make-booster VS gRPC

For a very different approach, check out make-booster:

https://github.com/david-a-wheeler/make-booster

Make-booster provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases). Released as open source software.

https://github.com/david-a-wheeler/make-booster

I think a lot of hate on make is due to poor use. If your makefile is complex, refactor it. Auto-generate dependencies (it only takes a few lines in GNU make). And don't use recursive make, that way lies madness. I also think GNU make is the wiser tool; POSIX make lacks too much in many cases.

https://github.com/david-a-wheeler/make-booster

From its readme:

"This project (contained in this directory and below) provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases)."

"For example, imagine that Python file BBB.py says include CC, and file CC.py reads from file F.txt (and CC.py declares its INPUTS= as described below). Now if you modify file F.txt or CC.py, any rule that runs BBB.py will automatically be re-run in the correct order when you use make, even if you didn't directly edit BBB.py."

This is NOT functionality directly provided by Python, and the overhead with >1000 files was 0.07seconds which we could live with :-).

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