warm up for sap hana

7/28/2019 Warm Up for Sap Hana

1/19

Chapter 5:

Foundations for a New

Enterprise Application

Development Era

Learning Map

Founda'onsfora

NewEnterprise

Applica'on

DevelopmentEra

Founda'onsof

DatabaseStorage

Techniques

TheFutureof

Enterprise

Compu'ng

Advanced

Database

Storage

Tech-

niques

In-Memory

Database

Operators


2/19

Implications on Application

Development

Dr.-Ing. Jrgen Mller

Simplified Application

DevelopmentTradi&onal Column-oriented

Applica&on

cache

Database

cache

Prebuiltaggregates

Rawdata

Nocachesneeded Noredundantdata/

objects

Nomaintenanceofindexesoraggregates

Datamovementsareminimized

111


3/19

SAP ERP Financials onIn-Memory Technology

In-memorycolumndatabaseforanenterprisesystem

Combinedworkload(parallelOLTP/OLAPqueries) Leveragein-memorycapabili&esto Reduceamountofdata Aggregateon-the-fly Runanaly&c-stylequeries(toreplacematerializedviews) Executestoredprocedures

UseCase:SAPERPFinancialssolu&on Postandchangedocuments Displayopenitems Rundunningjob Analy&calqueries,suchasbalancesheet

112

Current Financials

Solutions

113


4/19

Onlybasetables,algorithms,andsomeindexes

The TargetFinancials Solution

114

BKPFaccounting documents

BSEG

sum tables

secondary indices

dunning data

change documents

CDHDRCDPOS

MHNKMHNDBSAD

BSAKBSASBSIDBSIKBSIS

LFC1

KNC1

GLT0

In-Memory Financials on

SAP ERP

115


5/19

BKPFaccounting documents

BSEG

116

In-Memory Financials onSAP ERP (prototype)

Feasibility of Financials on

In-Memory Technology

Modifica&onsonSAPFinancials Removedsecondaryindices,sumtablesandpre-calculatedandmaterialized

tables

Reducecodecomplexityandsimplifylocks InsertOnlytoenablehistory(changedocumentreplacement) Addedstoredprocedureswithbusinessfunc&onality

Europeandivisionofaretailer ERP2005ECC6.0EhP3 5.5TBsystemdatabasesize Financials:

23millionheaders/8GBinmainmemory 252millionitems/50GBinmainmemory

(includinginvertedindicesforjoina_ributesandinsertonlyextension)117


6/19

DBMS IMDBBKPF 8.7 GB 1.5 GBBSEG 255 GB 50 GB

Secondary Indices 255 GB -Sum Tables 0.55 GB -Complete 519.25 GB 51.5 GB

263.7 GB 51.5 GB

Reduction by a Factor 10

118

Dunning Run

Dunningrundeterminesallopenanddueinvoices Customerdefinedquerieson250Mrecords Currentsystem:20min Newlogic:1.5sec

In-memorycolumnstore Parallelizedstoredprocedures SimplifiedFinancials

119


7/19

Why?

Beingabletoperformthedunningruninsuchashort&melowersTCO

Addmorefunc&onality! Runotherjobsinthemean&me!-inamul&-tenancycloud

setuphardwaremustbeusedwisely

120

Bring Application Logic Closer

to the Storage Layer

Selectaccountstobedunned,foreach: SelectopenaccountitemsfromBSID,foreach:

Calculateduedate Selectdunningprocedure,levelandarea

CreateMHNKentries Createandwritedunningitemtables

121


8/19

Bring Application Logic Closerto the Storage Layer




1 SELECT

10,000 SELECTs

10,000 SELECTs

31,000 Entries

122

Bring Application Logic Closer

to the Storage Layer




123

1 SELECT

10,000 SELECTs

10,000 SELECTs

31,000 Entries

Onesingle

storedprocedure


9/19




1 SELECT

10,000 SELECTs

10,000 SELECTs

31,000 Entries

Onesingle

storedprocedure

Calculatedon-the-fly

Bring Application Logic Closerto the Storage Layer

124

Results

125

Hardware: 4 CPUs x 6 Cores (Intel Dunnington), 256GB RAMCustomer Data: 250mio line items, 380k open, 200k due

# Opera'on HANA2Version Variant2 Variant31 SelectOpenItems 0.63s 1.01s

(incl.T047KNB5Join) 0.6s(incl.T047KNB5Join)2 Duedate,dunninglevel 27s deferredtoaggrega&on 0.5s3 Filter1(VerifyDunninglevels) 19s 1.1s 0.5s4 Filter2(CheckLastDunning) 15s 0.8s 0.4s5 GenerateMHNK(Aggregate) donein#1 1.2s donein#16 GenerateMHND(ExecuteFilters) donein#1 140ms donein#1

Total 1Minute 3.0s(#3,#4exec.inparallel) 1.5s(#3,#4exec.inparallel)

OriginalVersionneededabout20minutes

Factor800xaccelera&onachieved


10/19

Dunning Application

126

Dunning Application

127


11/19

Available-to-PromiseCheck

CanIgetenoughquan&&esofarequestedproductonadesireddeliverydate?

Goal:Analyzeandvalidatethepoten&alofin-memoryandhighlyparalleldataprocessingforAvailable-to-Promise(ATP)

Challenges Dynamicaggrega&on Instantreschedulinginminutesvs.nightlybatchruns

Real-&meandhistoricalanaly&cs Outcome

Real-&meATPcheckswithoutmaterializedviews Ad-hocrescheduling Nomaterializedaggregates

128

In-Memory Available-to-Promise

129


12/19

Demand Planning

Flexibleanalysisofdemandplanningdata

Zoomingtochoosegranularity Filterbycertainproductsor

customers

Browsethrough&mespans Combina&onofloca&on-based

geodatawithplanningdatainanin-memorydatabase

Externalfactorssuchasthetemperature,orthelevelofcloudinesscanbeoverlaidtoincorporatetheminplanningdecisions

130

GORFID

HANAforStreamingDataProcessing UseCase:In-MemoryRFIDDataManagement Evalua&onofSAPOER Prototypicalimplementa&onof:

RFIDReadEventRepositoryonHANA DiscoveryServiceonHANA(10billion

datarecordswithca.3secondsresponse&me) FrontendsforiPhone,iPad2

KeyFindings: HANAissuitedforstreamingdata

(usingbulkinserts)

Analy&csonstreamingdataisnowpossible131


13/19

Near Real-Time as aConcept

Discovery Service

Read Event

Repositories

Verification

Services

SAP HANA

up to 8,000 read

event notifications

per second

up to 2,000

requests

per second

PA

Bulk load every

2-3 seconds:

> 50,000 inserts/s

132

Learning Map

Founda'onsfora

NewEnterprise

Applica'on

DevelopmentEra

Founda'onsof

DatabaseStorage

Techniques

TheFutureof

Enterprise

Compu'ng

Advanced

Database

Storage

Tech-

niques

In-Memory

Database

Operators


14/19

Views


Dynamic ViewsExcel SAP

BusinessObjectsExplorer

Any SoftwarePresentation Layer

View View

View View View...

View

View Layer(Calculations, Filter, ...)

Persistency Layer

(Main Memory)

Object Hierarchy

Node Tables Node TablesNode Tables Node Tables

View Other DB

LogicalLog

i

i i i i

DBPersistence

Store

Restart

Write CompleteObjects

Recovery

135


15/19

Learning Map

Founda'onsfora

NewEnterprise

Applica'on

DevelopmentEra

Founda'onsof

DatabaseStorage

Techniques

TheFutureof

Enterprise

Compu'ng

Advanced

Database

Storage

Tech-

niques

In-Memory

Database

Operators

Bypass Solution



16/19

Bypass Solution Allows a SmoothTransition to In-Memory Technology

Millionsofoldun-op&mizedlinesofcodeatthecustomerssiteTransi&onrequired

Row-storereplacement Part-for-partreplacementwithbypass Transformrow-storetocolumn-storeonthefly Changeofapplica&oncode

140

OLAPEngine

Traditional DBw/ Cubes

Bypass Solution for Transition

ERP

Traditional DB

ETL

Traditional BITodays System

141

BIA

SAP

Excel

.

.

.

BusinessObjects


17/19

OLAPEngine

ETL

Traditional DBIMDB

SSD


Traditional BITodays System with IMDB

ERP


BIA

SAP

Excel

.

.

.

BusinessObjects

STEP 1: Install and run the in-memory database in parallel

NewApplications

OLAPEngineETL

Traditional DBIMDB

SSD


Traditional BITodays System with IMDB

ERP


BIA

SAP

Excel

.

.

.

BusinessObjects


STEP 2: Generate business value from the first day on


18/19

NewApplications

OLAPEngine

EL

Traditional DBIMDB

SSD


Traditional BI with IMDBTodays System with IMDB

ERP

IMDBw/o cubes

BIA

SAP

Excel

.

.

.

BusinessObjects



STEP 3: Re-create traditional BI w/o cubes in IMDB

NewApplications

OLAPEngineEL

IMDB

SSD


ERP

IMDBw/o Cubes

BIA

SAP

Excel

.

.

.

BusinessObjects

SAP

BusinessObjects

Excel

BI 2.0

.

.

.

OLAP

Engine

Traditional DB

Traditional BI with IMDBTodays System with IMDB




STEP 4: Introduce next-gen BI running in parallel without materialized views


19/19

NewApplications

IMDB

SSD


ERP

SAP

BusinessObjects

Excel

BI 2.0

.

.

.

OLAP

Engine

Traditional DB

Todays System with IMDB





STEP 5: Eliminate all the traditional BI, virtualize all in-memory BI, using non-materialized views

NewApplications

IMDB

SSD

OLTP & OLAP


ERP FutureReleases

SAP

BusinessObjects

Excel

BI 2.0

.

.

.

OLAP

Engine

Future System with IMDB





STEP 5: Eliminate all the traditional BI, virtualize all in-memory BI, using non-materialized views

STEP 6: Run ERP and BI on IMDB

warm up for sap hana

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