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Query Optimizer Improvements for Apache Derby


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Possible Improvements for Derby Query Optimizer disscussed

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Query Optimizer Improvements for Apache Derby

  1. 1. Apache DerbyQuery Optimizer Improvements CS 4420 Group 13 Presented by: Nufail, Nadeeshani, Amila, Malith
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 11. Questions?
  12. 12. Thank you!