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openAn 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

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accessing

Somethin

And the o

database

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It was mill

thing was

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itself.

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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

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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

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e?

very straigh

ional algebr

ata manag

pecialized s

ay to mana

eans. It's a

the RDB wa

lemented rel

ble now.

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ady very dif

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ere are laye

isk. And ac

k.

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

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structured

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lational data

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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

the backgro

e relational

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uery langua

bases was s

e started thi

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from mem

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ch smaller t

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re.

use of impr

running into

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ufacturing b

w kind of da

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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

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s now. Also,

vement in t

a physical

f light and t

nefits of Mo

tabase para

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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

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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

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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

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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,

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mnar datab

same relati

abases like

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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

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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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ses were in

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olumnar dat

as clear th

ormance

er quantitie

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ings, first of

hnology for

back here i

around 199

inking abou

s.

own by that

ced me to

a technolog

an and the

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billion recor

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ory; and the

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nal applicati

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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

advent of

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fact.

ns, and

ics.

umption,

two things

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

to rethinkHANA, as

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database,

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for buildin

HANA.

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unbelieva

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history of

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launch of

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than a bill

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in 2 years

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implemen

started to t

ion into how

gust of 200

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riving home

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t of where t

started wo

this column

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starting to d

ble journey.

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enterprise s

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product. Oc

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fastest gro

oftware.

it is Septem

ally, it's exa

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ths ago. In t

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hs. That's pr

customers h

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tic perform

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at night, I t

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ng like 1100


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And it ha

hardware

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Inside, rig

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chip cam

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running s

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it was cle

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consultan

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5/31

UNIT 2A: S

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AP HANA

So let's ta

technolog

We can't

Hasso ha

those.

The basic

data local

plus the f

And espe

run, that

That is b

the fact th

We have

are parall

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server, it

2 terabyt

persisten

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us to fully

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low, and

the less y

that high,

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gigahertz

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means is

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calculate

echnology:

lk some HA

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at it runs m

the ability in

el.

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as up to 80

s of DRAM,

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utilize all of

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you get 80

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of clock spe

elievable a

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anufacturin

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anghai to


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Seattle,

anything

one core,

or more o

from. We

because

per secon

And this

anything t

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major op

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throw tha

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UNIT 2B: S

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AP HANA

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ows,

en pulling

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mns.

ancy


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cocktail t

So that's

they were

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transactio

very quic

merge, in

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r who is

usly

rt to write

at's our


9/31

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main inve

that enab

for transa

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ntion, and in

les us to be

ctions.

in mind: On

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to buffer up

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her people

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able to achi

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for analytic

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but also

t people

can do

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romise

ch more


10/31

UNIT 2C: S

00:00:00

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AP HANA

So the thi

Dynamic

And whe

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that you g

That icon

compress

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when youis maybe

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and more

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out.

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that you c

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mentione

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cache tha

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looking for

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more like a

is going on i

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a dynamic

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ld of HANA

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that.

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database,

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a half billion

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tions. And I'

t's call it int

it goes so

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nd the inte

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a batch pro

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quickly.

bject cache

to be store

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ll explain wh

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rated comp

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that, grab

processing

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to fifteen m

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into these

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se 10 fields

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need.

verything

on that.

that I

on. The

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n HANA can

s it even

ic


11/31

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transactio

because

to group t

unrestrict

do that li

the weekl

If you wa

You don't

Because

have to b

Here, dir

time you

And then

reason I

is becaus

You just t

even if yo

there are

that there

So you c

encoding

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is someth

that we h

person, y

so there's

ridiculous

can think

So the int

especiall

when you

of the fiel

What we

customer

routinely

know, up

what I wa

every tim

it was like

And out o

700 giga

nal data,

ou can thin

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t to know th

have to be l

hen you ar

limited to

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are only 20

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one bit, an

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who run a

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their imagin

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untry that is

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pression in

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alytical data

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when it us

was that it

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hing like 1.1

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11

egations, yo

hings togeth

ation. So if

for the big t

last night mi

se aggregat

ose questio

at informati

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ion: This is

columns in

gs by colu

e necessary

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of data. And

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maining


12/31

00:06:59

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So this is

mean, if y

if you get

you have

and data

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landscap

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quite amazi

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ount of savi

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g that we a

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totals, all th

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nd copies othe raw dat

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re able to g

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f data, so

warehouse

is. In HANA

terprise

ANA.


13/31

UNIT 2D: S

00:00:00

00:00:18

00:00:31

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AP HANA

The next

which we

are some

fact, for a

And thos

and hot a

store,

a great w

And so y

record he

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informati

informati

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ple of more,

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application.

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ng change

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or multipl

database

And then

Then you

Typically,

On a 16

like that.

So of cou

inherently

But HAN

able to ru

And som

which is

one of th

transactio

And we t

1,200 billi

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That's a v

three qua

So you c

Each nod

And basi

case wer

The only

was quart

So this is

other infr

And here

and scale

And onesemantic

In Financi

the curre

and so a

the open

as well a

months o

so then y

servers. A

in one mac

unleash the

get really a

we see perf

ode cluster

rse it depen

it is.

gives us th

n workloads

of the mor

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

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structures.

in HANA, th

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ther capabilof 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

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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

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on, or a co

re sitting on

ormance.

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e performa

ture of the q

apabilities th

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m one of ou

e world. An

ta. Wow! So

ounting, th

t's one and

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on over 10

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year. So th

rtition colum

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we have ac

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they were

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t's 1.2 and t

a half times

.

nd we ran t

memory.

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ears.

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old. So, ther

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his 3.1 seco

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ear and we

rd into the n

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ave one pie

things.

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table or ho

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an that in


15/31

00:06:59

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the hot...

You can

additional

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this kind

so that w

HANA en

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in active me

old store th

compressio

n take 42 y

f a tiered st

can get ev

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rest of it,

n that way.

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16/31

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

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people ar

I mean b

amount o

if you wa

10% to all

That is a

inverse o

you want

demonstr

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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

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ing on,

e think abo

and stores,

e,

gine for pla

regations,

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o on. This is

have IMSL..

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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

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of HANA.

ack and ad

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res.

data, and s

tore.

t it. And tex

we have the

ning,

omplex pla

ory, and thin

also extens

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elines of ho

cause of co

m used to

kinds of thi

ular SQL pr

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t we use th

engines th

ning-orient

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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


18/31

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more eng

And so o

And then

And other

about.

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able to ch

HANA.

And then

these are

PAL, text,

And then,

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with som

which incl

All kinds

users, an

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like the m

or master

data, or t

And all thoutside o

So as yo

us and fo

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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 tthe 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

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kinds of libr

FL,

urs, others,

re than a d

ch we are b

e call the ap

guage runti

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at.

kinds of new

rvice,

for MDM, o

e.

at used to tre; all are av

NA goes fa

nd that is s

ers are start

years? HA

18

graph engin

ngines that

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and the oth

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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

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ing to think

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e will come

are inside H

execution, r

er language

plans that

mazing thin

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braries like t

ds of platfor

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rt for JavaS

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atabase, an

at is super-p

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e make in

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er language

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or in middlside HANA.

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at I talked

ractively be

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entioned,

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ware

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guess what


19/31

UNIT 3: SA

00:00:00

00:00:15

00:00:24

00:00:35

00:00:45

00:01:01

00:01:14

00:01:25

00:01:37

00:01:51

00:02:04

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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 wmade 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

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usly walk a

6 minutes

000 times f

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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

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walk from

the speed

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t if you look

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opportunit

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s on legacy

have to als

arks, and s

e have mor

, eleven hu

have now

r than they

n extraordin

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of the firm.

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rs in Japan.

are loyalty.

n Oracle dat

tives to payases made

system on

n instead of

125,000 tim

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provement,

an Francisc

f 3 miles an

t sort of give

00 times fa

resting way

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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

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at has

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f HANA

f various

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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

So there

performa

The first

gets, and

And the s

The ques

be pretty

finding m

on data.

of the sys

And then

the syste

A fourth d

How quic

answered

that the h

At less th

and at les

system wi

This is th

at these fi

the more

So, a sim

tremendo

and think

and bring

ome in. It's

t to calculat

n did. So w

rge amount

bably a few

amount of

nging as w

erformanc

o we think a

have to ret

k the notion

at the ICD

that it com

re five dim

ce, by Dr.

ne is, of co

so forth.

econd one i

ions can ra

ime-consu

dians, perc

he more co

tem, and so

the third on

absorb ne

imension is,

ly can we g

in less than

uman brain

n 3 second

s than one

ith interactivi

way our br

ve dimensio

of these five

ple way to th

us value wo

about what

HANA to b

very compl

the purcha

en you take

of data, it is

billion recor

data, compl

speak, this

.

out this in

hink the con

of benchma

Conferenc

s down to fi

nsions of p

ikka.

rse, the dat

the query c

ge from sim

ing, to highl

ntiles, doin

plex our q

forth.

is, let us ca

informatio

is the data

et our questi

3 seconds,

tarts to lose

, we can ca

econd, or le

ty, with realt

ins, our se

ns. And I be

dimensions

ink about th

ld be when

inds of sce

ar on those

20

ex query.

ses made b

a combinati

22 million r

s.

x questions

is the kind

more scien

cept of perf

rks for infor

in Australia

ive dimensio

rformance

size. Typic

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

arios do we

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

tinuous flo

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

. And it tur

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


21/31

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know, as

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P HANA Ro

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23/31

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picture. E

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