1. CPS216: Advanced Database Systems Notes 07:Query Execution Shivnath Babu

2. parse Query rewriting Physical plan generation execute result SQL query parse tree logical query planstatistics physical query plan Query Processing - In class order 2; 16.1 3; 16.2,16.3 1; 13, 15 4; 16.4—16.7

3. Roadmap Path of a SQL query Plans –Operator trees –Physical Vs Logical plans –Plumbing: Materialization Vs pipelining

4. Modern DBMS Architecture Disk(s) Applications OS Parser Query Optimizer Query Executor Storage Manager Logical query plan Physical query plan Access method API calls SQL File system API calls Storage system API calls DBMS

5. Locial Plans Vs. Physical Plans  B,D  R.A = “c” R S Natural join Best logical plan RS Index scan Table scan Hash join Project

6.  B,D  R.A = “c” R S Operator Plumbing Materialization: output of one operator written to disk, next operator reads from the disk Pipelining: output of one operator directly fed to next operator

7.  B,D  R.A = “c” R S Materialization Materialized here

8.  B,D  R.A = “c” R S Iterators: Pipelining  Each operator supports: Open() GetNext() Close()

9. Iterator for Table Scan (R) Open() { /** initialize variables */ b = first block of R; t = first tuple in block b; } GetNext() { IF (t is past last tuple in block b) { set b to next block; IF (there is no next block) /** no more tuples */ RETURN EOT; ELSE t = first tuple in b; } /** return current tuple */ oldt = t; set t to next tuple in block b; RETURN oldt; } Close() { /** nothing to be done */ }

10. Iterator for Select Open() { /** initialize child */ Child.Open(); } GetNext() { LOOP: t = Child.GetNext(); IF (t == EOT) { /** no more tuples */ RETURN EOT; } ELSE IF (t.A == “c”) RETURN t; ENDLOOP: } Close() { /** inform child */ Child.Close(); }  R.A = “c”

11. NLJ (conceptually) for each r  Lexp do for each s  Rexp do if Lexp.C = Rexp.C, output r,s Iterator for Nested Loop Join LexpRexp

12. Iterator for Sort Open() { /** Bulk of the work is here */ Child.Open(); Read all tuples from Child and sort them } GetNext() { IF (more tuples) RETURN next tuple in order; ELSE RETURN EOT; } Close() { /** inform child */ Child.Close(); }  R.A

13. Example 1: Left-Deep Plan R1(A,B) TableScan R2(B,C) TableScan R3(C,D) TableScan TNLJ Question: What is the sequence of getNext() calls?

14. Example 1 (contd.) Assume Statistics: –B(R1) = 1000 blocks, T(R1) = 10,000 tuples –B(R2) = 500 blocks, T(R2) = 5000 tuples –B(R3) = 1000 blocks, T(R3) = 10,000 tuples – Let X = R1 Join (R1.B = R2.B) R2 –T(X) = 1,000,000 tuples, B(X) = 200,000 blocks –Let Output = 1000 tuples Questions: –Number of getNext() calls? –Number of disk I/Os? –Assume we have 1000 blocks of memory, how can we improve the plan?

15. Example 2: Right-Deep Plan R3(C,D) TableScan TNLJ R1(A,B) TableScan R2(B,C) TableScan TNLJ Question: What is the sequence of getNext() calls?

16. Example 2 (contd.) Assume Statistics: –B(R1) = 1000 blocks, T(R1) = 10,000 tuples –B(R2) = 500 blocks, T(R2) = 5000 tuples –B(R3) = 1000 blocks, T(R3) = 10,000 tuples – Let X = R1 Join (R1.B = R2.B) R2 –T(X) = 1,000,000 tuples, B(X) = 200,000 blocks –Let Output = 1000 tuples Questions: –Number of getNext() calls? –Number of disk I/Os? –Assume we have 1000 blocks of memory, how can we improve the plan?

17. Questions to think about What "shape" of plan works best for nested loop joins: 'Left deep' (Example 1) or 'right deep' (Example 2)? Will sorting help for nested loop join? (Hint: think about clustered vs. unclustered indexes) Can materialization help for nested loop join? Generalize Example 1 (and 2) to 'n' relations: What is the optimal use of M blocks of memory? (I don't know the answer :-) )

18. Example 3: Hash-Join Plan R1(A,B) TableScan R2(B,C) TableScan R3(C,D) TableScan HJ

19. Example 3 (contd.) Naive materialization: –Compute hash join of R1, R2 (called X) –Write output X to disk –The (outer) hash join reads X (table scan) and reads R3 and performs hash join What is the cost of naive materialization? Suggest an improved processing strategy that shaves 2 B(X) from the above cost

20. Example 3 (contd.) Can this be completely pipelined if you have limited memory? How much memory do you need to be able to pipeline this plan?

21. Questions to think about If you are designing/building the basic set of physical operators for your database system, would you implement the hash-join as a single operator or as two operators -- one that partitions and the second that joins? If you are designing physical operators for your database system, would you implement sort- merge join as a single operator or as two operators -- one that sorts and one that merges? Think about pipelining vs. materialization issues in plans involving sort-merge joins