Sap mass data generator for banking

BP_MassDataGenerator_V30.doc – 10.06.2009

Best Practice

SAP Mass Data Generatorfor Banking

Dietmar-Hopp-Allee 16D-69190 Walldorf

CS STATUScustomer published

DATE VERSION

June 10 2009 3.0

SOLUTION MANAGEMENT PHASE SAP SOLUTION

Operations Implementation SAP for BankingTOPIC AREA SOLUTION MANAGER AREA

SAP Solution Management Change Management

Best PracticeSAP Mass Data Generator for Banking

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Table of Contents1 Management Summary 3

1.1 Goal of Using this Service 31.2 Duration and Timing 51.3 Staff and Skills 61.4 Conditions and Limitations 71.5 Alternative Practices 7

2 Best Practice: Mass Data Generation: Methodology 92.1 Design 9

2.1.1 Implementation details 102.1.2 Scenario synchronization 11

2.2 Setup and Implementation 122.3 Operation of the Mass Data Generator 12

2.3.1 General start screen 122.3.2 Scenario: master data creation 142.3.3 Scenario: Payment Items 162.3.4 Active Monitoring 21

2.4 Known Issues 232.4.1 Ineffective configuration changes during monitoring 24

3 Further Information 25Index of Figures 26Index of Tables 26


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1 Management Summary

Many banking projects face the challenge of creating mass test data to evaluate new business scenarios andtest new functionality in a pseudo-realistic productive environment. From a performance perspective,optimized core business processes for productive environments are the key requirement for any successfuloperation in a banking system.

In a typical Banking project the system landscape consists of various SAP solutions. Peak times of a bankingsystem are usually end-of-day, month-end, quarter-end and year-end processing, when high data volumesare transferred and need to be processed in short time frames. For example, banks may need to runsettlement on millions of accounts during the quarter-end processing and still have to fulfill their dailybusiness like money transfer from and to the ATM. The requirement on hardware is to process increasingworkload during peak time within the given time frame.

SAP offers the Mass Data Generator (MDG), an easy to use and transparent tool for creating high datavolumes, to help banks to simulate peak times and test their critical process chains with mass volumes.

The Mass Data Generator for Banking offers the following key advantages:

Reduce infrastructure expenses

Find optimal workload settings for your systems by using mass data generator dynamic functionality forchanging your process settings on the fly and observing the impact on your system as soon as the changesare applied.

Improve data quality

Improve quality of development and testing activities by using business-relevant and up-to-date mass data fortesting.

Increase efficiency

Increase your system performance from the beginning by working in an environment with expected end statedata volumes.

Decision support

Effectively simulate new business scenarios in your specific environment using high data volumes beforetaking critical business decisions.

It often takes longer to create data to test your solution than writing the code for it. It is extremely timeconsuming to ensure correct data formats and object relations. SAP Mass Data Generator for Banking canreduce these efforts significantly. It is easy to use, created for SAP Banking customer in particular and highlyefficient when it comes to real mass data creation.

1.1 Goal of Using this Service

This document focuses on the best practices and the advantages of applying SAP’s Mass Data Generator forBanking when facing the challenge of handling high application data volumes efficiently. The objective of the


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document is to enable the customer to setup, configure and operate the SAP Mass Data Generator forBanking. This service can help banking customers to better test and plan their mission critical processes.

Following our RunSAP recommendations means implementing a Best Practices which will help you to reducethe test preparation efforts and allow a more efficient test planning and later operation your Banking solution.The mass data generation approach and the proven method of SAPs End-to-End (E2E) solution operationsare aimed in particular for technical risk management.

Improved IT decision process and therefore lowering costs are reached by getting information aboutperformance impacts and data growth sooner and to a broader degree during the implementation project.This document gives guidance on how IT decision makers can take advantage of generated mass data forSAP Banking solutions.

Efficient operation of high data volumes depends on the right selection of tools, optimized processes and theright skill set of people involved. The focus on this topic should be set before real mass data has to beprocessed in productive environment. The MDG tool provides the functionality to generate high data volumesfor early performance optimizations and data volume management tests.

MDG enables you to create a future productive environment for your core banking processes. Productivedata can be extracted out of template transaction and master data of a small system and can be replicatedlong before real mass data migrations are possible. This is shown in the next picture:

Figure 1-1: Mass Data Creation


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The concept is comparable with the more common SAP Test Data Migration Server (TDMS). The differenceis that TDMS tries to specifically select from a huge system, where MDG selects from a small system tocreate a future mass data scenario.

MDG service can effectively help in at least three major banking scenarios:

1. End of Day processing – This is one of the most important business processes in the bankingsystems. It gets especially critical at the month end because month end activities (like settlement)have to be performed for each existing account, which results in the processing of high volumes in ashort period of time. Being able to quickly and efficiently produce relevant high test data volumes tosimulate and optimize EoD runs in peak times is the great advantage MDG offers. It is particularlyuseful for initial performance measurements and benchmarking of the customer specific EoDprocess, especially to test the performance of customer developed reports in the EoD job chains.

2. Transaction History – Customers often face difficulties to efficiently search through the transactionhistory of millions of transactions. MDG offers the possibility to produce a reliable test environment,where the payment items are spread across many accounts so the necessary optimization oftransaction history can be done during the test phase.

3. Volume – and Stress Tests for Payment Processing – Managing and efficiently processing paymentitems is the core transactional business process and therefore most sensitive for a bank. MDG canhelp you perform volume and stress tests for your system so you can more efficiently configure it tobe able to handle high peaks and volumes.

1.2 Duration and Timing

SAP recommends the implementation as soon as functional implementation provides real customer specificbusiness objects. This is usually already possible during an early stage in the implementation project. Thenext picture depicts two ideal points in a project when MDG service brings the highest value.

Figure 1-2: Recommended test process of SAP-centric solutions


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SAP Mass Data Generator for Banking is provided as a transport, which can be obtained form SAP AGSBanking directly. To establish contact you can either ask your support advisor or technical quality manager oryou open a customer message for component SV-SMG-SER.

For an initial implementation of SAP Mass Data Generator for Banking and test data generation for therelevant scenario the following duration and effort estimation are given as guidelines: Setup of SAP Mass Data Generator for Banking in system landscape will take approximately up to 2 days. The implementation of customer specific requirements will take additional efforts. The efforts can be

estimated only after the requirements have been made and the complexity of the change has beenanalyzed. The main effort is usually to define objects to be generated in the customer system. This will bedone in an initial workshop and implemented within the successive delivery phase.

Because there is the need of adapting existing implementation fast and flexible, SAP Master Data Generatorfor Banking is provided with full access to its source code as customer report. After this service you “own”your version of MDG for Banking, adapted to your specific requirements. You will know implementationdetails and will be able to execute changes within the processing logic on your own. The principal structure ofthis service delivery is depicted in the following slide:

Figure 1: Principal Service Delivery

In an initial phase of 1-2 days relevant packages are be transported into your system and manual steps arecarried out to enable your system environment for SAP Mass Data Generator for Banking. In a second stepwe initiate a workshop to define customer requirements in detail. This includes definition of required datavolumes, time frames for data creation and the structure of the business objects. With this knowledge we canimplement changes on your system and start creation of mass runs. We roughly expect a duration of up totwo weeks until useful test data is available at your disposal.

1.3 Staff and Skills

In a typical bank IT-operation environment, three roles exist. All three are relevant for the Mass DataGenerator service.

Roles Responsibility

Function support Define business requirement, solve business issue.

Results

• Real application massdata as defined

• Wrap-UpSummary of findings

•

Initial Setup Obtain

softwarepackages

Adapt systemsettings

Ongoing Workshop

Define requirements on business objects togenerate

Implement changes

Execute runs


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Roles Responsibility

SAP Basis System administration, maintain solution landscape, monitoring

Table 1: Roles and responsibilities

Functional support team is responsible for the definition of IT processes in particular with high data volumes.The function support team needs to define the most critical processes and appropriate service levels forthese processes. With SAP Mass Data Generator for Banking we offer a tool which allows an early testing ofcritical processes with high data volumes.

The Mass Data Generator is available for the following SAP Components.

SAP-Component SAP-Component Version SAP-Component Description

FSAPPL 100, 200 SAP Banking Services

Table 2: Required component release

1.4 Conditions and Limitations

The current release of Mass Data Generator for Banking serves specific for deposit management scenarios.These scenarios comprise most sensitive business processes for Deposit Management on SAP BankingServices: Retail business with business partners and current accounts Creation of payment items

Currently it is not supported to process loan accounts (because they require special functionality and checks),Master Contract Management and Analytics. Additional functional requirements can be discussed with SAPAGS Banking.

From a technical perspective the following system environments are required:

Banking Services environment with sufficient hardware and system settings for mass processing;For an efficient run of SAP Mass Data Generator there should be at least 20 DIA processes available and 20GB free memory. The database sizing should be accordingly.

1.5 Alternative Practices

Manual data creation is a very time consuming task and prone to human error. There are already severaltools and frameworks available to create test data. So, why do you need a new tool? All other approacheshave their specific focus.

The most simple solution for creation of mass data – a system copy – can only be used in late projectphases, when at least one system (or client) sufficient data was created, which is very often already too late.

There are other generic test data creation tools available. The most common one and – since integrated inSE80 - most widespread one is eCATT, SAP’s automated testing framework. It provides easy to use features


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like recording and replaying on transaction basis. However it does not provide a parallel processingframework and replaying transactions has been proved to be overall relatively slow. Thus it is not sufficient forhandling mass volumes.

For extracting specific test data out of existing SAP installations, a newer approach called SAP Test DataMigration Server (SAP TDMS) is available. It works closer on data storage level and provides transparentviews for extraction and mappings. However, it requires already existing data from a source system, thusbeing an appropriate tool for later project phases.


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2 Best Practice: Mass Data Generation: Methodology

In this section, the methodology is described in three different parts: design, setup and operation. In thedesign part, the program’s concept, structure and requirements are described. In the 2nd part we focus onsetup and implementation details. Operations are explained with examples in the third part of the document.

2.1 Design

In a typical project, there will be the requirement to create real mass data on a broad scale. The tool used toexecute this task is expected to be easy to use on the one hand and flexible on the other. To overcome thiscontradiction we decided to use a template based approach. First, the template master object needs to bemanually created with the specific characteristics. In the currently available scenario the template businesspartner and a template current account need to be created in your test system. Note: It is recommended totest the template account and BP with the required functionality already before you start to generate masstest data. If the template account doesn’t work you will create useless mass data on your system.

The business partner should be an account holder of the created template account. The business partner andaccount are then replicated on a high degree of parallelization. Progress of data replication is displayed to theuser showing progress and key figures, like throughput. Parallelization characteristics like parallel tasks andpackage size can be changed on the fly and direct impact on throughput can be observed. Therefore it ispossible to find the optimal setup for your system, before an actual mass run is started. For the creation ofhigh volumes of accounts you can reduce the parallelization during the day when other test activities run inparallel and increase the parallelization during the night, when no other activities will run in parallel.


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Figure 2: General flow – Mass Data Generation for SAP Banking

The architecture brings a clear differentiation between setup and actual data creation. The project team caneasily monitor the process and understand the basic system behavior for mass runs, thus a general forecastfor processing times of high data volumes is possible.

2.1.1 Implementation details

In SAP banking solutions, it is common to use parallel processing for jobs which are part of the critical path,like end of day process. Through parallel processing, large amount of data can be split into different workpackages and be assigned to different work processes. These parallelized jobs are called mass runs.

Because of the requirement of flexibility during mass data processing we decided to use RFC instead of fixedbatch based parallel processing frameworks (e.g. parallel processing framework). The general architecture isdisplayed in the next picture:


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Figure 3: General architecture – mass data generation for sap banking

Through the initial screen the user selects the scenario and defines the main settings of the mass run. Thenaccording to the chosen scenario the user sets the relevant attributes. After the execution of the run the useris forwarded to the graphical monitor to observe key figures during data creation. The batch triggers calls tothe function module via asynchronous RFC and collects statistical data for monitoring.

Every Mass Data Generator functionality (like Posting of Payment Items) is encapsulated in one functionmodule.

2.1.2 Scenario synchronization

For MDG there are two specific database tables controlling mass runs (where NS stands for customerspecific name space):

1. Run list information <NS>_RULI

2. Work list for mass processing <NS>_WOLI

For each scenario an entry in a database table <NS>_RULI is created to store run details like run id, time,number of created objects and number of total objects. During processing a run can create or process worklist items, which are stored in database table <NS>_WOLI. The MDG scenarios are not independent. First


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master data needs to be created (BPs and accounts) and then the payment items can be posted. Thefollowing flow was implemented to synchronize these scenarios.

1. Initial DLG scenario screens creates an entry in <NS>_RULI with a unique run ID and run type

2. Triggered BTC jobs are reading the last entry in <NS>_RULI based on the run type.

3. Master data creation run type will create entries in database table <NS>_WOLI which will then beread by the payment creation run type and used as input data.

According to the scenario logic <NS>_WOLI is filled during each run of master data scenario with accountdetails. This is done directly by each RFC function module to make use of parallelization. The subsequentpayment scenario (batch control job) reads items in <NS>_WOLI to build packages for posting payments oneach account in parallel RFC calls.

2.2 Setup and Implementation

The real process to implement and operate Mass Data Generator for SAP Banking is straightforward. Since itrequires the definition of data by business department as well as support from SAP basis for sensitive topicslike resource consumption, it needs cooperation of both departments. This could turn out to be very timeconsuming and organizational topics should be addressed before this service.

Following steps are generally performed during implementation:

Obtain SAP Mass Data Generator for Banking from SAP AGS Banking

At the beginning we will consult you on how to obtain transports and what manual steps are necessary toenable your system for Mass Data Generator for Banking. This could be done remotely or by experts onsite.As mentioned in chapter 1.2 not more than two days are necessary for this step.

Enable your system settings for mass processing

In this step we check your system settings to provide the resources necessary for mass data processing.These include general system parameter checks, number of on-hand work processes and memory checks.Besides general recommendations it always requires checking your system by a basis/performance expert todefine valid parameters to enable efficient mass processing.

2.3 Operation of the Mass Data Generator

In the previous section, we have discussed design and setup of Mass Data Generator for SAP Banking. Inthis section we explain how to start different scenarios and how to use monitoring results to find optimalsystem settings. The graphical output is called Mass Data Generator Monitor. We will first explain how tochoose and setup a scenario, and then explain the results in the MDG Monitor and how to influence them. Ina typical use case you start with mass creation of master data (BP and accounts) and then you createtransactional data (payment items) on top of it.

2.3.1 General start screen

Mass Data Generator is started as a Dialog (online transaction) to setup your run. After the set up andsuccessful start of the run, the screen switches to the monitoring tool. This feature provides useful information


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about current processing status and enables the user to influence the run by changing the level ofparallelization as well as the package size. It can either be used for active online monitoring or will also helpto identify the root cause of performance problems during mass processing.

After installing, Mass Data Generator can be started from the transaction SE38. Enter the program name<NS>_MDG_START and press execute.

Figure 4: Start Mass Data Generator for SAP Banking

In the start screen of Mass Data Generator you can choose your scenario and make initial settings for themass data creation run.

Figure 5: Start screen – Mass Data Generator for SAP Banking

At the moment there are two scenarios available.The first one is called Master Data Creation and will create business partners and current accounts in yoursystem. This is always the first step that should be done because it is the prerequisite for almost all furtherscenarios.

The second scenario to choose is Payment Item Creatio” and offers the possibility to post payment items tothe already created accounts.

Enter the program name andthen execute it.

Select the scenario youwant to start.

Click here to execute theselected scenario

Enter the initial number ofparallel tasks and packagesize.


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From the start screen you can also do the initial setting for the mass runs. You can set the initial number ofparallel tasks as well as the package size. This is only the initial setting. You can change both attributes onthe fly, during the mass run (see chapter 2.3.4 Active Monitoring).

2.3.2 Scenario: master data creation

The Master Data Generation scenario enables you to create business partners and accounts in the mostflexible way.

This scenario is based on a working business partner – called a BP template – with all properties relevant foryour business process (e.g. role assignments). At the beginning (after choosing your scenario) the followingstart screen is displayed. Press Load BP Template button to search and load your BP template.

Figure 6: Scenario Master Data Creation - start screen

Into the subsequent displayed dialog you enter the name of your BP template directly. If you don’t know theexact ID of the BP, you can use the provided standard search functionality to look it up.

Figure 7: Scenario screen – search and input business partner template

Click this button to openBusiness Partner load dialog.

Search your BP template. Clickcheck when ready.


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Now your BP template is loaded with all details. This includes all accounts belonging to this business partner.The loaded information is displayed on the next screen, where you are allowed to define structural details forsuccessive mass data creation.

Figure 8: Scenario Master Data Creation – define mass data object properties

The first view shows the loaded business partner template. The second view shows all accounts owned bythe loaded business partner.

For Business Partner you have the possibility to change the number of business partners to be created inthe first column “NUMBER TO BE CREATED” and define the two character template for the BP ID column“TEMPLATE STRING”. For example if you enter 100 for the number of BPs to be created and you enter THas a template, the result will be 100 BPs created with IDs: TH00000001, TH00000002, … TH00000100. Inthe next run, if you still define the template to be TH, the program will automatically determine that there arealready 100 BPs with the same template and create the new ones by incrementing the numbers:TH00000101, TH00000102, etc.

For Accounts in column “NUMBER TO BE CREATED” you can define how many copies of this specificaccount should be created per one single copy of BP. In case you provide zero, the account is skipped andbusiness partners are created without this account. Or if you enter 1 for the first account and 2 for the secondaccount, the final result will be a business partner with one account of the first type and two accounts of thesecond type.

Enter the number of BPs tobe created.

Enter the number ofaccounts to be created perone BP.


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There is always one business partner created in one LUW and all accounts belonging to it in the next. This isdone within one RFC, while package size defines how many BP are create in this call. After you finished yoursettings you can start mass processing, by clicking button Start Creation of Master Data.

Figure 9: Scenario Master Data Creation – start run

You are prompted with the common SAP batch job planning screen, to schedule your run. For immediatestart choose button immediate and click the save icon. This triggers the father batch job and forwards you tothe monitoring screen to observe progression. Details are described in the chapter below “Active Monitoring”.Mass processing has started now.

Example (Screenshot Figure 7): You enter 100 for the number of BPs to be created and for each of the twoaccounts you enter 1 in the “NUMBER TO BE CREATED”. This results in total of 100 BPs and 100 accountsof the first type plus 100 accounts of the second type, which makes a total of 200 accounts to be created.With default package size equals 10 there are 10 BP and 20 accounts created per RFC, all together 30objects – which is the figure relevant for throughput computation. How exactly you define number of accountsand package size influences processing time per RFC. Any change in package size or number of processes(see chapter 2.3.4 Active Monitoring) has to wait for RFC calls to return until changes become active. Thishas to be taken into consideration before executing any call.

2.3.3 Scenario: Payment Items

This scenario enables the creation of payment items on already created accounts. Payment items will beposted on all accounts created in the previous step, the Master Data Creation scenario. When you executethis scenario the following dialog is displayed.

Press this button to start themass run.


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Figure 10: Scenario Payment Items – initial screen

In the dialogue screen you can specify the following attributes:

Transaction type

Figure 11: Scenario Payment Items – transaction type selection

Enter the transaction type orchoose it from the input help


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Posting Date and Days offset

Figure 12: Scenario Payment Items – posting dates definition

Posting Date

Enter the posting dates range. For example if you enter 01.01.2009 to 31.12.2009, you will have paymentitems posted each day for the whole year 2009.

Note

Please allow backdated postings in your customizing settings, if the postings will be done in the past.

Days Offset

If you do not want to have payment items posted each day you can specify here how many days should beskipped between the two postings. For example, if you enter 2, you will have posting every second day, if youenter 3 every third day and so on.

Therefore if you specify posting dates from 01.01.2009 to 31.12.2009 and enter 3 for days offset you will havepostings on 01.01.2009, 04.01.2009, 07.01.2009, etc.

Payment note template and Number of payment notes

Figure 13: Payment note template

Enter the posting date orchoose it from the inputcalendar


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Payment note template

Enter up to four characters as a payment note template if you would like to have a payment note attached tothe payment item. Full payment note text will consist of the template you entered plus the string of randomlygenerated characters, for example PMTE6TSUV02pj5.

Number of payment notes: If you would like to have more payment notes per payment item enter here howmany you would like to have.

Amount and Currency

Figure 14: Scenario Payment Items – currency and payment amount

Amount: If you would like different amounts to be posted for each payment item, you can specify a range –the amount will be randomly generated within the specified range. For example, if you enter amounts from20.00 to 150.00, a random amount between 20.00 and 150.00 will be generated for each payment item. Ifyou do not enter the upper value, all payment items will be posted with the same amount specified in thelower value. Note: Please ensure that the postings will be accepted with the correct product attributes

Currency: Enter the transaction currency or use the input F4 help and choose it from the list of all currenciesin the system.

Number of payment items per account per posting date

Figure 15: Number of payment items per account per posting date

If you would like to have more postings per day for each account you can enter this number here. Forexample, if you enter 3, and we assume that the posting dates were from 01.01.2009 to 10.01.2009, and thedays offset was 3, you will have the following payment items for each account.

Enter the transactioncurrency or choose it fromthe input help.


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Table 3: Scenario Payment Items – resulting payments

Post payments in bulk

Figure 16: Post payments in bulk

You can post the payment items either in bulk or do a single item posting. If you leave this field uncheckedeach payment item will be posted as a single item in a single BAPI call. If you check this field a number ofpayment items will be packed, depending on the package size you specified, and posted in bulk using asingle BAPI call. The second option is commonly used when mass payment posting needs to be done. Theperformance is better because the overhead per BAPI call is smaller if you choose bulk processing.

Mass Data Generator can help to determine the optimal package size and the level of parallelization forpayment processing in your system, since you can change both the package size and the level ofparallelization during the mass run and monitor the change in throughput.

Calculate the total number of payment items to be created

Figure 17: Calculate the total number of payment items to be created

Pstg Date Value Date Transactio Medium AmountAC AcctCrcy Item Type Status Des

10.01.2007 10.01.2007 Cash Deposit Internal 46,09 EUR Turnover Posted













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This option is used to help you determine the total number of payments to be created in the system based onthe input you specified. The calculation is done using the following formula:

Number of Accounts x Number of Posting Dates x Number of p. items per account per posting date

Start payment item creation

Figure 18: Start payment item creation

After you have specified the attributes for payment item creation you can click this button to start the run. Thiswill call the job scheduler dialog where you can specify when you would like to start the run.

Figure 19: Scenario Payment Items – start the run

This will create one background job which will trigger RFCs to process payment items. After the job has beensuccessfully started, monitor will be started automatically. You can monitor the progress and throughput ofthe run as well as change the level of parallelization and package size on the fly.

Note : Please ensure that you will have at least 10 additional dialog work processes in addition to the dialogprocesses used for the MDG otherwise you might not be able to work properly on the system anymorebecause no Dialog processes are available anymore for other users then MDG.

2.3.4 Active Monitoring

After you have successfully started the mass run, control panel and monitoring of Mass Data Generator willstart automatically. The tool is decoupled and can be started at any time via SE38 to display the current run.


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In the normal scenario you are forwarded automatically to this screen. In rare situations, e.g. you did not starta mass run but you want monitor it, you can start it isolated using SE38 with program name:

<NS>_ GRAPHICAL_MONITOR

Here you can monitor the progress and throughput of the run as well as change the level of parallelizationand package size on the fly. This makes processing very flexible and increases the scalability of the solutionlandscape to be monitored.

Figure 20: Active Monitoring – Mass Data Generator for SAP Banking

Control Panel Area

Click the refresh button to get the latest figures displayed. Output is refreshed only when you pressrefresh.

This option enables you to change the number of allowed task on the fly.

Control Panel Area

Logs Area

Throughput Graph

Tasks allowed andPackage Size Graphs

Progress Bar


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This option enables you to change the package size.

This option changes the time interval for collecting the latest progress and throughput figures. Forexample if time delta is set to 10s you will have the new figures computed every ten seconds. If yourrun will run for hours you should increase this number to at least 60 seconds and more.

If you need to stop the mass run for some reason you can do this by clicking on this button. The runwill not break in the middle of a RFC call, but would rather wait for all RFCs to return and then stopfurther processing.

Logs area

Due to performance reasons only the errors and the most important information will be logged. For example ifan error occurs during the mass run, the error message will be displayed here and the run will beautomatically stopped.

Throughput graph

Throughput, number of object created per hours, will be calculated and displayed in the graph so you caneasily see how the throughput changes when you change the number of parallel tasks or package size.

Tasks allowed and the package size graph

Whenever you change the number of parallel task and the package size, the new figure will be shown in thisgraph. This helps you to easily connect the change in the parallelization and package size to the change inthroughput.

Note that changes are not active until all RFC calls return to the controlling batch job.

Progress bar

Progress bar shows how many object have been created so far and what is the total number of objects thatshould be created. This helps you to easily determine the number of object yet to be processed.

A key feature of monitoring is the combination of changing package size and parallelization on the one handand observing direct system response key figures on the other. It helps to find answers to questions like:What is an optimal level of parallelization? When do we see system bottlenecks (DB/SAP)? Where does thelinear dependency of throughput to number of work processes end?

Note: When you change the parallelization or the package size for payment item processing you should beaware that the number of parallel tasks multiplied by the package size cannot be higher that the total numberof accounts on which you do the postings, because at the same time two or more payment items will try to beposted on the same account, which will lead to account locking and a posting error will occur. Mass DataGenerator will inform you if you have tried to make higher package size or parallel task and will adjust itautomatically.

2.4 Known Issues

In this chapter we will provide latest information about known issues and problems you may experienceduring work with SAP Mass Data Generator for Banking.


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2.4.1 Ineffective configuration changes during monitoring

Changes of in the package size or number of parallel tasks are not effective after some time. Any change inthese settings (see chapter 2.3.4 Active Monitoring) has to wait for RFC calls to return until activated. Thishas to be taken into consideration before executing any run. In worst case you can create huge packagesizes comprising all data to be processed only one RFC calls. In this case you are actually doing sequentialprocessing and the key figures will appear when the RFC has returned, when the run is already finished.

A good basis to start from is the default configuration as displayed in the initial screen:

Number of parallel tasks = 2

Package size = 5


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3 Further Information

This chapter contains useful links to different RunSAP Best Practice papers within the banking world. Theycomprise valuable additional information. Related Best Practice documents for performance topics, likeperformance tests, performance test preparation, performance monitoring, database setup can be foundunder SAP Service Marketplace https://service.sap.com/solutionmanagerbp and filter on Solution =“Banking”. Table partitioning for optimal performance of a banking services system

Best Practice parallel processing framework for customer reportsBest Practice parallelization and package settings for a banking services systemSAP Test Data Migration Server for Deposits ManagementBest Practice business process performance monitoring banking

https://service.sap.com/solutionmanagerbp


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Index of FiguresFigure 1: Principal Service Delivery 6Figure 2: General flow – Mass Data Generation for SAP Banking 10Figure 3: General architecture – mass data generation for sap banking 11Figure 4: Start Mass Data Generator for SAP Banking 13Figure 5: Start screen – Mass Data Generator for SAP Banking 13Figure 6: Scenario Master Data Creation - start screen 14Figure 7: Scenario screen – search and input business partner template 14Figure 8: Scenario Master Data Creation – define mass data object properties 15Figure 9: Scenario Master Data Creation – start run 16Figure 10: Scenario Payment Items – initial screen 17Figure 11: Scenario Payment Items – transaction type selection 17Figure 12: Scenario Payment Items – posting dates definition 18Figure 13: Payment note template 18Figure 14: Scenario Payment Items – currency and payment amount 19Figure 15: Number of payment items per account per posting date 19Figure 16: Post payments in bulk 20Figure 17: Calculate the total number of payment items to be created 20Figure 17: Start payment item creation 21Figure 18: Scenario Payment Items – start the run 21Figure 19: Active Monitoring – Mass Data Generator for SAP Banking 22

Index of TablesTable 1: Roles and responsibilities 7Table 2: Required component release 7Table 3: Scenario Payment Items – resulting payments 20


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