[brobdingnagians]

I've always been curious about graph DBs and dabbled a bit in them, but for those who have more extensive experience in them-- are they really worth it? Is it that for small scale SQL is better and graph DBs really only matter at scale, or for specific use cases with highly connected data?

[shaism]

I worked in a startup whose value proposition was largely derived from using graph data and graph databases under the hood. The main benefit is, as the repo even states "Fast data processing: Links traversal is processed with O(1) complexity."

So, technically, you can do deep traversals quicker. A few notes:

1. Few use cases truly need low-latency deep traversal on realtime data (>5 hops). There are some well known ones like fraud detection in payment processing and, possibly, social media recommendation engines. But I am not even sure how latest social media engines work, and whether they still rely on graph DBs.

2. However, in practice the advantage is often marginal. With modern analytical databases, or even an optimized PostgreSQL (ltree, materialized views, pgrouting, pg_duckdb, etc), you often get more than good enough performance. In addition those traditional SQL DBs scale with hardware more easily than graph databases. So, you can always use the lever: "Throw more hardware at it."

3. Even Graph DBs don't get good traversal performance under all conditions without hand tuning. For example, there is the "super node" issue, a node with an abnormally high number of connections (edges). And if you still need hand-tuning, you might as well choose something more versatile.

4. The ecosystem of a PostgreSQL and other popular DBs is just unbeaten. With graph DBs, you often prematurely put yourself in a corner that you don't want to be in.

Hence, my recommendation. Unless you are really sure that a graph DB is the right fit for your use case, start with something else, and go the graph db way when you have established a true need.

[hobofan]

Most graph databases only offer an advantage in query language, as it allows for more ergonomic graph traversal compared to e.g. recursive CTEs in SQL. However with SQL/PGQ, the same query ergonomics are coming to traditional databases.

If you look under the hood there is usually nothing special about graph databases that will make them more performant. If you lay out a query plan side-by side between e.g. Postgres and Neo4J, they will look identical, just that the leaf-nodes in Postgres will be a table-scan, while in Neo4J they will be either a vertex-scan or a edge-scan (which can both be seen as special cases of a table-scan).

As someone that has worked a lot with graph databases in the past, I'd largely recommend not using them. The price you pay in terms of worse ecosystem and less battle-tested maintenance tooling is not worth it to just have better syntax.

[adsharma]

Recent benchmarking: https://theconsensus.dev/p/2026/05/29/ladybug-duckdb-and-pos...

Response: https://blog.ladybugdb.com/post/better-graph-database-ball/

[lenkite]

Yes, now that Postgres offers property graphs: https://www.postgresql.org/docs/19/ddl-property-graphs.html, which allows ergonomic queries using graph pattern matching syntax, instead of joins, these dbs feel like dodos.

[andriy_koval]

> However with SQL/PGQ, the same query ergonomics are coming to traditional databases.

I like how PG19 with PGQ will kill lots of companies and startups.

[StrauXX]

Apache AGE seems like a good way to have the battle testedness of Postgres with a more fluent query language. I have only used AGE in smaller projects so far however.

[27183]

AGE actually looks very interesting. When I evaluated it a while back it seemed like the project wasn't very active and I couldn't find any information about folks using it at scale in production. If I was forced to use a graph database again, and I didn't immediately quit upon receiving that news, I might give AGE a look.

[IceDane]

Unfortunately AGE is very immature and optimized even simple traversal queries rather poorly.

[hobofan]

Postgres 19, which is about to be released is adding support for SQL/PGQ.

[IceDane]

But only just. It doesn't really replace graph databases. It's mostly a slightly more convenient ways to write certain kinds of queries, but it doesn't come with anything else you might expect from a graph database.

[27183]

I have had very bad experiences with graph dbs at scale. To the point where I will never again work on a project based on a graph db. YMMV, but I'm done with them forever.

[edit] To elaborate: tuning read and write performance is difficult, essentially everything must be indexed. This results in an explosion in data size--so data that might be X TB in csv format becomes maybe 10X when you consider all the indexes and bloat from the graph db's storage format. Which brings us to the topic of compression: there isn't any. Moreover, graph dbs are basically all quite immature compared to other db software, they haven't seen the test of production to the same degree, nor have they gotten the same attention in terms of bugfixes, performance tuning, etc. So they often have very sharp edges that you encounter under load. So if you like spending tons of money and time on zero value work, choose a graph db!

[another edit] Another problem with graph dbs is they encourage laziness in data modelling. I would echo others' recommendations here: just start with SQL. If it gets to the point where you actually need a graph query language--and you've already appropriately considered your life choices and determined that there's some value to what you're doing--only then should you consider trying to implement something on a graph query engine. But I'm not aware of one that is actually good. That's the problem. If you're at the point where you absolutely need a graph database you are probably at the point where you need to build your own. And you'll probably fail like all the other ones.

[codelikeawolf]

I'm a bit of a Forensic Files nerd, so I worked on a little side project a while back to pull the transcripts from several episodes, use entity recognition to categorize people, places, things, etc, and load them into a Neo4j database (via Cypher queries). It turns out there's something called the POLE data model[1] that can be used by law enforcement to help solve crimes. You load all the details into a graph database and evaluate the relationships to aid in solving crimes. I suppose you could argue a criminal investigation is essentially graph traversal.

[1] https://neo4j.com/blog/government/graph-technology-pole-posi...

Edit: typo

[ChicagoDave]

I played around with Neo4j and only ever found two domains where it excelled.

- custom app security

- social media

I also think cypher is a brilliant way to query a graph.

[pphysch]

Graph query languages like Cypher are great, but I am wholly unconvinced by the concept of a dedicated, general-purpose "graph database".

IMO, you're better off just using Postgres/etc, modeling your graph data there, and pulling in subsets of your graph for in-memory analysis. This is for the 99% of enterprises that aren't doing online streaming graph analysis on TB-scale graphs, and the other 1% should probably figure out something tailored* to their specific business model.

* Graph algorithms are more accessible than ever with GenAI code, and efficiently modeling a graph in memory is trivial (it's just structs with pointers to other edges/nodes, plus its nice to have full control over the memory layout).

[adsharma]

Just use postgres (or another SQL db) ignores the following innovations:

* Columnar storage * Compressed Sparse Row on disk * Factorized joins * ASP joins, WCO Joins

Details: https://vldb.org/cidrdb/2023/kuzu-graph-database-management-...

[pphysch]

I'm not saying "only use Postgres", but you need to explain why core graph data needs your listed features.

[reaanb2]

We had general-purpose graph databases before graph databases became a thing. I'm talking about relational databases. Values are the vertices, tuples are the edges, FK constraints are inclusion dependencies. In fact, n-ary relations means a hypergraph database, not just a binary graph db.

[adsharma]

Many recent graph databases are built like this. If you compare ladybugdb vs duckdb, the internals are very similar.

The main innovation is the "REL table". You can think of it as a many-to-many relationship table on steroids with optimizations at the storage layer and join algorithms.

Definition of what makes something a graph database (apart from the query language) is contested. But we seem to be moving towards: build a reasonable relational database and then add a "REL table" to it with join optimizations.

[snapetom]

I worked for the research branch of a children's hospital. At one point, I was brought into vendor evaluations a department wanted to buy. Their tool had something to do with visualizing protein interactions, which was highly networked. It was basically a Neo4j database with a React UI on top of it.