How I Learned to Stop Worrying and Love Re-optimization

@article{Perron2019HowIL,
  title={How I Learned to Stop Worrying and Love Re-optimization},
  author={Matthew Perron and Zeyuan Shang and Tim Kraska and Michael Stonebraker},
  journal={2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  year={2019},
  pages={1758-1761}
}
Cost-based query optimizers remain one of the most important components of database management systems for analytic workloads. Though modern optimizers select plans close to optimal performance in the common case, a small number of queries are an order of magnitude slower than they could be. In this paper we investigate why this is still the case, despite decades of improvements to cost models, plan enumeration, and cardinality estimation. We demonstrate why we believe that a re-optimization… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • We demonstrate that re-optimization improves the end-to-end latency of the top 20 longest running queries in the Join Order Benchmark by 27%, realizing most of the benefit of perfect cardinality estimation.

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 38 REFERENCES