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CompSci 296.2 Self-Managing Systems. Shivnath Babu. Today. IBM’s LE arning O ptimizer (LEO) Oracle’s Automatic Database Diagnostic Monitor. Background. Declarative queries Query plans Query optimizer Indexes, index-AND’ing, materialization

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Presentation Transcript
today
Today
  • IBM’s LEarning Optimizer (LEO)
  • Oracle’s Automatic Database Diagnostic Monitor
background
Background
  • Declarative queries
  • Query plans
  • Query optimizer
    • Indexes, index-AND’ing, materialization
    • Statistics, e.g., Histograms, quantiles (percentile)
  • Assumptions made by query optimizer
    • Uniformity, inclusion, independence
slide4
LEO
  • Feedback-based learning
    • Inter-query
slide5
LEO
  • Feedback-based learning
    • Intra-query
challenges
Challenges
  • Stability
  • Detecting a problem
  • Deciding to respond to a problem
  • Dealing with bias, incompleteness, uncertainty
oracle addm
Oracle ADDM
  • Root-cause identification
  • Recommend fix
  • Characterize improvement
oracle addm1
Oracle ADDM
  • DB-Time
    • Need common unit; DB-Time Vs. Wall-Clock time
  • DB-Time graph
  • Data collection
    • Time
    • Active Session History
  • Observe, Diagnose, Resolve
  • Experimental evaluation
discussion
Discussion
  • Relationship between the two papers?
  • Differences between the papers?
  • Can ADDM answer the What-if question on Page 3?
    • Dealing with concurrency
  • Accounting for DB-Time
  • Extending DB-Time
  • Experimental evaluation
  • Can we extend ADDM approach to 3-tier system?
  • Stability