Sizing sap s 4 hana using the quick sizer tool

PUBLIC
June, 2017
Sebastian Schmitt, SAP
Sizing SAP S/4HANA using the Quick Sizer Tool

2
PUBLIC
© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Disclaimer
This presentation outlines our general product direction and should not be relied on in making a
purchase decision. This presentation is not subject to your license agreement or any other
agreement with SAP. SAP has no obligation to pursue any course of business outlined in this
presentation or to develop or release any functionality mentioned in this presentation. This
presentation and SAP's strategy and possible future developments are subject to change and
may be changed by SAP at any time for any reason without notice. This document is provided
without a warranty of any kind, either express or implied, including but not limited to, the implied
warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes
no responsibility for errors or omissions in this document, except if such damages were caused
by SAP intentionally or grossly negligent.

3
PUBLIC
Sizing Introduction and Basics
Sizing Tools and Results
Sizing SAP S/4HANA
Wrap-up
Agenda

4
PUBLIC
SAP HANA Sizing KPIs – Game Changer
 Different sizing approach: HANA
sizing vs. sizing of traditional DB
 Memoryis the leading driver for
HANA sizing
 Massive parallelization in
analytical scenarios will have an
influence on Response Times;
hence CPU requirement will get
more important for analytical
scenarios
 Mixed transactionaland
analyticworkloads now possible
with SAP HANA but compete for
shared resources
 Disk is required for data persistence and for logging data
 Disk sizing depends on type of store which is used
 Sufficient I/O performance required to enable processes
to run with acceptable data throughput and storage
system latency.
Disk size
Disk I/O
 Compared to anyDB, more CPU power is required to
fully benefit from the parallel processing capabilities of
HANA for optimal response times
CPU
 Memory sizing is determined by the data footprint in SAP
HANA (business and meta data in column and row store)
 Memory is also used by other components (e.g. HANA
caches) and for processing of requests
Memory
 Network sizing typically focuses on the bandwidth and
is described in gigabits per second (gbps)
Front-end
Network
Load

5
PUBLIC
Memory: Leading Driver for SAP HANA Sizing
 The main driver for memory sizing is the table data of the planned SAP HANA system
 Most tables are located in the highly compressed column store of SAP HANA
 For working memory of the database and temporary calculations, almost the same size as for table datais
required additionally
 A SAP HANA database includes further memory areas, such as code, stack, caches, operating system
and other system files. These areas are typically small compared to a typical database

6
PUBLIC
Approaches to SAP HANA Sizing
General Information: Different Memory Areas in SAP HANA
Relevant memory parts
1. The memory area allocated for
table data, either row store or
column store.
2. Subsequently, the buffer for
dynamic and temporary
computations is assumed to be
equal to the table data size
3. “Offset” refers to space required
for code and stack, caches and
services and the operating system.
Note: Used memory consists of
temporary memory parts and
table data, either in row store or
in column store. HANA code and
stack is also included, but usually
negligible.
Operating System
HANA caches and services (code and
stack included)
HANA Table data
(row store and column store)
Temporary memory
Pool
Sizing
“offset”
Results
of sizing
reports
Room
to grow
Depends on node size
~50 GB
Table footprint
(measured)
Equal to table footprint
(assumed)
Sizes

7
PUBLIC
What are SAPS?
SAP Application Performance Standard (SAPS) is a hardware-independent measurement unit that
describes the throughput of hardware in an SAP environment.
Laptop
 1 processor
 Quad-core
 Approx. 10,000 SAPS
Commodity server
 2 processors
 40 cores
High-end server
 16 processors
 244 cores
Definition of SAPS:
 Derived from Sales & Distribution (SD) Standard Application
Benchmark
 100 SAPS = 2,000 fully-processed order line items per hour
For more information on SAPS, see www.sap.com/benchmark
→ Measuring in SAPS

8
PUBLIC
Three-Party Collaboration Model
Contributions
 Certified benchmarks 
scalable hardware
 Different configurations
together with technology
partners
 Service level agreements
 Final responsibility for sizing
at customer site
Expectations from
benchmarking & sizing
 Optimal performance
 Suggestion for HW config.
Contributions
 Responsetime
requirements
 Throughput requirements
 Provides business input
Contributions
 Development and provision
of benchmark toolkits
 Regression testing for new
releases
 Standard sizing guidelines
 Sizing verification processes
Hardware Vendors Customer
SAP
SizingRecommendation
 CPU (SAPS)
 Memory (GB)
 Database space (GB)
 Disk I/O operations per sec
 Frontend bandwidth mbps
Sizing is the joint responsibility of
customer (LoB), SAP and HW Vendor.
But:
The main responsibility have the HW
Vendor. They have to make sure that
the SAP software runs smoothly at
customersiteand that customers
don’t run intoperformance or TCO
issues due to under or over-sizedHW
Examples:
 Custom coding
 Different businesses require
different sizings
 Different applications need different
amounts of CPUs
 Additional needs might come from
additional not sized usages

9
PUBLIC
Sizing SAP S/4HANA
Wrap-up
Agenda

10
PUBLIC
General Available Sizing Page (www.sap.com/sizing)
Guidelines
 Access Sizing Guidelines
 Access Sizing-related Materials
Tools
 Access Quick Sizer *
 Sizing Decision tree
 Others
Training opportunities
FAQs
* Requires login credentials

11
PUBLIC
Initial Calculation
Method
Educated guess
Questionnaire
without formulas
For structured
questions
T-Shirt Sizing
Simple algorithms
with many
assumptions
Formulas
Simple or more
complex
Standard Sizing Methods and Tools
QuickSizer
Supports user-
based and
throughput-based
sizing

12
PUBLIC
Example: Quick Sizer, SAP’s Online Sizing Tool
 Structured sizing questionnaires
 Input for
– Greenfield sizing
– GoingLive Check
 Hardware vendor contact list
 Available online since 1996
 NewSpecialQuickSizerversionforSAP HANAavailable (since 09/2014)
 Free of charge
 As of 2016: avg. 35,000 new projects per year
Characteristics
Facts & Figures
Scope
 SAP Key applications
– SAP S/4HANA
– SAP Business Suite and Industries
– SAP NetWeaver®
– etc.
 Sizing by users and/or by throughput

13
PUBLIC
Sizing SAP S/4HANA
Wrap-up
Agenda

14
PUBLIC
SAP HANA Sizing – In a Nutshell
Customer Interested in
SAP S/4HANA
New SAP HANA system
(Greenfield Sizing)
Existing SAP system
migrated to HANA
(Migration Sizing)
Use HANA Quick
Sizer
Use Migration
Reports
Connect with hardware vendor and
check for sample configuration or get
started with SAP CAL
Find deployment options:
Appliances, SAP Tailored Datacenter
Integration (TDI), Cloud via SAP Cloud
Appliance Library (CAL)

16
PUBLIC
Greenfield Sizing for SAP S/4HANA
For greenfield sizing for SAP S/4HANA, use HANA
version of Quick Sizer
Please note:
The basics of the calculations
are the same in HANA QS and
in the Classic QS, e.g. the think
times of the different user
types (low, medium, high) are
the same

17
PUBLIC
Data Aging
Data aging is a business data management concept for reducing the memory footprint in
SAP HANA
 Only operationally relevant (“hot”/current) data is loaded into main memory of SAP HANA
 Other (“cold”/historical) data remains primarily stored on disk, not affecting hot data performance, yet cold
data remains accessible via SQL on request

18
PUBLIC
HANA Quick Sizer News
Data Aging in HANA Quick Sizer
Since February 2016, a dataaging logichas been implementedin QuickSizer.
There are two residenceperiods. One for memory (aging period)and one for disk(archiving period)
 There are aging objects available, if the aging column (residence time in memory) is changeable. Per
default the aging period has been set to 24 months
 There are no aging objects available, if the aging column (residence time in memory) is empty and
highlighted in blue.
 Since March 2017: Introduction of ‘What if analysis for the retention times (disk/memory)’

19
PUBLIC
Example What-If Analysis Data Aging
Option 1: HANAMemory Result – 4,2TB for S/4HANAServer (24 monthresidence time in memory)
Option 2: HANAMemory Result – 8,9TB for S/4HANAServer(no data aging)

20
PUBLIC
Quick Sizer News
Sizing Principles for SAP Fiori Frontend Server (FES)
SAP Fiori Launchpad (FLP) Logon is the most influencingsizing factor
Determine Your FLP Logon Scenario for FES Sizing
 The total resource consumption has two parts, the one on the SAP Fiori Frontend Server, and the other
one on the SAP S/4HANA Backend
 For Fiori Frontend Server Sizing, please use the SAP Fiori Frontend Server Sizing” in the Quick Sizer and
estimate the maximal number of FLP logons per hour at peak time
Please Don‘t Forget to Size the Backend for Your Application Areas

21
PUBLIC
Quick Sizer News
HANA Disk I/O
SAP HANA requires adequate I/O performance to support processes such as
 Savepoint writing
 Delta merges
 Database startup times
Storage systems running with SAP HANA must provide sufficient I/O performance to enable processes to
run with acceptable data throughput and storage system latency.

22
PUBLIC
Sizing SAP S/4HANA Embedded Analytics
The goal of sizing for Sizing SAP S/4HANA Embedded Analytics is:
 To determine how many CPU Cores/Threads and memory are required for the processing of target
number of parallel queries
 And at the same time achieving the average target response time.
Get the latest version of the Sizing Excel Questionnaire:

23
PUBLIC
Example Analytical Fiori Applications
Please note:
HANA is designed for OLTP+OLAP. OLTP workloads can be sized with the Quick Sizer, whereas Analytical
Fiori Applications (OLAP) have to be sized additionally.

24
PUBLIC
Example Analytical Fiori Applications
Result
Input Be Careful:
 The SAPS out of the analytics sizing are very “peaky
SAPS”, which are needed to get the best possible
response times
 Customers have to be asked, whether this load may be
shifted to low load phases
 Customers have to decide, whether this optimal
response times justify systems, which have X times more
CPU power compared to what is needed just for the usual
throughput out of the usual sizing.
 Customers and hardware partners have to find a balance
between optimal response time on the one side and
minimized costs for hardware on the other side (also
higher response times might be acceptable for the
business of the customer)

26
PUBLIC
Sizing Report for SAP S/4HANA
Report /SDF/HDB_SIZING
 Described in SAP Note 1872170 – Suite on HANA sizing report
Scope
 Runs on SAP_BASIS 620 and higher
 Is suitable for sizing of all Business Suite products (ERP, CRM, SCM, SRM, etc.)
 Not suitable for BW (Refer to SAP Note 1736976 – Sizing Report for SAP Business Warehouse on HANA)
Functionality
 Estimates the maximum memory consumption of the database, if migrated to SAP HANA
 Is independent of the source database provider
 Considers distribution of tables to row and column store
 Considers differences for secondary indexes
 Considers compression of legacy database

27
PUBLIC
SAP Simple Finance Sizing for Migration Sizings
The current version of the report is also valid for sizing of HANA 2.0. The report can also be used for
different sizing scenario such as SAP Suite on HANA, SAP S/4HANA Finance, SAP S/4HANA.
1872170 – Suite on HANA sizing report

28
PUBLIC
Algorithm of Sizing Report /SDF/HDB_SIZING
1
• The totalnumberof entriesof each tables is read
from the database statistics
2
• A random sample of data is readfrom every
tables in the system
3
• Out of this sample, the report calculates the
averagenumberof bytes per column
4
• Out of the average size per column and the total
record count, the uncompressed size
of a columnis calculated
5
• To the uncompressed size an average
compressionfactoris applied to get the
estimated size in HANA.
Example: A typical column
“mandt” has type c and values
such as ‘100’, ‘000’, ‘066’. The
report will calculate an average
size of 6 bytes for this column.
Example: Column “mandt” has an
average size of 6 bytes and the
tables has 100 records. Total
uncompressed size of the column
is 600 bytes.
The size estimation for keys
(primary, secondary keys, etc.) is
more complex (and more
accurate) but uses the same
metrics (avg. size per column and
record count)

29
PUBLIC
Results of Sizing Report /SDF/HDB_SIZING
The sizing report for SAP ERP on SAP HANA includes the sizing projections, based on the actual table sizes
in the legacy system as well as an estimation of how much the memory footprint can be reduced using
functionalities that HANA will enable.

30
PUBLIC
How to Interpret the Results of the Sizing Report
 Column store and row store estimations have good enough accuracy (10-20%). Still, do not forget it is an
estimation.
 Work Space (temporary memory) estimation uses a simple formula (data size in memory) * 2. Based on
experiences, if the work space is bigger than 3TB, it might be oversized.
 Always check the top tables. Very often, you will find basis tables with deletion/archiving potential such
as idoc, workflow, application log tables, etc. See SAP Note 706478 – “Preventing Basis tables from
increasing considerably” for more details.
 The total estimated memory requirement given by the report should not be considered as the final
memory sizing result. Take into account that:
– Not all the server physical memory will be available to HANA (OS and other processes are run too).
– There should be enough space left for future data growth or functional extension
 The sizing report takes a snapshot. Any growth between that date and the go-live date should be
considered.

31
PUBLIC
Memory Sizing Report News
(1872170 – Business Suite on HANA and S/4HANA sizing report)
Data aging estimationfor Workflowdocument
 Added to already existing data aging estimation for Application log, Change documents and iDocs. The
addition of this objects completes the full coverage of “service objects”.
Sizing of upgrade shadowinstances
Integration in S/4HANA readiness check

32
PUBLIC
Memory Sizing Report News
Outlook
 Improve sizing of the “Work Space” (or temporary memory)
 Add more data aging objects to the report (especially, estimation of saving on “application objects”)
Other planned enhancements
 Integrate IOPS Sizing
 Reflect further simplifications in data model

35
PUBLIC
Active-Active Read Enabled
Loadbalance read-intensive operations
between a primary and secondary
instanceof SAP HANAwith the active/
active-readenabledmode.
 Currently „out of the box“ about 60
Apps are Active-active read enabled.
 Customer build queries
– Usage of generic analytical clients that use
analytical CDS views
– Own Smart Business Apps (and respective
adjustment of the Tile configuration)
– Own purely reading Apps (and respective
adjustment of the App Descriptor)
 Sizing: Beware that in case of a fail
over the complete load is on one
instance
 For resource intensive operations
available resources are used

36
PUBLIC
SAP HANA Multitenant Database Containers (MDC)
 MultitenantDatabase Containersprovide strong isolation capabilities
– Each tenant database has its own database admin and end users, database catalog,
repository, persistence, backups, traces and logs
– Tenants Memory and CPU consumption can be configured independently
– Integration with SAP HANA data center operation procedures, housekeeping, backups
(full and/or on tenant level), etc.
 MultitenantDatabase Containersshare one SID and Revision
– Shared installation of database system software and therefore better usage of
hardware
– Tenant databases are identified by name or port
– SAP HANA system replication covers whole system (Sys. DB and tenants)
– Additive sizing for all tenant database
 Targeting MCOS-likeon premise and SAPCloudPlatformscenarios
with a reasonable number of tenantdatabases per system.
Application
SAP HANA
System
Application
System DB
Tenant
DB
Tenant
DB

37
PUBLIC
Newest Adaptions in Quick Sizer and its Sizing / Configuration Impact
Introduction of Data
Aging in Quick Sizer
Introduction of sizing
forAnalytical
Applications
HANAQuickSizer
calculatesCPU and
memoryindependently
DataAgingcan reducethe
memoryfootprintsignificantly
Impacton CPU requirementsby applyingtheC2M ratio:
Reduced memory footprint will also lead to reduced CPU requirements.
But: Data aging should not reduce the required CPU resources for OLTP load
 Increased risk of CPU under-sizing with wrongly perceived C2M*
To getthebestpossible
responsetimes,theCPU
requirementscan increase
significantly
The calculation of CPU requirement based on the memory footprint might not
reflect correctly the additional CPU requirements for OLAP workloads.
 Increased risk of CPU under-sizing with wrongly perceived C2M*
TheratiobetweenCPU and
memorydiffers significantly
amongsttheavailablesizing
elements
The calculation of CPU requirements based on the memory footprint doesn’t
take these differences into account
 Risk of CPU over-sizing
 Risk of CPU under-sizing with wrongly perceived C2M*
(e.g. systems with high DB load)
* Please note that the CPU requirements based on the C2M ratio are minimum CPU values. This is
often misunderstood by customers

38
PUBLIC
Distribution: User-based Sizing Elements vs. C2M Ratio
Expected shifts
through Data
Aging effects
Expected shifts
through Sizing of
Analytical Apps
Quick Sizer projects
with higher CPU
results as projected
by the C2M ratio

39
PUBLIC
Deployment Options
Virtualization – SAP Note1788665
Infrastructure as a Service(IaaS) – Certified and SupportedSAP HANA Directory
Physical server – Certified and SupportedSAP HANA Directory

41
PUBLIC
www.sap.com/sizing
– Access to Quick Sizer*
– Access to sizing guidelines, for example, SAP HANA accelerators
SAP Support Portal
– SAP Note 1872170 – SAP S/4HANA memory sizing
– SAP Note 1793345 – Sizing for Suite on HANA
– SAP Note 1736976 – Sizing Report for BW on HANA
– SAP Note 1514966 – SAP HANA 1.0: Sizing SAP In-Memory Database
HANA Sizing – General
Introduction to Sizing on SAP HANA video
HANA Quick Sizer (for greenfield sizing) & SAP SoH Migration Sizing
Greenfield Sizing with SAP Quick Sizer demo video
Sizing information and tools
Sources of published sizing documentation
* Requires login credentials

42
PUBLIC
7 Key Points to Take Home
 Sizing means translating business requirements into hardware requirements
 The success of the sizing exercise almost entirely depends on the quality of the data
 Sizing involves very different people and teams within an organization
 Expert sizing is recommended for custom development
 The HANA sizing approach is different from the sizing of traditional databases
 Initial Sizing for HANA should be done with Quick Sizer
 Migration sizing of an existing system to SAP S/4HANA should be done following SAP Note
1872170

Thank you.
Contact information:
Sebastian Schmitt
Product Management
Performance & Scalability
SAP SE
Email: sebastian.schmitt@sap.com

Sizing sap s 4 hana using the quick sizer tool

Recommended

More Related Content

What's hot (20)

Similar to Sizing sap s 4 hana using the quick sizer tool (20)

Recently uploaded (19)

Sizing sap s 4 hana using the quick sizer tool