[tor-commits] [torperf/master] Remove all unused graphing code.
karsten at torproject.org
karsten at torproject.org
Sat May 9 08:56:51 UTC 2015
commit 856c04702b0c99ea9f3f192b6ebbb1a4be5fb2f3
Author: Karsten Loesing <karsten.loesing at gmx.net>
Date: Sat May 9 10:54:22 2015 +0200
Remove all unused graphing code.
---
Makefile | 7 +--
README | 9 ---
metrics/HOWTO | 109 ------------------------------------
metrics/filter.R | 149 --------------------------------------------------
metrics/timematrix.R | 36 ------------
plot_results.R | 146 -------------------------------------------------
6 files changed, 1 insertion(+), 455 deletions(-)
diff --git a/Makefile b/Makefile
index e85b8d2..2cafa76 100644
--- a/Makefile
+++ b/Makefile
@@ -2,8 +2,6 @@
### See LICENSE for licensing information
CC=gcc -Wall -Werror -ggdb
-R=R CMD BATCH --vanilla
-IMAGES=first-download.png first-local.png first-net.png second-download.png second-local.png second-net.png
all: trivsocks-client
@@ -17,8 +15,5 @@ test: trivsocks-client
./trivsocks-client -4 tor.eff.org /
./trivsocks-client -5 tor.eff.org /
-$(IMAGES): plot_results.R
- $(R) $<
-
clean:
- rm -f *~ *.o trivsocks-client *.png *.Rout
+ rm -f *~ *.o trivsocks-client
diff --git a/README b/README
index 48b9617..71d4ce5 100644
--- a/README
+++ b/README
@@ -12,7 +12,6 @@ Contents
Makefile: Builds and tests trivsocks-client
[run_test.py: Script to automate running of trivsocks-client -- defect]
-[plot_results.R: Plot the results from run_test.py -- defect]
measurements-HOWTO: Instructions for setting up Torperf
example_start-tors_fastslow.txt: Example start script for Torperfs with
@@ -29,11 +28,3 @@ Contents
LICENSE: The Tor license (3-clause BSD)
README: This file
-Subdirectory /metrics
------------- --------
-
-A set of utilities for filtering and graphing Tor performance data.
-
- filter.R: filters torperf data and prepares it for graphing
- timematrix.R: graphs tordata for interpretation and visualization
- HOWTO: documentation and examples
diff --git a/metrics/HOWTO b/metrics/HOWTO
deleted file mode 100644
index ce6f7eb..0000000
--- a/metrics/HOWTO
+++ /dev/null
@@ -1,109 +0,0 @@
-HOWTO -- How to generate nifty graphs of tor performance
-
-Welcome traveler! You have reached the howto for some tor performance
-and metrics stuff. You will find here some techniques and scripts
-developed during several tasks including:
-#1919; in which we examine torperfs with fixed entry guards
-#2543; in which we create graphs of #1919 data
-#2563; in which we generalize techniques from #2543 for the future
-
-The remainder of this HOWTO will walk you through what you need to do
-to use the generalized techniques to generate graphs from performance
-data. We will use #2543 as an example, because it is from this
-example that the generalized technique was derived. This is intended
-to be a living document. If something is unclear, or if you wish to
-request a feature, please open a ticket:
-https://trac.torproject.org/projects/tor/newticket
-
-As far as I know, this document was written by Karsten, Mike Perry,
-and Tom Benjamin. If you are also an author of this document, please
-add yourself to this list.
-
-Step 1: Download Torperf request files
---------------------------------------
-
-The 15 Torperf request files are available here:
-
- https://metrics.torproject.org/data.html#performance
-
-The wget commands to download all of them are:
-
- wget https://metrics.torproject.org/data/torperf-50kb.data
- wget https://metrics.torproject.org/data/torperf-1mb.data
- wget https://metrics.torproject.org/data/torperf-5mb.data
- wget https://metrics.torproject.org/data/torperffastratio-50kb.data
- wget https://metrics.torproject.org/data/torperffastratio-1mb.data
- wget https://metrics.torproject.org/data/torperffastratio-5mb.data
- wget https://metrics.torproject.org/data/torperffast-50kb.data
- wget https://metrics.torproject.org/data/torperffast-1mb.data
- wget https://metrics.torproject.org/data/torperffast-5mb.data
- wget https://metrics.torproject.org/data/torperfslow-50kb.data
- wget https://metrics.torproject.org/data/torperfslow-1mb.data
- wget https://metrics.torproject.org/data/torperfslow-5mb.data
- wget https://metrics.torproject.org/data/torperfslowratio-50kb.data
- wget https://metrics.torproject.org/data/torperfslowratio-1mb.data
- wget https://metrics.torproject.org/data/torperfslowratio-5mb.data
-
-Note that the torperf-*.data files are quite big already (25M+).
-
-
-Step 2: Install R and ggplot2
------------------------------
-
-Install R 2.8 or higher.
-
-Run R as user and install ggplot2, quit R, start R again and try to load
-ggplot2:
-
- $ R
- > install.packages("ggplot2")
- > q() # No need to save the workspace image, ever.
- $ R
- > library(ggplot2)
- > q()
-
-
-Step 3: Filter the data
------------------------
-
-Before actually graphing the Torperf data, we should filter it to avoid
-reading 29M of data for each graph. filter.R is a script that
-accomplishes this task, writing it's output to filtered.csv
-It is used as follows:
-
-1) Decide which files you are interested in. If you only want graphs
-based on the fast guard nodes, you only need to crunch those files.
-
-2) Decide what date range you are interested in. The default is to
-include all data since 2001-02-01 until 2099-12-31, by which time I
-expect this script may be obsolete.
-
-usage: R --slave -f filter.R --args [-start=DATE] [-end=DATE] FILENAME(S)
-
-filename must be of the form guardname-basesizeSUFFIX.data
-where SUFFIX is one of kb, mb, gb, tb
- eg: R --slave -f filter.R --args -start=2011-02-01 -end=2099-12-31 *.data
- eg: R --slave -f filter.R --args torperf-50kb.data
-
-So, to filter all data from #1919 you would execute:
- $ R --slave -f filter.R --args *.data
-
-The script may take some time to run if the data files are large.
-
-
-Step 4: Visualize the data
---------------------------
-
-Let's start with plotting a matrix of completion times graphs for every
-file size and guard selection.
-
- $ R --slave -f timematrix.R
-
-This execution may take around 15 seconds.
-
-
-Step 5: Find a more useful visualization of the data
-----------------------------------------------------
-
-... TODO ...
-
diff --git a/metrics/filter.R b/metrics/filter.R
deleted file mode 100644
index f069856..0000000
--- a/metrics/filter.R
+++ /dev/null
@@ -1,149 +0,0 @@
-## A new and "improved" genericised version of the old filter script
-## This version was created for task 2563
-## See HOWTO to put this in context
-##
-## usage: R -f filter.R --args [-start=DATE] [-end=DATE] FILENAME(S)
-## filename must be of the form guardname-basesizeSUFFIX.data
-## where SUFFIX is one of kb, mb, gb, tb
-##
-## eg: R -f filter.R --args -start=2011-02-01 -end=2099-12-31 *.data
-## eg: R -f filter.R --args torperf-50kb.data
-##
-## This R script reads in Torperf files as specified on the command line
-## and writes a filtered version to filtered.csv for later processing.
-
-FilterMain <- function(ARGV) {
- kDebug <- FALSE # set TRUE for debugging output
- kVersion <- 0.3
- if (kDebug) { cat("filter.R version ", kVersion, "\n\n") }
- files <- NULL # files is a list of torperfFiles as definied below
- setClass("torperfFile",
- representation(
- filename = "character",
- guardLabel = "character",
- filesizeLabel = "character",
- filesize = "numeric"
- )
- )
-
- ## default values
- ## cutoff dates for observations
- start <- as.POSIXct("2011-02-01", origin = "1970-01-01")
- end <- as.POSIXct("2099-12-31", origin = "1970-01-01")
-
- ## process command line arguments
- args <- unlist(strsplit(ARGV, " "))
-
- ## there are better ways to process command line args, but this works for me :-)
- for (arg in args) {
- if (kDebug) { cat('arg: ', arg, "\n") }
- ## if start date specified
- if (length(splitArgL <- unlist(strsplit(arg, "-start="))) == 2) {
- if (kDebug) { cat('Starting from ', splitArgL[2], '\n') }
- start <- as.POSIXct(splitArgL[2], origin = "1970-01-01")
- next
- }
- ## if end date specified
- if (length(splitArgL <- unlist(strsplit(arg, "-end="))) == 2) {
- if (kDebug) { cat('Ending at ', splitArgL[2], '\n') }
- end <- as.POSIXct(splitArgL[2], origin = "1970-01-01")
- next
- }
- ## if the argument is -start= or -end= we will not reach this line
- ## now, if it isn't a parameter add it to the file list
- ## parse filename for metadata...
- ## examples:
- ## "torperf-50kb.data" should result in
- ## filename = "torperf-50kb.data"
- ## guardLabel = "torperf"
- ## filesizeLabel = "50kb"
- ## filesize = 50 * 1024
- my.file <- new("torperfFile", filename = arg)
-
- ## get base filename (strip out leading parts of filename such as dirname)
- baseFilename <- basename(my.file at filename)
- parseFileStr <- unlist(strsplit(baseFilename, "-")) ## split the two parts of the filename string
- if (length(parseFileStr) != 2) {
- cat("error: filenames must be of the form guard-filesize.data, you said \"", baseFilename, "\"\n")
- quit("no", 1)
- }
- my.file at guardLabel <- parseFileStr[1]
- cdr <- parseFileStr[2]
- parseFilesize <- unlist(strsplit(cdr, "\\."))
- if (length(parseFilesize) != 2) {
- cat("error: tail of filename must be filesize.data, you said \"", cdr, "\"\n")
- quit("no", 1)
- }
- my.file at filesizeLabel <- tolower(parseFilesize[1]) ## smash case to make our life easier
-
- fileBaseSize <- as.integer(unlist(strsplit(my.file at filesizeLabel, "[a-z]"))[1])
- fileSizeMultiplierStr <- unlist(strsplit(my.file at filesizeLabel, '[0-9]'))
- fileSizeMultiplierStr <- fileSizeMultiplierStr[length(fileSizeMultiplierStr)]
- fileSizeMultiplier <- 1 ## assume no suffix
- if (fileSizeMultiplierStr == "kb") { fileSizeMultiplier <- 1024 }
- if (fileSizeMultiplierStr == "mb") { fileSizeMultiplier <- 1024 * 1024 }
- if (fileSizeMultiplierStr == "gb") { fileSizeMultiplier <- 1024 * 1024 * 1024}
- ## yeah right, like we are really pushing TB of data
- if (fileSizeMultiplierStr == "tb") { fileSizeMultiplier <- 1024 * 1024 * 1024 * 1024 }
- my.file at filesize <- fileBaseSize * fileSizeMultiplier
-
- if (kDebug) {
- cat("i will read file: ", my.file at filename, ' ',
- my.file at guardLabel, ' ',
- my.file at filesizeLabel, ' ',
- my.file at filesize, "\n")
- }
-
- files <- c(files, my.file)
- }
-
- ## sanity check arguments
- if (start >= end) {
- cat("error: start date must be before end date\n");
- quit("no", 1)
- }
- if (length(files) == 0) {
- cat("error: input files must be specified as arguments\n")
- quit("no", 1) ## terminate with non-zero errlev
- }
-
- if (kDebug) {
- cat("filtering from ", as.character.POSIXt(start), " to ",
- as.character.POSIXt(end), "\n")
- }
-
- ## Turn a given Torperf file into a data frame with the information we care
- ## about.
- read <- function(filename, guards, filesize, bytes) {
- x <- read.table(filename)
- x <- x[as.POSIXct(x$V1, origin = "1970-01-01") >= start &
- as.POSIXct(x$V1, origin = "1970-01-01") <= end, ]
- if (length(x$V1) == 0)
- NULL
- else
- data.frame(
- started = as.POSIXct(x$V1, origin = "1970-01-01"),
- timeout = x$V17 == 0,
- failure = x$V17 > 0 & x$V20 < bytes,
- completemillis = ifelse(x$V17 > 0 & x$V20 >= bytes,
- round((x$V17 * 1000 + x$V18 / 1000) -
- (x$V1 * 1000 + x$V19 / 1000), 0), NA),
- guards = guards,
- filesize = filesize)
- }
-
- ## Read in files and bind them to a single data frame.
- filtered <- NULL
- for (file in files) {
- if (kDebug) { cat('Processing ', file at filename, "...\n") }
- filtered <- rbind(filtered,
- read(file at filename, file at guardLabel, file at filesizeLabel, file at filesize)
- )
- }
-
- # Write data frame to a csv file for later processing.
- write.csv(filtered, "filtered.csv", quote = FALSE, row.names = FALSE)
-
-}
-
-FilterMain(commandArgs(TRUE))
diff --git a/metrics/timematrix.R b/metrics/timematrix.R
deleted file mode 100644
index ec01a25..0000000
--- a/metrics/timematrix.R
+++ /dev/null
@@ -1,36 +0,0 @@
-# Load ggplot library without printing out stupid warnings.
-options(warn = -1)
-suppressPackageStartupMessages(library("ggplot2"))
-
-# Read in filtered data.
-data <- read.csv("filtered.csv", stringsAsFactors = FALSE)
-
-# Remove NA's
-data <- na.omit(data)
-
-# Remove "outliers"
-data <- data[(data$filesize == "50kb" & data$completemillis < 60000) |
- (data$filesize == "1mb" & data$completemillis < 120000) |
- (data$filesize == "5mb" & data$completemillis < 300000), ]
-
-# Plot a matrix of scatter plots; the first step is to define which data
-# we want to plot (here: data) and what to put on x and y axis.
-ggplot(data, aes(x = as.POSIXct(started), y = completemillis / 1000)) +
-
-# Draw a point for every observation, but with an alpha value of 1/10 to
-# reduce overplotting
-geom_point(alpha = 1/10) +
-
-# Draw a matrix of these graphs with different filesizes and different
-# guards.
-facet_grid(filesize ~ guards, scales = "free_y") +
-
-# Rename y axis.
-scale_y_continuous(name = "Completion time in seconds") +
-
-# Rename x axis.
-scale_x_datetime(name = "Starting time")
-
-# Save the result to a large PNG file.
-ggsave("timematrix.png", width = 10, height = 10, dpi = 150)
-
diff --git a/plot_results.R b/plot_results.R
deleted file mode 100644
index a291414..0000000
--- a/plot_results.R
+++ /dev/null
@@ -1,146 +0,0 @@
-### Copyright 2007 Steven J. Murdoch
-### See LICENSE for licensing information
-
-### XXX This script is broken, but we should look at it some more and
-### maybe reuse parts of it when implementing Trac ticket #2563 (Add R
-### code for processing Torperf data to the Torperf repository)
-
-UFACTOR = 1e6
-
-## Subtract to timevals, maintaining precision
-todelta <- function(startsec, startusec, tsec, tusec) {
- tsec[tsec == 0] <- NA
- dsec <- tsec - startsec
- dusec <- tusec - startusec
- return(dsec*UFACTOR + dusec)
-}
-
-
-parsedata <- function(filename, size) {
-
- filename <- paste("data/run2/", filename, sep="")
-
- t = read.table(filename, header=TRUE)
-
- tStart <- t$startsec*UFACTOR + t$startusec
- dSocket <- todelta(t$startsec, t$startusec, t$socketsec, t$socketusec)
- dConnect <- todelta(t$startsec, t$startusec, t$connectsec, t$connectusec)
- dNegotiate <- todelta(t$startsec, t$startusec, t$negotiatesec, t$negotiateusec)
- dRequest <- todelta(t$startsec, t$startusec, t$requestsec, t$requestusec)
- dResponse <- todelta(t$startsec, t$startusec, t$responsesec, t$responseusec)
- dDRequest <- todelta(t$startsec, t$startusec, t$datarequestsec, t$datarequestusec)
- dDResponse <- todelta(t$startsec, t$startusec, t$dataresponsesec, t$dataresponseusec)
- dDComplete <- todelta(t$startsec, t$startusec, t$datacompletesec, t$datacompleteusec)
- cbWrite <- t$writebytes
- cbRead <- t$readbytes
-
- results <- data.frame(tStart, dSocket, dConnect,
- dNegotiate, dRequest, dResponse,
- dDRequest, dDResponse, dDComplete,
- cbWrite, cbRead)
-
- invalid <- abs(results$cbRead - size) > 64
- results[invalid,] <- NA
-
- return(results)
-}
-
-plotdist <- function(data, factor, labels, title, ylim=c(NA,NA)) {
- ## Scale units
- if (factor == 1e6)
- ylab <- "Time (s)"
- else if (factor == 1e3)
- ylab <- "Time (ms)"
- else {
- ylab <- "Time (us)"
- factor <- 1
- }
-
- d <- na.omit(data)/factor
-
- ## Find plotting range
- MinY<- NULL
- MaxY <- NULL
-
- range <- 1.5
-
- for (col in d) {
- s <- summary(col)
- Q1 <- as.vector(s[2])
- Q3 <- as.vector(s[5])
- InterQ <- Q3-Q1
- a <- Q1 - range*InterQ
- b <- Q3 + range*InterQ
-
- if (is.null(MinY) || a<MinY)
- MinY <- a
-
- if (is.null(MaxY) || b>MaxY)
- MaxY <- b
- }
-
- if (!is.na(ylim[1]))
- MinY <- ylim[1]
-
- if (!is.na(ylim[2]))
- MaxY <- ylim[2]
-
- ## Find how many points this will cause to be skipped
- skipped <- vector()
- for (i in (1:length(d))) {
- col <- d[[i]]
- isSkipped <- col<MinY | col>MaxY
- d[[i]][isSkipped] <- NA
- s <- length(which(isSkipped))
- ss <- paste("(",s,")",sep="")
- skipped <- append(skipped, ss)
- }
-
- labels <- mapply(paste, labels, skipped)
- if (length(d)>1)
- title <- paste(title, " (", length(d[[1]]), " runs)", sep="")
- else
- title <- paste(title, " (", length(d[[1]]), " runs, ", s, " skipped)", sep="")
-
- ## Plot the data
- boxplot(names=labels, d, frame.plot=FALSE, ylab=ylab, range=range,
- ylim=c(MinY, MaxY), xlab="Event (# points omitted)", main=title,
- pars=list(show.names=TRUE, boxwex = 0.8, staplewex = 0.5, outwex = 0.5))
-}
-
-first <- parsedata("first-big.data", 1048869)
-second <- parsedata("second-big.data", 1048868)
-
-EventNames <- c("start",
- "socket()", "connect()", "auth", "SOCKS req", "SOCKS resp",
- "HTTP req", "HTTP resp", "HTTP done")
-
-png("first-local.png", width=800, height=533, bg="transparent")
-par(mar=c(4.3,4.1,3.1,0.1))
-plotdist(first[2:5], 1e3, EventNames[2:5], "Local events -- first request", c(0,2))
-dev.off()
-
-png("second-local.png", width=800, height=533, bg="transparent")
-par(mar=c(4.3,4.1,5.1,0.1))
-plotdist(second[2:5], 1e3, EventNames[2:5], "Local events -- second request", c(0,2))
-dev.off()
-
-png("first-net.png", width=800, height=533, bg="transparent")
-par(mar=c(4.3,4.1,3.1,0.1))
-plotdist(first[6:8], 1e6, EventNames[6:8], "Network events -- first request", c(0,8))
-dev.off()
-
-png("second-net.png", width=800, height=533, bg="transparent")
-par(mar=c(4.3,4.1,5.1,0.1))
-plotdist(second[6:8], 1e6, EventNames[6:8], "Network events -- second request", c(0,8))
-dev.off()
-
-png("first-download.png", width=600, height=533, bg="transparent")
-par(mar=c(0.3,4.1,3.1,0.1))
-plotdist(first[9], 1e6, EventNames[9], "HTTP download -- first request", c(0,150))
-dev.off()
-
-png("second-download.png", width=600, height=533, bg="transparent")
-par(mar=c(0.3,4.1,3.1,0.1))
-plotdist(second[9], 1e6, EventNames[9], "HTTP download -- second request", c(0,150))
-dev.off()
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