299 lines
10 KiB
R
Executable File
299 lines
10 KiB
R
Executable File
#!/usr/bin/env Rscript
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library('ggplot2')
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library('ggthemes')
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library('tidyverse')
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library('repr')
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library('jcolors')
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library("reticulate")
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args <- commandArgs(trailingOnly = TRUE)
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#setwd(source_dir)
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use_python("/mnt/lustre01/work/ku0598/k202107/software/install/python/3.8.0/bin/python3", required=T)
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source_python("/work/ku0598/k202107/git/mistral-job-evaluation/scripts/jupyter/r_visual_jobs#pickle_reader.py")
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global = list()
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global[['source_dir']] = '/work/ku0598/k202107/git/mistral-job-evaluation/data/eval_20200117'
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global[['eval_dir']] = '../evaluation'
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global[['fig_dir']] = sprintf('%s/figures/job_visualization', global[['eval_dir']])
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global[['key']] = 22897682
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config = list()
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config[['crypted_jobid']] = strtoi(args[1])
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config[['jobid']] = bitwXor(config[['crypted_jobid']], global[['key']])
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config[['cat_fn']] = sprintf("%s/600/cats/%s.json", global[['source_dir']], config[['jobid']])
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config[['raw_fn']] = sprintf('%s/600/jobdata/%s.pkl', global[['source_dir']], config[['jobid']])
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graph_config = list()
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# View
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graph_config[['cols']] = c('metric', 'host', 'name') # Colorized entities: "name" : file systems; "host" : compute nodes, "metric" : I/O metrics
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graph_config[['views']] = c('jscore', 'default', 'nscore', 'mscore') # Enable views: 'default', 'jscore', 'nscore', 'mscore'
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#graph_config[['views']] = c('nscore')
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graph_config[['n_x_breakpoints']] = 5 # Number of breakpoints on x-axis
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graph_config[['seg_size']] = 10 # Segments size in minutes
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# Size
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graph_config[['plot_size']] = list(
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'default' = list('height'=1, 'width'=10),
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'jscore' = list('height'=3, 'width'=10),
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'nscore' = list('height'=1, 'width'=14),
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'mscore' = list('height'=1, 'width'=1))
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# Dimensions Limits
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graph_config[['max_dimensions']] = list(
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'default' = list('seg'=1000, 'host'=13, 'name'=2, 'metric'=9),
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'jscore' = list('seg'=1000, 'host'=13, 'name'=2, 'metric'=9),
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'nscore' = list('seg'=1000, 'host'=50, 'name'=2, 'metric'=9),
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'mscore' = list('seg'=1000, 'host'=50, 'name'=2, 'metric'=9))
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# Legend Limits
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graph_config[['max_legend_size']] = list(
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'default' = list('seg'=1000, 'host'=15, 'name'=2, 'metric'=9),
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'jscore' = list('seg'=1000, 'host'=15, 'name'=2, 'metric'=9),
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'nscore' = list('seg'=1000, 'host'=15, 'name'=2, 'metric'=9),
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'mscore' = list('seg'=1000, 'host'=15, 'name'=2, 'metric'=9))
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rename_metrics <- function(data) {
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data['metric'] <- lapply(data['metric'], gsub, pattern = "host.lustre.", replacement = "", fixed = TRUE)
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data['metric'] <- lapply(data['metric'], gsub, pattern = "stats.", replacement = "", fixed = TRUE)
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data['metric'] <- lapply(data['metric'], gsub, pattern = ".bytes", replacement = "_bytes", fixed = TRUE)
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data['metric'] <- lapply(data['metric'], gsub, pattern = ".calls", replacement = "_calls", fixed = TRUE)
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return(data)
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}
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visualize_categories <- function(fn, gconf, cconf, vconf, data, view, col, x_breakpoints, dims) {
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# Set legend title
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if (col == 'host') {
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gtitle = 'Node'
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}
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else if (col == 'metric') {
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gtitle = 'Metric'
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}
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else if (col == 'name') {
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gtitle = 'File system'
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}
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title = sprintf('JOBID: %d / %d (M:H:F:S)=(%d:%d:%d:%d)', cconf$jobid, cconf$crypted_jobid, dims$metric, dims$host, dims$name, dims$seg)
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# The palette with black:
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#cbp2 = c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")
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# General plot
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p <- (
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ggplot(data, aes_string(x='seg', y='score', fill=col))
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#+ geom_bar(stat='summary', fun.y = "mean")
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+ ggtitle(title)
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+ geom_bar(stat='identity')
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+ scale_x_discrete(breaks=x_breakpoints)
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#+ scale_fill_manual(values= cbp2)
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#+ geom_line(data=dat,aes(x='rmin', y='value', color="Second line"))
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+ guides(
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fill = guide_legend(title=gtitle, nrow=15)
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)
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#+ theme(aspect.ratio = 1)
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+ xlab('Runtime in minutes')
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+ theme_linedraw()
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#+ theme_classic()
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+ theme(
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#guide_legend.title = element_text('File system'), #element_blank(),
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#legend.text=element_text(size=6),
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legend.spacing.y = unit(0, 'cm'),
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#legend.spacing.x = unit(0, 'cm'),
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legend.text = element_text(size = 8, margin = margin(t = 1)),
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strip.text.x = element_text(size = 8, color = "black"),
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strip.text.y = element_text(size = 8, color = "black"),
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legend.key = element_rect(size = 1),
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legend.key.size = unit(0.5, 'lines'),
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strip.background = element_rect(color="black", fill="#FFFFFF", linetype="solid")
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# panel.grid.major=element_line(size=0.25, color=alpha('black', 0.25)),
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# panel.grid.minor=element_line(size=0.25, color=alpha('black', 0.25))
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)
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)
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# Dimensions modifier
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if (col == 'host') {
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# do nothing
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}
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else if (col == 'metric') {
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p <- (p
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+ scale_fill_jcolors("pal12")
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)
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}
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else if (col == 'name') {
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# do nothing
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}
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else if (col == 'seg') {
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# do nothing
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}
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# View modifiers
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if (view == 'default') {
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p <- (p
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+ facet_grid(metric ~ .)
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+ ylab('Score')
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+ theme(
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strip.text.y = element_text(angle=0)
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)
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)
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# Disable legend if dimensions are too large
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if (dims[[col]] > vconf$max_legend_size[[view]][[col]]) {
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p <- p + theme (legend.position='none')
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}
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ggsave(fn, width=vconf$plot_size[[view]][['width']], height=vconf$plot_size[[view]][['height']] * dims[['metric']])
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}
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else if (view == 'jscore') {
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p <- (p
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+ ylab('JScore')
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+ theme (
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strip.text.y = element_text(angle=0),
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)
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)
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# Disable legend if dimensions are too large
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if (dims[[col]] > vconf$max_legend_size[[view]][[col]]) {
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p <- p + theme (legend.position='none')
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}
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ggsave(fn, width=vconf$plot_size[[view]][['width']], height=vconf$plot_size[[view]][['height']])
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}
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else if (view == 'nscore') {
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p <- (
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p
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+ facet_grid(host ~ .)
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+ ylab('NScore')
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+ theme(
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strip.text.y = element_text(angle=0),
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aspect.ratio = vconf$plot_size$nscore$height / vconf$plot_size$nscore$width,
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#legend.position='bottom'
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)
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)
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# Disable legend if dimensions are too large
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if (dims[[col]] > vconf$max_legend_size[[view]][[col]]) {
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p <- p + theme (legend.position='none')
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}
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extra_space = 2
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ggsave(fn, width=vconf$plot_size[[view]][['width']], height=vconf$plot_size[[view]][['height']] * (dims[['host']] + extra_space))
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}
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else if (view == 'mscore') {
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p <- (
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p
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+ facet_grid(host ~ metric)
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#+ coord_fixed(ratio=dims[['host']]/dims[['metric']])
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#+ coord_fixed(ratio=dims[['metric']]/dims[['host']])
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#+ coord_fixed(ratio=1)
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+ ylab('MScore')
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+ theme(
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axis.text.x = element_text(angle=90, hjust=1),
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aspect.ratio = 1,
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)
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)
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# Disable legend if dimensions are too large
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if (dims[[col]] > vconf$max_legend_size[[view]][[col]]) {
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p <- p + theme (legend.position='none')
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}
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extra_space = 2
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ggsave(fn, width=vconf$plot_size[[view]][['width']] * dims[['metric']], height=vconf$plot_size[[view]][['height']] * (dims[['host']] + extra_space))
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}
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}
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# Check if dimensions exceed limits
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exceeds_limits <- function(view, dims, graph_config) {
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max_dims <- graph_config$max_dimensions[[view]]
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if ((dims[['seg']] > max_dims[['seg']])) {
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return(T)
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}
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if (view == 'default') {
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if ((dims[['metric']] > max_dims[['metric']])) {
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return(T)
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}
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}
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else if (view == 'jscore') {
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}
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else if (view == 'nscore') {
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if ((dims[['host']] > max_dims[['host']])) {
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return(T)
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}
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}
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else if (view == 'mscore') {
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if ((dims[['host']] > max_dims[['host']]) || dims[['metric']] > max_dims[['metric']]) {
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return(T)
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}
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}
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else {
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print("Unknown view")
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exit(1)
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}
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return(F)
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}
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# Create 10 minutes segments
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cat_data <- rename_metrics(read.csv(config[['cat_fn']])) # categorized data
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cat_data['rmin'] = cat_data['runtime'] / 60 # runtime in minutes
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duration = max(ceiling(cat_data['rmin']))
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bins = seq(0, duration, graph_config[['seg_size']] )
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d2 <- cat_data %>%
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group_by(cat) %>%
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mutate(seg = cut(rmin, breaks = bins, labels = bins[-1]))
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d3 <- d2 %>%
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group_by(name, metric, host, seg) %>%
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summarise(score = sum(cat))
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dimensions = list()
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dimensions[['metric']] <- length(unique(d3$metric))
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dimensions[['name']] <- length(unique(d3$name))
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dimensions[['host']] <- length(unique(d3$host))
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dimensions[['seg']] <- length(unique(d3$seg))
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x_breakpoints <- bins[seq(1, length(bins), dimensions[['seg']]/graph_config[['n_x_breakpoints']]+1)]
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#x_breakpoints[length(x_breakpoints)+1] <- (dimensions[['seg']]-0)*10
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out_dir = sprintf('%s/%d_%d', global[['fig_dir']], config[['jobid']], config[['crypted_jobid']])
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dir.create(out_dir, recursive=TRUE)
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for (col in graph_config[['cols']]) {
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for (view in graph_config[['views']]) {
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fn = sprintf('%s/%s_%s.png', out_dir, view, col)
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fn_skip = sprintf("%s.skip", fn)
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if (exceeds_limits(view, dimensions, graph_config)) {
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if (file.exists(fn)) {
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file.remove(fn)
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}
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f_skip<-file(fn_skip)
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writeLines(c("dimensions too large"), f_skip)
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close(f_skip)
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print(sprintf('Skipping %s', fn))
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}
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else {
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if (file.exists(fn_skip)) {
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file.remove(fn_skip)
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}
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print(sprintf('Processing %s', fn))
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visualize_categories(fn, global, config, graph_config, d3, view, col, x_breakpoints, dimensions)
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}
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}
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}
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## TODO
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#visualize_rawdata <- function(data) {
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#}
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#pickle_data <- rename_metrics(read_pickle_file(config[['raw_fn']])) # raw data
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#print(head(pickle_data))
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#offset = min(pickle_data$timestamp)
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#dat = pickle_data[complete.cases(pickle_data),]
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#dat$runtime = dat$timestamp - offset
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#dat['rmin'] = dat['runtime'] / 60 # runtime in minutes
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#visualize_rawdata(dat)
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