Query Processing and Optimization

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• Statistics Operation 2: n followed by a π • Relation statistics: r CheckedOut = 40,000 b CheckedOut = 2,000 • Attribute statistics: s FilmID = 8 • Index statistics: Secondary B + -Tree Index for CheckedOut on FilmID with 2 levels. • Result relation size: 8 tuples. • Operation: Index join using B + -Tree, then project on CustomerID. Leave result in main memory (one block). • Cost: C 2 = 1 +1 + 8 = 10 42
• Statistics Operation 3: σ followed by a π • Relation statistics: r Customer = 200 b Customer = 10 • Attribute statistics:s Street = 10 • Result relation size: 10 tuples. • Operation: Linear search of Customer. Leave result in main memory (one block). • Cost: C 3 = 10 43
Page 1 and 2: Query Processing + Optimization: Ou
Page 3 and 4: Architectural Context DDL Statement
Page 5 and 6: Overview of Query Processing 1. Par
Page 7 and 8: • Linear search Selection Strateg
Page 9 and 10: Joins • Join Strategies • Neste
Page 11 and 12: Nested Loop • Basically, for each
Page 13 and 14: Index-based Join • Requires (at l
Page 15 and 16: Sort-Merge Join • Sort each relat
Page 17 and 18: External or Disk-based Sorting, Con
Page 19 and 20: Example of Sort-Merge Join • Cost
Page 21 and 22: Partitioning Phase • Partitioning
Page 23 and 24: Hash Join Cost • Cost Total = Cos
Page 25 and 26: Cost and Applicability of Join Stra
Page 27 and 28: Query Optimization • Aim: Transfo
Page 29 and 30: Apply Selections Early π Name σ F
Page 31 and 32: Form Joins π Name n CU.CustomerID
Page 33 and 34: Some Transformation Rules • Casca
Page 35 and 36: Heuristic Query Optimization Summar
Page 37 and 38: • Per relation • Tuple size Rel
Page 39 and 40: • Per Index • Base relation Rel
Page 41: • Statistics Operation 1: σ foll
Page 45 and 46: Comparison • Heuristic query opti
Page 47 and 48: Query Tuning • Query optimization
Page 49 and 50: Join on Clustering Index, and Integ
Page 51 and 52: Unnesting Nested Queries • Uncorr
Page 53: Summary • Query processing & opti

Query Processing and Optimization

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