WHY SAP HANA ?

Ugur CANDANAugust 2011

2

Struggling to Keep Up With Expectations?

3

The Gap Between CPU and GPU

ref: Tesla GPU Computing Brochure

4

The Gap Between CPU and GPU

5

Evolution of Intel PentiumPentium I Pentium II

Pentium III Pentium IV

Chip areabreakdown

Q: What can you observe? Why?ref: Zhenyu Ye / Bart Mesman / Henk Corporaal “GPU Architecture and Programing”

6

Extrapolation of Single Core CPU

If we extrapolate the trend, in a few generations, Pentium will look like:

Of course, we know it did not happen. 

Q: What happened instead? Why?

7

Evolution of Multi-core CPUs

Penryn Bloomfield

Gulftown Beckton

Chip areabreakdown

Q: What can you observe? Why?

8

The Brick Wall -- UC Berkeley's View

Power Wall: power expensive, transistors freeMemory Wall: Memory slow, multiplies fastILP Wall: diminishing returns on more ILP HW

Power Wall + Memory Wall + ILP Wall = Brick Wall

David Patterson, "Computer Architecture is Back - The Berkeley View of the Parallel Computing Research Landscape", Stanford EE Computer Systems Colloquium, Jan 2007, link

Big Table

DynamoMapReduceGoogle File System

Fusion ioDrive Octal Capacity 5.12TB 6GB/s Read 4GB/s Write 1,000,000 IOPS

Xpress disk (xpd) 1 TB: 20 GB/s IOPS: 600,000 reads 500,000 writesThe NVIDIA Tesla

c2070 memory 6GB interface 384bit 144 GB/s

8K Camera: 260 km by a fiber optic network 3 GB/sa 20 minute broadcast would require roughly 4 TB of storage

11

RDBMS in MPP Architecture

Each SMB 6.4 GT/s

Useful work of a RDBMS

12

Reporting

“Typical” Business Intelligence Today

Slow

Painful

Expensive

Operational Data Store

Data Warehouse

Indexes

Aggregates

DataBusiness Applications

Copy

ETLCalculation EngineBusiness Intelligence

Query ResultsQuery

Slow

Painful

Expensive

Operational Data Store

Data Warehouse

Indexes

Aggregates

DataBusiness Applications

Copy

ETL

Calculation EngineBusiness Intelligence

Query ResultsQuery

DataMarts

13

In-Memory Computing

Disk is 1Mx slower than direct memory: like a chef doing his shopping on mars

14

In-Memory Computing Costs have Plummeted

Cost of 1 Mb of memory in 2000: ≈$1

Bosphorus bridge:105m / 344ft

15

In-Memory Computing Costs have Plummeted

Cost of 1 Mb of memory today: <1 cent

Child:1.5m

And shrinking….

Dell: Quad 10 Core server 512GB Ram, Hosting Services$5,200/month

16

A Shift of Frontiers in Computer ScienceFreely Adapted from Jim Gray, Turing Award Winner 1998

Tape is Dead Disk is new Tape Main Memory is new

Disk CPU Cache is new

Main Memory

17

SAP ve In-Memory

2010 SAP High-Performance Analytic Appliance

2006 SAP NetWeaver BW Accelerator (BWA)

2004 SAP NetWeaver Enterprise Search (TREX)

1999 SAP Advanced Planner and Optimizer

18

Operations and Analytics Together

Copy

Business Applications

Analytic ApplianceBusiness Intelligence

Add ACID-compliant, row-based, in-memorySingle source of dataFaster, better BI and actionable intelligenceFaster, better applicationsNew application opportunities

Data

SAP HANA

20

In-Memory Computing – The Time is NOW

HW Technology Innovations

64bit address space – 2TB in current servers

100GB/s data throughput

Dramatic decline in price/performance

Multi-Core Architecture (8 x 8core CPU per blade)

Massive parallel scaling with many blades

One blade ~$50.000 = 1 Enterprise Class Server

Row and Column Store

Compression

SAP SW Technology Innovations

21

Fast – Software Optimization for Memory

Conventional databases store records in rows

Storing data in columns enables faster in-memory processing of operations such as aggregates Columnar layout supports sequential memory access A simple aggregate can be processed in one linear scan

A 10 € B 35 $ C 2 € D 40 € E 12 $

A B C D E 10 35 2 40 12 € $ € € $

memory address

organize by row

organize by column

A 10 €

B 35 $

C 2 €

D 40 €

E 12 $

conceptual view

mapping to memory

22

In-Memory Computing – The Time is NOW

HW Technology Innovations

64bit address space – 2TB in current servers

100GB/s data throughput

Dramatic decline in price/performance

Multi-Core Architecture (8 x 8core CPU per blade)

Massive parallel scaling with many blades

One blade ~$50.000 = 1 Enterprise Class Server

Row and Column Store

Compression

Partitioning

No Aggregate Tables

SAP SW Technology Innovations

23

SAP HANA™■ In-Memory software + hardware

(HP, IBM, Fujitsu, Cisco, Dell)■ Data Modeling and Data Management■ Real-time Data Replication■ SAP BusinessObjects Data Services for ETL

capabilities from SAP Business Suite, SAP NetWeaver Business Warehouse (SAP NetWeaver BW), and 3rd Party Systems

Capabilities Enabled■ Analyze information in real-time at

unprecedented speeds on large volumes of non-aggregated data

■ Create flexible analytic models based on real-time and historic business data

■ Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category

■ Minimize data duplication

SAP In-Memory Appliance (SAP HANA™)

SAP HANA

SQL MDXBICSSQL

SAP BusinessObjects tools Other query tools

SAP BusinessSuite

Other data sources

SAP NetWeaver Business

Warehouse

SAP In-Memory Computing Studio

SAP In-Memory Database

Calculation and Planning Engine

Row & Column Storage

Real-Time Data Replication

SAP Business Objects Data

Services

24

HANA Combines Software and Hardware

In-Memory Computing Engine (Software)

Pre-Installed Systems (Hardware)

+

25

• 2 x 8 core Intel Nehalem EX ( 2 socket system) • 128 GB Main memory • 160 GB PCIe-Flash / SSD for Log volume• 1 TB SAS / SSD for Data volume• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~ 256 GB to ~500 GB

• Replication Data load 5GB / hr

• 2 x 8 core Intel Nehalem EX ( 2 or 4 sockets system) • 256 GB Main memory • 320 GB PCIe-Flash / SSD for Log volume• 1 TB SAS / SSD for Data volume• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~ 500 GB to ~1.25TB

• Replication Data load 5GB / hr

• 2 x 8 core Intel Nehalem EX (4 sockets system) • 256 GB Main memory (expandable up to 512 GB)• 320 GB PCIe-Flash / SSD (expandable up to 640 GB)• 1 TB SAS / SSD for Data volume (expandable up to 2 TB)• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~ 500 GB to ~2.5 TB

• Replication Data load 5GB / hr

HANA Appliance “T-shirt” sizes Specifications & Approximate Data Volumes

S+

S

XS

Starts at S and scales up to M

26

• 4 x 8 core Intel Nehalem EX ( 4 socket system) • 512 GB Main memory • 640 GB PCIe-Flash / SSD • 2 TB SAS / SSD for Data volume• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~1.25TB to ~2.5 TB

• Replication Data load 5GB - 20 GB/ hr

• 4 x 8 core Intel Nehalem EX ( 8 socket system) • 512 GB Main memory (expandable up to 1 TB)• 640 GB PCIe-Flash / SSD (expandable up to 1.2 TB)• 2 TB SAS / SSD for Data volume (expandable up to 4 TB)• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~ 1.25TB to ~5TB

• Replication Data load 5GB – 20 GB / hr

• 8 x 8 core Intel Nehalem EX • 1 TB Main memory • 1.2 TB PCIe-Flash / SSD • 4 TB SAS / SSD for Data volume• 3 x 1 GB n/w or 1 x 10GB n/w (trunk) • Redundant n/w infrastructure

• Uncompressed Data ~ 2.5TB to ~5TB

• Replication Data load 5GB – 20 GB / hr

HANA Appliance “T-shirt” sizes Specifications & Approximate Data Volumes

M

M+

L

Starts at M and scales up to L

27

Speed-up Existing Work

28

Discover New Options

29

Customer Testimonials

Consumer and Health Products

Manufacturing

Energy

Communications and Media

Services

Technology

30

The First 35 Years: Innovated with ERP & LOB Apps

ERP + LOBSystems of Record

Data “In” Business AnalyticsSystems of Engagement

BICS Info “Out”

MobilityAccessible Systems

Oracle DB2 SQL Other

Business Applications Performance Bound by Data

ELT or ETL HANAIn Memory Database

ELT or ETLOracleSQL

DB2, etc.HANA

In Memory Database

Three Years Ago: Innovated with AnalyticsLast Year: Innovated with MobilityThis Year: Innovating the DatabaseHANA Accelerates Data, Applications, AnalyticsLong Term: HANA Is the Database

31

The Truth About Enterprise Data Landscapes

Operational

Warehouses

Marts

Dimensional

Semantic

Information

Oracle DB2 SQL Other

BW TeraData Netezza

Mart Mart Mart

OLAP OLAP

IQ

Universe?Queries Ad-Hoc Dashboard

ETL

DATA QUALITY

Applications

Reports

OLAP

Mart Mart Mart

OLAP

Mart

The Value of HANA

HANA

Oracle/DB2/SQL/Other

BW/Netezza/Teradata/IQ

The Future of “The Stack” – HANA Nirvana

HANA

Distribution Center

Retailer ADistribution Center

Supplier

DSD DistributionCenter

Retail Store B

Mfg Plant

Retail Store A

Event Insight

Node

Event Insight

Event Insight

Node

Event Insight

Node

Event InsightNode

Event InsightNode

Event InsightNode

Event InsightNode

SAP BusnessObjects Event Insight

Custom DB

Apps

Apps

Apps

WWW

BW

Apps

Distribution Center

Retailer ADistribution Center

Supplier

DSD DistributionCenter

Retail Store B

Mfg Plant

Retail Store A

Event Insight

Node

Event Insight

Node

Event Insight

Node

Event Insight

Node

Event InsightNode

Event InsightNode

Event InsightNode

Custom DB

BW

Event InsightNode

Apps

Apps

Apps

Apps

WWW Event Pattern

Define Event Pattern

Event Pattern Identified

SAP BusnessObjects Event Insight