Renjin | Integrating R and Java | About Renjin - the JVM-based interpreter for the R language

Bigger data

Renjin will allow R scripts to transparently interact with data wherever it’s stored, whether that’s on disk, in a remote database, or in the cloud.

R has been traditionally limited by the need to fit data sets into memory, and working with even modest sets of data can quickly exhaust memory due to historical limitations in GNU R interpreter’s implementation.

While there have been attempts to bring big data to the original interpreter, these have generally provided a parallel set of data structures and algorithms, threatening a fragmentation of the language and platform. Renjin, in contrast, will allow existing R code to run on larger datasets with no modification, using R’s familiar and standard data structures and algorithms.

Better performance

Renjin offers performance improvements in executing R code on several fronts: Vector operations, Matrix operations, Scalar operations.

Vector operations: Renjin’s deferred computation engine automatically parallelizes and optimizes vector operation to run an order of magnitude faster, without the memory demands of computing intermediate structures,
Matrix operations: Renjin allows the user to plugin best-of-class implementations of BLAS, LAPACK, and FFT,
Scalar operations: Renjin will compile frequently used portions of R code to JVM byte code on the fly, dramatically increasing performance of R’s notorious performance on for loops and other predominantly scalar code.

These improvements make it possible to perform real-time analyses using complex models.

Cloud-ready

Renjin enables R developers to deploy their code to Platform-as-a-Service providers like Google Appengine, Amazon Beanstalk or Heroku.

Renjin enables R developers to deploy their code to Platform-as-a-Service providers like Google Appengine, Amazon Beanstalk or Heroku without worrying about scale or infrastructure. Renjin is pure Java - it can run anywhere.

Seamless integration

Renjin will allow existing R code to run on larger datasets with no modification, using R’s familiar and standard data structures and algorithms.

Built on the JVM, Renjin allows R code to interact directly with JVM libraries and data structures, without the need for expensive data transfer or brittle inter-process communication.

Java and Scala developers can expose their data directly to R scripts by implementing a simple interface. To the R developer, the data looks no different than a normal R data structure.

Renjin runs safely in multithreaded application servers, allowing embedders to create as many independent “apartments” to run existing single-threaded R code, or to allow multi- threaded access if the user’s R code is written in a pure, functional manner. Renjin allows developers to choose the right language for the different components of an enterprise system, without worrying how they’ll interact.

More productive cycle

With Renjin, analysts can move directly from prototype to production.

Many enterprises will prototype analyses and models in R because of it’s ease of use, but then port the results to Java/C++ for production use, either because of performance limitations or due to poor interoperability between the original R VM and other enterprise systems.

With Renjin, analysts can move directly from prototype to production. R scripts can be packaged as JARs and called directly from JVM-based environments and referenced directly by other JVM languages like Java, Scala, JRuby, or Clojure.

Open source and cutting edge

Like R itself, the Renjin source code is available under an open-source license.

Like R itself, the Renjin source code is available under an open-source license. That means you get to stand on the shoulders of giants at little or no cost.