For example, I see they do this for Postgres:
`let max_wal_gb = (shared_buffers_gb).clamp(2, 16);`
2-16GB of WAL is not a lot, but I have no idea how large is the data set.
On the parameters, the relational tests use 5 million records per test. The exceptions are the key-value category, which uses 15 million records, and the embedded category, which uses 1 million records. The same dataset shape, workload, harness, and hardware are used across the engines being compared.
For WAL, the 2 to 16 GB range is not intended to be a limit based on the dataset size. For the published runs, the dataset is small enough that this should not be a bottleneck. The persistent runs are also full-durability runs, with Postgres using fsync and synchronous_commit.
We will update the benchmarks page so the versions, dataset sizes, and tuning details are easier to find without digging through the Rust source.
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.
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.
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.