googleapis VS differential-dataflow

Rich Error Model: This model enables servers to return and clients to consume additional error details expressed as one or more protobuf messages. It further specifies a standard set of error message types to cover the most common error (QuotaFailure, PreconditionFailure, BadRequest, etc). When an error occurs, the server returns the appropriate status code along with an optional error message.

They list search in their public api

https://github.com/googleapis/googleapis/blob/288aa7fb71c9b6...

We structure the repo according to proto packages. It's quite similar to how the googleapis repository is structured.

It's unfortunate that the spec doesn't contain custom fields to a sub-object like other RPC specs, like proto Status [1]. They should have had the message go into a field named "message" and not "detail". And have a field like "details" where the opaque type is serialized, which should be named by the "type" field. The problem is that systems with existing error types may have field name conflicts with type, title, status, detail, or instance, so we'd just dump the actual error into a custom "extension member" which by definition, isn't standard.

[1] https://github.com/googleapis/googleapis/blob/1c8a25ab153eef...

Protobuf (https://github.com/googleapis/googleapis)

All the required changes can be viewed in our last demo, the grpc-rest-app implementation. First, we need to update our proto service interface to help the proxy service make our gRPC service methods available at the right URLs and for the correct HTTP operations. To do this, the Google API HTTP library provides annotations we can add to our proto to describe the correct mappings. The buf tool allows us to include the googleapis dependency as a plugin in our buf.yaml file).

I think this only makes sense if the 3rd party is also throwing custom exceptions.

If you want to reduce coupling you should avoid throwing custom exceptions at all. Semantic information can go in the error message and log. The error type should be used to indicate to your program whether an error is recoverable, retriable or some other action needs to be taken. For example google on has 16 canonical error codes for all APIs.

https://github.com/googleapis/googleapis/blob/master/google/...

OpenAPI and possibly developing reusable, versioned client libraries could help, but it's a major undertaking that gRPC makes redundant. I'd be tempted to use grpc-gateway even if I had to implement a REST API. Try looking into buf and monorepo structures for proto management, e.g. something like GoogleCloudPlatform/microservices-demo. For more thorough proto and grpc-gateway definition examples, see googleapis/googleapis.

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.

Well there is Timestamp defined as a well known type which is available to all implementations despite not being a primitive type [1]. Plus one is obviously able to define any other custom types if necessary- eg as seen in [2].

[1] https://developers.google.com/protocol-buffers/docs/referenc...

[2] https://github.com/googleapis/googleapis/blob/master/google/...

Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.

https://github.com/timelydataflow/differential-dataflow

https://materialize.com/

I'm looking for this but can't find it, how does this project compare to differential dataflow?

As a sibling commenter mentioned, it's built on timely dataflow (which is lower-level), but that already has differential dataflow[0] built on top of it by the same authors.

How do they differ?

[0]: https://github.com/TimelyDataflow/differential-dataflow

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/

differential dataflow which is used to power materialize db

It's partially inspired by Linq, so the similarity you see is expected.

It's not really arbitrary structures so much, though you're mostly free in what record type you use in a relation (structs and tagged enums are typical, though).

The incremental part is that you can feed it changes to the input (additions/retractions of facts) and get changes to the outputs back with low latency (you can alternatively just use it to keep an index up-to-date, where you can quickly look up based on a key (like a materialized view in SQL)).

This [0] section in the readme of the underlying incremental dataflow framework may help get the concept across, but feel free to follow up if you're still not seeing the incrementality.

[0]: https://github.com/TimelyDataflow/differential-dataflow#an-e...