Database Systems - Reducing Error-Prone Selectivity Space for the Plan Bouquet algorithm
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ESSReduction.py
ESSReduction.pyc
README.md
parallelESSReduction.py
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README.md

IKEBANA - ESS Dimensions Reduction For Plan Bouquet

Aim

Database Systems - IISc Course Project

  • We aim to enhance the performance of the Plan Bouquet algorithm for query processing, proposed in the paper presented at VLDB 2014. The paper defines plan bouquet approach as:

In this paper, we investigate a conceptually new approach, wherein the compile-time estimation process is completely eschewed for error-prone selectivities. Instead, these selectivities are systematically discovered at run-time through a calibrated sequence of cost- limited executions of a set of bouquet plans.

Algorithm IKEBANA

  • Firstly, the above code attempts to find the ESS dimension, which on elimination would result in least performance impact on query execution by bouquet approach
  • Secondly, it identifies the "best" bouquet of plans along with the budgets to be executed in the reduced ESS space
  • Further Details of this project are here in my blog: http://adarshpatil.in/timewarp/projects/error-prone-predicates.html

License

MIT

Free Software, Hell Yeah!