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"Using R and Hadoop for Statistical Computation at Scale"

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- 1. Model Building with RevoScaleR Using R and Hadoop for Statistical Computation Strata and Hadoop World 2013 Joseph Rickert, Revolution Analytics
- 2. Model Buliding with RevoScaleR Agenda: The three realms of data What is RevoScaleR? RevoScaleR working beside Hadoop RevoScaleR running within Hadoop Run some code 2
- 3. The 3 Realms of Data Bridging the gaps between architectures
- 4. The 3 Realms of Data Number of rows The realm of “chunking” >1012 1011 The realm of massive data Data in Data in a File 106 Data In Memory Multipl e Files Architectural complexity 4
- 5. RevoScaleR Revolution R Enterprise
- 6. RevoScaleR An R package ships exclusively with Revolution R Enterprise Revolution R Enterprise Implements Parallel External Memory Algorithms (PEMAs) Provides functions to: DeployR ConnectR – Import, Clean, Explore and Transform Data – Statistical Analysis and Predictive Analytics – Enable distributed computing RevoScaleR DistributedR Scales from small local data to huge distributed data The same code works on small and big data, and on workstation, server, cluster, Hadoop 6
- 7. Parallel External Memory Algorithms (PEMA’s) Built on a platform (DistributeR) that efficiently parallelizes a broad class of statistical, data mining and machine learning algorithms Process data a chunk at a time in parallel across cores and nodes: 1. 2. 3. 4. Initialize Process Chunk Aggregate Finalize Revolution R Enterprise DeployR ConnectR RevoScaleR DistributedR 7
- 8. RevoScaleR PEMAs Statistical Modeling Machine Learning Predictive Models Covariance, Correlation, Sum of Squares Multiple Linear Regression Generalized Linear Models: All exponential family distributions, Tweedie distribution. Standard link functions user defined distributions & link functions. Classification & Regression Trees Decision Forests Predictions/scoring for models Residuals for all models Data Visualization Histogram Line Plot Lorenz Curve ROC Curves Variable Selection Stepwise Regression PCA Cluster Analysis K-Means Classification Decision Trees Decision Forests Simulation Parallel random number generators for Monte Carlo 8
- 9. GLM comparison using in-memory data: glm() and ScaleR’s rxGlm() Revolution R Enterprise 9
- 10. PEMAs: Optimized for Performance Arbitrarily large number of rows in a fixed amount of memory Scales linearly with the number of rows with the number of nodes Scales well with the number of cores per node with the number of parameters Efficient Computational algorithms Memory management: minimize copying File format: fast access by row and column Heavy use of C++ Models pre-analyzed to detect and remove duplicate computations and points of failure (singularities) Handle categorical variables efficiently 10
- 11. Write Once. Deploy Anywhere. Hadoop Hortonworks Cloudera EDW IBM Teradata Clustered Systems Platform LSF Microsoft HPC Workstations & Servers Desktop Server Linux In the Cloud Microsoft Azure Burst Amazon AWS DeployR ConnectR RevoScaleR DistributedR DESIGNED FOR SCALE, PORTABILITY & PERFORMANCE 11
- 12. RRE in Hadoop or beside inside 12
- 13. Revolution R Enterprise Architecture Use Hadoop for data storage and data preparation Use RevoScaleR on a connected server for predictive modeling Use Hadoop for model deployment
- 14. A Simple Goal: Hadoop As An R Engine. Hadoop Run Revolution R Enterprise code In Hadoop without change Provide RevoScaleR Pre-Parallelized Algorithms Eliminate: The Need To “Think In MapReduce” Data Movement 14
- 15. Revolution R Enterprise HDFS Name Node Architecture MapReduce Data Node Use RevoScaleR inside Hadoop for: • Data preparation • Model building • Custom small-data parallel programming • Model deployment • Late 2013: Big-data predictive models with ScaleR Data Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker Task Tracker Task Tracker Job Tracker
- 16. RRE in Hadoop HDFS Name Node MapReduce Data Node Data Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker Task Tracker Task Tracker Job Tracker 16
- 17. RRE in Hadoop HDFS Name Node MapReduce Data Node Data Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker Task Tracker Task Tracker Job Tracker 17
- 18. RevoScaleR on Hadoop Each pass through the data is one MapReduce job Prediction (Scoring), Transformation, Simulation: – Map tasks store results in HDFS or return to client Statistics, Model Building, Visualization: – Map tasks produce “intermediate result objects” that are aggregated by a Reduce task – Master process decides if another pass through the data is required Data can be cached or stored in XDF binary format for increased speed, especially on iterative algorithms Revolution R Enterprise 18
- 19. Let’s run some code.
- 20. Backup slides
- 21. Sample code: logit on workstation # Specify local data source airData <- myLocalDataSource # Specify model formula and parameters rxLogit( ArrDelay>15 ~ Origin + Year + Month + DayOfWeek + UniqueCarrier + F(CRSDepTime), data=airData ) 21
- 22. Sample code for logit on Hadoop # Change the “compute context” rxSetComputeContext(myHadoopCluster) # Change the data source if necessary airData <- myHadoopDataSource # Otherwise, the code is the same rxLogit(ArrDelay>15 ~ Origin + Year + Month + DayOfWeek + UniqueCarrier + F(CRSDepTime), data=airData) 22
- 23. Demo rxLinMod in Hadoop - Launching Revolution R Enterprise 23
- 24. Demo rxLinMod in Hadoop - In Progress Revolution R Enterprise 24
- 25. Demo rxLinMod in Hadoop - Completed Revolution R Enterprise 25

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