Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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C Candidates for partiioning Data Warehouse and Data Store Objects 14 Multi-Dimensional Models 14 PSA Persistent staging area (PSA) 14 Change log table 275 Change-run 272 characteristic value 265, 281 key figures 286 particular combination 265 place holder 309 characteristics 264 clustering index 108 CLUSTER option 112 Compression 273 Compression result 273 Condense defined 27 Continuous operations 5 corresponding InfoCube dimension tables 271 D Data Access Monitor 278 Data granularity defined 28 data load 56, 100, 155 Data Load Monitor 278 Data Partitioned Secondary Index (DPSI) 109 data set 284 data source 77, 282 data target 56, 73, 155, 284 Data targets defined 76 Data warehouse 221, 257, 275 data warehousing e-busines operations 2 robust solution 280 database management system 5, 287 dataset 282 DataSore tables 104 DataSource 58, 258, 261, 275 DataStore object types DataStore object for direct update 104 Standard ODS 104 Write-Optimized DataStore object 104 352 Best Practices for SAP BI using DB2 9 for z/OS DB2 155 DB2 compression 28, 69, 132, 256 defined 28 DB2 connect diagnostics 139 DB2 subsystem 114 DB2 utility 109, 114 complete details 114 DB2 V8 3, 56, 133 new support 109 recommended method 114 DBA 99, 166 DBA Guide 114 DBA responsibility 4 dimension table 74, 99, 265–266 dimension/snowflake 211 disaster recovery (DR) 7 distributed relational database architecture (DRDA) 174 DPSI defined 21 Difference between DPSI and NPSI 22 Usage of Data Partitioned Secondary Indexes 21 DSO defined 11 E E-Fact table 273 partitioning 18 END 2, 7, 268 Enhanced Star Join access method 36 Enterprise Portal 285 Business Intelligence content 285 ETL - see extraction, transformation, and loading example sale 281 Extract, Transform, and Load 260 Extraction, Transfer and Load (ETL) 66 extraction, transformation, and loading (ETL) 260 F F fact table 100, 273 fact table 27–28, 74, 88, 96, 104, 162, 211, 224, 256–257, 265, 293 compressed data 28 compression results 99 consolidated data 98 initial compression 100 many records 28
same record 98 Shortens RUNSTATS processing time 96 Front End Processing Time 164 Front-end processing time 158 function module RSDU_ANALYZE_TOOL_DB2 163 G generally available (GA) 6 geographical information system (GIS) 285 geographically dispersed Parallel Sysplex (GDPS) 7 given SQL statement access path 174 Global Work Process Overview SM37 152 SM66 152 Goals of this book 3 granularity 275 H Health checker application server SM21 139 ST06 139 database connection. 139 DRDA network connectionr DB2 139 z/OS database server health 139 hierarchy 263, 269, 272 hierarchy node 264, 286 corresponding leafs 264 High granularity defined 28 Histogram what it does 46 HTML page 285 I IBIOlap interface 287 IBM zSeries 2 IDocs - see Intermediate Documents index availability 109, 113 Index compression 147 Index manipulation 147 index-controlled partitioning 108 Index-sequential access 112 InfoCube 27–28, 77, 96, 158, 211, 256–257, 259, 261–262, 269, 271, 282 Aggregate - see Aggregate Characteristics - see characteristics defined 10, 74 InfoCubes Basic InfoCubes defined 76 Real-Time InfoCubes defined 76 InfoObject 27, 256–257, 261–262 classes of metadata 257 See information model 257 templates 257 InfoPackage 60, 155, 273 InfoProvider 27, 256, 262, 285 defined 27 information model 257 business definitions 257 conceptual layers 257 InfoCube - see InfoCube InfoSource - see InfoSource master data - see master data Persistent Staging Area - see Persistent Staging Area InfoSource 257, 259, 261, 292 Intermediate Documents 279 Internet Engineering Task Force (IETF) 311 J J2EE Connector Architecture (JCA) 287 JCA specification 292 JDBC driver 287 K Key figure 2, 77, 97, 264, 281 key figures 264 L line item dimension 266 Load Data 148 local predicate 78, 111, 204, 211, 220, 225 master data tables 223 Only fields 111 Low granularity defined 28 Index 353
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Best Practices for SAP BI using DB2
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Note: Before using this information
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4.4 Extended sparse index and in-me
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11.3 SAP BI database and InfoCube l
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viii Best Practices for SAP BI usin
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Trademarks The following terms are
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The team that wrote this IBM Redboo
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Thanks to these authors from the Pr
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► Send your comments in an e-mail
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1.1 Product overview The business w
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In general, database administrators
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► Horizontal growth is achieved b
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8 Best Practices for SAP BI using D
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2.1 BI architecture Figure 2-1 show
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New term Old term Analysis authoriz
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negative values means missing stati
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- When calculating database statist
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If you specify a partitioning schem
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Important: Never partition a PSA ta
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Figure 2-10 DPSIs versus NPSIs Figu
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Finally, Figure 2-12 explains which
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Figure 2-14 shows how the snowflake
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► Realignment This refers to the
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3.1 Base recommendations - SAP BI i
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the F- fact table, the E- fact tabl
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34 Best Practices for SAP BI using
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4.1 Dynamic index ANDing for star s
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Figure 4-2 and Figure 4-3 on page 3
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4.2.1 Processing RUNSTATS for BI ta
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RUNSTATS will then be called over t
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DSNU610I -DB2T 081 16:36:03.85 DSNU
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Often the single-value based freque
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Several things worth noting are:
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The unit of a value specified to MX
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condition, generated by SAP BI upon
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There is a separate IBM Redbooks pu
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5.1 Load PSA In this section we dis
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5.1.1 Load PSA To load data into th
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After the source system, the DataSo
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Note the available tabs (registers)
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Figure 5-8 shows the provided optio
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Figure 5-10 shows the following:
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The PSA table name is created autom
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5.1.4 PSA partitioning Partitioning
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An InfoCube, whether data target or
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6.1 Types of InfoCubes Data targets
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After selecting at least one time d
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6.2.1 Repartitioning implementation
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You will then be presented with a w
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From within the monitor you are abl
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Once your repartitioning process is
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Figure 6-14 is displayed once you h
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When you remodel an InfoProvider yo
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Figure 6-17 demonstrates how you wo
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At this step of the process you are
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6.3.2 Benefits of remodeling InfoPr
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6.4.3 Cumulative data versus non-cu
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Important: After the initial compre
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► Use DPSIs for the cube’s E- f
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7.1 Differences between DataStore a
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Figure 7-1 shows the initial status
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4. By selecting the Create button,
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Partitioning and clustering are bot
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7.4.2 Choosing and changing the clu
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7.4.4 Partitioning a DataStore obje
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From the ABAP Dictionary: Utility f
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Figure 7-9 DB2/390 Storage Attribut
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partitioning was used. The CREATE T
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, "/BIC/RECORDMODE" POSITION( 00289
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Upon completion of the job, you wil
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| "/BIC/DIVISION" VARGRAPHIC (00000
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| sql: | DELETE FROM DDSTORAGE | WH
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| "/BIC/CRMEM_VAL" DECIMAL (000017,
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| DDL time(___8): .........4 millis
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8.1 Troubleshooting general system
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You need to ascertain: ► Which us
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Further information about the subje
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8.2 Analyzing load performance In t
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We use transaction ST03 → Expert
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Click the Details tab of the RSMO w
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Select the component of interest to
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Figure 8-11 All statistical records
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Figure 8-13 Single statistical reco
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Loading from a flat file You can av
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and right-click to select the Simul
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Note: You must activate the saving
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5. In the right-hand panel of the w
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If your query has long run times th
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that the query runs against → Mai
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If the main component of query perf
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► Display Statistics Data (to sho
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Select the appropriate button, ente
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Figure 8-29 Statement details To ch
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The ratio (ST04 statement cache “
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Figure 8-32 Visual Explain - SQL te
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8.5 SAP Query Analysis Tool The SAP
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Display of sql access path SQL quer
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The access path is displayed hierar
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The table information tab shown in
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DB2 Plan Table Figure 8-42 DB2 plan
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Graphical view of each query block
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Now you see the graphical view of t
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8.5.4 Example of using the query an
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9.1 The BIA architecture Figure 9-1
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Figure 9-2 BIA t-shirt sizing The B
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From the hardware side, the BIA is
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9.7 Overview The improvement of the
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10.1 SQL efficiency for DSO Queries
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The first join predicate is "/BIC/A
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Figure 10-5 Associate local predica
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Step 3 is repeated for each master
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Using the InfoCube query example in
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The first join predicate in Figure
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Thus, we have broken down the origi
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Figure 10-14 Comparing filtering Th
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AND "/BIC/AZOSASALE00"."PLANT" = "/
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Column Local or join CALMONTH Local
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10.4 Indexing of InfoCubes The defa
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11.1 Background To be more competit
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11.2.1 Hardware and software config
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11.2.2 Network infrastructure IBM S
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the chassis for blade-to-blade conn
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As part of the pre-benchmark planni
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11.4 Test case scenarios The goal o
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Figure 11-3 explains the data flow
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In addition, each cube, especially
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Measurement results Table 11-2 prov
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Figure 11-7 shows the average CPU u
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The number of queries per hour show
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Figure 11-10 shows that the average
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11.5.1 Recommendations for configur
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12.1 Terminology: BI objects and pr
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Metadata plays a fundamental role i
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12.3 Dataflow in SAP BI Data flow i
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12.4 Information access As previous
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Predecessor Successor WW Sales Euro
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An InfoCube in SAP BI can have up t
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12.8 Aggregates 4. The dimension ta
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The next example is an aggregate th
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For example, assume that different
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can be updated into InfoCubes or ot
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Figure 12-11 shows the steps that o
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Figure 12-12 on page 279 shows the
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The content, depicted in Figure 12-
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considerably more data records are
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The nodes stand for data sources, t
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values, hierarchies, hierarchy node
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BI SAP Query Connector A resource a
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Business Intelligence Cockpit Web-b
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processors in terms of macros that
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Crystal Reports Designer Design com
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Similar to the OLAP-standard term c
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P table Master data table for time-
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The process instance contains the m
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Page 334 and 335: IT scenario The environment we used
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Page 340 and 341: List of SAP BW OSS Notes Table C-1
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Page 346 and 347: START CU091060 START CU0A1070 START
Page 348 and 349: Table name Average row length Total
Page 350 and 351: Table name Average row length Total
Page 352 and 353: Parameters BPK16K0 BPK16K1 VIRTUAL
Page 354 and 355: drwxr-xr-x 3 t11adm sapsys 2048 200
Page 356 and 357: Example D-6 shows a cube index crea
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Page 360 and 361: -rw-r--r-- 1 t11adm sapsys 2084482
Page 362 and 363: -rw-r--r-- 1 t11adm sapsys 547449 2
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Page 366 and 367: Online resources These Web sites ar
Page 368 and 369: Help from SAP SAP Support and downl
Page 372 and 373: M master data 155, 211, 258, 277 si
Page 374 and 375: star schema 265-266 also see InfoCu
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