rethinkdb_rebirth VS flow

I know [RethinkDB][1] used to do this with their SQL-like ReQL language, but I looked around a bit and can't find much else about it - and I would have thought it would be more common.

If we think about modern frontends using SQL-based backends, essentially every time we render, its ultimately the result of a tree of SQL queries (queries depend on results of other queries) running in the backend. Our frontend app state is just a tree of materialized views of our database which depend on each other. We've got a bunch of state management libraries that deal with trees but they don't fit so well with relational/graph-like data.

I came across a Postgres proposal for [Incremental View Maintenance][2] which generates a diff against an existing query with the purpose of updating a materialized view. Oracle also has [`FAST REFRESH`](https://docs.oracle.com/database/121/DWHSG/refresh.htm#DWHSG8361) for materialized views.

I guess it's relatively easy to do until you start needing joins or traversing graphs/hierarchies - which is why its maybe avoided.

[1]: https://github.com/rethinkdb/rethinkdb_rebirth

We use a macaddr8 that embeds a wall-clock timestamp (so they're ascending order, achieving data locality) with some additional randomness. It's worked really well for us:

https://github.com/estuary/flow/blob/master/supabase/migrati...

we use macaddr8 instead of bigint, because it has a postgres serialization / JSON encoding which lossless-ly round-trips with browsers and it works well with PostgREST. The same CANNOT be said for bigint, which is a huge footgun.

I can't claim to know much about CloudQuery, but we are an open-source platform with CDC connectors from PostgreSQL and materializations to BQ and elsewhere. We also have fully-managed connectors if you don't want to deal with hosting.

This is essentially how we model things in Flow (disclosure: I work there). We call them Derivations, which are data products that are built (derived) from other data products. Each data product (we call them Collections) is backed by a set of append-only logs, so they can be read by many different consumers at different times. IDK if our product can work for you since we don't (yet) support stuff like MQTT, but there's a pretty generous free tier if you'd be able to push the data over HTTP. Either way, I just think it's cool that others have independently arrived at similar ideas about how to model streaming tasks!

We Estuary Git Docs have an open-source connector for Kafka -> Snowflake that could perform the tasks of a) flattening the data and b) removing duplicates via exactly once end to end delivery

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Flow Gazette We are looking for a backend engineer who is early in their career (around 1-3 years of industry experience) to join our team.

Estuary Technology | Junior Backend Engineer | Rust, Go | REMOTE OR HYBRID | Regional offices in NYC & Columbus, OH

Estuary (https://www.estuary.dev/) is the first real-time Data Operations platform for future-poof pipelines, including both historical and real-time data set up in minutes.

Our team is rapidly growing, VC funded and led by two successful, repeat founders.

We primarily develop in rust and go and are heavily built on top of gazette which is an internally developed streaming engine.

Flow: https://github.com/estuary/flow

Gazette: https://gazette.readthedocs.io/en/latest/

We are looking for a junior backend engineer with 2-3 years of industry experience.

For engineers who have an unquenched curiosity and drive to solve complex distributed systems problems, this is an opportunity to advance your career alongside a team of subject matter experts.

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It does seem like a weird bug that this would cause errors https://github.com/estuary/flow/runs/5642694619?check_suite_... seems like it should be some kind of warning instead of an error?

where you'd subscribe for live updates.

[1]: https://github.com/estuary/flow