[zozbot234]

You can actually do "inference" or "deduction" over large amounts of data using any old-fashioned RDBMS, and get broadly equal or better performance than the newfangled "graph" based systems. Graph databases may be a clear win for very specialized network analysis, but that is rare in practice.

[p_l]

Graph databases win in flexibility and ease of writing the queries over graphs, honestly.

Of course the underlying storage can be (and often is) a bunch of specially prepared relational tables.

But the strength in graph databases comes from restating the problem in different way, with query languages targeting the specific problem space.

Similarly there are tasks where SQL will be plainly better.

[zozbot234]

> ease of writing the queries over graphs, honestly

The SQL standard now includes syntactic sugar for 'Property Graph Query'. Implementations are still in the works AIUI, but can be expected in the reasonably near future.

[p_l]

Having seen PGQL, I think I'll stay with SPARQL.

And for efficient implementation the database underneath still needs to have extended graph support (in fact, I find it hilarious that Oracle seems to be spearheading it, as they have previously canceled their graph support around 2012 - enough that I wrote about how it was deprecated and removed from support in my thesis in 2014.

[totetsu]

Every time I try to write a query for GitHub’s graphql API I lose a few hours and go back to rest. May be it’s easy if all the edges and inputs are actually implemented in ways you would expect.

[p_l]

GraphQL isn't exactly a proper graph database query language. The name IIRC comes from Facebook Graph API, and the language isn't actually designed as graph database interface.

[totetsu]

Thanks for the correction