RHadoop
A collection of R packages for interacting with Hadoop ecosystems, enabling big data analysis from R.
What is RHadoop?
RHadoop is a collection of R packages that provide interfaces to Hadoop ecosystem components, enabling R users to perform distributed big data analysis. It solves the problem of analyzing large datasets that exceed single-machine memory limits by allowing R code to run on Hadoop clusters. The project bridges statistical computing with enterprise-scale data processing infrastructure.
Data scientists, statisticians, and analysts who use R for statistical computing and need to work with Hadoop-based big data platforms. Researchers and organizations with large datasets who want to leverage R's statistical capabilities on distributed systems.
RHadoop provides native R interfaces to Hadoop components without requiring users to learn Java or other Hadoop-native languages. It maintains R's expressive statistical syntax while enabling scalable distributed computing, making big data analysis accessible to the R community.
Overview
RHadoop
Use Cases
Best For
- Performing statistical analysis on datasets too large for single-machine R
- Running R-based MapReduce jobs on Hadoop clusters
- Accessing and manipulating HDFS files directly from R scripts
- Interacting with HBase NoSQL databases using R data frames
- Processing Avro-formatted data within R workflows
- Building data pipelines that combine R's statistical libraries with Hadoop's scalability
Not Ideal For
- Real-time or stream processing applications, as RHadoop is built on Hadoop's batch-oriented MapReduce framework.
- Projects in cloud environments using object storage like S3 without HDFS, since rhdfs is tightly coupled to Hadoop Distributed File System.
- Teams prioritizing modern data processing frameworks like Apache Spark, which offer more active development and better R integration via SparkR.
- New implementations requiring ongoing support and updates, given the project's archival status and fragmentation into separate repos.
Pros & Cons
Pros
Provides direct access to Hadoop components like HDFS and HBase from R, eliminating the need for Java coding, as evidenced by packages like rhdfs and rhbase in the modular suite.
Follows a modular approach with separate packages for different Hadoop technologies, allowing users to pick components like rmr2 or plyrmr based on specific workflow needs, as outlined in the README.
Enables running R's advanced statistical analyses on distributed datasets via rmr2, bridging the gap between statistical methods and scalable processing for data scientists.
plyrmr offers a higher-level interface with dplyr-like syntax, making distributed data manipulation more intuitive for R users, as highlighted in the key features.
Cons
The main repository is read-only with packages moved to separate repos, as stated in the README, indicating reduced maintenance and potential compatibility issues for users.
Requires a functioning Hadoop cluster and proper configuration, which can be challenging for teams without existing infrastructure, adding overhead to initial deployment.
Using R on Hadoop may introduce performance penalties compared to native Java implementations, especially for large-scale data processing with MapReduce via rmr2.
As Hadoop's popularity wanes in favor of Spark and other frameworks, RHadoop's ecosystem might lack updates and community support, making it less viable for cutting-edge projects.
Frequently Asked Questions
Related Projects
Oryx 2Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
Spark Machine LearningSpark’s ML library consisting of common learning algorithms and utilities
Apache MahoutApache Hivemall (incubating)Apache Hivemall is a scalable machine learning library that runs on Apache Hive, Spark and Pig
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.