Extensions to Multi Query Optimization

Extensions to Multi Query Optimization Amit Gupta IIT Bombay

Recap of MQO • AND-OR DAG of the set of Queries • Transformation • Greedy Algorithm • Choose highest benefit shared node to be cached

MQO for Fixed Cache Size • Greedy heuristic • Choose shared node with highest benefit/Size to be cached • Disadvantage of Greedy • less search space

Problem Definition • Given set of shared nodes S = ( s, s,..) and cache size C. • Choose subset P from S, such that •  size(p) <= C , where p  P • benefit of caching P is maximized.

Subset sum Problem • Given set S = ( s, s,..) and C, • choose the subset P from S such that •  p <= C , where p  P and •  p is maximized.

Subset sum Algorithm • Given set S = ( s, s,..) • Exponential Algorithm • Search Space: Power set of S. • Approximation Algo • Given  as error constant • Search Space: Trimmed Power Set of S. • Approximation Ratio = 

MQO for fixed Size Cache • Given • S = { set of shared nodes} • C = Cache Size • Error constant  • Search Space of trimmed Power set of S. • Trimming procedure

MQO cont. • Advantage of Subset sum Algorithm • More Search space •  can be changed

Scheduling in MQO nodes to be cached Plan DAG

Extensions to Multi Query Optimization

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