Mario Inchiosa

Chief Scientist, Revolution Analytics

In-Database Analytics Deep Dive with

Teradata and Revolution R

Tim Miller Partner Integration Lab, Teradata

• Introduction

• Revolution R Enterprise

• Case Study – Global Internet Marketplace

• Under the Hood

• Summary & Questions

Agenda

• What data storage/management software do you use?

> Hadoop

> Teradata

> LSF Clusters/Grids

> Servers

Please choose all that apply

Poll Question #1

• Most powerful statistical programming language

– Flexible, extensible and comprehensive for productivity

• Most widely used data analysis software

– Used by 2M+ data scientists, statisticians and analysts

• Create beautiful and unique data visualizations

– As seen in New York Times, Twitter and Flowing Data

• Thriving open-source community

– Leading edge of analytics research

• Fills the talent gap

– New graduates prefer R

What is R?

R is Hot bit.ly/r-is-

hot

WHITE PAPER

Exploding growth and demand for R

• R is the highest paid IT skill

> Dice.com, Jan 2014

• R most-used data science language after SQL

> O’Reilly, Jan 2014

• R is used by 70% of data miners

> Rexer, Sep 2013

• R is #15 of all programming languages

> RedMonk, Jan 2014

• R growing faster than any other data science language

> KDnuggets, Aug 2013

• More than 2 million users worldwide

R Usage Growth Rexer Data Miner Survey, 2007-2013

70% of data miners report using R

R is the first choice of more

data miners than any other

software

Source: www.rexeranalytics.com

Debt<10% of Income Debt=0%

Good

Credit

Risks

Bad

Credit

Risks

Good

Credit

Risks

Yes

YesYes

NO

NONO

Income>$40K

SQL Request

Sample Data

Debt<10% of Income Debt=0%

Good

Credit

Risks

Bad

Credit

Risks

Good

Credit

Risks

Yes

YesYes

NO

NONO

Income>$40K

Results

Desktop and Server Analytic Architecture

In-Database Analytic Architecture

Results

Server Based vs. In-Database Architectures

Why Is Teradata Different?

Exponential Performance Improvement

Analyst

Node level calculation: 1 2 7 9 = 4.5

• R is distributed across nodes or servers

• Runs independently of the other nodes/servers

> Great for row independent processing such as Model Scoring

> However, for analytic functions requiring all the data such as Model Building…

– Onus is on the R programmer to understand data parallelism

Challenges Running R in Parallel

1 1 1 1 2 9 1 7 9 3 9 9

System level calculation: 1 1 1 1 1 2 3 7 9 9 9 9 = 2.5

Example: Median (Midpoint)

Node Level

1. Find median per node

2. Consolidate and find the midpoint of the results

3. Produce the wrong answer

System Level

1. Sort all the data

2. Take midpoint

3. Produce the right answer

< Wrong

< Right

R Operations on Data

R operates on independent rows > Score models for a given observation

> Parsing Text field

> Log(x)

R operates on independent partitions

> Fit a model to a partition such as region, time, product or store

R operates on the entire data set > Global sales average

> Regression on all customers

R Client

R Client

R Client

• What statistical programming tools do you use?

> R/RRE

> SAS

> SPSS

> Statistica

> KXEN

Please choose all that apply

Poll Question #2

Who is Revolution Analytics?

Revolution Analytics

OUR COMPANY

The leading provider

of advanced

analytics software

and services

based on open source

R, since 2007

OUR SOFTWARE

The only Big Data, Big

Analytics software

platform based on the

data science language R

SOME KUDOS

Visionary

Gartner Magic Quadrant

for Advanced Analytics

Platforms, 2014

Finance Insurance

Healthcare & Pharma Digital Economy Analytics Service Providers

Manufacturing & High Tech

Revolution R Enterprise

is…. the only big data big analytics platform

based on open source R, the de facto statistical computing language for modern analytics

• High Performance, Scalable Analytics

• Portable Across Enterprise Platforms

• Easier to Build & Deploy Analytics

Big Data In-memory bound Hybrid memory & disk scalability

Operates on bigger volumes & factors

Speed of Analysis

Single threaded Parallel threading Shrinks analysis time

Enterprise Readiness

Community support Commercial support Delivers full service production support

Analytic Breadth & Depth

5000+ innovative analytic packages

Leverage open source packages plus Big Data ready packages

Supercharges R

Commercial Viability

Risk of deployment of open source

Commercial license Eliminate risk with open source

It Has Some Limitations for Enterprises R: Open Source that Drives Innovation, but…

The Big Data Big Analytics Platform

Introducing Revolution R Enterprise (RRE)

DistributedR

DevelopR DeployR

ScaleR

ConnectR

• Big Data Big Analytics Ready

> Enterprise readiness

> High performance analytics

> Multi-platform architecture

> Data source integration

> Development tools

> Deployment tools

The Platform Step by Step: R Capabilities

R+CRAN • Open source R interpreter

• UPDATED R 3.1.1

• Freely-available R algorithms

• Algorithms callable by RevoR

• Embeddable in R scripts

• 100% Compatible with existing R scripts, functions and packages

RevoR • Based on open source R

• Adds high-performance math

Available On: • Teradata Database

• Hortonworks Hadoop

• Cloudera Hadoop

• MapR Hadoop

• IBM Platform LSF Linux

• Microsoft HPC Clusters

• Windows & Linux Servers

• Windows & Linux Workstations

DeployR • Web services software

development kit for integration analytics via Java, JavaScript or .NET APIs

• Integrates R Into application infrastructures

Capabilities:

• Invokes R Scripts from web services calls

• RESTful interface for easy integration

• Works with web & mobile apps, leading BI & Visualization tools and business rules engines

DevelopR • Integrated development

environment for R

• Visual ‘step-into’ debugger

• Based on Visual Studio Isolated Shell

Available on:

• Windows

DevelopR DeployR

The Platform Step by Step: Tools & Deployment

DevelopR - Integrated Development Environment Script with type ahead

and code snippets Solutions window for organizing code and

data

Packages installed and

loaded

Objects loaded in the R

Environment

Object details

Sophisticated debugging with breakpoints , variable values etc.

DeployR - Integration with 3rd Party Software

• Seamless

– Bring the power of R to any web enabled application

• Simple

– Leverage common APIs including JS, Java, .NET

• Scalable

– Robustly scale user and compute workloads

• Secure

– Manage enterprise security with LDAP & SSO

Data Analysis

Business Intelligence

Mobile Web Apps

Cloud / SaaS

R / Statistical

Modeling Expert

DeployR Deployment

Expert

The Platform Step by Step: Parallelization & Data Sourcing

ConnectR • High-speed & direct connectors

Available for: • High-performance XDF

• SAS, SPSS, delimited & fixed format text data files

• Hadoop HDFS (text & XDF)

• Teradata Database

• ODBC

ScaleR • Ready-to-Use high-performance

big data big analytics

• Fully-parallelized analytics

• Data prep & data distillation

• Descriptive statistics & statistical tests

• Correlation & covariance matrices

• Predictive Models – linear, logistic, GLM

• Machine learning

• Monte Carlo simulation

• Tools for distributing customized algorithms across nodes

DistributedR • Distributed computing framework

• Delivers portability across platforms

Available on:

• Teradata Database

• Hortonworks / Cloudera / MapR

• Windows Servers / HPC Clusters

• IBM Platform LSF Linux Clusters

• Red Hat Linux Servers

• SuSE Linux Servers

Revolution R Enterprise ScaleR: High Performance Big Data Analytics

Data Prep, Distillation & Descriptive Analytics

R Data Step Descriptive Statistics

Statistical Tests

Sampling

• Data import – Delimited, Fixed, SAS, SPSS, ODBC

• Variable creation & transformation using any R functions and packages

• Recode variables • Factor variables • Missing value handling • Sort • Merge • Split • Aggregate by category

(means, sums)

• Min / Max • Mean • Median (approx.) • Quantiles (approx.) • Standard Deviation • Variance • Correlation • Covariance • Sum of Squares (cross

product matrix) • Pairwise Cross tabs • Risk Ratio & Odds Ratio • Cross-Tabulation of Data • Marginal Summaries of

Cross Tabulations

• Chi Square Test • Kendall Rank Correlation • Fisher’s Exact Test • Student’s t-Test

• Subsample (observations & variables)

• Random Sampling

Revolution R Enterprise ScaleR (continued)

Predictive Models

• Covariance/Correlation/Sum of Squares/Cross-product Matrix

• Multiple Linear Regression • Logistic Regression • Generalized Linear Models

(GLM) - All exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions including: cauchit, identity, log, logit, probit. - User defined distributions & link functions.

• Classification & Regression Trees and Forests

• Gradient Boosted Trees • Residuals for all models

• Histogram • ROC Curves (actual

data and predicted values)

• Lorenz Curve • Line and Scatter Plots • Tree Visualization

Data Visualization

Variable Selection

• Stepwise Regression • Linear • Logistic • GLM

• Monte Carlo • Run open source R

functions and packages across cores and nodes

Cluster Analysis

• K-Means

Classification & Regression

• Decision Trees • Decision Forests • Gradient Boosted Trees

• Prediction (scoring) • PMML Export

Simulation and HPC

Deployment

Statistical Modeling Machine Learning

DistributedR

ScaleR

ConnectR

DeployR

Write Once…Deploy Anywhere.

DESIGNED FOR SCALE, PORTABILITY & PERFORMANCE

In the Cloud Amazon AWS

Workstations & Servers Windows Linux

Clustered Systems IBM Platform LSF Microsoft HPC

Hadoop Hortonworks, Cloudera, MapR

EDW Teradata Database

• Challenge: Model and score 250M customers

• Server-based workflow was taking 3 days

• Move calculation in-database to drastically reduce runtime, process twice as many customers, and increase lift

Case Study - Global Internet Marketplace

• Binomial Logistic Regression

> 50+ Independent variables including categorical with indicator variables

> Train from small sample (many thousands) – not a problem in and of itself

> Scoring across entire corpus (many hundred millions) – slightly more challenging

Existing Open Source R model

• Same Binomial Logistic Regression

> 50+ Independent variables including categorical with indicator variables

> Train from large sample (many millions) – more accurately captures user patterns and increases lift

> Scoring across entire corpus (many hundred millions) – completes in minutes

Revolution R Enterprise model

By moving the compute to the data

RRE Used to Optimized the Current Process

Before After

Reduced 3 day process to 10 minutes

Scaling study: Time vs. Number of Rows

Benchmarking the Optimized Process

rows

tim

e

NOTE: • Teradata Environment

> 4 node, 1700 Appliance • RRE Environment

> version 7.2, > R 3.0.2

Server-based (Not In-DB) In-DB

• Before

trainit <- glm(as.formula(specs[[i]]), data = training.data, family='binomial', maxit=iters)

fits <- predict(trainit, newdata=test.data, type='response')

• After

trainit <- rxGlm(as.formula(specs[[i]]), data = training.data, family='binomial', maxIterations=iters)

fits <- rxPredict(trainit, newdata=test.data, type='response')

Recode Open Source R to Revolution R Enterprise

Optimization process

Revolution R Enterprise

How RRE Scale R Actually Works

Open

Source R

Revolution R

Enterprise

Computation (4-core laptop) Open Source R Revolution R Speedup

Linear Algebra1

Matrix Multiply 176 sec 9.3 sec 18x

Cholesky Factorization 25.5 sec 1.3 sec 19x

Linear Discriminant Analysis 189 sec 74 sec 3x

General R Benchmarks2

R Benchmarks (Matrix Functions) 22 sec 3.5 sec 5x

R Benchmarks (Program Control) 5.6 sec 5.4 sec Not appreciable

1. http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php

2. http://r.research.att.com/benchmarks/

Customers report 3-50x performance improvements

compared to Open Source R — without changing any code

RevoR - Performance Enhanced R Revolution R Enterprise:

Across Cores and Nodes

Scalable and Parallelized

• Anatomy of a PEMA: 1) Initialize, 2) Process Chunk, 3) Aggregate, 4) Finalize

• Process a chunk of data at a time, giving linear scalability

• Process an unlimited number of rows of data in a fixed amount of RAM

• Independent of the “compute context” (number of cores, computers, distributed computing platform), giving portability across these dimensions

• Independent of where the data is coming from, giving portability with respect to data sources

“Parallel External Memory Algorithms”

Scalability and Portability of PEMAs

• Efficient computational algorithms

• Efficient memory management – minimize data copying and data conversion

• Heavy use of C++ templates; optimal code

• Efficient data file format; fast access by row and column

• Models are pre-analyzed to detect and remove duplicate computations and points of failure (singularities)

• Handle categorical variables efficiently

ScaleR Performance

Speed and Scalability Comparison

• Unique PEMAs: Parallel, external-memory algorithms

• High-performance, scalable replacements for R/SAS analytic functions

• Parallel/distributed processing eliminates CPU bottleneck

• Data streaming eliminates memory size limitations

• Works with in-memory and disk-based architectures

In-Database Billion Row Logistic Regression

• 114 seconds on Teradata 2650 (6 nodes, 72 cores), including time to read data

• Scales linearly with number of rows

• Scales linearly with number of nodes: 3x faster than on 2 node Teradata system

Allstate compares SAS, Hadoop, and R for Big-Data Insurance Models

Approach Platform Time to fit

SAS 16-core Sun Server 5 hours

rmr/MapReduce 10-node 80-core Hadoop Cluster

> 10 hours

R 250 GB Server Impossible (> 3 days)

Revolution R Enterprise In-Teradata on 6-node 2650

3.3 minutes

Generalized linear model, 150 million observations, 70 degrees of freedom

http://blog.revolutionanalytics.com/2012/10/allstate-big-data-glm.html

• At what stage are you in your in-database analytics deployment project?

> Still researching tools and methods

> Evaluating/Selecting data storage/management platform

> Evaluating/Selecting analytics programming tools

> Launched the project/working on it now

> We’re done and looking for another one!

Please select one answer

Poll Question #3

• Revolution R Enterprise has a new “data source”, RxTeradata (ODBC and TPT)

# Change the data source if necessary

tdConn <- "DRIVER=…; IP=…; DATABASE=…; UID=…; PWD=…“

teradataDS <- RxTeradata(table=“…", connectionString=tdConn, …)

• Revolution R Enterprise has a new “compute context”, RxInTeradata

# Change the “compute context”

tdCompute <- rxInTeradata(connectionString=..., shareDir=..., remoteShareDir=...,

revoPath=..., wait=.., consoleOutput=...)

• Sample code for R Logistic Regression

# Specify model formula and parameters

rxLogit(ArrDelay>15 ~ Origin + Year + Month + DayOfWeek + UniqueCarrier

+ F(CRSDepTime), data=teradataDS)

RRE End-User’s Perspective

• Table User Defined Functions (UDFs) allow users to place a function in the FROM clause of a SELECT statement

• Table Operators extend the existing table UDF capability:

> Table Operators are Object Oriented – Inputs and outputs can be arbitrary and not “fixed” as Table UDF’s require

> Table Operators have a simpler row iterator interface – Interface simply produces output rows providing a more natural application development

interface than Table UDF’s

> Table operators operate on a stream of rows. – Rows are buffered for high-performance, eliminating row at a time processing

> Table operators support PARTITON BY and ORDER BY

– Allows the development of Map Reduce style operators in-database

Table Operators – Teradata 14.10+

RRE Architecture in Teradata 14.10+

Worker Process

Message Passing Layer

Master Process …

Request Response Teradata 14.10+

Data Partition

Data Partition

Data Partition

Data Partition

Master Process

Worker Process

Worker Process

Worker Process …

* All communication is done by binary BLOB’s

PE Layer

AMP Layer

1. RRE commands are sent to a “Master Process” - an External Stored Procedure (XSP) in the Parsing Engine that provides parallel coordination

2. RRE analytics are split into “Worker Process” tasks that run in a Table Operator (TO) on every AMP.

a. HPA analytics iterate over the data, and intermediate results are analyzed and managed by the XSP.

b. HPC analytics do not iterate, and final results from each AMP are returned to the XSP

3. Final combined results are assembled by the XSP and returned to the user

tdConnect <- rxTeradata(<data, connection string, …>)

tdCompute <- rxInTeradata(<data, server arguments, …>)

** PUT-based Installer

• High-performance, scalable, portable, fully-featured algorithms

• Integration with R ecosystem

• Compatibility with Big Data ecosystem

Summary

PARTNERS Mobile App

InfoHub Kiosks

teradata-partners.com

WE LOVE FEEDBACK

Questions

Rate this Session

Questions?

Resources for you (available on RevolutionAnalytics.com):

• White Paper: Teradata and Revolution Analytics: For the Big Data Era, An Analytics Revolution

• Webinar: Big Data Analytics with Teradata and Revolution Analytics

PARTNERS Mobile App

InfoHub Kiosks

teradata-partners.com

WE LOVE FEEDBACK

Questions

Rate this Session

Thank You!

www.RevolutionAnalytics.com www.Teradata.com