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

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9.1 The BIA architecture Figure 9-1 BIA architecture The BIA is based on the principles of cutting a big problem into smaller pieces. To do that it uses multiple techniques: ► Vertical decomposition ► Horizontal partitioning ► Smart compression ► Massive parallel execution The architecture of BIA and its components are shown in Figure 9-1. IBM SAP Global Alliance BIA Architecture : TREX Index server and BIA Engine TREX Index server � Indexes and searches � Contains BIA engine RFC TREX Why BIA is so fast � Vertical decomposition � Horizontal partitioning � Smart compression � Massive parallel execution 2 IBM Systems Solution for SAP NetWeaver BI Accelerator 4/4/2007 Vertical decomposition The BIA stores the data based on columns, a process called vertical decomposition. This increases the query’s performance, minimizing the data 196 Best Practices for SAP BI using DB2 9 for z/OS BIA Engine � Indexes BI InfoCubes � Materializes joins � Aggregation “on the fly” Index server BIA Engine Indexes © 2007 IBM Corporation
9.2 Sizing BIA managed because only it retrieves the data for the selected columns, instead of the traditional approach that gets the whole row. Horizontal partitioning The sizing of the BIA, covered in 9.2, “Sizing BIA” on page 197, is based on the memory needs of the data to be managed by the BIA. Based on that is the number of Blades that can manage that memory. For each Blade’s CPU, it creates one partition. Each partition can hold one or a part of a InfoCube/dimension data, depending on its size. Smart compression The BIA uses dictionary-based compression allowing it to reduce the size of the data stored. It also allows it to manage all the data in memory. For this reason it is important do correct sizing. If some paging is involved, the BIA performance is going to be affected. Massive parallel execution (MPE) These techniques mentioned prepare the system for MPE. The queries and loads can be partitioned on small independent parts and run on the different partitions. Every step uses its own data on its own process and does not affect any other step. The number of parallel sessions that can run on a BIA system is one of the metrics of capacity sizing. The purpose of the BIA is to improve the performance of queries on selected InfoCubes. The customer has to evaluate the potential InfoCubes selected for using BIA. SAP recommends not using the BIA for all InfoCubes. It should be used for large InfoCubes (thousands of millions of rows) to speed up query performance. For the sizing process SAP Note 917803 explains how to calculate the memory consumption using report ZZ_BIAMEMCONSUMPTION_BW3X. Note: We recommend using the option P_DETAIL to get more precise results for sizing. To get the number of Blades needed for the BIA system you can use the following algorithm: Result of report x 2 (GB) / amount of memory on Blade (GB) = number of Blades Chapter 9. SAP BI Accelerator 197
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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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Page 168 and 169: Figure 8-11 All statistical records
Page 170 and 171: Figure 8-13 Single statistical reco
Page 172 and 173: Loading from a flat file You can av
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Page 176 and 177: Note: You must activate the saving
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Page 180 and 181: If your query has long run times th
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Page 188 and 189: Select the appropriate button, ente
Page 190 and 191: Figure 8-29 Statement details To ch
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Page 194 and 195: Figure 8-32 Visual Explain - SQL te
Page 196 and 197: 8.5 SAP Query Analysis Tool The SAP
Page 198 and 199: Display of sql access path SQL quer
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Page 202 and 203: The table information tab shown in
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Page 208 and 209: Now you see the graphical view of t
Page 210 and 211: 8.5.4 Example of using the query an
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Page 216 and 217: Figure 9-2 BIA t-shirt sizing The B
Page 218 and 219: From the hardware side, the BIA is
Page 220 and 221: 9.7 Overview The improvement of the
Page 222 and 223: 10.1 SQL efficiency for DSO Queries
Page 224 and 225: The first join predicate is "/BIC/A
Page 226 and 227: Figure 10-5 Associate local predica
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Page 230 and 231: Using the InfoCube query example in
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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
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Page 262 and 263: 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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Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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