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Multi-Databases & Mediators

Multi-Databases & Mediators. DBMS. DBMS. DBMS. DBMS. data. data. data. data. Distributed Databases. Distributed Database System. Advantages of a DDBS. Modularity Fault Tolerance High Performance Data Sharing Low Cost Components. Issues. Data Distribution Exploiting Parallelism

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Multi-Databases & Mediators

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  1. Multi-Databases & Mediators

  2. DBMS DBMS DBMS DBMS data data data data Distributed Databases Distributed Database System

  3. Advantages of a DDBS • Modularity • Fault Tolerance • High Performance • Data Sharing • Low Cost Components

  4. Issues • Data Distribution • Exploiting Parallelism • Concurrency and Recovery • Heterogeneity

  5. Parallelism: Pipelining • Example: • T1 SELECT * FROM A WHERE cond • T2 JOIN T1 and B select join A B (with index)

  6. Parallelism: Concurrent Operations • Example: SELECT * FROM A WHERE cond data location is important... merge select select select A where A.x < 10 A where 10  A.x < 20 A where 20  A.x

  7. join strategy Join Processing • Example: JOIN A, B over attribute X B1 B2 A1 A2 A.x < 10 A.x  10 B.x < 10 B.x  10

  8. Join Processing • Example: JOIN A, B over attribute X B1 B2 A1 A2 A.z < 10 A.z  10 B.z < 10 B.z  10 join strategy

  9. Concurrency & Recovery • Two Phase Commit Bank Mainframe ATM

  10. 2PC: ATM Withdrawl • Mainframe is coordinator • Phase 1: ATM checks if money available; mainframe checks if account has funds (money and funds are “reserved”) • Phase 2: ATM releases funds; mainframe debits account

  11. Heterogeneous Databases Distributed Database System DBMS1 DBMS2 legacy web site data data data data

  12. Example: Amazon.com must specify at least one of these author: title: this attribute not returned subject: format: menu of choices price: cannot query on this attribute

  13. Example: BarnesAndNoble.com must specify at least one of these author: title: Menu of choices subject: format: can query if one of other attributes specified price:

  14. Why Limited Capabilities? • Search forms • Security • Indexes • Legacy

  15. Capability vs. Content • Capability description • Can only search for subject = “art,” “history,” “science” • Content description • Source only contains subject = “art,” “history,” “science”

  16. Outline • Describing source capabilities • Extending source capabilities • How mediators cope with limited capabilities • Mediator capabilities • Other topics mediator source source source

  17. Describing Query Capabilities R(X, Y, ... Z) • Adornments: • f: may or may not specify • u: cannot be specified • b: must be specified • c[S]: specified from list S • o[S]: optional, chose from S

  18. Describing Query Capabilities R(X, Y, ... Z) • With output restriction • f’ • u’ • b’ • c’[S] • o’[S] • Adornments: • f: may or may not specify • u: cannot be specified • b: must be specified • c[S]: specified from list S • o[S]: optional, chose from S

  19. Example • Relation R(X, Y, Z) • Description Templates: bu’f, uf’c[z1, z2] • Answerable queries: R(x1, Y, Z), R(X, Y, z1) • Unanswerable queries: R(X, y1, Z), R(X, Y, z3)

  20. Other Description Mechanisms • Tsimmis • query templates • Information Manifold • capability records (# bound attrs, conditions ok,...) • Disco • Garlic • black box • Contex-free grammars

  21. Extending Source Capabilities Query: author=“Freud” AND price > 10 wrapper amazon Source: R(author, price, ...) Template: b, u, ...

  22. Extending Source Capabilities Query: author=“Freud” AND price > 10 Wrapper Filter: price > 10 wrapper Source Query: author=“Freud” amazon Source: R(author, price, ...) Template: b, u, ...

  23. Another Example Query: (author = “Freud” OR author = “Jung”) AND price < 10 wrapper Barnes&Noble R(author, price, ...) No disjunctive conditions; Price can only be specified with author

  24. Another Example Query: (author = “Freud” OR author = “Jung”) AND price < 10 Union Operation wrapper Q1: author = “Freud” AND price < 10 Q2: author = “Jung” AND price < 10 Barnes&Noble R(author, price, …) No disjunctive conditions; Price can only be specified with author

  25. Extending Source Capabilities • General scheme: • try many query rewritings • check if query fragments supported by source • check if wrapper can combine answer fragments • do all this very efficiently!! [See ICDE99 paper] • Tsimmis, Info Manifold: no disjunctive queries • DISCO: no query splitting • Garlic: only CNF queries

  26. Mediator Processing Query: M(5, Y, Z, W, 3) M(X, Y, Z, W, U) = Join(R, T) mediator source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b

  27. Plan 1 Query: M(5, Y, Z, W, 3) (3) Join answers M(X, Y, Z, W, U) = Join(R, T) mediator (1) R(5, Y, Z) (2) T(Z, W, 3) source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b

  28. Plan 2 Query: M(5, Y, Z, W, 3) (3) Join answers (2) for each (z,w,u)  P: R(5, Y, u) M(X, Y, Z, W, U) = Join(R, T) mediator (1) P = T(Z, W, 3) source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b

  29. Mediator Plan Generation • Need feasible and efficient plan • Search space is huge • Tsimmis, Info Manifold, Garlic: • exponential algorithms • Polynomial algorithms: • often find optimal or near-optimal plan • bounded performance • [See ICDT99 Paper]

  30. Conclusion • Not all sources are created equal! • Need to • describe what sources can do • efficiently process queries with limited sources • describe what mediators can do • exploit content information • deal with unavailable sources

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