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Copyright © 2010, SAS Institute Inc. All rights reserved. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Staying Highly Available using SAS® Grid Manager: The Performance Management Application at Commerzbank AG Thomas Braun and Miriam Schipper, Commerzbank AG, Frankfurt, Germany Cheryl Doninger, SAS Institute Inc., Cary, NC Jochen Kirsten, SAS Institute Inc., Heidelberg, Germany

PC-RB Business Management and Vertrieb Seattle April 2010

Staying Highly Available using SAS® Grid ManagerThe Performance Management Application at Commerzbank AG Seattle, April 13, 2010

3PC-RB Business Management and Vertrieb Seattle April 2010

Agenda

10PageCommerzbanks ”Performance Manager” as solution3.

Page

Page

Integrated sales performance management required

Commerzbank AG – key facts

2.

1.

6

2


Milestones of corporate history

1870: Commerz- and Disconto-Bank founded in Hamburg

1971: Transformation of New York representative office into a branch – being the first German bank to do so

1990: Opening of the first branch in east Germany following the reunification

1995: comdirect bank AG - Germany‘s leading online broker - established

2003: Increase of stake in BRE Bank, Poland, to more than 70 %

2004: Acquisition of the branch network of SchmidtBank (70 branches, 29 self service centres)

2005: Acquisition of Eurohypo making Commerzbank Germany‘s second largest bank

2008: No. 1 in Affluent Customers, market share approx. 15% (source: Source: FMDS 2008, TSN Infratest)

2009: Acquisition of Dresdner Bank – integration is well on track

Commerzbank headquarters in Frankfurt


Integration well on trackJoint management structure

Stable governance at Head Office

Joint marketing activities

Yellow gradually integrated

First joint products

Tangible integration for customers

Joint customer advisory council

Customers have a strong voice

Joint employee events

Cultural interaction

Legal merger

Merger on May 11, 2009

Committee negotiations concluded

Works Council agreement reached

Allianz Banking spun off

Allianz Bank independent since June

Call Centers merged

Commerz Direktservice GmbH

Consequent Divestments

First EU requirements met

Head offices merged

Employees in key functions

Branches merged from 2009

First joint branches


Commerzbank’s Strategic Position

Profitability Drivers

Strategic Goals

Leading retail bank in Germany with 11 million private customers:- Comprehensive nationwide branch network of 1,200 branches for

Commerzbank and Dresdner Bank altogether- No. 1 Retail Bank - No. 1 Online Broker- No. 1 Private Wealth Manager - No. 3 in Retail Credit

Sustainable profit generation and value added for investors by means of synergy effects:

- Optimized branch network - Stable revenue basis: focus on client base & high-end product mix as

well as benefit from strong sales partnership with Allianz Group- Further reduced cost base - lean headquarter organization and

efficient back office processes- Risk-return-orientation in loan business / credit portfolio management

Successful integration of Dresdner Bank to create the No. 1 bank for sophisticated private customers

Thereby above-average participation in long-term growth trends (e.g. demographics, development of savings ratio)

Private Customers


Agenda


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Page



2.

1.

6

2


Branch network & customer management modelBranch network Commerzbank AG in Germany as of 2010/01/01

Private- & Business Customers1,695 Locations1)

18

173

1,504

regional branches

branches2)

main branches

Provision of identical sales performance information at all levels

Depiction of different branch types including “Branch of the Future”, “Branch of the Future Plus” and b&p advisory centers

Integration of Dresdner Bank branches in new distribution channels

Depiction of separate customer market segments

Consideration of different relationship management models (generalists & specialists)

Allocation of users based on special superior & agent-rules

Dynamic accommodation of changes to branch network – data reorganisation

Complexity added through need for:

1) Dresdner Bank branches incl. 2) advisory centres incl.


Integrated performance management of complete product offering

Complete and uniform

depiction of all sales management

relevant information

Depiction of the strategic product groupings

- Wealth management- Insurance- Investments- Credit & loan products- Payment instruments (incl. current accounts)

High flexibility allowing inclusion of new products

Depiction of both new- and existing business

Availability of revenue-, volume- und profitability information

High breath and depth of available information with simultaneous focus on Key Performance Indicators

Requirements

Goal


Immediate translation of information into performance management

Availability

Daily information: following day 8am

Weekly information: following Monday 8am

Monthly information: 2 working days after provision of raw data

Heterogeneous addressees Differentiated Performance

Management focus Complex rights

management-Functions -Level of Information

Processing

Performance

Users: soon >10,000 Peak processing periods Soon 4 terabytes SAN

storage Read- and write

capabilities 30,000 individual queries

processed every Monday

Intuitive usability Supportive analysis

process Depiction management-

process

Usability


Agenda


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Page



2.

1.

6

2


Third stage of extension prepares for integration of Dresdner Bank

2007

2008

2009 / 2010

Rollout of Performance Manger as application for integrated sales performance management

Integrated presentation of new and existing business

Analysis of consultant type, region, consumer segment and ratios of customers to full-time staff possible

Processing time: 60 hours1)

SAS Business Intelligence Award 2007

Extension of existing published KPIs and creation of new ones

Implementation of daily investments sales report

Shift of Servers’ operating system from Windows to UNIX®; smooth transition due to SAS platform independence

Proof of Scalability: Implementation of Unix more than doubles performance

Change to ZFS file system (zone concept)

Consolidation of application server, aggregation server and SPD server into one SUN server unit

Reduction of processing time to 20 hours1)

New requirements with integration of Dresdner Bank: - Maintain same online performance

despite twice as many users- Keep processing time the same

despite twice as much data Build-up of data history: Flexibility of

SAS data model allows processing of Dresdner Bank data

Innovative and scalable Architecture: - Change to QFS file system, which

allows independent data access from multiple servers

- Independent process optimization and control by QFS

- Load balancing through SAS® Grid Manager and BIG IP

- Parallelization of processing Further reduction of processing time

to 7 hours1) Processing time simulated on current data volume


Reporting in ”Performance Manager”

Standard Reporting

Emphasis

AdvancedAnalysis Options

DailySales Support

WeeklyActivities

MonthlyRevenues

Sales support- Investments

High-level reports- Sales performance (weekly)- Activities- Appointments

Detail Reports- Product details & drivers

Ranking- Overview branch network

High-level reports- Sales performance (monthly)- Asset-levels & customer numbers- Revenue

Detail Reports- Product details & drivers

Ranking- Overview branch network

Basic reports- High breadth and depth- Time series analysis

Activity management Sales performance management

Revenue management

Management-commentary

Management-commentary

Deal overview- Itemization of weekly sales

performance for consultants


Overview of the new SAS-platform

Architectural principles

Asynchronous mirrored design in two locations

– Disaster management

– Guaranteed availability

Layered model with explicit separation of functions

– Efficient maintenance– Simple expansion and

upgrading

Zone concept

– In event of failure automatic switch to another function

– Maintenance on ”live“ system possible

QFS

– Regulates server access to data

– Multiple servers can operate at the same time in the database

SAS® Grid Manager with 4 SPD Servers

– Optimizes parallel processing – Improved I/O performance


Grid ClientGrid Client

SAS® Metadata ServerSAS® Metadata Server

SAS® Management ConsoleGrid Manager and Schedule Manager

SAS® Management ConsoleGrid Manager and Schedule Manager

SAS ProgramSAS Program

Grid ControlServer

Grid ControlServer

Base SAS SAS CONNECT®

SAS Workspace Server

SAS Grid Server SAS Data Step

Batch Server SAS SPD Server


SAS Workspace Server



Platform Process MgrTMPlatform Process MgrTM

Platform LSFPlatform LSF

PlatformTM Grid Management Service

PlatformTM Grid Management Service

Grid Node 1Grid Node 1








Grid Node nGrid Node n








SAS® Data Integration Studio SAS® Enterprise

Miner SAS® Enterprise

GuideOther SASClients

Central File Server for: Job Deployment Directories Source and Target Data SAS Executables

PlatformTM LSFPlatformTM LSF

SAS Grid Computing Architecture


Candidates for High Availability

SAS Metadata Server

SAS Object Spawner

SAS OLAP Server

SAS/Share Server

Platform Process Manager

Platform Grid Management Service

Web application tier components

Any other critical services including 3rd party (JBOSS)


Controlling Grid Resources With Queues

LSF queue: a grid-wide holding place for jobs

Implementation of job scheduling/control policies

Various policies can be defined on queues:• priority and preemption

• job slots and slot pools

• dispatch and run windows

• hosts and host groups

• users and user groups


PROC SCAPROC;

RECORD ‘record.txt‘

GRID

‘GridCount.txt‘ ;

Run;

SAS Job

STEP 1

STEP 2

STEP 3

STEP 4

SAS Job

Instrumented for

Execution on the grid

PC 1

STEP 1

PC 2

STEP 2

STEP 4

PC 3

STEP 3

Grid-Enabling Legacy SAS CodeUsing the SAS Code Analyzer


Grid-Enabling Legacy SAS CodeWrapping the legacy source code using SCAPROC

proc scaproc; record "c:\temp\comments_out.txt" /* Code Analyzer comments */ grid "c:\temp\grid_out.txt" /* Grid exploiting code */ resource "SASApp"; /* SAS server*/

run;

proc summary data=wk_data.a; var x; output out=new1 mean=mx; run;

proc summary data=wk_data.a; var y; output out=new2 mean=my; run;

proc scaproc; write; run; /* Closes the results files */


Grid-Enabling Legacy SAS CodeInitializing the grid infrastructure and signon

/*---------------------------------------------------------------------------*/ /* There are 2 tasks in this job. */ /* 2 of these tasks can be RSUBMITed. */ /* These 2 tasks used 15 units of time. */ /* The longest task took 15 units of time, 100% of total time. */ /*---------------------------------------------------------------------------*/ %let SCAGRID_SESSIONS=2; /*---------------------------------------------------------------------------*/ /* Enable grid service */ /*---------------------------------------------------------------------------*/ %let rc=%sysfunc(grdsvc_enable(_all_, resource=SASApp)); /*---------------------------------------------------------------------------*/ /* This macro starts up the connect sessions */ /*---------------------------------------------------------------------------*/ %macro scagrid_sessions(count); %do i = 1 %to &count; signon sess&i connectwait=no; %put Session started on grid node %sysfunc(grdsvc_getname(sess&i)); %end; %mend scagrid_sessions; /*---------------------------------------------------------------------------*/ /* Start up our connect sessions. */ /*---------------------------------------------------------------------------*/ %scagrid_sessions(&SCAGRID_SESSIONS);


Grid-Enabling Legacy SAS CodeExecuting the tasks

/*---------------------------------------------------------------------------*/ /* TASK 1 run in rsubmit */ /*---------------------------------------------------------------------------*/ /* I/O Activity */ /*---------------------------------------------------------------------------*/ /* DATASET OUTPUT SEQ created in task 1 WORK.NEW1.DATA */ /*---------------------------------------------------------------------------*/ /* Symbol activity */ /*---------------------------------------------------------------------------*/ /* SYMBOL GET task: 0 used by subsequent task:no Name:SYS_IOUSEEE */ /*---------------------------------------------------------------------------*/ /* ELAPSED 0 */ /*---------------------------------------------------------------------------*/ /*---------------------------------------------------------------------------*/ /* Get an available session with the scagrid_gs function */ /*---------------------------------------------------------------------------*/ proc scaproc; getsession 1 "sess"; run; %put sess=&sess; /*---------------------------------------------------------------------------*/ /* rsubmit for task 1 */ /*---------------------------------------------------------------------------*/ rsubmit &sess sysrputsync=yes cmacvar=scagrid_task_1; proc summary data=wk_data.a; var x; output out=new1 mean=mx; run; endrsubmit;


Grid-Enabling Legacy SAS CodeSigning off

/*---------------------------------------------------------------------------*/ /* This macro issues waitfors and signoffs for our sessions. */ /*---------------------------------------------------------------------------*/ %macro scagrid_waitfors(count); %do i = 1 %to &count; waitfor sess&i; signoff sess&i; %end; %mend scagrid_waitfors; /*---------------------------------------------------------------------------*/ /* Wait for and sign off all sessions. */ /*---------------------------------------------------------------------------*/ %scagrid_waitfors(&SCAGRID_SESSIONS); /*---------------------------------------------------------------------------*/ /* Termination for our SCAGRID functions. */ /*---------------------------------------------------------------------------*/ proc scaproc; shutdown; run; /*---------------------------------------------------------------------------*/ /* All done. */ /*---------------------------------------------------------------------------*/


SAS Command-Line Grid Submission Utility

Standalone utility

User can submit and forget (no need to remain connected to process job)

User can view job status and output while job is running

Can kill submitted jobs

Allows for SAS checkpoint/restart capability

Uses SAS Grid Manager metadata for centralized control

Requires shared file system between client and grid


SAS stores Job information• Last execution of DATA and PROC

steps

• Checkpoint catalog

When a Job is terminates prematurely

• Start in Restart mode

• SAS reads checkpoint catalog

• Starts where the last successful step ended

• WORK tables can be stored and reused

• Globale statements and macro’s will be reexecuted

DATA step

DATA step

PROC step

DATA step

PROC step

PROC step

DATA step

Xxxxxxxxx

Xxxxxxxxx

Xxxxxxxxx

xxxxxxxxx

Checkpoint Library

SAS Checkpoint/Restart Capability


Data Storage is Key

Shared file system

Throughput across the grid• 30-50 MB/sec/core (~1.8 – 3GB/sec with 64 cores)

Scalable• Handle increasing load gracefully

• Readily enlargeable as data volumes grow

Locality of data• input/output files

• temporary files

• external data access


SPD Server Grid Deployment

Multiple SMP hosts, one name and data server each

Shared access to data on SAN

+ Client access can scale across multiple hosts

+ Shared read access to same tables

+ Shared write access to disparate tables

+ Highly available and transparent to client

+ Grid routes data acess requests transparently to the least used node

- More complex to deploy


SPD Server Grid Deployment

What can be shared• Server executables (binaries)

− Benefit grows as number of grid nodes grows

− Single location for version/patch mgmt

• Data domains

• User database

• Data domains definition file

• Server parameter definition file (unless different parms are required per node)

What cannot be shared• Log and audit files

• Startup scripts


Clustered Table

Jan 2003

New Months

Feb 2003 Apr 2003

Jul 2003 Aug 2003

Mar 2003

May 2003 Jun 2003

Sep 2003 Oct 2003 Dec 2003 Nov 2003

Jan 2004 Feb 2004 Apr 2004

Jul 2004 Aug 2004

Mar 2004

May 2004 Jun 2004

Sep 2004 Oct 2004 Dec 2004 Nov 2004

Jan 2005

Feb 2005

Mar 2005

Apr 2005

May 2005

Jun 2005

SPD Server Cluster TableParallel On-the-Side Loading

ETL1

ETL2

ETL3

ETL4

ETL5

ETL6


Original Clustered Table

Jan 2003 Feb 2003 Apr 2003

Jul 2003 Aug 2003

Mar 2003

May 2003 Jun 2003

Sep 2003 Oct 2003 Dec 2003 Nov 2003

Jan 2004 Feb 2004 Apr 2004

Jul 2004 Aug 2004

Mar 2004

May 2004 Jun 2004

Sep 2004 Oct 2004 Dec 2004 Nov 2004

New Months

Jan 2005

Feb 2005

Mar 2005

Snap-in

Apr 2005

May 2005

Jun 2005

SPD Server Cluster TableSnap in New Data in Seconds


Cluster Table Loading Scalability


Cluster_Year

Cluster Table Reading

Table_Q1

Jan

...

Mar

Jan

...

Mar

Jan

...

Mar

Table_Q4

Oct

...

Dec

Oct

...

Dec

Oct

...

Dec

Table_Q2

Apr

...

Jun

Apr

...

Jun

Apr

...

Jun

Table_Q3

Jul

...

Sep

Jul

...

Sep

Jul

...

Sep

x

x xselect ...

where date in(‚Apr‘,

‚May‘, ‚Jun‘);

…


As a Result ...

Number of member tables hit by query

Run time

Dynamic cluster table

Regular partitioned table


Materialized Views

Technically a temporary table

Materializes at view creation time

Speeds up view access drastically

Re-computes on next access when underlying data changes

Best for multi-user queries with N-way joins

Solid state disk placement being researched


Data Compression

At libname assignment or data set level(COMPRESS=YES)

Run-length encoding (RLE) algorithm

Compression rate data-dependent(best for character variables)

Disk footprint and I/O bandwidth

Needs additional CPU-cycles both ways


• SAS Grid Manager

•SAS SPD Server (grid deployment )

Challenges Solution Results

Customer SuccessCommerzbank AG

- Doubling of data volume due to merger- Same or better execution times- Terabytes of data - 1000s of users- 10000s queries/reports- Separate IT centers for disaster recovery

- SAS/Connect (MP Connect)- SAS Batch jobs (ETL)- SAS SPD Server (standard deployment)

Commodity hardware high performancescalable, resilient, dynamic management of grid infrastructure

SAS/Grid4 Opteron-based grid nodes(4 quad-core CPUs each)4 TB Disk Storage in SAN(mirrored at remote IT center)




Customer SuccessFinancial Industry

Saturated SAS environment had to be addressed

SMP Server36 Dual Core CPUs144 GB RAM<Disk storage extra><Internal bus connectivity>$2.4 Million

Commodity hardware high performancescalable, resilient

SAS/Grid36 Dual Core CPUs144 GB RAM7 TB SAS Work Disk StorageInfiniband Connectivity$212,000



• SAS SPD Server


Customer SuccessBanking Industry

• High performance processing

• at the data layer• at the compute layer

• Scoring Jobs • 10% of data in 8

hours

• Scoring Jobs• 100% of data in 2

hours and 30 minutes



Jochen KirstenSAS Institute Inc.Heidelberg, Germany

Email: [email protected]

copyright © 2010, sas institute inc. all rights reserved. sas and all other sas institute inc....

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