BW Basic Architecture

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BW Basic Architecture

• Klaus MajenzSAP Product Line BI

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Overview

• complete DW BI product, comprising ...
• ETL tools (extractors, transformation,
  monitoring, scheduling, ...)
• OLAP engine
• data mining engine
• repository
• analytical front-end (web- or Excel-based,
  agents, GIS, ...)
• prepacked models, built by SAP application
  departments
• client-server architecture
• SAP web application servers
• database server 7 commercial RDBMS platforms
  supported (Oracle, MS, 4IBM, SAP)
• part of SAP Netweaver
• SAP's open integration and application platform
• more details http//www.sap.com/solutions/netweav
  er/

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Overview
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Scenario (1)
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Scenario (2)

• Dimension Time
• Day
• Month
• Year
• Dimension Region
• City
• Region
• Country
• Dimension Sales Org
• Sales Person
• Division
• Distribution Channel
• Sales Organization

• Dimension Product
• Product
• Product Group
• Key Figures
• Quantity (in pieces)
• Profit (in US)

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An adequate BW Infocube IUSALES

• Dimension IUSALEST
• 0CALDAY
• 0CALMONTH
• 0CALYEAR
• Dimension IUSALES1
• IUCITY
• IUREGION
• IUCOUNTRY
• Dimension IUSALES2
• IUSALPER
• IUDIV
• IUDCHAN
• IUSALORG

• Dimension IUSALES3
• IUPROD
• IUPRODGRP
• Key Figures
• IUQUAN
• IUPROFIT

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Data Flow in BW
Aggregate
Initial Fill, Roll-Up
Infocube E fact table
Cube Query
Compression
Infocube F fact table
Infocube Upload (from PSA)
Infocube Upload (from ODS)
Operational Data Store (ODS)
ODS Query
ODS Activate
ODS Upload
Persistent Staging Area (PSA)
V.P. Query
Extraction
Source System (e.g. R/3, other DB, File, ...)
V.P. Query
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Data Flow in BW what we will look at
Aggregate
Initial Fill, Roll-Up
Infocube E fact table
Infocube
Cube Query
Compression
Infocube F fact table
Infocube Upload (from PSA)
Operational Data Store (ODS)
ODS
ODS Activate
ODS Upload
Persistent Staging Area (PSA)
PSA
Extraction
Source System (e.g. R/3, other DB, File, ...)
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PSA
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PSA table

• huge number of individual INSERTs
• no UPDATE
• SELECT FROM WHERE "REQUEST"
• mass deleteion DELETE WHERE "PARTNO" /
  DROP PARTITION

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ODS
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ODS object 3 tables

• active data /BIC/AOIUSALES00
• modified data ("activation queue")
  /BIC/AOIUSALES40
• delta data ("change log") /BIC/B0008215000
  (PSA)
• ODS upload
• INSERT INTO "/BIC/AOIUSALES40"
• ODS data activation
• UPSERT "/BIC/AOIUSALES00"
• delta records INSERT INTO "/BIC/B0008215000"
• (mass) DELETE FROM "/BIC/AOIUSALES40"
• infocube delta upload from ODS
• SELECT FROM "/BIC/B0008215000"

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ODS tables /BIC/AOIUSALES00, /BIC/AOIUSALES40
active data
same as in PSA table
modified data
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ODS Object (BW 3.0)
Active data
Change log
Req.ID I Pack.ID I Rec.No
Doc.No I Value
Activation
Activation queue
Req1
Req2
Req3
Staging Engine
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Infocube
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InfoCube Star Schema
(1) Fact Table (2) Dimension (3)
time-independent-SID time-dependent-SID
master SID Char (4) SID Attr
Y
X
S
F, E
D
S
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Infocube IUSALES
Facttable
Dimension 1
S (Population)
X (City)
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Infocube Indexing (1) Oracle
line item dimension
Facttable
Dimension 1
S (Population)
X (City)
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Infocube Indexing (2) MS SQL Server
line item dimension
Facttable
Dimension 1
S (Population)
X (City)
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Infocube Indexing (3) Oracle
F Facttable
partitioning column (for E facttable)
E Facttable
"P-index"

• single column indexes support queries
• P-index compress
• additional bitmap index on part. column

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Infocube Indexing (4) MS SQL Server
F Facttable
Does not exist on MS-SQL
E Facttable
"P-index"

• single column indexes support queries
• P-index compress

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Infocube Operations (1)

• INSERT only F facttable
• array INSERT
• if array INSERT fails UPSERT logic
• DELETE request (mass deletion) only F facttable
• DELETE FROM "/BIC/FIUSALES" WHERE KEY_IUSALESP
  
• alternatively DROP PARTITION
• DELETE specified data
• DELETE FROM WHERE
• UPSERT only E facttable
• infocube compression (separate slide)
• SELECT
• separate slide

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Infocube Compression (ex. request 3)
before
after
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Infocube Compression (2)

• Oracle (via stored procedure on DB server)
• loop over rows for request REQ in F facttable
• attempt UPDATE of E facttable
• if UPDATE fails then INSERT rowid into temporary
  table INS
• do mass INSERT INTO E facttable using INS
• DROP PARTITION corresponding to REQ in F
  facttable
• MS SQL-Server (via ABAP via application server)
• loop over rows for request REQ in F facttable
• attempt UPDATE of E facttable
• if UPDATE fails then attempt INSERT
• DELETE FROM F facttable WHERE requestid REQ

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Aggregate Fill

• INSERT INTO /BIC/E100010
• SELECT D1.SID_IUCITY AS KEY_1000101,
• D2.SID_IUSALPER AS KEY_1000102,
• 0 AS KEY_100010P,
• SUM (F./BIC/IUPROFIT),
• SUM (F./BIC/IUQUAN),
• COUNT() AS FACTCOUNT
• FROM /BIC/FIUSALES F,
• /BIC/DIUSALES1 D1,
• /BIC/DIUSALES2 D2,
• /BIC/DIUSALESP DP
• WHERE F.KEY_IUSALES1 D1.DIMID AND
• F.KEY_IUSALES2 D2.DIMID AND
• F.KEY_IUSALESP DP.DIMID AND
• DP.SID_0CHNGID 0 AND
• ( F.KEY_IUSALESP 0 OR
  F.KEY_IUSALESP 2 ) AND
• DP.SID_0REQUID BETWEEN 0 AND 40
• GROUP BY D1.SID_IUCITY,
• D2.SID_IUSALPER

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Aggregate Roll-Up

• INSERT INTO /BIC/F100011
• SELECT D1.SID_IUCITY AS KEY_1000111,
• D3.SID_IUPROD AS KEY_1000112,
• 7 AS KEY_100011P,
• SUM (F./BIC/IUPROFIT),
• SUM (F./BIC/IUQUAN),
• COUNT() AS FACTCOUNT
• FROM /BIC/FIUSALES F,
• /BIC/DIUSALES1 D1,
• /BIC/DIUSALES3 D3,
• /BIC/DIUSALESP DP
• WHERE F.KEY_IUSALES1 D1.DIMID AND
• F.KEY_IUSALES3 D3.DIMID AND
• F.KEY_IUSALESP DP.DIMID AND
• DP.SID_0CHNGID 0 AND
• F.KEY_IUSALESP 5 AND
• DP.SID_0REQUID 498
• GROUP BY D1.SID_IUCITY,
• D3.SID_IUPROD

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Infocube Query Example Infocube IUSALES
city
region
country
(1) Fact Table (2) Dimensions (3)
Characteristics (simplified)
sales person
day
division
month
distribution channel
year
sales organization
product
product group
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Query Example Processing (under Oracle)
region
country 'US'
(1) Fact Table (2) Dimensions (3)
Characteristics (simplified)
month
year 98-99
product group
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Step 1 Restrictions Master Data è Dimensions
region
country 'US'
(1) Fact Table (2) Dimensions (3)
Characteristics (simplified)
month
year 98-99
product group
Typical Query Processing
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Step 2 Restrictions Dimensions è Fact Table
(1) Fact Table (2) Dimensions (3)
Characteristics (simplified)
bitmap index
bitmap index
product group
Typical Query Processing
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Step 3 Assemble Result
region
country 'US'
(1) Fact Table (2) Dimensions (3)
Characteristics (simplified)
small subset of facttable
month
year 98-99
product group
Typical Query Processing
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Query Example (1) simple

• SELECT "DT"."SID_0CALMONTH" AS "S____081"
• ,"DT"."SID_0CALYEAR" AS "S____083"
• ,"D1"."SID_IUCOUNTRY" AS "S____520"
• ,"D3"."SID_IUPRODGRP" AS "S____524"
• , COUNT( ) AS "1ROWCOUNT"
• , SUM ( "F"."/BIC/IUPROFIT" ) AS
  "IUPROFIT"
• , SUM ( "F"."/BIC/IUQUAN" ) AS "IUQUAN"
• FROM "/BIC/FIUSALES" "F"
• , "/BIC/DIUSALEST" "DT"
• , "/BIC/DIUSALES1" "D1"
• , "/BIC/DIUSALES3" "D3"
• , "/BIC/DIUSALESP" "DP"
• WHERE "F"."KEY_IUSALEST" "DT"."DIMID" AND
• "F"."KEY_IUSALES1" "D1"."DIMID" AND
• "F"."KEY_IUSALES3" "D3"."DIMID" AND
• "F"."KEY_IUSALESP" "DP"."DIMID" AND
• ( "DT"."SID_0CALMONTH" 200007 AND
• "DT"."SID_0CALYEAR" 2000 AND
• "DP"."SID_0REQUID" lt 745 )

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Query Example (2) navigational attribute

• SELECT "DT"."SID_0CALMONTH" AS "S____081"
• ,"DT"."SID_0CALYEAR" AS "S____083"
• ,"D1"."SID_IUCOUNTRY" AS "S____520"
• ,"X1"."S__IUCOLOR" AS "S____530"
• , COUNT( ) AS "1ROWCOUNT"
• , SUM ( "F"."/BIC/IUPROFIT" ) AS
  "IUPROFIT"
• , SUM ( "F"."/BIC/IUQUAN" ) AS "IUQUAN"
• FROM "/BIC/FIUSALES" "F"
• , "/BIC/DIUSALEST" "DT"
• , "/BIC/DIUSALES1" "D1"
• , "/BIC/DIUSALES3" "D3"
• , "/BIC/XIUPROD" "X1"
• , "/BIC/DIUSALESP" "DP"
• WHERE "F"."KEY_IUSALEST" "DT"."DIMID" AND
• "F"."KEY_IUSALES1" "D1"."DIMID" AND
• "F"."KEY_IUSALES3" "D3"."DIMID" AND
• "D3"."SID_IUPROD" "X1"."SID" AND
• "F"."KEY_IUSALESP" "DP"."DIMID" AND
• ( "DT"."SID_0CALMONTH" 200007 AND
  "DT"."SID_0CALYEAR" 2000 AND

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Query Example (3) external hierarchy

• SELECT "DT"."SID_0CALYEAR" AS "S____083"
• ,"DT"."SID_0CALMONTH" AS "S____081"
• ,"D1"."SID_IUCOUNTRY" AS "S____520"
• ,"H1"."PRED" AS "S____524"
• , COUNT( ) AS "1ROWCOUNT"
• , SUM ( "F"."/BIC/IUPROFIT" ) AS
  "IUPROFIT"
• , SUM ( "F"."/BIC/IUQUAN" ) AS "IUQUAN"
• FROM "/BIC/FIUSALES" "F"
• , "/BIC/DIUSALES3" "D3"
• , "/BIC/DIUSALEST" "DT"
• , "/BIC/DIUSALES1" "D1"
• , "/BIC/DIUSALESP" "DP"
• , "/BI0/0300148611" "H1" / This is a
  (UNION) view! /
• WHERE "F"."KEY_IUSALES3" "D3"."DIMID" AND
• "F"."KEY_IUSALEST" "DT"."DIMID" AND
• "F"."KEY_IUSALES1" "D1"."DIMID" AND
• "F"."KEY_IUSALESP" "DP"."DIMID" AND
• "D3"."SID_IUPRODGRP" "H1"."SUCC" AND
• ( "DT"."SID_0CALYEAR" 2000 AND
  "DP"."SID_0REQUID" lt 745 AND

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Examples of Conceptual Modeling in SAP BW
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Examples

• Reveal why pure RDBMS technology ...
• sometimes requires an additional conceptual
  layer on top,
• is not sufficient is some cases,
• has no chance in some situations because it has
  to be more general than necessary.
• Examples
• example 1 infoproviders in SAP BW
• uniform view on differing physical layouts
• example 2 non-cumulative key figures in SAP BW
• semantic relationship between table columns
• example 3 aggregates in SAP BW
• could be implemented by using materialized views
  (or equivalent)
• but they have proved to be inferior

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Example 1 Infoprovider (1)

• An infoprovider in SAP BW ...
• comprises a reporting scenario,
• is the entity on which a query is defined,
• combines (aggregated or non-aggregated)
  operational data with master data (e.g. product,
  customer, ... data),
• or constitutes a master data entity

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Example 1 Infoprovider (2) Examples

• Example A
• a cube is an infoprovider
• fact table holds operational data on certain
  granularity
• dimensions hold master data
• Example B
• customer master data can be an infoprovider
• same UI as for other infoproviders
• selections, projections, summaries using
  attributes (e.g. address, customer category,
  region, ...)

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Example 1 Infoprovider (3) -- Overview (SAP BW
3.x)
Infoprovider
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Example 2 Non-Cumulative Key Figures (1)

• also "semi-additive measures"
• example account balance
• conceptually

• physically

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Example 2 Non-Cumulative Key Figures (2)

• non-cumulative key figures / semi-additive
  measures
• balance can be reconstructed for any moment in
  the past
• ? that information has not to be physically
  stored
• advantages
• significantly reduced data volumes
• better performance
• more flexibility
• however algorithms are required for
• reconstruction ? read
• insertion ? load

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Example 3 Aggregates in SAP BW

• SAP BW constraints
• only SUM, MIN, MAX aggregations are materialized
• uploaded data (in an infocube) can still be
  identified
• ? delta roll-ups are simple
• Materialized or Indexed Views / Automatic Summary
  Tables
• could be used in theory
• however maintenance is considerably slower
• ... due to expensive tracking and logging
  mechanisms that are necessary if the general case
  has to be covered

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Summary
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Summary

• brief introduction to SAP BW
• three examples
• an additional conceptual layer on top of the
  relational one
• a semantical pattern that is frequently used in
  business
• an object that might suffer from the generic
  approach
• Do the examples reveal shortcomings of RDBMS or
  are they application domain specific ?