API Gateway Proxy Queue Architecture

class QueueBenefits:
    # 1. Rate limiting and traffic shaping
    rate_control = "Protects backend systems from traffic spikes"
    
    # 2. Improved throughput
    throughput = "Processes 3-5x more requests via batch optimization"
    
    # 3. Enhanced reliability
    reliability = "Queue persistence prevents data loss during failures"
    
    # 4. Cost optimization
    cost_savings = "40-60% reduction in infrastructure costs"
    
    # 5. Scalability
    scalability = "Horizontal scaling without service disruption"

class DistributedQueueSystem:
    def __init__(self, queue_backend="rabbitmq"):
        self.queue = QueueBackendFactory(queue_backend)
        self.workers = []
        self.monitor = QueueMonitor()
    
    def add_worker(self, worker_count=5):
        # Create worker pool
        for i in range(worker_count):
            worker = QueueWorker(
                queue=self.queue,
                batch_size=100,
                max_retries=3
            )
            self.workers.append(worker)
    
    async def process_batch(self, requests):
        # Distribute requests across workers
        batches = self._create_balanced_batches(requests)
        results = await asyncio.gather(
            *[worker.process(batch) for worker, batch in zip(self.workers, batches)]
        )
        return self._aggregate_results(results)

class ProductionQueueGateway:
    def __init__(self, config):
        self.config = config
        self.queue = PriorityQueue()
        self.metrics = MetricsCollector()
        self.retry_strategy = ExponentialBackoffRetry()
    
    async def handle_request(self, request):
        # Validate and prioritize request
        validated = await self._validate_request(request)
        priority = self._calculate_priority(validated)
        
        # Enqueue with tracking
        tracking_id = self._generate_tracking_id()
        queue_item = {
            'id': tracking_id,
            'request': validated,
            'priority': priority,
            'timestamp': time.time()
        }
        
        await self.queue.put(queue_item)
        self.metrics.record_enqueue()
        
        # Return tracking information
        return {
            'tracking_id': tracking_id,
            'queue_position': self.queue.qsize(),
            'estimated_time': self._estimate_processing_time()
        }
    
    async def _process_batch(self):
        while True:
            try:
                batch = await self._gather_batch()
                if batch:
                    results = await self._process_with_monitoring(batch)
                    await self._deliver_results(results)
            except Exception as e:
                self.metrics.record_error(e)
                await self._handle_processing_error(e)