flsplit VS gRPC

My project Restruct[1] does Kaitai-like things but also supports serialization. Unfortunately, it only supports Go and only deals with Go struct tags rather than YAML manifests. Still, it totally can be used for serialization. I use it to sketch out quick projects against arbitrary binary formats. Two examples: one, parsing PNG headers to implement a quick binwalk-like program for just PNG that looks for the IEND chunk to extract accurately[2], two, a program that splits FL Studio FLP projects by playlist track[3].

I feel like I’ve self-promoted Restruct like four times on Hacker News, and I feel kind of bad because it could use improvements and even some bug fixes and I never seem to get around to it. Oh well. It’s still useful for me, I hope it’s useful for others, too.

That said, Kaitai has a fairly clear path towards adding serialization from a design PoV; many things that would be calculated for parsing structures in deserialization could just become checks/assertions in serialization. As an example, checking that an expression calculates out to the expected value would be a reasonable approach. Reversible expressions could be implemented for some cases, too, if you want it to do more of the heavy lifting. I think the biggest obstacle is actually implementing it, and frankly my Scala is too weak to help with such a relatively big undertaking.

I’ve also played with the rust nom library, which implements functional programming style parser combinators. It is quite cool how it can express fairly complex grammars and binary formats pretty much equally well, albeit optimizing it effectively requires serious magic that I do not think nom has. (I assume in Haskell, the same thing can be done with mind-boggling optimization power.)

[1]: https://github.com/go-restruct/restruct

[2]: https://github.com/jchv/pngextract

[3]: https://github.com/jchv/flsplit

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