Seems similar ideas, although SlateDB seems a bit more lightweight and using Parquet as primitive (even using Arrow) might mean more compute-heavy on client-side?
>SlateDB is designed for key/value (KV) online transaction processing (OLTP) workloads. It is optimized for lowish-latency, high-throughput writes. It is not optimized for analytical queries that scan large amounts of columnar data. For online analytical processing (OLAP) workloads, we recommend checking out Tonbo.
1) your serverless and edge runtime needs to have internet access, so it can contact anyone
2) you're obviously not going to be able to efficiently write to S3 while providing guarantees, so it'll be expensive
3) you're writing in rust, so you really care about correctness and efficiency
This seems like a contradiction. Why would you do this as opposed to hosting a redundant postgres on 2 Hetzner/OVH/... servers and writing to that?
What's shifting is workloads. More and more compute runs in short-lived sandboxes: WASM runtimes (browser, edge), Firecracker, etc. These are edge environments, but not just for web applications.
We're exploring a different architecture for these workloads: ephemeral, stateless compute with storage treated as a format rather than a service.
This also maps to how many AI agent service want per-user or per-workspace isolation at large scale, without operating millions of always-on database servers.
If you're happy running a long-lived Postgres service, Neon or Supabase are great choices.
And you use Rust ... so you care about speed and correctness. This seems like a very wrong approach.
I have this product I have tried and stopped before: https://github.com/pixlie/dwata and I want to restart it. The idea is to create a knowledge graph (use Gliner for NER). Compute would either be on desktop or cloud (instances).
Then store the data on S3 or Cloudflare Workers KV or AWS Dynamo DB and access with cloud functions to hook up to WhatsApp/Telegram bot. I may stick with Dynamo or Cloudflare options eventually though (both have cloud functions support).
I need a persistent storage of key/value data (the graph, maybe embedding) for cloud functions. Completely self-hosted email/calendar bot with LLM, own cloud, own API keys. Super low running cost.
That model worked when you had a small number of stable app servers. It becomes much harder when compute fans out into thousands or millions of short-lived sandboxes.
We're already seeing parts of the data ecosystem move away from this assumption. Projects like Iceberg and DuckDB decouple storage from long-running database services, treating data as durable formats that many ephemeral compute instances can operate on. That's the direction we're exploring as well.