googleapis VS gRPC

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/...

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