DB2 – Differentiating Business Value

IBM Information Management

DB2 – Differentiating Business Value

Les King
Director, Product Management
lking@ca.ibm.com

May, 2012 © 2006 IBM Corporation

IBM Client Confidential – Do Not Distribute

DB2 Overview - Agenda
 Reducing Storage Costs

 A hybrid database with pureXML

 Industry unique clustering for storage optimization, performance & scalability

 Autonomics, Workload Management and Advanced Tooling – let DB2 do the work

 High Availability and Extreme Scalability for all workloads

 Protecting your existing application investment

 The form-factor of your choice

 Security, Data Governance & Regulatory Compliance

 Going forward

Data Server Innovation to Deliver True Business Value


DB2 – A continuous delivery of content !!

DB2 10.1
Just GA’d in 2Q 2012

DB2 9.8
Introduction of DB2 pureScale

DB2 9.7
About 2/3 of our customers are here

DB2 9.5
About 1/3 of our customers are here

DB2 9.1
Coming up to end of support



Compression
Reducing Overall Storage Costs



Breakthrough Savings with Adaptive Compression
Lower Storage Costs; Lower Administration Costs
Galileo
Adaptive
Compression
DB2 9.7
Temp Space &
Index
DB2 9.5 Compression
On-line
Enablement
DB2 9.1 of
Table Compression
Compression

• Adaptively apply both table-level compression and page-level compression
• Table re-orgs not required to maintain high compression
• Compress archive logs

5 Data Server Innovation to Deliver True Business Value


Row Compression
Reduces the cost of data storage

Fred, Dept 500, 10000, Plano, TX, 24355…

John, Dept 500, 20000, Plano, TX, 24355, Site 3

Fred, (01), 10000, (02),
John, (01), 20000, (02)

179.9 GB
01 Dept 500
76%
02 Plano, TX, 24355 Smaller!

… …
42.5 GB

Dictionary contains repeated information from the rows.



…. And if that’s not enough

 Performance gains of up to 40% ….
– All data is compressed in memory
 Reduced outages for utilities
– Backup, Reorg now run 2X-4X faster
 6X+ volume of data as compared to production size
– Dev/Test, HA, DR, Backup repositories



Improving the Best Compression in the Industry

– Multiple algorithms for automatic index compression Unique in
the
industry

– Automatic compression for temporary tables Unique in
the
Table Temp Table industry
Order By Order By Temp

– Intelligent compression of large objects and XML



Adaptive Compression – What is it

 Technology provides compression rates approaching 7X

 Provides significant costs benefits
– Storage reduction
• Acquisition cost, floor space, power and cooling
– I/O reduction
• Reduced response time and improved throughput
– Reduced backup and recovery times

 Very simple to configure and use

 Next generation compression is adaptive
– Improve compression rates by up to 2X (approaching 15X)
– Maintain compression rates with data skew

9


Almost 40% Improvement over DB2 9.7 Compression
When Using Offline Reorg and Compression

60

54.1
50
Storage Size (GB)

40

30

5.2x
20
8.5x
10
10.5
6.4
0
Uncompressed DB2 9.7 Compression Galileo Adaptive
Compression



Up to 60% Improvement over DB2 9.7 Compression
When Using Online Automatic Dictionary Creation (ADC)

60

54.1
50
Storage Size (GB)

40

30
2.5x
20
21.2
6.4x
10
8.4

0
Uncompressed DB2 9.7 Compression Galileo Adaptive
Compression



Adaptive Compression Shrinks your Data Storage Needs

 Higher performance
– Faster queries for I/O-bound environments
– Faster backups

 Lower costs
– Postpone upcoming storage purchases
– Lower ongoing storage needs
– Easier administration with reduced need for table re-orgs
“Page-level dynamic compression is one of the new DB2 features that will
reduce planned outages by 40% and storage savings up to 50%”
—Jessica Tatiana Flores Montiel, DAFROS Multiservicios

“Our migration from Oracle Database to DB2 resulted in a 40% storage savings.
Upgrading to DB2 9.7 and index compression brought our average savings to 57%.
Now adaptive compression brings our average savings to 77%, dramatic savings!”
—Andrew Juarez, Lead SAP Basis / DBA, Coca Cola Bottling Company.



More Proof Points ….



pureXML
A Hybrid Database



DB2 XML - A New Generation Hybrid Data Server

High cost development Streamlined development
Poor performance High performance

Or

Business data in XML form Business data in XML form
managed in relational database managed with DB2 pureXMLTM



DB2 XML - Benefits
XPath
XPath
XML XML Store

Retrieve
Mapping Retrieval
Client Client
Code Code
XML XML

Shred
Compose

Shredded Content Catalog
DB2 XML

 Simplified and streamlined solution
 No mapping code to write and maintain
 No complex schema to manage and maintain
 No proprietary catalog
 No XPath parsing and result set composition
 Improved performance and flexibility
 Lower development and maintenance costs and faster to market


DB2 XML – A First Class Citizen

 Data Definition
create table dept(deptID int, deptdoc xml);

 Insert
insert into dept(deptID, deptdoc) values (?,?)

 Retrieve
select deptID, deptdoc from dept

 Query
select deptID, xmlquery('$d/dept/name' passing deptdoc as “d") from dept
where deptID <> “PR27”;



SQL/XML: Use SQL to produce XML

SELECT
Available Functions:
XMLELEMENT(NAME "Department", XMLELEMENT

XMLATTRIBUTES (e.dept AS "name" ), XMLATTRIBUTES
XMLFOREST
XMLAGG( XMLELEMENT(NAME "emp", e.firstname) ) XMLCONCAT

) AS "dept_list" XMLAGG
XML2CLOB
FROM employee e XMLNAMESPACES
WHERE ….. XMLCAST

GROUP BY e.dept;
Start With Produce
dept_list
<Department name="A00">
firstname lastname dept
<emp>CHRISTINE</emp>
<emp>VINCENZO </emp>
SEAN LEE A00
<emp>SEAN</emp>
MICHAEL JOHNSON B01
</Department>
VINCENZO BARELLI A00
<Department name="B01">
CHRISTINE SMITH A00
<emp>MICHAEL</emp>
</Department>



The FLWOR Expression

 FOR: iterates through a sequence, bind variable to items
 LET: binds a variable to a sequence
 WHERE: eliminates items of the iteration
 ORDER: reorders items of the iteration
 RETURN: constructs query results

FOR $movie in xmlcolumn(‘movies.doc’)
LET $actors := $movie//actor
WHERE $movie/duration > 90 <movie>
<title>Chicago</title>
ORDER by $movie/@year <actor>Renee Zellweger</actor>
RETURN <movie> <actor>Richard Gere</actor>
<actor>Catherine Zeta-Jones</actor>
{$movie/title, $actors} </movie>
</movie>


Storage Optimization and
Multi Dimensional Clustering
Unique in the Industry



Problem : Optimizing for Multiple Access Keys
 Database systems try to store the records of a table in a particular order (e.g. in part
number order) to enable fast/ordered retrieval
– Called ‘Clustering’
– Speeds up queries – but only for a single ‘key’ (aka ‘dimension’)
– Queries involving other dimensions suffer
– Clustering eventually degrades

SELECT * FROM Sales WHERE Region = SW
– Usually do not require a page I/O when reading the next record
(because it’s usually on same page as previous record)
Region – The page I/Os that are required, are sequential (efficient)
… NW SW SW SW …

SELECT * FROM Sales WHERE Year = 2009
– Usually do require a page I/O when reading the next record (because
it’s usually on a different page than the previous record)
… 2009, 2010, 2010, 2010, … – Each of these page I/Os is random (inefficient)
Year



Solution : Multi-Dimensional Clustering (MDC)
 Divides the table up into ‘extents’ and ensures that each record in an extent contains the
same value in all interesting dimensions
– Extent = consecutive group of pages, big enough for efficient I/O (typically 32 pages; 4 in the e.g. below)
– Queries in all dimensions benefit
– This clustering is always maintained by DB2; it never degrades

SELECT * FROM Sales WHERE Region = SW
– 2 big block I/Os to retrieve pages containing region SW
Region – All sequential I/O

NW,2010 SW,2010 SW,2011
SELECT * FROM Sales WHERE Year = 2010
– 2 big block I/Os to retrieve pages containing year 2010
Year – All sequential I/O

Have your cake and eat it too !


MDC : Simple and Flexible Syntax

Example 1:
CREATE TABLE Sales
(YEAR DATE, REGION CHAR(12), PRODUCT CHAR(30), …
ORGANIZE BY (YEAR, REGION, PRODUCT);

Example 2:

CREATE TABLE Sales
(SALES_DATE DATE, REGION CHAR(12), PRODUCT CHAR(30),…
MONTH GENERATED ALWAYS AS ((INTEGER(DATE)/100)…
ORGANIZE BY (MONTH, REGION, PRODUCT)

For the query:
select * from sales where sales_date>”2010/03/03” and date<“2011/01/01”..

The compiler generates the additional predicates:
month>=201003 and month<=201101



Range Partitioning
 Allows a single logical table to be broken up into multiple separate physical
storage objects
 Each corresponds to a ‘partition’ of the table
 Partition boundaries correspond to specified value ranges in a specified partition key

 Examples of Benefits
 Allows for partition elimination during SQL processing
 Allows for optimized roll-in / roll-out processing (e.g.. minimized logging)
 Allows for “divide and conquer” table management

Without Partitioning With Partitioning
Tablespace A Tablespace B Tablespace C
Tablespace 1

Table_1 Table_1.p1 Table_1.p2 Table_1.p3



Distributing, Partitioning, Clustering
CREATE TABLE ORDERS
(ORDER_ID, SHIP_DATE, REGION, CATEGORY)
IN TBSP1, TBSP2, TBSP3, TBSP4
DISTRIBUTE BY (ORDER_ID) World’s Richest
PARTITION BY (SHIP_DATE) Slice & Dice
(STARTING FROM('01-01-2010’) ENDING (‘9-31-2010’) EVERY (3 MONTHS)) Capability

MONTH GENERATED ALWAYS AS ((INTEGER(DATE)/100)
ORGANIZE BY (MONTH, REGION, PRODUCT)

Data Rows

Distribute via Hash
ORDER_ID
Partition By Range Partition By Range
SHIP_DATE SHIP_DATE
Part 1 Part 2 Part 3
Part 1 Part 2 Part 3
Month Month Month
Month Month Month

Region

Region

Region
Region

Region

Region

Organize By Organize By Organize By
Organize By Organize By Organize By

Product Product Product
Product Product Product



No Partitioning

Data



Distribute by Hash
Divide & Conquer Parallelism

P1 P2 P3 P4



Hash + Partition by Range - Partition Elimination
Massive Parallelism with Massive IO Reduction

P1 P2 P3 P4

2
0
1
0

2
0
1
1



Hash + Range + MDC
High density, High Value, Low IO Reads

P1 P2 P3 P4

2
0
1
0

2
0
1
1



Multi-Temperature Data Management
Increase Ability to Meet SLAs; Postpone Hardware Upgrades
 Storage pools for different tiers of storage
– For range partitions, policy-based automated movement of data

HOT WARM COLD ARCHIVE
SSD RAID SAS RAID SATARAID Optim
Data
Growth

– Improved ability to meet SLAs while retaining greater amount of data for analysis
 Lower costs
– Embrace new lower-cost storage technology
– Further reduces the cost for meeting SLAs
“The multi-temperature database management feature of DB2 V10.1 is great because the hardware world is not just
RAM and hard disks. There are many types of storage options with different I/O speeds and prices. This feature allows
administrators to make optimal use of these different devices, balancing expensive SSDs with cheaper SATA disks and
everything in between. Using SSDs for indexes and logs and a SATA array for the data, we noticed fantastic
improvements in I/O speeds, especially for synchronous reads. Additionally, the background movement of data
between the storages groups is very fast.” —Thomas Kalb, CEO ITGAIN GmbH



Autonomics, Workload Management
and Advanced Tooling
Intelligence and Simplicity



Adaptive Self Tuning Memory Management
 DB2 9 introduced a revolutionary memory tuning system called the Self Tuning Memory Manager (STMM)
– Works on main database memory parameters
• Sort, locklist, package cache, buffer pools, and total database memory
– Hands-off online memory tuning
• Requires no DBA intervention
– Senses the underlying workload and tunes the memory based on need
– Adapts quickly to workload shifts that require memory redistribution
– Adapts tuning frequency based on workload

Interval BenefitPerPage
Interval
Tuner
Tuner
DB2
Step Tuner Clients
Entry Size
MIMO
MIMO
Control Algorithm
Control Algorithm
Y
Memory

Accurate
Greedy

Accurate
Model
Model
(Constraint) Statistics
Builder
Builder Collector
N
Fixed 4-Bit Entry
Step (Oscillation) Size DB2 UDB
Server

BenefitPerPage



STMM in action – Two databases on the same box
7000000

Second
6000000 database
stopped

5000000

Second
Memory (in 4K Pages)

database
4000000
started

3000000

2000000

1000000

0
0 10000 20000 30000 40000 50000 60000 70000
Time (in seconds)



STMM in Action – Dropping an Important Index

TPCH Query 21 - After drop index - Average times for the 10 streams
7000

Avg = 6205
6000

Reduced 63%
Time in seconds

5000

4000
Indexes Dropped

Avg = 2285
3000

2000

Avg = 959
1000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Order of execution



STMM – Comparing Different Configurations

70000

STMM also edges 63796
63302 out benchmark
60000
tuned system
Transactions Per Minute

50000

40000

STMM beats default
configuration by nearly
30000 4x

20000
16713

10000

0

Default (No tuning) Benchmark Tuned STMM Tuned
Configuration



“Time Spent” Metrics (example)
Total Time
Default Time Metrics

Bufferpool Read Wait

Bufferpool Write Wait

Direct I/O Read Wait

Direct I/O Write Wait

Lock Wait

Agent Wait

WLM Queue Wait

FCM Send Wait

FCM Receive Wait

Network Send Wait

Network Receive Wait

Log Write Wait

Log Buffer Insert Wait

Wait Times Processing / Non-Wait Time



“Component Time” Metrics (example)



Health Monitor

Context: All

Heat Alert Dashboar
Chart s d
Alerts System Database
us
tat c e
nc
e
nt
S
ge an na
ge ve
r e ce Us
a
for
m
on
s ns int
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rA er ag pa er cti tio
ito l ng Us ry g ac ng Ma
S ca ni S
mo ck
in L P onne s gg
i
Mon atatus Criti
D ta W
ar CP
U
Di
sk
Me Lo SQ C Tr
an Lo
 Name S
Context
 Production   3 5         
 Web   1 0         
eCommerce   1 0         
Support   0 0         
 Retail   0 2         
New York   0 1         
Los Angeles   0 1         
Accounts   1 0         
Marketing   1 3         
 Test   1 6         
 Development   0 11         



Meet Your Service Level Agreements

 Optimize Performance with Workload Management
– Create controls ahead of time
– Override them on the fly
– Adjust to changing priorities throughout the day
 Lower your costs by automating resource
allocation and utilization
– Control for both applications and users
– Establish controls based on business priority
 Workload management
– Part of database engine
– Request management
– Time based resource management



Summary of Key WLM Features

 DB2 Service Class
– Serves as the primary point of resource control for executing work
– Acts as point of integration with AIX WLM for work being done within database
 DB2 Workload
– Serves as the primary point of control for submitters of work
– Acts as primary router of work to a specific DB2 Service Class
 DB2 Threshold
– Provides limits to control behaviours of database activities based on predictive and
reactive elements
– Provides limits to control rate of concurrency for database activities
 DB2 Work Action Set
– Provides ability to discriminate between different types of database activities for service
subclass mapping or for DB2 Threshold assignment
 DB2 WLM Monitor and Control capabilities
– New table functions, event monitors, and stored procedures to provide monitoring and
control mechanisms for DB2 WLM



Workload Management
 Enhanced Thresholds
– Rows Read
• Does not include index access
• Checked on user configurable time interval
• Database, work action set, service class, workload
– Processing Time (CPU)
• Database, work action set, service class, workload
• Calculated on a user specified check interval
– Aggregate System Temp
• Controls overall aggregate temp space within a service class
 Tiered service class model
– Use in service class thresholds to remap to new class
– Processing time or number rows read
 Defining thresholds on workload domain
– Estimated SQL cost, SQL rows returned, actitivity total time, SQL temp space
– Rows read
– Processing time
 Bufferpool sensitivity to I/O priority
– Introduce h/m/l bufferpool priority for service class
– Pages will be swapped out based on the priority they were fetched under



Accelerate Value for New Features
Increase Ability to Meet SLAs; Lower Administration Costs
 Updated Database Admin solutions:
– IBM Data Studio
– InfoSphere Data Architect

 Updated Performance Mgmt solutions:
– InfoSphere Optim Performance Manager
– InfoSphere Optim Query Workload Tuner
– InfoSphere Optim Configuration Manager

– Immediate support for new performance features
– Enhanced Visual Explain, Access Plan Explorer and Index Advice
– Extended Insight identifies source of performance issues

 Lower costs
– Immediate support for new time saving features (incl. Temporal,
Multi-Temperature Data Management & Row and Column Access Control)
– IBM solutions are integrated and consistent



New and Enhanced Tooling

 IBM Data Studio 3.1.1
– Merges functionality from Data Studio, Optim Development Studio and Optim Database
Administrator
– Includes all functionality available in Control Center and MORE!!
– Supports DB2 Galileo features

 Optim Query Workload Tuner: Tunes multiple queries in parallel

 IBM Data Studio Console replaces Health Center

 Optim Performance Manager: Monitors workloads and events

 Workload Manager (WLM) replaces some of Query Patroller and Governor
functionality
Adding Additional Value to Advanced Enterprise Edition


High Availability and Extreme Scalability
Optimized for the workload



HADR now Supports Multiple Standby Servers
Increase Ability to Meet SLAs; Disaster Recovery

 HADR now supports more than
one stand-by server
 If Primary Server fails,
Principal Standby takes over
 If Principal Standby then fails,
can switch to Auxiliary Standby
 Auxiliary Standby can provide complete
offsite availability,
while maintaining speed of
local standby



DB2 pureScale –
OLTP Workloads

 Unlimited Capacity
– Buy only what you need, add capacity as your
needs grow

 Application Transparency
– Avoid the risk and cost of
application changes

 Continuous Availability
– Deliver uninterrupted access to your data with
consistent performance

Learning from the undisputed Gold Standard... System z



DB2 pureScale Architecture

Automatic workload balancing

Cluster of DB2 members running on
Linux or Power servers

Leverages the global lock
and memory manager
technology from z/OS

Integrated Tivoli
System Automation

InfiniBand network & DB2
Cluster Services

Shared Data



DB2 pureScale: Near Linear Scaling 112 Members
OLTP Workloads
81% Scalability

88 Members
87% Scalability
2, 4 and 8
64 Members
Members
91% Scalability
Over 95%
Scalability 32 Members
Over 95%
Scalability
16 Members
Over 95%
Scalability

Number of Members in the Cluster


DB2 pureScale Enhancements
Increase Ability to Meet SLAs; Easily Add or Remove Capacity

 Further Improving IBM’s Shared-Disk Cluster Capability
– NEW! Workload management for DB2 pureScale
– NEW! Multiple database support
• Easy multi-tenancy
– NEW! Range partitioning support
– NEW! Additional backup/restore options
– NEW! Support for 10-gigabit Ethernet
– NEW! Support for multiple Infiniband adapters and switches

 Configurable geographically-dispersed clusters

“Vormetric’s integration with DB2 pureScale GPFS provides IBM customers with a fantastic combination
of Vormetric Data Security with pureScale availability, capacity and scalability. Improved performance
and availability with data security offers our mutual customers a phenomenal solution.”
-- Todd Thiemann, Senior Director, Product Marketing Vormetric, Inc.
49



Real-Time Data Warehousing
Faster Business Decisions; More Accurate Business Decisions

 Continuous feed of data
 Parallel processing
 Supports multiple connections
– Faster availability of data
– Minimal impact on query performance
– No downtime (even for large volumes of data)

 Lower costs
– Costs less than solutions outside database
– Reduced infrastructure costs

“You can now continuously feed data into your data warehouse at a high rate
even whilst you are running queries against the tables in your data warehouse.
DB2 10 represents a greatly strengthened offering for the data warehouse market.”
—Ivo Grodtke, LIS.TEC GmbH
50



Active - Active Warehousing
Full database or subset
Bi-directional or uni-
directional
Integrated packaging
Multiple stand-bys
Read-Only or
Read/Write Time Delay Read/Write
Application Applications
Seamless application failover

IBM IBM IBM IBM

p e s
S rie pSeries pSeries pSe s
rie

server server se r
rve server

Storage Area Network

part1 part5 part9 part13
IBM IB M IB M IB M

IBM IBM IBM IBM

se rve r
pSeries

s erv er
pSer ie s

ser ver
pSer ies

ser ver
pSer ies

DB2 Warehouse C s erver
pSeries

server
pSerie s

serv er
pSeries

serv er
pSeries

(Standby)

Storage Area Network
Q replication Storage Area Network


Database Log buffers

DB2 Warehouse A DB2 Warehouse B
(Primary) Primary DB2 Client connection path (Standby)
DB2 Client Reroute connection path

Data Server Innovation to Deliver True Business Value 51


Application Portability
Protection of existing investment



Move Your Applications to DB2 … as-is
Proven
Results

 Use the Oracle skills you have with  Simple drag and drop of schemas to
DB2 DB2
– Achieve high productivity
 Applications moved to DB2 run with  Integrated, cross-platform tools
full native execution
– Deliver high performance  IBM can rapidly assess your
– 98%+ application code runs as-is application

3. Ease of Use


PL/SQL in DB2

 Built in PL/SQL compiler
 Source level debugging and profiling

Editor Data Studio

PL/SQL SQL PL
Compiler Compiler
Debugger

SQL Unified Runtime Engine Profiler

DB2 Server

Data base



Debugging PL/SQL in DB2



Oracle Compatibility Layer in DB2
– Aggressive delivery into DB2 9.7 ship vehicles

– Significant reduction in cost and risk of application migration from Oracle to DB2
• Many applications simply work as-is, with equal or better performance
• Now over 98% compatibility with DB2 10

– Prioritized Focus on ….
– Extending Reach
> Eliminating show stoppers
> More client APIs to increase number of applications which can be enabled to DB2 in an
enterprise.
– Increase “out of the box” experience
> Popular features which reduce effort significantly
> 98% → 99% compatibility => Reduction of effort by 50%

– Utilize with partners and community
– Support for Oracle Forms via multiple partners
– Support for more Oracle package via Developerworks



Average PL/SQL Compatibility Moves Above 98%
Easily Move from the More Expensive Oracle Database; Leverage Oracle Skills with DB2
9.7.1 SUB STRB Increase compatibility

9.7.1 UDF Parameters: INOUT Increase compatibility
Reliance Life Insurance
9.7.1 FORALL/BULK COLLECT Increase compatibility

“The total cost of ownership with 9.7.1 Improve BOOLEAN Increase compatibility
DB2 running on IBM systems is almost 9.7.1 Conditional Compilation Enhancement
half the cost of Oracle Database on 9.7.1 Basic DPF Support Broaden coverage
Sun systems.” 9.7.1 OCI Support Broaden coverage

9.7.2 UDF Parameters: DEFAULT Increase compatibility

9.7.2 Obfuscation Enhancement
Banco de Crédito del Peru
9.7.2 NCHAR, NVARCHAR, NCLOB Increase compatibility
“We switched from Oracle Database 9.7.3 NUMBER Performance Performance
to IBM DB2 and cut our costs in half, 9.7.3 Runtime “purity level” Enforcement Increase compatibility
while improving performance and 9.7.3 RATIO_TO_REPORT Function Increase compatibility
reliability of business applications.” 9.7.3 RAISE_APPLICATION_ERROR Increase compatibility

9.7.3 Small LOB Compare Increase compatibility
JSC Rietumu Banka 9.7.4 Multi-action Trigger & Update Before Trigger Increase compatibility

• Moved from Oracle Database to IBM DB2 9.7.4 Autonomous Tx Improvements Increase compatibility

• Used “compatibility features” 9.7.4 LIKE Improvements, LISTAGG Increase compatibility

• 3-30x faster query performance 9.7.4 ROW & ARRAY of ROW JDBC Support Increase compatibility

9.7.5 Pro*C Support Increase compatibility
• 200% improvement in data availability
9.7.5 Nested Complex Objects Increase compatibility

10 Local Procedure Definitions Increase compatibility

10 Local Type Definitions Increase compatibility
57
10 PL/SQL Performance Performance
* Based on internal tests and reported client experience from 28 Sep 2011 to 07 Mar 2012.



Workload Optimized Systems
One Size Does NOT fit all …..



IBM - Proven Track Record of Optimizing Systems
1950s…1960s
1990s
TPF: Airline Reservation System
IMS & s/360: Transaction & Database IMS, CICS, and DB2 Parallel Sysplex:
System High-scale Application and Data Serving

2000s
IBM Smart Analytics System
1970s...1980s
High-scale Business Intelligence and
System 38 and AS/400: Relational / XML Data Warehousing
Integrated Application and Data Serving
DB2 pureScale on PowerHA:
DB2 & S/370: Online Transaction High-Scale Database Management
Processing
WebSphere Edge Server:
High-scale Web Application Serving
Datapower:
XML & Web services appliance



Workload Optimized Right Out of the Box

• 3x to Database
throughput improvement
on brokerage application.
• System configuration
POWER7 (Model 9117-MMB)
DB2 9.7 FP1
4.6 TB SSD + 38.4 TB 15K HDD



IBM Smart Analytic System – For Operational Analytics
Leveraging DB2 10 - Coming soon
Faster Time to Value, Faster Business Results

5600
Based on System x
…Designed for business analytics workloads
Meeting clients where …Optional Solid State Disk – reducing data latency
their information is…

5710 7700
Based on System x Based on POWER7 Servers
…Cost-effective solutions for analytics …Scaling to hundreds of terabytes of data
and BI, reporting …Extract insights from untapped information
…Compact, integrated single analytics solutions
…Available for mid-market

7710
Based on Power7 Server 9600
…A single server warehousing and analytics solution Based on Based on System z
…Built on POWER7 based servers and designed for …Advanced query /workload management
production data warehouses - sizes under 10 TB, …Database designed and optimized for system
…For development and non-production use. …Disk controller, optimized to reduce data latency



Form-factor Coverage
Expert Optimised
Flexible Deployment Configurable System Warehouse Appliance

•Deployed On Platform of Choice •Highly Optimised & adaptable Platform •Fully Integrated High Performance
Analytical Appliance
•Sophisticated Architectures and workloads •Sophisticated Architectures and workloads
•Low Cost of Management
•Mixed Updates and Queries •Mixed updates and Queries
•Rapid Deployment
•Configurable/Tunable platform •Configurable/Tunable platform
•Industry Vertical Applications
•No Copy Analytics •No Copy Analytics

InfoSphere Warehouse IBM Smart Analytics System IBM Netezza

Choice of Platform Workload Optimized
Linux, Unix, Windows Simplify - Accelerate Value - Reduce Cost

DB2 or Z or Power or Linux
Customers who want
Customers with Operational Analytic requirements
a Warehouse Appliance for deep analytics
Software Only Solutions or Expert Optimized Systems

Configurable/Customisable Low Touch



Security, Data Governance &
Regulatory Compliance
A complete set of capabilities



Security, Regulatory Compliance and Data Governance
Components of a Data Compliance Offering
Leveraging DB2, Optim and Guardium

Audit updates made to data within Encryption for data at rest, backup
the database data or data within the database
• • Minimal impact to performance required
Applicable US Regs.:
• Sarbanes, HIPAA, PCI… • Encryption on disk, backups and on the
wire

Archive Strategy Creation of test databases
Corporate
• Keep data for a specified period of time • “Changing” sensitive data
• Includes audit record repository as well while maintaining “production-like”
Data Servers
as sensitive data data and referential integrity
• For development and test

Proactively detect areas Restrict and Manage access in DB2
• Individual users
of vulnerability
• Map database to business security models
• Important to ensuring
• SECADM
compliance of all other pillars
• Label Based Access Control
• Roles
• Finely Grained Access Control and Masking
• Identity Assertion and Trusted Context



Conclusion
Continued
Investment
Consistency of focus



Accelerating an Information-Led Transformation…
IBM has invested $12B in R&D and Acquisitions

Cognos SPSS
ILOG

InfoSphere

DB2 Informix
Netezza
FileNet Optim

66


IBM DB2 Technology Roadmap
Investment, Innovation and Industry Leading Capabilities
2011 2012 2013 2014 and Beyond
DB2 DB2 DB2 Overall
•Oracle compatibility •Temporal Query •Simple integrated Disaster • Unbreakable DB2
enhancements • Fine Grain Access Control Recovery (logical HADR) • Seamless application
• 2x Compression • SSD and PCM memory recovery
PureScale DB2 • Oracle compatibility hierarchy • Continuous application
•Geographic Cluster enhancements • Reorg free database availability
• HADR Multiple standby • Oracle compatibility • Transparent archival
• XML • Java object cache • Hibernate optimizations • Cross-database de-
• Multiple CF HCA integration duplication
Support pureScale DB2 • Virtual database and
pureScale DB2 • Online rolling upgrades tenant support
• Multiple Database •Range Partitioned Tables • Built-in encryption
• Incremental backup
Consolidation Platform • Tablespace recovery • Explicit Hierarchical Locking • no-sql, triple store, RDF etc
• Performance Improvements support
•Database as a Service • RDMA over 10Gb-E for P
for Tier 2 and below Consolidation Platform
applications • Common platform with SMAS
Consolidation Platform
• Virtualized •DbaaS for Tier 1 • Single model for Tier 1, Tier
applications 2, Tier 3 and
• Web workload
deployment • Bare metal DbaaS
deployment of pureScale
• Deployment into DB2
pureScale DB2 • Multitenancy support and more…

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DB2 – Differentiating Business Value

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