22  gRPC vs REST

Excellent question! Understanding when to use gRPC versus RESTful APIs is crucial for making informed architectural decisions, especially in medical systems where you need to balance performance, maintainability, and interoperability. Let me break down the fundamental differences and help you understand when each approach shines.

22.1 Conceptual Differences: RPC vs Resource-Oriented Thinking

The fundamental difference between gRPC and REST lies in their conceptual approaches to distributed computing. gRPC follows the Remote Procedure Call paradigm - you’re essentially calling functions that happen to execute on remote machines. REST (Representational State Transfer) follows a resource-oriented approach where you manipulate resources through standard HTTP operations.

Think of it this way: with gRPC, you might call calculatePatientRisk(patientId, symptoms), which feels like calling a local function. With REST, you would POST /patients/123/risk-assessments with symptom data in the request body, thinking in terms of creating a risk assessment resource for a patient.

Let me illustrate this with a practical comparison using our medical service example.

22.2 Side-by-Side Implementation Comparison

Here’s how the same medical functionality would look implemented in both approaches:

22.2.1 gRPC Implementation (Protobuf Definition)

service MedicalService {
  // Function-oriented thinking: "calculate BMI for this patient"
  rpc CalculateBMI(BMIRequest) returns (BMIResponse);
  
  // Action-oriented: "analyze medical data"
  rpc AnalyzeMedicalData(AnalysisRequest) returns (AnalysisResponse);
  
  // Real-time streaming: "stream vital signs"
  rpc StreamVitalSigns(Empty) returns (stream VitalSignReading);
  
  // Interactive: "conduct consultation"
  rpc InteractiveConsultation(stream ConsultationMessage) returns (stream ConsultationResponse);
}

message BMIRequest {
  string patient_id = 1;
  double weight_kg = 2;
  double height_m = 3;
}

22.2.2 REST API Implementation (OpenAPI/Swagger)

# RESTful API thinking: resources and HTTP verbs
paths:
  /patients/{patientId}/bmi:
    post:
      summary: Create BMI calculation for patient
      parameters:
        - name: patientId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              properties:
                weight_kg:
                  type: number
                height_m:
                  type: number
      responses:
        '201':
          description: BMI calculation created
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/BMIResult'

  /patients/{patientId}/analyses:
    post:
      summary: Create new medical data analysis
      # Resource-oriented: creating an analysis resource
      
  /patients/{patientId}/vital-signs:
    get:
      summary: Get vital signs for patient
      # RESTful approach: polling for data

components:
  schemas:
    BMIResult:
      type: object
      properties:
        patient_id:
          type: string
        bmi:
          type: number
        category:
          type: string
        calculated_at:
          type: string
          format: date-time
        links:
          type: object
          properties:
            self:
              type: string
            patient:
              type: string

Notice the fundamental difference in thinking. gRPC focuses on actions you want to perform, while REST focuses on resources you want to manipulate.

22.3 Technical Architecture Differences

Let me create a visual comparison of how these approaches differ technically:

gRPC Architecture:
Client App ←→ gRPC Client Stub ←→ HTTP/2 + Protobuf ←→ gRPC Server ←→ Business Logic
   |              |                       |                    |             |
   |              |                       |                    |             |
Strong Types   Generated Code         Binary Protocol      Generated Code   Type Safety

REST Architecture:
Client App ←→ HTTP Client ←→ HTTP/1.1 + JSON ←→ REST Framework ←→ Business Logic
   |             |              |                    |              |
   |             |              |                    |              |
Manual Types  Manual Code   Text Protocol        Manual Code    Manual Validation

22.3.1 Performance Characteristics

gRPC Performance Profile: - Serialization: Protocol Buffers are 3-10x faster than JSON - Network: HTTP/2 multiplexing eliminates head-of-line blocking - Payload Size: Binary format is typically 20-50% smaller than JSON - Connection Management: Single persistent connection with multiplexing

REST Performance Profile: - Serialization: JSON parsing overhead (though widely optimized) - Network: HTTP/1.1 requires multiple connections for concurrency - Payload Size: Human-readable but verbose - Connection Management: Often requires connection pooling strategies

Here’s a practical example showing the performance difference:

// REST API call - multiple round trips for related data
async function getPatientDataREST(patientId: string) {
  const patient = await fetch(`/api/patients/${patientId}`);
  const vitals = await fetch(`/api/patients/${patientId}/vital-signs`);
  const analyses = await fetch(`/api/patients/${patientId}/analyses`);
  const medications = await fetch(`/api/patients/${patientId}/medications`);
  
  // 4 separate HTTP requests, each with connection overhead
  return {
    patient: await patient.json(),
    vitals: await vitals.json(),
    analyses: await analyses.json(),
    medications: await medications.json()
  };
}

// gRPC call - single request for comprehensive data
async function getPatientDataGRPC(patientId: string) {
  // Single call that can return comprehensive patient data
  const response = await client.getComprehensivePatientData({
    patient_id: patientId,
    include_vitals: true,
    include_analyses: true,
    include_medications: true
  });
  
  return response; // All data in one efficient binary response
}

22.4 Developer Experience and Ecosystem

22.4.1 gRPC Developer Experience

// Go server - type safety enforced at compile time
func (s *MedicalServer) AnalyzeMedicalData(ctx context.Context, req *pb.AnalysisRequest) (*pb.AnalysisResponse, error) {
    // req.TestValues is guaranteed to be []float64
    // req.PatientId is guaranteed to be string
    // No runtime type checking needed
    
    if req.PatientId == "" {
        return nil, status.Errorf(codes.InvalidArgument, "patient_id required")
    }
    
    // Compile-time guarantee that response matches contract
    return &pb.AnalysisResponse{
        PatientId:    req.PatientId,
        AverageValue: calculateAverage(req.TestValues),
        Status:       determineStatus(req.TestValues),
    }, nil
}

// TypeScript client - generated types provide IDE support
const analysis = await client.analyzeMedicalData({
  patient_id: "PATIENT_001",        // IDE knows this should be string
  test_values: [95, 102, 88, 91],   // IDE knows this should be number[]
  test_type: "blood_glucose"        // IDE provides autocomplete
});

// Response is strongly typed
console.log(analysis.average_value); // IDE knows this is number
console.log(analysis.status);        // IDE knows this is string

22.4.2 REST API Developer Experience

// REST API - manual type handling
app.post('/api/patients/:patientId/analyses', async (req, res) => {
  // Manual validation required
  const { test_values, test_type } = req.body;
  
  if (!Array.isArray(test_values)) {
    return res.status(400).json({ error: 'test_values must be array' });
  }
  
  if (!test_values.every(v => typeof v === 'number')) {
    return res.status(400).json({ error: 'test_values must contain numbers' });
  }
  
  // Manual response construction
  const result = {
    patient_id: req.params.patientId,
    average_value: calculateAverage(test_values),
    status: determineStatus(test_values),
    analyzed_at: new Date().toISOString()
  };
  
  res.status(201).json(result);
});

// REST client - manual type definitions
interface AnalysisRequest {
  test_values: number[];
  test_type: string;
}

interface AnalysisResponse {
  patient_id: string;
  average_value: number;
  status: string;
  analyzed_at: string;
}

// Manual HTTP handling
const response = await fetch(`/api/patients/${patientId}/analyses`, {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    test_values: [95, 102, 88, 91],
    test_type: 'blood_glucose'
  })
});

if (!response.ok) {
  throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}

const analysis: AnalysisResponse = await response.json();

22.5 Use Case Analysis: When to Choose What

22.5.1 Choose gRPC When:

High-Performance Internal Services In your radiology AI context, imagine multiple AI models that need to process DICOM images rapidly. The efficiency of binary serialization and HTTP/2 multiplexing makes gRPC ideal for this scenario:

// Efficient AI model chaining with gRPC
const imageAnalysis = await Promise.all([
  client.detectAnomalies({ dicom_data: imageBuffer }),
  client.segmentOrgans({ dicom_data: imageBuffer }),
  client.measureDensity({ dicom_data: imageBuffer, region: 'liver' })
]);

Real-Time Communication Medical monitoring systems benefit enormously from gRPC’s streaming capabilities:

// Real-time patient monitoring
const vitalStream = client.streamVitalSigns({ patient_id: 'ICU_001' });
vitalStream.on('data', (reading) => {
  if (reading.heart_rate > 120) {
    triggerAlert('Tachycardia detected', reading);
  }
});

Type-Critical Applications When data integrity is paramount (like medication dosing calculations), gRPC’s compile-time type checking provides an extra safety layer:

message MedicationDose {
  string medication_name = 1;
  double dose_mg = 2;           // Guaranteed to be number
  string frequency = 3;         // Guaranteed to be string
  int32 duration_days = 4;      // Guaranteed to be integer
}

Microservices Architecture When building internal service meshes where different teams need to integrate quickly:

// Service discovery and type safety make integration straightforward
patientService := pb.NewPatientServiceClient(conn)
imagingService := pb.NewImagingServiceClient(conn)
aiService := pb.NewAIServiceClient(conn)

// All services have guaranteed contracts

22.5.2 Choose REST When:

Public APIs and Third-Party Integration REST’s universality makes it ideal when you need broad compatibility. Every programming language and platform understands HTTP and JSON:

# Any tool can call REST APIs
curl -X POST /api/patients/123/prescriptions \
  -H "Content-Type: application/json" \
  -d '{"medication": "aspirin", "dose": "81mg", "frequency": "daily"}'

Web Application Frontends Browser-based applications naturally work with REST APIs:

// Natural browser integration
const response = await fetch('/api/patients/123', {
  method: 'GET',
  headers: { 'Authorization': `Bearer ${token}` }
});
const patient = await response.json();

CRUD Operations and Resource Management When your domain naturally maps to resources and HTTP verbs:

GET    /api/patients           # List patients
POST   /api/patients           # Create patient
GET    /api/patients/123       # Get specific patient
PUT    /api/patients/123       # Update patient
DELETE /api/patients/123       # Delete patient

Integration with Existing HTTP Infrastructure When you need to leverage existing load balancers, caches, and monitoring tools:

# Standard HTTP tools work seamlessly
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: medical-api
spec:
  rules:
  - host: api.hospital.com
    http:
      paths:
      - path: /api
        backend:
          service:
            name: medical-rest-service

22.6 Practical Example: Hybrid Architecture

In real-world medical systems, you often use both approaches strategically. Here’s how you might architect a comprehensive radiology system:

Medical System Architecture:

┌─────────────────┐    REST/HTTP    ┌──────────────────┐
│   Web Dashboard │ ←──────────────→ │   API Gateway    │
│   (TypeScript)  │                 │                  │
└─────────────────┘                 └──────────────────┘
                                            │
                                            │ REST for external
                                            │ gRPC for internal
                                            ▼
┌─────────────────┐    gRPC         ┌──────────────────┐
│ DICOM Processor │ ←──────────────→ │  Orchestration   │
│     (Go)        │                 │     Service      │
└─────────────────┘                 │    (Python)      │
                                    └──────────────────┘
        │                                   │
        │ gRPC streaming                    │ gRPC
        ▼                                   ▼
┌─────────────────┐                ┌──────────────────┐
│   AI Analysis   │                │   Patient Data   │
│   Service       │                │     Service      │
│   (Python)      │                │   (PostgreSQL)   │
└─────────────────┘                └──────────────────┘

External Interface (REST):

// Public API for hospital systems integration
app.get('/api/v1/patients/:id/reports', async (req, res) => {
  // REST for external consumption - universal compatibility
  const reports = await getPatientReports(req.params.id);
  res.json({
    data: reports,
    links: {
      self: `/api/v1/patients/${req.params.id}/reports`,
      patient: `/api/v1/patients/${req.params.id}`
    }
  });
});

Internal Communication (gRPC):

# High-performance internal service communication
async def process_dicom_image(image_data):
    # Efficient binary communication between services
    analysis_result = await ai_service.analyze_image(
        AnalyzeImageRequest(
            image_data=image_data,
            analysis_types=['tumor_detection', 'organ_segmentation']
        )
    )
    
    # Stream results as they become available
    async for partial_result in analysis_result:
        await notify_radiologist(partial_result)

22.7 Decision Framework for Your Medical Applications

Given your background in radiology AI, here’s a practical decision framework:

Use gRPC for: - AI model inference services (high throughput, type safety) - Real-time image processing pipelines - Inter-microservice communication within your hospital’s infrastructure - Streaming DICOM data analysis - High-frequency monitoring data transmission

Use REST for: - Hospital information system (HIS) integration - Web-based radiologist workstations - Mobile applications for doctors and nurses - Third-party integration (PACS systems, external labs) - Public APIs for research collaboration

Example in Your Context:

// Public REST API for external systems
GET /api/v1/studies/123/analysis
{
  "study_id": "123",
  "status": "completed",
  "findings": ["Normal chest X-ray"],
  "confidence": 0.95
}

// Internal gRPC for AI pipeline
const analysis = await aiService.analyzeChestXray({
  dicom_data: imageBuffer,
  models: ['pneumonia_detection', 'fracture_detection'],
  priority: 'urgent'
});

// Stream results as AI processes different regions
for await (const region_result of analysis) {
  updateRadiologistWorkstation(region_result);
}

Understanding these differences helps you make informed architectural decisions. In medical systems, you often need the performance and type safety of gRPC for critical internal operations, while maintaining the accessibility and interoperability of REST for external interfaces. The key is recognizing that these aren’t competing technologies—they’re complementary tools that excel in different scenarios within your overall system architecture.