1. IBM Cognos Business Intelligence Performance
Jason Tavoularis – Product Manager
March 2015
IBM Cognos Business Intelligence Performance
IBM Big Data & Analytics © 2013 IBM Corporation

2. Agenda Architecture and platform capabilities Best Practices
Recent performance improvements

3. mobile / web interfaces or SDK
IBM Cognos Business Intelligence 10.x architecture
mobile / web interfaces or SDK
report service (RSVP)
BIBusTKServerMain
32 or 64 bit
query service (XQE)
Java
64 bit
C8 query stack (UDA)
BIBusTKServerMain
32 bit
Dynamic
Query mode
Compatible
data sources

4. The Data Access layer of IBM Cognos Business Intelligence
Generates SQL/MDX specifically optimized for the type and version of underlying data source(s)
Security-aware in-memory caching avoids redundant queries
Blends multiple sources of business data together
Powerful, efficient data summarization
Dynamic query mode employs a 64-bit extensible Java query engine
Compatible query mode for easy upgrades from Cognos 8
Dynamic Query
Compatible Query
Dynamic Cubes

5. Data Source Updates (DQM)
Hitachi HADB
IBM Domino
Cloudera Impala x
Pivotal Greenplum
HP Vertica
EXASOL EXASolution
Actian ParAccel (now Matrix) x
IBM DB2 i
SAP HANA
SAP Sybase IQ
Apache Hive
MySQL
Postgres
IBM Informix
IBM IMS
IBM BigInsights
SAP/ECC
Siebel
Salesforce.com x
IBM DB2 LUW & Z
IBM Netezza
Teradata
MS SQL Server
MS Analysis Services
Oracle
Amazon Redshift
Cisco Composite
CA IDMS
OData
JSON
IBM Cognos TM1
SAP/BW
Oracle Essbase

6. Dynamic Cubes Feature mission
High performance analytics over growing data volumes
Optimize in-memory caching with in-database processing
Aggregate awareness
Aggregate acceleration

7. Dynamic Cubes Lifecycle
3. Reporting & analytics
2. Deploy, manage
Dynamic Cube Server
4. Optimize
Dynamic
Cube
1. Model & publish
Logs CM
Warehouse

8. Dynamic Cubes find the shortest path to the answer
Result Set Cache
Security is applied on top of the caches, so all users benefit
Expression Cache
Member Cache
Query Data Cache
Aggregate Cache
BI query service
Over 80% of queries are < 3 seconds
Over half of queries are sub-second
Aggregates
Warehouse
Database
© 2013 IBM Corporation 8

9. TPC-DS 10 TB warehouse performance with Dynamic Cubes
28.8 billion row fact table
65 million members in largest dimension (Customer)
First open
Subsequent open

10. “ ” “ ” University Colorado
After running the Aggregate Advisor, a report that used to take over 90 minutes ran in 3 seconds.
Dynamic Cubes helps us turn Cognos from a packaged reporting engine into a self-service BI engine.
—Molly Doyle, Assistant Director for IRM, University Information Systems, University of Colorado, Office of the President ” “ ”
One of our customers, the University of Colorado, recognized early on that the performance gains we were observing in our labs might solve some major bottleneck problems they were having. They jumped early into a Dynamic Cube implementation, and then shared with us what they accomplished.
After updating to the Dynamic Cube technology, they saw an immediate gain in the preparation of a critical university report. This was report was so large that in order to get all the data and the calculations, they had to run a section of the report and then import it into Microsoft Access, run the next section of the report, shift it also into Access, and so on. Once all the report pieces were gathered in Access, the analyst could add the calculations and deliver the report. This took most on an entire day, including 90 minutes of report run time,. Dynamic Cubes, because of its ability to process massive amounts of data and because of its aggregation recommendation, it took this entire process down to under 3 seconds. So they saw an immediate impact on existing reports. But because of the fast response times of Dynamic Cubes, they state that the university has moved from a packaged reporting company into a true, self-service responsive IT organization. Now the questions that they need to ask of their data can be asked and answered in seconds, on the glass, rather than waiting for creation or modification of a report. This takes the strain off the IT department and their backlog of report requests, and frees the university’s business analysts to solve problems and gain insights more quickly
This quote did not fit on the slide….“With Dynamic Cubes, performance will continue to be fast
even as our data volumes grow.”
© 2013 IBM Corporation

11. Technology Selection Guidance
Application objective
Preferred technology
static reports (no interactivity)
simple list reporting
reporting on leaf-level records
Pure relational
volatile data because of planning and budgeting applications
users writing back to the same data source being analyzed
what-if analysis
TM1
data warehouse structured in a star or snowflake schema
self-service interactive analysis
large and growing data volumes
Dynamic Cubes
interactive analysis on operational/transactional data
tight control over latency (caching)
tight control over security
DMR

12. Online Technical Resources
IBM Redbooks Publications
Dynamic Query
Dynamic Cubes
IBM Knowledge Center
Guidelines for Modeling Metadata
IBM developerWorks
Business Analytics Proven Practices
Youtube
IBM Business Analytics

13. Learn more about these exciting innovations at www.AnalyticsZone.Com
IBM IOD 2012
4/21/2017
Learn more about these exciting innovations at
See the new features in action
Read blogs on key topics from product experts
Test drive a trial version of Cognos BI V10.2.2
Let us know what you think!
‘Sign up’ or ‘Sign in’ to
Click on Downloads and Trials and select “Business Intelligence” on the menu  
Drury Design Dynamics

14. Performance troubleshooting
Find the bottleneck: eliminate, simplify, reduce, narrow down
in Report Studio, you can test a Query or Page independently
Open two instances of Report Studio and copy and paste
Dynamic Query Analyzer
Tracing
Review statistics and other metrics in the underlying data source(s)
Chapter 7 to the IBM Cognos Dynamic Query Redbooks publication

15. all else equal, less is faster
Optimizing SQL for performance
avoid unnecessary complexity
avoid unnecessary conversions
consider Display values different from Use values
take advantage of indexes and table organization features
chapter 6 of IBM Cognos Dynamic Query Redbooks publication
all else equal, less is faster

16. #Macros#
Macros are fragments of code that you can insert in the expression editors and several other interfaces of Cognos BI
you can nest macro functions and reference session parameters (user info), parameter maps (look up tables)
macros are evaluated during query planning and fully expanded before query execution
macros can give significant performance improvements
macros can allow your applications to be much more flexible
chapter 4 of IBM Cognos Dynamic Query Redbooks publication

17. Dimensional report authoring – Performance Tips (applies to PowerCubes, TM1, DMR, Dynamic Cubes, Essbase, SSAS, and SAP BW)
Filtering on a Member Unique Name (MUN) is fastest
Avoid filtering on attributes
Use parent members for summaries
Specify Automatic in your summaries instead of an explicit summary (such as Total)
the function that computes automatic summaries is Aggregate()
especially useful when detail summaries are required, such as in a list report
If you know which members have the data you care about, explicitly add those into the report
Step-by-step report creation:
Add one data item at a time and filter that item down to the smallest number of members before proceeding to the next data item
Read Writing Efficient OLAP Queries on developerWorks

18. Active Reports performance and responsiveness
Simplification of the report reduces the size of the output and improves opening time
Performance improvements in v10.2.2
new JSON data store for most client side controls (including extensible visualization)
reduced complexity in report_output.xml which reduces size and improves opening time
if the same vizbundle is being used multiple times, now only 1 vizspec is being stored
Examples without re-authoring the report:
Opening a report: 25s down to 5s (iPad Air)
File size : 13MB down to 10MB
Improvements vary depending on the Active Report

19. Dimensionally Modeled Relational (DMR)
IBM IOD 2011
4/21/2017
4/21/2017 8:29 AM
Dimensionally Modeled Relational (DMR)
CQM
relatively complex SQL generated to simulate OLAP experience
temporary cubes built on file system when needed
report authors can use relational functions in certain scenarios
DQM
relatively simple SQL generated to populate in-memory cubes
a true OLAP experience
authors must use dimensional functions
Prensenter name here.ppt 19

20. IBM IOD 2011
4/21/2017
4/21/2017 8:29 AM
DMR Performance
no one-size-fits-all strategy to optimizing performance
in-memory cube approach of DQM
best for small-to-medium volumes of data
excellent performance when cache is primed
DQM cold-cache performance improvements in every new version
more being developed in the IBM Labs
recommendations
if cache won’t be used, set Use Local Cache to No
chapter 7 of IBM Cognos Dynamic Query Redbooks publication
Prensenter name here.ppt 20

21. Performance improvements with BI 10.2.1+ and TM1 10.1.1+
DQM’s local MDX engine (LOLAP) now employed for TM1
generally faster and more versatile than TM1’s MDX engine
Much more BI side caching
BIG performance improvements for interactive analysis
automatic detection of changes to TM1 cube -> stale data cleared
Internally suppression on always (by default)
large sparse results is the #1 performance problem in earlier versions
DQM will push NON EMPTY suppression on every data query to TM1
UseProviderCrossJoinThreshold now obsolete and ignored 
TM1 Java API is now employed
Faster loading of members through this interface

22. Other recent performance improvements
Master-detail optimizations
Smarter cache reuse
Crosstab spacer performance
Filter Join Optimization
Many Dynamic Cube performance improvements
IBM Big Data & Analytics © 2014 IBM Corporation

23. Q&A 23

24. IBM Big Data & Analytics © 2013 IBM Corporation
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