3.ibm cognos-tm1-9.5.2-why-upgrade

© 2011 IBM Corporation

Information Management

TM1 9.5.2 – Why Upgrade?

Brian Simpson

Product Manager – Cognos TM1



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.



3

TM1 9.5.2 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

• Red Hat Linux* • Excel 2010 • BI Pkg. Con. / C10

Language

• New Languages

Rules & TI

• Min, Max, Avg • Count, DCount

TM1Top

• Sandbox • Job Queue



4

Parallel Interaction

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

Goal

• Greater scale for read/write environments

• Faster performance for writers

• Simultaneous Activities (data maintenance writes / TI & reads)

• Maintain reader performance

• Greater CPU utilization

Benefits

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



5

Demonstrating 9.5.1 Locking Behavior – a brief review

Cubes linked by rules will „lock‟ together - A read to Cube A will lock a write to Cube A & B

Rules create a dependency

between Cube A & Cube B

Cube A

Cube 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

When the cube is „free‟, the writers process serially (they block each other)

Read

ers

W

rite

rs



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Without Concurrency, 9.5.1 performance will be on par with 9.5.2 PI

This is a TM1 9.5.1 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

9.5.2 with PI will not improve performance of this scenario because there is no concurrency conflict



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9.5.2 PI Enhances Writer Performance during High Concurrency – No Waiting!

TM1 9.5.2 PI removes object locking that occurs as a result of data reads or writes

Writers are no longer blocked by readers (or other writers) – they process without delay*

9.5.2 PI performance improvement vs. 9.5.1 is noticed when in 9.5.1 scenarios demonstrating contention - and when there are sufficient Server Cores to handle the greater level of concurrent transactions

* 9.5.2 still has object „locking‟ scenarios caused by Meta Data updates (including Dynamic Subsets, element updates, etc..)

….. We‟ll fix that in another release



8

Existing TM1 Read Concurrency

Cube A

Read 1 Cube A

Read 2 Cube A

Read 3 Cube A

Processing Time

Completion



9

Existing TM1 Read/Write Concurrency

Cube A

Read 1 Cube A

Read 2 Cube A

Read 3 Cube A

Write 1 Cube A

Write 2 Cube A

Processing Time

Completion

Wait Time

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10

Read/Write Concurrency with Parallel Interaction

Cube A

Read 1 Cube A

Read 2 Cube A

Read 3 Cube A

Write 1 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



11

How does PI work?

Cube A

Read 1 Cube A

Read 2 Cube A

Write 1 Cube A

Read 3 Cube A

Write 2 Cube A

t t+1 t+2

Time line

TM1 previously manage a single Data Tree to Access Cube Data

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

Reads access the latest Data Tree Access Point to get the most recent updates

Access „trees‟ to cube data are versioned, then updated



12

Starwood Read/Write Concurrency Test

25.4 37.3189.7136.7

499.8

2003.4

0

500

1000

1500

2000

250 Users 500 Users 1000 Users

TM1 Release

AV

G A

gg

reg

ate

Resp

on

se

Tim

e

9.5.2 9.5.1

Read / Write Concurrency Improvements 9.5.1 -> 9.5.2 PI

Windows 2003 / 8 Core

Load Runner / Cube Views

2 Hour Test

9.5.1 -> 9.5.2 Optimizations C-Lock, Data Spreading, and Auto

Recalc (very applicable to this test)

Server‟s 8 cores maxed out @

1000 users PI runs

93% Faster PI runs

81% Faster

PI runs

90% Faster



13

0

10

20

30

40

50

60

70

80

90

100

00:00

00:04

00:08

00:12

00:17

00:21

00:25

00:29

00:34

00:38

00:42

00:46

00:51

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

01:55

01:59

02:03

Duration of test (hh:mm)

% o

f T

ota

l CP

U

0

10

20

30

40

50

60

70

80

90

100

0:00

0:17

0:34

0:51

1:08

1:25

1:42

1:59

2:16

2:33

2:50

3:07

3:24

3:41

3:58

4:16

4:33

4:50

5:07

Duration of Test (hh:mm)

% o

f T

ota

l CP

U

0

10

20

30

40

50

60

70

80

90

100

0:00

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)

% o

f T

ota

l CP

U

1000 User R/W

500 User R/W

250 User R/W 9.5.2

9.5.1

9.5.2

9.5.1

9.5.2

9.5.1

Lower Volume not taxing 8 core server

500 User test reaches CPU

capacity

1000 Users saturates server -

delays due to CPU constraints



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)

– 1st 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



15

BI Integration

TM1 9.5.2 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

Drag „n Drop TM1 iWidgets to Business

Insight dashboards

TM1 iWidgets adopt BUX toolbar

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

Notes:

• Supports Cognos 10 „Dynamic Query‟ mode

• BI Package Support for DMR / SAP BW



16

Single Portal with Zero-footprint Web Interface

RDBMS DW OLAP Data

TM1 OLAP

PowerPlay

SAP BW

ESSBASE

MS Analysis

Services

Heterogeneous Flat Files

Common Metadata, Security, Integration Services, Query Engine, Automation, Process Mgt

Cognos BI with TM1

Google

Search Cognos

Mobile Cognos

Office

BI Analysis

For Excel

Reporting , Dashboarding, Ad-hoc Query, Analysis, Scorecarding, Event Management

TM1 Web, Executive Viewer



17

Cognos 10 Business Insight Dashboard with TM1 iWidget



18



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Excel Planning Templates What-If Analysis

Web Reports BI Dashboards & Scorecards



20

Cognos 10.1 FP1 on TM1 9.5.2: 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.



21

Contributor Usability

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

Text measures used for commentary can have fixed column widths and height

Consolidation data spreading when all children are null / zero

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

Notes:

• Next TM1 release has many Contributor

functional and scale enhancements



22

Single Workflow Hierarchy

Single Hierarchy for Approvers / Contributors



23

Workflow Page Re-Design Permissions appear in grid view

Ownership node identifies single or multi-node package



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



25

Consolidated Cell Spreading: Auto Proportional / Spread to All Leaves

Spread lump sum to all leaves : spread even if all leaves are null/zero

Enter 52k in Total Year Equipment

ProportionSpreadToZeroCells=T

ProportionSpreadToZeroCells=T

Tm1s.cfg



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Conformance

Red Hat Enterprise Linux 5.3 (Introduced with 9.5.1 Silent Refresh Pack)

Windows 2008 for TM1 Workflow (Introduced with 9.5.1 Silent Refresh Pack)

Excel 2010 Client and Web Server



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Languages

Traditional Chinese

Korean

Brazilian Portuguese

Russian

Polish

----

More CEEMA languages coming in 2011



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 multi-dimensional count

• Distinct Count identifies a single dimension for counting distinct occurrences of a value



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



30



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