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

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11.4 Test case scenarios The goal of the Jupiter project was to prove the scalability of a BI Accelerator for increasing data volumes and increasing blade resources. The test plan consisted of a suite of tests on three database sizes (5 TB, 15 TB, and 25 TB), various blade configurations (27, 81, and 135 blades), the BI Accelerator index creation, and a multi-user query workload. Note that for every 27 blades, one backup blade was configured. This brought our total blade requirements to 28, 84, and 140 for the three database sizes. 11.4.1 BI Accelerator index creation When all the data was populated into the InfoCubes in a DB2 database on System z, the next step was the BI Accelerator index creation. This phase read data from a row-based database and transformed it into in-memory column-based files in a cluster of blades. In the Jupiter study, all InfoCube data from a DB2 database was indexed to BI Accelerator blades before it was available for query processing. Objectives The purpose of the BI Accelerator index creation test scenarios was to measure key performance indicators (KPIs) on 5 TB, 15 TB, and 25 TB database size points with blade resources of 27, 81, and 135 systems, respectively. These KPIs were: ► Elapsed time to create indexes into the BI Accelerator ► BI Accelerator index creation throughput rate ► CPU utilization on BI server and blade servers Description Since the goal of BI Accelerator index creation is to read data from a database server to a GPFS on blades, certain sizing and configuration settings need to be considered. Proper BI Accelerator blade memory sizing is an important consideration. SAP note 917803 provides an official sizing estimate and guideline for blades. In this study, 135 blades plus five backups (10 HS21 chassis of 14 Woodcrest blades each) were used. Each blade had 16 GB RAM and two dual-core processors at 3 GHz. Thus, 140 blades had about 2.2 TB of memory (RAM). A rule of thumb is to allocate 50% of blade memory for storing the indexed cube data and 50% for the Linux system for processing runtime objects and storing query result sets. This avoids performance impacts from memory swapping to disk. 238 Best Practices for SAP BI using DB2 9 for z/OS
Table 11-1 lists our Jupiter BI Accelerator resource requirements. Table 11-1 BI Accelerator requirements Total DB size RAM # blades @16 GB RAM 5 TB 432 GB 27 + 1 backup 6 B 15 TB 1296 GB 81 + 3 backups 18 B 25 TB 2160 GB 135 + 5 backups 30 B Number of rows In addition, the minimum GPFS size requirement is three times the total blade memory size, which is approximately 6 TB. IBM DS4000 servers are normally employed when using a smaller number of blades. In this study, the DS8300, an enterprise class storage system, was used. Our next consideration was the configuration settings for SAP parameters. They can influence data transfer performance: BATCHPARA, PKSIZE, NUMPROC, and SUBPKSIZE. They are defined in the RSDDTREXADMIN table, which contains some of the BI Accelerator administration settings. For details and guidance, refer to the SAP document Technical Operations Manual for BI Accelerator 7.0. Here is a brief description of these parameters: ► BATCHPARA defines the number of parallel background processes used to read data from the fact table of each InfoCube in the database into the data buffer at the SAP BI application server. This parameter essentially governs the degree of parallelism and can require significant system resource. ► PKSIZE defines the data size in bytes that is transferred from the fact table in the database to the data buffers at the SAP BI application server for each batch process. Parameters PKSIZE and BATCHPARA can affect significant BI system resource requirements. Make sure that a minimum memory size equal to (PKSIZE x BATCHPARA x number of cubes to be indexed in parallel) is available in the BI system. ► NUMPROC defines the number of dialog processes for each batch process to transfer data from the SAP BI data buffer to the BI Accelerator via Asynchronous Remote Function Call (aRFC). It is important to ensure that there are enough dialog processes and RFC connections defined. ► SUBPKSIZE defines the data size, in number of records, to be sent from the SAP BI data buffer to the BI Accelerator via aRFC. Chapter 11. Project Jupiter: large BI Accelerator scalability evaluation 239
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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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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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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 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 242 and 243: 10.4 Indexing of InfoCubes The defa
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 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
Page 290 and 291: For example, assume that different
Page 292 and 293: can be updated into InfoCubes or ot
Page 294 and 295: Figure 12-11 shows the steps that o
Page 296 and 297: Figure 12-12 on page 279 shows the
Page 298 and 299: The content, depicted in Figure 12-
Page 300 and 301: considerably more data records are
Page 302 and 303: The nodes stand for data sources, t
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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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