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Transcript of openS An Int SAP troduc ction t to SAP P HAN NA by D Dr. Vis ...
openSAn Int
UNIT 1: Intr
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SAP troduc
roduction a
So why dihow we e
So the ba80s, early
when SQLaway from
and manastructured
And that'sAnd at the
the hardwhardware
Or even athe hardw
How was have main
and... wel
And then accessing
Somethin
And the odatabase
The DRAM
It was millthing was
And so Mitself.
And alrealimitation,
governed the proces
in order toby 2003 areach.
ction t
nd Backgro
id we do HANnded up here
sic idea wasy 90s,
L was a relatm file-based d
agement of sd relational w
s what SQL me time when
ware that impthat is availa
already 10, 1ware was alre
it different? Sn memory,
l, actually the
there is the dg it out of dis
g like 10,000
ther thing thawhen it was
M was much
lions of timesthat CPUs b
oore's Law w
dy by 2003 i
by things thassors were n
o continue thalso, it was cl
to SAP
ound of SAP
NA, and whae?
s very straigh
tional algebradata manage
pecialized stway to manag
means. It's a the RDB wa
lemented relable now.
1, 12 years aeady very diff
So if you loo
ere are layer
disk. And acck.
0 times or mo
at happenedfirst built.
h more expen
s worse in prback then we
was largely a
t was clear t
at we cannotnot going to b
e performanlear that a co
P HAN
P HANA
at is some of
htforward. Th
a, and SQL sement
tructures andge data.
structured qs designed,
lational data
ago, when wfferent then.
ok at this pict
rs in between
cessing data
ore faster. So
d was.... so th
nsive and mu
rice/performaere single co
attained beca
that this was
t control, likebe single cor
nce and manuompletely ne
NA by D
f the backgro
he relational d
started to be
d so forth, an
query langua
bases was s
we started thi
ure, typically
n as well; the
a from memo
o this is slow
his was not c
uch smaller t
ance than it ire.
ause of impro
running into
e the speed ore any more
ufacturing beew kind of da
Dr. Vis
ound, how HA
database wa
ecome popula
nd objects, a
ge, that was
significantly d
nking about
y in computer
e on board ca
ory is dramat
w, and it is ug
clear in the d
han it is now
s now. Also,
ovement in th
a physical w
of light and th
enefits of Motabase parad
shal S
ANA came a
as designed
ar. People w
nd get into a
s the original
different than
re-doing the
rs we have C
aches and s
tically faster t
gly, and we d
days of the re
w.
, the other fu
he single cor
wall, into a ha
hings like tha
oore's Law. Sdigm was wi
Sikka
bout and
in the late
wanted to get
a more
name for it.
n the
database,
CPUs, we
o forth.
than
don't like it.
elational
ndamental
re CPU
ard
at, and that
So already thin our
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And whendatabase,
That is mucolumnar
The reasoor so year
Some sorother sort
And analythis was th
that, you kare very, v
columnar retrieve th
And colummore than
and for a performan
and this isoffer some
as well as
So, when was that S
The reasoparadigm
The good
So if you land Rudi
and built ahis friends
started to projects th
And someto one of m
who was awas an in
Sort of a sproject, w
back in 20was pretty
n we started , then it has t
ulticore procestructures.
on why columrs ago, OLTP
rts of compans of compan
ytical workloahe belief tha
know, you arvery differen
structures whem significa
mnar technoln 20 years ol
long time, pence. In the ca
s more than 2e unique adv
s multicore pr
we looked aSAP had to b
on for it was in database
news was th
look at the timMunz did his
a database ts
build TREX.hat Hasso as
e of the workmy PhD adv
also his stud-memory row
second genewhich was de
003. When Fy remarkable
thinking aboto be built ar
essing; mass
mnar structurP and OLAP
nies were buies were sta
ads are quitet everybody
re either anat. Neverthele
were inventedantly faster.
ogy itself is nd,
eople have kase of Sybas
20 years old vantages for
rocessing an
at this, when build a new d
very straights is possible
hat SAP had
me line of whs thesis,
hat we all kn
. And I joinedsked me to th
k that Franz hisors, Gio W
dent. And Sanw store techn
eration databmonstrated a
Franz showede.
2
ut this, it becround the new
sively larger
res are interehad become
uilding thingsrting to build
e different thain the indust
alyzing thingsess, the colu
d to store the
not new. Dat
known that cose IQ, it is a d
now. So by improving a
nd large avai
I started thindatabase.
tforward. Ame.
d worked on d
here we hav
now now as M
d in 2002, anhink about w
had done waWiederhold, an
nk had beennology.
base. And Fraand was the
d the EUCLID
came clear thw reality of h
and cheape
esting is becae very differe
for OLTP, fod things prima
an OLTP; or try had,
s or you are wmnar databa
e same relati
tabases like
olumnar datadisk-based c
this time, it wnalytical perf
lability of larg
nking about t
mong other th
database tec
e been, way
MaxDB. And
nd I started thwas database
as already knnd he introdu
n working on
anz and Stefwinner of the
D running a
hat if SAP hahardware:
r main memo
ause alreadyent kinds of in
or transactionarily for OLA
at least this
writing thingsases were inv
onal informa
for example
abases are bcolumnar dat
was clear thaformance
ger quantitie
his back in 2
ings, first of
chnology for
back here in
around 1999
hinking aboues.
own by that uced me to S
a technology
fan and the ge very first D
billion record
as to build a
ory; and the
y by that timendustries, in
nal applicatioAP, for analyt
was the ass
s, and these vented,
ation, but to b
our own Syb
better for retrtabase,
at columnar d
es of DRAM.
2002. My firs
all, a comple
quite some t
n the past wa
9, Franz and
ut, one of the
time. I went Sang Cha
y called P*TI
guys built theDKOM that SA
ds in one sec
new
advent of
e, about 10 fact.
ons, and tics.
umption,
two things
be able to
base IQ are
ieval
databases
t conclusion
etely new
time.
as 1977,
d some of
first
and I talked
IME, which
e EUCLID AP ever did,
cond, that
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So Hassoinvestigat
And in Auhe had, th
And he waamazing a
from this nguess waaggregate
indices, das I was d
I had just this was adatabase
to rethink HANA, as
That's sor
And so wedatabase,
at SIGMOHANA dev
for buildinHANA.
It went int
So that, b
So HANASapphire,
who wereunbelieva
Part of thehistory of
As we arelaunch of HANA bec
So 2 yearthan a bill
A billion d
in 2 years
And moreimplemen
o started to teion into how
ugust of 2006hat we could
anted to rewability to calc
new databass 70% or so es,
aily totals, wdriving home
come back fa fundamenta
the architects Hasso's new
rt of where th
e started wor, this column
OD in New Envelopment p
ng the HANA
to RTC, and
oys and girls
A became gen I had showe
starting to dble journey.
e... by far theenterprise so
e taping this HANA. Actucame genera
rs and 3 monion dollars in
dollars. Yes, t
s and 3 mont
e than 2,000 ntations going
each these thall of these t
6, about 30 mrewrite Fina
write Financiaculate things
se technologyof the code
weekly totals, ,
from Hawaii, ally new idea
ture of the apw architectur
he HANA nam
rking on thatnar in-memor
ngland. And roject
product. Oc
then June 20
s, is a little hi
nerally availaed the first 25
do all kinds o
e fastest growoftware.
it is Septembally, it's exacally available
nths ago. In tn revenue.
that is 1 with
hs. That's pr
customers hg already.
3
hings at HPI things could
metres from hncials.
als for the fouon the fly, a
y, that we coin Financials
monthly tota
and as I waa, not only in
pplication itsre.
me comes fr
t. In 2009, Hary database,
it was extrem
ctober of 200
011, HANA w
istory of how
able in June 5 or so custo
of amazing th
wing product
ber of 2013, ctly 2 years ae.
those 2 years
h 9 zeros
retty unbeliev
have already
around 2004be combine
here, in his o
urth time in hnd the drama
ould actually s was about c
als, things of
s driving homdatabases b
elf. And so t
rom. August 2
asso present
mely well rec
9. On Decem
went genera
w HANA cam
of 2011, shoomers that w
hings with HA
t in our histor
so it's about and 3 months
s and 3 mon
vable.
purchased H
4, and startedd into a full d
office, he told
is life. And thatic performa
get rid of all creating and
this sort. An
me that nightbut in how we
hat night, I th
2006, right h
ted his pape
ceived. That
mber 1st of 2
lly available.
e to be.
ortly after Sape had worke
ANA. Since th
ry. By my ca
2 years ands! It was Jun
ths, HANA h
HANA. We h
d to launch adatabase.
d me about th
his time, becance that we
the aggregad managing th
nd I remembe
t, it was verye can apply t
hought of the
here in Palo A
r on the in-m
Fall, we star
2010, we lau
pphire, backed with
hen it has be
alculation also
d 3 months sine 20th of 20
has already m
ave somethi
a real
his idea that
cause of this e get
ates. And his hese
er that night
y clear that the
e name
Alto.
memory
rted the
nched
k then. At
een an
o in the
ince the 11 that
made more
ng like 1100
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And it hashardware
HANA itseInside, rig
It is now 1chip came
which hadrunning so
We just reit was clea
was someWithout yo
So 10 har
Dell, Leno
And we haconsultan
And all kinkinds of s
consulting
SAP itselfalready ru
or is in therecently, t
is ISP. Ouusers in o
Our wholehas been
So that is supposed
Everybodymost miss
including the next 1
And we arsystems o
s just been a vendors are
elf is the resught?
10 years since out,
d two CPU coomething like
ecompiled thar that this m
ething that wou, HANA w
rdware vendo
ovo, Hitachi,
ave all kindsts from arou
nds of compaervices, edu
g, training aro
f: we have nouns on HANA
e process of that we are r
ur internal ERour company
e company drunning on H
something. d to be simply
y used to thision-critical, m
our own, of a0 days we'll
re one of aboon HANA; so
hell of a joue manufactur
ult of a deep
ce we started
ores, and Dae 85% faster
e software fomulticore ben
as going to rould not be p
ors, IBM, HP
all kinds of c
of storage pnd the indus
anies in the ecation,
ound HANA,
ow 77 producA
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depends on thHANA.
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nk that HANAmost comple
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rney since thring hardware
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or the Woodcnefit that we g
really... So, tpossible.
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ecosystem a
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cts of ours th
HANA. And oproud of,
ow runs on H
he ISP syste
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hen. Beyond e for HANA.
n with Intel..
th Intel. I rem
ss had calledhad 2 cores.
crest chip. Aget from HAN
hanks to our
tsu,
re making se
stuff like thisen trained on
are building a
quite an inc
hat run on HA
one of the m
HANA. And t
em, and ISP
ment to this p
ytical producle ERP syste
ny is runningks on HANA
right now thaextraordinary
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member when
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ctually, it waNA
r friends at In
ervers for HA
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ERP
r
UNIT 2A: S
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SAP HANA T
So let's tatechnolog
We can't b
Hasso hathose.
The basic
data local
plus the fa
And esperun, that w
That is bathe fact th
We have are paralle
So every server, it h
2 terabytepersistenc
And that ius to fully
Unlike thelow, and s
the less ythat high,
This is 80gigahertz
It's an unbSo everyt
One of theprocessor
Three andis nearly u
with the nmeans is
or some mkind of a t
calculate
Technology:
alk some HANgy.
be all about
s this amazin
c thing to kno
ity in memor
act that we re
cially thankswe could wor
asically the sehat it runs ma
the ability in el.
operator in Hhas up to 80
es of DRAM, ce on the ser
s a pretty amutilize all of
e assumptionso forth, here
ou have to syou get 80 C
0 CPUs, rougof clock spe
believable amhing on HAN
e most impor 3.5 billion s
d a half billiounlimited
umber of cothat if you ha
manufacturinthing,
a risk for a b
Parallelism
NA technolog
PowerPoint
ng set of icon
ow about HA
ry, and colum
e-thought ev
s to Colgate, rk with while
ecret for howassively para
HANA, beca
HANA operatCPU cores,
and you canrver.
mazing amouthem.
ns of the pase, our belief i
store, the moCPU cores.
ghly 3 gigaheeed available
mount of comNA was desig
rtant statisticscans per sec
n integer sca
res, the numave some bu
g run to plan
bank, or figur
5
m
gy. And yes,
and traffic lig
ns with rega
NA is that th
mnar structur
verything, and
who gave uswe were bu
w HANA camallel.
ause we rede
tes in paralle
n put maybe
unt of compu
st, where peois the more y
ore you calcu
ertz clock spee to you.
mputing capagned to take
cs to remembcond, per co
ans per seco
mber of CPUsudget forecas
n, or some sa
re out the opt
every once
ghts and thin
rd to HANA t
e combinatio
res,
d worked tog
s the very firsilding HANA
me about. The
esigned ever
el. We have t
5 or even m
ting power. T
ople used to tyou burn the
late on the fl
eed on every
acity. And yomaximum ad
ber is that HAre.
ond per core.
s, the numbest to run,
ales analytics
timal path to
in a while, w
gs of this so
that he uses
on of multico
gether with c
st actual ERP.
e power that
rything from
the ability to.
ore terabyte
The 80 CPU
try to keep thCPUs the fa
ly, and so on
y single one o
u have 2 teradvantage of
ANA does on
This means
er of servers.
s to run, or if
ship a conta
we have to ta
ort all the time
. I'll go over
ore, parallelis
customers to
P system of
t HANA deriv
scratch, all o
... If you take
es of SSD as
cores — HA
he CPU consaster you get
n. On a single
of them, this
abytes of dathese two th
n the Ivy Brid
s that basical
So basically
f you were to
ainer from Sh
alk
e.
some of
sm,
do this.
theirs to
ves is from
operators
e a modern
the
ANA enables
sumption t the results,
e box, about
is 240
ta in DRAM.hings.
dge
lly, the scale
y, what this
o run any
hanghai to
s
t
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Seattle,
anything oone core,
or more ofrom. We
because oper secon
And this manything t
So, the comajor ope
In fact, thethrow that
What thatprocessor
and then intra-operitself.
And of cohave nativfrom.
So that isthis, Sanja
and with atrying to d
That is soparallelism
of that sort: I
or less in onealso do in ad
of the native nd per core.
means that wthat we can t
ore of HANA erators in cal
ey use what t word aroun
t means is thrs, we can ev
run that alsorator paralleli
urse, today'sve parallelism
: Number onay has this fu
a little windowdepict a CPU
ort of the iconm. So that is
f it requires l
e second on addition to the
algorithm in
we can basicathink of.
is built arouculations, in
we call intrad: Vishal sai
at basically tven take, wit
o within an opism runs som
s relational dm. So this is
e is the paraunky icon tha
w-like thing iU... like that.
n. When you number one
6
let's say 350
a hundred coese scans,
the operato
ally aggrega
nd these prinjoins, in sca
a-operator paid intra-opera
that not only thin the opera
perator in pamething like 6
databases or where some
allel operatorat sort of loo
n the middle
look at Hasse.
billion scans
ores. This is
rs, we do ab
te anything o
ncipals of paans, all use p
arallelism. Thator parallelis
do we take ator itself, we
rallel. So it's 6 and a half
yesterday's e of the treme
rs. Let me seks like that
e, and then lit
so's notes an
s, you can do
basically wh
out 12.5 to 1
on the fly tha
rallelism in tharallelism.
at is, in a cosm.
a little job ane can take a
quite extraotimes faster
relational daendous adva
ee... where is
ttle things lik
nd stuff, that
o this in 100
here the pow
15 million agg
at we can ima
he operators
ocktail party y
nd distribute part of the jo
ordinary. Actuthan just par
atabases donantage of HA
s the? Yeah,
ke that. I think
is the icon fo
seconds on
er is derived
gregations
agine,
s. All the
you can
that across ob
ually, the rallelism by
n't even ANA comes
there is
k he was
or
UNIT 2B: S
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SAP HANA T
The secon
The colum
The colum
But basicastore ever
So this is store is in
like optimmemory, y
And so weteam had
So we havtransactio
is that youalready th
I hope youwhen you
one of ouBSEG,
which is othe heade
The accou
The docunot — 320
So it is a vstructures
wants to khandle ma
So in a coyou are in
and demodisk, you
then you athis inform
Here, not massively
In fact, yo
Technology:
nd big one in
mn stores are
mns, they are
ally, you takerything abou
column store-memory, an
istic, latch-freyou have to
e do latch-fredesigned, a
ve both of thons quite quic
u can do anahe number.
u guys reme think about
r sales order
one of the twoers and this is
unting line ite
ment name, 0 pieces of in
very, what ws and whenev
know somethaybe 10, 20
olumnar datanterested in g
onstrate that.are grabbing
are identifyinmation up. So
only do you y parallel, bec
ou can take m
Row and C
n HANA is th
e basically lik
e not all unifo
e a relation wt it in column
e, and you hnd it has som
ee index travbe able to do
ee index travnd so on and
ese. And theckly. The ben
alytics and re
mbered this:enterprise d
rs, for examp
o core tabless the line item
ems. And the
number, typnformation a
we would call ver somebod
hing about, leout of these
a structure, thgetting inform
. In a row stog things row
ng the columo it's dramati
get just the ccause you ca
more than on
7
Column Stor
e row and co
ke that.
orm, and I'll g
which sort of ns.
ave the row me pretty ama
versal, whicho that withou
versal. So thid so forth.
e benefits, ofnefit of the co
eads dramati
: three and aata structure
ple, or the ma
s; BKPF is thms.
e BSEG table
e, is it creditbout that.
wide data stdy, a normal
et's say, acco320 fields.
hat means thmation on, qu
ore, in a tradiby row, and
ns out of thecally slower.
columns thaan assign dif
ne core for on
res
olumn stores
get into the r
looks like a
store, whichazing inventi
h is when youut locking up
s was a new
f course, of tolumn store,
cally faster.
a half billion ses,
anufacturing
he other one,
e has somet
, is it debit, is
tructure. Andhuman bein
ounting line i
hat you just puickly assem
itional disk-bthen after yo
e rows that yo.
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cocktail th
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00:06:33
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main inve
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UNIT 2C: S
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SAP HANA T
So the thi
Dynamic a
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object cache to be stored
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transactio
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nt to know thhave to be l
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an create a dfor which co
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11
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So this is mean, if y
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UNIT 2D: S
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SAP HANA T
The next o
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13
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or multipledatabase
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00:06:59
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the hot...
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15
meaning put i
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UNIT 2E: S 00:00:00
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AP HANA T
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00:06:41
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UNIT 3: SA 00:00:00
00:00:15
00:00:24
00:00:35
00:00:45
00:01:01
00:01:14
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AP HANA Pe
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that has c
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00:07:09
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know, as
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UNIT 4: SA 00:00:00
00:00:19
00:00:29
00:00:39
00:00:48
00:01:00
00:01:12
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picture. Ev
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AP HANA in
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