Sizing BW 3 - Archive€¦ · SAP AG2002, Title of Presentation, Speaker Name 7 BW Standard...

Sizing BW 3.0

BW Performance Services, SAP AG

SAP AG 2002, Title of Presentation, Speaker Name 2

Agenda

Sizing Objectives

Disk Sizing

CPU Sizing

Memory Sizing


Sizing Objectives

Proper hardware sizing of a BW system is prerequisite for good performance :n Data load and stagingn Query execution

Sizing includes :n Database layer and application layern Disk storage sizingn CPU sizing (number of CPUs, clock rate, CPU architecture)n Memory sizing


Sizing Objectives

BW is a toolset and therefore difficult to size:

Sizing depends on

ØInitial data volume

ØPeriodic data volume

ØData staging requirements (aggregates, change runs, etc.)

ØMaintenance window for data staging

ØNumber of users accessing the data

ØUser profiles

ØQueries

ØQuality of aggregates

Ø...


The more accurate a sizing must be, the more complex is the sizing process:

accuracy

Complexity / effort / costs

Basic tools / T

-Shirt sizing

More sophisticated sizing

app

roach

Extrapolation from

evaluation / test system

s

Custom

er tailored benchm

ark

... and the more detailed information is required from customers

Sizing Objectives


most customers are not able to provide required information in an early project phase

The sizing information produced by Quick Sizer is then mapped to a vendor specific hardware configuration by our hardware partners

Provide best possible sizing based on a minimum set of customer input

An easy to use sizing tool needs to be publicly available:SAP Quick Sizer in SAPNet (alias: quicksizer)

Our goal for a sizing approach:

Sizing Objectives


BW Standard Application Benchmark 3.0

Step 1: Load Phasen Load master data from flat files into master data tables n Load transactional data from flat file into PSA

n Load transactional data into ODS incl. activationn Load transactional data into infocube 0SD_C01

u Includes refresh of statistics (threshold 10%) and bitmap indices on Fact Table and filling of 10 aggregates

n Key figure: Throughput of data in rows/hour

Step 2: Query Phasen The multi-user query phase simulates navigation steps

uCalling a query with various step of drill downs (based on infoCube and ODS)

n Key figure: Number of navigation steps/hour


Master data / data processed Ø 100,000 productsØ 100,000 customers

Ø 9 divisionsØ 9 distribution channelsØ 3650 dayØ 100 versionsØ 1000 salesorgsØ 100 types

Ø 22 countries

Possible sizes of InfoCube (depends on memory available on database server): Ø 29.200.000 rows in fact table (2 GB main memory)Ø 58.400.000 rows in fact table (4 GB main memory)Ø 116.800.000 rows in fact table (8 GB main memory, and so on)

BW Standard Application Benchmark 3.0


Disk Sizing

Factors that influence disk sizeØ InfoCubesØ ODS objects

Ø IndexesØ Master dataØ PSAØ Temporary data (PSAPTEMP)Ø Archiving / backupØ Redo logs

Ø Software / executablesØ Swap space


Disk space is required for:Ø InfoCubesØ ODS objects

Ø IndexesØ Master dataØ PSAØ Temporary data (PSAPTEMP)Ø Archiving / backupØ Redo logs

Ø Software / executablesØ Swap space

Disk Sizing


1. Determine 5 - 10 largest Infoproviders (Cube or ODS)

1.1 For each cube, provideØ Number of dimensions

Ø Number of key figures

Ø Number of records loaded initiallyØ Number of records loaded during

a delta load

Ø Number of delta loads during the life time of the cube

Disk Sizing

Record length

Total number of records


Add uplifts (in percent of the total size of infoproviders) forØ Indexes 100%Ø Aggregates/change_log(ODS) 100%

Ø PSAPTEMP 2*largest Infoprovider > 10 GBØ PSA 20%Ø Master data 10%

Ø Simplified: sum of the size * 3,3 + PSAPTEMP

Recommendation: a large number of small disks should be preferred to a small number of large disks

Only many small disks and a sufficient number of disk controllers can provide the I/O throughput required in a data warehouse

Disk Sizing


CPU Sizing

CPU load is mainly created by

Ø Data StagingØLoad / Delta load

Ø Index generation

ØRollup into aggregates

ØCube condensation

ØChange runs

Ø Query executionØNavigation in reports, Slicing and dicing

Ø „heavy“ queries scanning fact tables or large ODS objects


User Types

A "user" is a very abstract notion

ØJust a few thoughts

ØA named user is a user with an account / license

Øactive users work simultaneously or concurrently in the system

ØDifference between logged-on users and concurrent users

ØMobile, pervasive, Wap, Web users also generate load

ØInternet users create a different load than professional users or users using Bex Analyzer (analysts,admin)

ØActive Users (BW) are be defined by activity (# of Nav. Steps/h)


Assumptions :

Ø BW user categories according to typical user profile:ØNormal users : Typically run static / predefined reports

ØAdvanced users : Navigate within reports, do slicing and dicing, but usually hit aggregates

ØPower users : Run ad-hoc queries with a high probabilityof full table scans

Ø Query categories according to imposed load:Ø „Easy“ queries : Predefined reports using optimal aggregates

Ø „Medium“ queries : Slicing and dicing, navigating in reports,using various aggregates

Ø „Heavy“ queries : Ad-hoc reports with unpredictable navigationpaths, access of detail data, full table scans

CPU Sizing - Query


User types – classification

Ø normal users : 1 navigation step / hour

Ø advanced users : 11 navigation steps / hour

Ø power users : 33+ navigation steps / hour

Report types – classification

Ø Easy reports

Ø Medium reports : 1,5 times more load than easy reports

Ø Heavy reports : 5 times more load than easy reports

CPU Sizing - Query

Based on BW benchmark


CPU Sizing – Query

100%0%0%Z Power U.

33 Nav/h

0%50%50%Y Adv. U.

11 Nav/h

0%20%80 %X Normal U.

1 Nav/h

Heavy Q.

5

Medium Q.

1,5

Easy Q.

1

Nav/h = (X * 0,8 *1 + X * 0,2 * 1,5 + X * 0 *5) *1 +(Y * 0,5 *1 + Y * 0,5 * 1,5 + Y * 0,5 *5) *11 +(Z * 0 *1 + Z * 0 * 1,5 + Z * 1 *5) *33

71 %

27%

2%

User distribution

SAPS = (Nav/h * 1,33 * 2,28 * 9 / 60) / 0,65 (at 65% CPU consumption)

Remarks: * smallest possible result is 460 SAPS (at least 2 CPU)1.33 = adjustment factor 2.28 = factor comparing SD : BW9 = number dialogsteps produced by 1 Navstep


CPU Sizing – Data Staging

Assumptions:

Ø ThroughputØ Rec / hour / job : 750000 (incl. Load,rollup,change_run,…)Ø SAPS / batchjob : 350 SAPS Ø Maintenance window : Input of customer

#jobs =

{ sum (delta upload) / maintenance window } / 750000

SAPS= #jobs * 350 SAPS / 0,65 (at 65% CPU consumption)

Remark: With the benchmark scenario we are able to reach a troughput of

2000000 rec / hour / job having optimal conditions

Based on BW benchmark

and customer experiences


Memory Sizing

Memory consumption is mainly created by

Ø Data StagingØNumber of parallel jobs

ØNumber of records per package (default 50000)

Ø Query executionØNumber of connected users (BEx and WWW)

ØAmount of records select within the result set


Memory Sizing - Query

Application Layer:

Assumptions :• Each user needs 30 MB of memory• Users are connected via WAS• WAS keep alive time is 60 seconds (after 60 seconds a reconnect is required)• Minimum of buffer is 500 MB (table buffer program buffer, export/import,...)

Memory = 500 MB + (#high user + #medium user + #low user )/3600 * keep_alive * 30 MB

DB Layer:

Assumptions:• Each dialog process needs 120 MB of memory on the database server (default)• Minimum size of buffer is 700 MB • Buffer is increased by 10 MB for each mio. records of the largest infoprovider• Max size of buffer is limited to 10GB• One dialog process can handle 1 high or 2 medium or 5 low users concurrently• concurrent users ó keep_alive_time = 60

Memory = 700 MB + max(rows of infoprovider)/1 mio. * 10 MB +(#high user + #medium user / 2 + #low user / 5 ) / 3600 * 60 * 120MB


Memory Sizing – Data Staging

Application layer:

Assumptions:• Each job needs 300 MB of extended memory• Minimum of Buffer is 500 MB (table buffer program buffer, export/import,...)• #jobs = {sum (delta upload)/ maintenance window } / 750000 (see CPU sizing – data staging)

Memory = 500 MB + #jobs * 300 MB

DB layer:

Assumptions:• Each dialog/batch process needs 120 MB of memory (default)• Minimum size of buffer is 700 MB • Buffer is increased by 10 MB for each mio. records of the largest infoprovider• Max size of buffer is limited to 10GB • For parallel processing 2 times of #jobs are used by the RDBMS

Memory = 700 MB + max(rows of info provider) / 1 mio. * 10 MB +#Jobs * 120MB + 2 * #Jobs * 120MB


Multi-Tier Architecture – Overview BW

Database

Presentation

Layer

User Dialog: Graphical Information Processing

Processing Application Logic: System ManagementTransaction Monitoring

Handling Internet Access

Processing Internet Transactions

Information StorageDatabase Backup

Internet Architecture

20-50%

30-80%

5-10%

Load

Application

CreateProduction

Orders

ReleaseProduction

Orders

ScheduleProduction

Accept Customer

Order

ConfirmDelivery

BuildProducts

ExplodeBill-of-

Material

ReserveMaterial

CustomerService

Rep

PlantPersonnel

ProductionOrder

CustomerOrder

Part Material Task WEB Application ServicesWEB Application Services

Database ServicesDatabase Services

WebBrowser

WebBrowser

Presentation Services




2-tier 3-tier Multi-tier

WEB Application

Services

WEB Application

Services

Database Services

Database Services

Internet

Enterprise Portal

Enterprise Portal

5-10%


Complete Resources 2-Tier Enviroment

Disksize is the targetdisksize incl. all periods

SAPS =Max(SAPS data staging or SAPS QUERY)

Memory =Max(Memory data staging or Memory QUERY)


Complete Resources 3-Tier Enviroment

Disksize is the targetdisksize incl. all periods

SAPS =Max(50 % SAPS data staging or 20 % SAPS QUERY)


SAPS =Max(50% SAPS data staging or 80% SAPS QUERY)


DB

APPL


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Copyright 2002 SAP AG. All Rights Reserved


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