Query Result Caching

1
Query Result Caching

• Prasan Roy, Krithi Ramamritham, S. Seshadri,
• S. Sudarshan
• Pradeep Shenoy, Jinesh Vora

2
Model

• Predictive Caching - use history
• Query results/ intermediate
• Single user stream - very similar queries
• Global sequence of queries - long term patterns
• Leverages off MQO (P. Roy, et.al.)

3
Issues

• Matching and reuse of cached items
• Choice of items to cache

4
Matching

• Integrated into optimization
• Hash-based storage of DAGs and plans
• New plans unified with old identical plans
• Cache items chosen in cost based manner

5
Review of MQO

• Basic idea
• Sharable nodes considered for caching
• Benefit of all subsets computed, choose best set
• Greedy heuristic take highest benefit node at
  each step
• Several optimizations included

6
Adaptation

• Characterizing the query workload
• Weighted set of queries - frequency based
• Candidates for caching is varied

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Local Commonality

• Use small window
• Candidate set current cache contents new
  execution plan
• Make greedy choice on this set
• Re-check if old nodes are relevant (cleanup)
• Any nodes in current plan worth caching?
  (scavenge)
• Metric benefit to representative set.

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Disadvantage

• DAG is small - no long term patterns
• Candidate set is small - only local minimum
• similar to "quick-and-dirty" method Volcano-RU

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Global Commonality

• Dynamic "view selection"
• Large DAG, full-scale MQO
• Candidate set includes all sharable nodes
• Extended-predictive no immediate caching
• compute and materialize during slack time
• cache on first use

10
Status

• In Progress!