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CSE544 Query Optimization Tuesday-Thursday, February 8 th -10 th , 2011 Dan Suciu -- 544, Winter 2011 1
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CSE544 Query Optimization. Tuesday-Thursday , February 8 th -10 th , 2011. Outline. Chapter 15 in the textbook. Query Optimization Algorithm. Enumerate alternative plans Compute estimated cost of each plan Compute number of I/Os Compute CPU cost Choose plan with lowest cost - PowerPoint PPT Presentation

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CSE544Query OptimizationTuesday-Thursday,February 8th-10th, 2011Dan Suciu -- 544, Winter 2011 11OutlineChapter 15 in the textbookDan Suciu -- 544, Winter 2011 22Dan Suciu -- 544, Winter 2011 3Query Optimization AlgorithmEnumerate alternative plans

Compute estimated cost of each planCompute number of I/OsCompute CPU cost

Choose plan with lowest costThis is called cost-based optimization3Dan Suciu -- 544, Winter 2011 4ExampleSome statisticsT(Supplier) = 1000 recordsT(Supply) = 10,000 recordsB(Supplier) = 100 pagesB(Supply) = 100 pagesV(Supplier,scity) = 20, V(Supplier,state) = 10V(Supply,pno) = 2,500Both relations are clusteredM = 10Supplier(sid, sname, scity, sstate)Supply(sid, pno, quantity)SELECT snameFROM Supplier x, Supply yWHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA4Dan Suciu -- 544, Winter 2011 5Physical Query Plan 1SupplierSupplysid = sidscity=Seattle sstate=WA pno=2sname(File scan)(File scan)(Block-nested loop)(On the fly)(On the fly)B(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 105Dan Suciu -- 544, Winter 2011 6Physical Query Plan 1SupplierSupplysid = sidscity=Seattle sstate=WA pno=2sname(File scan)(File scan)(Block-nested loop)(On the fly)(On the fly)Selection and project on-the-fly-> No additional cost.

Total cost of plan is thus cost of join:= B(Supplier)+B(Supplier)*B(Supply)/M= 100 + 10 * 100= 1,100 I/OsB(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 106Dan Suciu -- 544, Winter 2011 7SupplierSupplysid = sidscity=Seattle sstate=WAsname(File scan)(File scan)(Sort-merge join)(Scanwrite to T2)(On the fly) pno=2(Scan write to T1)Physical Query Plan 2B(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 107Dan Suciu -- 544, Winter 2011 8SupplierSupplysid = sidscity=Seattle sstate=WAsname(File scan)(File scan)(Sort-merge join)(Scanwrite to T2)(On the fly) pno=2(Scan write to T1)Physical Query Plan 2Total cost= 100 + 100 * 1/20 * 1/10 (1)+ 100 + 100 * 1/2500 (2)+ 2 (3)+ 0 (4)Total cost 204 I/OsB(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 108SupplySuppliersid = sidscity=Seattle sstate=WAsname(Index nested loop)(Index lookup on sid)Doesnt matter if clustered or not(On the fly) pno=2(Index lookup on pno )Assume: clusteredPhysical Query Plan 3(Use index)(On the fly)9B(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 109SupplySuppliersid = sidscity=Seattle sstate=WAsname(Index nested loop)(Index lookup on sid)Doesnt matter if clustered or not(On the fly) pno=2(Index lookup on pno )Assume: clusteredPhysical Query Plan 3(Use index)(On the fly)4 tuples10B(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 1010SupplySuppliersid = sidscity=Seattle sstate=WAsname(Index nested loop)(Index lookup on sid)Doesnt matter if clustered or not(On the fly) pno=2(Index lookup on pno )Assume: clusteredPhysical Query Plan 3Total cost= 1 (1)+ 4 (2)+ 0 (3)+ 0 (3)Total cost 5 I/Os(Use index)(On the fly)4 tuples11B(Supplier) = 100B(Supply) = 100T(Supplier) = 1000T(Supply) = 10,000V(Supplier,scity) = 20V(Supplier,state) = 10V(Supply,pno) = 2,500M = 1011Dan Suciu -- 544, Winter 2011 12SimplificationsIn the previous examples, we assumed that all index pages were in memory

When this is not the case, we need to add the cost of fetching index pages from disk1213LessonsNeed to consider several physical planeven for one, simple logical plan

No plan is best in generalneed to have statistics over the datathe Bs, the Ts, the VsDan Suciu -- 544, Winter 2011 13The Contract of the OptimizerHigh-quality execution plans for all queries, While taking relatively small optimization time, andWith limited additional input such as histograms.Dan Suciu -- 544, Winter 2011 1414Dan Suciu -- 544, Winter 2011 15Query OptimizationThree major components:

Search space

Algorithm for enumerating query plans

Cardinality and cost estimation

15History of Query OptimizationFirst query optimizer was for System R, from IBM, in 1979It had all three components in place, and defined the architecture of query optimizers for years to comeYou will see often references to System RRead Section 15.6 in the bookDan Suciu -- 544, Winter 2011 16161. Search SpaceThis is the set of all alternative plans that are considered by the optimizer

Defined by the set of algebraic laws and the set of plans used by the optimizer

Will discuss these laws nextDan Suciu -- 544, Winter 2011 1717Left-Deep Plans andBushy PlansDan Suciu -- 544, Winter 2011 18R3R1R2R4R3R1R4R2Left-deep planBushy planSystem R considered only left deep plans, and so do some optimizers today18Dan Suciu -- 544, Winter 2011 19Relational Algebra LawsSelectionsCommutative: c1(c2(R)) = c2(c1(R))Cascading: c1c2(R) = c2(c1(R))ProjectionsJoinsCommutativity : R S = S R Associativity: R (S T) = (R S) T Distributivity: R (S T) = (R S) (R T)Outer joins get more complicated19ExampleAssumptions:Every join selectivity is 10%That is: T(R S) = 0.1 * T(R) * T(S) etc.B(R)=100, B(S) = 50, B(T)=500All joins are main memory joinsAll intermediate results are materialized

Dan Suciu -- 544, Winter 2011 20Which plan is more efficient ?R (S T) or (R S) T ?2021ExampleExample: R(A, B, C, D), S(E, F, G) s F=3 (R D=E S) = ? s A=5 AND G=9 (R D=E S) = ?Dan Suciu -- 544, Winter 2011 2122Simple LawsExample R(A,B,C,D), S(E, F, G)PA,B,G(R D=E S) = P ? (P?(R) D=E P?(S)) Dan Suciu -- 544, Winter 2011 PM(R S) = PM(PP(R) PQ(S))PM(PN(R)) = PM(R) /* note that M N */22Laws for Group-by and JoinDan Suciu -- 544, Winter 2011 23A, agg(D)(R(A,B) B=C S(C,D)) = A, agg(D)(R(A,B) B=C (C, agg(D)S(C,D)))These are very powerful laws.They were introduced only in the 90s.23Semantic Optimizations = Laws that use a ConstraintDan Suciu -- 544, Winter 2011 24Product(pid, pname, price, cid)Company(cid, cname, city, state)Foreign keyNeed a second constraint for this law to hold. Which ?Ppid, price(Product cid=cid Company) = Ppid, price(Product)24ExampleDan Suciu -- 544, Winter 2011 25Product(pid, pname, price, cid)Company(cid, cname, city, state)Foreign keyCREATE VIEW CheapProductCompany SELECT * FROM Product x, Company y WHERE x.cid = y.cid and x.price < 100SELECT pname, priceFROM CheapProductCompanySELECT pname, priceFROM Product2526Law of SemijoinsRecall the definition of a semijoin:R S = P A1,,An (R S)Where the schemas are:Input: R(A1,An), S(B1,,Bm)Output: T(A1,,An)The law of semijoins is:Dan Suciu -- 544, Winter 2011 R S = (R S) S26Laws with SemijoinsVery important in parallel databasesOften combined with Bloom Filters (next lecture)Read pp. 747 in the textbookDan Suciu -- 544, Winter 2011 272728Semijoin ReducerGiven a query:

A semijoin reducer for Q is

such that the query is equivalent to:

A full reducer is such that no dangling tuples remainQ = Rk1 Rk2 . . . Rkn Ri1 = Ri1 Rj1Ri2 = Ri2 Rj2. . . . .Rip = Rip RjpDan Suciu -- 544, Winter 2011 Q = R1 R2 . . . Rn2829ExampleExample:

A semijoin reducer is:

The rewritten query is:Q = R(A,B) S(B,C)R1(A,B) = R(A,B) S(B,C)Q = R1(A,B) S(B,C)2930Why Would We Do This ?Large attributes:

Expensive side computationsQ = R(A, B, D, E, F,) S(B, C, M, K, L, )Q = A,B,count(*)R(A,B,D) C=value(S(B,C))R1(A,B,D) = R(A,B,D) C=value(S(B,C))Q = A,B,count(*)R1(A,B,D) C=value(S(B,C))3031Semijoin ReducerExample:

A semijoin reducer is:

The rewritten query is:Q = R(A,B) S(B,C)R1(A,B) = R(A,B) S(B,C)Are there dangling tuples ?Q = R1(A,B) S(B,C)3132Semijoin ReducerExample:

A full semijoin reducer is:

The rewritten query is:Q = R(A,B) S(B,C)R1(A,B) = R(A,B) S(B,C)S1(B,C) = S(B,C) R1(A,B) Q :- R1(A,B) S1 (B,C)No more dangling tuples3233Semijoin ReducerMore complex example:

A full reducer is:Q = R(A,B) S(B,C) T(C,D,E)S(B,C) := S(B,C) R(A,B)T(C,D,E) := T(C,D,E) S(B,C)S(B,C) := S(B,C) T(C,D,E)R(A,B) := R (A,B) S(B,C)Q = R(A,B) S(B,C) T(C,D,E)3334Semijoin ReducerExample:

Doesnt have a full reducer (we can reduce forever)

Theorem a query has a full reducer iff it is acyclic[Database Theory, by Abiteboul, Hull, Vianu]Q = R(A,B) S(B,C) T(A,C)Dan Suciu -- 544, Winter 2011 34Example with Semijoins35CREATE VIEW DepAvgSal As (SELECT E.did, Avg(E.Sal) AS avgsalFROM Emp EGROUP BY E.did)[Chaudhuri98]Emp(eid, ename, sal, did)Dept(did, dname, budget)DeptAvgSal(did, avgsal) /* view */SELECT E.eid, E.salFROM Emp E, Dept D, DepAvgSal VWHERE E.did = D.did AND E.did = V.didAND E.age < 30 AND D.budget > 100kAND E.sal > V.avgsalView:Query:Goal: compute only the necessary part of the view35Example with Semijoins36CREATE VIEW LimitedAvgSal As (SELECT E.did, Avg(E.Sal) AS avgsalFROM Emp E, Dept D WHERE E.did = D.did AND D.buget > 100kGROUP BY E.did)[Chaudhuri98]New viewuses a reducer:Emp(eid, ename, sal, did)Dept(did, dname, budget)DeptAvgSal(did, avgsal) /* view */SELECT E.eid, E.salFROM Emp E, Dept D, LimitedAvgSal VWHERE E.did = D.did AND E.did = V.didAND E.age < 30 AND D.budget > 100kAND E.sal > V.avgsalNew query:36Example with Semijoins37CREATE VIEW PartialResult AS(SELECT E.eid, E.sal, E.didFROM Emp E, Dept DWHERE E.did=D.did AND E.age < 30AND D.budget > 100k)

CREATE VIEW Filter AS(SELECT DISTINCT P.did FROM PartialResult P)

CREATE VIEW LimitedAvgSal AS(SELECT E.did, Avg(E.Sal) AS avgsalFROM Emp E, Filter FWHERE E.did = F.did GROUP BY E.did)[Chaudhuri98]Full reducer:Emp(eid, ename, sal, did)Dept(did, dname, budget)DeptAvgSal(did, avgsal) /* view */3738Example with SemijoinsSELECT P.eid, P.salFROM PartialResult P, LimitedDepAvgSal VWHERE P.did = V.did AND P.sal > V.avgsalDan Suciu -- 544, Winter 2011 New query:38Pruning the Search SpacePrune entire sets of plans that are unpromising

The choice of partial plans influences how effective we can pruneDan Suciu -- 544, Winter 2011 3939Complete PlansDan Suciu -- 544, Winter 2011 40SELECT *FROM R, S, TWHERE R.B=S.B and S.C=T.C and R.A