gRPC VS Apache Thrift

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

There are various notable implementations of RPC like Apache Thrift and gRPC.

> That's just your choice of how to build your app, right? You could've avoided this by rendering templates on the server and sending static HTML to the client, keeping the business logic on the server.

No, that's a requirement on most business cases, my comment stated 'complex and dynamic web apps'. Re-rendering the whole page everytime the user checks a box or clicks a button is (a) terrible UX, (b) hard to track the state between page refresh, (c) wrong practice and (d) bad performance.

> Here's just one of ten-thousand other battle-tested options you can use: https://github.com/apache/thrift/

Sure, I should setup a complex and huge dependency for just one of the many problems I highlighted. What a great idea

Thus, we released Baseplate.py v2.1 with deadline propagation. Each request between Baseplate services has an associated THeader, which includes relevant information for Baseplate to fulfill its functionality, such as tracing request timings. We added a “Deadline-Budget” field to this header that propagates the remaining timeout so that information is available to the following request, and this timeout continues to get updated with every new request made. With this update, we save production costs by allowing resources to work on requests awaiting a response, and gain overall improved latency.

Here’s an example of the Thrift changelog. Knock yourself out. Or you can get your sense of productivity by actually doing something of value.

The biggest addition is native async reading via futures::AsyncRead and futures::AsyncSeek, which required a lot of (to be merged) changes upstream (changes to thrift rust compiler and parquet-format-rs). I placed those changes on a temporary crate until things are released there.

We were sharing instances of a Thrift TDeserializer across threads. We knew TProtocol was not thread-safe, but the TDeserializer constructor accepts a TProtocolFactory, so we naively assumed the deserialize method would use that to create a new instance of TProtocol for each invocation, but unfortunately, the TDeserializer constructor immediately creates TProtocol and stores it in a member variable, so TDeserializer is not actually thread-safe.