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

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4.4 Extended sparse index and in-memory workfile . . . . . . . . . . . . . . . . . . . . 49 4.5 TRUNCATE TABLE statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.6 FETCH FIRST n ROWS ONLY in subselects . . . . . . . . . . . . . . . . . . . . . . 51 4.7 RENAME INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.8 RANK expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.9 Further DB2 9 enhancements and new features. . . . . . . . . . . . . . . . . . . . 53 Chapter 5. Best practices for PSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.1 Load PSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.1.1 Load PSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.1.2 Management of PSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.1.3 Load PSA DB2 compression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.1.4 PSA partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Chapter 6. Best practices for InfoCubes . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.1 Types of InfoCubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.1.1 Physical data stores (data targets) . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.1.2 VirtualProviders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.2 Repartitioning of InfoCubes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.2.1 Repartitioning implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.2.2 Advantages of partitioning/repartitioning. . . . . . . . . . . . . . . . . . . . . . 88 6.3 Remodeling InfoProviders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.3.1 Procedure for remodeling an InfoProvider . . . . . . . . . . . . . . . . . . . . 91 6.3.2 Benefits of remodeling InfoProviders . . . . . . . . . . . . . . . . . . . . . . . . 96 6.4 SAP compression for loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.4.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.4.2 Data validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.4.3 Cumulative data versus non-cumulative key figures. . . . . . . . . . . . . 98 6.4.4 Areas of impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6.4.5 Implementing SAP compression . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.4.6 Speed up compression of non-cumulative cubes . . . . . . . . . . . . . . 100 6.4.7 Best practices for compressing large requests . . . . . . . . . . . . . . . . 100 Chapter 7. Best practices for DataStore . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.1 Differences between DataStore and ODS. . . . . . . . . . . . . . . . . . . . . . . . 104 7.2 Types of DataStore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 7.2.1 Standard DataStore object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 7.2.2 DataStore object for direct update . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.2.3 Write-optimized DataStore object . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.3 Secondary indexes on DataStore objects . . . . . . . . . . . . . . . . . . . . . . . . 105 7.3.1 Creating a secondary index on a DataStore object (DSO) . . . . . . . 107 7.4 Partitioning and clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 7.4.1 Choosing the partitioning key . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 7.4.2 Choosing and changing the clustering index . . . . . . . . . . . . . . . . . 112 iv Best Practices for SAP BI using DB2 9 for z/OS
7.4.3 Data Partitioned Secondary Indexes (DPSIs) . . . . . . . . . . . . . . . . . 112 7.4.4 Partitioning a DataStore object . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 7.5 DB2 compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7.5.1 Materialized query tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Chapter 8. Query and Load performance . . . . . . . . . . . . . . . . . . . . . . . . . 135 8.1 Troubleshooting general system performance . . . . . . . . . . . . . . . . . . . . 136 8.2 Analyzing load performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 8.3 Analyzing query performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.3.1 Starting the analysis from ST03 . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 8.3.2 Determining whether aggregates are required . . . . . . . . . . . . . . . . 161 8.3.3 Front-end processing time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 8.3.4 Database performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 8.4 DB2 Visual Explain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 8.5 SAP Query Analysis Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 8.5.1 Display of SQL access plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 8.5.2 SAP Query Visualizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 8.5.3 Features of SAP Query Visualizer . . . . . . . . . . . . . . . . . . . . . . . . . 190 8.5.4 Example of using the query analysis tool . . . . . . . . . . . . . . . . . . . . 192 Chapter 9. SAP BI Accelerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 9.1 The BIA architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 9.2 Sizing BIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 9.3 System z requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 9.4 BIA installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 9.5 BIA administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 9.6 Benefits of BIA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 9.7 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Chapter 10. Tips for SQL efficiency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 10.1 SQL efficiency for DSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 10.2 SQL efficiency for InfoCube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.3 Indexing of DSOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 10.3.1 Index design for post-DSO access . . . . . . . . . . . . . . . . . . . . . . . . 223 10.4 Indexing of InfoCubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 10.4.1 facttable indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 10.4.2 Index design for dimension/master data tables. . . . . . . . . . . . . . . 224 Chapter 11. Project Jupiter: large BI Accelerator scalability evaluation 227 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 11.1.1 Project overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 11.2 Jupiter environment infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 11.2.1 Hardware and software configuration . . . . . . . . . . . . . . . . . . . . . . 230 11.2.2 Network infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 Contents v
Page 1: Best Practices for SAP BI using DB2
Page 4 and 5: Note: Before using this information
Page 8 and 9: 11.3 SAP BI database and InfoCube l
Page 10 and 11: viii Best Practices for SAP BI usin
Page 12 and 13: Trademarks The following terms are
Page 14 and 15: The team that wrote this IBM Redboo
Page 16 and 17: Thanks to these authors from the Pr
Page 18 and 19: ► Send your comments in an e-mail
Page 20 and 21: 1.1 Product overview The business w
Page 22 and 23: In general, database administrators
Page 24 and 25: ► Horizontal growth is achieved b
Page 26 and 27: 8 Best Practices for SAP BI using D
Page 28 and 29: 2.1 BI architecture Figure 2-1 show
Page 30 and 31: New term Old term Analysis authoriz
Page 32 and 33: negative values means missing stati
Page 34 and 35: - When calculating database statist
Page 36 and 37: If you specify a partitioning schem
Page 38 and 39: Important: Never partition a PSA ta
Page 40 and 41: Figure 2-10 DPSIs versus NPSIs Figu
Page 42 and 43: Finally, Figure 2-12 explains which
Page 44 and 45: Figure 2-14 shows how the snowflake
Page 46 and 47: ► Realignment This refers to the
Page 48 and 49: 3.1 Base recommendations - SAP BI i
Page 50 and 51: the F- fact table, the E- fact tabl
Page 52 and 53: 34 Best Practices for SAP BI using
Page 54 and 55: 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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72 Best Practices for SAP BI using
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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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Relational Query Model An abstracti
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SAP Business Information Warehouse
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source object element Component of
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elements to be displayed in the Web
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Web application object catalog Web
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You insert one or more queries in t
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IT scenario The environment we used
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Checks under z/OS and DB2 We recomm
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To check that the SAPCL procedure w
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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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