mirror of
https://github.com/clearlinux/cloud-native-setup.git
synced 2026-08-26 18:36:03 +00:00
metrics: report: Error more cleanly
Clean up the rest of the report R files to allow them to quit cleanly when they find an error or missing data, so that the final PDF report gives meaninful errors such as 'No data found', rather than cryptic R errors. Signed-off-by: Graham Whaley <graham.whaley@intel.com>
This commit is contained in:
Graham Whaley
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Graham Whaley
Graham Whaley
parent
058e1753ae
commit
07fd8412da
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@@ -13,95 +13,105 @@ 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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"k8s-parallel.json",
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"k8s-scaling.json",
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"k8s-rapid.json"
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)
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render_dut_details <- function()
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{
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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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"k8s-parallel.json",
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"k8s-scaling.json",
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"k8s-rapid.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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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$'kubectl-version') != 0 ) {
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# We have kata-runtime data
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dirstats=tibble("Client Ver"=as.character(fdata$'kubectl-version'$clientVersion$gitVersion))
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dirstats=cbind(dirstats, "Server Ver"=as.character(fdata$'kubectl-version'$serverVersion$gitVersion))
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numnodes= nrow(fdata$'kubectl-get-nodes'$items)
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dirstats=cbind(dirstats, "No. nodes"=as.character(numnodes))
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if (numnodes != 0) {
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first_node=fdata$'kubectl-get-nodes'$items[1,]
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dirstats=cbind(dirstats, "- Node0 name"=as.character(first_node$metadata$name))
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havekata=first_node$metadata$labels$'katacontainers.io/kata-runtime'
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if ( is.null(havekata) ) {
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dirstats=cbind(dirstats, " Have Kata"=as.character('false'))
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} else {
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dirstats=cbind(dirstats, " Have Kata"=as.character(havekata))
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}
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dirstats=cbind(dirstats, " CPUs"=as.character(first_node$status$capacity$cpu))
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dirstats=cbind(dirstats, " Memory"=as.character(first_node$status$capacity$memory))
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dirstats=cbind(dirstats, " MaxPods"=as.character(first_node$status$capacity$pods))
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dirstats=cbind(dirstats, " PodCIDR"=as.character(first_node$spec$podCIDR))
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dirstats=cbind(dirstats, " runtime"=as.character(first_node$status$nodeInfo$containerRuntimeVersion))
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dirstats=cbind(dirstats, " kernel"=as.character(first_node$status$nodeInfo$kernelVersion))
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dirstats=cbind(dirstats, " kubeProxy"=as.character(first_node$status$nodeInfo$kubeProxyVersion))
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dirstats=cbind(dirstats, " Kubelet"=as.character(first_node$status$nodeInfo$kubeletVersion))
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dirstats=cbind(dirstats, " OS"=as.character(first_node$status$nodeInfo$osImage))
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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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datasetname=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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break
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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$'kubectl-version') != 0 ) {
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# We have kata-runtime data
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dirstats=tibble("Client Ver"=as.character(fdata$'kubectl-version'$clientVersion$gitVersion))
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dirstats=cbind(dirstats, "Server Ver"=as.character(fdata$'kubectl-version'$serverVersion$gitVersion))
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numnodes= nrow(fdata$'kubectl-get-nodes'$items)
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dirstats=cbind(dirstats, "No. nodes"=as.character(numnodes))
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if (numnodes != 0) {
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first_node=fdata$'kubectl-get-nodes'$items[1,]
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dirstats=cbind(dirstats, "- Node0 name"=as.character(first_node$metadata$name))
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havekata=first_node$metadata$labels$'katacontainers.io/kata-runtime'
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if ( is.null(havekata) ) {
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dirstats=cbind(dirstats, " Have Kata"=as.character('false'))
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} else {
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dirstats=cbind(dirstats, " Have Kata"=as.character(havekata))
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}
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dirstats=cbind(dirstats, " CPUs"=as.character(first_node$status$capacity$cpu))
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dirstats=cbind(dirstats, " Memory"=as.character(first_node$status$capacity$memory))
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dirstats=cbind(dirstats, " MaxPods"=as.character(first_node$status$capacity$pods))
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dirstats=cbind(dirstats, " PodCIDR"=as.character(first_node$spec$podCIDR))
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dirstats=cbind(dirstats, " runtime"=as.character(first_node$status$nodeInfo$containerRuntimeVersion))
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dirstats=cbind(dirstats, " kernel"=as.character(first_node$status$nodeInfo$kernelVersion))
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dirstats=cbind(dirstats, " kubeProxy"=as.character(first_node$status$nodeInfo$kubeProxyVersion))
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dirstats=cbind(dirstats, " Kubelet"=as.character(first_node$status$nodeInfo$kubeletVersion))
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dirstats=cbind(dirstats, " OS"=as.character(first_node$status$nodeInfo$osImage))
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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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cat(paste("No valid data found for directory ", currentdir, "\n\n"))
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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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if ( length(dirstats) == 0 ) {
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warning(paste("No valid data found for directory ", currentdir))
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if ( length(stats_names) == 0 ) {
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cat("No system details found\n\n")
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return()
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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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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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# Set up as left hand justify, so the node data indent renders.
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tablefontsize=8
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tbody.style = tbody_style(hjust=0, x=0.1, size=tablefontsize)
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stats_plot = suppressWarnings(ggtexttable(data.frame(spun_stats, check.names=FALSE),
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theme=ttheme(base_size=tablefontsize, tbody.style=tbody.style),
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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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}
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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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# Set up as left hand justify, so the node data indent renders.
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tablefontsize=8
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tbody.style = tbody_style(hjust=0, x=0.1, size=tablefontsize)
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stats_plot = suppressWarnings(ggtexttable(data.frame(spun_stats, check.names=FALSE),
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theme=ttheme(base_size=tablefontsize, tbody.style=tbody.style),
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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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render_dut_details()
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@@ -0,0 +1,26 @@
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library('elasticsearchr')
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for_scaling <- query('{
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"bool": {
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"must": [
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{ "match":
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{
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"test.testname": "k8s scaling"
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}
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}
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]
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}
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}')
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these_fields <- select_fields('{
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"includes": [
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"date.Date",
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"k8s-scaling.BootResults.launch_time.Result",
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"k8s-scaling.BootResults.n_pods.Result"
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]
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}')
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sort_by_date <- sort_on('[{"date.Date": {"order": "asc"}}]')
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x=elastic("http://192.168.0.111:9200", "logtest") %search% (for_scaling + sort_by_date + these_fields)
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@@ -32,7 +32,7 @@ This [test](https://github.com/clearlinux/cloud-native-setup/metrics/scaling/k8s
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measures the time taken to launch and delete pods in parallel using a deployment. The times
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are how long it takes for the whole deployment operation to complete.
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```{r parallel, echo=FALSE, fig.cap="K8S parallel pods"}
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```{r parallel, echo=FALSE, fig.cap="K8S parallel pods", results='asis'}
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source('parallel.R')
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```
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@@ -57,7 +57,7 @@ This table describes the test system details, as derived from the information co
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in the test results files.
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```{r dut, echo=FALSE, fig.cap="System configuration details"}
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```{r dut, echo=FALSE, fig.cap="System configuration details", results='asis'}
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source('dut-details.R')
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```
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@@ -67,6 +67,6 @@ source('dut-details.R')
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This table describes node details within the Kubernetes cluster that have been used for test.
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```{r node, echo=FALSE, fig.cap="Node information within Kubernetes cluster"}
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```{r node, echo=FALSE, fig.cap="Node information within Kubernetes cluster", results='asis'}
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source('node-info.R')
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```
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@@ -13,83 +13,96 @@ 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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"k8s-scaling.json"
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)
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render_node_info <- function()
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{
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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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"k8s-scaling.json"
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)
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stats=c()
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stats_names=c()
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max_char_name_node=18
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stats=c()
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stats_names=c()
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datasetname=c()
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complete_data=c()
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max_char_name_node=18
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# list for each dirstats
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dirstats_list=list()
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j=1
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# list for each dirstats
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dirstats_list=list()
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j=1
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# For each set of results
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for (currentdir in resultdirs) {
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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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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$'kubectl-version') != 0 ) {
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numnodes= nrow(fdata$'kubectl-get-nodes'$items)
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for (i in 1:numnodes) {
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node_i=fdata$'kubectl-get-nodes'$items[i,]
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node_info=fdata$'socketsPerNode'[i,]
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# Substring node name so it fits properly into final table
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node_name=node_i$metadata$name
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if ( nchar(node_name) >= max_char_name_node) {
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dirstats=tibble("Node \nname"=as.character(substring(node_name, 1, max_char_name_node)))
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} else {
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dirstats=tibble("Node \nname"=as.character(node_name))
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}
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dirstats=cbind(dirstats, "CPUs"=as.character(node_i$status$capacity$cpu))
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dirstats=cbind(dirstats, "Memory"=as.character(node_i$status$capacity$memory))
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dirstats=cbind(dirstats, "Max \nPods"=as.character(node_i$status$capacity$pods))
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dirstats=cbind(dirstats, "Count \nsockets"=as.character(node_info$num_sockets))
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dirstats=cbind(dirstats, "Have \nhypervisor"=as.character(node_info$hypervisor))
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dirstats=cbind(dirstats, "kernel"=as.character(node_i$status$nodeInfo$kernelVersion))
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dirstats=cbind(dirstats, "OS"=as.character(node_i$status$nodeInfo$osImage))
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dirstats=cbind(dirstats, "Test"=as.character(datasetname))
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dirstats_list[[j]]=dirstats
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j=j+1
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# For each set of results
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for (currentdir in resultdirs) {
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dirstats=c()
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for (resultsfile in resultsfiles) {
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fname=paste(inputdir, currentdir, resultsfile, sep="/")
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if ( !file.exists(fname)) {
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next
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}
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# Derive the name from the test result dirname
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datasetname=basename(currentdir)
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# Import the data
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fdata=fromJSON(fname)
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if (length(fdata$'kubectl-version') != 0 ) {
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numnodes= nrow(fdata$'kubectl-get-nodes'$items)
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for (i in 1:numnodes) {
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node_i=fdata$'kubectl-get-nodes'$items[i,]
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node_info=fdata$'socketsPerNode'[i,]
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# Substring node name so it fits properly into final table
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node_name=node_i$metadata$name
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if ( nchar(node_name) >= max_char_name_node) {
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dirstats=tibble("Node \nname"=as.character(substring(node_name, 1, max_char_name_node)))
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} else {
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dirstats=tibble("Node \nname"=as.character(node_name))
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}
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dirstats=cbind(dirstats, "CPUs"=as.character(node_i$status$capacity$cpu))
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dirstats=cbind(dirstats, "Memory"=as.character(node_i$status$capacity$memory))
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dirstats=cbind(dirstats, "Max \nPods"=as.character(node_i$status$capacity$pods))
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dirstats=cbind(dirstats, "Count \nsockets"=as.character(node_info$num_sockets))
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dirstats=cbind(dirstats, "Have \nhypervisor"=as.character(node_info$hypervisor))
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dirstats=cbind(dirstats, "kernel"=as.character(node_i$status$nodeInfo$kernelVersion))
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dirstats=cbind(dirstats, "OS"=as.character(node_i$status$nodeInfo$osImage))
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dirstats=cbind(dirstats, "Test"=as.character(datasetname))
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dirstats_list[[j]]=dirstats
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j=j+1
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}
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complete_data = do.call(rbind, dirstats_list)
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}
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complete_data = do.call(rbind, dirstats_list)
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}
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if ( length(complete_data) == 0 ) {
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cat(paste("No valid data found for directory ", currentdir, "\n\n"))
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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, complete_data)
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stats_names=rbind(stats_names, datasetname)
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}
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if ( length(complete_data) == 0 ) {
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warning(paste("No valid data found for directory ", currentdir))
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if ( length(stats_names) == 0 ) {
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cat("No node stats found\n\n");
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return()
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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, complete_data)
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stats_names=rbind(stats_names, datasetname)
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# Build us a text table of numerical results
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# Set up as left hand justify, so the node data indent renders.
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tablefontsize=8
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tbody.style = tbody_style(hjust=0, x=0.1, size=tablefontsize)
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stats_plot = suppressWarnings(ggtexttable(data.frame(complete_data, check.names=FALSE),
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theme=ttheme(base_size=tablefontsize, tbody.style=tbody.style),
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rows=NULL))
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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(stats_plot,
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nrow=1,
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ncol=1 )
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}
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# Build us a text table of numerical results
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# Set up as left hand justify, so the node data indent renders.
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tablefontsize=8
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tbody.style = tbody_style(hjust=0, x=0.1, size=tablefontsize)
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stats_plot = suppressWarnings(ggtexttable(data.frame(complete_data, check.names=FALSE),
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theme=ttheme(base_size=tablefontsize, tbody.style=tbody.style),
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rows=NULL))
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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(stats_plot,
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nrow=1,
|
||||
ncol=1 )
|
||||
render_node_info()
|
||||
|
||||
@@ -13,113 +13,123 @@ suppressMessages(suppressWarnings(library(ggpubr))) # for ggtexttable.
|
||||
suppressMessages(library(jsonlite)) # to load the data.
|
||||
suppressMessages(library(scales)) # For de-science notation of axis
|
||||
|
||||
testnames=c(
|
||||
"k8s-parallel*"
|
||||
)
|
||||
render_parallel <- function()
|
||||
{
|
||||
testnames=c(
|
||||
"k8s-parallel*"
|
||||
)
|
||||
|
||||
data=c()
|
||||
stats=c()
|
||||
rstats=c()
|
||||
rstats_names=c()
|
||||
cstats=c()
|
||||
cstats_names=c()
|
||||
data=c()
|
||||
stats=c()
|
||||
rstats=c()
|
||||
rstats_names=c()
|
||||
cstats=c()
|
||||
cstats_names=c()
|
||||
|
||||
skip_points_enable_smooth=0 # Should we draw the points as well as lines on the graphs.
|
||||
skip_points_enable_smooth=0 # Should we draw the points as well as lines on the graphs.
|
||||
|
||||
for (currentdir in resultdirs) {
|
||||
dirstats=c()
|
||||
for (testname in testnames) {
|
||||
matchdir=paste(inputdir, currentdir, sep="")
|
||||
matchfile=paste(testname, '\\.json', sep="")
|
||||
files=list.files(matchdir, pattern=matchfile)
|
||||
if ( length(files) == 0 ) {
|
||||
#warning(paste("Pattern [", matchdir, "/", matchfile, "] matched nothing"))
|
||||
}
|
||||
for (ffound in files) {
|
||||
fname=paste(inputdir, currentdir, ffound, sep="")
|
||||
if ( !file.exists(fname)) {
|
||||
warning(paste("Skipping non-existent file: ", fname))
|
||||
next
|
||||
for (currentdir in resultdirs) {
|
||||
dirstats=c()
|
||||
for (testname in testnames) {
|
||||
matchdir=paste(inputdir, currentdir, sep="")
|
||||
matchfile=paste(testname, '\\.json', sep="")
|
||||
files=list.files(matchdir, pattern=matchfile)
|
||||
if ( length(files) == 0 ) {
|
||||
#warning(paste("Pattern [", matchdir, "/", matchfile, "] matched nothing"))
|
||||
}
|
||||
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)
|
||||
# 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]]
|
||||
# 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
|
||||
testname=datasetname
|
||||
|
||||
# convert ms to seconds
|
||||
cdata=data.frame(boot_time=as.numeric(fdata$BootResults$launch_time$Result)/1000)
|
||||
cdata=cbind(cdata, delete_time=as.numeric(fdata$BootResults$delete_time$Result)/1000)
|
||||
cdata=cbind(cdata, npod=as.numeric(fdata$BootResults$n_pods$Result))
|
||||
# convert ms to seconds
|
||||
cdata=data.frame(boot_time=as.numeric(fdata$BootResults$launch_time$Result)/1000)
|
||||
cdata=cbind(cdata, delete_time=as.numeric(fdata$BootResults$delete_time$Result)/1000)
|
||||
cdata=cbind(cdata, npod=as.numeric(fdata$BootResults$n_pods$Result))
|
||||
|
||||
# If we have more than 20 items to draw, then do not draw the points on
|
||||
# the graphs, as they are then too noisy to read.
|
||||
# But, do draw the smoothed lines to help read the now dense and potentially
|
||||
# noisy graphs.
|
||||
if (length(cdata[, "boot_time"]) > 20) {
|
||||
skip_points_enable_smooth=1
|
||||
# If we have more than 20 items to draw, then do not draw the points on
|
||||
# the graphs, as they are then too noisy to read.
|
||||
# But, do draw the smoothed lines to help read the now dense and potentially
|
||||
# noisy graphs.
|
||||
if (length(cdata[, "boot_time"]) > 20) {
|
||||
skip_points_enable_smooth=1
|
||||
}
|
||||
|
||||
cdata=cbind(cdata, testname=rep(testname, length(cdata[, "boot_time"]) ))
|
||||
cdata=cbind(cdata, dataset=rep(datasetname, length(cdata[, "boot_time"]) ))
|
||||
|
||||
# Store away as a single set
|
||||
data=rbind(data, cdata)
|
||||
}
|
||||
|
||||
cdata=cbind(cdata, testname=rep(testname, length(cdata[, "boot_time"]) ))
|
||||
cdata=cbind(cdata, dataset=rep(datasetname, length(cdata[, "boot_time"]) ))
|
||||
|
||||
# Store away as a single set
|
||||
data=rbind(data, cdata)
|
||||
}
|
||||
}
|
||||
|
||||
# If we found nothing to process, quit early and nicely
|
||||
if ( length(data) == 0 ) {
|
||||
cat("No results files found for parallel tests\n\n")
|
||||
return()
|
||||
}
|
||||
|
||||
# Show how boot time changed
|
||||
boot_line_plot <- ggplot( data=data, aes(npod, boot_time, colour=testname, group=dataset)) +
|
||||
geom_line( alpha=0.2) +
|
||||
xlab("parallel pods") +
|
||||
ylab("Boot time (s)") +
|
||||
ggtitle("Deployment boot time (detail)") +
|
||||
#ylim(0, NA) + # For big machines, better to not 0-index
|
||||
theme(axis.text.x=element_text(angle=90))
|
||||
|
||||
if ( skip_points_enable_smooth == 0 ) {
|
||||
boot_line_plot = boot_line_plot + geom_point(alpha=0.3)
|
||||
} else {
|
||||
boot_line_plot = bool_line_plot + geom_smooth(se=FALSE, method="loess", size=0.3)
|
||||
}
|
||||
|
||||
# And get a zero Y index plot.
|
||||
boot_line_plot_zero = boot_line_plot + ylim(0, NA) +
|
||||
ggtitle("Deployment boot time (0 index)")
|
||||
|
||||
# Show how boot time changed
|
||||
delete_line_plot <- ggplot( data=data, aes(npod, delete_time, colour=testname, group=dataset)) +
|
||||
geom_line(alpha=0.2) +
|
||||
xlab("parallel pods") +
|
||||
ylab("Delete time (s)") +
|
||||
ggtitle("Deployment deletion time (detail)") +
|
||||
#ylim(0, NA) + # For big machines, better to not 0-index
|
||||
theme(axis.text.x=element_text(angle=90))
|
||||
|
||||
if ( skip_points_enable_smooth == 0 ) {
|
||||
delete_line_plot = delete_line_plot + geom_point(alpha=0.3)
|
||||
} else {
|
||||
delete_line_plot = delete_line_plot + geom_smooth(se=FALSE, method="loess", size=0.3)
|
||||
}
|
||||
|
||||
# And get a 0 indexed Y axis plot
|
||||
delete_line_plot_zero = delete_line_plot + ylim(0, NA) +
|
||||
ggtitle("Deployment deletion time (0 index)")
|
||||
|
||||
# See https://www.r-bloggers.com/ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/ for
|
||||
# excellent examples
|
||||
master_plot = grid.arrange(
|
||||
boot_line_plot_zero,
|
||||
delete_line_plot_zero,
|
||||
boot_line_plot,
|
||||
delete_line_plot,
|
||||
nrow=2,
|
||||
ncol=2 )
|
||||
}
|
||||
|
||||
# Show how boot time changed
|
||||
boot_line_plot <- ggplot( data=data, aes(npod, boot_time, colour=testname, group=dataset)) +
|
||||
geom_line( alpha=0.2) +
|
||||
xlab("parallel pods") +
|
||||
ylab("Boot time (s)") +
|
||||
ggtitle("Deployment boot time (detail)") +
|
||||
#ylim(0, NA) + # For big machines, better to not 0-index
|
||||
theme(axis.text.x=element_text(angle=90))
|
||||
|
||||
if ( skip_points_enable_smooth == 0 ) {
|
||||
boot_line_plot = boot_line_plot + geom_point(alpha=0.3)
|
||||
} else {
|
||||
boot_line_plot = bool_line_plot + geom_smooth(se=FALSE, method="loess", size=0.3)
|
||||
}
|
||||
|
||||
# And get a zero Y index plot.
|
||||
boot_line_plot_zero = boot_line_plot + ylim(0, NA) +
|
||||
ggtitle("Deployment boot time (0 index)")
|
||||
|
||||
# Show how boot time changed
|
||||
delete_line_plot <- ggplot( data=data, aes(npod, delete_time, colour=testname, group=dataset)) +
|
||||
geom_line(alpha=0.2) +
|
||||
xlab("parallel pods") +
|
||||
ylab("Delete time (s)") +
|
||||
ggtitle("Deployment deletion time (detail)") +
|
||||
#ylim(0, NA) + # For big machines, better to not 0-index
|
||||
theme(axis.text.x=element_text(angle=90))
|
||||
|
||||
if ( skip_points_enable_smooth == 0 ) {
|
||||
delete_line_plot = delete_line_plot + geom_point(alpha=0.3)
|
||||
} else {
|
||||
delete_line_plot = delete_line_plot + geom_smooth(se=FALSE, method="loess", size=0.3)
|
||||
}
|
||||
|
||||
# And get a 0 indexed Y axis plot
|
||||
delete_line_plot_zero = delete_line_plot + ylim(0, NA) +
|
||||
ggtitle("Deployment deletion time (0 index)")
|
||||
|
||||
# See https://www.r-bloggers.com/ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/ for
|
||||
# excellent examples
|
||||
master_plot = grid.arrange(
|
||||
boot_line_plot_zero,
|
||||
delete_line_plot_zero,
|
||||
boot_line_plot,
|
||||
delete_line_plot,
|
||||
nrow=2,
|
||||
ncol=2 )
|
||||
|
||||
render_parallel()
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
|
||||
suppressMessages(library(jsonlite)) # to load the data.
|
||||
|
||||
options(digits=22)
|
||||
|
||||
x=fromJSON('{"ns": 1567002188374607769}')
|
||||
|
||||
print(x)
|
||||
print(fromJSON('{"ns": 1567002188374607769}'), digits=22)
|
||||
@@ -205,7 +205,7 @@ render_tidy_scaling <- function()
|
||||
# Check if we got any stats at all by checking the memstats data. If we found no data,
|
||||
# abort early and nicely
|
||||
if ( length(memstats) == 0 ) {
|
||||
cat("No results files found for scaling tests\n")
|
||||
cat("No results files found for scaling tests\n\n")
|
||||
return()
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user