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

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Often the single-value based frequency statistics can hardly help DB2 with the predicate selectivity other than uniform interpolation on the rest of the (uncollected) value range. This would be a wild guess and may lead to an undesirable access path. Histogram describes the data distribution over the entire value range. The predicate selectivity gets a more accurate calculation if the searching range matches the boundary of any one quantile or any group of consecutive quantiles. Even if there is no perfect match, the predicate selectivity interpolation now is done within one or two particular quantiles. With the interpolation done in a much smaller granularity, the predicate selectivity is expected to be evaluated with more accuracy. Histograms in detail Histogram statistics are introduced to DB2 9 for enhancing predicate selectivity estimation and to enhance the DB2 access path selection in general. Histogram statistics is a way of summarizing data distribution on an interval scale (either discrete or continuous). It divides up the range of possible values in a dataset into quantiles, for which a set of statistics parameters is collected. There are several types of histogram statistics being researched. DB2 for z/OS uses only Equal-depth histogram statistics. Histogram statistics enable DB2 to improve access path selection by estimating predicate selectivity from value-distribution statistics that are collected over the entire range of values in a data set. DB2 chooses the best access path for a query based on predicate selectivity estimation, which in turn relies heavily on data distribution statistics. Histogram statistics summarize data distribution on an interval scale by dividing the entire range of possible values within a data set into a number of intervals. DB2 creates equal-depth histogram statistics, meaning that it divides the whole range of values into intervals that each contain about the same percentage of the total number rows. The following columns in a histogram statistics table define an interval: QUANTILENO An ordinary sequence number that identifies the interval LOWVALUE A value that serves as the lower bound for the interval HIGHVALUE The value that serves as the upper bound for the interval Note the following characteristics of histogram statistics intervals: ► Each interval includes approximately the same number, or percentage, of the rows. A highly frequent single value might occupy an interval by itself. 46 Best Practices for SAP BI using DB2 9 for z/OS
► A single value is never broken into more than one interval, meaning that the maximum number of intervals is equal to the number of distinct values on the column. The maximum number of intervals cannot exceed 100, which is the maximum number that DB2 supports. ► Adjacent intervals sometimes skip values that do not appear in the table, especially when doing so avoids a large range of skipped values within an interval. For example, if the value 30 above has 1% frequency, placing it in the seventh interval would balance the percentage of rows in the sixth and seventh intervals. However, doing so would introduce a large skipped range to the seventh interval. ► HIGHVALUE and LOWVALUE can be inclusive or exclusive, but an interval generally represents a non-overlapped value range. ► NULL values, if any exist, occupy a single interval. ► Because DB2 cannot break any single value into two different intervals, the maximum number of intervals is limited to the number of distinct values in the column, and cannot exceed the DB2 maximum of 100 intervals. Example of equal-depth histogram statistics Equal-depth Histogram cuts the whole value range so that each quantile has about the same number of rows. The important parameters to describe equal-depth histogram statistics includes the total number of quantiles (N) and for each quantile, the pair of LOWVALUE/HIGHVALUE, the number of distinctive values (CARD) and the frequency (or the number of rows). To give an example, on a table IBM_EMPLOYEE column YRS_OF_EXPERIENCE, it may have (N=7) quantiles, as shown in Table 4-1. Table 4-1 Histogram statistics example SEQ.NR LOWVALUE HIGHVALUE CARD FREQUANCY 1 0 3 4 14% 2 4 15 8 14% 3 18 24 7 12% 4 25 25 7 12% 5 26 26 1 15% 6 27 30 4 16% 7 35 40 6 14% Chapter 4. Benefits of DB2 9 for SAP BI 47
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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
Page 14 and 15: The team that wrote this IBM Redboo
Page 16 and 17: 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
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Page 58 and 59: 4.2.1 Processing RUNSTATS for BI ta
Page 60 and 61: RUNSTATS will then be called over t
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Page 66 and 67: Several things worth noting are:
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Page 70 and 71: condition, generated by SAP BI upon
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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
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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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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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