Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
Join GitHub today
GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Sign upSimple hash-join optimization #362
Open
+57
−43
Conversation
|
Thanks! Strange, I was sure I am already doing this :) Seems like not. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
In hash-join of two relations, a simple optimization is to create the hash table on the smaller side of the relations. This simple change can noticeably improve query performance in some cases.
For instance, if we change
q2of the benchmark from[:find ?e ?a :where [?e :name "Ivan"] [?e :age ?a]]to[:find ?e ?a :where [?e :sex ?a] [?e :name "Ivan"]]. On my machine, before this optimization, the query time is 9.5ms, after the optimization, it is 6.5ms.Credit: This optimization is one of the techniques introduced in this paper:
Fan et al. 2019, Scaling-up in-memory datalog processing: observations and techniques, Proceedings of the VLDB Endowment, vol. 12. Full text: http://www.vldb.org/pvldb/vol12/p695-fan.pdf