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Query Result Caching

Query Result Caching. Prasan Roy, Krithi Ramamritham, S. Seshadri, S. Sudarshan Pradeep Shenoy, Jinesh Vora. Model. Predictive Caching - use history Query results/ intermediate Single user stream - very similar queries Global sequence of queries - long term patterns

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Query Result Caching

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  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

  7. 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.

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

  9. 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!

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