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Query Optimizer Improvements for Apache Derby
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Query Optimizer Improvements for Apache Derby

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Possible Improvements for Derby Query Optimizer disscussed

Possible Improvements for Derby Query Optimizer disscussed

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  • 1. Apache DerbyQuery Optimizer Improvements CS 4420 Group 13 Presented by: Nufail, Nadeeshani, Amila, Malith
  • 2. Query optimization• Evaluates the least cost execution plan to be sent to the evaluation engine• A key factor in deciding DBMS performance• Cost-based vs. Heuristic
  • 3. Derby Optimizer• Considers left-deep trees• Represent the tables in an array• Goes through the search space in depth-first manner• Exhaustive search of query plans• Cost based search space pruning
  • 4. Performance Statistics H2 PostgreSQL Derby Query embedded (ms) (ms) (ms) 1 28 43 1416 2 438 26 151733 3 420 35 147261 4 84256 31 125356 5 312 52 2026 6 63456 68 142458Complex join queries for 8 relations, each with 400 records
  • 5. Concurrency• Derby optimizers poor design is its main drawback, which executes serially• Uses two while loops to iterate through each join order, and access path per each order o getNextPermutation() o getNextDecoratedPermutation() o costPermutation()• As easy approach: Use loop-parellel programming pattern.• Make each iteration independent and execute each iteration in new threads.
  • 6. Bushy Trees• Left deep trees & Bushy trees• More flexibility in query plan generation• Has a large search space• Best plan may be bushy
  • 7. Bushy Trees contd.More than half of the queries have bettersolutions in the bushy tree solution space M. Steinbrunn, et. al. 1997. Heuristic and randomized optimization for the join ordering problem
  • 8. Randomized Algorithms• Deterministic - Start from base relations and build plans by adding one relation at each step• Randomized - Search for optimal solutions around a particular starting point• Trade optimization time for execution time• No guarantee of the best solution• Useful for joins with a high number of relations
  • 9. Heuristics For TimeoutnImprovements• Consequence of a miserable timeout value• Time wasted for generation of numerous plans + estimating their costs• Applicability of optimal solution over sub- optimal• Heuristic based values for timeout• Improvement over time
  • 10. Genetic Algorithms• Genetic?• Used by PostgreSQL• Evolution o Removal of least fit individuals o Recombination of individuals of high fitness• Initial population: query plans with possible join orders• Fitness function: to minimize cost• Lower cost join sequence has higher fitness
  • 11. Questions?
  • 12. Thank you!

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