API Gateway Proxy Event-Driven Architecture
Build scalable, resilient AI systems with event-driven architecture. Decouple components, handle async workloads, and achieve horizontal scalability through message queues and event sourcing patterns.
Architecture Benefits
Why event-driven architecture excels for AI workloads.
Horizontal Scalability
Scale processing independently from API endpoints. Add more workers to handle increased load without changing gateway configuration.
Resilience & Fault Tolerance
Isolate failures to individual components. Message queues provide durability and automatic retries without blocking the API.
Improved Performance
Non-blocking API responses. Clients receive immediate acknowledgment while processing happens asynchronously in the background.
Loose Coupling
Components communicate through events rather than direct calls. Change processors without affecting the gateway or other services.
Observability
Complete audit trail of all events. Track request lifecycle from API call through processing to completion.
Cost Optimization
Process during off-peak hours. Batch similar requests together. Scale down workers when demand decreases.
Event-Driven Patterns
Proven patterns for building event-driven AI systems.
Request-Reply Pattern
Handle synchronous API requests through async processing with correlation IDs.
- Client sends request, receives request ID
- Gateway publishes event to queue
- Worker processes and stores result
- Client polls for completion
Competing Consumers
Multiple workers process events from the same queue for parallel execution.
- Events published to shared queue
- Multiple workers subscribe
- Queue distributes events evenly
- Failed events return to queue
Dead Letter Queue
Capture failed events for analysis and manual intervention.
- Event fails after max retries
- Moved to dead letter queue
- Alerts triggered for ops team
- Manual reprocessing possible
Event Sourcing
Store all state changes as immutable events for complete history.
- All changes stored as events
- Current state derived from history
- Time-travel debugging possible
- Audit trail maintained automatically
Implementation Example
Build an event-driven AI gateway with message queues.
class EventDrivenGateway: """Event-driven API gateway for AI workloads""" def __init__(self, message_queue, event_store): self.queue = message_queue self.event_store = event_store self.response_cache = RedisCache() async def handle_request( self, request: AIRequest ) -> Response: """Handle incoming AI request asynchronously""" # Generate correlation ID correlation_id = str(uuid.uuid4()) # Create event event = AIRequestEvent( id=correlation_id, type="ai.request", payload=request.to_dict(), timestamp=datetime.utcnow(), status="pending" ) # Store event for audit trail await self.event_store.append(event) # Publish to message queue await self.queue.publish( topic="ai.requests", message=event.to_json(), headers={ "correlation_id": correlation_id, "priority": request.priority } ) # Return immediate acknowledgment return Response( status=202, body={ "request_id": correlation_id, "status": "processing", "status_url": f"/requests/{correlation_id}" } ) async def check_status( self, request_id: str ) -> dict: """Check status of async request""" # Check cache first cached = await self.response_cache.get(request_id) if cached: return { "status": "completed", "result": cached } # Get event history events = await self.event_store.get_events( aggregate_id=request_id ) if not events: return {"status": "not_found"} # Return latest status latest = events[-1] return { "status": latest.status, "events": [e.to_dict() for e in events] } class EventProcessor: """Worker that processes AI request events""" def __init__(self, queue, ai_client, event_store): self.queue = queue self.ai_client = ai_client self.event_store = event_store async def start(self): """Start consuming events""" await self.queue.subscribe( topic="ai.requests", handler=self.process_event ) async def process_event(self, event_data): """Process a single AI request event""" event = AIRequestEvent.from_json(event_data) try: # Update status to processing await self.emit_status(event.id, "processing") # Call AI provider result = await self.ai_client.complete( event.payload ) # Emit completion event await self.emit_completion(event.id, result) except Exception as e: # Emit failure event await self.emit_failure(event.id, str(e)) # Re-queue for retry raise