TL;DR: gRPC is a high-performance RPC framework that enables fast, strongly typed service-to-service communication using Protocol Buffers over HTTP/2. It is designed for low latency, high throughput, and streaming workloads, making it ideal for internal microservices and distributed systems where efficiency and reliability matter.

gRPC is a high-performance RPC framework that lets services call each other like local functions using Protocol Buffers (compact, strongly typed messages) over HTTP/2 (multiplexed, low-overhead connections). It’s best suited for service-to-service communication in microservices, internal APIs, and streaming workloads where latency, throughput, and efficiency matter more than browser-native compatibility.

If your REST APIs are getting “chatty,” CPU-heavy (JSON), or slow under load, gRPC can reduce payload size and network overhead while improving reliability with deadlines, retries, and standardized error handling. (gRPC)

What problem does gRPC solve

_gRPC standardizes fast, typed, cross-language communication between service_s.

gRPC is a universal RPC framework: a client calls a remote method on a server as if it were a local function, and gRPC handles the network transport, serialization, and plumbing. It’s particularly effective in distributed systems where you need:

• Low latency and high throughput
• Strong contracts (typed requests/responses)
• Streaming (real-time telemetry, events, updates)
• Consistent reliability controls (deadlines/timeouts, retries) (gRPC)

Why was gRPC created, and what limits does it address in REST

REST is great for web-facing APIs; gRPC targets internal service efficiency and streaming.

REST over HTTP/1.1 (or even HTTP/2) commonly uses JSON and resource-oriented patterns, which is perfect for broad compatibility but can become inefficient when:

• APIs become chatty (many small calls for one user request)
• JSON parsing becomes CPU-expensive
• You need streaming updates (telemetry, real-time feeds)
• You want strict, evolvable contracts across many services

gRPC’s design leans toward efficient binaries (Protobuf) and HTTP/2 streaming primitives to reduce overhead and support richer communication patterns. (gRPC)

How gRPC works

Think “remote function call,” with generated clients and servers enforcing the contract.

In gRPC, you define a service with methods in a .proto file. From that schema, tooling generates:

• Server interfaces (what you must implement)
• Client stubs (what you call)

That contract-first approach makes it harder to “accidentally” break clients because the schema is the source of truth.

Text diagram: RPC mental model

Why protocol buffers are faster than JSON

Protobuf gives compact binary messages + a schema-first workflow.

Protocol Buffers (Protobuf) are a binary serialization format and IDL (interface definition language). Key benefits:

• Smaller payloads than typical JSON for structured data
• Faster serialization/deserialization (less text parsing)
• Strong typing (clear fields, enums, oneofs)
• Code generation across many languages (Protocol Buffers)

Why HTTP/2 makes gRPC efficient under load

HTTP/2 reduces connection and header overhead and supports true multiplexing.

gRPC runs over HTTP/2, which introduces mechanisms that are especially useful for high-performance APIs:

• Multiplexing: many concurrent streams on one connection
• Header compression: reduces repeated metadata overhead
• Long-lived connections: avoids reconnect churn under load (RFC Editor)

Note: RFC 9113 is the current HTTP/2 spec and obsoletes RFC 7540, though 7540 is still widely referenced. (RFC Editor)

The 4 gRPC RPC types

Unary is “request/response”; streaming unlocks real-time and high-throughput flows.

gRPC supports four RPC types: (gRPC)

How does unary RPC work

One request, one response—closest to a REST call.

Client —> Request —> Server

Client <— Response <— Server

How does server-side streaming work

Client asks once; server streams multiple responses (updates, progress, feed).

Client —> Request —> Server

Client <— Resp #1 <— Server

Client <— Resp #2 <— Server

…

How does client-side streaming work

Client streams many messages; server responds once (batch upload, aggregation).

Client —> Req #1 —> Server

Client —> Req #2 —> Server

…

Client <— Summary <— Server

How does bidirectional streaming work

Both sides stream independently (chat, telemetry + commands, real-time sync).

Client <–> Stream <–> Server

(messages flow both directions, ordered per stream)

gRPC vs REST for High-Performance APIs

Use gRPC when efficiency + streaming matter; use REST when ubiquity + browsers matter.

Dimension	gRPC	REST (typical JSON over HTTP)
Payload format	Binary (Protobuf) (Protocol Buffers)	Text (JSON)
Transport	HTTP/2 by default (RFC Editor)	HTTP/1.1 or HTTP/2
Contract	Schema-first, strongly typed (Protocol Buffers)	Often spec-first (OpenAPI) or ad hoc
Streaming	Built-in patterns (gRPC)	Possible (SSE/WebSocket), not standardized per REST
Browser support	Needs gRPC‑Web / proxy (gRPC)	Native
Tooling familiarity	Strong but more “systems” oriented	Extremely widespread

Rule of thumb

• Choose gRPC for internal microservices, high QPS, low latency, and streaming.
• Choose REST for public APIs, third-party developers, caching/CDN friendliness, and browser-first requirements.

gRPC vs REST: Which should you use

Pick based on clients, performance needs, and operational constraints.

When should you choose gRPC

• Service-to-service calls in microservices and internal platforms
• Mobile clients where payload size matters (with proper gateway strategy)
• Real-time streaming: telemetry, live updates, collaboration signals
• Polyglot orgs that want generated clients and strong contracts (gRPC)

When should you prefer REST

• Your primary clients are browsers and you don’t want a proxy layer
• You need simple debuggability with curl and plain JSON
• Your API is public and you need maximal interoperability

How do browsers call gRPC services with gRPC-Web

Browsers can’t implement “native” gRPC-over-HTTP/2 directly, so you use gRPC-Web and a proxy.

It’s not feasible to implement the full gRPC HTTP/2 framing in browsers due to missing low-level APIs and raw frame access. That’s why gRPC-Web exists: a browser-compatible protocol that typically runs through an intermediary (often Envoy) to reach your gRPC backend. (gRPC)

Common deployment shape

SCSS

Browser (gRPC-Web) -> Envoy (gRPC-Web filter) -> gRPC service (HTTP/2)

Envoy explicitly supports bridging gRPC-Web clients to upstream gRPC services via its gRPC-Web filter. (Envoy Proxy)

How to expose REST/JSON while keeping gRPC internally

Use gRPC-Gateway (or platform transcoding) when you need both worlds.

A common pattern is: gRPC internally, REST externally.

• gRPC-Gateway reads protobuf service definitions and generates a reverse proxy that translates RESTful JSON HTTP calls into gRPC, based on annotations like google.api.http. (GitHub)
• Cloud platforms and frameworks may also support JSON/HTTP transcoding tied to gRPC service definitions. (Google Cloud Documentation)

Minimal example: Build a gRPC API

This gives you a copy-paste starting point (unary + server streaming).

Step 1: Define the API contract in a .proto file

Schema-first is the core gRPC workflow. (Protocol Buffers)

Proto

// api/demo/v1/greeter.proto
syntax = "proto3";
package demo.v1;
service Greeter {
rpc SayHello (HelloRequest) returns (HelloReply);
rpc WatchHellos (HelloRequest) returns (stream HelloReply);
}
message HelloRequest {
string name = 1;
}
message HelloReply {
string message = 1;
}

Step 2: Run a quick Node.js server (no codegen, dynamic loading)

Great for demos; for production, many teams prefer generated stubs for stricter typing.

js

// server.js (Node 18+)
import grpc from "@grpc/grpc-js";
import protoLoader from "@grpc/proto-loader";
const PROTO_PATH = "./api/demo/v1/greeter.proto";
const pkgDef = protoLoader.loadSync(PROTO_PATH, {
keepCase: true,
longs: String,
enums: String,
defaults: true,
oneofs: true,
});
const proto = grpc.loadPackageDefinition(pkgDef);
const greeter = proto.demo.v1.Greeter;
function sayHello(call, callback) {
const name = call.request.name || "world";
callback(null, { message: `Hello, ${name}!` });
}
function watchHellos(call) {
const name = call.request.name || "world";
let i = 0;
const interval = setInterval(() => {
i += 1;
call.write({ message: `Hello #${i}, ${name}!` });
if (i >= 5) {
clearInterval(interval);
call.end();
}
}, 500);
call.on("cancelled", () => clearInterval(interval));
}
const server = new grpc.Server();
server.addService(greeter.service, { SayHello: sayHello, WatchHellos: watchHellos });
server.bindAsync("0.0.0.0:50051", grpc.ServerCredentials.createInsecure(), () => {
server.start();
console.log("gRPC server listening on :50051");
});

Install deps:

Bash

npm i @grpc/grpc-js @grpc/proto-loader
node server.js
});

Step 3: Call it from Python (unary + server streaming)

Python uses the official gRPC libraries, and you can generate stubs with protoc. (gRPC)

Bash

pip install grpcio grpcio-tools
python -m grpc_tools.protoc \
-I ./api \
--python_out=. \
--grpc_python_out=. \
./api/demo/v1/greeter.proto

Python

# client.py
import grpc
from demo.v1 import greeter_pb2, greeter_pb2_grpc
def main():
channel = grpc.insecure_channel("localhost:50051")
stub = greeter_pb2_grpc.GreeterStub(channel)
# Unary
resp = stub.SayHello(greeter_pb2.HelloRequest(name="John"))
print("Unary:", resp.message)
# Server streaming
for msg in stub.WatchHellos(greeter_pb2.HelloRequest(name=" John ")):
print("Stream:", msg.message)
if __name__ == "__main__":
main()

Run :

Python

python client.py

Step 4: Add deadlines early (don’t ship without them)

Without deadlines, clients can wait indefinitely; set realistic time limits per call. (gRPC)

Python example:

Python

resp = stub.SayHello(greeter_pb2.HelloRequest(name="John"), timeout=0.5)

How do you implement the same patterns in .NET

.NET has first-party guidance and generated clients from .proto files.

If you’re in a .NET ecosystem, generated clients are the normal path, and official docs cover unary and streaming call types. (Microsoft Learn)

Versioning protobuf without breaking clients

Protobuf compatibility is mostly about field numbers and safe evolution rules.

The core safety rules developers rely on in practice:

• Never change or reuse field numbers once released.
• Prefer adding new fields over changing existing ones.
• When removing a field, reserve the field number/name to prevent reuse.
• Be cautious changing field types; treat it as a breaking change unless you know the wire-compatibility implications (Protocol Buffers).

If your org needs strict governance (multi-team protobufs), this may need clarification from your product/engineering team (e.g., “do we enforce API review for .proto changes?”).

Security in production: TLS and mTLS for gRPC

gRPC typically runs over TLS; mTLS is common for zero-trust service-to-service auth.

At a high level:

• TLS encrypts traffic and authenticates the server
• mTLS authenticates both client and server commonly used inside clusters/service meshes

Your exact setup depends on whether you terminate TLS at:

• The application
• A sidecar proxy (service mesh)
• An edge proxy

(Implementation details vary by runtime and mesh; align with your platform’s security baseline.)

Production reliability: Deadlines, retries, and load balancing

Deadlines bound work; retries must be intentional; load balancing must match your topology.

Why should every gRPC call set a deadline

By default, gRPC may wait “forever,” which is dangerous in distributed systems. Deadlines bound latency and prevent stuck calls from consuming resources. (gRPC)

When do retries help and when do they hurt

Retries can improve resilience for transient failures, but they can also amplify load during outages (retry storms). gRPC provides retry guidance and mechanisms; use them with backoff, budgets, and idempotency awareness. (gRPC)

How does Envoy fit into gRPC traffic management

Envoy can proxy gRPC traffic and provides filters for gRPC-Web bridging; it’s commonly used for edge-service mesh patterns where you want centralized routing and policy. (Envoy Proxy)

What production use cases benefit most from gRPC

These are the patterns where teams typically feel gRPC’s advantages fastest.

1. Microservices internal APIs at high QPS Lower overhead per request and stronger contracts help keep p99 latency stable. Streaming telemetry and observability pipelines Bidirectional or server streaming fits “continuous updates” naturally. (gRPC)
2. Real-time updates (presence, pricing, live dashboards) Server streaming avoids polling and reduces wasted requests.
3. Platform APIs inside Kubernetes/service meshes Long-lived connections + standardized contracts integrate cleanly with mesh routing and policy layers.

What best practices keep gRPC fast, secure, and operable

These are the “you’ll thank yourself later” rules.

• Design schema-first and treat .proto as an API surface (review changes like public APIs). (Protocol Buffers)
• Always set deadlines/timeouts, and propagate them across service boundaries. (gRPC)
• Avoid chatty APIs: Prefer coarse-grained methods or streaming for batch/event flows.
• Plan for compatibility: Don’t reuse field numbers; reserve removed fields. (Protocol Buffers)
• Add observability hooks: Interceptors/middleware for logs, metrics, and traces; capture status codes and latency per method.
• Keep payloads intentional: Don’t “stuff” huge objects into one response; stream where appropriate.
• Be explicit about retries: Only retry what’s safe, and cap retry budgets. (gRPC)

Common gRPC mistakes

Most issues come from missing time bounds and weak contract discipline.

1. No deadlines → hung requests and resource exhaustion (gRPC)
2. Retrying everything → outage amplification (retry storms) (gRPC)
3. Breaking .proto changes → subtle client failures (field number reuse, type changes) (Protocol Buffers)
4. Using gRPC for public/browser-first APIs without a gateway plan → friction and proxy complexity (gRPC)
5. Treating streaming like “just a loop” → leaks and backpressure bugs (always handle cancellation, timeouts, and slow consumers)

key takeaways

• gRPC is a strong fit for high-performance internal APIs and streaming workloads. (gRPC)
• Protobuf + schema-first design improves contract safety and cross-language client generation. (Protocol Buffers)
• HTTP/2 features (multiplexing, compression) help reduce overhead under concurrent load. (RFC Editor)
• Browsers need gRPC-Web (usually via Envoy), or expose REST via gRPC-Gateway/transcoding. (Envoy Proxy)
• Production success depends on deadlines, careful retries, and disciplined versioning. (gRPC)

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Note: This blog was originally published at boldsign.com