Query Processing and Optimization

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Recursive Partitioning • Required if number of partitions n h is greater than number of available buffer blocks n B -1. • instead of partitioning n h ways, use n B – 1 partitions for s • Further partition the n B – 1 partitions using a different hash function • Use same partitioning method on r • Rarely required: e.g., recursive partitioning not needed for relations of 1GB or less with memory size of 2MB, with block size of 4KB. • Cost hjrp = 2 × (b r + b s ) ×⎡log nB -1 (b s ) -1⎤ + b r + b s • Cost hjrp = 2 × (2000 + 10) ×⎡log 5 (10) -1⎤ + 2000 + 10 = 6030 24
Cost <strong>and</strong> Applicability of Join Strategies • Nested-loop join • Brute-force • Can h<strong>and</strong>le all types of joins (=, ) • Index-based join • Requires minimum one index on join attributes • Sort-merge join • Requires that the files are sorted on the join attributes. • Sorting can be done for the purpose of the join. • A variation is also applicable when secondary indices are available instead. • Hash join • Requires good hashing functions to be available. • Performance best if smallest relation fits in memory. ? 25
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: Hash Join Cost • Cost Total = Cos
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 and 42: • Statistics Operation 1: σ foll
Page 43 and 44: • Statistics Operation 3: 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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