I only mentioned it because I clicked it wondering if someone had found a way to "cheat" CAP for graph databases. When I saw that it was being used as an analogy and not literally, I figured I'd comment.
I still don't quite get the analogy. What are the 2 out of 3 that you can have? The second paragraph I quoted gives a classic 1 out of 2 dilemma - either scalable _or_ open-source.
DuckDB is scalable (can handle TPC-H 1TB) and open source, but doesn't support graphs natively. It supports some graph queries on a SQL native columnar storage.
With the proposed solution, you'll be able to query larger graphs on an open source graph native engine. Thus beating the "CAP theorem for graphs".
Comments
I only mentioned it because I clicked it wondering if someone had found a way to "cheat" CAP for graph databases. When I saw that it was being used as an analogy and not literally, I figured I'd comment.
I still don't quite get the analogy. What are the 2 out of 3 that you can have? The second paragraph I quoted gives a classic 1 out of 2 dilemma - either scalable _or_ open-source.
DuckDB is scalable (can handle TPC-H 1TB) and open source, but doesn't support graphs natively. It supports some graph queries on a SQL native columnar storage.
With the proposed solution, you'll be able to query larger graphs on an open source graph native engine. Thus beating the "CAP theorem for graphs".