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

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10.4 Indexing of InfoCubes The default indexes for an InfoCube facttable are generally sufficient for optimal query performance. This is especially true when combined with partition elimination that can be exploited by DB2, such as a query with a time restriction on a partitioned E facttable. The InfoCube query example shown in Figure 10-1 on page 204 actually contains a custom time dimension (D6), and therefore the query does not exploit the SAP-provided time dimension (referred to as DT in queries). Since E facttable partitioning is by the time characteristic, then InfoCubes with custom time dimensions may not benefit from partition elimination. This makes custom indexing a possible requirement. 10.4.1 facttable indexing From Figure 10-1 on page 204, the filtering dimensions (in order of filtering) are D3, D6, and D1. A facttable index may be created to support the combined filtering, although you must consider the Cartesian product size of before facttable dimensions. After filtering, the dimension/snowflake sizes are: ► D3 - 281 ► D6 - 37 ► D1 - 171,787 A Cartesian product of D3 x D6 x D1 = 1.78 billion is not desirable. The size of the D1 snowflake is too large to include in a Cartesian product. Given that this dimension provides the least filtering of the three listed, this can be discarded in choosing the optimal post-facttable access. A Cartesian product of D3 x D6 = 10,397 is very desirable given the facttable size of 10 million rows. At most, the facttable will be probed 10,397 times from this Cartesian result (potentially less, if not all combinations of the two dimensions exist in the facttable). This query may benefit from an index on KEY_ZIC_SFC3, KEY_ZIC_SFC6. 10.4.2 Index design for dimension/master data tables Indexes on dimension/master data tables provide two main functions: ► Efficient data access and join performance ► Easy statistics collection 224 Best Practices for SAP BI using DB2 9 for z/OS
For efficient join performance, it is important that an index exists to support the join predicates, regardless of the table size (small or large). For anything other than very small dimensions/master data tables (a few data pages), you must ensure that these tables have indexes that support the join and local filtering predicates. This will ensure efficient nested loop join performance for all tables. Local predicates are preferred as the leading index columns because they support this table accessed as the leading table accessed or non-leading of a table join sequence. Additionally, they allow frequency statistics to be collected on the local predicate column with default RUNSTATS. Both the local and join predicates should be included in the index because both columns provide filtering as part of the join — that is, unless the dimension/master data table is very small, in which case an index on the join predicate alone may be sufficient for join performance. Aside from data access and join performance, statistics collection on the dimension and master data tables becomes very important. An incorrect estimation of filtering from dimension/snowflake will propagate to the large facttable. Thus a 10% error in estimation on a 100-row dimension is only 10 rows, but when joined to the facttable (10 million), the error factor becomes 1 million rows. Single column indexes that support the local predicates on the dimension and master data tables allow frequency statistics to be collected by default. For dimensions/master data tables that have multiple filtering predicates, indexes that contain all filtering columns allow default collection of correlation information on these tables. This information is crucial for correct access path selection (and table join sequence). Dimensions and master data tables (large or small) can support many more indexes than can multi-million row facttables. This should be encouraged for the purpose of efficient data access/join performance, and also for effective statistics collection. Chapter 10. Tips for SQL efficiency 225
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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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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
Page 200 and 201: The access path is displayed hierar
Page 202 and 203: The table information tab shown in
Page 204 and 205: DB2 Plan Table Figure 8-42 DB2 plan
Page 206 and 207: Graphical view of each query block
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
Page 212 and 213: 194 Best Practices for SAP BI using
Page 214 and 215: 9.1 The BIA architecture Figure 9-1
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
Page 228 and 229: Step 3 is repeated for each master
Page 230 and 231: Using the InfoCube query example in
Page 232 and 233: The first join predicate in Figure
Page 234 and 235: Thus, we have broken down the origi
Page 236 and 237: Figure 10-14 Comparing filtering Th
Page 238 and 239: AND "/BIC/AZOSASALE00"."PLANT" = "/
Page 240 and 241: Column Local or join CALMONTH Local
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
Page 252 and 253: the chassis for blade-to-blade conn
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
Page 266 and 267: The number of queries per hour show
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
Page 282 and 283: Predecessor Successor WW Sales Euro
Page 284 and 285: An InfoCube in SAP BI can have up t
Page 286 and 287: 12.8 Aggregates 4. The dimension ta
Page 288 and 289: The next example is an aggregate th
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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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