CSE 344 APRIL 27 TH COST ESTIMATION
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1 CSE 344 APRIL 27 TH COST ESTIMATION
2 ADMINISTRIVIA HW5 Out Please verify that you can run queries Midterm May 9 th 9:30-10:20 MLR 301 Review (in class) May 7th Practice exam May 4 th Through parallelism: next week s material
3 INDEX BASED SELECTION Example: B(R) = 2000 T(R) = 100,000 V(R, a) = 20 cost of s a=v (R) =? Table scan: B(R) = 2,000 I/Os Index based selection: If index is clustered: B(R) * 1/V(R,a) = 100 I/Os If index is unclustered: T(R) * 1/V(R,a) = 5,000 I/Os Lesson: Don t build unclustered indexes when V(R,a) is small!
4 OUTLINE Join operator algorithms One-pass algorithms (Sec and 15.3) Index-based algorithms (Sec 15.6) Note about readings: In class, we discuss only algorithms for joins Other operators are easier: read the book
5 JOIN ALGORITHMS Hash join Nested loop join Sort-merge join
6 HASH JOIN Hash join: R S Scan R, build buckets in main memory Then scan S and join Cost: B(R) + B(S) Which relation to build the hash table on? CSE au 6
7 HASH JOIN Hash join: R S Scan R, build buckets in main memory Then scan S and join Cost: B(R) + B(S) Which relation to build the hash table on? One-pass algorithm when B(R) M M = number of memory pages available
8 HASH JOIN EXAMPLE Patient(pid, name, address) Insurance(pid, provider, policy_nb) Patient Insurance Two tuples per page Patient 1 Bob Seattle 2 Ela Everett 3 Jill Kent 4 Joe Seattle Insurance 2 Blue Prem Prem GrpH 554
9 HASH JOIN EXAMPLE Patient Insurance Memory M = 21 pages Some largeenough # Showing pid only Patient 1 2 Disk Insurance This is one page with two tuples
10 HASH JOIN EXAMPLE Step 1: Scan Patient and build hash table in memory Can be done in Memory M = 21 pages method open() Hash h: pid % Patient 1 2 Disk Insurance Input buffer
11 HASH JOIN EXAMPLE Step 2: Scan Insurance and probe into hash table Done during Memory M = 21 pages calls to next() Hash h: pid % Disk Patient Insurance Input buffer Output buffer Write to disk or pass to next operator
12 HASH JOIN EXAMPLE Step 2: Scan Insurance and probe into hash table Done during Memory M = 21 pages calls to next() Hash h: pid % Disk Patient Insurance Input buffer Output buffer
13 HASH JOIN EXAMPLE Step 2: Scan Insurance and probe into hash table Done during Memory M = 21 pages calls to next() Hash h: pid % Disk Patient Insurance Input buffer Output buffer Keep going until read all of Insurance Cost: B(R) + B(S) 13
14 NESTED LOOP JOINS Tuple-based nested loop R S R is the outer relation, S is the inner relation for each tuple t 1 in R do for each tuple t 2 in S do if t 1 and t 2 join then output (t 1,t 2 ) What is the Cost?
15 NESTED LOOP JOINS Tuple-based nested loop R S R is the outer relation, S is the inner relation for each tuple t 1 in R do for each tuple t 2 in S do if t 1 and t 2 join then output (t 1,t 2 ) Cost: B(R) + T(R) B(S) Multiple-pass since S is read many times What is the Cost?
16 PAGE-AT-A-TIME REFINEMENT for each page of tuples r in R do for each page of tuples s in S do for all pairs of tuples t 1 in r, t 2 in s if t 1 and t 2 join then output (t 1,t 2 ) Cost: B(R) + B(R)B(S) What is the Cost?
17 PAGE-AT-A-TIME REFINEMENT 1 2 Input buffer for Patient Disk 2 4 Input buffer for Insurance Patient Insurance Output buffer
18 PAGE-AT-A-TIME REFINEMENT 1 2 Input buffer for Patient Disk 4 3 Input buffer for Insurance Patient Insurance Output buffer
19 PAGE-AT-A-TIME REFINEMENT 1 2 Input buffer for Patient Patient Disk Insurance Cost: B(R) + B(R)B(S) Input buffer for Insurance Keep going until read 2 2 all of Insurance Output buffer Then repeat for next page of Patient until end of Patient
20 BLOCK-NESTED-LOOP REFINEMENT for each group of M-1 pages r in R do for each page of tuples s in S do for all pairs of tuples t 1 in r, t 2 in s if t 1 and t 2 join then output (t 1,t 2 ) Cost: B(R) + B(R)B(S)/(M-1) What is the Cost?
21 SORT-MERGE JOIN Sort-merge join: R S Scan R and sort in main memory Scan S and sort in main memory Merge R and S Cost: B(R) + B(S) One pass algorithm when B(S) + B(R) <= M Typically, this is NOT a one pass algorithm
22 SORT-MERGE JOIN EXAMPLE Step 1: Scan Patient and sort in memory Memory M = 21 pages Patient 1 2 Disk Insurance
23 SORT-MERGE JOIN EXAMPLE Step 2: Scan Insurance and sort in memory Memory M = 21 pages Patient 1 2 Disk Insurance
24 SORT-MERGE JOIN EXAMPLE Step 3: Merge Patient and Insurance Memory M = 21 pages Patient 1 2 Disk Insurance Output buffer
25 SORT-MERGE JOIN EXAMPLE Step 3: Merge Patient and Insurance Memory M = 21 pages Patient 1 2 Disk Insurance Output buffer Keep going until end of first relation
26 INDEX NESTED LOOP JOIN R S Assume S has an index on the join attribute Iterate over R, for each tuple fetch corresponding tuple(s) from S Cost: If index on S is clustered: B(R) + T(R) * (B(S) * 1/V(S,a)) If index on S is unclustered: B(R) + T(R) * (T(S) * 1/V(S,a))
27 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 1 π sname σ pno=2 scity= Seattle sstate= WA SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA sid = sid Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
28 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 1 π sname σ pno=2 scity= Seattle sstate= WA T = SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA sid = sid Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
29 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 1 π sname T < 1 σ pno=2 scity= Seattle sstate= WA T = SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA sid = sid Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
30 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 2 π sname sid = sid SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA σ pno=2 σ scity= Seattle sstate= WA Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
31 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 2 π sname sid = sid T = 4 T= 5 SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA σ pno=2 σ scity= Seattle sstate= WA Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
32 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 2 π sname sid = sid T = 4 T= 5 σ σ scity= Seattle sstate= WA pno=2 SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA Very wrong! Why? Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
33 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 2 π sname T = 4 sid = sid T = 4 T= 5 σ σ scity= Seattle sstate= WA pno=2 SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA Very wrong! Why? Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
34 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) LOGICAL QUERY PLAN 2 σ pno=2 π sname T = 4 sid = sid Different estimate L T = 4 T= 5 σ scity= Seattle sstate= WA SELECT sname FROM Supplier x, Supply y WHERE x.sid = y.sid and y.pno = 2 and x.scity = Seattle and x.sstate = WA Very wrong! Why? Supply T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 Supplier T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
35 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 1 π sname T < 1 σ pno=2 scity= Seattle sstate= WA T = Total cost: 100/10 * 100 = 1000 sid = sid Block nested loop join Scan Supply Scan Supplier T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
36 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 1 π sname T < 1 σ pno=2 scity= Seattle sstate= WA T = sid = sid Block nested loop join Total cost: *100/10 = 1100 Cost: B(R) + B(R)B(S)/(M-1) Scan Supply Scan Supplier T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
37 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 2 π sname T = 4 Cost of Supply(pno) = 4 Cost of Supplier(scity) = 50 Total cost: 54 T = 4 sid = sid Main memory join T= 5 σ sstate= WA Unclustered index lookup Supply(pno) σ pno=2 Supply σ scity= Seattle Supplier T= 50 Unclustered index lookup Supplier(scity) T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
38 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 2 π sname T = 4 Cost of Supply(pno) = 4 Cost of Supplier(scity) = 50 Total cost: 54 T = 4 sid = sid Main memory join T= 5 σ sstate= WA Unclustered index lookup Supply(pno) σ pno=2 Supply σ scity= Seattle Supplier T= 50 Unclustered index lookup Supplier(scity) T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
39 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 2 π sname T = 4 Cost of Supply(pno) = 4 Cost of Supplier(scity) = 50 Total cost: 54 T = 4 sid = sid Main memory join T= 5 σ sstate= WA Unclustered index lookup Supply(pno) σ pno=2 Supply σ scity= Seattle Supplier T= 50 Unclustered index lookup Supplier(scity) T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
40 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 3 T = 4 π sname σ scity= Seattle sstate= WA Cost of Supply(pno) = 4 Cost of Index join = 4 Total cost: 8 Unclustered index lookup Supply(pno) T = 4 σ pno=2 Supply sid = sid Clustered Index join Supplier T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
41 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 3 T = 4 π sname σ scity= Seattle sstate= WA Cost of Supply(pno) = 4 Cost of Index join = 4 Total cost: 8 Unclustered index lookup Supply(pno) T = 4 σ pno=2 Supply sid = sid Clustered Index join Supplier T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
42 Supplier(sid, sname, scity, sstate) Supply(sid, pno, quantity) PHYSICAL PLAN 3 T = 4 π sname σ scity= Seattle sstate= WA Cost of Supply(pno) = 4 Cost of Index join = 4 Total cost: 8 Unclustered index lookup Supply(pno) T = 4 σ pno=2 Supply sid = sid Clustered Index join Supplier T(Supply) = B(Supply) = 100 V(Supply, pno) = 2500 T(Supplier) = 1000 B(Supplier) = 100 V(Supplier, scity) = 20 V(Supplier, state) = 10 M=11
43 QUERY OPTIMIZER SUMMARY Input: A logical query plan Output: A good physical query plan Basic query optimization algorithm Enumerate alternative plans (logical and physical) Compute estimated cost of each plan Choose plan with lowest cost This is called cost-based optimization
44 DISK SCHEDULING Query optimization Good DB design Good estimation Hardware independent All Disk I/Os are not created equal Sectors close to each other are more preferable to read
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