Understanding Firebird optimizer, by Dmitry Yemanov (in English)

1. Understanding Firebird optimizer Dmitry Yemanov [email_address] Firebird Project

4. Cost-based (statistics)

5. Mixed

7. Separate predicates: «base», «parent», «missing»

8. Distribute equalities

10. Choose the best index coverage for the given join order

11. Ensure early predicates evaluation

12. Decide about navigation or sorting

15. Not ready for «ad hoc» queries

18. Slower optimization process

19. Requires up-to-date statistics

23. Is a function of the estimated cardinalities

24. Linearly depends on the number of logical reads (page fetches)

26. N represents a number of the leaf pages to be scanned and thus depends on the average key length

27. Cost Aggregation SELECT * FROM T1 JOIN T2 ON T1.PK = T2.FK WHERE T1.VAL < 100 ORDER BY T1.RANK PLAN SORT ( JOIN ( T1 NATURAL, T2 INDEX (FK) ) ) Table T1: base cardinality = 1000 Table T2: base cardinality = 5000 Index FK: selectivity = 0.001 Final Row Set cost = 5000 cardinality = 2500 Sort cost = 5000 cardinality = 2500 Full Scan cost = 1000 cardinality = 1000 Filter cost = 1000 cardinality = 500 Index Scan cost = 7 cardinality = 5 Loop Join cost = 4000 cardinality = 2500

30. On index creation / activation

31. On database restore

33. For large tables: number of data pages / average record length

35. Maintained per segment: (A), (A, B), (A, B, C)

36. Uniform value distribution is assumed

37. Stored on the index root page, visible through RDB$INDICES and RDB$INDEX_SEGMENTS

40. Average key length

42. Value distribution histograms

43. Clustering Factor Bad Clustering Factor Good Clustering Factor Index Key 1 Index Key 2 Index Key 3 Index Key 5 Index Key 4 Data Page 12 Data Page 25 Data Page 28 Data Page 57 Data Page 44 Data Page 12 Data Page 13 Data Page 14

46. Estimate how many indices would work best

47. Consider segment operations for compound indices

48. Special handling of different comparisons

49. Calculate selectivities for AND / OR operations

50. Thank you!