TM Why Upgrade?

Transcription

1 Brian Simpson Product Manager Cognos TM1 TM Why Upgrade? Information Management

2 Important Disclaimer THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILE EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, 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 PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, OR SHALL HAVE THE EFFECT OF: CREATING ANY WARRANTY OR REPRESENTATION FROM IBM (OR ITS AFFILIATES OR ITS OR THEIR SUPPLIERS AND/OR LICENSORS); OR ALTERING THE TERMS AND CONDITIONS OF THE APPLICABLE LICENSE AGREEMENT GOVERNING THE USE OF IBM SOFTWARE. 2

3 TM Enhancements Read / Write Scalability & Performance Parallel Interaction Data Reservation BI Integration BI Report Performance TM1 iwidgets Contributor Usability Multi-Node Edit Text Wrap Spreading Shortcuts Conformance Language Rules & TI TM1Top Red Hat Linux* Excel 2010 BI Pkg. Con. / C10 3 New Languages Min, Max, Avg Count, DCount Sandbox Job Queue

4 Parallel Interaction Goal Greater scale for read/write environments Faster performance for writers Simultaneous Activities (data maintenance writes / TI & reads) Maintain reader performance Greater CPU utilization Notes: PI provides benefits when contention exists PI is not targeted at reader performance Meta Data maintenance still blocks Memory consumption will rise ~ 10 30% Watch for core saturation as you scale Benefits During periods of read/write contention, writer performance is improved Performance is steady as users scale Analysis of updates in real-time Concurrent data spreading Eliminates Writer Partitioning Best Practice Intra-Day Data Imports do not inhibit writers Intra-Day TI Processes do not inhibit writers Faster parallel data loads to same cube Scale leverages server core capacity Job Queue multi-threads processeing 4

5 Writers Readers Information Management Demonstrating Locking Behavior a brief review Rules create a dependency between Cube A & Cube B Cube B Cube A Cubes linked by rules will lock together - A read to Cube A will lock a write to Cube A & B Blocked writers will stack up behind concurrent readers and wait for the reads to flush out. Readers are free to process together, so multiple reads can occur in parallel to all cubes 5 When the cube is free, the writers process serially (they block each other)

6 Without Concurrency, performance will be on par with PI This is a TM example demonstrating behavior without concurrency conflicts These readers and writers are not conflicting with each other, therefore there is no waiting occurring due to object locking with PI will not improve performance of this scenario because there is no concurrency conflict 6

7 9.5.2 PI Enhances Writer Performance during High Concurrency No Waiting! TM PI removes object locking that occurs as a result of data reads or writes 7 * still has object locking scenarios caused by Meta Data updates (including Dynamic Subsets, element updates, etc..).. We ll fix that in another release Writers are no longer blocked by readers (or other writers) they process without delay* PI performance improvement vs is noticed when in scenarios demonstrating contention - and when there are sufficient Server Cores to handle the greater level of concurrent transactions

8 Existing TM1 Read Concurrency Processing Time Read 1 Cube A Completion Read 2 Cube A Cube A Read 3 Cube A 8

9 Existing TM1 Read/Write Concurrency Wait Time Processing Time Read 1 Cube A Completion Write 1 Cube A Cube A Read 2 Cube A Read 3 Cube A Write 2 Cube A 9

10 Read/Write Concurrency with Parallel Interaction Read 1 Cube A Write 1 Cube A Cube A Read 2 Cube A Read 3 Cube A Write 2 Cube A Read 2 will not include data impact from Write 1 (because it begins before Write 1 completes) Read 3 will include data impact from Write 1 (because it begins after Write 1 completes), but not Write 2 10

11 How does PI work? Time line Read 1 Cube A Read 2 Cube A Write 1 Cube A Read 3 Cube A Write 2 Cube A TM1 previously manage a single Data Tree to Access Cube Data Access trees to cube data are versioned, then updated t+2 t+1 t Reads could share a Tree, but Writes had to wait for Exclusive Access to update the Tree Parallel Interaction creates a new Data Tree Access Point version for each Write, allowing concurrent Reads & Writes Cube A Reads access the latest Data Tree Access Point to get the most recent updates 11

12 AVG Aggregate Response Time Information Management Read / Write Concurrency Improvements > PI Windows 2003 / 8 Core Load Runner / Cube Views 2 Hour Test Starwood Read/Write Concurrency Test > Optimizations C- Lock, Data Spreading, and Auto Recalc (very applicable to this test) PI runs 81% Faster PI runs 93% Faster Users 500 Users 1000 Users TM1 Release PI runs 90% Faster Server s 8 cores maxed 1000 users

13 % of Total CPU % of Total CPU % of Total CPU Information 80 Management User R/W :00 00:04 00:08 00:12 00:17 00:21 00: :29 00:34 00:38 00:42 00:46 00:51 Duration of test (hh:mm) 00:55 00:59 01:04 01:08 01:12 01:16 01:21 01:25 01:29 01:33 01:38 01:42 01:46 01:50 01: User R/W 01:59 02:03 Lower Volume not taxing 8 core server 500 User test reaches CPU capacity :00 0:08 0:17 0:25 0:34 0:42 0:51 0:59 1:08 1:16 1:25 1:33 1:42 1:50 1:59 2:08 2:16 2:25 2:33 2:42 2:50 2:59 Duration of test (hh:mm) User R/W 1000 Users saturates server - delays due to CPU constraints :00 :17 :34 :51 :08 :25 :42 :59 :16 :33 :50 :07 :24 :41 :58 :16 :33 :50 :07

14 Parallel Interaction Notes No benefit / possible negative impact to Read performance Reads now compete with Writes for system resources Availability to cache is lessened (due to more frequent write invalidation) Separate Reader environments still a Best Practice Read Only environments operate with PI disabled CPU utilization will be greater, raising importance of server core capacity PI performance benefits are reduced when server CPU power is fully utilized Increase cores in alignment with concurrent read/write scale Dimension Updates still wait for Reads and block other Dimension Updates Isolate Meta Data Updates from Data Updates in TI Processes Other activities remain subject to blocking Save Data All Views with Dynamic Subsets Public Views with UDCs (including Subsets in a Subset) 1 st Time View activity following Cube Loading (Cube Dependencies) Best Practice - Separate Meta Data Loads from Data Loads to lessen lock duration Run TI command line 14

15 BI Integration Goal Cognos 10 Conformance Improve BI Report Performance against TM1 Data Sources Leverage TM1 for suppression and filter reports Optimize MDX generated by BI Reduce network data transfers Polish TM1 iwidgets for Business Insight Benefits TM Conforms with C8.4 and Cognos 10 BI Reporting on TM1 is faster BI Server memory not a bottleneck Zero Suppression reporting against large, sparse TM1 Databases having big dimensions is much faster Top Count, Measure filtering, and Attribute filtering reports are much faster Reports indirectly referencing members via levels or children of consolidation are faster Notes: Supports Cognos 10 Dynamic Query mode BI Package Support for DMR / SAP BW Drag n Drop TM1 iwidgets to Business Insight dashboards TM1 iwidgets adopt BUX toolbar 15

16 Cognos BI with TM1 Google Search Cognos Office BI Analysis For Excel Cognos Mobile Single Portal with Zero-footprint Web Interface Reporting, Dashboarding, Ad-hoc Query, Analysis, Scorecarding, Event Management TM1 Web, Executive Viewer Common Metadata, Security, Integration Services, Query Engine, Automation, Process Mgt 16 Heterogeneous RDBMS DW OLAP Data TM1 OLAP PowerPlay SAP BW ESSBASE MS Analysis Services Flat Files

17 Cognos 10 Business Insight Dashboard with TM1 iwidget 17

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19 Excel Planning Templates What-If Analysis Web Reports BI Dashboards & Scorecards 19

20 Cognos 10.1 FP1 on TM : Moderate Sized TM1 Datasource Reporting Only the first report execution time is measured as this more closely matches a real life scenario of a user running a report once in a given user session. 20

21 Contributor Usability Goal Extend Contributor 9.5.x Multi-Node Editing Enhance Contributor Workflow page layout and content Provide pre-defined column width and text wrapping configuration Enhance data spreading intuitiveness Benefits Planners having budget responsibility for multiple nodes (cost centers, projects, etc.) can update some/all nodes in a single Contributor Session Perform update while comparing nodes Data Spread across nodes Planners can submit all nodes of a single consolidation in one action Approvers can review / update a submission of multiple nodes in one session Workflow page presents a single hierarchy for dual role Approvers/Contributors Notes: Next TM1 release has many Contributor functional and scale enhancements Text measures used for commentary can have fixed column widths and height Consolidation data spreading when all children are null / zero 21

22 Single Workflow Hierarchy Single Hierarchy for Approvers / Contributors 22

23 Workflow Page Re-Design Permissions appear in grid view Ownership node identifies single or multi-node package 23

24 Text Column Wrapping (for Contributor UI and Web Cube Views) Edit Web.config configuration for all text columns (character width and count) Web.config setting 24

25 Consolidated Cell Spreading: Auto Proportional / Spread to All Leaves Enter 52k in Total Year Equipment ProportionSpreadToZeroCells=T Spread lump sum to all leaves : spread even if all leaves are null/zero Tm1s.cfg ProportionSpreadToZeroCells=T 25

26 Conformance Red Hat Enterprise Linux 5.3 (Introduced with Silent Refresh Pack) Windows 2008 for TM1 Workflow (Introduced with Silent Refresh Pack) Excel 2010 Client and Web Server 26

27 Languages Traditional Chinese Korean Brazilian Portuguese Russian Polish ---- More CEEMA languages coming in

28 New Rule and TI Functions for Statistical Analysis Min, Max, Avg, Count, Distinct Count Min, Avg, Count can include or exclude zeros Count is a multidimensional count Distinct Count identifies a single dimension for counting distinct occurrences of a value 28

29 TM1Top Enhancements Expand Capabilities of TM1Top to : Monitor Sandbox Activity Monitor Job Queue Activity Enabled by new TM1Top.ini commands View Sandbox Memory impact / # of Sandboxes View Job Queue activity, kill Jobs 29

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31 Questions & Comments Information Management