TestON/JenkinsFile/scripts/SCPFbatchFlowResp.R

# Copyright 2017 Open Networking Foundation (ONF)
#
# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
#
#     TestON is free software: you can redistribute it and/or modify
#     it under the terms of the GNU General Public License as published by
#     the Free Software Foundation, either version 2 of the License, or
#     (at your option) any later version.
#
#     TestON is distributed in the hope that it will be useful,
#     but WITHOUT ANY WARRANTY; without even the implied warranty of
#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#     GNU General Public License for more details.
#
#     You should have received a copy of the GNU General Public License
#     along with TestON.  If not, see <http://www.gnu.org/licenses/>.
#
# If you have any questions, or if you don't understand R,
# please contact Jeremy Ronquillo: jeremyr@opennetworking.org

# **********************************************************
# STEP 1: File management.
# **********************************************************

print( "STEP 1: File management." )

# Command line arguments are read. Args usually include the database filename and the output
# directory for the graphs to save to.
# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
print( "Reading commmand-line args." )
args <- commandArgs( trailingOnly=TRUE )

# Import libraries to be used for graphing and organizing data, respectively.
# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
#                     reshape2: https://github.com/hadley/reshape
print( "Importing libraries." )
library( ggplot2 )
library( reshape2 )
library( RPostgreSQL )    # For databases

# Check if sufficient args are provided.
if ( is.na( args[ 7 ] ) ){
    print( "Usage: Rscript SCPFbatchFlowResp <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
    q()  # basically exit(), but in R
}

# Filenames for output graphs include the testname and the graph type.
# See the examples below. paste() is used to concatenate strings.

errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
errBarOutputFile <- paste( errBarOutputFile, "_PostGraph.jpg", sep="" )

print( "Reading from databases." )

con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )

command <- paste( "SELECT * FROM batch_flow_tests WHERE branch='", args[ 6 ], sep="" )
command <- paste( command, "' ORDER BY date DESC LIMIT 3", sep="" )

print( paste( "Sending SQL command:", command ) )

fileData <- dbGetQuery( con, command )

chartTitle <- paste( "Single Bench Flow Latency - Post", "Last 3 Builds", sep = "\n" )

# **********************************************************
# STEP 2: Organize data.
# **********************************************************

avgs <- c()

print( "Sorting data." )
avgs <- c( fileData[ 'posttoconfrm' ], fileData[ 'elapsepost' ] )

dataFrame <- melt( avgs )
dataFrame$scale <- fileData$scale
dataFrame$date <- fileData$date
dataFrame$iterative <- dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )

colnames( dataFrame ) <- c( "ms", "type", "scale", "date", "iterative" )

# Format data frame so that the data is in the same order as it appeared in the file.
dataFrame$type <- as.character( dataFrame$type )
dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )

dataFrame <- na.omit( dataFrame )   # Omit any data that doesn't exist

print( "Data Frame Results:" )
print( dataFrame )

# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************

print( "Generating fundamental graph data." )

theme_set( theme_grey( base_size = 20 ) )   # set the default text size of the graph.

mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, fill = type ) )
xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$date )
xLabel <- xlab( "date" )
yLabel <- ylab( "Latency (ms)" )
fillLabel <- labs( fill="Type" )
theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )

fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme


print( "Generating bar graph with error bars." )
width <- 0.3
barGraphFormat <- geom_bar( stat="identity", width = width )
title <- ggtitle( chartTitle )
result <- fundamentalGraphData + barGraphFormat + title


print( paste( "Saving bar chart to", errBarOutputFile ) )
ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )

print( paste( "Successfully wrote stacked bar chart out to", errBarOutputFile ) )


# **********************************************************
# STEP 2: Organize data.
# **********************************************************

avgs <- c()

print( "Sorting data." )
avgs <- c( fileData[ 'deltoconfrm' ], fileData[ 'elapsedel' ] )

dataFrame <- melt( avgs )
dataFrame$scale <- fileData$scale
dataFrame$date <- fileData$date
dataFrame$iterative <- dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )

colnames( dataFrame ) <- c( "ms", "type", "scale", "date", "iterative" )

# Format data frame so that the data is in the same order as it appeared in the file.
dataFrame$type <- as.character( dataFrame$type )
dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )

dataFrame <- na.omit( dataFrame )   # Omit any data that doesn't exist

print( "Data Frame Results:" )
print( dataFrame )

# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************

print( "Generating fundamental graph data." )

theme_set( theme_grey( base_size = 20 ) )   # set the default text size of the graph.

mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, fill = type ) )
xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$date )
xLabel <- xlab( "Build Date" )
yLabel <- ylab( "Latency (ms)" )
fillLabel <- labs( fill="Type" )
theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )

fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme


print( "Generating bar graph with error bars." )
width <- 0.3
barGraphFormat <- geom_bar( stat="identity", width = width )
chartTitle <- paste( "Single Bench Flow Latency - Del", "Last 3 Builds", sep = "\n" )
title <- ggtitle( chartTitle )
result <- fundamentalGraphData + barGraphFormat + title

errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
errBarOutputFile <- paste( errBarOutputFile, "_DelGraph.jpg", sep="" )

print( paste( "Saving bar chart to", errBarOutputFile ) )
ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )

print( paste( "Successfully wrote stacked bar chart out to", errBarOutputFile ) )