Lesson plan
Worst-case optimal join algorithm
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. 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. Worst-case optimal join algorithms have been implemented in commercial database systems, including the LogicBlox system. Worst-case optimal joins have been applied to build a worst-case optimal algorithm for e-matching.
Article summary
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. 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. Worst-case optimal join algorithms have been implemented in commercial database systems, including the LogicBlox system. Worst-case optimal joins have been applied to build a worst-case optimal algorithm for e-matching.
Key claims
- publication date: 2012 — source: Wikipedia · confidence 0.95
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- The record states that the publication date is "2012". What evidence would count against this claim?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
- Wikipedia https://en.wikipedia.org/w/index.php?oldid=1314136590
Key claims and bibliography are generated from the same tracked claims and sources as the article page.