From Wikipedia, the free encyclopedia
An illustration of properties of join algorithms. When performing a join between more than two relations on more than two attributes, binary join algorithms such as hash join operate over two relations at a time, and join them on all attributes in the join condition; worst-case optimal algorithms such as generic join operate on a single attribute at a time but join all the relations on this attribute. [1]

A worst-case optimal join algorithm is an algorithm for computing relational joins with a runtime that is bounded by the worst-case output size of the join. Traditional binary join algorithms such as hash join operate over two relations at a time; joins between more than two relations are implemented by repeatedly applying binary joins. Worst-case optimal join algorithms are asymptotically faster in worst case than any join algorithm based on such iterated binary joins.

The first worst-case optimal join algorithm, generic join, was published in 2012. [2] Worst-case optimal join algorithms have been implemented in commercial database systems, including the LogicBlox system. [3] [4] Worst-case optimal joins have been applied to build a worst-case optimal algorithm for e-matching. [5]

References

Notes

  1. ^ Wang, Yisu Remy; Willsey, Max; Suciu, Dan (2023-01-27). "Free Join: Unifying Worst-Case Optimal and Traditional Joins". arXiv: 2301.10841 [ cs.DB].
  2. ^ Ngo, Hung Q.; Porat, Ely; Ré, Christopher; Rudra, Atri (2012-03-08). "Worst-case Optimal Join Algorithms". arXiv: 1203.1952 [ cs.DB].
  3. ^ Veldhuizen, Todd L. (2013-12-20). "Leapfrog Triejoin: a worst-case optimal join algorithm". arXiv: 1210.0481 [ cs.DB].
  4. ^ Freitag, Michael; Bandle, Maximilian; Schmidt, Tobias; Kemper, Alfons; Neumann, Thomas (2020-07-01). "Adopting worst-case optimal joins in relational database systems". Proceedings of the VLDB Endowment. 13 (12): 1891–1904. doi: 10.14778/3407790.3407797. ISSN  2150-8097. S2CID  221115321.
  5. ^ Zhang, Yihong; Wang, Yisu Remy; Willsey, Max; Tatlock, Zachary (2022-01-12). "Relational e-matching". Proceedings of the ACM on Programming Languages. 6 (POPL): 35:1–35:22. doi: 10.1145/3498696. S2CID  236924583.

Sources

External links

From Wikipedia, the free encyclopedia
An illustration of properties of join algorithms. When performing a join between more than two relations on more than two attributes, binary join algorithms such as hash join operate over two relations at a time, and join them on all attributes in the join condition; worst-case optimal algorithms such as generic join operate on a single attribute at a time but join all the relations on this attribute. [1]

A worst-case optimal join algorithm is an algorithm for computing relational joins with a runtime that is bounded by the worst-case output size of the join. Traditional binary join algorithms such as hash join operate over two relations at a time; joins between more than two relations are implemented by repeatedly applying binary joins. Worst-case optimal join algorithms are asymptotically faster in worst case than any join algorithm based on such iterated binary joins.

The first worst-case optimal join algorithm, generic join, was published in 2012. [2] Worst-case optimal join algorithms have been implemented in commercial database systems, including the LogicBlox system. [3] [4] Worst-case optimal joins have been applied to build a worst-case optimal algorithm for e-matching. [5]

References

Notes

  1. ^ Wang, Yisu Remy; Willsey, Max; Suciu, Dan (2023-01-27). "Free Join: Unifying Worst-Case Optimal and Traditional Joins". arXiv: 2301.10841 [ cs.DB].
  2. ^ Ngo, Hung Q.; Porat, Ely; Ré, Christopher; Rudra, Atri (2012-03-08). "Worst-case Optimal Join Algorithms". arXiv: 1203.1952 [ cs.DB].
  3. ^ Veldhuizen, Todd L. (2013-12-20). "Leapfrog Triejoin: a worst-case optimal join algorithm". arXiv: 1210.0481 [ cs.DB].
  4. ^ Freitag, Michael; Bandle, Maximilian; Schmidt, Tobias; Kemper, Alfons; Neumann, Thomas (2020-07-01). "Adopting worst-case optimal joins in relational database systems". Proceedings of the VLDB Endowment. 13 (12): 1891–1904. doi: 10.14778/3407790.3407797. ISSN  2150-8097. S2CID  221115321.
  5. ^ Zhang, Yihong; Wang, Yisu Remy; Willsey, Max; Tatlock, Zachary (2022-01-12). "Relational e-matching". Proceedings of the ACM on Programming Languages. 6 (POPL): 35:1–35:22. doi: 10.1145/3498696. S2CID  236924583.

Sources

External links


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