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The Relational Model The Relational Model Serge Abiteboul INRIA Saclay, Collège de France et ENS Cachan 20/03/2012 1 Organization The priilinciples – Abstraction – UiUniversa lity – Independence Abs trac tion: the reltilationa l modldel Universality: main functionalities Independence: the views revisited Optimization Complexity and expressiveness Conclusion 20/03/2012 2 The principles 3/20/2012 3 DBMS Goal: the management of large amounts of data – Large amounts of dtdata: dtbdatabase – Software that does this: DBMS Management systems, databases Characteristics of the data – Persistence over time (years) – Size (giga, tera, etc.). – Shared among many users and programs – May be distributed geographically – Heterogeneous storage : hard disk, network 3/20/2012 4 Mediation The data management system acts as a mediator between intelligent users and objects that store information tdt,d ( Film(t, d, «Bogart ») Séance(t, s, h) ) Où et à quelle intget(intkey){ heure puis-je inthash=(key%T voir un film S);while(table[h avec Bogart? ash]=NU LL&&ta ble[hash]‐ >getKey()=key) hash=(hash… The questions are translated into first order logic and then into pgprograms with precise and unambiguous syntax and semantics Alice does not want to write this program; she does not have to 3/20/2012 5 1st principle: abstraction • Data modldel – Definition language for describing the data – Manipulation language (queries and updates) • Simple data structure – Relations – Trees – Graphs • Formal language for queries – LiLogics – Declarative vs. Procedural – Graphical languages 3/20/2012 Complex graphical queries with MS Access6 Towards abstraction: high‐level data models The relational model: Codd 1970 Data are represented as tables Queries are expressed in relational calculus: « declarative » In practice, a richer language: SQL Very su ccessful both scientifically and indu strially – Commercial systems such as Oracle, IBM’s DB2 – Popular free software like mySQL – DBMS on personal computers such as MS Access 3/20/2012 7 2nd principle: universality DBMSs are didesigne d to capture all dtdata in the world for all kinds of applications – Powerful languages – Rich functionalities: see further – To avoid to multiply developments In reality – Less structured data are often stored in files – Too intense applications require specialized software – Today more and more specialized systems 3/20/2012 8 Towards universality We need services such as – Concurrency and transactions – Re lia bility and SitSecurity – Data distribution – More Scaling – Vooulum e of data – Volume of requests Performance – Response time: The time per operation – Throughput: The number of operations per time unit 3/20/2012 9 Large variety of applications with important needs for data management Two main classes OLTP: Online Transaction Processing – Transactional – E‐commerce, banking, etc.. – Simple transactions, known in advance – Very high load in number of transactions per second* OLAP: Online Analytical Processing – Decision making – Business intellige nce queries – Often very complex queries involving aggregate functions – Multidimensional queries: e.g., date, country, product 3/20/2012 10 3rd principle: Independence physical/logical/external Views ANSI‐SPARC Architecture (75): 3 levels External level Separation into three levels – Physical level: physical organization of data on disk, disk management, schemas, indexes, transaction , log – Logic: logical organization of data in a schema, qyquery Logical and update processing level – Externally: views, API, programming environments Independence – Physical: We can change the physical organization withou t chihanging the lillogical lllevel – Logical: We can evolve the logical level without Physical level modifying the applications – External: We can change or add views without 3/20/2012 affecting the logical level 11 Abstraction The relational model 20/03/2012 12 Data are organized in relations 20/03/2012 13 Queries are expressed in relational calculus • qHB = { s, h | d, t ( Film(t, d, « Humphrey Bogart ») Séance(t, s, h ) } • In practice, using a syntax that is easier to understand: • SQL: select salle, heure from Film, Séance where Film.titre = Séance.titre and acteur= «Humphrey Bogart» 3/20/2012 14 Queries are translated in algebraic expressions and evaluated efficiently 20/03/2012 15 The main predecessors Trees Graphs • IMS, IBM late 60s, 70s • Codasyl • Still very used • A graph of records with keys • A hierarchy of records with keys Supplier(sno, Supplier(sno, Part(pno, sname, sadd) sname, sadd) pname) Little abstraction Part(pno, Languages pname, qty, • Navigational Order(ono, price) • Procedural qty, pri)ice) 16 3/20/2012 • Record‐at‐at‐time The main successors: semistructured data models Trees Graphs • XML • Semantic Web & RDF • Exchange format for the • Format for representing Web knowledge • Standard • Standard • Query languages: Xpath, • Query language: SPARQL Xquery • Developing very fast • Developing very fast Abstraction • Logic foundations • High‐level languages 3/20/2012 • We will discuss them 17 Universality: functionalities Here with a very relational viewpoint 3/20/2012 18 Performance and scaling The core of the problem Be able to support – Terabytes of data – Millions of requests per day For this two main tools – Optimization – Parallelism 20/03/2012 19 Dependencies Laws abtbout the dtdata – To protect data To design schemas – To optimize queries To explain data Examples – Séance[titre] Film[titre] Inclusion dependency Only known films are shown – Séance: salle heure titre Functional dependencies Only one movie is shown at a time in a theater Logical formulas – t, s, h (Séance(t, s, h ) d, a ( Film(t, d, a) ) )tgds – t, t’, s, h (Séance(t, s, h ) Séance(t’, s, h ) t=t’) egds Some of the most sophisticate developments in db theory 3/20/2012 20 Dependencies and schema design Use silimple ddidependencies up to complex semantic dtdata models Help choose a better relational schema Person Child Car John Toto BMW John Toto 2chevaux John Zaza BMW John Zaza 2Chevaux Sue Lulu Sue Mimi Update anomalies NllNull values 3/20/2012 21 Concurrency and transactions ‐ ACID Atomicity: the sequence of operations is indbldivisible; in case of flfailure, either all operations are completed or all are canceled Consistency: The consistency pppropert y ensures that any transaction the database performs will take it from one consistent state to another. (So, consistency states that only consistent data will be written to the database). Isolation: When two transactions A and B are executed at the same time, the changes made by A are not visible to B until transaction A is completed and validate d (i)(commit). Durability: Once validated, the state of the database must be permanent, and no technical problem should lead to cancelling of transaction operations 20/03/2012 22 Recovery from failures The DBMS must resist to failures A variety of techniques – Journal – Back‐up copies – Shadow pages “Hot‐standby“: second system running simultaneously Availability: users should not have to wait beyond what is seen as reasonable for an application 3/20/2012 23 Distributed data Typically the case – When integrating several data sources – Organizations with many branches – Activities involving several companies – When using distribution to get better performance Query processing over distributed data – Data localization & global query optimization – Data fragmentation – Typically horizontal partitioning Distributed transactions – Two‐phase commit – Typically too heavy for Web applications 3/20/2012 24 More Security – Protect content against unauthorized users (humans or programs) – Confidentiality: access control, authentication, authorization Data monitoring Data cleaning Data mining Data streaming Spatiotemporal data Etc. 20/03/2012 25 Independence: views 20/03/2012 26 Views • Definition: – Function f: Database View • One of the most fundamental topics in databases db1 v1 db2 View states Database db3 states db4 db5 v2 3/20/2012 db6 27 View definition • Classical query <stttate s=‘C ‘Clolora d’do’> <resort n=‘Aspen’> – Define view … <sc> Unisyy(p)s.com/snow(“Aspen”) </sc> • Implicit definition and <sc> Yahoo.com/GetHotels(“Aspen”)</sc> recursion </resort> … </resorts> – Datalog – Dependencies (tgds) state • Mix between explicit/implicit: Active n resort resort XML Colorado n tf g n 3/20/2012 Aspen 12 metermeters Lake Tahoe28 To materialize or not IilIntentional MilidMaterialized Update: do nothing Update: propagate Query : com pl ex – Base view: costly view maintenance – View base: Query vs. Update ambiguous The database trade‐off Query: simple 3/20/2012 29 Integration: view over several bases Intentional: mediatior • Materialized: warehouse Queries are complex • Updates are complex • Definitions • Global‐as‐view: v = (db1,… , dbn) • Local‐as‐view: dbi= i(v) for each I • Arbitrary complex constraints between the database and the views • Sometimes called alignments between them 3/20/2012 30 Optimization 20/03/2012 31 The reasons of the success The queries are based on relational calculus, a logical language, simple and understandable by people especially in variants such as SQL A calculus query can easily be translated into an expression of algebra that it is simple to evaluate (Codd Theorem) Relational algebra is a limited model of computation (it does not allow comppguting arbitrary functions). That is why it is possible to optimize algebraic expressions evaluation Finally, for this language, parallelism allows scaling to very large databases (class AC0) 3/20/2012
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