# Query Optimization Example. Steps Query op5miza5on steps: 12/13/09

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1 Query Optimization Example (sid, sname, ra5ng, age) (bid, bname, color) (sid, bid, day, rname) Query: SELECT S.sid, S.sname, S.age FROM S, B, R WHERE B.bid = R.rid AND B.bid = R.bid AND B.color = Red AND S.age < 30; has 1000 pages, 10 tuples/page has 500 pages, 50 tuples/page has 160 pages, 10 tuples/page Data is evenly distributed (assumption) CPSC 421, Steps Query op5miza5on steps: Translate SQL Query to Rela5onal Algebra Query Create a query tree Create ley deep alterna5ve trees Create query plans for our trees Es5mate costs of plans Pick best one CPSC 421,

2 Step 1 Translate SQL Query to Rela5onal Algebra Query π sid, sname, age ( ( (σ bid=bid (B σ sid=sid (S R))))) CPSC 421, Step 2 Create a query tree σ bid=bid Note our query only involves joins (as opposed to plain old crossproducts)! Lets draw the trees with joins! σ sid=sid CPSC 421,

3 Step 3 Create ley deep alterna5ve trees We replace cross products if they are really joins How? Using RA equivalences! This is a ley deep plan with joins CPSC 421, Step 3 More Alternatives Would we expect these to have different costs over previous alterna5ve? No! Yes! CPSC 421,

4 Lets create some plans and es5mates Total cost: 160, ,002,000 = 1,162,160 I/Os!!! M + M*N =?+?*500 =??? How large is the result of the first join? Every reservation has a bid bid is unique for each boat So 10,000 tuples How many pages? 1 / ((1/10) + (1/10)) = 10/2 = 5 tuples/page M + M*N = 2,000+2,000*500 = 1,002,000 M + M*N = *1,000 = 160,160 (this is the cost of reading, not writing) age<30 (Page NL) (Page NL) CPSC 421, Assume has a clustered B+ Tree on bid (key) Assume S has a clustered B+ Tree on sid (key) Total cost: 5, ,500 = 13,660 I/Os!!! Sort(M) + Sort(N) + M + N = 6, , = 8,500 Result must be sorted on sid: 2*2, *1*2,000 = 6,000 I/Os Sort(M) + Sort(R) + M + N = 0 + 4, ,000 = 5,160 sorted on bid, isn t Assume buffers B = 50 Cost of sorting reserves: 2*1, *1*1,000 = 4,000 I/Os age<30 * We are assuming merge (in Sort Merge) takes M + N this may not always be the case. Why? CPSC 421,

5 Note on Sort Merge This is a much better plan than our previous page nested loops one But it is an overestimate for Sort-Merge join! In Pass 0 we read all pages then write all pages (2*M) We do not need to read all pages if they are pipelined to the next join (1*M) In last merge pass, we don t have to write the last set of pages since we always pipeline them to the next operator We also don t have to read in the file again: Sort(M) + Sort(N) if N needs to be sorted Sort(M) + N if N is sorted CPSC 421, Assume has a clustered B+ Tree on bid (key) Assume S has a clustered B+ Tree on sid (key) Total cost: 3, ,500 = 7,660 I/Os!!! Sort(M) + N = 4, = 4,500 Result must be sorted on sid: 1*2, *1*2,000 = 4,000 I/Os Sort(M) + N = 3, = 3,160 sorted on bid, isn t Assume buffers B = 50 Cost of sorting reserves: 2*1, *1*1,000 = 3,000 I/Os age<30 * We are assuming merge (in Sort Merge) takes M + N this may not always be the case. Why? CPSC 421,

6 Lets try block nested loop join (B=50) Total cost: 4, ,500 = 26,660 I/Os!!! age<30 M + (M/(B-1))*N = 2, *500 = 22,500 M + (M/(B-1))*N = 160+4*1,000 = 4,160 (this is the cost of reading, not writing) CPSC 421, Can we do bejer? Total cost: 5, ,500 = 16,910 I/Os!!! age<30 M + (M/(B-1))*N = 1, *160 = 5,410 How many pages in the answer? 1,250 why? M + (M/(B-1))*N = *500 = 11,500 (this is the cost of reading, not writing) CPSC 421,

7 Can we do even bejer? Assume has a hash index on color Total cost: , = 3,106 I/Os!!! (less than sort-merge plan) M + (M/(B-1))*N = *125 = 500 File Scan = 500 Assume 25% reduction factor M + (M/(B-1))*N = *1000 = 2,050 Assume 1/16 fewer match boats So result is: 625 tuples at 5 tuples/page = 125 pages has 1600 tuples Assume 16 colors Thus, 100 tuples are Red Assume scattered across 50 pages And the hash index takes 6 pages Cost = 56 (File Scan) (Hash Index) CPSC 421, Can we s5ll do bejer? Yes! E.g., we could project on bid ayer first join No5ce that by adding an index on color to We reduced our best query 5me in half! This is one reason we talk about query op5miza5on It drives physical database design Improving performance (by adding indexes, e.g.) should be driving on how query op5miza5on works! CPSC 421,

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