Jan 2012 HUG: RHadoop

sapply(data, function)
mapreduce(data, function)
#!/usr/bin/Rscript
library(rmr)
mapreduce(…)

Hive,
Pig
Rmr, Rhipe,
Dumbo,
Pydoop
Hadoopy
Java,
C++
Cascalog,
Scalding,
Scrunch
Cascading,
Crunch
Rmr
Expose MR Hide MR

#!/usr/bin/python
import sys
from math import fabs
from org.apache.pig.scripting import Pig
filename = "student.txt"
k = 4
tolerance = 0.01
MAX_SCORE = 4
MIN_SCORE = 0
MAX_ITERATION = 100
# initial centroid, equally divide the space
initial_centroids = ""
last_centroids = [None] * k
for i in range(k):
last_centroids[i] = MIN_SCORE + float(i)/k*(MAX_SCORE-MIN_SCORE)
initial_centroids = initial_centroids + str(last_centroids[i])
if i!=k-1:
initial_centroids = initial_centroids + ":"
P = Pig.compile("""register udf.jar
DEFINE find_centroid FindCentroid('$centroids');
raw = load 'student.txt' as (name:chararray, age:int, gpa:double);
centroided = foreach raw generate gpa, find_centroid(gpa) as centroid;
grouped = group centroided by centroid;
result = foreach grouped generate group, AVG(centroided.gpa);
store result into 'output';
""")
converged = False
iter_num = 0
while iter_num<MAX_ITERATION:
Q = P.bind({'centroids':initial_centroids})

if results.isSuccessful() == "FAILED":
raise "Pig job failed"
iter = results.result("result").iterator()
centroids = [None] * k
distance_move = 0
# get new centroid of this iteration, caculate the moving distance with last iteration
for i in range(k):
tuple = iter.next()
centroids[i] = float(str(tuple.get(1)))
distance_move = distance_move + fabs(last_centroids[i]-centroids[i])
distance_move = distance_move / k;
Pig.fs("rmr output")
print("iteration " + str(iter_num))
print("average distance moved: " + str(distance_move))
if distance_move<tolerance:
sys.stdout.write("k-means converged at centroids: [")
sys.stdout.write(",".join(str(v) for v in centroids))
sys.stdout.write("]n")
converged = True
break
last_centroids = centroids[:]
initial_centroids = ""
for i in range(k):
initial_centroids = initial_centroids + str(last_centroids[i])
if i!=k-1:
initial_centroids = initial_centroids + ":"
iter_num += 1
if not converged:
print("not converge after " + str(iter_num) + " iterations")
sys.stdout.write("last centroids: [")
sys.stdout.write(",".join(str(v) for v in last_centroids))
sys.stdout.write("]n")

import java.io.IOException;
import org.apache.pig.EvalFunc;
import org.apache.pig.data.Tuple;
public class FindCentroid extends EvalFunc<Double> {
double[] centroids;
public FindCentroid(String initialCentroid) {
String[] centroidStrings = initialCentroid.split(":");
centroids = new double[centroidStrings.length];
for (int i=0;i<centroidStrings.length;i++)
centroids[i] = Double.parseDouble(centroidStrings[i]);
}
@Override
public Double exec(Tuple input) throws IOException {
double min_distance = Double.MAX_VALUE;
double closest_centroid = 0;
for (double centroid : centroids) {
double distance = Math.abs(centroid - (Double)input.get(0));
if (distance < min_distance) {
min_distance = distance;
closest_centroid = centroid;
}
}
return closest_centroid;
}
}

mapreduce(input, output, map, reduce)
one or more hdfs paths
or output of other
mapreduce jobs
hdfs path, default to
temp location
a function of two args
returning a keyval(),
default identity
a function of two args
returning a keyval(),
default none

map = function(k, v) if (hash(k) %% 10 == 0)keyval(k, v)
reduce = function(k, vv) keyval(k, length(vv))

condition = function(x) x >10
out = mapreduce(
input = input,
map = function(k,v)
if (condition(v)) keyval(k,v))

x = from.dfs(hdfs.object)
hdfs.object = to.dfs(x)

INSERT OVERWRITE TABLE pv_gender_sum
SELECT pv_users.gender, count (DISTINCT pv_users.userid)
FROM pv_users
GROUP BY pv_users.gender;
mapreduce(input =
mapreduce(input = "pv_users",
map = function(k, v) keyval(v['userid'], v['gender']),
reduce = function(k, vv) keyval(k, vv[[1]]),
output = "pv_gender_sum",
map = function(k,v) keyval(v, 1)
reduce = function(k, vv) keyval(k, sum(unlist(vv)))

kmeans =
function(points, ncenters, iterations = 10,
distfun = function(a,b) norm(as.matrix(a-b), type = 'F')){
newCenters = kmeans.iter(points, distfun, ncenters = ncenters)
for(i in 1:iterations) {
newCenters = lapply(values(newCenters), unlist)
newCenters = kmeans.iter(points, distfun, centers = newCenters)}
newCenters}
kmeans.iter =
function(points, distfun, ncenters = length(centers), centers = NULL) {
from.dfs(
mapreduce(input = points,
map = if (is.null(centers)) {
function(k,v)keyval(sample(1:ncenters,1),v)}
else {
function(k,v) {
distances = lapply(centers,function(c)distfun(c,v))
keyval(centers[[which.min(distances)]],v)}},
reduce = function(k,vv)
keyval(NULL,apply(do.call(rbind,vv),2,mean))))}

input.specs, output.specs
combine
reduce.on.data.frame
tuning.params
verbose
local, hadoop backends
profiling
managed IO
optimize

mapreduce(mapreduce(…
mapreduce(input = c(input1, input2), …)
equijoin(left.input = input1, right.input = input2, …)
out1 = mapreduce(…)
mapreduce(input = out1, <xyz>)
mapreduce(input = out1, <abc>)
abstract.job = function(input, output, …) {
…
result = mapreduce(input = input, output = output)
…
result}

repo
github.com/RevolutionAnalytics/RHad
oop/
license
Apache 2.0
documentation
R help, github wiki
Q/A
github issue tracking
email
rhadoop@revolutionanalytics.com
project lead
David Champagne

Jan 2012 HUG: RHadoop

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