What is grpc?

gRPC (pronounced “gee‑RPC”) is an open‑source framework created by Google that lets different computer programs talk to each other over a network. It uses a language‑neutral, platform‑neutral way to define the messages (called “protocol buffers”) and the methods (called “services”) that can be called remotely, so one program can run a function that actually lives on another machine as if it were local.

Let's break it down

  • Protocol Buffers: A compact binary format for defining data structures and automatically generating code in many languages (C++, Java, Python, Go, etc.).
  • Service Definition: In a .proto file you write something like rpc GetUser(UserRequest) returns (UserResponse);. This describes the remote function, its input, and its output.
  • Code Generation: The .proto file is compiled to client‑side stubs (code you call) and server‑side skeletons (code you implement).
  • Transport: gRPC uses HTTP/2 under the hood, giving it features like multiplexed streams, flow control, and built‑in support for TLS encryption.
  • Streaming: Besides simple request‑response, gRPC supports client‑side, server‑side, and bidirectional streaming, allowing continuous data flow.

Why does it matter?

  • Performance: Binary protobuf messages are much smaller and faster to parse than JSON or XML, and HTTP/2 reduces latency with multiplexing.
  • Cross‑language: Write a service once and call it from any language that has gRPC support.
  • Strong contracts: The .proto file acts as a single source of truth, preventing mismatched APIs.
  • Built‑in security: TLS is part of the transport layer, making secure communication easier.
  • Scalability: Streaming and efficient multiplexing let you build high‑throughput, low‑latency systems (e.g., microservices, real‑time data pipelines).

Where is it used?

  • Microservices: Companies like Netflix, Square, and Cockroach Labs use gRPC for internal service‑to‑service communication.
  • Mobile & IoT: Apps that need fast, low‑bandwidth communication (e.g., real‑time gaming, sensor data) often choose gRPC.
  • Cloud services: Google Cloud APIs, Kubernetes API server, and many other cloud platforms expose gRPC endpoints.
  • Machine learning: TensorFlow Serving uses gRPC to serve models efficiently.
  • Edge computing: Distributed systems that require quick, reliable RPCs across edge nodes.

Good things about it

  • High performance thanks to protobuf and HTTP/2.
  • Clear, version‑controlled contracts via .proto files.
  • Automatic code generation for many languages reduces boilerplate.
  • Supports multiple communication patterns (unary, client streaming, server streaming, bidirectional).
  • Integrated authentication, load balancing, and health checking in many ecosystems.
  • Strong community and official support from Google and the Cloud Native Computing Foundation (CNCF).

Not-so-good things

  • Steeper learning curve for beginners compared to simple REST/JSON APIs.
  • Binary format makes debugging with plain text tools harder; you need special utilities (e.g., grpcurl, protobuf viewers).
  • Requires HTTP/2 support; older infrastructure or proxies may need upgrades.
  • Less human‑readable than REST, which can be a drawback for quick ad‑hoc testing.
  • Streaming can add complexity to client code, especially when handling back‑pressure or reconnection logic.
  • Ecosystem is still maturing; some language implementations lag behind others in features or stability.