microsoft r server for distributed computing
Transcript of microsoft r server for distributed computing
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IntroducingMicrosoft R Server &Microsoft R Open
Krit KamtuoTechnical Evangelist
Microsoft (Thailand) Limited
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What is R?
Language
Platform
Community
Ecosystem
• A programming language for statistics, analytics, and data science
• A data visualization framework
• Provided as Open Source
• Used by 2.5M+ data scientists, statisticians and analysts
• Taught in most university statistics programs
• Active and thriving user groups across the world
• CRAN: 7000+ freely available algorithms, test data and evaluation
• Many of these are applicable to big data if scaled
• New and recent graduates prefer it
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20152009200420032000199719951993
Research Project in
New Zealand
Open Source Project
R-Core Group
R-1.0.0 released
R Foundation
First user
New York Times article
R-3.2.0 and R Consortium (founded by Microsoft)
History of R
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$?
Challenges posed by open source R
Uncertain
total cost of ownership
Inadequate
access to important
business data
Limited
business agility
Limited
business value
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R from Microsoft brings
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• Free and open source R distribution
• Enhanced and distributed by Revolution Analytics
Microsoft R Open
• Built in Advanced Analytics and Stand Alone Server
Capability
• Leverages the Benefits of SQL 2016 Enterprise Edition
SQL Server R Services
Microsoft R Products
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Microsoft R Server
• Microsoft R Server for Redhat Linux
• Microsoft R Server for SUSE Linux
• Microsoft R Server for Teradata DB
• Microsoft R Server for Hadoop on Redhat
Microsoft R Server
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Introducing SQL Server 2016 R Services
Enterprise speed and performance
Near-DB analytics
Parallel threading and processing
Model on-premises, store in cloud—or vice versa
Hybrid memory and disk scalability
Not bound by memory-enabling limits of larger
datasets
Included in SQL Server 2016
Reuse and optimize existing R code
Eliminate data movement across machines
Write once, deploy anywhere
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Microsoft R server for distributed computing
The First NIDA Business Analytics and Data Sciences Contest/Conference
วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
-แนะนํา Microsoft R Server
-Distributed Computing มีวิธีการอย่างไร และมีประโยชน์อย่างไร
-แนะนําวิธีการ Configuration สําหรับ Distributed Computing
https://businessanalyticsnida.wordpress.com
https://www.facebook.com/BusinessAnalyticsNIDA/
กฤษฏิ์ คําตื้อ,
Technical Evangelist,
Microsoft (Thailand)
-Distributed computing กับ Big Data
-Analytics บน R server
-สาธิตและสอนในลักษณะ workshop
Computer Lab 2 ชั้น 10 อาคารสยามบรมราชกุมารี
1 กันยายน 2559 เวลา 9.00-12.30
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Scalable in-database analytics
Data Scientist
Interacts directly with data
Creates models
and experiments
Data Analyst/DBA
Manages data and
analytics together
Example Solutions• Fraud detection
• Sales forecasting
• Warehouse efficiency
• Predictive
maintenance
010010100100
010101
Relational Data
Extensibility
?R
R Integration
Analytic LibraryOpen Source RRevolution PEMA
T-SQL Interface
How is it Integrated?• T-SQL calls a Stored Procedure
• Script is run in SQL through
extensibility model
• Result sets sent through Web API
to database or applications
Benefits• Faster deployment of ML models
• Less data movement, faster
insights
• Work with large datasets: mitigate
R memory and scalability
limitations
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Cost effectiveness
• Best Advanced Analytics Value
• R Services and Polybase are built-ino Part of SQL Server 2016 Enterprise Edition
• In DB analytics shrinks analysis cost and timeo No data movement reduces costs
• No Proprietary Hardware Requirement
o Can be installed in commodity hardware
• Integration between cloud and open source offerings
SQL SERVER 2016$ 648 K
+ $120 Per user for PowerBI
Costs based on a Server with2 proc/ 8 Cores
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High-performance open source R plus:• Data source connectivity to big-data objects
• Big-data advanced analytics
• Multi-platform environment support
• In-Hadoop and in-Teradata predictive modeling
• Development and production environment support
• IDE for data scientist developers
• Secure, Scalable R Deployment
DeployR
R Open R Server
DevelopR
Microsoft R Server is a broadly deployable enterprise-class analytics platform based on R that is
supported, scalable and secure. Supporting a variety of big data statistics, predictive modeling and
machine learning capabilities, R Server supports the full range of analytics – exploration, analysis,
visualization and modeling
Introducing Microsoft R Server
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R Open Microsoft R Server
DeployRDevelopR
The Microsoft R Server Platform
ConnectR• High-speed & direct
connectors
Available for:• High-performance XDF
• SAS, SPSS, delimited & fixed format text data files
• Hadoop HDFS (text & XDF)
• Teradata Database & Aster
• EDWs and ADWs
• ODBCScaleR• Ready-to-Use high-performance
big data big analytics
• Fully-parallelized analytics
• Data prep & data distillation
• Descriptive statistics & statistical tests
• Range of predictive functions
• User tools for distributing customized R algorithms across nodes
• Wide data sets supported – thousands of variables
DistributedR• Distributed computing framework
• Delivers cross-platform portability
R+CRAN• Open source R interpreter
• R 3.1.2
• Freely-available huge range of R algorithms
• Algorithms callable by RevoR
• Embeddable in R scripts
• 100% Compatible with existing R scripts, functions and packages
RevoR• Performance enhanced R
interpreter
• Based on open source R
• Adds high-performance math library to speed up linear algebra functions
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ScaleR – Parallel + “Big Data”
Stream data in to RAM in blocks. “Big Data” can be any data
size. We handle Megabytes to Gigabytes to Terabytes…
Our ScaleR algorithms work
inside multiple cores / nodes
in parallel at high speed
Interim results are collected
and combined analytically to
produce the output on the
entire data set
XDF file format is optimised to work with the ScaleR library and
significantly speeds up iterative algorithm processing.
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SQL Server 2016 Enterprise Edition
SQL Server R Services
Integration Facilities:
• Component Integration• Launchers• Parameter Passing• Results Return• Console Output
Return• Parallel Data Exchange
(RTM)• Stored Procedures• Package Administration
SQL Server
Query
Processor
Algorithm Library
• Data Prep
• Descriptive Stats
• Sampling
• Statistical Tests
• Predictive Models
• Variable Selection
• Clustering
• Classification
• Custom APIs for R + CRAN
• Parallel Scoring
Fast, Parallel, Storage Efficient Algorithms
Microsoft R Open
• 100% Open Source R• Fully CRAN Compatible• Accelerated Math
Open Source R Interpreter
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Run R In-Database from TSQL
SQL
Server
2016
In-Database
Execution of R
+ CRAN
+ SQL
In-Database Execution of:
R Code
CRAN Packages
Move the
Work to
the Data
Run R From
the Query
Processor
Retrieve
Models,
Scores,
Transformed
Data,
Plots/Images
Operationalise
scoring/predictio
n in database for
data batches or
real-time
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SQL
In-Database Execution:
Remote Execution
Parallelized Compute SQL
Server
Remote
Execution
Context
Explore and Model:
In Parallel, In-Database
Parallelize distributable R and CRAN
Operationlize:
Score In Parallel
ParallelWorker Tasks
Move
BIG
Work to
the DataLarge Data Sets in Chunks
Parallel Algorithm
Iterate/ Sequence
Run Parallel Algorithms in Database from an R client
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SQL 2016
ScaleR PEMAs: Fast, Parallel, Storage Efficient Algorithms
R Interpreter
Conceptual Flow
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SQL
Processor
Data Segments
(CTP3 is
via files)
R IDE
XSP
RTerm.exe
R.dll
(MSLP$
SQL16) BxlServer.exe
(MSLP$SQL16)
Input Data Set
via ODBC
ScaleR Master Process
Worker Process
Worker Process
Worker Process
Data Segments
Console OutSpawn WorkerProc’s.
Assemble Intermediate
Results
Iterate/ Sequence
MPI Ring
Results – Models, Data
Parallelized Algorithms in Database
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Introducing Microsoft R Server
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Gradient Boosted Decision Trees
Naïve Bayes
Scale R – Parallelized Algorithms & Functions
Data import – Delimited, Fixed, SAS, SPSS,
OBDC
Variable creation & transformation
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 for set
variables)
Pairwise Cross tabs
Risk Ratio & Odds Ratio
Cross-Tabulation of Data (standard tables & long
form)
Marginal Summaries of Cross Tabulations
Chi Square Test
Kendall Rank Correlation
Fisher’s Exact Test
Student’s t-Test
Subsample (observations & variables)
Random Sampling
Data Preparation Statistical Tests
Sampling
Descriptive Statistics
Sum of Squares (cross product matrix for set
variables)
Multiple Linear Regression
Generalized Linear Models (GLM) exponential
family distributions: binomial, Gaussian, inverse
Gaussian, Poisson, Tweedie. Standard link
functions: cauchit, identity, log, logit, probit. User
defined distributions & link functions.
Covariance & Correlation Matrices
Logistic Regression
Classification & Regression Trees
Predictions/scoring for models
Residuals for all models
Predictive Models K-Means
Decision Trees
Decision Forests
Cluster Analysis
Classification
Simulation
Variable Selection
Stepwise Regression
Simulation (e.g. Monte Carlo)
Parallel Random Number Generation
Combination
rxDataStep
rxExec
New
PEMA-R API Custom Algorithms
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ScaleR - Performance comparisonMicrosoft R Server has no data size limits in relation to size of available RAM. When open source R operates on data sets that exceed RAM it will fail. In contrast Microsoft R Server scales linearly well beyond RAM limits and parallel algorithms are much faster.
US flight data for 20 years
Linear Regression on Arrival Delay
Run on 4 core laptop, 16GB RAM and 500GB SSD
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DistributedR
ScaleR
ConnectR
DevelopR
Distributed R - Model development and model compute choice: “Write Once. Deploy Anywhere.”
Code Portability Across Platforms
In the Cloud
Workstations & Servers LinuxWindows
EDW Teradata
HadoopHortonworksClouderaMapR
+ HD Insights
+ Hadoop Spark
+ R Tools for Visual Studio
+ Azure ML
Ro
ad
map
Azure Marketplace
+ SQL Server v16
Microsoft R Server
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Distributed R - How Does Remote Execution Work?
Algorithm
Master
Big
Data
Predictive
Algorithm
Analyze
Blocks In
Parallel
Load Block
At A Time
Distribute Work,
Compile Results
The Results:
• Even Faster Computation
• Larger Data Set Capacity
• Fewer Security Concerns
• No Data Movement, No Copies
Work
“Pack and Ship” Requests
to Remote Environments
Results
Microsoft R Server functions
• A compute context defines remote connection• Microsoft R functions prefixed with rx
• Current compute context determines processing
location
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DistributedR - Revolution Code Portability
### SETUP HADOOP ENVIRONMENT VARIABLES ###
myHadoopCCC <- RxHadoopMR()
### HADOOP COMPUTE CONTEXT ###
rxSetComputeContext(myHadoopCC)
### CREATE HDFS, DIRECTORY AND FILE OBJECTS ###
hdfsFS <- RxHdfsFileSystem()
AirlineDataSet <-
RxXdfData(“AirlineDemoSmall/AirlineDemoSmall.xdf”)
, fileSystem = hdfsFS)
### ANALYTICAL PROCESSING ###
### Statistical Summary of the data
rxSummary(~ArrDelay+DayOfWeek, data= AirlineDataSet, reportProgress=1)
### CrossTab the data
rxCrossTabs(ArrDelay ~ DayOfWeek, data= AirlineDataSet, means=T)
### Linear Model and plot
hdfsXdfArrLateLinMod <- rxLinMod(ArrDelay ~ DayOfWeek + 0 , data = AirlineDataSet)
plot(hdfsXdfArrLateLinMod$coefficients)
### SETUP LOCAL ENVIRONMENT VARIABLES ###
myLocalCC <- “localpar”
### LOCAL COMPUTE CONTEXT ###
rxSetComputeContext(myLocalCC)
### CREATE LINUX, DIRECTORY AND FILE OBJECTS ###
linuxFS <- RxNativeFileSystem() )
AirlineDataSet <-
RxXdfData(“AirlineDemoSmall/AirlineDemoSmall.xdf”,
fileSystem = linuxFS)
Local Parallel processing – Linux or Windows In – Hadoop
ScaleR models can be deployed from a server or edge node to run in Hadoop
without any functional R model re-coding for map-reduce
Compute
context R script
– sets where the
model will run
Functional
model R script –
does not need
to change to run
in Hadoop
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DistributedR - In-Hadoop
Uses Hadoop nodes for R
computations
Eliminate data movement
latency on very large data
Remove data duplication
Faster model development
No MapReduce R coding
Develop better models
using all the data= Microsoft R Server
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MRS and Hadoop Architecture options
R R R R R
R R R R R
ScaleR Production
RStudio Server Pro
Microsoft R Server
1. Copy
2. Stream
3. Send
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DistributedR - Hadoop Processing Methods
Method 1: Local (Linux) parallel processing using all
cores on one node, copying data from HDFS to store
in local Linux file-system.
Compute Context
HadoopCompute Context
HadoopCompute Context
Local Parallel
Linux (Local) File-System
HDFS
Csv, Xdf
Processing
Data
1 Edge node 1:n data nodes
1:n disks 1:(n x number of nodes) disks
Csv, Xdf
Linux FS
Read / write
Method 1
(“Beside” or “Edge”)
Copy
to Local
File
Method 2: Local (Linux) parallel processing using all cores on one node, streaming data from / to HDFS
Compute Context
HadoopCompute Context
HadoopCompute Context
Local Parallel
Compute Context
Hadoop
Linux (Local) File-System
HDFS
Csv, Xdf
1:n nodes
1:n disks 1:(n x number of nodes) disks
1 Edge node
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Method 3
Method 3: Hadoop (Map-Reduce) parallel processing
using all cores on n nodes, using HDFS data on each
node
Compute Context
HadoopCompute Context
HadoopCompute Context
Local Parallel
Compute Context
Hadoop
Linux (Local) File-System
HDFS
Csv, Xdf
Processing
Data
1:n nodes
1:n disks 1:(n x number of nodes) disks
Csv, Xdf
HDFS
Read / write
(“inside”)
R script
sent to data
nodes
1 Edge node
R model script sent to Master Node:
1. Starts a master process
2. Distribute work
3. Master tasks for each node
4. Master initiates distributed work1.Hadoop schedules mapper for each split2.Algorithm computes intermediate result3.Reducer combines intermediate results
5. Master process evaluates
completion
6. Iterates as required by the
algorithm
7. Returns consolidated answer to
script
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DistributedR - What processing mode to use, when?
Analytic data set size and processing complexity (e.g. simple summary statistics vs iterative algorithm)
guide the use of Method 1 and 2 (Edge Node / Server Linux local processing) vs Method 3 (in-Hadoop
processing)
Low Medium High
Small Data
< 10GB
Medium Data
< 50GB
Bigger Data
> 50GB
Edge Node Linux
processingIn-Hadoop
processing
Local Linux
file-systemHadoop
file-system
LegendProcessing
Complexity
Data Size
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While Open Source R delivers:
• Capability• 6500+ Algorithm &
Connector Packages Available for Free in CRAN
• Simplicity• R Skills Transfer / Lower cost
of Talent• Ease of Integration with Other
Analytics Packages & Data• Access to Huge Libraries of R
Analytical Algorithms
• Speed• Intel-Optimized Computation
• Peace of mind• Knowledge that your business is using a stable platform backed with
commercial support and services
• Platform longevity for more predictability around costs
• Speed and scalability• Faster decisions using advanced analytics that were previously unachievable
• In-Hadoop & In Teradata Analysis
• Efficiency• Continue getting returns on existing hardware and software investments
• Developers can write code once and deploy it anywhere, keeping costs low
• Flexibility and agility• Model data in a hybrid environment: on-premises, in the cloud, or both
• Scripting, modeling, and in-database analytics across platforms shrinks analysis time and enables agile response to business needs
SQL Server R Services and Microsoft R Server deliver:
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Introducing Microsoft R Open• Enhanced Open Source R distribution
• Based on the latest Open Source R (3.1.2)
• Built, tested and distributed by Microsoft
• Enhanced by Intel MKL Library to speed up linear algebra functions
• Compatible with all R-related software• CRAN packages, RStudio, third-party R integrations, …
• Revolutions Open-Source R packages• Reproducible R Toolkit – Checkpoint , miniCRAN
• ParallelR – parallelise execution via ‘foreach’ loop
• Rhadoop – rhdfs, rhbase, ravro, rmr2, plyrmr
• AzureML – read/write data to AzureML, publish R code as ML API
• MRAN website mran.revolutionanalytics.com• Enhanced documentation and learning resources
• Discover 6500 free add-on R packages
• Open source (GPLv2 license) - 100% free to download, use and share
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DatasizeIn-memory
In-memory In-Memory or Disk Based
Speed of AnalysisSingle threaded Multi-threaded
Multi-threaded, parallel
processing 1:N servers
SupportCommunity Community Community + Commercial
Analytic Breadth
& Depth 7500+ innovative analytic
packages7500+ innovative analytic
packages
7500+ innovative packages +
commercial parallel high-speed functions
LicenceOpen Source
Open Source
Commercial license.
Supported release with indemnity
CRAN, MRO, MRS ComparisonMicrosoft
R Open
Microsoft
R Server
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More efficient and multi-threaded math computation.
Benefits math intensive processing.
No benefit to program logic and data transform
CRAN R compared to Microsoft R Open
• Matrix calculation – upto 27x faster
• Matrix functions – upto 16x faster
• Programation – 0x faster