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

Agenda Architecture and platform capabilities Best Practices Recent performance improvements

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

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

Data Source Updates (DQM) 10.2.2.0 10.2.1.4 10.2.1.3 Hitachi HADB 10.2.1.2 IBM Domino 10.2.1.0 Cloudera Impala 10.2.0.x Pivotal Greenplum HP Vertica EXASOL EXASolution Actian ParAccel (now Matrix) 10.1.1.x IBM DB2 i SAP HANA SAP Sybase IQ Apache Hive MySQL Postgres IBM Informix IBM IMS IBM BigInsights SAP/ECC Siebel Salesforce.com 10.1.0.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

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

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

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

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

“ ” “ ” 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

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

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

Learn more about these exciting innovations at www.AnalyticsZone.Com IBM IOD 2012 4/21/2017 Learn more about these exciting innovations at www.AnalyticsZone.Com 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 www.AnalyticsZone.Com Click on Downloads and Trials and select “Business Intelligence” on the menu   Drury Design Dynamics

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

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 http://www.redbooks.ibm.com/abstracts/sg248121.html all else equal, less is faster

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

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

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

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

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 http://www.redbooks.ibm.com/abstracts/sg248121.html Prensenter name here.ppt 20

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

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

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