© 2015 IBM Corporation IBM Cognos Business Intelligence Performance Jason Tavoularis – Product...

© 2015 IBM Corporation

IBM Cognos Business IntelligencePerformance

Jason Tavoularis – Product Manager

March 2015

© 2014 IBM Corporation2

Agenda

Architecture and platform capabilities Best Practices Recent performance improvements


Query modeQuery mode

CompatibleDynamicquery service query service (XQE)(XQE)

• JavaJava• 64 bit64 bit

query service query service (XQE)(XQE)

• JavaJava• 64 bit64 bit

C8 query stack C8 query stack (UDA)(UDA)

•BIBusTKServerMainBIBusTKServerMain• 32 bit32 bit

C8 query stack C8 query stack (UDA)(UDA)

•BIBusTKServerMainBIBusTKServerMain• 32 bit32 bit

data sourcesdata sourcesdata sourcesdata sources

report service report service (RSVP)(RSVP)

• BIBusTKServerMainBIBusTKServerMain• 32 or 64 bit32 or 64 bit

report service report service (RSVP)(RSVP)

• BIBusTKServerMainBIBusTKServerMain• 32 or 64 bit32 or 64 bit

mobile / web interfaces or SDKmobile / web interfaces or SDKmobile / web interfaces or SDKmobile / web interfaces or SDK

IBM Cognos Business Intelligence 10.x architecture


DynamicQuery

CompatibleQuery

DynamicCubes

DynamicCubes

The Data Access layer of IBM Cognos Business Intelligence

4

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


Data Source Updates (DQM)

• IBM Cognos TM1• SAP/BW• Oracle Essbase

10.1.0.x

10.1.1.x

• IBM DB2 LUW & Z• IBM Netezza• Teradata• MS SQL Server• MS Analysis Services• Oracle

10.2.0.x

• IBM Informix• IBM IMS• IBM BigInsights• SAP/ECC• Siebel• Salesforce.com

• IBM DB2 i• SAP HANA• SAP Sybase IQ• Apache Hive • MySQL• Postgres

10.2.1.0

• Pivotal Greenplum• HP Vertica• EXASOL EXASolution• Actian ParAccel (now Matrix)

10.2.1.3

• IBM Domino

10.2.1.4 10.2.2.0

• Hitachi HADB

• Amazon Redshift• Cisco Composite• CA IDMS• OData• JSON

10.2.1.2

• Cloudera Impala


High performance analytics over growing data volumes

Aggregate awarenessAggregate acceleration

Optimize in-memory caching with in-database processing

Dynamic CubesFeature mission


1. Model & publish

2. Deploy, manage3. Reporting & analytics

4. Optimize

Dynamic Cube Server

DynamicCube

DynamicCube

Logs

CMCM

Warehouse

Dynamic Cubes Lifecycle

© 2014 IBM Corporation8© 2013 IBM Corporation8

• Security is applied on top of the caches, so all users benefit

BI query service

DatabaseDatabase

Warehouse

Aggregates

Result Set Cache

Expression Cache

Member Cache

Query Data Cache

Aggregate Cache

Over 80% of queries are < 3 seconds

Over half of queries are sub-second

Dynamic Cubes find the shortest path to the answer


TPC-DS 10 TB warehouse performance with Dynamic Cubes

28.8 billion row fact table 65 million members in largest dimension (Customer)

Subsequent open

First open


University Colorado 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

“

”

”“


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

Technology Selection Guidance


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

12


Learn more about these exciting innovations at www.AnalyticsZone.Com

See the new features in actionRead blogs on key topics from product expertsTest drive a trial version of Cognos BI V10.2.2Let us know what you think! 1. ‘Sign up’ or ‘Sign in’ to www.AnalyticsZone.Com

2. Click on Downloads and Trials and select “Business Intelligence” on the menu


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

o http://www.redbooks.ibm.com/abstracts/sg248121.html

Performance troubleshooting


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

o http://www.redbooks.ibm.com/abstracts/sg248121.html

all else equal, less is faster

Optimizing SQL for performance


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


http://www.redbooks.ibm.com/abstracts/sg248121.html

Macros are fragments of code that you can insert in the expression editors and several other interfaces of Cognos BI

#Macros#


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

Dimensional report authoring – Performance Tips(applies to PowerCubes, TM1, DMR, Dynamic Cubes, Essbase, SSAS, and SAP BW)


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


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

Dimensionally Modeled Relational (DMR)


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


http://www.redbooks.ibm.com/abstracts/sg248121.html

DMR Performance


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

Performance improvements with BI 10.2.1+ and TM1 10.1.1+


Other recent performance improvements

Master-detail optimizations Smarter cache reuse Crosstab spacer performance Filter Join Optimization Many Dynamic Cube performance improvements


Q&A


Legal Disclaimer

• © IBM Corporation 2014. All Rights Reserved.• The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is

provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software.

• References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results.

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