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

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the chassis for blade-to-blade connections. Data traffic between System p and the BI Accelerator cluster of blades was mainly through Remote Function Calls (RFCs). A standard Maximum Transmission Unit (MTU) size of 1500 was used because the data package traffic was small. Connection between blade cluster and DS8300 A GPFS file system was mounted and accessible to all 140 blades. Generally, a Storage Area Network (SAN) is effective with 64 blades or fewer. As the number of blades scales up, GPFS topology with its cluster of network shared disk (NSD) server nodes performs better. In our large environment with 10 BladeCenter® chassis and 140 blades in total, 10 GPFS NSD servers were employed. They were System x3650 machines (two dual-core CPUs at 3.0 GHz with 4 GB RAM) running Linux SLES9 SP3. Since InfiniBand (IB) has better bandwidth and latency than GbE, we chose IB for the network between the blades and the GPFS NSD servers. Each configured HS21 blade chassis had two embedded Cisco SDR IB switches. Blades 1 through 7 were configured to use the first IB switch, and blades 8 through 14 were on the second one, to have a non-blocking configuration. Each SDR IB daughter card on each blade is rated at 10 Gbps, or 4x, where x = 2.5 Gbps. Each SDR IB switch had two external 12x ports and two external 4x ports, with an aggregate bandwidth of 80 Gbps. Each GPFS NSD server had 4x or 10 Gbps IB connection. On the other end, the 10 GPFS NSD servers were connected to GPFS storage (DS8300) via twenty 4 Gbps Fibre Channel connections, two connections per GPFS NSD server. Since each blade had GbE and IB interfaces, blade-to-blade communication could use GbE or IB. In our case, IB was mainly used for reading and writing to the GPFS file system and GbE was used for blade-to-blade communication. 11.3 SAP BI database and InfoCube load One of the major challenges for project Jupiter was to build a large, complex (25 TB) BI database within a very short period of time. According to the plan, a minimum InfoCube data load rate of 1 TB per day was needed in order to meet the aggressive schedules. In the following sections, we describe the database structures and the InfoCube load techniques that we implemented to meet the project requirements. 234 Best Practices for SAP BI using DB2 9 for z/OS
11.3.1 SAP BI database The four most relevant objects used for SAP BI (as described in Chapter 2, “Overview of SAP BI and related data” on page 9) are the master data, persistent staging area (PSA), data store (DSO), and InfoCubes (or simply cubes). For our Jupiter project, three database sizes were created and populated to supply the data volume requirements for the suite of tests. These are 5 TB, 15 TB, and 25 TB. In reference to the BI database characteristics, it is important to note that: ► The growth in database size was reached by increasing the size of the InfoCubes only. The data volume for PSA, DSO, and master data remained constant. ► The data volume refers to cubes (F fact table and indexes) only. The actual SAP BI database size was larger, if we include the statistics for PSA, DSO, and master data. ► DB2 data compression was not turned on for this environment. Since the focus of the test was the performance scalability of the BI Accelerator, and since this feature is not available in all database platforms, SAP opted not to enable this DB2 feature. 11.3.2 BI database InfoCube load The initial build of the SAP BI database involved the population of master data, PSA, DSO, and InfoCubes. There were several challenges encountered during this phase of the study. Some were caused by the level of DB2 code that we had installed at the time. Others were due to the characteristics of the database, such as the large volume of data being inserted and the concurrency of the jobs during data population. The execution of this project took six months. As standard benchmark practice, the software code level was not updated from the initial level unless we encountered a defect. However, due to the duration of this project, we did apply a few maintenance updates. The focus of the study was to showcase the performance scalability of the BI Accelerator in various data volumes. However, the amount of time needed to load the InfoCubes was a significant portion of the project schedule. Thorough planning for disk storage allocation, layout, and backup/restore procedures was essential to meet the demanding milestones. Chapter 11. Project Jupiter: large BI Accelerator scalability evaluation 235
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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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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 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 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
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
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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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