1. Discover Informix
IBM Information Management
Informix Ultimate Warehouse Edition
- Extreme Performance for Faster Decisions
sandor.szabo@de.ibm.com
© 2011 IBM Corporation
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2. Discover Informix
The State of Data Warehouse
A Glimpse Into the Future
 Vendor solutions began to focus even more on the ability to isolate and prioritize workload
types including strategies for dual warehouse deployments and mixing OLTP and OLAP
on the same platform.
 In-memory DBMS solutions provide a technology which enables OLTP/OLAP combined
solutions. Organizations should increase their emphasis on financial viability during 2011
and even into 2012 as well as aligning their analytics strategies with vendor road maps
when choosing a solution.
Source: The State of Data Warehousing in 2011, 1/31/2011 by Mark Beyter, Roxane Edjlali, Donald Feinberg (ID Number: G00209643)
© 2011 IBM Corporation

3. Discover Informix
Data Warehouse Trends for the CIO, 2011-2012
Data Warehouse Appliances:
 DW appliances are not a new concept…Most vendors have developed an
appliance offering or promote certified configurations…Although there are many
reasons why organizations consider buying an appliance, the main reason is
simplicity.
The Resurgence of Data Marts:
 Data marts can be used to optimize DW by offloading part of the workload,
returning greater performance to the warehousing environment
Column-Store DBMSs
 CIOs should be aware that their current DBMS vendor may offer a column-store
solution. Don’t just buy a column-store-only DBMS because a column store was
recommended by your team.
In-Memory DBMSs
 IMDBMS technology also introduces a higher probability that analytics and
transactional systems can share the same database.
Source: Data Warehousing Trends for the CIO, 2011-2012, 1/27/2011 by Mark Beyter, Roxane Edjlali, Donald Feinberg (ID Number:
G00210272)
© 2011 IBM Corporation

4. Discover Informix
IT & Business Challenges for Analytics & Data Warehouse
 Costly for IT  Challenges for Business
– Cost for new hardware for – Lack of real-time operational
processors and disks information
– Administering OLTP and Data – Lack of Insight from lengthy
Warehouses concurrently analyses
– Expertise to tune databases – Inability to adopt new solutions
© 2011 IBM Corporation
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5. Discover Informix
What’s New in Data Warehousing (and Analytics)?
Columnar
© 2011 IBM Corporation
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6. In-Memory Computing Technology – Defined
© 2011 IBM Corporation
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7. Discover Informix
Row Oriented Data Store
Each row stored sequentially
• Optimized for record I/O
• Fetch and decompress
entire row, every time
• Result –
• Very efficient for
transactional workloads
• Not always efficient for
analytical workloads
If only few columns are required the complete row is
still fetched and uncompressed
© 2011 IBM Corporation

8. Discover Informix
Columnar Data Store
Data is stored sequentially by column
• Data is compressed
sequentially for column:
•Aids sequential scan
•Slows random access
If attributes are not required for a specific query execution,
they are skipped completely.
© 2011 IBM Corporation

9. Discover Informix
DW Appliance, Columnar and In-Memory Databases
DW Appliance Columnar Database
DataAllegro (Microsoft) Calpont
Dataupia Exasol
Infobright
Greenplum (EMC)
ParAccel
Kognito
Sand Technology
Netezza (IBM) Vertica (HP)
Sybase IQ (SAP)
In-Memory OLAP Tools
QlikTech/QlikView In-Memory Data Warehouse
Applix TM-1 (IBM-Cognos) HANA (SAP)
PALO ISAO-DB2 Z (IBM)
Exalytics (Oracle) IWA (IBM)
© 2011 IBM Corporation
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10. Discover Informix
Informix Warehouse Accelerator – Breakthrough Technology for Performance
Extreme Compression Row & Columnar Database
3 to 1 compression ratio Row format within IDS for transactional workloads
and columnar data access via accelerator for
OLAP queries.
Multi-core and Vector In Memory Database
Optimized Algorithms 3 generation database technology avoids
rd
Avoiding locking or synchronization 7 1 I/O. Compression allows huge databases
to be completely memory resident
6 2
5 3
Predicate evaluation on 4 Frequency Partitioning
compressed data Enabler for the effective parallel access of
Often scans w/o decompression the compressed data for scanning.
during evaluation Horizontal and Vertical Partition
Elimination.
Massive Parallelism
All cores are used for each query
Comes with Smart Analytics Studio, a GUI tool, for configuring data mart and monitoring IWA
© 2011 IBM Corporation

11. Discover Informix
Informix Ultimate Warehouse Edition
What it is
*Informix Warehouse Accelerator requires a Linux Intel system as it is relies on optimizations in that environment
© 2011 IBM Corporation

12. Discover Informix
Informix Warehouse Accelerator (Key Technologies)
values
Rare
Frequency
Partitioning
Occurrences
Number of
64-bit processor
Common
Values
RAM in TB
… … … …
11111 0 &1111 0
01001 0 == 1110 0
Compressed Predicate Evaluation
A1 D1 G1
A2 D2 G2
A3 D3 G3
A4 D4 G4
SIMD
© 2011 IBM Corporation
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13. Discover Informix
Compression: Frequency Partitioning
values
Rare
Trade Info (volume, product, Histogram Column Partitions
origin country) on Origin
Vol Prod Origin
Occurrences
Number of
China GER,
USA FRA,
… Rest
Common
Values
Origin
Top 64
traded goods
Cell Cell Cell 4
– 6 bit code 1 3
Product
Cell Cell Cell 6
Rest 2 5
Histogram
on Product Table partitioned
into Cells
• Field lengths vary between cells
• Higher Frequencies  Shorter Codes (Approximate Huffman)
• Field lengths fixed within cells
© 2011 IBM Corporation

14. Discover Informix
Data is Processed in Compressed Format
• Within a Register – Store, several
columns are grouped together.
• The sum of the width of the compressed
columns doesn‘t exceed a register
compatible width. This utilizes the full
capabilities of a 64 bit system. It doesn‘t
matter how many columns are placed
within the register – wide data element.
• It is beneficial to place commonly used
columns within the same register – wide
data element. But this requires dynamic
knowledge about the executed workload
(runtime statistics).
• Having multiple columns within the same
register – wide data element prevents
ANDing of different results.
Predicate evaluation is done against compressed data!
The Register – Store is an optimization of the Column – Store approach where we try to make the
best use of existing hardware. Reshuffling small data elements at runtime into a register is time
consuming and can be avoided. The Register – Store also delivers good vectorization capabilities.
© 2011 IBM Corporation

15. Discover Informix
Defining, What Data to Accelerate
• A MART is a logical collection of tables which are related to each other. For
example, all tables of a single star schema would belong to the same MART.
• The administrator uses a rich client interface or SmartMart to define the
tables which belong to a MART together with the information about their
relationships.
• IDS creates definitions for these MARTs in the own catalog. The related data
is read from the IDS tables and transferred to IWA.
• The IWA transforms the data into a highly compressed, scan optimized
format which is kept locally (in memory) on the Accelerator
IDS + IWA
Coordinator Worker
Process Processes
Define
© 2011 IBM Corporation

16. Discover Informix
Informix IWA in Action At A Retail Company
IWA
Store Managers & 160 GB data ~ 40GB
Home Office compressed RAM
Managers across IWA with 24 cores
thousands of stores single Linux Intel < 10 secs average
want to analyze box response with 500
promotional items users and 10x better
Data set is ~200GB price/performance
Current database Able to change
unable to provide promotional items on
quick enough a daily basis
turnaround
Challenge Solution Result
© ۲۰۱۱ IBM Corporation
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17. Discover Informix
IWA in Action for Public Sector
Long response when Seconds response time
police calls dispatcher to queries
Informix IWA with 2
Uncoordinated data cpus & 64 GB of Dispatcher can provide
from State, County, memory at nominal coordinated data
Dept, Specialty price
databases
No solution offered
Challenge Solution Result
© 2011 IBM Corporation
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18. Discover Informix
POC with Informix Warehouse Accelerator
 Data Warehouse query Performance without Perspiration
 Analysis query run time reduced from 45 minutes to 4
seconds
 Acceleration from 60x to 1400x – average acceleration of
450x
 More questions, faster answers, better business insights
© 2011 IBM Corporation

19. Discover Informix
POC: Datamart at a Government Agency
• Microstrategy report was run, which generates
• 667 SQL statements of which 537 were Select statements
• Datamart for this report has 250 Tables and 30 GB Data size
• Original report on XPS and Sun Sparc M9000 took 90 mins
• With IDS 11.7 on Linux Intel box, it took 40 mins
• With IWA, it took 67 seconds.
© 2011 IBM Corporation

20. Discover Informix
Informix Growth Warehouse Edition
IUWE IGWE
Components Informix Ultimate Edition Informix Growth Edition
Compression IWA
IWA ISAO Studio
ISAO Studio
Limits Max memory available Informix Growth
No core limits 16 cores, 16 GB Memory max
Informix on 4 platforms: Informix on 4 platforms:
AIX64, Sol64, HPUX64, Linux- AIX64, Sol64, HPUX64, Linux-Intel64
ntel64
IWA on Linux-Intel 64
IWA on Linux-Intel 64
48 GB Max, 16 core limit
Target > 300 GB of raw data < 300 GB of raw data
List Price $463 per PVU $150 per PVU
© 2011 IBM Corporation
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21. Discover Informix
Target Informix clients in the Ultimate Warehouse sweet spot
Informix Warehouse Editions
Informix Ultimate Warehouse
Edition (IUWE) and Growth
Warehouse Edition (IGWE)
means higher performance
and lower costs for Informix Informix, XPS, Red Brick
clients
< 5 TB Star schema Mixed
data mart Workloads
"Gemini Systems is extremely excited about the Informix Ultimate Warehouse Edition. Combining deep
columnar technology with the super fast performance of in-memory databases solves many problems for
both legacy and future warehouse customers. The investment preservation proposition of this offering just
can't be beat. No rip-and-replace, no code rewrites, no data migrations, no tuning. Just plug-in and go for
immediate business value return." - Michael "Mick" Bisignani , Senior Vice President and CTO
,Gemini Systems LLC
© 2011 IBM Corporation
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22. Discover Informix
Do you struggle with…
… performance issues on
analytics and business reports ?
•Reports taking too long to run
•Ad-hoc queries with unpredictable
response times
… cost and flexibility for mixed … ongoing warehouse
workloads? maintenance and administration?
•Unable to optimize on a single
platform •Constant tuning
•Building/Maintaining cubes
•Constant storage optimization
… leaving you at a competitive disadvantage ?
This is an example text. Go ahead and replace it with your own text. It is meant to give you
… feeling of how the designs looks including text.
a Introducing the Informix Ultimate Warehouse Edition
© 2011 IBM Corporation

23. Discover Informix
New order of Performance! Take “No” for
No Near zero
Storage
maintenance!! allocation/data administration!!
partitioning
10s to
1000s of Index Statistics
times faster maintenance maintenance
Predictable
NO
response
times
Cube
Application maintenance
changes or summary
tables
© 2011 IBM Corporation

24. Discover Informix
Informix Ultimate Warehouse – Performance, Simplicity,
Transparency
BI App
Configure, offload data mart
HPUX-64, AIX-64, SOL-64, Linux-64 Linux-64, Intel
Redirect queries
Informix env Informix Warehouse Accelerator
Query Results
Warehouse DataMart
© 2011 IBM Corporation

25. Discover Informix
Informix Hybrid Engine Overview
.
.
.
.
© 2011 IBM Corporation

26. Discover Informix
IWA Design Studio
DB connections
Accelerator
© 2011 IBM Corporation

27. Workload Advisor for Mart Definition
• Takes the guesswork out of defining a data mart for IWA
• Run selected queries (presumably the most time-
consuming ones) through advisor
• Advisor will generate mart definition in XML format to be
loaded onto IWA
• Can be fully automated

28. Typical Data Warehouse Architecture
Discover Informix
All databases as marked above including OLTP, data warehouse/data mart/ODS can
run on Informix
© 2011 IBM Corporation

29. Discover Informix
What Is IWA Ideally Suited For?
REGION
Star or snowflake schema
Complex, OLAP-style queries that typically:
MONTH • Need to scan large subset of data (unlike
QUARTER
CITY OLTP queries)
• Involve aggregation function such as
COUNT, SUM, AVG.
• Look for trends, exceptions to assist in
STORE
PERIOD making actionable business decisions
SALES SELECT PRODUCT_DEPARTMENT, REGION, SUM(REVENUE)
FROM FACT_SALES F
INNER JOIN DIM_PRODUCT P ON F.FKP = P.PK
INNER JOIN DIM_REGION R ON F.FKR = R.PK
LEFT OUTER JOIN DIM_TIME T ON F.FKT = T.PK
PRODUCT
WHERE T.YEAR = 2007
GROUP BY PRODUCT_DEPARTMENT, REGION
CATEGORY
BRAND
© 2011 IBM Corporation

30. Discover Informix
Sizing Guidelines
Number of Intel cores
T-shirt size Raw data * Main Memory
(X7560)
XL >1.5 TB to 3 TB 1 TB 24-32
L >750 GB to 1.5 TB 512 20-24
M > 400 GB to 750 GB 256 GB 16-20
S > 250 GB to 400 GB 192 GB 12-16
XS ≥ 100 GB to 250 GB 96 GB 8-12
XXS < 100 GB 48 GB 8
XXXS < 50 GB 24 GB 4
* Raw data represents only table data and excludes any indices, temp table space etc
Important Considerations
T-shirt sizes are a reference guideline only and are not officially available
configurations.
© 2011 IBM Corporation

31. Discover Informix
Configuration Scenarios
 Alternative 1: Install IWA on a separate Linux box
Database Server InformixWarehouse Accelerator
RHEL 5,6/SUSE 11 -64
Solaris 10/AIX 6.1/HP-UX 11.31 64 RHEL 5,6/SUSE 11 -
 Alternative 2: Install Informix and IWA in the same symmetric multiprocessing system
Database Server Informix Warehouse Accelerator
RHEL 5,6/SUSE 11-64
 Note: IWA requires Linux on Intel x64 (64-bit EM64T) Xenon
© 2011 IBM Corporation

32. Discover Informix
The Differentiation
Deep Columnar Technology In-Memory
Data is stored and accessed Entire data set being queried is
using columnar approach compressed and in-memory
eliminating disk I/O
IUWE
Run mixed workloads No Maintenance
OLTP transactions and OLAP No requirements for indexes,
queries can run against the query tuning or MOLAP cubes
same system 450
times
330
900
times
The Result!!
times 1350
times
ORDERS OF MAGNITUDE PERFORMANCE IMPROVEMENTS!!
© 2011 IBM Corporation

33. Discover Informix
Motto for UWE
“Everything should be made
as simple as possible, but not
simpler.”
―Albert Einstein
© 2011 IBM Corporation

34. Discover Informix
Questions?
contact Sandor Szabo,
Sandor.szabo@de.ibm.
com
© 2011 IBM Corporation