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Week 1 Unit 4: BI Sizing - Methodology

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OpenSAP BIFOUR1 Week 1 Unit 4 Presentation BISM

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  • Week 1 Unit 4: BI Sizing -Methodology

  • 2013 SAP AG. All rights reserved. 2Public

    BI is I/O Intensive I/O can be more difficult to measure than

    CPU and Memory Aggregation of millions of rows more

    costly than streaming transactions Balance constant load vs. peak load

    BI designed to use all systemresources

    Resource Greedy for optimalperformance

    No reason to restrict resourceconsumption on per system basis

    Throttling outside servers can be donewith vLANs and Storage Tiering

    SAP Business Intelligence Considerations

  • 2013 SAP AG. All rights reserved. 3Public

    Strategies for Deploying SAP BI

    Map out the deployment CMS database is the key to overall

    performance and scalability of BIPlatform

    Use a dedicated CMS DB, on its ownhardware, to ensure performance

    Monitor individual server tiers to identifybottlenecks

    Start small and scale out Increase load in 50-200 user steps,

    adding services as needed Immediately jumping to 1000 users (or

    more) makes root cause analysisimpossible

    Test, Analyze Results, Repeat

  • 2013 SAP AG. All rights reserved. 4Public

    Evolution of SAP BI 4.x Architecture

    BI 4.x is 64-bit XI 3.1 was designed for the entire suite

    to fit within a 32-bit architecture BI 4.x is not artificially limited for

    resources and is capable of stretchingout to take advantage of modernhardware

    BI 4.x is architecturally inclusive XI 3.1 was a collection of apps with their

    own connectivity stacks BI 4.x shares a common Semantic Layer

    for data connectivity BI 4.x is a first-class and highly

    integrated SAP client for BI

  • 2013 SAP AG. All rights reserved. 5Public

    Homogeneous vs. Heterogeneous

    Scale Up Scaling up has limits but modern hardware

    is too powerful to host only singleprocesses

    5 instances of Web Intelligence on amachine might make sense but watch outfor bottlenecks such as Disk I/O

    If you schedule Crystal Reports mostly atnight, CR Job/Processing servers may co-exist with Web Intelligence

    Scale Out Virtualization enables easier scale out as

    there is little/no incremental hardware cost

    Design principles for scaling out are nodifferent from other enterprise software

    When scaling out you may not needservers with vast system resources aseach server will handle part of the overallload

  • 2013 SAP AG. All rights reserved. 6Public

    Role of External Systems

    Poorly provisioned databases haveinvisible effects

    CMS DB latencies have cascadingeffects

    I/O bottlenecks have severeimpacts

    Starving a BI system for I/O willguarantee poor performance

    A slow file server hosting the FRS canalso impact performance

    Patch SAP BW systems Incremental performance gains can be

    significant Poorly performing WebI documents can

    often be traced back to a lack of BWpatches

  • 2013 SAP AG. All rights reserved. 7Public

    Understanding the Management / Intelligence Tiers

    CMS 400 500 Heavy Users

    per instance Add CMS every 500

    active concurrent users Best practice is not to

    have 2 CMS on thesame physical systemin order to enable faulttolerance and effectiveload balancing

    FRS FRS performance

    dependent on Disk I/O Input FRS should be

    kept close toProcessing Servers asbest practice

    Setting Instance Limitsis essential for optimalOutput FRSperformance

    RepositoryDatabases System & Audit DB

    should be kept close toCMS

    If monitoring is enabled,the trend DB shouldalso be kept close

    Best practice is not tochoose all servers formonitoring as it cannegatively impactperformance

  • 2013 SAP AG. All rights reserved. 8Public

    Sizing Virtual Environments Best Practices

    Use strict CPU Reservations for each VM on everyhost

    Leverage Memory Reservations to ensure allocatedmemory is always available

    Do not use shares, limits, affinity, or other artificialmechanisms to divide VM resources on the host

    To avoid excessive swapping, Size VMs large enoughto accommodate very I/O intensive BI workloads.

    Use more, smaller sized VMs rather than a few verylarge VM (>16 CPU)

  • 2013 SAP AG. All rights reserved. 9Public

    Processing architecture Do you have enough CPU power? Go beyond SAPS Are you set to properly scale your systems out? Are your processes properly distributed across nodes?

    Evaluate I/O requirements Consider reporting databases, inter-node communication, I/O links, etc.. CMS DB properly provisioned to ensure low latency/high throughput? DB vendor specific, not part of SAP BI documentation

    Memory do you really have enough? Nature of application means spikey and dynamic memory allocations

    Building Better BI Systems

    Always think about system design and read the manualsDefault systems are just that the default, not optimal

  • 2013 SAP AG. All rights reserved. 10Public

    Goal: focus on reliability and consistency, then performance: Reliability: ensure the system is highly available including fault tolerance, etc. Consistency: ensure acceptable performance at heavy loads Performance: move from acceptable to enjoyable experience

    Approach: Start with a clean/proven system Capture vital statistics Gradually scale up # of users Analyze tests using the collected metrics Make changes and iterate

    Testing & Tuning SAP BI

  • 2013 SAP AG. All rights reserved. 11Public

    Aim for 60-65% average CPU utilization That enables peaks to 80-90% w/o setting off data center alerts for high utilization Systems peaking at 100% will impact user sessions

    Emphasizes how important it is to scale slowly so you know what to add! What service in that APS is needing more headroom? Whats a better use of your resources, another WEBI service or an additional MDAS?

    CMS-specific: CMS are usually on their own machines add additional CMS hosts at 65% utilization Dont get carried away but the old myth of a 2-4 CMS limit is gone as of BI 4.0 SP4

    When to Scale

    There is no hard and fast rule for scale out, but common practice of 80%utilization is completely inappropriate for bursty, I/O heavy applications like

    BI

  • 2013 SAP AG. All rights reserved. 12Public

    Be generous with RAM Java allocates memory in heaps which can quickly grab large amounts of RAM Java garbage collection happens more frequently under memory pressure Heavy memory pressure can cause swapping at the OS level

    Look at max usage, not average usage: In virtualized scenarios VMware may report low active memory and your infrastructure

    team may overcommit memory (which some consider a best practice)

    Make sure your infrastructure team understands how BI is different: Most do not understand BI isnt like MS Exchange or a database, BI is bursty!

    What about memory?

    Business Intelligence is one of the very few applications that does NOThave the performance profile of a typical enterprise application

  • Thank you

    Contact information:

    [email protected]

  • 2013 SAP AG. All rights reserved. 14Public

    2013 SAP AG. All rights reserved.

    No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG.The information contained herein may be changed without prior notice.

    Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.

    National product specifications may vary.

    These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation orwarranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Groupproducts and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothingherein should be construed as constituting an additional warranty.

    SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG inGermany and other countries.Please see http://www.sap.com/corporate-en/legal/copyright/index.epx#trademark for additional trademark information and notices.

    2013 SAP AG. All rights reserved.

    No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG.The information contained herein may be changed without prior notice.

    Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.

    National product specifications may vary.

    These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation orwarranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Groupproducts and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothingherein should be construed as constituting an additional warranty.

    SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG inGermany and other countries.Please see http://www.sap.com/corporate-en/legal/copyright/index.epx#trademark for additional trademark information and notices.