CPS216: Advanced Database Systems Notes 03:Query Processing (Overview, contd.)

1. CPS216: Advanced Database SystemsNotes 03:Query Processing (Overview, contd.) Shivnath Babu

2. Overview of Query Processing SQL query parse parse tree Query rewriting Query Optimization statistics logical query plan Physical plan generation physical query plan execute Query Execution result

3. SQL query Initial logical plan parse Rewrite rules parse tree Query rewriting Logical plan statistics logical query plan Physical plan generation “Best” logical plan physical query plan execute result

4. Query Rewriting B,D B,D R.C = S.C R.A = “c” Λ R.C = S.C R.A = “c” X X R S R S We will revisit it towards the end of this lecture

5. SQL query parse parse tree Query rewriting statistics Best logical query plan Physical plan generation Best physical query plan execute result

6. Physical Plan Generation B,D Project Natural join Hash join R.A = “c” S Index scan Table scan R R S Best logical plan

7. SQL query parse parse tree Enumerate possible physical plans Query rewriting statistics Best logical query plan Find the cost of each plan Physical plan generation Best physical query plan Pick plan with minimum cost execute result

8. Physical Plan Generation Logical Query Plan P1 P2 …. Pn C1 C2 …. Cn Pick minimum cost one Physical plans Costs

9. Plans for Query Execution • Roadmap • Path of a SQL query • Operator trees • Physical Vs Logical plans • Plumbing: Materialization Vs pipelining

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

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

12. B,D R.A = “c” S R 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

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

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

15. 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 */ }

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

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

18. Iterator for Tuple Nested Loop Join • TNLJ (conceptually) for each r  Lexp do for each s  Rexp do if Lexp.C = Rexp.C, output r,s Lexp Rexp

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

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

21. Example Worked on blackboard

22. Cost Measure for a Physical Plan • There are many cost measures • Time to completion • Number of I/Os (we will see a lot of this) • Number of getNext() calls • Tradeoff: Simplicity of estimation Vs. Accurate estimation of performance as seen by user

23. Textbook outline Chapter 15 15.1 Physical operators - Scan, Sort (Ch. 11.4), Indexes (Ch. 13) 15.2-15.6 Implementing operators + estimating their cost 15.8 Buffer Management 15.9 Parallel Processing

24. Textbook outline (contd.) Chapter 16 16.1 Parsing 16.2 Algebraic laws 16.3 Parse tree  logical query plan 16.4 Estimating result sizes 16.5-16.7 Cost based optimization

25. Background Material Chapter 5 Relational Algebra Chapter 6 SQL

26. SQL query parse parse tree Query rewriting statistics logical query plan Physical plan generation physical query plan execute result Query Processing - In class order 2; 16.1 First: A quick look at this 3; 16.2,16.3 4; 16.4—16.7 1; 13, 15

27. Why do we need Query Rewriting? • Pruning the HUGE space of physical plans • Eliminating redundant conditions/operators • Rules that will improve performance with very high probability • Preprocessing • Getting queries into a form that we know how to handle best • Reduces optimization time drastically without noticeably affecting quality

28. Some Query Rewrite Rules • Transform one logical plan into another • Do not use statistics • Equivalences in relational algebra • Push-down predicates • Do projects early • Avoid cross-products if possible

29. Equivalences in Relational Algebra R S = S R Commutativity (R S) T = R (S T) Associativity Also holds for: Cross Products, Union, Intersection R x S = S x R (R x S) x T = R x (S x T) R U S = S U R R U (S U T) = (R U S) U T

30. Apply Rewrite Rule (1) B,D B,D R.C = S.C R.A = “c” Λ R.C = S.C R.A = “c” X X R S R S B,D [sR.C=S.C [R.A=“c”(R X S)]]

31. Rules: Project Let: X = set of attributes Y = set of attributes XY = X U Y pxy (R) = px [py (R)]

32. [sp (R)] S R [sq (S)] Rules:s + combined Let p = predicate with only R attribs q = predicate with only S attribs m = predicate with only R,S attribs sp (R S) = sq (R S) =

33. Apply Rewrite Rule (2) B,D B,D R.C = S.C R.C = S.C R.A = “c” X R.A = “c” S X R S R B,D [sR.C=S.C [R.A=“c”(R)] X S]

34. Apply Rewrite Rule (3) B,D B,D R.C = S.C Natural join R.A = “c” X S R.A = “c” S R R B,D [[R.A=“c”(R)] S]

35. Rules:s + combined (continued) spq (R S) = [sp (R)] [sq (S)] spqm (R S) = sm[(sp R) (sq S)] spvq (R S) = [(sp R) S] U [R(sq S)]

36. Which are “good” transformations? sp1p2 (R) sp1 [sp2 (R)] sp (R S)  [sp (R)] S R S  S R px [sp(R)] px {sp [pxz(R)]}

37. Conventional wisdom: do projects early Example: R(A,B,C,D,E) P: (A=3)  (B=“cat”) pE {sp(R)} vs. pE {sp{pABE(R)}}

38. But: What if we have A, B indexes? B = “cat” A=3 Intersect pointers to get pointers to matching tuples

39. Bottom line: • No transformation is always good • Some are usually good: • Push selections down • Avoid cross-products if possible • Subqueries  Joins

40. Avoid Cross Products (if possible) • Which join trees avoid cross-products? • If you can't avoid cross products, perform them as late as possible Select B,D From R,S,T,U Where R.A = S.B  R.C=T.C  R.D = U.D

41. More Query Rewrite Rules • Transform one logical plan into another • Do not use statistics • Equivalences in relational algebra • Push-down predicates • Do projects early • Avoid cross-products if possible • Use left-deep trees • Subqueries  Joins • Use of constraints, e.g., uniqueness

42. Announcements • First homework will be posted next week • I will send an email announcement when it is posted • We will start discussing past projects and project ideas from next week • Your first goal is to have a concretely defined project by the last week of September (the earlier the better!) • Small class  single student projects • October and November should be used to work on the projects • Final “demo’s” will be in the first week of December