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0 pointsthreeseed1y ago0 comments

Neo4J is mature not dated which is why it's so popular.

And couldn't disagree more that graph is a feature. You really want something optimised for it (query language / storage approach) as the data structure is so different in every way from a relational or document store.

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4 comments · 2 top-level

tucnak1y ago· 2 in thread

How is it different? Isn't a graph basically two sets of tuples: edges and nodes? I played with Cayley (Google) for a little while, & that was my impression.

bubblyworld1y ago

I think it's less a matter of "can you represent graphs in a relational DB" (of course you can), and more about what kind of queries the DB is optimised for. Graph databases are intended for complex recursive queries on relatively unstructured data. You could certainly do that in SQL if you wanted to, but you'll pay for it performance-wise.

Graph query languages also make those kinds of queries much easier to express in the first place.

tucnak1y ago

So the underlying storage is conventional, it's still tuples of some kind, and it's only a matter of how indexes are laid out? Otherwise, I'm struggling to see how it could "optimise" for certain access patterns. How would a typical graph database index be different from a btree access method in Postgres?

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thesz1y ago

> ...as the data structure is so different in every way from a relational or document store.

No, it is not.

[1] https://en.wikipedia.org/wiki/Worst-case_optimal_join_algori...

Graph processing can create substantial amount of intermediate data if it is done in typical join implementation fashion (nested loops or hash join). So it may appear that graph processing needs a tailored approach.

But what can help graph algorithms can help SQL query execution as well and vice versa, see the link above.

For example, TPC-DS contains queries that (indirectly) joins same tables multiple times (query 4, for example). This is, basically, a kind of centrality metric computation for a graph represented by the tables.

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