OpenSAP HANAINTRO1 Transcripts

8/20/2019 OpenSAP HANAINTRO1 Transcripts

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open An In

UNIT 1: Int

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AProdu

roduction a

So why di

how we e

So the ba

80s, early

when SQ

away fro

and man

structured

And that'

And at th

the hard

hardware

Or even

the hard

How was

have mai

and... wel

And then

accessing

Somethin

And the o

database

The DRA

It was mill

thing was

And so M

itself.

And alrea

limitation,

governed

the proce

in order t

by 2003

reach.

tion

nd Backgro

d we do HA

nded up her

sic idea was

90s,

was a rela

file-based

gement of s

relational

what SQL

time when

are that imp

that is avail

lready 10, 1

are was alr

it different?

memory,

l, actually th

there is the

it out of dis

g like 10,00

ther thing th

when it was

was much

lions of time

that CPUs

oore's Law

dy by 2003 i

by things th

sors were

continue th

lso, it was cl

o SA

und of SAP

A, and wh

e?

very straigh

ional algebr

ata manag

pecialized s

ay to mana

eans. It's a

the RDB wa

lemented rel

ble now.

1, 12 years

ady very dif

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ere are laye

isk. And ac

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times or m

at happened

first built.

more expe

worse in p

ack then w

as largely a

t was clear t

at we canno

ot going to

e performan

lear that a c

HA

HANA

t is some of

tforward. Th

a, and SQL

ment

ructures an

e data.

structured

s designed,

lational data

go, when

erent then.

k at this pict

s in betwee

cessing dat

re faster. S

was.... so t

sive and m

ice/perform

re single co

ttained bec

hat this was

control, lik

e single co

ce and man

mpletely ne

A by

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e relational

tarted to be

so forth, a

uery langua

bases was s

e started thi

ure, typicall

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from mem

o this is slo

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ch smaller t

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re.

use of impr

running into

the speed

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ufacturing b

w kind of da

r. Vi

und, how H

atabase w

come popul

d objects, a

ge, that was

ignificantly d

king about

in compute

on board c

ry is dramat

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lear in the d

han it is no

s now. Also,

vement in t

a physical

f light and t

nefits of Mo

tabase para

hal S

NA came a

s designed

ar. People w

nd get into

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ifferent than

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rs we have

aches and s

ically faster

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ays of the r

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the other fu

he single co

all, into a h

ings like th

ore's Law.

digm was wi

ikka

bout and

in the late

anted to get

more

name for it.

the

database,

PUs, we

o forth.

han

on't like it.

lational

ndamental

e CPU

rd

t, and that

o already

thin our



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And when

database,

That is m

columnar

The reas

or so yea

Some sor

other sort

And anal

this was t

that, you

are very,

columnar

retrieve th

And colu

more tha

and for a

performa

and this i

offer som

as well as

So, when

was that

The reas

paradigm

The good

So if you l

and Rudi

and built

his friend

started to

projects t

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to one of

who was

was an in

Sort of a

project, w

back in 2

was prett

we started

then it has

lticore proc

structures.

n why colu

s ago, OLT

ts of compa

s of compan

tical worklo

he belief tha

now, you a

ery differen

structures

em significa

nar technol

20 years ol

long time, p

ce. In the c

more than

e unique ad

multicore p

we looked

AP had to

n for it was

in database

news was t

look at the ti

Munz did hi

database t

build TREX

at Hasso a

of the work

my PhD adv

also his stud

-memory ro

econd gen

hich was de

03. When

remarkabl

thinking abo

o be built ar

essing; mas

nar structur

and OLAP

ies were bu

ies were sta

ds are quit

t everybody

e either ana

t. Neverthel

ere invente

ntly faster.

ogy itself is

d,

ople have k

se of Syba

20 years old

antages for

rocessing an

t this, when

uild a new d

very straight

s is possible

at SAP had

me line of w

thesis,

hat we all kn

And I joine

ked me to t

that Franz

isors, Gio W

ent. And Sa

store tech

ration datab

monstrated

ranz showe

.

2

ut this, it be

ound the ne

ively larger

es are inter

had becom

ilding things

rting to build

different th

in the indus

lyzing thing

ss, the colu

to store th

ot new. Da

nown that c

e IQ, it is a

now. So by

improving a

d large avai

I started thi

atabase.

forward. Am

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worked on

here we hav

ow now as

in 2002, a

ink about

ad done wa

iederhold, a

nk had been

ology.

ase. And Fr

nd was the

the EUCLI

ame clear t

w reality of

and cheape

sting is bec

very differ

for OLTP, f

things prim

n OLTP; or

ry had,

or you are

mnar datab

same relati

abases like

lumnar dat

disk-based

this time, it

nalytical per

lability of lar

king about t

ong other th

atabase te

e been, way

axDB. And

d I started t

as databas

s already kn

nd he introd

working on

nz and Ste

winner of th

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ardware:

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use alread

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r transactio

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at least this

riting thing

ses were in

onal informa

for example

bases are b

olumnar dat

as clear th

ormance

er quantitie

his back in

ings, first of

hnology for

back here i

around 199

inking abou

s.

own by that

ced me to

a technolog

an and the

very first D

billion recor

s to build a

ory; and the

by that tim

ndustries, in

nal applicati

P, for analyt

was the ass

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vented,

tion, but to

our own Sy

etter for retr

abase,

t columnar

s of DRAM.

002. My firs

all, a compl

quite some

the past w

9, Franz an

t, one of the

time. I went

ang Cha

called P*TI

uys built th

KOM that S

s in one se

new

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fact.

ns, and

ics.

umption,

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e able to

ase IQ are

ieval

atabases

t conclusion

tely new

ime.

s 1977,

some of

first

and I talked

ME, which

EUCLID

P ever did,

ond, that



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So Hasso

investigat

And in Au

he had, th

And he w

amazing

from this

guess wa

aggregat

indices, d

as I was

I had just

this was

database

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database,

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for buildin

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history of

As we ar

launch of

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So 2 year

than a bill

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in 2 years

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implemen

started to t

ion into how

gust of 200

at we could

anted to rew

bility to cal

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riving home

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started wo

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ble journey.

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enterprise s

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than 2,000

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oftware.

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ths ago. In t

revenue.

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customers h

already.

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plication itsre.

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mers that w

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e that nigh

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at night, I t

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And it ha

hardware

HANA its

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chip cam

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it was cle

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UNIT 2A: S

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AP HANA

So let's ta

technolog

We can't

Hasso ha

those.

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data local

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the fact th

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Seattle,

anything

one core,

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from. We

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UNIT 2B: S

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AP HANA

The seco

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ancy



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cocktail t

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00:06:33

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main inve

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ch more



UNIT 2C: S

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AP HANA

So the thi

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out.

So this is

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transactio

because

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egations, yo

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maining



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So this is

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ANA.



UNIT 2D: S

00:00:00

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00:00:43

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AP HANA

The next

which we

are some

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or multipl

database

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On a 16–

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inherently

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And we t

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case wer

The only

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And here

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And onesemantic

In Financi

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n workloads

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nonymous r

biggest co

ns per day.

ok 10 years

on records.

trillion, in c

ery, very lar

rter times th

n kind of gu

e is 40 CPU

ally the incr

between 6

uestion tha

er over qua

like, these a

structures.

in HANA, th

-out.

ther capabil of the appli

als, for exa

t year. The

ear, of cour

items from t

the items fr

information

u'll do also

d of course,

ine, and so

cores that a

esome per

ormance sc

, we often s

s on the na

ese native c

across mac

extreme ex

etail data fro

panies in t

' worth of da

se you are

ge number. I

e population

ess who this

cores and 1

dible thing

0 milliseco

went past

ter compari

re the kind

at runs withi

lity in HANAcation.

ple, we kno

ear is typic

se, is typical

e previous

om this year

for one yea

the previous

14

, you can pa

on, or a co

re sitting on

ormance.

le-up and s

e performa

ture of the q

apabilities th

hines.

amples that

m one of ou

e world. An

ta. Wow! So

ounting, th

t's one and

of the world

retailer is.

terabyte of

as, all the

ds and 3.1

seconds w

on over 10

f questions

n a couple o

is hot and c

w that the a

lly the mea

lly a little bit

ear that ca

that you wil

r. And then i

year. So th

rtition colum

bination of

each one of

cale-out pret

ce improve

uestion, and

at other dat

we have ac

r largest, su

they were

that is how

t's 1.2 and t

a half times

.

nd we ran t

memory.

uestions th

econds res

as this one,

ears.

hat on this

f seconds. S

old. So, ther

ctive data in

ure of a pu

longer than

e into this

l carry forwa

you want t

t's it. You d

ns, so you h

oth of these

these mach

ty much line

ent of arou

how distrib

bases don't

ieved: We

er, super la

oing somet

much data?

hen 12 zero

he populati

is thing on

t we could t

onse time.

his 3.1 seco

ind of data

o that is the

are times

a financial r

lic compan

ne year, be

ear and we

rd into the n

do year ov

n't need to

ave one pie

things.

ines into all

arly.

nd 11 times,

table or ho

have, whic

nce took re

rge Retail c

hing like 33

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after that.

n of the wo

a 100 node

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nd question

ould run fo

benefit of p

hen we kn

ecord of a c

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cause you

e carried fo

ext year: So

r year com

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e of the

f them.

and stuff

distributed

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ail data,

stomers,

million

oximately

ld. One and

luster.

s particular

, and this

days on

rtitioning

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mpany is

nformation,

ant to keep

ward,

maybe 14

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an that in



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00:07:28

the hot...

You can

additional

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this kind

so that w

HANA en

So that w

in active me

old store th

compressio

n take 42 y

f a tiered st

can get ev

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as INSERT

mory.

rest of it,

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ce. And



UNIT 2E: S

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AP HANA T

The next

That's the

completel

And yet, f

using for

Anyhow,

25 million

There is,

even in th

depends

So yeah,

support f

and the r

The ends

compliant

this is reg

SQL-like l

for multidi

inside HA

text functi

forth,

for specia

not in the

We also

people ar

I mean b

amount o

if you wa

10% to all

That is a

inverse o

you want

demonstr

And then

language

so people

We have,

the librari

echnology:

one I want t

language t

y redesigne

rom the out

ecades.

ere in the

people aro

ou know, t

e consumer

n SQL.

don't let the

ll SQL, so b

w store, an

see SQL 9

, no learning

ular SQL. A

anguage, st

mensional d

NA. We hav

ons, and th

l syntaxes a

standard S

upport map

e so fascina

sically, whe

data when

t to, let's sa

l your plan,

ap operati

that, that y

to do a filter

ated map-re

we have the

called L, wh

can write c

of course H

s for GIS d

SQL, Libra

talk about i

access HA

from scrat

ide, the inte

alley, there

nd the worl

ns and tons

world, all ou

fool you. S

oth on the c

both of the

and then s

, no special

d beyond S

arted at Micr

ata traversa

e support fo

n all kinds o

round that,

L.

reduce oper

ed by.

n you think

you want to

, increment

r something

n that appli

u want to re

or somethi

duce inside

stored proc

ich is a part

de, low-lev

ANA itself is

ta, for text

16

ries, and S

s SQL.

NA. Now th

h, ground u

rfaces are e

is a lot of tal

who know

and tons of

r ability to a

QL is no le

lumn store,

se have the

bsequent v

voodoo of o

QL we offer

osoft,

l, especially

r text-orient

f functional

eographical

ations, whic

bout it, a m

map an ope

everything

like that.

s widely, a

duce somet

g of that so

REX at the

edure langu

of the LLVM

l code direc

written in C

ata, for wh

mmary

beauty of

p.

actly the sa

k about no S

ow to progr

usage of S

k questions

s important

SQL front e

rsions of S

urs, no prop

all kinds of

used in anal

d things,

nhanceme

syntaxes, s

h is what ty

p is like a s

ration,

r do an agg

d we have

ing, you wa

t. So we off

DKOM in 2

ge SQLScr

,

tly in L, and

++, and ther

t we call the

ANA is that

me interfac

QL and stuf

am SQL.

L. The entir

and do tran

ow than it

d on top of

L that cam

ietary thing

dditional thi

ytics. We su

ts for busin

uff like that,

ically the “N

an that you

regation ove

map API. It

nt to do an

r map-redu

06.

pt. We have

have that b

are lots of

Business F

we did all of

s that peopl

f like this. T

e enterprise

sactions ba

as ever bef

them.

in, fully sta

here,

ngs: MDX,

pport MDX

ss function

which som

o SQL” mov

distribute o

r something

reduces ty

ggregation,

ce. In fact,

a native, lo

attached in

C++ librarie

unction Libr

this stuff

e have been

ere are like

world, and

ically

ore. So we

ndards-

hich is a

atively

and so

times are

ement

er a large

, or add

ically the

e

-level

o HANA.

in HANA,

ry, and the



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Predictiv

We integr

function li

and we a

anybody'

Now safe

HANA is

When we

HANA,

there is al

really sup

And that'

comes int

with Live

memory

because t

each oth

And so wi

happen.

working o

training w

So, those

If I forgot

So the ov

have sort

So you b

So these

things lik

And then

We also

and other

store for.

And then

these. So

calc engi

which incl

operation

We have

Analytics Li

ate R, which

braries like t

e coming u

library, insi

y here is ex

ntirely runn

insert code

ways a dan

er careful a

why we ha

o HANA. Yo

ache, we h

nd things lik

he program

r every onc

th HANA, w

o there are

n any datab

hatsoever, g

were five of

some, the b

erall picture,

of looks like

sically have

are things li

this.

these are st

ave the gra

stores that

So this is al

we have be

these are th

e, join engi

udes things

, rolling for

the geograp

brary.

is a statisti

his,

with a gen

de HANA in

remely imp

ing in-memo

like this and

er that bad

out how thi

e written so

u know, in t

d lots of ex

e that,

area of the

in a while,

have gone

all kinds of li

se that you

et up to spe

the most im

auty of the

when you t

this.

the core, le

e integers,

red in the r

h store that

e can add l

o text.

ond the cor

ings like the

e. We have

like aggreg

casts, and

hic engine in

17

al package

ric way, wh

a safe way.

rtant, beca

ry.

run that in t

things can h

code is int

me extreme

e early day

erience wit

ystem and

and stuff lik

to great len

ibraries like

can think of

ed and runn

portant tech

e MOOCs i

ink about th

's say data

nd text, an

w store, in

we are wor

ater on, as

e data types

OLAP engi

a special e

tions, disag

ersions of p

here, and s

in there, we

t we call th

se when we

e same ser

appen. So

grated into

ly rigorous a

of APO,

the system

he data are

that.

gths to ensu

hat. But, if y

in the world

ing on HAN

nical aspect

that I'll go

e power of

ypes and st

geographic

he column

ing on,

e think abo

and stores,

e,

gine for pla

regations,

lans in mem

o on. This is

have IMSL..

AFL, as a

insert code

er, in the s

e have to b

ANA.

nd strict gui

crashing b

of the syst

re that thes

ou are a reg

, you can go

. It's fully st

of HANA.

ack and ad

ANA, the o

res.

data, and s

tore.

t it. And tex

we have the

ning,

omplex pla

ory, and thin

also extens

., let's see...

ay to integr

like this and

me process

really, reall

elines of ho

cause of co

m used to

kinds of thi

ular SQL pr

and, withou

andards-co

them later

erall pictur

o on, and str

t we use th

engines th

ning-orient

gs like this.

ible, so we

all kinds of

ate people,

it runs,

space as

y, really,

w this code

rruption of

ollide with

gs don't

grammer,

t any

pliant.

on.

that we

ings, and

column

t work on

d

an also add



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more eng

And so o

And then

And other

about.

In fact, w

able to ch

HANA.

And then

these are

PAL, text,

And then,

It is a plat

with som

which incl

All kinds

users, an

And then

like the m

or master

data, or t

And all thoutside o

So as yo

us and fo

And just

happene

ines here as

. So there a

we have SQ

language s

have the a

ange them.

we have all

things like

statistics, o

HANA is m

form, on whi

thing that w

udes the la

f things to

stuff like th

we have all

essaging se

data servic

e rule engin

ese things t the databa

can see, H

our future.

hen the oth

in the last

we go. The

re all these

L. So the S

pport: MDX

ility to fully

These are t

kinds of libr

FL,

urs, others,

re than a d

ch we are b

e call the ap

guage runti

anage user

at.

kinds of new

rvice,

for MDM, o

e.

at used to tr e; all are av

NA goes fa

nd that is s

ers are start

years? HA

18

graph engin

ngines that

L plan and

and the oth

isualize the

e kinds of a

ries up her

ll kinds of li

tabase.

uilding all ki

plication ser

e, so supp

sessions, u

capabilities

r data servi

aditionally bailable as e

r beyond a

omething th

ing to think

A became

e will come

are inside H

execution, r

er language

plans that

mazing thin

. And these

braries like t

ds of platfor

vices of the

rt for JavaS

er authenti

from the pl

es for extra

e in integratitensions an

atabase, an

at is super-p

bout buildin

a platform.

ere as well.

ANA.

nning and

, SQLScrip

e make in

s that we h

libraries are,

is.

m capabiliti

S engine,

cipt and oth

ation and a

tform that w

ting, transf

on platform libraries in

d into beco

owerful.

g in-memor

.

xecution.

and stuff th

QL and inte

ve the abilit

, I already m

s. So we st

er language

thorization,

e are buildin

rming, and l

or in middlside HANA.

ing a real p

databases,

at I talked

ractively be

to do in

entioned,

rted off

in there.

memory for

g in here,

oading

ware

latform for

guess what



UNIT 3: SA

00:00:00

00:00:15

00:00:24

00:00:35

00:00:45

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P HANA Pe

So, what

When we

existed b

and betw

or betwe

we have t

itself. We

The reas

already,

sorts

and the a

somethin

So they a

first exam

A couple

Yodobas

so his gra

told me th

So that's

do is, the

once a mpurchase

And this

And whe

a perform

Which is

when you

that is ba

that, if yo

if you wer

were to fl

then you

faster 10,

10,000 ti

customer

And the r

on data t

So every

rformance

does all that

think about

tween OLT

en structur

n being abl

o... one of

have to reth

n I say that

nd a thousa

mazing thin

in HANA a

re in this 10,

ple of that.

of years ago

i, and he's

ndfather fou

at they hav

2 million. A

used to do

nth, they w made by t

as a three

they ran thi

ance improv

very, very l

look at 10,

ically like, if

were to fly

e to continu

there in lik

ould be 10

000 times is

es faster th

in the 10,0

ason this h

at is operati

last transact

enchmark

technology

HANA and t

and OLAP

d processin

to build ne

y conclusio

ink the notio

is, I mention

nd or so imp

that I found

least 10,00

000 club. A

, Fujisawa s

lso the son

nded the co

22 million t

nd out of tho

in our ERP

uld calculatem as well

ay long run,

s thing on H

ement of ap

arge numbe

00 times pe

you were to

there,

usly walk a

6 minutes

000 times f

Usain Bolt i

an a snail. T

00 club.

ppens is th

onal in natur

ion that is n

19

s

mean? Wha

he ability, th

,

g of informa

application

s is that we

n of bench

ed earlier,

lementation

was that w

0 times fast

d it's quite

an told me t

of the owne

mpany and

tal custom

se, 5 million

ystem on a

e what inces the purch

on our ER

ANA, this ra

proximately

r. That is dif

rformance i

walk from

the speed

r so,

ster. So tha

s about 10,

hat's an inte

t if you look

e.

t aggregate

t can it do fo

opportunit

ion and pro

s on legacy

have to als

arks, and s

e have mor

, eleven hu

have now

r than they

n extraordin

at — he is t

of the firm.

is father is

rs in Japan.

are loyalty.

n Oracle dat

tives to payases made

system on

n instead of

125,000 tim

icult for the

provement,

an Francisc

f 3 miles an

t sort of give

00 times fa

resting way

at the Yodo

d, they have

r us?

to shatter t

essing of u

applications

rethink the

on.

than 2,000

dred imple

7 or 28 cus

id before.

ary situation

he head of I

He's from th

ow the hea

So this is th

abase,

these guys,y everybod

n Oracle d

three days, i

s.

human mind

o to New Yo

hour, and i

s you an ide

ter than a s

o think abo

bashi exam

to look at e

his barrier th

structured i

, and so on,

notion of pe

customers

entations

omers who

. Yodobashi

T and opera

e founding f

of the com

e total. And

based on th.

tabase.

in 2 second

to compreh

rk, and com

stead of tha

a about ho

nail.

t it. Well an

le, they we

ery single t

at has

formation,

rformance

f HANA

f various

run

was the

ions at

mily

pany. So he

hat they

e

. So that is

end this. So

ared to

t, if you

much

how, so 28

e doing this

ansaction



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that has c

They wan

this perso

and this l

that is pr

So a larg

that is ch

dramatic

So how d

HANA, w

and rethi

about this

and I thin

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performa

The first

gets, and

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The ques

be pretty

finding m

on data.

of the sys

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the syste

A fourth d

How quic

answered

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At less th

and at les

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the more

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tremendo

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rge amount

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amount of

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have to ret

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at the ICD

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absorb ne

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in less than

uman brain

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ith interactivi

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ve dimensio

of these five

ple way to th

us value wo

about what

HANA to b

very compl

the purcha

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of data, it is

billion recor

data, compl

speak, this

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out this in

hink the con

of benchma

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nsions of p

ikka.

rse, the dat

the query c

ge from sim

ing, to highl

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forth.

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informatio

is the data

et our questi

3 seconds,

tarts to lose

, we can ca

econd, or le

ty, with realt

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ns. And I be

dimensions

ink about th

ld be when

inds of sce

ar on those

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ex query.

ses made b

a combinati

22 million r

s.

x questions

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more scien

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rks for infor

in Australia

ive dimensio

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omplexity.

ple scans a

y complex s

clustering

estions are,

ll it the chan

n?

repared, or

ions answer

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attention; t

rry out a tas

ss than 800

ime, in a co

ses are wir

lieve that H

are in there

e value, of

we take mo

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scenarios in

everybody

on of a com

cords, purc

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f a combina

tific way? W

rmance itse

ation proc

earlier this

ns of perfor

hese are Dr

ally, the larg

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atistical ana

nalyses, an

the longer i

ge, the rate

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d? And ide

chological

pically, we

k more or le

millisecond

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d. So, how

NA,

, the more H

here HANA

re and more

have which

the busines

who bought

lex query

ases made

ggregated,

ion that ena

ll, my sens

lf,

ssing syste

ear,

ance.

. Sikka's fiv

er the data,

are our qu

straightforw

lysis, media

d other kind

takes, the

of change o

d finally, it i

lly, we can

tudies show

tart to lose

s continuou

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of thought.

uch is the

ANA's perfo

would really

of these fiv

exercise th

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the same th

by 22 millio

n real oper

bles HANA

is that in th

s. And I wr

dimension

the slower t

stions?

rd joins, wh

ns,

s of comple

lower the p

data. How

the respon

get the ques

ttention at

sly,

ically opera

response? I

rmance stan

, really dem

dimension

se five dim

s out that th

ings that

people, so

tional data

o achieve

e age of

te a paper

of

e system

ich can also

analytics

rformance

uickly does

e time.

tions

seconds.

te on the

mean, look

ds out.

nstrate

nsions,

ere are, you



00:07:09

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00:07:22

know, as

Imaginati

to.

So there'

make tha

And we'v

itself; giv

many as yo

n is our onl

the paper t

available.

been starti

n the abiliti

can imagin

limitation

hat I have w

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UNIT 4: SA

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00:09:57

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responsiv

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UNIT 5: SA

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P HANA in

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00:13:51

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the best i

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nd thank yo

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u for listenin

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news with

u. All the



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