Comment by PunchyHamster
4 hours ago
So, just use PostgreSQL? 50% faster write at cost of 25% slower reads (which usually are prevailing workload) doesn't warrant moving into far smaller ecosystem
4 hours ago
So, just use PostgreSQL? 50% faster write at cost of 25% slower reads (which usually are prevailing workload) doesn't warrant moving into far smaller ecosystem
Not in anyway related with the surreal folks.
I think it is not so clear cut. I mean, the multi-model nature it is pretty neat. Yes, you can use pgvector on PostgreSQL, but here you also have native graph support. If you want to have both you need to also add something like apache AGE, but arguably that is also a small ecosystem (at least IMHO as I never heard it until I actually started looking for Neo4J alternatives). Also, pgvector has a hard limit on embedding size, while surrealdb does not. For instances in which you have less than 1M elements and retrieval performance matters surreal already has an advantage.
In my personal opinion is a great overall product. Probably not the best at anything, but close enough without having to fiddle with PostgreSQL extensions or adding another piece of machinery to support graph workloads.
The only thing I don't like is that they didn't use either pure SQL nor Cipher for the query(ies) language(s). They roll their own blend, meaning that you will likely need more work to move in the ecosystem and you can't fully use the muscle memory of users that worked with other DBs before.
Outside of the most trivial use cases, I've found that AGE will not get anywhere near Neo4j in terms of performance and there's a lot of edge cases that just flat out won't work. The interesting types of queries you'd want to do in the graph end up being quite limited in AGE openCypher; I could not write very complex Cypher that would otherwise work well in Neo4j.
I appreciate having the option, but for most use cases on Pg, you are better off just using JOINs or switch to Neo4j for your graph workloads. I switched some workloads back to using different approaches of approximating "connectedness" in Pg (e.g. using Jaccard similarity)
If you do go down this route, the easiest way to get coding agents to figure out AGE is actually their regressions SQL tests: https://github.com/apache/age/tree/master/regress/sql
This has a lot of examples for the agent to know what will/won't work with AGE versus Neo4j Cypher.
Projects that's using already existing that is using Postgres already should keep it in Postgres.
It is worth a try for startups if you won't mind. Try to vibe code around it and give the data model a new look. I have a prototype project that combines both tree-sitter AST and converted it to JSON, then since SurrealDB accepts JSON as native input I now get free graph lookup on the control flow and easily did ancestry analysis and finding what functions potentially calls to this segement. All of it is in SurrealDB nested graph queries and the performance is alright, but is abysmal in Postgres JSONB since JSONB does not linearize the JSON data structure.
ps: I'm building a K8S operator for deploying SurrealDB with TiKV operator integration too.
Unless you want to build a startup specifically around that new hot database to do something very specific that's hard with other systems, do not build your startup around a hot new database.
The innovation points you spend on this should generally be spent in other areas, not seeing if someone's unproven db is your breadwinner.
Well, I'm still in a very early phase, but I'm indeed combining both Restate and SurrealDB together for a project that I'm building, where I persist the temporary state on Restate and permanent state on SurrealDB, and since both uses JSON as its lingua franca, it is pretty easy to serailize data between Restate and SurrealDB, very much so better than using MongoDB with BSON as many people would have naturally thought of what is supposed to be a better replacement than SurrealDB.
Oh, that's the reason the SurrealDB operator was here in the first place because I need the full K8S lifecycle to maintain the database state such as backing up, that is not really doable with Helm.
Your reasoning is very solid, and something I'd also consider before picking a DB.
No one should pick us because we're the new hot thing (at least I'd hope not). But at SurrealDB, we've got real enterprises in production at scale. For a lot of startups building today, having LLM/vector features, graph, auth, and the database in one place can really help you ship faster without stitching a bunch of tools together.
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What is correct depends on your workload. There is never a case for comparing the performance for Postgres vs Redis. The are intended for very different uses and so they are should never substitute, as a feature analysis will reveal which you really need.
Though to be honest most people won't scale enough that DB performance is important in the first place. For most people they don't even need a database, your language has built in containers that will do everything you need.
Postgres is definitely one of the strongest databases out there, and we are not trying to hand-wave that away with benchmarks. The point is more that SurrealDB v3 is getting much closer on raw performance while offering a multi-model database, which feels especially relevant today.
On the ecosystem side, we have also grown a lot over the last few years across the community, integrations, cloud offering, and customers. Still work to do, but we are not as far off as people might assume.