| my feeling is that graph databases face an uphill battle for mass adoption not because their architects or vendors doing anything wrong but some intrinsic aspects of information exchange in most current situations and use cases * information tends to be private and/or commercially sensitive, this severs the links that graph dbs are good at representing (and made the "node focused" SQL approach the ubiquitous model that it currently is) * objects in typical schemas have many more attributes that relations. while you could model things RDF style where everything is a relation, it is not the most intuitive for people * when the previous constraint does not apply (e.g. data from a centralized social network), it is typically not too hard to emulate an adequate graph structure on a Pareto 20/80 basis using an RDBMS so graph dbs end up being optimal only for a niche of situations and probably not the impact that the people / investors involved in their development would be happy with on the other hand the ginie is out of the bottle and the decades-long SQL monoculture seems to be coming to an end. but maybe what results is a relational database+ type thingy [0] rather than two disconnected paradigms [0] https://postgresconf.org/conferences/2020/program/proposals/... |
I saw very niche use cases 10 years ago but have seen more and more common use cases recently. Knowledge Graphs in particular have a lot of use. I’d expect to continue to see explosive growth in graph over the next 10 years.
As ubiquitous as SQL or document databases? Probably not. But very likely more and more use cases will be uncovered.