There is no single machine constraint, just the observation that we routinely run non-graph databases at similar scale on single machines without issue. It doesn't scale on in-memory supercomputers either, so the hardware details are unrelated to the problem:

- Graph database with good query performance typically has terrible write performance. It doesn't matter how fast queries are if it takes too long to get data into the system. At this scale there can be no secondary indexing structures into the graph; you need a graph cutting algorithm efficient for both scalable writes and join recursion. This was solved.

- Graph workloads break cache replacement algorithms for well-understood theory reasons. Avoiding disk just removes one layer of broken caching among many but doesn't address the abstract purpose for which a cache exists. This is why in-memory systems still scale poorly. We've known how to solve this in theory since at least the 1980s. The caveat is it is surprisingly difficult to fully reduce to practice in software, especially at scale, so no one really has. This is a work in progress.

- Most implementations use global synchronization barriers when parallelizing algorithms such as BFS, which greatly increases resource consumption while throttling hardware scalability and performance. My contribution to research was actually in this area: I discovered a way to efficiently use error correction algorithms to elide the barriers. I think there is room to make this even better but I don't think anyone has worked on it since.

The pathological cache replacement behavior is the real killer here. It is what is left even if you don't care about write performance or parallelization.

I haven't worked in this area for many years but I do keep tabs on new graph databases to see if someone is exploiting that prior R&D, even if developed independently.

There are countless smaller graphs for narrow domains that may be <1B edges but many people have the ambition to stitch together these narrow graphs into a larger graph. When stitching graphs together, the number of edges is usually super-linear. A billion edges is kind of considered “Hello World” for system testing.

The Semantic Web companies in the 2000s had graphs that were 100B+ edges. They wanted to go much larger but hit hard scaling walls around that point. That scaling wall killed them.

Classic mapping data models are typically 10-100B edges. These could be much, much larger if they could process all the data available to them.

Of course, intelligence agencies had all kinds of graphs far beyond trillions of edges 20 years ago. People, places, things, events.

Any type of spatiotemporal entity graphs with large geographic scope are quadrillions of edges. It isn’t just a lot of inferred relationships between entities, the relationships evolve over time which also must be captured. These are probably the most commercially valuable type of graph. You could build hundreds of different graphs of this type with 1T+ edges in most regions, never mind doing it at scale. These are so large that we usually don’t store them. Subgraphs are generated on demand, which is computationally expensive.

These spatiotemporal entity graphs also have the largest write loads. Single sources generate tens of PB/day of new edges. There is a ton of industrial data that looks like this; it isn’t just people slinging structured data.

Graphs are everywhere but we furiously avoid them because the scalability of operations over anything but severely constrained graphs is so poor. Selection bias.

NSA in particular heavily funded foundational theoretical and applied computer science research into scaling graph computing for decades. They had all kinds of boring graphs where trillions of edges was their Tuesday. The US military also uses large graph databases in fairly boring applications that probably didn’t require a graph database.

every example you mentioned is not something you should delegate to LLMs, unless quick prototyping