Slides from Joseph Rickert's presentation at Strata NYC 2013 "Using R and Hadoop for Statistical Computation at Scale" http://strataconf.com/stratany2013/public/schedule/detail/30632

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
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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