Query Processing and Optimization

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External Sorting (Multiway Merge Sort) • Buffer size is n B = 4 (N) Orginal rel. 32 5 1712 1 14 8 3 3130232629 2 2511 6 7 151628 4 9 221821102013272419 Initial runs First merge Second merge 5 121732 1 3 8 14 23263031 2 112529 6 7 1516 4 9 2228 10182021 13192427 1 3 5 8 1214172326303132 2 4 6 7 9 11151622252829 1013181920202427 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132 • Cost: 2 × b relation + 2 × b relation ×⎡log nB -1 (b relation /n B )⎤ • 2 × 32 + 2 × 32 × log 3 (32/4) = 192 18
Example of Sort-Merge Join • Cost to sort CheckedOut (b CheckedOut = 10) • Cost Sort ChecedOut = 2 × 2.000 + 2 × 2.000 × ⎡log 5 (2.000/6)⎤ = 20.000 • Cost to sort Customer relation (b Customer = 10) • Cost Sort Customer = 2 × 10 + 2 × 10 × ⎡log 5 (10/6)⎤ = 40 • Cost for merge join • Cost to scan sorted Customer + cost to scan sorted CheckedOut • Cost merge join = 10 + 2.000 = 2.010 • Cost sort-merge join = Cost Sort Customer + Cost Sort ChecedOut + Cost merge join • Cost sort-merge join = 20.000 + 40 + 2.010 = 22.050 19
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: External or Disk-based Sorting, Con
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 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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