1. Query Evaluation and Optimization Main Steps 1.Translate into RA: select/project/join 2.Greedy optimization of RA: by pushing selection and projection into RA expression 3.Select best implementation for each operator 4.Cost-based estimation and selection of best join order 1

2. Greedy optimization of RA: pushing selection and projection into RA expression 2

3. Selection and Indexing If we have a S.C condition supported by an existing index we use the index If we have a conjunction, such as S.A>5 and S.B <10 with indexes on both, then we can select the better of the two (optimization) If there is no index, do we create one? 3

4. Selecting best implementation for each operator Selection: use an index if appropriate is available. Otherwise visit all the blocks Projection: trivial, unless we need to eliminate duplicates—using sorting or hashing. Joins –Nested loop, reasonable only with indexes –Nested Block Join –Sort-Merge Join –Has Join 4

5. 5 40T1 60T2 30T3 10T4 20T5 10T6 60T7 40T8 20T9 R S C C R JOIN S?

6. 6 For each r  R do For each s  S do if r.C = s.C then output r,s pair 40T1 60T2 30T3 10T4 20T5 10T6 60T7 40T8 20T9 R S Nested-Loop Join (NLJ) R is called the outer relation and S the inner relation of the join. Requires no indices and can be used with any kind of join condition. Expensive since it examines every pair of tuples in the two relations.

7. Nested-Loop Join (Cont.) In the worst case, if there is enough memory only to hold one block of each relation, the estimated cost is n R  b S + b R block transfers If the smaller relation fits entirely in memory, use that as the inner relation. – Reduces cost to b r + b s block transfers and 2 seeks Block nested-loops algorithm (next slide) is preferable.

8. Block Nested-Loop Join Variant of nested-loop join in which every block of inner relation is paired with every block of outer relation. for each block B r of r do begin for each block B s of s do begin for each tuple t r in B r do begin for each tuple t s in B s do begin Check if (t r,t s ) satisfy the join condition if they do, add t r t s to the result. end end end end

9. Block Nested-Loop Join (Cont.) Worst case estimate: b R  b S + b R block transfers –Each block in the inner relation S is read once for each block in the outer relation R. Best case: b R + b S block transfers. Many Improvements proposed to nested loop and block nested loop algorithms including: –Scan inner loop forward and backward alternately, to make use of the blocks remaining in buffer (with LRU replacement) –Use index on inner relation if available (next slide) E.g.Compute student takes, with R=student as the outer relation, S= takes the inner relation, –Number of records of student: 5,000 takes: 10,000 –Number of blocks of student: 100 takes: 400

10. 10 (1)Create an index for S.C if one is not already there (2)For each r  R do X  index-lookup(S.C, r.C) For each s  X, output (r,s) 40T1 60T2 30T3 10T4 20T5 10T6 60T7 40T8 20T9 R S Index Join (IJ)

11. Merge-Join 11

12. 12 –If the two tables fit in memory we can use bubble sorting –External sorting must be used otherwise. This is the litmus test for big data processing http://sortbenchmark.org/ 2014, 4.27 TB/min Apache Spark 100 TB in 1,406 seconds 207 Amazon EC2 i2.8xlarge nodes x (32 vCores - 2.5Ghz Intel Xeon E5-2670 v2, 244GB memory, 8x800 GB SSD) Reynold Xin, Parviz Deyhim, Xiangrui Meng, Ali Ghodsi, Matei Zaharia Databricks Sort-Merge Join (SMJ)

13. 13

14. 14 Hash function h(v), range 1  k Algorithm (1)Hashing stage (bucketizing): hash tuples into buckets Hash R tuples into G1,…,Gk buckets Hash S tuples into H1,…,Hk buckets (2)Join stage: join tuples in matching buckets For i = 1 to k do match tuples in Gi, Hi buckets R … S … … Hash Join (HJ)

15. 15 H(k) = k mod 3 0 1 2 R 0 1 2 S 40T1 60T2 30T3 10T4 20T5 10T6 60T7 40T8 20T9 Hash Join (HJ)

16. 16 Step (1): Hashing stage Step (2): Join stage... Memory buckets G1 G2 Gk R S... Gi memory... Hi R H1 H2 G1 G2 G3 Hash Join (HJ)

17. Summary of Join Algorithms Nested-loop join ok for “small” relations (relative to memory size) Hash join usually best for equi-join if relations not sorted and no index Merge join for sorted relations Sort merge join good for non-equi-join Consider index join if index exists DBMS maintains statistics on data 17

18. Query Evaluation and Optimization Main Steps 1.Translate into RA: select/project/join 2.Greedy optimization of RA: by pushing selection and projection into RA expression 3.Select best implementation for each operator 4.Cost-based estimation and selection of best join order 18

19. ©Silberschatz, Korth and Sudarshan1.19Database System Concepts - 6 th Edition Statistics collection commands DBMS has to collect statistics on tables/indexes – For optimal performance – Without stats, DBMS does stupid things… DB2 – RUNSTATS ON TABLE. AND INDEXES ALL Oracle – ANALYZE TABLE COMPUTE STATISTICS – ANALYZE TABLE ESTIMATE STATISTICS (cheaper than COMPUTE) Run the command after major update/index construction

20. ©Silberschatz, Korth and Sudarshan1.20Database System Concepts - 6 th Edition Join Ordering Example For all relations r 1, r 2, and r 3, (r 1 r 2 ) r 3 = r 1 (r 2 r 3 ) (Join Associativity) If r 2 r 3 is quite large and r 1 r 2 is small, we choose (r 1 r 2 ) r 3 so that we compute and store a smaller temporary relation.

21. ©Silberschatz, Korth and Sudarshan1.21Database System Concepts - 6 th Edition Estimation of the Size of Joins (Cont.) If R  S = {A} is not a key for R or S. If we assume that every tuple t in R produces tuples in R S, the number of tuples in R S is estimated to be: If the reverse is true, the estimate obtained will be: The lower of these two estimates is probably the more accurate one. Can improve on above if histograms are available Use formula similar to above, for each cell of histograms on the two relations V(A,s), V(A,r) the average number of A-values in s and r A special case: foreign keys

22. Statistics collection commands DBMS has to collect statistics on tables/indexes – For optimal performance – Without stats, DBMS does stupid things… DB2 – RUNSTATS ON TABLE. AND INDEXES ALL Oracle – ANALYZE TABLE COMPUTE STATISTICS – ANALYZE TABLE ESTIMATE STATISTICS (cheaper than COMPUTE) Run the command after major update/index construction