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Query Optimization and Perspectives

Query Optimization and Perspectives. March 12 th , 2003. Administration. Exam next Wednesday, 2:30pm. Special office hours next week will be announced. Problem. Given: a query R1 R2 … Rn Assume we have a function cost() that gives us the cost of every join tree

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Query Optimization and Perspectives

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  1. Query Optimization and Perspectives March 12th, 2003

  2. Administration • Exam next Wednesday, 2:30pm. • Special office hours next week will be announced.

  3. Problem • Given: a query R1 R2 … Rn • Assume we have a function cost() that gives us the cost of every join tree • Find the best join tree for the query

  4. Dynamic Programming • For each subquery Q ⊆ {R1, …, Rn} compute the following: • Size(Q) • A best plan for Q: Plan(Q) • The cost of that plan: Cost(Q)

  5. Dynamic Programming • Step 1: For each {Ri} do: • Size({Ri}) = B(Ri) • Plan({Ri}) = Ri • Cost({Ri}) = (cost of scanning Ri)

  6. Dynamic Programming • Step i: For each Q ⊆ {R1, …, Rn} of cardinality i do: • Compute Size(Q) • For every pair of subqueries Q’, Q’’ s.t. Q = Q’ U Q’’compute cost(Plan(Q’) Plan(Q’’)) • Cost(Q) = the smallest such cost • Plan(Q) = the corresponding plan

  7. Dynamic Programming • Return Plan({R1, …, Rn})

  8. Dynamic Programming • Summary: computes optimal plans for subqueries: • Step 1: {R1}, {R2}, …, {Rn} • Step 2: {R1, R2}, {R1, R3}, …, {Rn-1, Rn} • … • Step n: {R1, …, Rn} • We used naïve size/cost estimations • In practice: • more realistic size/cost estimations • heuristics for Reducing the Search Space • Restrict to left linear trees • Restrict to trees “without cartesian product” • need more than just one plan for each subquery: • “interesting orders”

  9. Plan with Materialization HashTable  Srepeat read(R, x) y  join(HashTable, x) write(V1, y) HashTable  T repeat read(V1, y) z  join(HashTable, y) write(V2, z) HashTable  Urepeat read(V2, z) u  join(HashTable, z) write(Answer, u) ⋈ V2 ⋈ U V1 ⋈ T R S

  10. pipeline Plan with Pipelining ⋈ HashTable1  SHashTable2  T HashTable3  Urepeat read(R, x) y  join(HashTable1, x) z  join(HashTable2, y) u  join(HashTable3, z) write(Answer, u) ⋈ U ⋈ T R S

  11. Key Lessons in Optimization • There are many approaches and many details to consider in query optimization • Classic search/optimization problem! • Not completely solved yet! • Main points to take away are: • Algebraic rules and their use in transformations of queries. • Deciding on join ordering: System-R style (Selinger style) optimization. • Estimating cost of plans and sizes of intermediate results.

  12. The Course in Perspective • The relational data model, SQL • Views, updates, transactions • Conceptual design • Expressing the constraints on the domain • Using them to get good schema designs • XML • A format for data exchange. Has its own query language. • Basis for web services.

  13. Perspective (continued) • Building a database system: • Storage and indexing • Query execution (join algorithms) • Query optimization • Data integration and sharing: • Databases were created independently • Schemas need to be matched • Need to access multiple databases in one query. • Interoperability through web services.

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