1. Exploring R Services and Advanced Analytics
SQL Saturday #473 – Cleveland
Exploring R Services and Advanced Analytics
in SQL Server 2016
David Eldersveld
Solution Consultant, BlueGranite
@dr_eldersveld

2. Pop Quiz Besides R Services, name another improvement
to analytics capabilities in SQL Server 2016
Real-time operational analytics
Significant columnstore index improvements
Does not replace need for EDW or SSAS in most cases
Feature summary
DavidEldersveld

3. How to deploy and use R in SQL Server
Expectations
Today’s Key Takeaway
How to deploy and use R in SQL Server
Session targeted to SQL users who have never used R
Black box approach to R itself
Main focus is not on R development or learning language
DavidEldersveld

4. Agenda Setting the stage Microsoft R investment R Services
Advanced analytics, data science
What is R?
Microsoft R investment
R Services
Dev & Ops workflows
Operationalized R
R in Reporting Services and Power BI
Demo: Overtime Prediction
Images courtesy of R Project and Microsoft

5. Advanced Analytics Moving beyond traditional business intelligence
Modeling future results based on existing data
“Not Advanced”
“Advanced”
Descriptive
Predictive
Prescriptive
Analysis
What happened
What may happen
Recommendation due to prediction
Use cases
Not that →
Forecasting, classification, sentiment, anomaly detection, pattern recognition, text analysis, image classification & more
Execution
Batch
Batch, streaming or
combo (lambda architecture)
Setting the stage
DavidEldersveld

6. Data Science Strong foundation in basic statistics
Incorporates statistical or machine learning models
Large data stores, ecosystems like Hadoop
Language such as R for reproducible analysis
Summarizes, visualizes, and presents to different audiences
Setting the stage
DavidEldersveld

7. What is R? Open source language & environment
Built for statistics and data science workloads
Popular in academia, with influence expanding
Traditional performance and scalability issues
Setting the stage
Image courtesy of R Project

8. Traditional R Issues Built by statisticians, for statisticians
Lack of governance
Memory limitations
No built-in parallel execution
Setting the stage
Image courtesy of R Project

9. R Seemingly Everywhere
On-premises/Hybrid
SQL Server 2016 R Services
Microsoft R Open
Microsoft R Server
Power BI
R Tools for Visual Studio
Cloud / Cortana Analytics
Azure Machine Learning
HDInsight
Microsoft R investment
Images courtesy of Microsoft

10. Why R, Why Now?
In General
Appropriate timing due to increased corporate R adoption
Many businesses now require advanced analysis
SQL Server R Services (SSRS*) Specifically
Investment in popular open source language
Existing skills, redeploy existing models
On-premises answer to R capabilities in Azure
*Unfortunately, SSRS acronym already taken
Microsoft R investment

11. Revolution Analytics Acquired by Microsoft in 2015
“Enterprise-ready” R
Overcomes many traditional R issues
R capabilities extended
Microsoft R investment
Images courtesy of Microsoft

12. Re-Branded Revolution Products
Microsoft R Open (MRO)
Formerly Revolution R Open (RRO)
Enhanced version of open source R
Microsoft R Server (MRS)
Formerly Revolution R Enterprise (RRE)
Performance enhancements
Operationalized analytics at scale
Portability
Microsoft R investment
Image courtesy of Microsoft

13. Re-Branded Revolution Products
Microsoft R investment
Image courtesy of Microsoft

14. R Server Components ConnectR ScaleR DistributeR
Data source integration
Storage: SQL Server, Hadoop, HBase & more
ScaleR
Numerous functions for increased performance over base R
“rx” function prefixes (i.e. rxLinMod, rxLogit)
PEMAs (parallel external memory algorithms)
Transformation, statistics, modeling, machine learning & more
DistributeR
Compute context integration
Compute: SQL Server in-database, Hadoop, local workstation & more
Microsoft R investment
DavidEldersveld

15. SQL Server R Services Microsoft R Server + SQL Server = R Services
Arguably two very distinct audiences
Development <- data scientists / analysts
Operationalization <- DBAs / analysts / business
DEV
OPS
R Services
NOTE: Any resemblance, either real or imagined, to a practice called DevOps is strictly unintentional

16. R Services – Development Scenario
Typically advanced analytics practitioner
Use existing R IDE on local workstation
Connect to SQL Server
Change compute context
Offload computation to SQL Server
Bring results back to local environment
Deploy in SQL Server or handoff
R Services
DavidEldersveld

17. R Services – Operational Scenario
Typically SQL professional
Use existing SQL tools
Deployment
Embed R code or model in stored proc
Store artifacts in database tables
Consumption
SQL Server
Reporting Services
Power BI
R Services
DavidEldersveld

18. For the DBA New software that competes for server resources
Setup and configuration now includes:
Advanced Analytics Extensions
Trusted Launchpad service
Microsoft R Open
Microsoft R Server (node version)
Several post-installation tasks
Admin
Session isolation for R
Resource governor for R
Securable by user, role and database
R Services configuration file
R Services

19. For the DBA R Services config file in depth Adjustable options
<SQL instance folder>\binn\rlauncher.config
Adjustable options
MEMORY_LIMIT_PERCENT – 20% default
TRACE_LEVEL – Error (1) default
USER_POOL_SIZE – 10 user default
Defaults recommended
JOB_CLEANUP_ON_EXIT
LOG_DIRECTORY
MPI HOME
RHOME
WORKING_DIRECTORY
R Services

20. For Everyone Else New software and capabilities… yeah!
Push analysis to SQL Server
Embed R in stored procedures
Store models and visual output
R Services

21. Operationalized R New stored procedure
sp_execute_external_script
SQL input --> R black box --> SQL output
SQL Server Comfort Zone
R Services Twilight Zone
SQL Server Comfort Zone
SQL recordset
Transform
and/or
Model
Plot
Magic
R data frame
output
SELECT
FROM
WHERE
SQL query
R data frame input
Plots / Models varbinary(max)
R Services
DavidEldersveld

22. Operationalized R
EXEC = = N’[R code goes = N’[SQL input]’ [ = N‘InputDataSet’ ] [ = N’OutputDataSet’ ] [ = N’parameter’ ] WITH RESULT SETS (([SQL output]));
input_data_1_name and output_data_1_name are optional and default to InputDataSet and OutputDataSet respectively
R Services
DavidEldersveld

23. If output is a model or plot, specify varbinary(max) in
Operationalized R
EXEC = = N’[R code goes = N’[SQL input]’ [ = N‘InputDataSet’ ] [ = N’OutputDataSet’ ] [ = N’parameter’ ] WITH RESULT SETS (([SQL output]));
If output is a model or plot, specify varbinary(max) in
WITH RESULT SETS
R Services
DavidEldersveld

24. Three Main Types of Output
Dataset
Standard resultset of rows and columns
Data types will vary
Plot
Static images
Binary
Model
Trained models such as linear regression, naïve bayes, etc.
R Services
DavidEldersveld

25. Output a Dataset
R Services
DavidEldersveld

26. Output a Plot
R Services
DavidEldersveld

27. Output a Model
R Services
DavidEldersveld

28. Consumption - Reporting Services
If R output is dataset
Retrieve stored results from table
Execute stored proc directly
If R output is image
Retrieve stored image from table
R in Reporting Services and Power BI

29. Consumption - Power BI Native R support Additional R support
R script as data source in PBI Desktop
R visual executes R code and displays plot
R coming in some form to PBI Service
Additional R support
“Pin” Reporting Services R charts to PBI Service
R in Reporting Services and Power BI
Image courtesy of Microsoft

30. Demo: Overtime Prediction
Public dataset with city payroll from 2011 to 2014
Can we predict future overtime expenses?
Explore basic R
Build plots & view in SSRS
Build simple regression model
Deploy the model
Demo
DavidEldersveld

31. Evaluations Session Event
@dr_eldersveld