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

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6.3.2 Benefits of remodeling InfoProviders Remodeling allows you to add, delete, or replace a characteristic or key figure to an InfoCube without having to reload the data. Remodeling is not as involved as in previous versions, thus providing you with improved flexibility. 6.4 SAP compression for loads 6.4.1 Advantages SAP compression is the consolidation and summarization of data from an iInfoCube’s F- fact table into its E- fact table. Compression in this manner differs from traditional DB2 storage compression in that it does more than potentially reduce data storage requirements. Implementing SAP compression from the F- fact table to the E- fact table, in conjunction with SAP partitioning of the E- fact table, is one of the most important factors in having good load and query performance. A request is always loaded into the F- fact table. Compression copies the data into the E- fact table. Records in different requests that characteristics are all the same are merged into one record of the E- fact table. For non-cumulative cubes, an additional request containing the actual values of key figures is updated. These records are called reference points. At the end, the request is deleted from the F- fact table. There are several advantages to using SAP compression for loading data into E- fact tables. Some of those advantages are: ► SAP compression reduces the number of rows in the F- fact table (sometimes to zero). – A smaller F- fact table results in accelerated loading into the F- fact table. – SAP compression permits faster updating of the F- fact table indexes. – It accelerates aggregate rollups, since the F- fact table is the source of data for rollup. – It shortens RUNSTATS processing time on the F- fact table. – It reduces index REBUILD processing time if indexes are dropped as part of load. 96 Best Practices for SAP BI using DB2 9 for z/OS
6.4.2 Data validation ► SAP compression places summarized records in the E- fact table. – The E- fact table can be partitioned (at the time the InfoCube is designed) to optimize query performance. If the InfoCube is not compressed, SAP partitioning will not take effect. – Data from overlapping packages (based upon time characteristics) is consolidated into E, and thus rows are somewhat pre-summarized. – Similarly, data from overlapping packages (based upon time characteristics) is consolidated into E, and thus rows are somewhat pre-summarized. – SAP compression simplifies query optimization of the InfoCube because the optimizer does not have to factor in filtering from the package ID. ► SAP compression resets the reference point for non-cumulative key figures. This can improve query performance, since fewer rows are needed to calculate key figure value queries for dates near to the reference point. ► SAP compression sometimes reduces data storage requirements when rows from more than one package have the same time value (packages 1 and 2 were loaded on the same day, for example). In situations in which the F- fact table contains packages that do not overlap time periods, SAP compression may not reduce disk storage requirements. But even in this situation, SAP compression is always recommended because of the other advantages. Data in the F- fact table must be carefully checked (via an acceptance and approval process) prior to compression. Compression results in the package ID in the F- fact table being replaced with a 00 or a 99 (see 6.4.3, “Cumulative data versus non-cumulative key figures” on page 98) and all records summarized into the E- fact table. The replacement of the package ID means that these summarized records may not be deleted from the InfoCube after SAP compression using the corresponding package ID. For this reason, it is important that the data be carefully checked and validated prior to being summarized and prior to being compressed. However, data can be deleted from the infoCube after SAP compression by other characteristics such as time. If packages have been consolidated due to overlapping time, then package deletion may not be possible. Chapter 6. Best practices for InfoCubes 97
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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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Page 74 and 75: 5.1 Load PSA In this section we dis
Page 76 and 77: 5.1.1 Load PSA To load data into th
Page 78 and 79: After the source system, the DataSo
Page 80 and 81: Note the available tabs (registers)
Page 82 and 83: Figure 5-8 shows the provided optio
Page 84 and 85: Figure 5-10 shows the following:
Page 86 and 87: The PSA table name is created autom
Page 88 and 89: 5.1.4 PSA partitioning Partitioning
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Page 92 and 93: An InfoCube, whether data target or
Page 94 and 95: 6.1 Types of InfoCubes Data targets
Page 96 and 97: After selecting at least one time d
Page 98 and 99: 6.2.1 Repartitioning implementation
Page 100 and 101: You will then be presented with a w
Page 102 and 103: From within the monitor you are abl
Page 104 and 105: Once your repartitioning process is
Page 106 and 107: Figure 6-14 is displayed once you h
Page 108 and 109: When you remodel an InfoProvider yo
Page 110 and 111: Figure 6-17 demonstrates how you wo
Page 112 and 113: At this step of the process you are
Page 116 and 117: 6.4.3 Cumulative data versus non-cu
Page 118 and 119: Important: After the initial compre
Page 120 and 121: ► Use DPSIs for the cube’s E- f
Page 122 and 123: 7.1 Differences between DataStore a
Page 124 and 125: Figure 7-1 shows the initial status
Page 126 and 127: 4. By selecting the Create button,
Page 128 and 129: Partitioning and clustering are bot
Page 130 and 131: 7.4.2 Choosing and changing the clu
Page 132 and 133: 7.4.4 Partitioning a DataStore obje
Page 134 and 135: From the ABAP Dictionary: Utility f
Page 136 and 137: Figure 7-9 DB2/390 Storage Attribut
Page 138 and 139: partitioning was used. The CREATE T
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Page 154 and 155: 8.1 Troubleshooting general system
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Page 160 and 161: 8.2 Analyzing load performance In t
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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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