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Optimizing Recursive Queries in SQL

Optimizing Recursive Queries in SQL. Carlos Ordonez ACM SIGMOD 2005. Introduction. Recursion available in ANSI SQL Deductive databases; graph problems Challenging query optimization Syntax now available in Teradata SQL V2R6. Directed Graphs. Consider G=(V,E)

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Optimizing Recursive Queries in SQL

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  1. Optimizing Recursive Queries in SQL Carlos Ordonez ACM SIGMOD 2005

  2. Introduction • Recursion available in ANSI SQL • Deductive databases; graph problems • Challenging query optimization • Syntax now available in Teradata SQL V2R6

  3. Directed Graphs • Consider G=(V,E) • A vertex in V is identified with i or j and i,j=1..n. An edge i,j has a direction • G may have cycles • G: adjacency list in table T • Transitive closure problem: give me all vertices reachable from i

  4. Examples • V=cities,E=roads. Is there some path from San Diego to NYC?: path from i to j? shortest one? • V=employees, E=manager -> employee relationship q1: all employees under i q2: Is j supervised by i? • The well-known part/subpart manufacturing DB

  5. Recursive view Rk: rec. depth n: graph size

  6. Technical details • Linear recursion • Inner joins • SELECTs must have same term types • No GROUP-BY, DISTINCT, HAVING, NOT IN, OUTER JOIN clauses inside R • Any SQL construct is valid on top of R • Recursive view can used as any table • Recursion depth k: loop with k-1 joins

  7. Row selection: WHERE clause

  8. WHERE clause in base step

  9. Issues with WHERE clauses • Incorrect to use a WHERE clause involving a join expression column in the recursive step • Infinite recursion: cycles • Monotonically increasing v OK • Recursion depth

  10. Using DISTINCT: many paths

  11. Eliminating duplicate rows

  12. Indexing • T is self-joined many times • R is appended many times • Subset of R at depth d-1 used at each recursive step d

  13. Indexing schemes • Index on join expression: • T is indexed on (i) • R is indexed on (j) • Index on primary keys: • T is indexed on (i,j) • R is indexed on (d,i,j)

  14. Impact of early row selectionn: |V|, k=max recursion depth, times in secs

  15. Impact of duplicate eliminationn: |V|, k=max recursion depth, times in secs

  16. Indexingn: |V|, k=max recursion depth, times in secs

  17. Recommendations • Writing base step first • A wrong join expression may produce larger cartesian products than usual • Setting a maximum recursion depth k • Careful indexing

  18. Conclusions • Constraints inside recursive view • Three optimizations: WHERE, DISTINCT, indexing • Max recursion depth • Graphs with cycles impact performance

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