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

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The unit of a value specified to MXDTCACH is in MB (1 MB is 1048576 bytes). For example, if a value 300 is specified, the total amount of up to 300 MB memory can be allocated for data caching per thread. ► The DB2 default value of 20 MB is far to low. ► When the value of 0 (zero) is specified, the data caching method will not be used during query execution. Only sparse index (key+rid) can be applied. ► The possible range of the values is between 0 and 512. ► When data records are cached, the required memory amount is: (number of rows)*((maximum length of the keys) + (total maximum length of all the relevant columns)) If the required memory exceeds the limit that MXDTCACH specifies or the memory allocation fails, the query will continue without using data caching. Sparse index will be used instead. ► When the thread terminates, the allocated memory for data caching will be freed from the local pool above the 2 G bar. The new installation parameter MXDTCACH can be set on the DB2 installation panel. Note: The existing installation parameter SJMXPOOL will be replaced by MXDTCACH. Accordingly, the data caching space for star join will be moved to the local pool above the 2 G bar. 4.5 TRUNCATE TABLE statement The TRUNCATE TABLE statement deletes all data rows for either base tables or declared global temporary tables without activating delete triggers defined on the table (they are ignored). The base table can belong to a simple tablespace, a segmented tablespace, a partitioned tablespace, or a universal tablespace. If the table contains LOB or XML columns, its corresponding tablespaces and indexes are also emptied. DB2 9 transforms the new TRUNCATE TABLE operation into a mass delete operation to take advantage of the current, optimized design and to provide greater flexibilities for users to deactivate existing delete triggers and harden the results of truncate operation without issuing a normal commit. Under specific table attributes and statement options, the TRUNCATE TABLE statement may provide an optimal performance path to empty the table while in comparison with the current mass delete operation (that is, DELETE without 50 Best Practices for SAP BI using DB2 9 for z/OS
WHERE clause). The performance improvement can be gained only on a table with triggers defined. Otherwise, it will perform as today. Moreover, it provides a statement commit option (IMMEDIATE option) for the statement that allows the truncate operation to become permanent (that is, cannot undo) and immediately makes deallocated space available for new, subsequent inserts in the same unit of work. SAP BI will use the TRUNCATE TABLE statement of DB2 9, but because BI tables usually have no triggers, the performance is the same as the mass delete operation. 4.6 FETCH FIRST n ROWS ONLY in subselects DB2 8 for z/OS disallows the FETCH FIRST n ROWS ONLY clause in a subselect. That is, one could write: SELECT * FROM T ORDER BY c1 FETCH FIRST n ROWS ONLY (specifying the clauses as part of select-statement), but one could not write: INSERT INTO any_table (SELECT * FROM T ORDER BY c1 FETCH FIRST n ROWS ONLY) (specifying the clauses within the subselect). SAP BI needs this feature to implement a more sophisticated algorithm of the so-called BI Selective Deletion for InfoCube and DSO data. Here selective means that the user can choose an arbitrary selection criteria for the data to be deleted. In this general case the data to be deleted can be distributed over all table partitions of a partitioned table, so that the deletion of whole partitions cannot be used. When deleting a huge amount of data rows from a fact or DSO table, it is better to separate this deletion in many small portions of delete transactions. Otherwise, you have one probably very long-running delete transaction, with the danger of a day-long or even longer rollback in the case the delete transaction has to be canceled for any purpose. This is a nightmare for every database administrator. With the DB2 9 feature of using FETCH FIRST n ROWS ONLY within a subselect, the portionwise deletion is implemented in the following way. The large delete operation is split up into multiple smaller units of work. In Example 4-2, assume that the primary key of table consists of the columns kf1, kf2, ..., kfn. is a placeholder for the WHERE Chapter 4. Benefits of DB2 9 for SAP BI 51
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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
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
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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
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Page 54 and 55: 4.1 Dynamic index ANDing for star s
Page 56 and 57: Figure 4-2 and Figure 4-3 on page 3
Page 58 and 59: 4.2.1 Processing RUNSTATS for BI ta
Page 60 and 61: RUNSTATS will then be called over t
Page 62 and 63: DSNU610I -DB2T 081 16:36:03.85 DSNU
Page 64 and 65: Often the single-value based freque
Page 66 and 67: Several things worth noting are:
Page 70 and 71: condition, generated by SAP BI upon
Page 72 and 73: There is a separate IBM Redbooks pu
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
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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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134 Best Practices for SAP BI using
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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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Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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