schematic VS gRPC

schematic: Last updated in 2021. "It can be thought of as a subset of JSON Schema", "Schematic schemas can be exported to json-schema".

Thanks for Cap'n Proto. I think the article is clearly indicating the issues that a wider community of conventional type systems in their mainstream languages is not fully aware of. And I disagree with your comments. Firstly, I don't like that you are labelling the author of the article as a "PL design theorist who doesn't have a clue":

> his article appears to be written by a programming language design theorist who, unfortunately, does not understand (or, perhaps, does not value) practical software engineering.

I'm not the author, but they mention their prior industrial experience at Google with protobufs.

I'm not a PL theorist either, and I see that you don't fully understand the problems of composability, compatibility, and versioning and are too eager to dismiss them based on your prior experience with inferior type suystems. And here's why I think it is:

> > This is especially true when it comes to protocols, because in a distributed system, you cannot update both sides of a protocol simultaneously. I have found that type theorists tend to promote "version negotiation" schemes where the two sides agree on one rigid protocol to follow, but this is extremely painful in practice: you end up needing to maintain parallel code paths, leading to ugly and hard-to-test code. Inevitably, developers are pushed towards hacks in order to avoid protocol changes, which makes things worse.

You are conflating your experience with particular conventional tooling with the general availability of superior type systems and toolings out there. There's a high demand in utilising their properties in protocol design today.

Version negotiation is not the only option available to a protocol designer. It is possible to implicitly migrate and up/down-cast schemas in semi-automated way. Example [1]

> This seems to miss the point of optional fields. Optional fields are not primarily about nullability but about compatibility. Protobuf's single most important feature is the ability to add new fields over time while maintaining compatibility.

There are at least two ways to achieve compatibility, and the optional fields that expand a domain type to the least common denominator of all encompassing possibilities is the wrong solution to this. Schema evolition via unions and versioning is the proper approach that allows for automatic resolution of compatibility issues.

> Real-world practice has also shown that quite often, fields that originally seemed to be "required" turn out to be optional over time, hence the "required considered harmful" manifesto. In practice, you want to declare all fields optional to give yourself maximum flexibility for change.

This is false. In practice I want a schema versioning and deprecation policies, and not ever-growing domain expansion to the blob of all-optional data.

> It's that way because the "oneof" pattern long-predates the "oneof" language construct. A "oneof" is actually syntax sugar for a bunch of "optional" fields where exactly one is expected to be filled in.

this is not true either, and it doesn't matter what pattern predates which other pattern. Tagged unions are neither a language construct nor a syntax sugar, it's a property of Type Algebra where you have union- and product-compositions. Languages that implement Type Algebra don't do it to just add another fancy construct, they do it to benefit from mathematical foundations of these concepts.

> How do you make this change without breaking compatibility?

you version it, and migrate over time at your own pace without bothering your clients too often [1]

[1] https://github.com/typeable/schematic#migrations

gRPC, built on HTTP/2, inherently supports flow control. The server can push updates, but it must also respect flow control signals from the client, ensuring that it doesn't send data faster than what the client can handle.

Yes, grpc_cli tool uses essentially the same mechanism except implemented as a grpc service rather than as a stubby service. The basic principle of both is implementing the C++ proto library's DescriptorDatabase interface with cached recursive queries of (usually) the server's compiled in FileDescriptorProtos.

See also https://github.com/grpc/grpc/blob/master/doc/server-reflecti...

The primary difference between what grpc does and what stubby does is that grpc uses a stream to ensure that the reflection requests all go to the same server to avoid incompatible version skew and duplicate proto transmissions. With that said, in practice version skew is rarely a problem for grpc_cli style "issue a single RPC" usecases: even if requests do go to two or more different versions of a binary that might have incompatible proto graphs, it is very common for the request and response and RPC to all be in the same proto file so you only need to make one RPC in the first place unless you're using an extension mechanism like proto2 extensions or google.protobuf.Any.

While gRPC and Apache Thrift have served the microservice architecture well, CloudWeGo's advanced features and performance metrics set it apart as a promising open source solution for the future.

The loadBalancingConfig is what we use in order to decide which policy to go for (round_robin in this case). This JSON representation is based on a protobuf message, then why does the name resolver returns it in the JSON format? The main reason is that loadBalancingConfig is a oneof field inside the proto message and so it can not contain values unknown to the gRPC if used in the proto format. The JSON representation does not have this requirement so we can use a custom loadBalancingConfig .

The Dart implementation of gRPC which puts mobile and HTTP/2 first. It's built and maintained by the Dart team. gRPC is a high-performance RPC (remote procedure call) framework that is optimized for efficient data transfer.

gRPC is a high-performance, open-source RPC (Remote Procedure Call) framework initially developed by Google. It uses Protocol Buffers for serialization and supports bidirectional streaming.

In general, tunneling through HTTP2 turns out to be a great choice. There is a RPC protocol built on top of HTTP2: gRPC[1].

This is because HTTP2 is great at exploiting a TCP connection to transmit and receive multiple data structures concurrently - multiplexing.

There may not be a reason to use HTTP3 however, as QUIC already provides multiplexing.

I expect that in the future most communications will be over encrypted HTTP2 and QUIC simply because middleware creators can not resist to discriminate.

[1] <https://grpc.io>

Even in the https://grpc.io blog says this

gRPC