UPL628050451174393135 Abiteboul Cours 14 Mars Relationnel

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TheRelationalModel

Ser eAbiteboul

INRIASaclay,CollgedeFranceetENSCachan

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epr nc p es

Abstraction n versa ty

Independence

s rac on:

e

re a ona

mo eUniversality: mainfunctionalities

Independence: theviewsrevisited

OptimizationComplexityandexpressiveness

Conclusion

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Theprinciples

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Goal:the

management

of

large

amounts

of

data

argeamoun so a a: a a ase

Softwarethatdoesthis:DBMS

,

Characteristicsofthedata

Size(giga,tera,etc.).

Sharedamong

many

users

and

programs

Maybedistributedgeographically

Heterogeneousstorage:harddisk,network

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Thedatamanagementsystemactsasamediatorbetweenintelligentusers

andobjectsthatstoreinformation

, m , ,

Bogart)Sance(t,s,h))

O et quelle

heure puis-je

voir un film

intget(intkey){

inthash=(key%T

S);while(table[h

ash =NULL&&ta

Thequestions

are

translated

into

first

order

logic

and

then

into

programs

ble[hash]

>getKey()=key)

hash=(hash

withpreciseandunambiguoussyntaxandsemantics

Alicedoesnotwanttowritethisprogram;shedoesnothaveto

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st

Datamodel Definitionlanguage fordescribingthedata

Manipulationlanguage

(queries

and

updates)

Simpledatastructure Relations

Trees

Graphs

Formallanguageforqueries og cs

Declarativevs.Procedural

Graphical

languages

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Therelationalmodel:Codd1970

Dataare

represented

as

tables

Queriesareexpressedinrelationalcalculus:

declarative

Inpractice,

aricher

language:

SQL

ery success u o sc en ca yan n us r a y CommercialsystemssuchasOracle,IBMsDB2

DBMSonpersonalcomputerssuchasMSAccess

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nd

sare es gne ocap urea a a n ewor

for

allkinds

of

applications Richfunctionalities:seefurther

Inreality

Toointenseapplicationsrequirespecialized

softwareTodaymoreandmorespecializedsystems

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Weneedservicessuchas

Concurrency

and

transactions e a yan ecur y

Datadistribution

Scaling

Volumeofdata

Volumeofrequests

Performance Responsetime: Thetimeperoperation

Throughput: Thenumberofoperationspertimeunit

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Largevarietyofapplicationswithimportant

needsfor

data

management

Twomainclasses

OLTP:Online

Transaction

Processing

Transactional

Ecommerce,banking,etc..

Simpletransactions,knowninadvance

*

OLAP:Online

Analytical

Processing

Decision

making

Businessintelli ence ueries

Oftenverycomplexqueriesinvolvingaggregatefunctions

Multidimensionalqueries:

e.g.,

date,

country,

product

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3rd principle:

Independencephysical/logical/external

Separationinto

three

levels

External level

Physicallevel:physicalorganizationofdataondisk,diskmanagement,schemas,indexes,transaction,log

Lo ic:lo icalor anizationofdatainaschema uer

Logicallevel

andupdate

processing

Externally: views,API,programmingenvironments

Independence

Physical:

We

can

change

the

physical

organization

w ou c ang ng e og ca eve

Logical:Wecanevolvethelogicallevelwithoutmodifyingtheapplications

External:

We

can

change

or

add

views

without

affectingthelogicallevel

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Abstraction

Therelationalmodel

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qHB ={s,h| d,t(Film(t,d, HumphreyBogart)Sance

(t,

s,

h

)

}

Inpractice,usingasyntaxthatiseasiertounderstand:

:

select salle,heure

,

where Film.titre =Sance.titre and acteur= HumphreyBogart

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inalgebraic

evaluated

efficiently

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Trees

IMS,IBM

late

60s,

70s

Graphs

Codasyl

Stillveryused

Ahierarchyofrecordswith

Agraphofrecordswithkeys

keys

Supplier(sno,

sname,sadd)Supplier(sno,

sname,sadd)

Part(pno,

pname)

Littleabstraction

Part(pno,

pname,qty,Order(ono,

Languages Navigational

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price)q y,pr ce

Procedural

Recordatattime


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Trees

XML

Graphs

SemanticWeb

&

RDF

ExchangeformatfortheWeb

Formatforrepresentingknowledge

Standard Querylanguages:Xpath,

Standard Querylanguage:SPARQL

Xquery

Developing

very

fast

Developingveryfast

Abstraction

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Highlevel

languages

Wewilldiscussthem


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Universality:functionalities

Herewithaver relationalview oint

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Thecoreoftheproblem

Beable

to

support

Terabytesofdata

Millionsofrequestsperday

Forthis

two

main

tools

Parallelism

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awsa ou e a a Toprotectdata Todesignschemas

Tooptimize

queries

To

explain

data

Examples Sance[titre] Film[titre] Inclusiondependency

Sance:salle heure titre Functional

dependencies

Onlyonemovieisshownatatimeinatheater

t,s,h(Sance(t,s,h) d,a(Film(t,d,a))) tgds

t,t,

s,

h(Sance(t,

s,

h)

Sance(t,s,

h) t=t)

egds

Someofthemostsophisticatedevelopmentsindbtheory

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ses mp e epen enc esup ocomp exseman c a amodels

Person Child Car

John Toto BMW

John Toto 2chevauxJohn Zaza BMW

John Zaza 2Chevaux

Sue Lulu

Updateanomalies

u va ues

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Atomicity:thesequenceofoperationsisindivisible;incaseoffailure,either

all operationsarecompletedorallarecanceled

Consistenc :The

consistenc

ro ert

ensures

that

an

transaction

the

databaseperformswilltakeitfromoneconsistentstatetoanother.(So,

consistencystatesthatonlyconsistentdatawillbewrittentothe

.

Isolation:When

two

transactions

A

and

B

are

executed

at

the

same

time,

the

changesmadebyAarenotvisibletoBuntiltransactionAiscompleted

an va ate comm t .

Durability:Oncevalidated,thestateofthedatabasemustbepermanent,and

notechnical

problem

should

lead

to

cancelling

of

transaction

operations

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TheDBMSmustresisttofailures

A

variety

of

techniques Journal

Backupcopies

Hotstandby:

second

system

running

simultaneously

asreasonableforanapplication

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Typicallythecase

Whenintegratingseveraldatasources

Organizationswith

many

branches

Activitiesinvolvingseveralcompanies

Whenusingdistributiontogetbetterperformance

Datalocalization

&

global

query

optimization

Datafragmentation

Typicallyhorizontalpartitioning

Distributed

transactions Twophasecommit

TypicallytooheavyforWebapplications

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Security

Protect

content

against

unauthorized

users

(humans

or

programs

Confidentiality:accesscontrol,authentication,authorization

Datacleaning

Datamining

Data

streaming

Spatiotemporaldata

Etc.

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Independence:views

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Definition:

Functionf:

Database

View

Oneofthemostfundamentaltopicsindatabases

db1

db2

v1

db3Database

states

states

db4

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Classicalquery

Defineview

Unisys.com/snow(Aspen)

Implicitdefinitionandrecursion

Yahoo.com/GetHotels(Aspen)

DatalogDependencies(tgds)

Mixbetween

XMLn

Colorado

resor resort

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n

Aspen

n

LakeTahoe

f gt

2meters1

meter


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ntent ona ater a ze

Update:do

nothing

uer :com lexUpdate:

propagate

Base view:costly

viewmaintenance

View

base:

ambiguous

Query:simpleQueryvs.Update

The databasetradeoff

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Intentional:mediatior Materialized:warehouse

Queries

are

complex Updates

are

complex

Definitions

Global

as

view:

v

=

(db1,

,

dbn) Localasview: dbi=i(v) foreachI

Arbitrarycomplexconstraints betweenthedatabaseandtheviews Sometimescalledalignmentsbetweenthem

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Optimization

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Thequeriesarebasedonrelationalcalculus,alogicallanguage,

simpleandunderstandablebypeopleespeciallyinvariants

Acalculusquerycaneasilybetranslatedintoanexpressionof

Relationalalgebra

is

alimited

model

of

computation

(it

does

not

allowcom utin arbitrar functions .Thatiswh itis ossible

tooptimizealgebraicexpressionsevaluation

Finally,for

this

language,

parallelism

allows

scaling

to

very

large

databases(classAC0)

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Foreachsinsancedo complexityin n2

(b) If fewtuplespasstheselection complexityin n

(c) Usingtheindex complexity constant

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Usingaccessstructures

Hash

Usingsophisticatedalgorithm

Costevaluation

to

select

an

execution

plan

Technique:Rewritequeriesbasedonheuristicstoexploreonly

art of it

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Theseproblemscangreatly

benefitforparallelismFiltre

fyp ca y v e e a a

Thisisnottrueforallproblems

ff

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ff


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Complexityandexpressivity

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Complexity

http://www.cs.rice.edu/~vardi/papers/sigmod08.pdf

omp ex ty: ora xe queryq,

Testinggiven(I,t)whethertisinq(I)asafunctionofthe

FocusonBooleanquerytonotdependonoutputsize

Verydifferent

and

if

mixed

the

dependency

on

query

t icall hidesthede endenc onthedata

Datacomplexityasafunctionofthesizeofthedata(queryfixed)

Querycomplexityasafunctionofthesizeofthequery(datafixed)

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Relationalcalculusisinlogspace

The

test

can

be

performed

using

a

space

logarithmic

in

the

sizeofthedata

Thisisprimarilybecausethearityoftablesisfixed;soa

tup euses ogspace

logspace NC ( ptime)

Goodpotentialforparallelization;seefurther

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Thecomplexityispspace

Intuition:an

intermediary

result

may

be

very

large

is

it

isthejoinofmanyrelations

Dependsmoreonthenumberofvariablesusedinthe

query

that

in

its

actual

sizeNaiveevaluationof(PiA(RjoinS))requiresmorespacethat

thatof(PiA(R)cap^PiA(S))

Polynomial

in

the

tree

width

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Datacomplexity:Constantparalleltime

AC0

Acomplexityclassusedincircuitcomplexity

Theproblemsthatmaybesolvedwithcircuitsofconstantdepth

an po ynom a s ze,w t un m te an n gatesan

gates.

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Onecannotcomputetransitiveclosure

Addafixed

point

Inflationary:fixpoint

Ornot:while

Vardi theorem:with

an

order

on

the

domain

while=pspace

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Onecannottestifarelationhasanevennumberof

tuplesAbiteboulVianu

Characterizationofwhatcanbecomputedwithfixpoint

and

whileTheorem:fixpoint=whileiff ptime=pspace

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Conclusion

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Andthen:alwaysquestioneverything

Revisitthe

models,

languages,

principles

Why?

Toscaletoalwaysmoredataandqueries

Tosupportextremeapplicationsthatcannotbesupportedbystandardtechnology:

Visatransactions

To

facilitate

application

development Tooffermoreintermsofperformance,reliability,security,etc..

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Relationalmodel BeyondEntriesinrelations =atomic values Entriesaresetofvalues

Missingdata,probabilisiticdata

ACID Weakerconcurrency

Universal Specialized: noSQL

Dataarepersistant Queriesondataflows

Dataarestatic Data&behavior:Objectdatabases

c ve

a a ases

Constraints arestatic(FDs,etc.) Triggers

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Merci!

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UPL628050451174393135 Abiteboul Cours 14 Mars Relationnel

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