_plot,
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cpu_stats_plot,
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mem_text.p,
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cpu_stats_plot,
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cpu_text.p,
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boot_line_plot,
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zeroGrob(),
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nrow=4,
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ncol=2,
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heights=c(1, 0.8, 0.1, 1) )
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nrow=7,
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heights=c(1, 1, 0.8, 0.1, 0.8, 0.1, 1) )
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