warm up for sap hana
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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
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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
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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
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Current Financials
Solutions
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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
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BKPFaccounting documents
BSEG
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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
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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
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Dunning Run
Dunningrundeterminesallopenanddueinvoices Customerdefinedquerieson250Mrecords Currentsystem:20min Newlogic:1.5sec
In-memorycolumnstore Parallelizedstoredprocedures SimplifiedFinancials
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Why?
Beingabletoperformthedunningruninsuchashort&melowersTCO
Addmorefunc&onality! Runotherjobsinthemean&me!-inamul&-tenancycloud
setuphardwaremustbeusedwisely
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Bring Application Logic Closer
to the Storage Layer
Selectaccountstobedunned,foreach: SelectopenaccountitemsfromBSID,foreach:
Calculateduedate Selectdunningprocedure,levelandarea
CreateMHNKentries Createandwritedunningitemtables
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Bring Application Logic Closerto the Storage Layer
Selectaccountstobedunned,foreach: SelectopenaccountitemsfromBSID,foreach:
Calculateduedate Selectdunningprocedure,levelandarea
CreateMHNKentries Createandwritedunningitemtables
1 SELECT
10,000 SELECTs
10,000 SELECTs
31,000 Entries
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Bring Application Logic Closer
to the Storage Layer
Selectaccountstobedunned,foreach: SelectopenaccountitemsfromBSID,foreach:
Calculateduedate Selectdunningprocedure,levelandarea
CreateMHNKentries Createandwritedunningitemtables
123
1 SELECT
10,000 SELECTs
10,000 SELECTs
31,000 Entries
Onesingle
storedprocedure
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Selectaccountstobedunned,foreach: SelectopenaccountitemsfromBSID,foreach:
Calculateduedate Selectdunningprocedure,levelandarea
CreateMHNKentries Createandwritedunningitemtables
1 SELECT
10,000 SELECTs
10,000 SELECTs
31,000 Entries
Onesingle
storedprocedure
Calculatedon-the-fly
Bring Application Logic Closerto the Storage Layer
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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
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Dunning Application
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Dunning Application
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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
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In-Memory Available-to-Promise
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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
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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
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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
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Views
Dr.-Ing. Jrgen Mller
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
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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
Dr.-Ing. Jrgen Mller
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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
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OLAPEngine
ETL
Traditional DBIMDB
SSD
Bypass Solution for Transition
Traditional BITodays System with IMDB
ERP
Traditional DBw/ Cubes
BIA
SAP
Excel
.
.
.
BusinessObjects
STEP 1: Install and run the in-memory database in parallel
NewApplications
OLAPEngineETL
Traditional DBIMDB
SSD
Bypass Solution for Transition
Traditional BITodays System with IMDB
ERP
Traditional DBw/ Cubes
BIA
SAP
Excel
.
.
.
BusinessObjects
STEP 1: Install and run the in-memory database in parallel
STEP 2: Generate business value from the first day on
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NewApplications
OLAPEngine
EL
Traditional DBIMDB
SSD
Bypass Solution for Transition
Traditional BI with IMDBTodays System with IMDB
ERP
IMDBw/o cubes
BIA
SAP
Excel
.
.
.
BusinessObjects
STEP 1: Install and run the in-memory database in parallel
STEP 2: Generate business value from the first day on
STEP 3: Re-create traditional BI w/o cubes in IMDB
NewApplications
OLAPEngineEL
IMDB
SSD
Bypass Solution for Transition
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 1: Install and run the in-memory database in parallel
STEP 2: Generate business value from the first day on
STEP 3: Re-create traditional BI w/o cubes in IMDB
STEP 4: Introduce next-gen BI running in parallel without materialized views
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NewApplications
IMDB
SSD
Bypass Solution for Transition
ERP
SAP
BusinessObjects
Excel
BI 2.0
.
.
.
OLAP
Engine
Traditional DB
Todays System with IMDB
STEP 1: Install and run the in-memory database in parallel
STEP 2: Generate business value from the first day on
STEP 3: Re-create traditional BI w/o cubes in IMDB
STEP 4: Introduce next-gen BI running in parallel without materialized views
STEP 5: Eliminate all the traditional BI, virtualize all in-memory BI, using non-materialized views
NewApplications
IMDB
SSD
OLTP & OLAP
Bypass Solution for Transition
ERP FutureReleases
SAP
BusinessObjects
Excel
BI 2.0
.
.
.
OLAP
Engine
Future System with IMDB
STEP 1: Install and run the in-memory database in parallel
STEP 2: Generate business value from the first day on
STEP 3: Re-create traditional BI w/o cubes in IMDB
STEP 4: Introduce next-gen BI running in parallel without materialized views
STEP 5: Eliminate all the traditional BI, virtualize all in-memory BI, using non-materialized views
STEP 6: Run ERP and BI on IMDB
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