Skip to main content

R is the flavor of the season

By Sachin Ghai
With a series of announcements made for R inclusion in Hadoop distribution and relational database software packages, suddenly R is becoming the flavor of predictive analytics. It’s time to preview R offerings for Hadoop world and where the things seemed to be heading.

Hadoop distribution with R


R was developed around 1993 by Ross Ihaka, Robert Gentleman as a statistical computing language and environment. Open sourced in 1996, the free software is available under GNU licence from r-project.org/CRAN (Comprehensive R Archive Network) servers. Most of the commercial Hadoop distributions today have a R package inclusion or have similar plans. Cloudera and Hortonworks have recently announced partnership with Revolution Analytics, a popular vendor offering enterprise edition. IBM has been offering R as statistical analysis package as part of Infosphere BigInsights for couple of years now. Also, IBM Netezza/Pure Data hardware systems offer R package as a software inclusion. While IBM SPSS is more of a bete noir of R, it also offers R integration. SAS, not a Hadoop focused company, but a prominent player in predictive analytics also offers R integration. Intel which ventured into Hadoop distributions also has recently announced a partnership with Revolution Analytics. MapR, another leading Hadoop distribution company has a partnership with Revelytix which offers Loom software with R integration. Greenplum/Pivotal also offer R integration as part of its database and Hadoop offering.

R on Hadoop integration hacks


Among the non Hadoop distribution options, RHIPE (pronounced hree-pay') is the R and Hadoop Integrated Programming Environment. RHive is another tool from a Korean company(NexR) which offers a way to integrate R with Hive for querying HDFS. Oracle among the big players offers it own distribution of R software. Further, it provides a connector for integrating R and Hadoop. Amazon EMR also offers you an option of running R programs now. If you are adventurous yourself and would like to hack your own R with Hadoop nodes you could either copy libraries to each node or use a JDBC connection approach from R with Hive or HBase. With SQL on Hadoop tools offering JDBC support, hadoopsphere.com believes there will be more than a likely chance of integrating R with Cloudera Impala, Apache Tez, IBM BigSQL and the likes in future.

The following extract from a Google paper lists down one of the approach to use R with MapReduce.
1.First, the user's code calls google.apply() with a list of inputs and a provided function, FUN. An archive is dynamically created on the client including R and all of the needed libraries and then staged to the cluster management system in a datacenter with available resources. FUN and its environment are serialized and written out to a Bigtable in that datacenter.

2.Second, workers tasks are spawned using the dynamically generated virtual machines providing access to all of the R packages that were loaded in the calling  instance's R session. These workers read in the serialized environment from the Bigtable, execute the provided function over a unique element of the input list, and write out the serialized results to a different column of the Bigtable.

3.Third, and finally, the calling R instance reads back in the serialized return values from each worker task, performs the necessary error handling, and returns the computed list to the google.apply() caller


R on an unprecedented journey


It is no secret that the predictive modeling market is going to get hotter and hotter in the days to come. Inclusion of R in technology stacks offers one of the convenient ways of doing ensemble modeling. The amount of buzz suddenly around R is unprecedented in its lifetime. While there are a good number of folks having basic education on R, inclusion of another language in stacks is bound to complicate the skilled resource requirement. The commercial stack for R has been progressing rapidly but the open source counterpart has not benefited much from this pace. For instance, the open source R continues to rely on in-memory, single threaded model. There are limited enterprise R options and dependence on one particular commercial vendor leads to fears of vendor lock-in besides the fear of vendor acquisition considering its size and history. However, with ex-SPSS and ex-SAS folks forming the top management in Revolution Analytics, there is lesser doubt that R’s enterprise show is being run by experienced hands. Price point still remains one of the main winners for R vis-à-vis SPSS and SAS. We would not be surprised if more commercial vendors for R crop up which would be a good thing rather than a bad thing as long as the open source version continues to benefit from the commercial run.


Labels:

Comments

Post a Comment

Popular posts from this blog

In-memory data model with Apache Gora

By Sachin Ghai
Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…
4 comments
Read more

Data deduplication tactics with HDFS and MapReduce

By Desk Editor
As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
Post a Comment
Read more

Amazon DynamoDB datastore for Gora

By Sachin Ghai
What was initially suggested during causal conversation at ApacheCon2011 in November 2011 as a “neat idea”, would soon become prime ground for Gora's first taste of participation within Google's Summer of Code program. Initially, the project, titled Amazon DynamoDB datastore for Gora, merely aimed to extend the Gora framework to Amazon DynamoDB. However, it seem became obvious that the issue would include much more than that simple vision.

The Gora 0.3 Toolbox We briefly digress to discuss some other noticeable additions to Gora in 0.3, namely: Modification of the Query interface: The Query interface was amended from Query<K, T> to Query<K, T extends Persistent> to be more precise and explicit for developers. Consequently all implementors and users of the Query interface can only pass object's of Persistent type. Logging improvements for data store mappings: A key aspect of using Gora well is the establishment and accurate definitio…
1 comment
Read more