gRPC Streaming – Grape Up

Earlier articles offered what Protobuf is and the way it may be mixed with gRPC to implement easy synchronous API. Nonetheless, it didn’t current the true energy of gRPC, which is streaming, totally using the capabilities of HTTP/2.0.

Contract definition

We should outline the strategy with enter and output parameters just like the earlier service. To observe the separation of issues, let’s create a devoted service for GPS monitoring functions. Our present proto must be prolonged with the next snippet.

message SubscribeRequest 
  string vin = 1;

service GpsTracker 
  rpc Subscribe(SubscribeRequest) returns (stream Geolocation);

Essentially the most essential half right here of enabling streaming is specifying it in enter or output kind. To do this, a key phrase stream is used. It signifies that the server will hold the connection open, and we are able to count on Geolocation messages to be despatched by it.


public void subscribe(SubscribeRequest request, StreamObserver<Geolocation> responseObserver) 
            .setOccurredOn(TimestampMapper.convertInstantToTimestamp(On the

The straightforward implementation of the strategy doesn’t differ from the implementation of a unary name. The one distinction is in how onNext the strategy behaves; in common synchronous implementation, the strategy can’t be invoked greater than as soon as. Nonetheless, for methodology working on stream, onNext could also be invoked as many occasions as you need.

As it’s possible you’ll discover on the hooked up screenshot, the geolocation place was returned however the connection continues to be established and the consumer awaits extra information to be despatched within the stream. If the server desires to tell the consumer that there isn’t a extra information, it ought to invoke: the onCompleted methodology; nevertheless, sending single messages is just not why we need to use stream.

Use instances for streaming capabilities are primarily transferring important responses as streams of knowledge chunks or real-time occasions. I’ll attempt to reveal the second use case with this service. Implementation shall be based mostly on the reactor ( ) as it really works properly for the offered use case.

Let’s put together a easy implementation of the service. To make it work, internet flux dependency shall be required.

implementation 'org.springframework.boot:spring-boot-starter-webflux'

We should put together a service for publishing geolocation occasions for a particular automobile.

import com.grapeup.grpc.instance.mannequin.GeolocationEvent;
import org.springframework.stereotype.Service;
import reactor.core.writer.Flux;
import reactor.core.writer.Sinks;

public class InMemoryGeolocationService implements GeolocationService 

    personal remaining Sinks.Many<GeolocationEvent> sink = Sinks.many().multicast().directAllOrNothing();

    public void publish(GeolocationEvent occasion) 

    public Flux<GeolocationEvent> getRealTimeEvents(String vin) 
        return sink.asFlux().filter(occasion ->;

Let’s modify the GRPC service ready within the earlier article to insert the strategy and use our new service to publish occasions.

public void insert(Geolocation request, StreamObserver<Empty> responseObserver) 
    GeolocationEvent geolocationEvent = convertToGeolocationEvent(request);;


Lastly, let’s transfer to our GPS tracker implementation; we are able to exchange the earlier dummy implementation with the next one:

public void subscribe(SubscribeRequest request, StreamObserver<Geolocation> responseObserver) 
        .subscribe(occasion -> responseObserver.onNext(toProto(occasion)),

Right here we benefit from utilizing Reactor, as we not solely can subscribe for incoming occasions but in addition deal with errors and completion of stream in the identical means.

To map our inner mannequin to response, the next helper methodology is used:

personal static Geolocation toProto(GeolocationEvent occasion) 
    return Geolocation.newBuilder()


As it’s possible you’ll be seen, we despatched the next requests with GPS place and acquired them in real-time from our open stream connection. Streaming information utilizing gRPC or one other software like Kafka is extensively utilized in many IoT techniques, together with Automotive.

Bidirectional stream

What if our consumer want to obtain information for a number of automobiles however with out preliminary data about all automobiles they’re serious about? Creating new connections for every automobile isn’t one of the best method. However fear no extra! Whereas utilizing gRPC, the consumer might reuse the identical connection because it helps bidirectional streaming, which signifies that each consumer and server might ship messages utilizing open channels.

rpc SubscribeMany(stream SubscribeRequest) returns (stream Geolocation);

Sadly, IntelliJ doesn’t enable us to check this performance with their built-in consumer, so now we have to develop one ourselves.

localhost:9090/com. grapeup.geolocation.GpsTracker/SubscribeMany

com.intellij.grpc.requests.RejectedRPCException: Unsupported methodology is known as

Our dummy consumer might look one thing like that, based mostly on generated lessons from the protobuf contract:

var channel = ManagedChannelBuilder.forTarget("localhost:9090")
var observer = GpsTrackerGrpc.newStub(channel)
    .subscribeMany(new StreamObserver<>() 
        public void onNext(Geolocation worth) 

        public void onError(Throwable t) 
            System.err.println("Error " + t.getMessage());

        public void onCompleted() 
whereas (true)  // to maintain consumer subscribing for demo functions :)

Should you ship the updates for the next random VINs: JF2SJAAC1EH511148, 1YVGF22C3Y5152251, you need to be capable of see the output within the console. Test it out!

Tip of the iceberg

Introduced examples are simply gRPC fundamentals; there may be rather more to it, like disconnecting from the channel from each ends and reconnecting to the server in case of community failure. The next articles had been supposed to share with YOU that gRPC structure has a lot to supply, and there are many prospects for a way it may be utilized in techniques. Particularly in techniques requiring low latency or the flexibility to supply consumer code with strict contract validation.