R de Hadoop (Oracle R Advanced Analytics for Hadoopご説明資料）

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
> ore.connect
function (user = "", sid = "", host = "localhost", password = "",
port = 1521, service_name = NULL, conn_string = NULL, all = FALSE,
type = c("ORACLE", "HIVE"), tzone = Sys.getenv("TZ"))
{
type <- match.arg(type)
if (ore.is.connected())
ore.disconnect()
.ore.QueryEnvInit()
switch(type, ORACLE = .ore.oracleQuerySetup(), HIVE = .ore.hiveQuerySetup())
.ore.QueryEnv$connect(user, sid, host, password, port, service_name,
conn_string, tzone)
if (all) {
ore.sync()
ore.attach()
}
invisible()
}
<environment: namespace:OREbase>
R de Hadoop
Hadoop R
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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 2
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Oracle R Advanced Analytics for Hadoop
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Traditional Analytics
Hours, Days or Weeks
Data Extraction
Data Prep &
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Data Mining
Model Building
Data Mining
Model “Scoring”
Data Preparation
and
Transformation
Data Import
Source
Data
Dataset
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Data Preparation
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Oracle Data Mining
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Oracle’s Big Data Advanced Analytics Solutions
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Oracle Database Server
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R
Hadoop Cluster
Big Data
SQL
Oracle R Enterprise
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R Client
SQL Developer
SQL
SQL Client
Oracle ExadataBig Data Appliance
SQL
R
R Client
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Oracle R Advanced
Analytics for Hadoop

Big Data
~ ~
Hive HiveR
Java MahoutMahout

MapReduce
~ ~
Hive HiveR
Java MahoutMahout

Spark
~ ~
Hive HiveR
Java MahoutMahout
Scala Phython

MapReduce
~ ~
Hive HiveR
Java MahoutMahout
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Scala Phython

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Oracle R
Oracle R Distribution (ORD)
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Oracle R Distribution
http://www.oracle.com/technetwork/database/database-technologies/r/r-distribution/downloads/index.html
You are using Oracle's distribution of R. Please
contact Oracle Support for any problems you
encounter with this distribution.

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[1] "Intel Math Kernel Library (Intel MKL)"
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$vendor
[1] "R internal BLAS and LAPACK"
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Oracle R Advanced Analytics for Hadoop (ORAAH)
R Hadoop
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Hierarchical k-Means - MR
Feature Extraction
Non-negative Matrix Factorization MapReduce
Collaborative Filtering (LMF) MapReduce
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Basic Statistics
Correlation/Covariance Map-Reduce
GLM ORAAH Map-Reduce
* Random Forests
* Decision Trees
* Hierarchical k-Means
Principal Components Analysis Map-Reduce
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* Support Vector Machines
* Support Vector Machines * Random Forest
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OREstats ORCHcoreORCHstats
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ORCHtestkit

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Oracle Database Server Hadoop Cluster
R
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ore.frame DBSQL
> class(TITANIC01)
[1] "ore.frame"
attr(,"package")
[1] "OREbase"
> colnames(TITANIC01)
[1] "Class" "Sex" "Age" "Survived" "Freq"
> mean(TITANIC01$Freq)
[1] 68.78125
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> ore.create(iris,table="iris_hive2")
Warning message:
In ore.create(iris, table = "iris_hive2") :
column names modified by "ore.make.names" function
> colnames(iris_hive2)
[1] "sepal_length" "sepal_width" "petal_length" "petal_width" "species"
> class(iris_hive2$species)
[1] "ore.factor"
attr(,"package")
[1] "OREbase"
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> test.mat <- matrix(1:16,nrow=4,ncol=4)
> test.mat
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
> test.mat.hive <- ore.push(test.mat,table="hive_mat")
> test.mat.hive
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
> class(test.mat.hive)
[1] "ore.vecmatrix"
attr(,"package")
[1] "OREbase"
hive
Matrix

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library(ORCH)>
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> library(ORCH)
OREbase
OREcommon
: ‘OREbase’
The following objects are masked from ‘package:base’:
cbind, data.frame, eval, interaction, order, paste, pmax, pmin,
rbind, table
OREstats
MASS
ORCHcore
rJava
Oracle R Connector for Hadoop 2.5.1 (rev. 307)
Info: using native C base64 encoding implementation
Info: Hadoop distribution is Cloudera's CDH v5.5.1
Info: using auto-detected ORCH HAL v4.2
Info: HDFS workdir is set to "/user/oracle"
Warning: mapReduce checks are skipped due to "ORCH_MAPRED_CHECK"=FALSE
Warning: HDFS checks are skipped due to "ORCH_HDFS_CHECK"=FALSE
Info: Hadoop 2.6.0-cdh5.5.1 is up
Info: Sqoop 1.4.6-cdh5.5.1 is up
Info: OLH 3.5.0 is up
Info: Hive 1.1.0-cdh5.5.1-standalone is up
Info: loaded ORCH core Java library "orch-core-2.5.1-mr2.jar"
ORCHstats
orch.dbg.on("all")
orch.debug(T)
>
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> hdfs.mkdir("oratest")
[1] "/user/oracle/oratest"
attr(,"orch.isdir")
[1] TRUE
attr(,"orch.dfs.path")
[1] TRUE
> hdfs.cd("oratest")
[1] "/user/oracle/oratest"
attr(,"orch.isdir")
[1] TRUE
[1] TRUE
> hdfs.ls()
NULL
> hdfs.put(iris,dfs.name="iris.hdfs")
[1] "/user/oracle/oratest/iris.hdfs"
attr(,"orch.dfs.id")
[1] TRUE
> hdfs.ls()
[1] "iris.hdfs"
> hdfs.head("iris.hdfs",5)
[1] "5.1,3.5,1.4,0.2,setosa" "4.9,3,1.4,0.2,setosa" "4.7,3.2,1.3,0.2,setosa"
[4] "4.6,3.1,1.5,0.2,setosa" "5,3.6,1.4,0.2,setosa"
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ore.connect(
host="localhost",
port=10000, user="hive",
schema="default",
type="HIVE")
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> ore.connect(type="HIVE")
> ore.create(cars, table="hive_cars")
> summary(hive_cars)
speed dist
Min. : 4.00 Min. : 2.00
1st Qu.:11.38 1st Qu.: 24.25
Median :14.67 Median : 35.00
Mean :15.40 Mean : 42.98
3rd Qu.:18.83 3rd Qu.: 55.50
Max. :25.00 Max. :120.00
> class(hive_cars)
[1] "ore.frame"
attr(,"package")
[1] "OREbase"
hive_cars
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> ore.connect(host="localhost",port=10000,user="hive",schema="default",type="HIVE")
> ore.sync()
> ore.attach()
> ore.ls()
[1] "cmnt" "cust" "datx2_hive"
[4] "datx2_tohive" "datx_hive2"
> ore.create(cbind(iris,id=1:150) ,table="iris_hive")
The following object is masked _by_ .GlobalEnv:
iris_hive
Warning message:
In ore.create(cbind(iris, id = 1:150), table = "iris_hive") :
column names modified by "ore.make.names" function
> nrow(iris_hive)
[1] 150
> colnames(iris_hive)
[1] "sepal_length" "sepal_width" "petal_length" "petal_width" "species"
[6] "id"
> row.names(iris_hive) <- iris_hive$id
> summary(iris_hive$sepal_length)
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.300 5.100 5.800 5.843 6.400 7.900
> iris_hive$newcol <- 150:1
> head(iris_hive,1)
sepal_length sepal_width petal_length petal_width species id newcol
1 5.1 3.5 1.4 0.2 setosa 1 150
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Access previously created HIVE tables
> ore.sync(table=“tab_input")
> ore.sync(table=“tab_input2")
List the HIVE tables
> ore.ls()
HIVE
Oracle R Advanced Analytics
for Hadoop Client Packages
HIVE
Transparency Engine
4
/user/oracle/tab_input
HDFS Storage
HDFS Storage
3
HIVE
Thrift
Server
1
HQL
Metastore
Metastore
Metastore
Metastore
2
[1] “tab_input" “tab_input2"
Check variable names on the files
> names(tab_input)
[1] “target" "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8"
[10] "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"
[19] "v18" "v19" "v20" "v21"
> names(tab_input2)
[1] “target" "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8"
[10] "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"
[19] "v18" "v19" "v20" "v21"
Oracle Distribution of R version 3.2.0 (--) -- "Full of Ingredients"

Join the two tables by one common variable
> joined <- merge(tab_input,tab_input2,by="value")
JOIN
HIVE
HIVE
Transparency Engine
The new table is temporary, so it’s not pushed to HIVE
> ore.ls()
[1] “tab_input" “tab_input2"
But, it’s part of the local R objects
> ls()
[1] "joined"
> names(joined)
[1] "value" "v1.x" "v2.x" "v3.x" "v4.x" "v5.x" "v6.x" "v7.x" "v8.x" "v9.x"
[11] "v10.x" "v11.x" "v12.x" "v13.x" "v14.x" "v15.x" "v16.x" "v17.x" "v18.x" "v19.x"
[21] "v20.x" "v21.x" "v1.y" "v2.y" "v3.y" "v4.y" "v5.y" "v6.y" "v7.y" "v8.y"
[31] "v9.y" "v10.y" "v11.y" "v12.y" "v13.y" "v14.y" "v15.y" "v16.y" "v17.y" "v18.y"
[41] "v19.y" "v20.y" "v21.y"
4
/user/oracle/tab_input
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master="yarn-client",
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)
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kyp.rdd <- hdfs.toRDD("kyp.hdfs")

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kyp.fit <- orch.glm2(
Kyphosis ~ Age + Number + Start,
dfs.dat = kyp.hdfs)

> spark.connect(master="yarn-client",memory="1G",dfs.namenode="bigdatalite.localdomain")
> spark.connected()
[1] TRUE
> spark.session()
Reference class object of class ".orch.SparkSession"
Field ".jRef":
[1] "Java-Object{com.oracle.orch.spark.Session@4a901445}"
Field ".jSig":
[1] "Lcom/oracle/orch/spark/Session;"
Field ".stop":
[1] TRUE
Field ".error":
[1] FALSE
Field ".active":
[1] TRUE
> kyp.hdfs <- hdfs.put(kyphosis)
> kyp.rdd <- hdfs.toRDD(kyp.hdfs)
> kyp.rdd
[1] "/tmp/orch66e040bd0859"
[1] TRUE
attr(,"orch.spark.rdd")
[1] "Java-Object{com.oracle.orch.spark.TextRDD@784d9bc}"
> kyp.fit <- orch.glm2(Kyphosis ~ Age + Number + Start, dfs.dat = kyp.hdfs)
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> orch.connect("oreuser","ora12c","shiva",port=1521, driver ="olh")
Connecting ORCH to RDBMS via [olh]
Host: shiva
Port: 1521
SID: ora12c
User: oreuser
Enter password for [oreuser]: ********
Connected to database "shiva:ora12c".
> hdfs.pull(cars.dfs,db.name="CARS_OLH")
> orch.connect("oreuser","ora12c","shiva",port=1521, driver ="sqoop")
Connecting ORCH to RDBMS via [sqoop]
Host: shiva
Port: 1521
SID: ora12c
User: oreuser
Enter password for [oreuser]:******
Connected to database "shiva:ora12c".
> cars.dfs2 <- hdfs.push("CARS_TABLE")
>
> hdfs.toHive(cars.dfs2,table="cars_hive")
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SFO
ontime <- ore.pull(ONTIME_S[ONTIME_S$YEAR==2000,])
ontime.dfs <- hdfs.put(ontime, key='UNIQUECARRIER' )
res <- NULL
res <- hadoop.run(
ontime.dfs,
mapper = function(key, ontime) {
if (ontime$DEST == 'SFO') {
keyval(key, ontime)
}
},
reducer = function(key, vals) {
sumAD <- 0; count <- 0
for (x in vals) {
if (!is.na(x$DISTANCE)) {
sumAD <- sumAD + x$DISTANCE; count <- count + 1
}
}
if (count > 0) { res <- sumAD / count }
else {res <- 0 }
keyval(key, res)
}
)
hdfs.get(res)
key val1
1 AA 1361.4643
2 AS 515.8000
3 CO 2507.2857
4 DL 1601.6154
5 HP 549.4286
6 NW 2009.7273
7 TW 1906.0000
8 UA 1134.0821
9 US 2387.5000
10 WN 541.1538

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R MapReduce
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res <- hdfs.get(dfs.res)
finalres = list()
for (i in 1:nrow(res))
{ finalres[[i]] <-
orch.unpack(res[i,])}
/user/oracle/iris
Mapper(s)
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-D stream.io.identifier.resolver.class=com.oracle.orch.streaming.io.NoSplitIdentifierResolver
-D mapred.job.name=ORCH_20160414091432-1
-input /tmp/orch66c568b00bbe
-output /tmp/orch66c58105bce
-mapper "/usr/bin/env R --vanilla --quiet --slave --no-save --no-restore -e
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-reducer "/usr/bin/env R --vanilla --quiet --slave --no-save --no-restore -e
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-file /tmp/orch66c57801bd90.R
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-cmdenv R_HOME= -cmdenv R_PROFILE_USER= -cmdenv HADOOP_HOME= 2>&1; echo "?$?"
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> USARRESTS <- hdfs.put(USArrests)
> arrestsModel <- orch.princomp(USARRESTS, cor = TRUE)
> arrestsModel
Call:
princomp(cor = cor, covmat = list(cov = cov, center = center,
n.obs = n.obs))
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.5748783 0.9948694 0.5971291 0.4164494
4 variables and 50 observations.
> res <- orch.predict(arrestsModel, USARRESTS)
> hdfs.head(res,3)
[1] "-0.985565884503144,1.13339237770997,0.444268787550733,0.156267144919712"
[2] "-1.95013775033503,1.07321325616849,-2.04000333289159,-0.438583439947189"
[3] "-1.76316353972298,-0.745956780637291,-0.0547808243262842,-0.834652924308098"
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3
89

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90
> irisModel <- princomp(~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
data = iris)
> iris.hdfs.put <- hdfs.put(iris)
> iris.hdfs.pred <- orch.predict(irisModel, iris.hdfs.put)
> hdfs.head(iris.hdfs.pred,5)
[1] "-2.68412562596953,-0.319397246585102,0.0279148275894149,0.0022624370713179,setosa"
[2] "-2.71414168729432,0.17700122506478,0.210464272378243,0.0990265503235873,setosa"
[3] "-2.88899056905929,0.144949426085557,-0.0179002563208903,0.019968389709029,setosa"
[4] "-2.74534285564141,0.318298979251915,-0.0315593736056814,-0.0755758166136827,setosa"
[5] "-2.72871653655453,-0.326754512934921,-0.0900792405512,-0.0612585925856928,setosa"
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91
> u <- sample(1:100, 300, replace=TRUE)
> i <- sample(1:10, 300, replace=TRUE)
> ui <- unique(cbind(u,i))
> r <- sample(1:5, nrow(ui), replace=TRUE)
> input <- cbind(ui,r)
> inputFile <- ORCHcore:::.orch.tmpfile()
> write.table(input, file=inputFile, sep=",", col.names=FALSE, row.names=FALSE)
> fit <- orch.lmf(inputFile, method="mahout-als", rank=3, iterations=5)
> fit
Input HDFS Directory : /tmp/orch6f9c5c0fbbf7
Model HDFS Directory : /user/oracle/orch6f9c7790aff3
> class(fit)
[1] "orch.mahout.lmf.als" "orch.lmf"
> orch.recommend(fit, n=2, maxRating=5)
[1] "/user/oracle/orch6f9c2ddd6da4"
[1] TRUE
• ~
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Number of columns 46 columns
Missing values yes
# hidden Elapsed time Elapsed time
neurons (sec) nnet (sec) orch.neural
10 934.176 44.181
20 1861.812 44.969
30 2634.434 35.196
40 3674.379 39.217
50 4400.551 49.527
• Hardware spec: Single BDA node
MemTotal: 49GB
CPUs: 24 (3058MHz each)
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orch.ml.kmeans(formula = ~ Number + Age, data = data)
predict(model, newdata = data, supplemental = c("Kyphosis", "Age"))

> spark.connect( master="yarn-client", memory="1G",dfs.namenode="bigdatalite" )
> kyp.mod <- orch.ml.kmeans(formula = ~ Number + Age, data =kyp.dfs)
OBX Model Matrix: created MLlib Vector RDD (81 rows) 0.014 sec
OBX Machine Learning: MLlib K-means elapsed time 4.284 sec
> kyp.mod
$formula
[1] "~Number + Age"
$predictMetadata
[1] "Java-Object{com.oracle.obx.csv.CSVPredictMetadata@5cf1bbd3}"
$predictor
[1] "Java-Object{com.oracle.obx.ml.MLKMeans@204a02a4}"
$technique
[1] "MLlib K-means"
attr(,"class")
[1] "orch.ml.kmeans"
> kyp.pred <- predict(kyp.mod, newdata=kyp.dfs, supplemental = c("Kyphosis", "Age"))
OBX Model Matrix: created predict RDD (81 rows) 0.030 sec
> hdfs.write(kyp.pred,outPath="kyp_pred_km")
> hdfs.attach("kyp_pred_km")
Info: sampling data types of hdfs:"/user/oracle/kyp_pred_km"
Data class : data.frame
Column types: character, numeric, integer
Column count: 3
Has key : no
Pristine : yes
Key delim :
Value delim : ,
[1] "/user/oracle/kyp_pred_km"
[1] TRUE
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¥MR K O W NOV Oi kd D@BO Oi kd Ieb[
¥MR ¥O K¥O W NOV WK ¥Sb Oi kd fh]ê,f kmbq
RNP a¥S O Oi kd fh]ê,f mkbq D@BO –
Oi kd ¥MR ¥O K¥O D@BO Oi kd ~
¥MR _X ¥O K¥O Oi kd fh]ê,f mkbq
102

ORAAH
C K¥U VSL Bu
s r
• ) -
103
C K¥U VSL
C K¥U VSL
C K¥U VSL K
C K¥U VSL
C K¥U VSL
C K¥U VSL
C K¥U VSL
C K¥U VSL i
C K¥U VSL OKX

B11
KN B s
S O B s
C K¥U B s
KN i w
104

B11
KN B s
S O B s
C K¥U B s
KN i w
105

ORCH/ORE
108

HDFS (ORCH )
RNP cXM B3
RNP K KMR 46C u B3 i
RNP NOVSW 46C UOc i # KV_O i e
RNP UOc O 46C UOc i e NOPK_V I
RNP KV_O O 46C KV_O i e NOPK_V I
RNP MKMRO B3 X PP
RNP aN 46C w
RNP MaN 46C w
RNP MN 46C f
RNP WUNS¥ 46C
RNP ¥WNS¥ 464C
RNP ¥ 46C i
RNP O ¥ 46C i
109

HDFS ( , )
RNP V i 46C f
RNP WO K 46C B3 i e
RNP ObS v 46C z f
RNP SN 46C B3 NP SN f
RNP M 46C i f
RNP W 46C f
RNP ¥W 46Cu f
RNP SdO 46C f
RNP K¥ 46C f
RNP XM V 46C
RNP X¥ a 46C
RNP NO M¥SLO 46C y B i f
RNP NSW 46C NSW
110

HDFS ( , )
RNP KW VO 46C i
RNP KSV 46C u e i f
i i RNP _ B B 46C i
RNP QO 46C i B i
111

Hadoop(MapReduce)
R MapReduce
RKN ObOM D ]hhi ~ ~ N ~
D@BO ~
RKN T L D ]hhi –
RKN ¥_X D ]hhi ~ ~ N ~
D@BO a ]hhi,kng ~ D@BO ~
¥MR N¥c¥_X ~ D ]hhi ~ N ~
¥MR Ob ¥ ~ ~ ~ N N D ]hhi –
I iN^]n ^ –
¥MR KMU ~ ~ ~ / ~N
¥MR _X KMU hk a,i d N
¥MR M¥OK O K¥ KL ~ Dbo^ KN D I iN^]n ^ ~ ~ –
¥MR OW K R ~
112

HDFS ( )
RNP KW VO 46C i
RNP KSV 46C u e i f
113
i i RNP _ B B 46C i
RNP QO 46C i B i
RNP _ R i i i 46C i
RNP _VV 46C i i i i
RNP S O 46C S O i
RNP P¥ W S O S O 46C i
RNP B44 46C C K¥U B44 i i

ORAAH + ORE /Hive
Hive Oracle DB
6_XM S X 4O M¥S S X
a]_l,_khfDbo^ KN D Dbo^ D@BO
a]_l,mhDbo^ D@BO hk^,_k f^ Dbo^
hk^, k^ m^ ] m ,_k f^ hk^,_k f^ ~ ~
hk^,]khi ~ ~ ~
hk^, ^m hk^,_k f^
hk^,inee Dbo^ N ~ ~
hk^,inla N Dbo^ ~ ~
hk^,k^ h]^ hk^,o^ mhk
114

Hadoop(MapReduce)
k^ m^ ~ • ~
_bo^gnf ~ Pnd^r 3 & '
EMN
f^]b g
jn gmbe^
l]
o k /
115

6_XM S X 4O M¥S S X
¥MR M ¥ L^ klhg
¥MR M
¥MR QO HVO OV fh]ê,f mkbq qeô – ~ , ^mTeôêl
¥MR QVW D@BO ~
¥MR QVW)
¥MR UWOKX D@BO ~ G
¥MR VW
m ee+ g]+ldbggr MN &POMN' Kk e^ N Agm^kikbl^ hk^,ef
~
¥MR VWP
cêer_bla I ahnm em^kg mbg e^ lm ljn k^l pbma p^b am^] k^ ne kbs mbhg &=HO+SN'
¥MR XO_¥KV ~ ~ ~ ~
¥MR XWP
cêer_bla
~ N JIB ~ ~ –
¥MR XWP 6KVQ N JIB ~ ~ ~ ~
¥MR ¥SXM W
¥MR ¥OM WWOXN hk a,f ahnm,ef_, le – ~ ~ g
¥MR KW VO
¥MR MKVO ~
116

R
117
K ¥O ~N KNA
K ¥O MRK¥KM O¥ ~N KNA
K ¥O NK O ~N KNA
K ¥O NK O SWO ~N KNA
K ¥O NSPP SWO ~N KNA
K ¥O PKM ¥ ~N KNA
K ¥O P¥KWO ~N KNA ~
K ¥O SX OQO¥ ~N KNA
K ¥O VS ~N KNA
K ¥O V QSMKV ~N KNA
K ¥O WK ¥Sb ~N KNA
K ¥O X_WO¥SM ~N KNA
K ¥O LTOM ~N KNA
K ¥O OM ¥ ~N KNA

R
Hive
118
S ¥O Kk e^ N Agm^kikbl^
S ¥O MRK¥KM O¥
S ¥O NK O
S ¥O NK O SWO
S ¥O NSPP SWO
S ¥O PKM ¥
S ¥O P¥KWO ~
S ¥O SX OQO¥
S ¥O VS
S ¥O V QSMKV
S ¥O WK ¥Sb
S ¥O X_WO¥SM
S ¥O LTOM
S ¥O OM ¥

hk a,][ ,e lm^kk ~ ~
hk a,][ ,h__ ~
hk a,][ ,hg ~ N KN
¥KMVO B 1N KXMON 1XKVc SM P ¥ KN
hk a,][ ,hnminm
hk a,o^klbhg KN D ~ ~
hk a,]^[n I iN^]n ^ N N

HIVE/R
9F5 B
bgm bgm^ ^k
mbgrbgm bgm^ ^k
lf eebgm bgm^ ^k
[b bgm bgm^ ^k
]hn[e^ gnf^kb
_eh m gnf^kb
]^ bf e gnf^kb
lmkbg a k m^k
a k a k m^k
o k a k a k m^k
[hhe^ g eh b e
] m^ @ m^
mbf^lm fi LKOET m
ohb] Akkhk
120

Spark Mlib
Spark Mlib
¥MR WV N Oi kd Ieb[
¥MR WV VSXOK¥ Oi kd Ieb[
¥MR WV VK Oi kd Ieb[ H llh
¥MR WV ¥SNQO Oi kd Ieb[
¥MR WV V QS SM Oi kd Ieb[
¥MR WV MK Oi kd Ieb[
¥MR WV ¥KXN W P ¥O Oi kd Ieb[
¥MR WV W Oi kd Ieb[ ~
¥MR WV UWOKX Oi kd Ieb[ G+I^ gl
¥MR W NOV WK ¥Sb Oi kd fh]ê,f mkbq
¥MR V KN W NOV D@BO Oi kd Oi kd Ieb[ ~
¥MR K O W NOV Oi kd D@BO Oi kd Ieb[
¥MR ¥O K¥O W NOV WK ¥Sb Oi kd fh]ê,f kmbq
RNP a¥S O Oi kd fh]ê,f mkbq D@BO –
Oi kd ¥MR ¥O K¥O D@BO Oi kd ~
¥MR _X ¥O K¥O Oi kd fh]ê,f mkbq
121

R de Hadoop (Oracle R Advanced Analytics for Hadoopご説明資料）

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