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

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For example, assume that different plants deliver their data at different times to be loaded into an InfoCube. It would not be valid to make any of the data visible until all plants have delivered their data to be loaded into the InfoCube as a consistent set. ► Another option to start the roll-up manually is by executing the program RSDDK_AGGREGATES_ROLLUP. This program can either be scheduled periodically or an event chain can be constructed to include aggregate roll-up. Change-run: hierarchy-attribute realignment run If you change master data, navigation attributes or hierarchies usually change as well. We therefore recommend that you adjust the data in the aggregates after loading the master data. To assure that reporting delivers consistent results, the master data and hierarchies are kept in two versions: ► The active version, where you can see the query ► The modified version, which at some point will become the active version The change-run, also called hierarchy-attribute realignment run, adjusts the data in the aggregates and turns the modified version of the navigation attributes and hierarchies into an active version. In almost every phase of the change-run, you can continue reporting on the old master data and hierarchies. You can either start the change-run manually in the Administrator Workbench or with the program RSDDS_AGGREGATES_MAINTAIN. You can give the program a list of characteristics and hierarchies that are to be taken into account for the change-run. By default, all those characteristics are taken into account whose master data you loaded or changed manually, and all the hierarchies that were marked for the realignment run. The change-run takes places in different phases: ► A list is generated of all navigation attributes and hierarchy levels that have changed. Then the relevant aggregates that are to be adjusted are generated from this list. ► The system adjusts the aggregates and calculates the percentage of change per aggregate. If the percentage is small, the changes are calculated explicitly. If the percentage is large, the aggregate is completely rebuilt in a temporary table. ► Master data and hierarchies are activated. ► The temporary aggregate tables are renamed. When running on DB2, the indexes are also renamed at this stage. 272 Best Practices for SAP BI using DB2 9 for z/OS
12.9 Compression of requests Data is loaded into an InfoCube by using smaller pieces, called requests. The data is first stored in the F- fact table of the InfoCube. Requests can be deleted from the F- fact table if, for example, the data is inconsistent. If the quality status of a request is appropriate, the request can be compressed, which significantly reduces the required storage space for the data. This form of compression is called SAP compression and is a consolidation and summarization of data from the InfoCube’s F-fact table into its E- fact table. Compression results in the package ID being stripped off and all records summarized into the E-fact table. During compression, data is transferred from the F- fact table to the E- fact table of the InfoCube or aggregate, as shown in Figure 12-9. Compressed requests can only be deleted by deleting the entire contents of the InfoCube. Figure 12-9 Compression of requests 12.10 DataStore (DSO) Request 1 to n E- Fact table (compressed data) Condensor Request n Request n+2 Request n+3 F- Fact table (uncompressed) A DSO object stores consolidated transaction data from one or several InfoSources. In contrast to the multidimensional data models of InfoCubes, data in DSO objects is stored in flat, transparent, database tables. DSO object data Chapter 12. Concepts, activities, and terminology 273
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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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Page 242 and 243: 10.4 Indexing of InfoCubes The defa
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Page 246 and 247: 11.1 Background To be more competit
Page 248 and 249: 11.2.1 Hardware and software config
Page 250 and 251: 11.2.2 Network infrastructure IBM S
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Page 254 and 255: As part of the pre-benchmark planni
Page 256 and 257: 11.4 Test case scenarios The goal o
Page 258 and 259: Figure 11-3 explains the data flow
Page 260 and 261: In addition, each cube, especially
Page 262 and 263: Measurement results Table 11-2 prov
Page 264 and 265: Figure 11-7 shows the average CPU u
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Page 268 and 269: Figure 11-10 shows that the average
Page 270 and 271: 11.5.1 Recommendations for configur
Page 274 and 275: 12.1 Terminology: BI objects and pr
Page 276 and 277: Metadata plays a fundamental role i
Page 278 and 279: 12.3 Dataflow in SAP BI Data flow i
Page 280 and 281: 12.4 Information access As previous
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Page 284 and 285: An InfoCube in SAP BI can have up t
Page 286 and 287: 12.8 Aggregates 4. The dimension ta
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Page 292 and 293: can be updated into InfoCubes or ot
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Page 334 and 335: IT scenario The environment we used
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List of SAP BW OSS Notes Table C-1
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SAP BI on DB2 for z/OS 390016 BW Da
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Network configuration: static multi
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START CU091060 START CU0A1070 START
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Table name Average row length Total
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Table name Average row length Total
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Parameters BPK16K0 BPK16K1 VIRTUAL
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drwxr-xr-x 3 t11adm sapsys 2048 200
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Example D-6 shows a cube index crea
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B49/S_RTNSVAL 24 DECIMAL 17 2 N 4 N
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-rw-r--r-- 1 t11adm sapsys 2084482
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-rw-r--r-- 1 t11adm sapsys 547449 2
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Online resources These Web sites ar
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Help from SAP SAP Support and downl
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C Candidates for partiioning Data W
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M master data 155, 211, 258, 277 si
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star schema 265-266 also see InfoCu
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Best Practices for SAP BI using DB2
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Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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