Oracle R Advanced Analytics for Hadoop: Part 1
Oracle R Advanced Analytics for Hadoop: Part 1
How to get started using Oracle R Advanced Analytics for Hadoop to access and process data in Oracle Database, HDFS, and Hive and how to create and manage map-reduce processes
by Brendan Tierney

In this article, which is Part 1 of a series, we will look at how you can run R analytics at scale on a Hadoop platform using Oracle R Advanced Analytics for Hadoop, which is a component of Oracle Big Data Connectors and provides a set of R functions allowing you to connect to and process data stored on Hadoop Distributed File System (HDFS) using Hive transparency as well as Oracle Database. Oracle R Advanced Analytics for Hadoop provides interfaces to Oracle Database, HDFS, and Hive and interfaces for initiating map-reduce jobs using by providing some simplified functions that abstract the underlying complexity away from the data scientist. Oracle R Advanced Analytics for Hadoop has a number of highly scalable machine learning algorithms and also utilizes some of the Apache Spark machine learning algorithms for greater performance of in-memory distributed machine learning.

This article focuses on showing how you can get started with using Oracle R Advanced Analytics for Hadoop, how to access and process data in Oracle Database, HDFS, and Hive and how to create and manage map-reduce processes. In the second part of this series, we will look at how you can use the various analytic features, machine learning, and Apache Spark to process and analyze your data.

Overview of Oracle R Advanced Analytics for Hadoop

Oracle R Advanced Analytics for Hadoop is one of the components of the Oracle Big Data Connectors, and it provides a set of R functions that allows you to connect to and manipulate data stored on HDFS using Hive transparency. It also allows you to build map-reduce analytics and use the prepackaged algorithms exposed through an R interface. Additionally, you can integrate with Apache Spark and other tools and languages for greater performance for multilayer neural networks and for logistic regression. (Note: In Figure 1, "Oracle R Advanced Analytics for Hadoop" is abbreviated as "ORAAH" and "SQL Developer" refers to Oracle SQL Developer.)

Figure 1: Oracle R Advanced Analytics for Hadoop overview
Figure 1: Oracle R Advanced Analytics for Hadoop overview

Check Oracle R Advanced Analytics for Hadoop 2.7.0 for a full listing of all the R functions contained in the Oracle R Advanced Analytics for Hadoop package: .

How to Get Access to an Oracle R Advanced Analytics for Hadoop Environment

When it comes to trying out Oracle R Advanced Analytics for Hadoop, you have three main options. The first is to get it installed on some of your existing servers that have Hadoop, Hive, and access to Oracle Database. If such an environment is not easily accessible, alternatively, you can use one of the Oracle VM VirtualBox prebuilt virtual machines. The virtual machine called Oracle Big Data Lite VM comes with a large number of Oracle products installed on it including Oracle R Advanced Analytics for Hadoop, Oracle Database12c, Hadoop, HBase, Hive, Impala, Kafta, and so on. This virtual machine is continually being updated with the latest version of this software, and it can be downloaded from the following website: http://www.oracle.com/technetwork/community/developer-vm/index.html.

As a third option, Oracle Big Data Cloud Service comes with Oracle R Advanced Analytics for Hadoop installed along with many of the products that are on the Oracle Big Data Lite VM. Check the following web page to see what cloud services are available: https://cloud.oracle.com/en_US/big-data.

Working with Data Across Datasources

Oracle R Advanced Analytics for Hadoop allows you to work seamlessly across many different locations for your data including Oracle Database, Hive, and HDFS, as shown in Figure 2. In this article, I will illustrate how you can use it to access and process data on each of these datasources.

Figure 2: Oracle R Advanced Analytics for Hadoop working with data across many datasources
Figure 2: Oracle R Advanced Analytics for Hadoop working with data across many datasources.

The first example of using Oracle R Advanced Analytics for Hadoop is with data located in Oracle Database. The following example code begins with loading the R package for Oracle R Advanced Analytics for Hadoop, which is called ORCH. It then downloads a CSV file from the internet that contains Beach Quality inspection reports for beaches located in Dublin, Ireland. This dataset will be used in each of the examples to illustrate the moving of data between each of the data storage environments. When this data is loaded into Oracle Database, you can use many of the R functions to inspect this data. The following code illustrates the loading and usage this dataset.

For our first step, we can load Oracle R Advanced Analytics for Hadoop using the ORCH package and then download the dataset from the internet.

# load the Oracle R Advanced Analytics for Hadoop library
library(ORCH)

# download the CSV file to the local environment and examine the data
data <- read.csv(
    file=url("http://data.fingal.ie/datasets/csv/BathingWaterStatus.csv"),
    head=TRUE, sep=",")
class(data)
head(data)

After loading the data into the local R environment, you can connect to a schema in Oracle Database:

# connect to Oracle Database
ore.connect(user="odmuser", sid="orcl", host="localhost", password="odmuser", port=1521, all=TRUE);

You can now save the local R data frame, containing the beach quality data, into your Oracle schema by creating new table. The ore.create() command creates a table in the database and uses the data in the R data frame (called data) into this table. This new table can now be referenced in the R session.

# save the R data frame as a table in the database
ore.create(data, table="DUBLIN_BEACH_QUALITY")
# refresh the list of database objects
ore.attach()
# list the objects in the database. The newly created table is listed
ore.ls()
class(DUBLIN_BEACH_QUALITY)

After the table has been created, you can use it as a proxy data frame (also called an ORE data frame) and run all your typical R functions on this data. But in this case, these R functions, via the ORE transparency layer, are being run on the data in the table in Oracle Database instead of in the local R environment. This allows you to use the benefits of Oracle Database. The following codes illustrates using some of the traditional R functions to analyze the data in the Oracle table.

# this table can be used in the same way as a tradition R data frame,
# but the data remains in the database. Only results are returned to your R 
# session
summary(DUBLIN_BEACH_QUALITY)
head(DUBLIN_BEACH_QUALITY)

The R code above illustrates how easy it is to work with data in the local R session and with data in Oracle Database.

But what about when you want to work with data in HDFS? Oracle R Advanced Analytics for Hadoop has number of R functions that facilitate this. Table 1 lists the functions for working with HDFS.

Table 1. HDFS functions in Oracle R Advanced Analytics for Hadoop
Function Name Function Name
hdfs.attach hdfs.mv
hdfs.cache hdfs.ncol
hdfs.cd hdfs.nrow
hdfs.cleanInput hdfs.parts
hdfs.cp hdfs.pull
hdfs.cwd hdfs.push
hdfs.delim hdfs.put
hdfs.describe hdfs.pwd
hdfs.dim hdfs.rm
hdfs.download hdfs.rmdir
hdfs.exists hdfs.root
hdfs.fromHive hdfs.sample
hdfs.fromRData hdfs.setroot
hdfs.get hdfs.size
hdfs.head hdfs.sync
hdfs.id hdfs.tail
hdfs.keysep hdfs.toHive
hdfs.levels hdfs.toRData
hdfs.ls hdfs.toRDD
hdfs.meta hdfs.upload
hdfs.mkdir hdfs.valuesep

The functions listed in Table 1 allow you to work with data in HDFS, to move or save data to HDFS, and to read data from HDFS. This is very useful for working with data as it lands in your Hadoop platform and to save newly created datasets that are based on your R analytics and can be used by other processes.

The following examples illustrate how you can work with data files in HDFS using a selection of the HDFS R functions listed in Table 1. The following R code will take the Dublin Beach Quality dataset and will save it to HDFS. Then using some of the Oracle R Advanced Analytics for Hadoop R functions, you can examine this data stored in HDFS as an alternative location to having data in Oracle Database or in your R environment, and you can easily examine these data files from your R environment. These functions allow you to easily persist your dataset, created during your data science projects, for reuse at a later time.

# What is the current working directory on HDFS?
# If needed the working directory can be changed using hdfs.cwd()
hdfs.pwd()

# Write the R data frame out to a file in HDFS
hdfs.put(data, dfs.name="Dublin_Beach_Quality_Data")

# Add the newly added file in HDFS to your search space 
hdfs.attach("Dublin_Beach_Quality_Data")

# List the properties of the file in HDFS, including path, class, types, 
# names, size, number of rows, number of variables/features, and so on
hdfs.describe("Dublin_Beach_Quality_Data")

# List the number of records and the number of variables/features
hdfs.dim("Dublin_Beach_Quality_Data")

# Check to see if a file with this name exists in HDFS. This is 
# a useful command before you try to create a file in HDFS.
hdfs.exists("Dublin_Beach_Quality_Data")

# Returns the first part of the file. 
hdfs.head("Dublin_Beach_Quality_Data")
# Returns the end of the file
hdfs.tail("Dublin_Beach_Quality_Data")

# Returns meta-data about the file. This includes the variable names, class 
# type, variable types (for example, factors), number of records, 
# if trimming is used, and so on.
hdfs.meta("Dublin_Beach_Quality_Data")

# Returns number of rows. Similar to R function
hdfs.nrow("Dublin_Beach_Quality_Data")
# Returns number of variables. Similar to R function
hdfs.ncol("Dublin_Beach_Quality_Data")
# Returns the size of the file in HDFS in bytes
hdfs.size("Dublin_Beach_Quality_Data")
# Lists the files stored in HDFS for the current working directory
hdfs.ls()

#
hdfsData <- hdfs.get("Dublin_Beach_Quality_Data")
# Check the class of the object. It will be an R data frame
# This shows that the data in the file has been loaded into
# the R environment as an R data frame
class(hdfsData)

# Finally, you can remove/delete the file from HDFS
hdfs.rm("Dublin_Beach_Quality_Data", force=TRUE)

When working with Hive, you will need to create a connection to Hive. This is similar to making a connection to Oracle Database, but for Hive it is a much simpler command:

# set up a connection to Hive
ore.connect(type="HIVE")
ore.attach()

# Displays the current database being used by Hive
ore.showHiveOptions()

# Take the Dublin Beach Quality dataset and push it out to Hive
# Hive cannot process factor variables. Convert these to character strings
f_filter <- sapply(data, is.factor)
data[f_filter] <- data.frame(lapply(data[f_filter], as.character), 
                             stringsAsFactors = FALSE) 
# Move the modified data frame out to Hive
hive_data <- ore.push(data)

# This data can be processed using the typical R functions
# while availing yourself of the scalability of Hive
summary(hive_data)
str(hive_data)
head(hive_data)
nrow(hive_data)
dim(hive_data)

# R data frames can be persisted to Hive to later use 
ore.create(data, table = "dublin_beach_hive")
ore.ls()

# Hive data can be deleted from Oracle R Advanced Analytics for Hadoop
ore.drop(table="dublin_beach_hive")
ore.ls()

We have seen from the examples above how we can use Oracle R Advanced Analytics for Hadoop as one tool to work with data in Oracle Database, in HDFS, and in Hive.

Map-Reduce Using Oracle R Advanced Analytics for Hadoop

Oracle R Advanced Analytics for Hadoop comes with a number of functions that allow you to create and manage map-reduce jobs in Hadoop. A map-reduce process takes a dataset that has been distributed over Hadoop, performs analysis on the distributed dataset, and finally calculates and returns the results. This allows you to utilize many of the CRAN packages and invoke these as Hadoop jobs from R.

With the ORCH package, you can define a map-reduce job and submit it using the hadoop.exec() function. Table 2 lists the various functions map-reduce function for ORAAH. There are three main steps to define a map-reduce job in ORCH.

  1. Define the dataset you are going to use. This dataset can exist on Hadoop, in Hive, or as an R object.
  2. Define the mapper function. This allows you to define what data you want to be selected from the dataset and used in the later steps.
  3. Specify how to apply the reducer function. This is the function to be applied to the selected data. The output from this will be results from applying the reducer function or calculation.
Table 2: Oracle R Advanced Analytics for Hadoop map-reduce functions
Function Name Description
hadoop.exec This function starts the Hadoop engine to send the mapper, reducer, and combiner R functions for execution. The data must exist in HDFS.
hadoop.jobs Lists the running Hadoop jobs.
hadoop.run This start the Hadoop engine and sends the mapper, reducer, and combiner R functions for execution. This is very similar to hadoop.exec above, except that the data is not in Hadoop. It will copy the data to Hadoop before commencing the map-reduce job.
orch.dryrun Changes the execution platform between the local host and the Hadoop cluster.
orch.export Makes R objects, in the local R session, available in Hadoop so that they can referenced in map-reduce jobs.
orch.keyval Outputs the key-value pairs in a map-reduce job.
orch.keyvals Outputs the sets of key-value pairs in a map-reduce job.
orch.pack Compresses an R object that map-reduce will write as the values in key-value pairs.
orch.tempPath Sets the path where temporary data is stored.
orch.unpack Uncompresses an R object that was compressed using the orch.pack function.
orch.create.parttab Enables partitioned Hive tables to be used with the ORCH map-reduce framework.

The following code illustrates the typical word count map-reduce process using Oracle R Advanced Analytics for Hadoop.

words_dataset <- hdfs.put(corpus)
wordcount <- function(input, output=NULL, pattern= "  " ) {
   result <- hadoop.exec(dfs.id=input,
                   mapper = function (k, v) {
                       lappy(strsplit(x=v, split=pattern) [[1]],
                       function(w)  orch.keyval(w, 1) [[1]])
                   },
                   reducer = function(k, vv) {
                       orch.keyval(k, sum(unlist(vv)))
                   },
                   config=new ("mapred.config",
                   job.name = "wordcount",
                   map.output = data.frame(key=0, val=''),
                   reduce.output = data.frame(key='', val=0))
               )
   result
}
Summary

In this article, we looked at what Oracle R Advanced Analytics for Hadoop is and at some of its capabilities. Oracle R Advanced Analytics for Hadoop provides a set of R functions that allow you to connect to and manipulate data stored in HDFS, using Hive transparency, as well as Oracle Database. In addition, you can build highly efficient and scalable advanced analytics using map-reduce and Spark, all through simplified R interfaces.

In Part 2 of this series, we will look at some of the more advanced features of Oracle R Advanced Analytics for Hadoop, including advanced analytics, how to access and run Spark, and how to use other machine learning algorithms.

About the Author

Oracle ACE Director Brendan Tierney is an independent consultant (Oralytics) and lectures on data science, databases, and big data at the Dublin Institute of Technology/Dublin Technological University. He has 24+ years of experience working in the areas of data mining, data science, big data, and data warehousing. As a recognized data science and big data expert, Tierney has worked on projects in Ireland, the UK, Belgium, Holland, Norway, Spain, Canada, and the US. He is active in the UK Oracle User Group (UKOUG) community and one of the user group leaders in Ireland. Tierney has also been editor of the UKOUG Oracle Scene magazine, is a regular speaker at conferences around the world, and writes for several publications. In addition, he has published four books, three with Oracle Press/McGraw-Hill (Predictive Analytics Using Oracle Data Miner, Oracle R Enterprise: Harnessing the Power of R in Oracle Database, and Real World SQL and PL/SQL: Advice from the Experts) and one with MIT Press (Essentials of Data Science).

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