I've been a fan of this series! The work by this team, as well as kuzudb (acquired by apple) and relational.ai, have similar vibes.
One area that has been especially interesting to me is identifying cases where new kinds of vector-friendly join operators are helpful . We've been doing a very different kind of oss gpu graph query language & engine (gfql), where we're solving how to turn declarative cypher property graph queries on big parquets / sql db results / etc -> query plans over scalable cpu/gpu dataframe operations that trounce neo4j etc at a fraction of the time & cost and without needing a DB, and these join algorithm results carry over quite enticingly despite not being datalog.
arxiv: https://arxiv.org/html/2311.02206v5
I've been a fan of this series! The work by this team, as well as kuzudb (acquired by apple) and relational.ai, have similar vibes.
One area that has been especially interesting to me is identifying cases where new kinds of vector-friendly join operators are helpful . We've been doing a very different kind of oss gpu graph query language & engine (gfql), where we're solving how to turn declarative cypher property graph queries on big parquets / sql db results / etc -> query plans over scalable cpu/gpu dataframe operations that trounce neo4j etc at a fraction of the time & cost and without needing a DB, and these join algorithm results carry over quite enticingly despite not being datalog.
I found this to be a great introduction to Datalog solving generally. Here is my summary: https://danglingpointers.substack.com/p/optimizing-datalog-f...