© 2012 SAP AG. All rights reserved. 1
SAP In-Memory Computing
SAP HANA Deep Dive
Chris Bullock10/9/2012
© 2012 SAP AG. All rights reserved. 2
Disclaimer
This presentation outlines our general product direction and should not be
relied on in making a purchase decision. This presentation is not subject to
your license agreement or any other agreement with SAP.
SAP has no obligation to pursue any course of business outlined in this
presentation or to develop or release any functionality mentioned in this
presentation. This presentation and SAP's strategy and possible future
developments are subject to change and may be changed by SAP at any
time for any reason without notice.
This document is provided without a warranty of any kind, either express or
implied, including but not limited to, the implied warranties of
merchantability, fitness for a particular purpose, or non-infringement. SAP
assumes no responsibility for errors or omissions in this document, except
if such damages were caused by SAP intentionally or grossly negligent.
© 2012 SAP AG. All rights reserved. 3
Big John
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Willis Tower – 1,451 ft Big John – 1,127 ft
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Burj Khalifa – 2,723 ft
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� An in-memory data management platform
� “Real Real-time” performance for huge data volumes
� Current focus: business analytics
� Technology foundation for new in-memory enterprise applications
� Delivered as a vertically integrated appliance via SAP HW partners
� Fastest growing productin SAP’s 40-year history
What is SAP HANA?
Speed
Scale
FlexibilityFlexibility
SpeedSpeed
Scale
Flexibility
© 2012 SAP AG. All rights reserved. 7
Cloud
SAP’s Business Strategy
Business Suite (ERP)
Business Analytics (BO, BW)
mobile
Data platform (SAP HANA)
HANA is at the heart of
SAP’s vision and strategy
Credit for illustration idea: Maarten de Vries, SAP
# 1, worldwide market share, 2010
# 1, worldwide market share, 2011
Gartner magic quarter leader
Fastest growing SAP product ever
15m paying cloud
subscribers
© 2012 SAP AG. All rights reserved. 8
SAP HANA: Business Value Proposition
Make Decisions in Real-time
Access to real time analysis; fast and easy creation of ad-hoc business statistics
Accelerate Business Processes
Increase speed of information processes such as planning, forecasting, pricing, offers…
Unlock New Insights
Remove constraints for analyzing massive data volumes, trends, data mining, predictive analytics…
Structured and unstructured data
Improve IT Efficiency
Manage growing data volume and complexity withlower cost of ownership
Speed
Scale
Flexibility
© 2012 SAP AG. All rights reserved. 9
A Combined and Simplified Architecture
In-Memory
Row +
Column Database
Massively Parallel
Processing
Calculation Engine
Columnar storage increases the amount of data that can be stored in limited memory
(compared to disk)
Column databases enable easier parallelization of
queries
Row database fast transactional processing
In-memory processing gives more time for
relatively slow updates to column data
In-memory allows sophisticated calculations
in real-time
MPP optimized software enables linear performance scaling
making sophisticated calculations like allocations possible
Each technology works well on its own, but combining them all is the real opportunity — provides all of the upside benefits while mitigating the downsides
© 2012 SAP AG. All rights reserved. 10
Sportsmart Demo
Image: Renjith Krishnan / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 11
Technology Trends
Image: Renjith Krishnan / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 12
Moore’s Law: DRAM Pricing
Source: OBJECTIVE ANALYSIS http://blogs-images.forbes.com/jimhandy/files/2011/12/DRAM-GB-Price.jpg
1 TB ~ $10K
100x
1000x
10x
Pri
ce
pe
r G
Byte
Multi-Terabyte servers are now completely affordable!
© 2012 SAP AG. All rights reserved. 13
Moore‘s Law: CPUs
2002
1 core32 bits4MB
2007
2 cores2 CPUs per serverExternal Controllers
8 cores -16 threads / CPU4 CPUs per serverOn-chip memory controlQuick interconnectVM and vector support64 bits; 256 GB - 1 TB
2010
More cores, bigger caches16 ... 64 CPUs per server Greater on-chip integration(PCIe, network, ...)Data-direct I/OTens - hundreds of TBs
2013
Images: Intel, Danilo Rizzuti / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 14
Database Evolution
� In the eighties, we had simple, general purpose Database Servers…
Client Application
DatabaseServer
t2000 201019901980
© 2012 SAP AG. All rights reserved. 15
Database Evolution
� Over time, a separate “application server” tier, processing the data from the DB, was introduced; the web popularized this architecture
Database Server
App Server
Client(web)
t2000 201019901980
© 2012 SAP AG. All rights reserved. 16
Data
transformation and aggregation
Database Evolution
� Bigger and bigger datasets forced the introduction of separate, specialized “Data Warehouses”, used for analytic processing (OLAP)
Transaction Server
App Server
Client(web)
AnalyticClient
App Server
Data Warehouse
t2000 201019901980
© 2012 SAP AG. All rights reserved. 17
Database Evolution
� Using in-memory computing, SAP HANA offers a vision of simplicity and integration, with most of the data-intensive operations pushed into the data layer
DataClient
App Server
SAP HANA
t2000 201019901980
© 2012 SAP AG. All rights reserved. 18
Elements of HANA Technology
Image: Renjith Krishnan / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 19
The New Challenges of In-memory Computing
� Challenge 1: Parallelism! Take advantage of tens, hundreds of cores� Challenge 1: Parallelism! Take advantage of tens, hundreds of cores
� Challenge 2: Data locality!
Yes, DRAM is 100,000 times faster than disk…
But DRAM access is still 4-60 times slower than on-chip caches
� Challenge 2: Data locality!
Yes, DRAM is 100,000 times faster than disk…
But DRAM access is still 4-60 times slower than on-chip caches
© 2012 SAP AG. All rights reserved. 20
SAP HANA Design Goals
Hardware innovation leads to software innovation
• In-memory: no disk access during read (updates are logged/persisted to disk)
• Highly parallel execution
• Cache-aware memory organization
Multi-engine data platform: beyond SQL
� Relational (Row and Column), Text, Graph, …
� Application-specific object models
� Embedded development environment (scripting)
� Business Function Library, intrinsic plan operators
� SAP Application server integration
Simplified System Architecture
• Reduced DB administration
• Integrated ERP transactions (roadmap)
Image: Renjith Krishnan / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 21
Additional SAP HANA Design Goals
Support for up to very large data-sets
• Data partitioning and Data Distribution
• Up to ten servers (20TB physical, ~500B records) tested
Failure recovery and High Availability
� Persistence, Redo-log, save-points
� Backup / Restore (go back in history)
� Hot standby, lazy table-load
� Disaster recovery (clusters)
Other
• Planning Engine, Predictive Analysis
• Application-specific object repository
• Multi-tenancy and cloud deployment (future)
Image: Renjith Krishnan / FreeDigitalPhotos.net
Insert only on change
Column and row store
++
No aggregatesMinimal
projections
Partitioning
Analytics onhistorical data
Single andmulti-tenancy
SQL interface on columns & rows
Reduction oftiers / layers
x
In-memoryCompression
Multi-core/parallelization
DynamicExtensibility
++ ++ ++
Active/passive& data aging
PAA
Bulk load
++
++
++++
T
Text Retrieval & Exploration
Multi-threading within nodes
Map reduce No diskGroup Key
t
SAP HANA Building Blocks
SQL
In-memory Apps
© 2012 SAP AG. All rights reserved. 23
� DBs typically use a row-based storage; SAP HANA supports rows, but is optimized for column-order data organization
Order Country Product Sales
456 France corn 1000
457 Italy wheat 900
458 Italy corn 600
459 Spain rice 800
Column and Row Store
456 France corn 1000
457 Italy wheat 900
458 Italy corn 600
459 Spain rice 800
456
457
458
459
France
Italy
Italy
Spain
corn
wheat
corn
rice
1000
900
600
800
Row order organization
Column order organization
Single-record access:SELECT * FROM SalesOrdersWHERE Order = ‘457’
SQL
Single-scan aggregation:SELECT Country, SUM(sales) FROM SalesOrders WHERE Product=‘corn’ GROUP BY Country
ΣΣΣΣ
© 2012 SAP AG. All rights reserved. 24
Order Country Product Sales
456 France corn 1000
457 Italy wheat 900
458 Spain rice 600
459 Italy rice 800
460 Denmark corn 500
461 Denmark rice 600
462 Belgium rice 600
463 Italy rice 1100
… … … …
Columnar Dictionary Compression
� Dictionary per column
� Uses data-driven fixed-length bit encodings
� Operations directly on compressed data, using integers
� More in cache, less main memory access
1 Belgium
2 Denmark
3 France
4 Italy
5 Spain
1 3
2 4
3 5
4 4
5 2
6 2
7 1
8 4
… …
1 7
2 5,6
3 1
4 2,4,8
5 3
Logical Table
Dictionary5 entries, so need 3 bits to encode!
Compressed column
(bit fields)Inverted
indexDictionary
Where was order 460?
Which orders in Italy?
© 2012 SAP AG. All rights reserved. 25
Columnar Run-length Encoding
� Compress repeated values in column memory
� Works best on sparse, sorted columns
� Other encodings in other cases
Order Country Product Sales
456 France corn 1000
457 Italy wheat 900
458 Spain rice 600
459 Italy rice 800
460 Denmark corn 500
461 Denmark rice 600
462 Belgium rice 600
463 Italy rice 1100
… … … …
1 Belgium
2 Denmark
3 France
4 Italy
5 Spain
3
4
5
4
2x2
1
4
…
Logical Table Country
1 corn
2 wheat
3 rice
1
2
2x3
1
3x3
…
Product
© 2012 SAP AG. All rights reserved. 26
Dramatic Simplification
© 2012 SAP AG. All rights reserved. 27
How HANA is Transforming Customers
Image: Renjith Krishnan / FreeDigitalPhotos.net
© 2012 SAP AG. All rights reserved. 28
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Over 400 New HANA Customers …
© 2012 SAP AG. All rights reserved. 29
HANA – Usage Scenarios Today
Operational Data Mart / Application Accelerator
• Flexible Real-Time Analytics/Reporting
• Accelerated SAP Applications
• Rapid Deployment Solutions for Quick Deployment
Agile Data Mart
• Enhance Existing Data Mart and Data Warehouse Investments
• Data Acquisition and Integration from Any Source
• Real-Time Consolidated Reporting/Analytics
SAP BW on HANA
• Dramatically Improved Performance
• Simplified Administration & Streamlined Landscape
• Unlock Data Across the Enterprise
• Preserve BW Investment without Disruption
© 2012 SAP AG. All rights reserved. 30
Southern California Edison Business Drivers
OperationalImprovement
Next GenerationAnalytics
• Faster reporting• Faster data loading• Lower TCO• Reduced maintenance costs• Reduced development costs
• Faster analytics• Modeling flexibility• Near real-time data
replication• Calculation engine and
built-in functions• Big Data footprint• New applications—
potential examples:• Smart meter analytics• Power outage management• Power procurement• Predictive analytics
Standalone HANA DW HANA
© 2012 SAP AG. All rights reserved. 31
The Southern California Edison Business Intelligence Journey
DW
2007 (1)DW
2009 (2)DW
2011 (3)DW HANA
2012 (4)Standalone HANA
2012 (5)Integrated HANA
2014+
Legacy DBWithout
BI
Legacy DBWithBI
Legacy DBWithBI
HANAWithBI
Calc Engines Enterprise Apps, DW,and Standalone
Integration
Baseline Faster Report Speed
• BI 2.5x fasterreporting
Faster Report Speed
• BI verystable
• Reports are faster:Avg (20%)
Legacy DB repl withHANA
• Reports are faster5.0 times
• Delta Data Loads aremuch faster (3.2x)
HANA Standalone Database
• Optimized calculation engines with parallel processing
• Radical Improvement
HANA Standalone Database
• HANA environmentsvirtually or physically merge
• Reports:• Avg 90 sec• CRM 400 sec
• Data Loads 15 hrs
• Reports:• Avg 40 sec• CRM 161 sec
• Data Loads 15 hrs
• Reports:• Avg 32 sec• CRM 23 sec
• Data Loads 15 hrs
• Reports:• Avg 6.4 sec • CRM 4.6 sec
• Data Loads 4.6 hrs
• Analytics: • Telecom CRM 55:1• Smart Meter
Analytics 40:1
• More agile• Seamless• Less costly
development
Performance Improvements
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5
Reports
Data
Analytics (New Capabilities)Operational Improvement (DW HANA)
Relative estimate
Projected
Based on Pilot Results
© 2012 SAP AG. All rights reserved. 32
HANA – Pilot
Validate:
• Compression• Data loading speed• Report response time• Back-up and restore• Security
W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 W17
Task Name 1/9/2012 1/16/2012 1/23/2012 1/30/2012 2/6/2012 2/13/2012 2/20/2012 2/27/2012 3/5/2012 3/12/2012 3/19/2012 3/26/2012 4/2/2012 4/9/2012 4/16/2012 4/23/2012 4/30/2012
HANA Pilot Program
Project Management
Pilot Planning
Production Planning
System Setup
Hardware
Software
Data Migration
DW HANA
HANA Standalone
DW HANA Enhancement
DW HANA Optimization
Data Modeling
Design
Build
Migrate and Validate
Security Setup
DW HANA
HANA Standalone
Report Development & Test
DW HANA
HANA Standalone
Explorer Testing
SAS Validation
SLT Validation
Backup Restore
© 2012 SAP AG. All rights reserved. 33
Pilot ResultsComparing DW HANA vs DW on Legacy Database
0
500
1000
Legacy HANA
Data Loading - Full
DSO to Cube
DSO to DSO
DSO Activation
PSA to DSO
Source to PSA
5.2 x
0
20
40
60
Legacy HANA
Data Loading - Delta
DSO to Cube
DSO to DSO
DSO Activation
PSA to DSO
Source to PSA
3.2 x
5 x 2 x
7.5 x
0
20
40
Response Time (Simple)
Response Time
0
10
20
30
40
Response Time (Complex)
Response Time
0 50 100 150 200 250
Legacy DB
HANA
Data Compression (column store)
Cube DSO Master Data
Projected for Production = 5.7 x
© 2012 SAP AG. All rights reserved. 34
HANA – Production
Goals:
• Migrate all of DW Legacy DB into DW HANA• Incorporate BCS and IP into DW HANA• Reduce nightly batch loading• Improve reporting performance• Migrate enterprise BI • Create the ability to handle “Big Data” analytics• Leverage built-in calculation engines• Create one or more new applications in Standalone HANA
W1 W3 W5 W7 W9 W11 W13 W15 W17 W19 W21 W23 W25 W27 W29 W31 W33
Phase 5/1/2012 5/15/2012 5/29/2012 6/12/2012 6/26/2012 7/10/2012 7/24/2012 8/7/2012 8/21/2012 9/4/2012 9/18/2012 10/2/2012 10/16/2012 10/30/2012 11/13/2012 11/27/2012 12/11/2012
Planning
Project Management
Pre-Trial
Development/ Unit Test
Reg Testing Cycle 1
Reg Testing Cycle 2
User Acceptance
Performance Testing
Production Cutover
Post Production
Stabilization
© 2012 SAP AG. All rights reserved. 35
Current Environment – DW Legacy DB
Sources
ECC
CRM
SRM
Others
Ext
racto
rs
EDW
DSO
DSO
DSO
INT 1
DSO
DSO
DSO
INT 2
DSO
DSO
DSO
Data Mart
PC PC PC PC CubeCube
CubeCube
CubeCube
Multi P
rovid
ers
BeX Universe
Unv
Unv
Unv
BEx
BEx
BEx
MDX
Reports
Crystal Reports
WebI
Xcelsius
BICSBICS
BWA
Legacy DB
New 2012 Environment – DW HANA, BI 4.0, and Data Services 4.0
Sources
ECC
CRM
SRM
Others
Ext
racto
rs
EDW
DSO
DSO
DSO
INT 1
DSO
DSO
DSO
INT 2
DSO
DSO
DSO
PC PC PC
Multi P
rovid
ers
BeX Universe
Unx
Unx
Unx
BEx
BEx
BEx
MDX
Reports
Crystal Reports
WebI
Xcelsius
BICSBICS
HANA
Business Intelligence Development Process
© 2012 SAP AG. All rights reserved. 36
HANA Standalone Environment – Standalone HANA
Sources
ECC
CRM
CRM
Teradata
Ext
racto
rs
Universe
Unx
Unx
Unx
MDX
Reports
Crystal Reports
WebI
Xcelsius
HANA
OracleData Services
Ext
ract
Tra
nsfo
rm
EDW
T1
T2
T3
T4
Load
Data Mart
T1
T2
T3
T4
T5
HA
NA
Mo
de
ler
AttributeView
AnalyticalView
CalculationView
Business Intelligence Development Process
© 2012 SAP AG. All rights reserved. 37
Storage Sizing - Projection
Object TypeCurrent Size in Legacy DB
Tighten DW House Keeping
Clean-up System Tables
Decommission Unused Objects
Reduce Layers
Near lineOld Data
PSA 5,842 100 100 100 100 100
Legacy DB Overhead 6,141 - - - - -
Change Log 4,174 - - - - -
DSO 1,487 1,487 1,487 1,312 1,201 1,201
System Tables 1,167 1,167 300 300 300 300
Cube 708 708 708 648 591 141
Master Data 408 408 408 408 408 408
Temp 7 7 7 7 7 7
Total 19,934 3,877 3,010 2,775 2,607 2,157
6 x 3322 646 502 462 434 359
* All Numbers are in GB
HANA DB Compression
© 2012 SAP AG. All rights reserved. 38-
Standalone HANA – Potential Opportunities
Smart Meter Analytics• Improve customer energy efficiency• Improve campaign effectiveness• Understand consumption behavior
Power Procurement• Lower short-term power purchases• Better forecasting• Introduce new pricing options
Outage Management• Reduce response time and outage costs• Increase customer satisfaction• Quickly analyze up-to-date outage information
Predictive Analysis• Forecast outages, usage patterns, and cost• Model customer segmentation• Rapidly analyze advanced statistical models
© 2012 SAP AG. All rights reserved. 39
Colgate-Palmolive begins budget/planning roll out
�Colgate uses a financial
application for their entire
budget and planning process.
The process starts with laying
out the budgets and plans for
future periods and then actuals
come into the system
�They are limited in what
analysis they can perform (due
to data volumes, aggregation,
hierarchies, etc.)
�Need a cross-enterprise
solution. Colgate started with
their Hills Pet Nutrition business
�Hills Metrics: 450M records
from ERP system, 600GB data.
100k-200k change records/day
�Had to be non-disruptive:
� If Colgate had to be on the latest
software stack they could not
go-live before End 2012
� Zero training or UI impact to
users
Reporting by Customer/Product
now available for 1st time
Flexible customizing of financial
application is key, (e.g., switch on
and off for specific users, reports,
operating concerns) allowing a
smooth and non-disruptive go-live
with minimal risk
Challenge SolutionConditions
© 2012 SAP AG. All rights reserved. 40
Accelerate Business Performance
Large CPG company wants to analyze all their POS of data to predict demand
Target -stock shelves with 48 hour turn-around
Data Set is 460 Billion records (40 Terabytes)
Unable to analyze data using current database platform
10 HANA blades with 500GB per blade (5TB) & 2TB SSD Storage
SAP BusinessObjects Explorer
Significant data compression
20x Faster Analysis with 200x Better Price/Performance
Moved from 5 days down to 2 days for shelf turnaround
Eliminates out of stock scenarios during promotions
Challenge ResultsSolution
SAP HANA for Data Intensive Point of Sale Analysis
© 2012 SAP AG. All rights reserved. 41
Consumer Products CompanyFinancials with SAP HANA
15-60 minutes to generate summary
profitability report
2.9 seconds for600 million records
Drill-down to detail
Analyze any SKU, product family, region, time period …
© 2012 SAP AG. All rights reserved. 42
Global Professional Services Firm— the “Finder” Application
In the sales process, the current approach to
understanding the depth of the relationship
with a customer or prospect is a global email
(spam)
To transform this, need to collect data from
several SAP and non-SAP sources:� HR, FI, CRM
� Recruiting DB
� Alumni DB
� Linked In, etc.
Once collected, you need to search based on
existing/historical relationship with the
customer or prospect
Then score the results based on an algorithm
measuring the depth of the relationship (e.g.,
senior-level, multi-year)
To support sales pursuits, respond on mobile
devices to queries about relationships, then
send targeted messages to the key relationship
owners to help in pursuit
© 2012 SAP AG. All rights reserved. 43
DW on HANA, Major Beer Brewer
� DW Source was 4TB – HANA was 400GB (10x compression is
fairly standard, not allow for dynamic memory demands of
HANA)
� Data load was 10x faster (specifically, the portion that does
transformation and activation inside DW, not extraction from
source data which happens outside HANA)
� Queries on HANA 3X faster than previous in memory solution
� Upgrade and Unicode migration was manageable – about 4
technical days to complete
� Adding fields & attributes, is done in minutes (max), no re-
aggregation or indexing required
� Of 30 DW resources, they feel they will be able to reassign 6
people to other task. 6 redeployed staff is significant
© 2012 SAP AG. All rights reserved. 44
T-Mobile Initial Production Scenario
New Marketing program initiative—to drive an aggressive Marketing initiative for targeted campaigns and offers
� Offers are available for approximately 21 million customers
� Presented via retail stores, customer care centers and eventually via SMS. Each offer has a limited lifespan
� Goal is to increase adoption rate & profitability, customer retention
� Marketing Operations team needs to collect, analyze and report results of these campaigns/offers very quickly and with great flexibility
� Combine subscriber, marketing data, and POS data (20 tables from four sources). Initial load of nine months of data: inbound takes table ~82 million records; outbound offers table ~600 million records.
�Lengthy data load times
�Need access to details of customers actions, stop aggregating data
�Two primary data sources:
�(1) Enterprise data warehouse; and
�(2) Marketing campaign application
�Teradata sourced—so used Data Services ETL. Business Objects reporting tools in use for both the operational reports and ad hoc reporting
�Short fuse on project time frame
� “50x improvement in analytics in offer optimization…”
� “Marketing now gets their reports in 3 hours rather than 1 week and can recalibrate offers real-time”
� “We could not find anything that we already owned to solve this problem including Greenplum, Netezza, Teradata….they were too expensive or too risky.”
Challenge SolutionConditions
© 2012 SAP AG. All rights reserved. 45
Medidata & Big Data
Leading Provider of SaaS technology solutions that drive efficiencies across clinical research processes for Life Sciences companies
Data Volumes
� Over 25,000 protocols
� Over 250,000 site relationships
� Over 2,000,000 patients
� Billions of rows of transactional data for every clinical trial activity
Today
• Use HANA for specific analytics and benchmarks
• Dashboards generated in real-time
• Dashboards that can be “explored” in real-time with interactive visualization
• Tomorrow Leverage for broader cross-functional analytics
Actionable Analytics to help our customers optimize operations
First experience, Complex Query:
� Inner & Outer Joins across 17 tables
� Large Tables (millions of rows each)
47 minutes
1.78 Sec
© 2012 SAP AG. All rights reserved. 46
Experience SAP HANA Use Cases
© 2012 SAP AG. All rights reserved. 47
In Summary
� HANA is a disruptive in-memory data management platform, demonstrating a breakthrough in big-data analytic performance
– Customers already use SAP HANA to gain “real real-time” business insights into massive amounts of data
� SAP HANA represents a new paradigm for developing data-intensive applications
– HANA is vertically integrated with analytic applications (such as SAP BW) and delivered as an appliance with top HW partners
– SAP plans to release several new in-memory applications
– SAP intends to integrate its ERP business applications with HANA
Speed
Scale
Flexibility
HANA is at the heart of
SAP’s vision and strategy
Thank You!
SAP HANA
© 2012 SAP AG. All rights reserved. 49
References
� SAP HANA on YouTube: http://www.youtube.com/watch?v=kwu5fndwz9Ahttp://www.youtube.com/watch?v=r3cw0yRKjsQ
� Excellent recent article:Faerber et al, SAP HANA Database - Data Management for Modern Business Applications; SigMOD Record December 2011, (Vol. 40, no. 4).http://www.sigmod.org/publications/sigmod-record/1112/pdfs/08.industry.farber.pdf
� More on HANA from SAP:www.sap.com/hana
� www.experiencesaphana.com
© 2012 SAP AG. All rights reserved. 50
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