Earlier quoted context omitted.
PGQ requires you to write using SQL and read using a graph query language. GQL is a standalone language that supports reads/writes. But much of the community is still using cypher. More on this here: https://adsharma.github.io/beating-the-CAP-theorem-for-graph...
As far as I can tell, this has nothing to do with CAP theorem or distributed systems. It's just being used as an analogy. > [CAP theorem] states that any distributed storage system can provide only two of these three guarantees: Consistency, Availability and Partition safety. > In the realm of graph databases, we observe a similar “two out three” situation. You can either have scalable systems that are not fully open…
The real question being raised in the blog post is - should the next generation graph databases pursue a local-only embedded strategy or build on top of object storage like many non-graph and vector embedded databases are doing.
Specifically, DuckLake (using system catalog for metadata instead of JSON/YAML) is interesting. I became aware of Apache GraphAr (incubating) after writing the blog post. But it seems to be designed for data interchange between graph databases instead of designing for primary storage.