Big Data Step-by-Step: Using R & Hadoop (with RHadoop's rmr package)

Big Data Step-by-Step
Boston Predictive Analytics
Big Data Workshop
Microsoft New England Research &
Development Center, Cambridge, MA
Saturday, March 10, 2012

by Jeffrey Breen

President and Co-Founder
http://atms.gr/bigdata0310 Atmosphere Research Group
email: jeffrey@atmosgrp.com
Twitter: @JeffreyBreen


Using R & Hadoop
with an emphasis on RHadoop’s rmr package

Code & more on github:
https://github.com/jeffreybreen/tutorial-201203-big-data

Introduction
• Hadoop streaming enables the creation of mappers,
reducers, combiners, etc. in languages other than Java
• Any language which can handle standard, text-based
input & output will do
• Increasingly viewed as a lingua franca of statistics and
analytics, R is a natural match for Big Data-driven
analytics
• As a result, a number of R packages to work with
Hadoop
• We’ll take a quick look at some of them and then dive
into the details of the RHadoop package


There’s never just one R package to do anything...
Package Latest Release Comments

misleading name: stands for "Hadoop interactIVE" &
hive 2012-03-06
has nothing to do with Hadoop hive. On CRAN.
focused on utility functions: I/O parsing, data
HadoopStreaming 2010-04-22
conversions, etc. Available on CRAN.

comprehensive: code & submit jobs, access HDFS, etc.
RHIPE “a month ago” Most links to it are broken. Look on github instead:
http://saptarshiguha.github.com/RHIPE/

Very clever way to use Amazon EMR with small or no
segue 0.02 in December
data. http://code.google.com/p/segue/

Divided into separate packages by purpose:
last week for rmr • rmr - MapReduce
RHadoop last month for rhdfs • rhdfs - ﬁle management w/HDFS
(rmr, rhdfs, rhbase) last fall for rhbase • rhbase - database management for HBase
Sponsored by Revolution Analytics & on github:
https://github.com/RevolutionAnalytics/RHadoop


Any more?
• Yeah, probably. My apologies to the authors of any
relevant packages I may have overlooked.
• R is nothing if it’s not ﬂexible when it comes to
consuming data from other systems
• You could just use R to analyze the output of
any MapReduce workﬂows
• R can connect via ODBC and/or JDBC, you
could connect to Hive as if it were just another
database
• So... how to pick?

Photo credit: http://en.wikipedia.org/wiki/File:Darts_in_a_dartboard.jpg

Thanks, Jonathan Seidman
• While Big Data big wig at Orbitz, Jonathan
(now at Cloudera) published sample code
to perform the same analysis of the airline
on-time data set using Hadoop streaming,
RHIPE, hive, and RHadoop’s rmr
https://github.com/jseidman/hadoop-R

• To be honest, I only had to glance at each
sample to make my decision, but let’s take
a look at each package he demonstrates


About the data & Jonathan’s analysis
• Each month, the US DOT publishes details of the on-time performance
(or lack thereof) for every domestic ﬂight in the country
• The ASA’s 2009 Data Expo poster session was based on a cleaned
version spanning 1987-2008, and thus was born the famous “airline” data
set:
Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,
FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,
Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,
WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
2004,1,12,1,623,630,901,915,UA,462,N805UA,98,105,80,-14,-7,ORD,CLT,599,7,11,0,,0,0,0,0,0,0
2004,1,13,2,621,630,911,915,UA,462,N851UA,110,105,78,-4,-9,ORD,CLT,599,16,16,0,,0,0,0,0,0,0
2004,1,14,3,633,630,920,915,UA,462,N436UA,107,105,88,5,3,ORD,CLT,599,4,15,0,,0,0,0,0,0,0
2004,1,15,4,627,630,859,915,UA,462,N828UA,92,105,78,-16,-3,ORD,CLT,599,4,10,0,,0,0,0,0,0,0
2004,1,16,5,635,630,918,915,UA,462,N831UA,103,105,87,3,5,ORD,CLT,599,3,13,0,,0,0,0,0,0,0
[...]

http://stat-computing.org/dataexpo/2009/the-data.html

• Jonathan’s analysis determines the mean departure delay (“DepDelay”)
for each airline for each month


“naked” streaming
hadoop-R/airline/src/deptdelay_by_month/R/streaming/map.R
#! /usr/bin/env Rscript

# For each record in airline dataset, output a new record consisting of
# "CARRIER|YEAR|MONTH t DEPARTURE_DELAY"

con <- file("stdin", open = "r")
while (length(line <- readLines(con, n = 1, warn = FALSE)) > 0) {
fields <- unlist(strsplit(line, ","))
# Skip header lines and bad records:
if (!(identical(fields[[1]], "Year")) & length(fields) == 29) {
deptDelay <- fields[[16]]
# Skip records where departure dalay is "NA":
if (!(identical(deptDelay, "NA"))) {
# field[9] is carrier, field[1] is year, field[2] is month:
cat(paste(fields[[9]], "|", fields[[1]], "|", fields[[2]], sep=""),
"t",
deptDelay, "n")
}
}
}
close(con)


“naked” streaming 2/2
hadoop-R/airline/src/deptdelay_by_month/R/streaming/reduce.R
#!/usr/bin/env Rscript

# For each input key, output a record composed of
# YEAR t MONTH t RECORD_COUNT t AIRLINE t AVG_DEPT_DELAY

delays <- numeric(0) # vector of departure delays
lastKey <- ""
split <- unlist(strsplit(line, "t"))
key <- split[[1]]
deptDelay <- as.numeric(split[[2]])

# Start of a new key, so output results for previous key:
if (!(identical(lastKey, "")) & (!(identical(lastKey, key)))) {
keySplit <- unlist(strsplit(lastKey, "|"))
cat(keySplit[[2]], "t", keySplit[[3]], "t", length(delays), "t", keySplit[[1]], "t", (mean(delays)),
"n")
lastKey <- key
delays <- c(deptDelay)
} else { # Still working on same key so append dept delay value to vector:
lastKey <- key
delays <- c(delays, deptDelay)
}
}

# We're done, output last record:
cat(keySplit[[2]], "t", keySplit[[3]], "t", length(delays), "t", keySplit[[1]], "t", (mean(delays)), "n")


hive
hadoop-R/airline/src/deptdelay_by_month/R/hive/hive.R

mapper <- function() {
# For each record in airline dataset, output a new record consisting of

cat(paste(fields[[9]], "|", fields[[1]], "|", fields[[2]], sep=""), "t",
deptDelay, "n")
}
}
}
close(con)
}

reducer <- function() {
delays <- numeric(0) # vector of departure delays
lastKey <- ""
split <- unlist(strsplit(line, "t"))
key <- split[[1]]
deptDelay <- as.numeric(split[[2]])

# Start of a new key, so output results for previous key:
if (!(identical(lastKey, "")) & (!(identical(lastKey, key)))) {
lastKey <- key
delays <- c(deptDelay)
} else { # Still working on same key so append dept delay value to vector:
lastKey <- key
delays <- c(delays, deptDelay)
}
}

# We're done, output last record:
}

library(hive)
DFS_dir_remove("/dept-delay-month", recursive = TRUE, henv = hive())
hive_stream(mapper = mapper, reducer = reducer,
input="/data/airline/", output="/dept-delay-month")
results <- DFS_read_lines("/dept-delay-month/part-r-00000", henv = hive())


RHIPE
hadoop-R/airline/src/deptdelay_by_month/R/rhipe/rhipe.R

# Calculate average departure delays by year and month for each airline in the
# airline data set (http://stat-computing.org/dataexpo/2009/the-data.html)

library(Rhipe)
rhinit(TRUE, TRUE)

# Output from map is:
map <- expression({
# For each input record, parse out required fields and output new record:
extractDeptDelays = function(line) {
rhcollect(paste(fields[[9]], "|", fields[[1]], "|", fields[[2]], sep=""),
deptDelay)
}
}
}
# Process each record in map input:
lapply(map.values, extractDeptDelays)
})

# Output from reduce is:
# YEAR t MONTH t RECORD_COUNT t AIRLINE t AVG_DEPT_DELAY
reduce <- expression(
pre = {
delays <- numeric(0)
},
reduce = {
# Depending on size of input, reduce will get called multiple times
# for each key, so accumulate intermediate values in delays vector:
delays <- c(delays, as.numeric(reduce.values))
},
post = {
# Process all the intermediate values for key:
keySplit <- unlist(strsplit(reduce.key, "|"))
count <- length(delays)
avg <- mean(delays)
rhcollect(keySplit[[2]],
paste(keySplit[[3]], count, keySplit[[1]], avg, sep="t"))
}
)

inputPath <- "/data/airline/"
outputPath <- "/dept-delay-month"

# Create job object:
z <- rhmr(map=map, reduce=reduce,
ifolder=inputPath, ofolder=outputPath,
inout=c('text', 'text'), jobname='Avg Departure Delay By Month',
mapred=list(mapred.reduce.tasks=2))
# Run it:
rhex(z)


rmr
hadoop-R/airline/src/deptdelay_by_month/R/rmr/deptdelay-rmr.R
#!/usr/bin/env Rscript

# Calculate average departure delays by year and month for each airline in the
# airline data set (http://stat-computing.org/dataexpo/2009/the-data.html).
# Requires rmr package (https://github.com/RevolutionAnalytics/RHadoop/wiki).

library(rmr)

csvtextinputformat = function(line) keyval(NULL, unlist(strsplit(line, ",")))

deptdelay = function (input, output) {
mapreduce(input = input,
output = output,
textinputformat = csvtextinputformat,
map = function(k, fields) {
keyval(c(fields[[9]], fields[[1]], fields[[2]]), deptDelay)
}
}
},
reduce = function(keySplit, vv) {
keyval(keySplit[[2]], c(keySplit[[3]], length(vv), keySplit[[1]], mean(as.numeric
(vv))))
})
}

from.dfs(deptdelay("/data/airline/1987.csv", "/dept-delay-month"))


shorter is better


rmr notes
• You have control over the input parsing, but without having
to interact with stdin/stdout directly
• Your code only needs to deal with R objects: strings,
lists, vectors & data.frames
• The result of the main mapreduce() function is simply the
HDFS path of the job’s output
• Since one job’s output can be the next job’s input,
mapreduce() calls can be daisy-chained to build
complex workﬂows
• Warning: Recently-released v1.2 has a new I/O model which
breaks compatibility with existing code, but adds ﬂexibility
and binary formats. 1.3 will focus on speed enhancements.


Using rmr: airline enroute time
• Since Hadoop keys and values needn’t be single-valued, let’s pull out a
few ﬁelds from the data: scheduled and actual gate-to-gate times and
actual time in the air keyed on year and airport pair
• For a given day (3/25/2004) and airport pair (BOS & MIA), here’s
what the data might look like:
2004,3,25,4,1445,1437,1820,1812,AA,399,N275AA,215,215,197,8,8,BOS,MIA,1258,6,12,0,,0,0,0,0,0,0
2004,3,25,4,728,730,1043,1037,AA,596,N066AA,195,187,170,6,-2,MIA,BOS,1258,7,18,0,,0,0,0,0,0,0
2004,3,25,4,1333,1335,1651,1653,AA,680,N075AA,198,198,168,-2,-2,MIA,BOS,1258,9,21,0,,0,0,0,0,0,0
2004,3,25,4,1051,1055,1410,1414,AA,836,N494AA,199,199,165,-4,-4,MIA,BOS,1258,4,30,0,,0,0,0,0,0,0
2004,3,25,4,558,600,900,924,AA,989,N073AA,182,204,157,-24,-2,BOS,MIA,1258,11,14,0,,0,0,0,0,0,0
2004,3,25,4,1514,1505,1901,1844,AA,1359,N538AA,227,219,176,17,9,BOS,MIA,1258,15,36,0,,0,0,0,15,0,2
2004,3,25,4,1754,1755,2052,2121,AA,1367,N075AA,178,206,158,-29,-1,BOS,MIA,1258,5,15,0,,0,0,0,0,0,0
2004,3,25,4,810,815,1132,1151,AA,1381,N216AA,202,216,180,-19,-5,BOS,MIA,1258,7,15,0,,0,0,0,0,0,0
2004,3,25,4,1708,1710,2031,2033,AA,1636,N523AA,203,203,173,-2,-2,MIA,BOS,1258,4,26,0,,0,0,0,0,0,0
2004,3,25,4,1150,1157,1445,1524,AA,1901,N066AA,175,207,161,-39,-7,BOS,MIA,1258,4,10,0,,0,0,0,0,0,0
2004,3,25,4,2011,1950,2324,2257,AA,1908,N071AA,193,187,163,27,21,MIA,BOS,1258,4,26,0,,0,0,21,6,0,0
2004,3,25,4,1600,1605,1941,1919,AA,2010,N549AA,221,194,196,22,-5,MIA,BOS,1258,10,15,0,,0,0,0,22,0,0


rmr 1.2 input formatter
• The input formatter is called to parse each input line.

• Jonathan’s code splits CSV file just fine, but we’re going to get fancy
and name the fields of the resulting vector.

• rmr 1.2’s new make.input.format() can wrap your own function:
asa.csvtextinputformat = make.input.format( format = function(line) {
values = unlist( strsplit(line, ",") )
names(values) = c('Year','Month','DayofMonth','DayOfWeek','DepTime',
'CRSDepTime','ArrTime','CRSArrTime','UniqueCarrier',
'FlightNum','TailNum','ActualElapsedTime','CRSElapsedTime',
'AirTime','ArrDelay','DepDelay','Origin','Dest','Distance',
'TaxiIn','TaxiOut','Cancelled','CancellationCode',
'Diverted','CarrierDelay','WeatherDelay','NASDelay',
'SecurityDelay','LateAircraftDelay')
return( keyval(NULL, values) )
} )

https://raw.github.com/jeffreybreen/tutorial-201203-big-data/master/R/functions.R


data view: input formatter
Sample input (string):
2004,3,25,4,1445,1437,1820,1812,AA,399,N275AA,215,215,197,8,8,BOS,MIA,
1258,6,12,0,,0,0,0,0,0,0

Sample output (key-value pair):
structure(list(key = NULL, val = c("2004", "3", "25", "4", "1445",
"1437", "1820", "1812", "AA", "399", "N275AA", "215", "215",
"197", "8", "8", "BOS", "MIA", "1258", "6", "12", "0", "", "0",
"0", "0", "0", "0", "0")), .Names = c("key", "val"),
rmr.keyval = TRUE)

(For clarity, column names have been omitted on these slides)


mapper
Note the improved readability due to named ﬁelds and the compound key-value
output:
#
# the mapper gets a key and a value vector generated by the formatter
# in our case, the key is NULL and all the field values come in as a vector
#
mapper.year.market.enroute_time = function(key, val) {

# Skip header lines, cancellations, and diversions:
if ( !identical(as.character(val['Year']), 'Year')
& identical(as.numeric(val['Cancelled']), 0)
& identical(as.numeric(val['Diverted']), 0) ) {

# We don't care about direction of travel, so construct 'market'
# with airports ordered alphabetically
# (e.g, LAX to JFK becomes 'JFK-LAX'
if (val['Origin'] < val['Dest'])
market = paste(val['Origin'], val['Dest'], sep='-')
else
market = paste(val['Dest'], val['Origin'], sep='-')

# key consists of year, market
output.key = c(val['Year'], market)

# output gate-to-gate elapsed times (CRS and actual) + time in air
output.val = c(val['CRSElapsedTime'], val['ActualElapsedTime'], val['AirTime'])

return( keyval(output.key, output.val) )
}
}



data view: mapper
Sample input (key-value pair):
structure(list(key = NULL, val = c("2004", "3", "25", "4", "1445",
"1437", "1820", "1812", "AA", "399", "N275AA", "215", "215",
"197", "8", "8", "BOS", "MIA", "1258", "6", "12", "0", "", "0",
"0", "0", "0", "0", "0")), .Names = c("key", "val"),
rmr.keyval = TRUE)

structure(list(key = c("2004", "BOS-MIA"),
val = c("215", "215", "197")),
.Names = c("key", "val"), rmr.keyval = TRUE)


reducer
For each key, our reducer is called with a list containing all of its values:
#
# the reducer gets all the values for a given key
# the values (which may be multi-valued as here) come in the form of a list()
#
reducer.year.market.enroute_time = function(key, val.list) {

# val.list is a list of row vectors
# a data.frame is a list of column vectors
# plyr's ldply() is the easiest way to convert IMHO
if ( require(plyr) )
val.df = ldply(val.list, as.numeric)
else { # this is as close as my deficient *apply skills can come w/o plyr
val.list = lapply(val.list, as.numeric)
val.df = data.frame( do.call(rbind, val.list) )
}
colnames(val.df) = c('actual','crs','air')

output.key = key
output.val = c( nrow(val.df), mean(val.df$actual, na.rm=T),
mean(val.df$crs, na.rm=T),
mean(val.df$air, na.rm=T) )

return( keyval(output.key, output.val) )
}



data view: reducer
Sample input (key + list of vectors):
key:
c("2004", "BOS-MIA")
value.list:
list(c("215", "215", "197"), c("187", "195", "170"),
c("198", "198", "168"), c("199", "199", "165"),
c("204", "182", "157"), c("219", "227", "176"),
c("206", "178", "158"), c("216", "202", "180"),
c("203", "203", "173"), c("207", "175", "161"),
c("187", "193", "163"), c("194", "221", "196") )

$key
[1] "2004" "BOS-MIA"
$val
[1] 12.0000 202.9167 199.0000 172.0000


submit the job and get the results
mr.year.market.enroute_time = function (input, output) {
mapreduce(input = input,
output = output,
input.format = asa.csvtextinputformat,
map = mapper.year.market.enroute_time,
reduce = reducer.year.market.enroute_time,
backend.parameters = list(
hadoop = list(D = "mapred.reduce.tasks=10")
),
verbose=T)
}

hdfs.output.path = file.path(hdfs.output.root, 'enroute-time')
results = mr.year.market.enroute_time(hdfs.input.path, hdfs.output.path)

results.df = from.dfs(results, to.data.frame=T)
colnames(results.df) = c('year', 'market', 'flights', 'scheduled',
'actual', 'in.air')

save(results.df, file="out/enroute.time.RData")


R can handle the rest itself
> nrow(results.df)
[1] 42612
> yearly.mean = ddply(results.df, c('year'), summarise,
scheduled = weighted.mean(scheduled, flights),
actual = weighted.mean(actual, flights),
in.air = weighted.mean(in.air, flights))
> ggplot(yearly.mean) +
geom_line(aes(x=year, y=scheduled), color='#CCCC33') +
geom_line(aes(x=year, y=actual), color='#FF9900') +
geom_line(aes(x=year, y=in.air), color='#4689cc') + theme_bw() +
ylim(c(60, 130)) + ylab('minutes')


Big Data Step-by-Step: Using R & Hadoop (with RHadoop's rmr package)

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