performance tuning for sap business information warehouse · 2019-11-12 · performance tuning for...

SAP TechEd ‘03 Basel

© 2003 SAP AG BW 354, Uwe Heinz; Alexander Peter 1

Performance Tuning for SAP Business Information WarehouseAlexander PeterProduct Manager BI, SAP AGUwe HeinzDeveloper BI, SAP AG

SAP AG 2003, TechED EMEA, BW354, Alexander Peter, Uwe Heinz

Learning Objectives

As a result of this workshop, you will be able to:

Differentiate and Analyse Performance IssuesExplain effects of modeling on performanceSuggest means to improve query and data load performanceTrace Web Queries




SAP BW Architecture


Agenda

Modeling

Query Performance

General Settings and Checklist

Extraction / Data Load Performance

Analysis Methods




Agenda

Modeling

Query Performance



Analysis Methods


Performance Tuning in BW

Database

Database

Performance Tuning

Application

Application

Performance Tuning

OLTP SystemsApplication Development and performance tuning separatedPerformance tuning by basis experts

Performance Tuning

SAP BWPerformance Tuning as holistic process over application design and database configuration

SAP BW

OLTP




General Performance Rules

Basic Rules for Performance Optimization:

Parallel processes are fully scalable

Do it parallel: Deploy parallelism on all Do it parallel: Deploy parallelism on all available levelsavailable levels

Be economic: Eliminate all unnecessary Be economic: Eliminate all unnecessary processesprocesses

Keep it small: Reduce the data volume to Keep it small: Reduce the data volume to be processedbe processed


Checklist

Sound Data ModelThe Data Model is one of the most important issues

BW Reporting PerformanceAggregatesPre-calculated Web TemplatesOLAP Cache

Extraction / Data Load PerformanceRecommendations in SAP notes 130253 and 417307




Performance Guidelines – General Aspects – 1 –

Database (or Physical) PartitioningDatabase tables are cut into smaller chunks (partitions)One logical database tableTransparent for userAvailable for the following database management systems

Range Partitioning: ORACLE, Informix, IBM DB2/390Hash Partitioning: IBM DB2/UDB

Year / Month Customer Sales2003/01 4711 102003/01 4712 152003/01 4713 202003/01 4714 232003/02 4711 442003/02 4713 322003/02 4714 17

DatabaseTable(logical)

DatabasePartition

DatabasePartition



Database (or Physical) PartitioningBenefits

Parallel accesses to partitionsRead smaller sets of dataFast Deletion of partitions (DROP PARTITION instead DELETE FROM WHERE)

Automatically Partitioned Database Tables (for Range Partitioning)InfoCube F-Fact table: partitioned by requestPSA table: partitioned by requestODS Change Log: similar to PSA table

User Defined Partitioning Criteria (for Range Partitioning)InfoCube E-Fact tablePartition Criteria: Time characteristics like month or fiscal periodNote: Both fact tables are extended by the SID of the chosen time characteristic





Database (or Physical) PartitioningQuery Access (Partition Pruning)

Jan - MarApr - JunJul - SepOct - Dec

Package 2Package 5Package 7

month

Master Data

Time

Package

Dimension

Fact Table

„F“

„E“

Example:

Month = August 2003

Apply Restrictions to Dimensions Apply Restriction

to fact table

Month Customer SalesAug 03 4711 15Aug 03 4712 66

Discard irrelevant partitions

F table has to be read, too


Agenda

Modeling

Query Performance



Analysis Methods




Checklist

Design of Operational Store, Data Warehouse Layer and Multidimensional Model

Dimensions of InfoCubes

Logical (MultiProvider) Partitioning

Non-Cumulative Key Figures


Operational Data Store and Data Warehouse Layer

Data WarehouseNon volatileGranularIntegratedHistorical foundationBuilt with ODS Objects

Operational Data StoreOperational Reporting Near Real-Time / VolatileGranularBuilt with ODS Objects

Multidimensional ModelsMultidimensional analysisAggregated viewIntegratedBuilt with InfoCubes

ODS Object ODS Object




InfoCubes SAP-Extended Star Schema

ONE Normalization of DimensionsMaster Data is shared, not part of the InfoCube

Fact TableFact Table

DimensionDimension

DimensionDimensionDimensionDimension

DimensionDimension

Dimension ID

MasterData

MasterData

MasterData

MasterData

MasterData

MasterData

MasterData

MasterData

Surrogate Key


InfoCubes Master Data

HierarchiesAttributesTexts (Multi-Language Support)

Fact TableFact Table

DimensionDimension

MasterData

MasterDataSID TableSID Table

Attributes

Hierarchies

TextsPlant UKPlant GermanyPlant Australia

Customer Street Account




Data Modeling – Line Item Dimensions – 1 –

Line Item DimensionsDirect Link from InfoCube Fact Table to Master Data SID Table – without dimension tableOnly one characteristic is possible in a line item dimensionRecommended for large dimensions (e.g. invoice number, order number, but also reasonable for large material or customer dimensions)

SID Dimension Fact Table Dimension SID SID Fact Table Dimension SID

Without lineitemdimension

With lineitemdimension


Data Modeling – Line Item Dimensions – 2 –

Line Item DimensionsBenefits

Saves one table join at query runtimeSaves determination of dimension ID’s at data load time

DisadvantagesF4 – Help not possible on dimension values (only on master data values)




Data Modeling – MultiProviders – 1 –

MultiProvider (or Logical) PartitioningPossible partitioning criteria: year, plan/actual, regions, business areaParallel sub-queries are started automatically to basic InfoCubesUse MultiProvider Partitioning to cut large amounts of data in chunks

MultiProvider

Basic InfoCubes Europe Asia USA

Consolidated view on all data



MultiProviders (SAP BW 3.x)Definition

Combination of InfoCubes, ODS objects, InfoObjects and InfoSets

With

Dat

a

Master Data

Basic InfoCube

ODS Object

No

Dat

a

InfoSet

Virtual InfoCubeMulti

ProviderMulti

Provider





MultiProvidersBenefits

No additional data storageSingle InfoProviders smaller, less complex and less sparsely filled than one big InfoProvider(Parallel) Data load into individual InfoProvidersQueries are split automatically and distributed to InfoProviders (parallel execution)Transparent usage for ReportingLocal queries on each InfoProvider possibleArchiving of single basic InfoProvider is very easy

DisadvantagesAdminstration (with aggregates)Additional I/O


Data Modeling – Non-Cumulative KFs – 1 –

Non-cumulative key figuresNon-cumulative key figures cannot be cumulated meaningfully over time, e.g. inventory, number of employeesStorage: Historical movements and reference point (in E fact table)Reference point is updated when InfoCube is compressed; if all requests are compressed, the reference point represents e.g. the current stockExample:

Month Material Plant Material flow Reference pointJan 02 4711 1000 10 0

10Feb 02 4711 1000 20 10Mar 02 4711 1000 5 10

35

Compressing

Compressing

Note: the reference points are only stored in the compressed E-table; they have the time value “infinity”, e.g. for day 31.12.9999





Performance Tips & Tricks:Compress InfoCubes with non-cumulative key figures as soon as possibleVery granular characteristic value combinations generate lots ofreference points in the E table

Aggregate rebuild is significantly influencedDelete unused values (e.g. old seasonal material) from the InfoCube by selective deletion without timely restriction)

LAST- and FIRST- key figures can use aggregates; AVG-key figures cannot use them. Try to split these key figures in separate queries, if semantically possibleAt query time use tight restrictions on time characteristics; ideally request only current valuesSuppress sum lines if not neededDo not use partial time characteristics (e.g. FISCPER3) if not needed



Validity ObjectsValidity Objects are characteristics for which a validity period can be definedThe validity is stored within the validity tableExample: If plant 1 is closed at the end of March, it should not show up any value (even no zero) in April and May

Every entry in the validity table generates an individual SELECT at query runtime.Use as few validity objects as possibleDo not misuse validity objects for termination (e.g. insurance contract)

Jan Feb Mar Apr May Reference pointPlant 1 +10 +20 +30 100Plant 2 +20 +10 +10 +20 +30 150




Checklist – Query Design

Multi-dimensional Query Design

Inclusion / Exclusion

MultiProvider Query

Cell Calculation

Customer Exits

Query Read Mode


Agenda

Modeling

Query Performance



Analysis Methods




Checklist – Query and Web Performance –Overview

ST03 / Technical ContentST03 / Technical Content

DatabaseDatabase OLAPOLAP FrontendFrontend

1. Data Model

2. Query Definition

3. Aggregates

4. OLAP Cache

5. Pre-Calculated Web Templates

6. Compressing

7. Indices

8. DB Statistics

9. DB and basis (Buffer) Parameter

1. Data Model

2. Query Definition

3. Aggregates

4. OLAP Cache

5. Pre-Calculated Web Templates

6. Compressing

7. Indices

8. DB Statistics

9. DB and basis (Buffer) Parameter

1. Data Model

2. Query Definition (including OLAP features)

3. Aggregates

4. OLAP Cache

5. Virtual Key Figures / Characteristics

6. Authorizations

1. Data Model

2. Query Definition (including OLAP features)

3. Aggregates

4. OLAP Cache

5. Virtual Key Figures / Characteristics

6. Authorizations

1. Query Definition

2. Network

3. WAN and BEx

4. Client Hardware

5. VBA / Java

6. Documents

7. Formatting

8. ODBO / 3rd party

1. Query Definition

2. Network

3. WAN and BEx

4. Client Hardware

5. VBA / Java

6. Documents

7. Formatting

8. ODBO / 3rd party

SQL Trace (ST05)

RSRV

RSRT, RSRTRACE

SQL Trace (ST05)

RSRV

RSRT, RSRTRACE

RSRT, RSRTRACE

SQL Trace (ST05)

ABAP Trace (SE30)

RSRT, RSRTRACE

SQL Trace (ST05)

ABAP Trace (SE30)IEMON

RSRT, RSRTRACE

IEMON

RSRT, RSRTRACE

Which component contributes most?

Tools

Check these points


Performance Layers

ReuseReuse

PerformancePerformance

Offline Analysis

Pre-Calculation

OLAP Cache

Aggregates

InfoCubes

Performance LayersFor specific Scenarios




Aggregates – 1 –

Aggregate DefinitionMaterialization of aggregated subsets of InfoCube fact table dataIndependent structures where summary data is stored within separate, transparent InfoCubesTransparency: Users do not notice if aggregate is hit or notImproved query performance by reducing the amount of data to be read from DB

Aggregates can be createdOnly on top of basic InfoCubesFor dimension characteristicsFor navigational attributesOn hierarchy levelsUsing time-dependent navigational attributes (as of BW 3.x)Using hierarchy levels where the structure is time-dependent (as of BW 3.x)

Note: Aggregates cannot be built on MultiProviders, SAP Remote Cubes, Remote Cubes or ODS ObjectsAggregates can improve query performance considerably, but keep in mind that they also impact the load performance. For more information, see chapter on data load performance.


Aggregates – 2 – Example

Example for flow with and without aggregates

Flow without aggregate

InfoCube Number of records read on the database Records

transferred to BW instance after

being summarized on the database

Month Material RevenueJuly Hammer 10July Nail 20August Hammer 10August Nail 20

Aggregate

Month RevenueJuly 30August 30

Month Material RevenueJuly Hammer 10July Nail 20August Hammer 10August Nail 20



Flow with aggregate

Database Selection OLAP Engine




Aggregates – 3 – Tips for Creating Aggregates

Do the following stepsCall query or InfoCube overview in technical content or ST03Sort by mean overall time to find queries/InfoCubes with highest runtimesCalculate the KPI ‘aggregation ratio’ = number of records read from DB / number of records transferred Check quota of database time to total runtimeAs a rule of thumb, an aggregate is reasonable and may be created, if:

Aggregation ratio > 10Aggregation ratio > 10, I.e. 10 times more records are read than are

displayed, ANDANDPercentage of DB time > 30%Percentage of DB time > 30%, I.e. the time spent on database is a substantial part of the whole query runtime


OLAP Cache – 1 –

Caching in SAP BW 3.x OLAP CacheGlobal cache which is accessible from all sessionsOLAP Cache is part of application buffer (Imp/Exp SHM) and can also be stored in a DB table or a fileThe Cache stores query results and navigation statuses as highly compressed cluster dataQuery Cache is used for equal queries or subsets of cached queriesBenefits: OLAP Cache reduces workload on database and application serverInvalidations:

Data Load into underlying DataTargetQuery Generation




Pre-Calculated Web Templates – 1 –

Pre-Calculated Web TemplatesPre-calculation is a set of techniques where you can distribute the workload of running the report to off-peak hours, and have the report result set ready for very fast access to the dataData Pre-Calculation or HTML Pre-Calculation

HTML Pre-Calculation can be used for Offline AnalysisBenefits

Fast response timeSystem workload shifted to off-peak hoursRe-use data that goes to many queriesReports also available offline


Pre-Calculated Web Templates – 2 –

ScenarioSupports “Newspaper” scenario

Quick access to many reportsReports are requested by many usersStatic reporting, usually little navigationrequirementsActive Reporting Authorisations

RestrictionsNo invalidation when new data is loadedRestricted navigation: only filtering possibles




Reporting Performance – Indices

IndicesIf you report on ODS objects with a restrictive selection key, check if indices are defined for this keyCheck if the InfoCube Indices exist

In ORACLE, you can choose between bitmap index and B-tree indexUse B-tree index, if dimension size exceeds 10% of the fact table size

If you select on navigational attributes, be sure that an appropriate index is available


Reporting Performance – ODS Objects

Indexing ODS ObjectsIndices can be defined in the

3.x ODS object maintenance; in BW 2.x indices must be defined via SE11

Use indices on characteristics which you are accessing regularly (for reporting or DataMart interface)

Doc.No. Customer Sales4711 Miller 104712 Smith 204713 Miller 254714 Cassano 354715 Birch 124716 Smith 44

ODS Object

Index

Doc.No. Customer Sales4711 Miller 10 Miller4712 Smith 20 Smith4713 Miller 25 Cassano4714 Cassano 35 Birch4715 Birch 124716 Smith 44

ODS ObjectExample: If you look for sales figures with customer Birch, a full table scan is necessary when there is no index (see picture above); if the index has been created, only two DB accesses are necessary (see picture on the left).

Full

table

scan




Reporting Performance – Indices

Index types (selection)Bitmap Index

Well-suited for few characteristic valuesSeveral bitmap indexes can be used for one table (bitmap merge join)

B-Tree IndexGood for range queries and for high cardinality

Miller

Leask Osbourne Smith

BrownLeask Miller Moloney

OsbournePageSmith Trapp

rowidMiller 00110000111Smith 10000011100Trapp 01001100000


Indexing in SAP BW

Fact TableNon-Unique Index on every dimension ID

ORACLE: Bitmap (except for “high cardinality” dimensions)ORACLE: B-tree on F-fact table for transactional InfoCubes(guarantees parallel read&write)ORACLE: additional bitmap index on time characteristic SID for partitioned InfoCubesDB2/AS400: EVIOther DBMSs: B-TreeDB2/OS390: B-Tree not on package dimension ID

Non-unique B-Tree on all dimension IDs (Primary Key)Used for compressionORACLE/Informix: only on E-fact table




Indexing in SAP BW

DimensionsDimension ID (primary key)

Unique B-TreeMaster Data SIDs

Non-Unique B-TreeORACLE / DB2/AS400: not on first SID (this is included in the index on all SIDs)

On all master data SIDsNon-Unique B-Tree

Master Data X- and Y- tables (SIDs for all navigational attributes)

Unique B-Tree on <SID, Objectversion> (primary key)Additional indices could be defined for individual SID-columns to avoid full table scans (for selective accesses)

S-table (SID)Unique B-Tree on master data key (primary key)Unique B-Tree on SID


Agenda

Modeling

Query Performance



Analysis Methods




Checklist – Data Load Performance – Overview 1

Technical Content, Data Load MonitorTechnical Content, Data Load Monitor

ExtractionExtraction TransferTransfer Load Into PSALoad Into PSA

1. Customer Exits

2. Resource Utilization

3. Load Balancing

4. Data Package Size

5. Indices on tables

6. Flat File format

7. Content vs. generic extractor

1. Customer Exits

2. Resource Utilization

3. Load Balancing

4. Data Package Size

5. Indices on tables

6. Flat File format

7. Content vs. generic extractor

1. Resource Contraint

2. CPU / Memory Bottleneck

3. Network

4. Application Buffer Synchronization

1. Resource Contraint

2. CPU / Memory Bottleneck

3. Network

4. Application Buffer Synchronization

1. I/O Contention1. I/O Contention

Extractor Checker (RSA3),

ABAP Trace (SE30),

SQL Trace (ST05)

Extractor Checker (RSA3),

ABAP Trace (SE30),

SQL Trace (ST05)

SM50

SQL Trace (ST05)

OS Monitor (ST06)

SM50

SQL Trace (ST05)

OS Monitor (ST06)OS Monitor (ST06)

DB Monitor (ST04)

OS Monitor (ST06)

DB Monitor (ST04)


Tools

Check these points


Checklist – Data Load Performance – Overview 2

1. Transformation Rules / ABAP Coding

2. Transformation Library Formulas

1. Transformation Rules / ABAP Coding

2. Transformation Library Formulas

1. Roll-up

2. Change Run

3. Compression

4. Indices

5. Load Master Data before Transaction Data

6. Buffering Number Ranges

1. Roll-up

2. Change Run

3. Compression

4. Indices

5. Load Master Data before Transaction Data


Debugger within Monitor

ABAP Trace (SE30),

SQL Trace (ST05)

Debugger within Monitor

ABAP Trace (SE30),

SQL Trace (ST05) SQL Trace (ST05)SQL Trace (ST05)


Tools

Check these points

Transfer RulesTransfer Rules

Update RulesUpdate RulesLoad Into Data TargetsLoad Into Data Targets

InfoCubesInfoCubes ODS ObjectsODS Objects

1. Parallel ODS activation

2. Unique Data Records

3. Flag BExReporting

4. Indices

1. Parallel ODS activation

2. Unique Data Records

3. Flag BExReporting

4. Indices

Technical Content, Data Load MonitorTechnical Content, Data Load Monitor

Master DataMaster Data


2. Change Run


2. Change Run




Loading Master Data – 1 –

Number RangesSID number range can be buffered instead of accessing the DB for each SIDIf you encounter massive accesses to DB table NRIV via SQL trace (ST05), increase the number range buffer in transaction SNROIf possible, reset the value to its original state after the load (to avoid unnecessary memory allocation)

SID Material Number1 47112 08153 4712

SID Buffer45678

New material: 1125Draw new SID from Buffer

SID


Roll-Up Performance – 1 –

Roll-upThe roll-up process populates all aggregates of an InfoCubewith the newly loaded delta loadBasic Rule: InfoCube and all depending aggregates must be in-sync, i.e. you must see the same data no matter if it has been derived from the InfoCube or the aggregateNewly loaded data is not available for reporting until it has been rolled up into the aggregates

Aggregate HierarchyAggregates can be built out of other aggregates to reduce the amount of data to be read and, hence, to improve the roll-up performanceAggregate hierarchy is determined automaticallyGeneral Guideline: define few basis (large) aggregates and many small aggregates that can be built from the hierarchy level before

Display aggregate hierarchy




Roll-Up Performance – 1 – Aggregate Hierarchy

Example: Optimized Aggregate Hierarchy

Basic InfoCube

{Material, Material Group, Customer, Day}

Example

Few large basicaggregates

{Material Group, Customer, Day}

Many smallaggregates

{Material Group, Month}


Change Run Performance – 1 –

Change RunAggregates can contain

Dimension CharacteristicsNavigational AttributesHierarchy Levels

When master data changes, the changes of the navigational attributes/hierarchies must be applied to the depending aggregates; this process is called change runNewly loaded master data is not active until the change run has been applied the changes to all aggregatesThreshold for delta and new build-up in customizingThe Change Run can be parallelized across InfoCubes; see SAP note 534630 for more detailsCheck aggregate hierarchy (see Roll-Up for more details)Try to build basis aggregates that are not affected by the change run, i.e. no navigational attributes nor hierarchy levelsThe following slides show details on the process itself …




Change Run – 2 – Before Master Data Activation

Country Customer Sales

USAGermanyUSAAustriaAustriaGermanyUSA

Winsoft Inc.InternetworksFunny Duds Inc.InternetworksThor IndustriesFunny Duds Inc.Winsoft Inc.

10155

10102025

Fact Table: Sales DataFact Table: Sales Data Aggregate Tables: Sales DataAggregate Tables: Sales Data

Country SpaceIndustry *


Industry Sales

602510

TechnologyConsumer ProductsChemical

TechnologyConsumer ProductsTechnologyChemicalConsumer Products

IndustryCustomer

Winsoft Inc.Funny Duds Inc.InternetworksThor IndustriesInternetworks

Navigational Attribute for Characteristic Customer


Changed master data not available for reporting

Old:

New:


Change Run – 3 – After Master Data Activation

Country Customer Sales

USAGermanyUSAAustriaAustriaGermanyUSA

Winsoft Inc.InternetworksFunny Duds Inc.InternetworksThor IndustriesFunny Duds Inc.Winsoft Inc.

10155

10102025

Fact Table: Sales DataFact Table: Sales Data Aggregate Tables: Sales DataAggregate Tables: Sales Data


Country Country SpaceSpaceIndustry Industry **

Industry Sales

355010

TechnologyConsumer ProductsChemical

TechnologyConsumer ProductsTechnologyChemicalConsumer Products

IndustryCustomer

Winsoft Inc.Funny Duds Inc.InternetworksThor IndustriesInternetworks



Old:

New:

Changed master data now available for reporting




Agenda

Modeling

Query Performance



Analysis Methods


Query Monitor (RSRT) – 1 –

Analyze specific queries

Debug Option




Query Monitor (RSRT) – 2 –

Analyze specific queriesCheck if the right aggregate(s) is(are) used by the query

Number of Database Access

Structureto be read

SuggestedAggregates

AvailableAggregates

* disaggregatedspace aggregated

F fixed valueH hierarchy level aggregation


Performance Analysis: SQL Trace (ST05) – 1 –

Analyze high database runtimeTrace specific process with SQL Trace

Activate SQL Trace Flag

Activate Trace

Execute process

Deactivate Trace

Analyze Trace




Performance Analysis: SQL Trace (ST05) – 2 –

Total Num

berof „statement-identical“ statem

ents

Percentageof „value-identical“ statem

ents

Total ExecutionTim

e in microsec

(Prep, Open,

Fetch, Close)

Total numberof row

s

Table name

orDB

procedure

Explain


Performance Analysis: SQL Trace – Explain

1. Find all bitmap streams for package restriction

3. Calculate “AND” of bitmap streams

2. Find all bitmap streams for fiscal periods

4. Access fact table partition by partition through local index

5. Join other requested dimensions to the result via hash join




SQL Explain

Basic Help to Read the Execution PlanRead from inside out of the hierarchy

Read a set of operations at the same level from top to bottomImportant node names (ORACLE)

Hash JoinSmaller Table is used to build up hash table on the join key (optimal: in memory)Then scanning the larger table with hash table

Nested LoopFor each relevant row in the first table, find all matching rows in the other table

Bitmap Merge (=Or) / Bitmap AndPartition Range Iterator


Web Query Analysis – Overview

2 different measuring points:

Web Browser

WAS / BW / DB

HTTPrequest

HTMLresponse

ICMOLAPINIT

DB

OLAP

Rendering

OLAP Cache

Monitoring of WAS / BW / DB internal activities

HTTP M

onitor

Frontend monitoringNetwork traffic monitoring

IEMonitor

„frontend“

„backend“




Web Query Analysis – Frontend Monitoring

Key Performance Indicators for Frontend Monitoring:Query response time

Time to load HTML pageFrontend rendering time

Transferred data volumeNumber of round-trips between frontend and server

IEMon.exeSimple Web browser replacement with protocol logging facilitiesUsed to analyze load and frontend rendering time of an HTML pageEasy deployment

httpmon.exeSet up as a proxy serverRequires changes of IE settingsRequires environment variables to be set for configuration


IEMon.exe – Log File Analysis in Excel

Output produced by

analyze_iemon

Graphical representation

with a few clicks




httpmon.exe – Setup

Set up as a proxy server

Configuration by environment variables:HTTP_PROXY: proxy_server:proxy_port(e.g. pwdf0754:1080)NO_PROXY: list of web server domainswhich do not need proxy (e.g. *.sap.corp;*.sap-ag.de)

httpmon listens on port 8000 by defaultchange Internet Explorer settings

Web Browser Proxy server

proxy_port

httpmon

port 8000

otherWeb servers

(no prxy required)


Httpmon.exe – Use Case

First execution: download of MIME

objects

Second execution: MIME objects were read from

browser cache




Summary

Now you are able to:Differentiate and Analyse Performance IssuesExplain effects of modeling on performanceSuggest means to improve query and data load performanceTrace Web Queries


Further Information

Public Web:www.sap.com Solutions Business IntelligenceSAP Customer Services Network: www.sap.com/services/

SAP BW Online Documentationhttp://help.sap.com

SAP Service Marketplacehttp://service.sap.com/bw Performance

Related SAP Training CoursesBW360: Performance & Administration

Collective Performance NotesNote 567745: DB-specific SettingsNote 567746: Query & Web PerformanceNote 567747: Extraction & Loading




Q&A

Questions?


Please complete your session evaluation anddrop it in the box on your way out.

Feedback

Thank You !

The SAP TechEd ’03 Basel Team




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