How to Combine R with Hadoop for Petabyte-Scale Data Processing
This article explains three practical approaches—Streaming APIs, the Rhipe package, and RHadoop—to integrate R with Hadoop, enabling R to process petabyte-scale datasets, compares their setup complexity, capabilities, and trade‑offs, and highlights key conclusions for choosing the right method.
Background
To meet the demand of processing petabyte‑scale data with R, it is necessary to combine R with Hadoop. This article outlines three different techniques for achieving such integration.
Method 1: Streaming APIs
Hadoop provides Streaming APIs that allow R functions to be passed to Hadoop and executed in a MapReduce fashion. Any R script that can read from standard input and write to standard output can be used without installing additional client software. An example command line (illustrated in the figure) shows how to pipe an R script into Hadoop.
Method 2: Rhipe Package
The Rhipe package enables users to write MapReduce jobs directly in R. Before using Rhipe, R must be installed on every node of the Hadoop cluster, and each node must also have Protocol Buffers (see Apache documentation). Once the environment is prepared, R scripts can invoke MapReduce functions through Rhipe.
Method 3: RHadoop
RHadoop, an open‑source library from Revolution Analytics, offers similar functionality to Rhipe. It consists of several packages: plyrmr for data manipulation, rmr for MapReduce job submission, rdfs for HDFS access, and rhbase for HBase connectivity. The following example demonstrates how to use functions from the rmr package to run an R‑Hadoop job.
Summary of Methods
All three approaches allow R to operate on data stored in HDFS, granting R the ability to handle large‑scale datasets. Streaming APIs are the simplest to set up, while Rhipe and RHadoop require additional configuration but provide richer MapReduce capabilities.
Key Conclusions
Streaming APIs are the easiest to install and configure; they do not require a client.
Rhipe and RHadoop need R installed on each Hadoop node and additional libraries, but they let developers define and invoke MapReduce functions directly within R.
Beyond these three options, Apache Mahout, Apache Hive, the Segue framework, or commercial versions of Revolution Analytics R can also be used for large‑scale machine learning.
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