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

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Important: After the initial compression of the F- fact table, ensure that RUNSTATS is run to maintain accurate statistics, as the E- fact table grows rapidly. The same applies as new partitions are populated. 6.4.5 Implementing SAP compression Initially when you go live with new InfoCubes, it may be a good idea to wait a few weeks before deciding to compress regularly. As explained earlier, you could delete data from F- fact tables based on the package ID. So if you discover any data quality issues, you could reload the data after deleting one or more packages from the F- fact tables. Once you have solved all data quality issues, and you become confident with data loads, we recommend that you implement a periodic compression process. Since compression will be adding rows to the E- fact table, which may make it necessary to drop/rebuild indexes and run runstats on the large E- fact table, it can be beneficial to compress periodically (for example, weekly, bi-weekly) rather than daily. This can help to reduce the administrative impact of compression. 6.4.6 Speed up compression of non-cumulative cubes For non-cumulative cubes, the most difficult part is the update of reference points due to lack of a full, qualifying index. SAP Note 919597 describes an important code change to speed up this update of reference points. The solution was to replace the correlated subselect by a materialization into a temp table, which is then joined by the update. 6.4.7 Best practices for compressing large requests Some best practices for compressing large requests are: ► Deletion of requests from an F- fact table is an expensive operation and often dominates elapsed time of compression. In order to speed up the deletion of requests: – Condense all requests. When all requests are condensed, the F- fact table can be deleted completely, which is the fastest way. Mass delete versus row deletion. Be aware that request-save rollup cannot utilize mass delete. 100 Best Practices for SAP BI using DB2 9 for z/OS
– When not condensing all requests: i. Drop the secondary indexes on the F- fact table. ii. Call function module RSDU_INFOCUBE_INDEXES_DROP for the F- fact table. iii. Condense the requests. iv. Call function module RSDU_INFOCUBE_INDEXES_REPAIR. This speeds up the deletion of the request from the F- fact table, because the deletion of entries from the secondary indexes is very expensive. – Activate F- fact table partitioning. See SAP notes 860830 and 894045. ► Tune the size of the bufferpool for the E- fact table index pages. If the bufferpool size is too small, it is the bottleneck for the insert into the E- fact table. The default bufferpool for all index pages is BP3. The specific setting for fact table index pages via the RSADMIN parameter is DB2_FACT_BPOOL_INDEX (see SAP note 536074). ► When condensing many large requests: a. Drop the secondary indexes on the E- fact table before starting the condensing. b. Recreate the secondary indexes after all requests are compressed. Call function module RSDU_INFOCUBE_INDEXES_DROP and specify the E- fact table name. c. Call function module RSDU_INFOCUBE_INDEXES_REPAIR to rebuild indexes. Do not drop the secondary indexes if the E- fact table is already very large and the compression adds less than about 10% of new rows into the E- fact table. ► Restrict the size of the requests. – Requests should not be larger than 10 million rows. – Compression of huge requests results in long-running uncommitted UR (unit of recovery) and long elapsed time in case of a rollback. ► Use the partitioning of the F- fact table. – Refer to SAP note 860830 (DB2_PARTITIONED_F_FACT = X). – Configure the fast deletion of the partition for the request deletion from the F-Fact able (SAP note 894045). Chapter 6. Best practices for InfoCubes 101
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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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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 114 and 115: 6.3.2 Benefits of remodeling InfoPr
Page 116 and 117: 6.4.3 Cumulative data versus non-cu
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
Page 140 and 141: , "/BIC/RECORDMODE" POSITION( 00289
Page 142 and 143: Upon completion of the job, you wil
Page 144 and 145: | "/BIC/DIVISION" VARGRAPHIC (00000
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Page 150 and 151: | DDL time(___8): .........4 millis
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Page 154 and 155: 8.1 Troubleshooting general system
Page 156 and 157: You need to ascertain: ► Which us
Page 158 and 159: Further information about the subje
Page 160 and 161: 8.2 Analyzing load performance In t
Page 162 and 163: We use transaction ST03 → Expert
Page 164 and 165: Click the Details tab of the RSMO w
Page 166 and 167: 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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Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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