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Firestone does provide global consistency, so the following quote is incorrect:
> In Cloud Firestore, the data on the client are loaded from the database at different points in time. Even if you listen for realtime updates, results from separate queries will not remain in sync. This creates consistency anomalies and bugs in your app.
Here is a link to the protocol documentation that the clients use to support it: https://github.com/googleapis/googleapis/blob/d0b394f188e8c3...
I'd link to the client implementation but it's quite involved.
I was an early developer at Firebase. I think we made Firebase so easy to use and never spoke on about the technicals that the whole software ecosystem now underestimates the complexity involved. I see various Firebase competitors asserting various "mistakes it makes" without really understanding what it delivers, which is understandable because we never marketed it like that because we spoke only about how it can help you build easier.
The idea that n queries instead of a join is slow is not as true as you would think. Firestore supports streaming and pipelines at its core, and can reuse cache across operations. At the end of the day, the data goes over a narrow network channel. If you can saturate the channel, and don't leave any gaps, what's the performance difference if the data comes from a single query or many that are back-to-back. The data is transferred to the client either way. Both Firebase databases are pipelined, so this "many round trip" argument is not a decent argument if the client can issue the queries without waiting for responses (such as the code in this article).
The other is consistency levels and correctness. I constantly see devs call Firebase an eventually consistent database which is wrong, its causally consistent [1], and this makes a huge difference when trying to do OLTP. The offline capabilities are built on the consistency primitives, and it's the only way it can work. So while this convex article is banging on about "End-to-End Correctness Philosophy", they miss the most important quality of correctness, and if they are not careful, will miss the required engineering, and then be unable to deliver an offline cache over real-time streams. I see this playing out with Supabase, I warned them personally before they got into YCombinator that what they were building was not causally consistent. Since then, they have had to rearchitect their real-time features after shipping them. (I have not reviewed their latest design yet so I have no idea whether they have it right yet).
Many things sucked about Firebase. The bespoke security rules and the lack of views. So Convex is on the money shipping functions on the backend. I think Supabase is shipping competitors' mistakes with row-level security language. Personally, I think Firebase's mistakes can be fixed with the addition of an open-source Firebase server [1], as the clients are already open source and the mistakes are all to do with just the server. The real tech was always in the clients anyway (offline cache, connection management, operation queues).
It will be interesting to see if building expressly for React is a good idea. Firebase shipped many adapters, like https://github.com/FirebaseExtended/reactfire, using the "thin-waist" principle of not over-fitting. But Javascript technology moved from callbacks to async while Firebase was in the field, so the current API is not now idiomatic. But convex is setting itself for even more ecosystem fragility, what if React changes API or falls out of favor? This is a big risk! I hope they can roll with whatever happens!
[1] https://observablehq.com/@tomlarkworthy/redis-backend-1
hi! sujay from convex here. I remember reading about your "reverse query engine" when we were getting started last year and really liking that framing of the broadcast problem here.
as james mentions, we entirely re-run the javascript function whenever we detect any of its inputs change. incrementality at this layer would be very difficult, since we're dealing with a general purpose programming language. also, since we fully sandbox and determinize these javascript "queries," the majority of the cost is in accessing the database.
eventually, I'd like to explore "reverse query execution" on the boundary between javascript and the underlying data using an approach like differential dataflow [1]. the materialize folks [2] have made a lot of progress applying it for OLAP and readyset [3] is using similar techniques for OLTP.
[1] https://github.com/TimelyDataflow/differential-dataflow
[2] https://materialize.com/
[3] https://readyset.io/
hi! sujay from convex here. I remember reading about your "reverse query engine" when we were getting started last year and really liking that framing of the broadcast problem here.
as james mentions, we entirely re-run the javascript function whenever we detect any of its inputs change. incrementality at this layer would be very difficult, since we're dealing with a general purpose programming language. also, since we fully sandbox and determinize these javascript "queries," the majority of the cost is in accessing the database.
eventually, I'd like to explore "reverse query execution" on the boundary between javascript and the underlying data using an approach like differential dataflow [1]. the materialize folks [2] have made a lot of progress applying it for OLAP and readyset [3] is using similar techniques for OLTP.
[1] https://github.com/TimelyDataflow/differential-dataflow
[2] https://materialize.com/
[3] https://readyset.io/
hi! sujay from convex here. I remember reading about your "reverse query engine" when we were getting started last year and really liking that framing of the broadcast problem here.
as james mentions, we entirely re-run the javascript function whenever we detect any of its inputs change. incrementality at this layer would be very difficult, since we're dealing with a general purpose programming language. also, since we fully sandbox and determinize these javascript "queries," the majority of the cost is in accessing the database.
eventually, I'd like to explore "reverse query execution" on the boundary between javascript and the underlying data using an approach like differential dataflow [1]. the materialize folks [2] have made a lot of progress applying it for OLAP and readyset [3] is using similar techniques for OLTP.
[1] https://github.com/TimelyDataflow/differential-dataflow
[2] https://materialize.com/
[3] https://readyset.io/
