Introducing gRPC and Streaming: A Modern Approach to Service Communication (Part 1)

As distributed systems grow more complex, the demand for efficient, low-latency communication intensifies. While HTTP/REST once ruled the API world, the push for better scalability and responsiveness is driving experienced engineers to explore alternatives — enter gRPC, complete with powerful streaming capabilities.

Why gRPC is Gaining Traction

At its core, gRPC is a high-performance Remote Procedure Call (fancy way of saying API) framework that uses Protocol Buffers (Protobuf) for defining service interfaces and serializing data. Protobuf is like JSON or XML, but smaller and faster, since it's binary. Combine that with HTTP/2, and you get more efficient data transport, improved performance, and less overhead compared to traditional REST.

gRPC is not faster than REST by default

The real speed boost comes from using Protobufs.

HTTP/2 Interlude

HTTP/2 introduces a few key features that help gRPC shine:

Multiplexing: Multiple requests/responses in parallel over one connection.
Streaming: Sending multiple messages in a single request, great for real-time data.
Header Compression: Smaller headers mean less overhead.

These optimizations reduce latency and make continuous data flows easier to handle.

Four Types of gRPC Methods

gRPC offers four interaction patterns beyond simple request-response:

Unary: One request, one response (just like REST).
Server Streaming: One request, but the server keeps sending data as it's available (stock price updates).
Client Streaming: The client sends multiple chunks, and the server responds once it's done (file uploads).
Bidirectional Streaming: Both sides send streams simultaneously (chat apps).

Why Streaming Matters

Traditional REST often relies on polling (periodically sending requests) or hacky workarounds to simulate real-time updates. With gRPC streaming, your services can push data as it happens, and clients react immediately — no extra round-trips, no wasted bandwidth.

Real-World Use Cases

Imagine a scenario where your backend ingests sensor data from thousands of IoT devices. With server streaming, your clients don't need to request updates every few seconds — they automatically receive fresh data as it becomes available. Similarly, client streaming simplifies large data ingestion: rather than sending a massive payload at once, clients can stream chunks of data, letting the server process it incrementally.

Here's a simple example of a gRPC service definition for streaming sensor data:


    // version of the .proto file 
    syntax = "proto3"; 

    message SensorRequest {
      // 1 is the field number
      string sensor_id = 1;
      // start time for data retrieval
      int32 start_time = 2;
    }

    message SensorData {
      // sensor reading
      float value = 1;
    }

    service SensorDataService {
      // return type = stream
      rpc StreamSensorData(SensorRequest) returns (stream SensorData); 
    }

Calling StreamSensorData will open a persistent channel, enabling the server to push sensor readings as soon as they're ready—no extra round-trips or manual polling needed.

Similarly, the other RPCs would look like:

Unary RPC:

rpc GetSensorData(SensorRequest) returns (SensorData); // no stream

Client Streaming RPC:

rpc UploadSensorData(stream SensorData) returns (SensorResponse); // stream in req

Bidirectional Streaming RPC:

rpc StreamSensorData(stream SensorData) returns (stream SensorResponse); // stream in req and res

What's Next

Now that we've laid the groundwork and highlighted why gRPC streaming matters, it's time to roll up our sleeves. In upcoming parts of this series, we'll cover:

Implementing streaming RPCs for real-time scenarios
Troubleshooting and best practices