class TenantResourceManager:
    def __init__(self):
        self.resource_pools = {
            'basic': ResourcePool(cpu_cores=2, memory_gb=4, storage_gb=10),
            'professional': ResourcePool(cpu_cores=8, memory_gb=16, storage_gb=100),
            'enterprise': ResourcePool(cpu_cores=32, memory_gb=128, storage_gb=1000)
        }
        self.tenant_allocations = {}
    
    async def allocate_resources(self, store_id, plan_type):
        pool = self.resource_pools[plan_type]
        
        # Check available resources
        if not pool.has_capacity():
            # Scale up the pool or move to different region
            await self.scale_resource_pool(plan_type)
        
        allocation = await pool.allocate(store_id)
        self.tenant_allocations[store_id] = allocation
        
        return allocation
    
    async def handle_tenant_spike(self, store_id, traffic_multiplier):
        current_allocation = self.tenant_allocations[store_id]
        
        # Temporary burst allocation
        burst_resources = current_allocation.scale(traffic_multiplier)
        
        # Apply burst pricing
        await self.billing_service.apply_burst_charges(
            store_id, burst_resources, duration_minutes=60
        )
        
        return burst_resources

class MultiTenantSharding:
    def __init__(self):
        self.shard_count = 128
        self.tenant_distribution = TenantDistribution()
        self.shard_rebalancer = ShardRebalancer()
    
    def get_shard_for_tenant(self, store_id):
        # Consistent hashing with tenant size consideration
        tenant_size = self.tenant_distribution.get_tenant_size(store_id)
        
        if tenant_size > 1000000:  # Large tenant
            # Dedicated shard for large tenants
            return self.get_dedicated_shard(store_id)
        else:
            # Shared shard for smaller tenants
            shard_key = hash(store_id) % self.shard_count
            return self.get_shared_shard(shard_key)
    
    async def rebalance_tenant(self, store_id, new_size):
        current_shard = self.get_shard_for_tenant(store_id)
        optimal_shard = self.calculate_optimal_shard(store_id, new_size)
        
        if current_shard != optimal_shard:
            # Migrate tenant to optimal shard
            await self.shard_rebalancer.migrate_tenant(
                store_id, current_shard, optimal_shard
            )

class ReadReplicaManager:
    def __init__(self):
        self.replica_pools = {}
        self.load_balancer = ReadLoadBalancer()
    
    async def scale_read_replicas(self, shard_id, read_load):
        current_replicas = len(self.replica_pools.get(shard_id, []))
        target_replicas = self.calculate_replica_count(read_load)
        
        if target_replicas > current_replicas:
            # Scale up replicas
            for i in range(target_replicas - current_replicas):
                replica = await self.create_read_replica(shard_id)
                await self.wait_for_replica_sync(replica)
                self.replica_pools[shard_id].append(replica)
                
        elif target_replicas < current_replicas:
            # Scale down replicas
            excess_replicas = current_replicas - target_replicas
            for i in range(excess_replicas):
                replica = self.replica_pools[shard_id].pop()
                await self.terminate_replica(replica)
    
    def calculate_replica_count(self, read_load):
        # Each replica can handle ~1000 QPS
        base_replicas = max(2, read_load // 1000)
        return min(base_replicas, 10)  # Cap at 10 replicas per shard

class ServiceAutoScaler:
    def __init__(self):
        self.scaling_policies = {
            'product_catalog': {
                'metric': 'requests_per_second',
                'target': 1000,
                'min_instances': 5,
                'max_instances': 100,
                'scale_up_cooldown': 300,
                'scale_down_cooldown': 600
            },
            'order_processing': {
                'metric': 'queue_depth',
                'target': 100,
                'min_instances': 10,
                'max_instances': 200,
                'scale_up_cooldown': 180,
                'scale_down_cooldown': 900
            },
            'checkout_service': {
                'metric': 'cpu_utilization',
                'target': 70,
                'min_instances': 20,
                'max_instances': 500,
                'scale_up_cooldown': 120,
                'scale_down_cooldown': 300
            }
        }
    
    async def evaluate_scaling(self, service_name):
        policy = self.scaling_policies[service_name]
        current_metric = await self.get_metric_value(service_name, policy['metric'])
        current_instances = await self.get_instance_count(service_name)
        
        if current_metric > policy['target'] * 1.2:  # 20% above target
            # Scale up
            target_instances = min(
                current_instances * 2,
                policy['max_instances']
            )
            await self.scale_service(service_name, target_instances)
            
        elif current_metric < policy['target'] * 0.5:  # 50% below target
            # Scale down
            target_instances = max(
                current_instances // 2,
                policy['min_instances']
            )
            await self.scale_service(service_name, target_instances)

class QueueBasedScaling:
    def __init__(self):
        self.queue_monitors = {}
        self.worker_pools = {}
    
    async def monitor_queue_depth(self, queue_name):
        while True:
            depth = await self.get_queue_depth(queue_name)
            processing_rate = await self.get_processing_rate(queue_name)
            
            # Calculate required workers
            target_workers = self.calculate_worker_count(depth, processing_rate)
            current_workers = len(self.worker_pools.get(queue_name, []))
            
            if target_workers > current_workers:
                await self.scale_up_workers(queue_name, target_workers - current_workers)
            elif target_workers < current_workers:
                await self.scale_down_workers(queue_name, current_workers - target_workers)
            
            await asyncio.sleep(30)  # Check every 30 seconds
    
    def calculate_worker_count(self, queue_depth, processing_rate):
        # Target: process queue within 5 minutes
        target_processing_time = 300  # seconds
        
        if processing_rate == 0:
            return 1  # Minimum workers
        
        required_rate = queue_depth / target_processing_time
        workers_needed = math.ceil(required_rate / processing_rate)
        
        return max(1, min(workers_needed, 100))  # Between 1 and 100 workers

class GlobalDeploymentManager:
    def __init__(self):
        self.regions = {
            'us-east-1': {'capacity': 0.4, 'latency_zones': ['NA']},
            'us-west-2': {'capacity': 0.2, 'latency_zones': ['NA', 'APAC']},
            'eu-west-1': {'capacity': 0.25, 'latency_zones': ['EU', 'AFRICA']},
            'ap-southeast-1': {'capacity': 0.15, 'latency_zones': ['APAC']}
        }
        self.traffic_router = GlobalTrafficRouter()
    
    async def route_request(self, request):
        client_location = await self.get_client_location(request)
        store_id = await self.extract_store_id(request)
        
        # Check data residency requirements
        data_region = await self.get_data_residency_region(store_id)
        if data_region:
            return self.regions[data_region]
        
        # Route to nearest region with capacity
        optimal_region = self.find_optimal_region(client_location)
        return optimal_region
    
    def find_optimal_region(self, client_location):
        # Calculate latency and capacity scores
        region_scores = {}
        
        for region, config in self.regions.items():
            latency_score = self.calculate_latency_score(client_location, region)
            capacity_score = self.get_capacity_score(region)
            
            # Weighted score (70% latency, 30% capacity)
            region_scores[region] = (latency_score * 0.7) + (capacity_score * 0.3)
        
        return max(region_scores, key=region_scores.get)

class CDNScalingManager:
    def __init__(self):
        self.cdn_providers = ['cloudflare', 'fastly', 'aws_cloudfront']
        self.cache_policies = {}
        self.purge_manager = CachePurgeManager()
    
    async def optimize_cache_strategy(self, store_id):
        # Analyze traffic patterns
        traffic_analysis = await self.analyze_store_traffic(store_id)
        
        cache_policy = {
            'static_assets': {
                'ttl': 86400,  # 24 hours
                'edge_locations': 'all'
            },
            'product_images': {
                'ttl': 3600,   # 1 hour
                'edge_locations': traffic_analysis.primary_regions
            },
            'product_data': {
                'ttl': 300,    # 5 minutes
                'edge_locations': traffic_analysis.primary_regions,
                'vary_headers': ['Accept-Language', 'Currency']
            },
            'dynamic_content': {
                'ttl': 0,      # No cache
                'edge_locations': []
            }
        }
        
        await self.apply_cache_policy(store_id, cache_policy)
        return cache_policy
    
    async def handle_cache_invalidation(self, store_id, content_type, identifiers):
        # Smart purging based on content relationships
        purge_patterns = []
        
        if content_type == 'product':
            # Purge product pages, collections, and search results
            for product_id in identifiers:
                purge_patterns.extend([
                    f"/products/{product_id}*",
                    f"/collections/*",  # Product might be in collections
                    f"/search*"         # Search results might include product
                ])
        
        elif content_type == 'inventory':
            # Purge only product availability data
            for product_id in identifiers:
                purge_patterns.append(f"/products/{product_id}/availability")
        
        # Execute purge across all CDN providers
        await asyncio.gather(*[
            self.purge_cdn_cache(provider, store_id, purge_patterns)
            for provider in self.cdn_providers
        ])

class SearchClusterManager:
    def __init__(self):
        self.clusters = {}
        self.index_manager = IndexManager()
        self.query_optimizer = QueryOptimizer()
    
    async def scale_search_cluster(self, cluster_id, metrics):
        cluster = self.clusters[cluster_id]
        
        # Analyze scaling needs
        if metrics.query_latency > 500:  # ms
            # Add more query nodes
            await self.add_query_nodes(cluster_id, 2)
        
        if metrics.indexing_rate > cluster.indexing_capacity * 0.8:
            # Add more data nodes
            await self.add_data_nodes(cluster_id, 1)
        
        if metrics.storage_usage > cluster.storage_capacity * 0.85:
            # Scale storage
            await self.expand_cluster_storage(cluster_id)
    
    async def optimize_tenant_indexes(self, store_id):
        store_size = await self.get_store_size(store_id)
        
        if store_size > 1000000:  # Large store
            # Dedicated index with custom settings
            index_config = {
                'number_of_shards': 5,
                'number_of_replicas': 2,
                'refresh_interval': '1s'
            }
        else:
            # Shared index with standard settings
            index_config = {
                'number_of_shards': 1,
                'number_of_replicas': 1,
                'refresh_interval': '30s'
            }
        
        await self.create_or_update_index(store_id, index_config)

class AnalyticsScalingManager:
    def __init__(self):
        self.kafka_clusters = {}
        self.stream_processors = {}
        self.data_warehouse = DataWarehouse()
    
    async def scale_analytics_pipeline(self, throughput_metrics):
        # Scale Kafka partitions based on throughput
        for topic, metrics in throughput_metrics.items():
            if metrics.messages_per_second > 10000:
                await self.scale_kafka_partitions(topic, metrics.messages_per_second)
        
        # Scale stream processing
        await self.scale_stream_processors(throughput_metrics)
        
        # Scale data warehouse
        await self.scale_data_warehouse(throughput_metrics)
    
    async def scale_stream_processors(self, metrics):
        for processor_name, processor_metrics in metrics.items():
            if processor_metrics.lag > 60000:  # 1 minute lag
                # Scale up processing instances
                current_instances = await self.get_processor_instances(processor_name)
                target_instances = min(current_instances * 2, 50)
                
                await self.scale_processor(processor_name, target_instances)

class ConnectionPoolManager:
    def __init__(self):
        self.pools = {}
        self.pool_monitor = PoolMonitor()
    
    async def optimize_connection_pools(self):
        for service_name, pool in self.pools.items():
            metrics = await self.pool_monitor.get_metrics(pool)
            
            # Optimize pool size based on usage patterns
            if metrics.utilization > 0.9:
                # Increase pool size
                new_size = min(pool.max_size * 1.5, 200)
                await pool.resize(new_size)
                
            elif metrics.utilization < 0.3:
                # Decrease pool size
                new_size = max(pool.max_size * 0.8, 10)
                await pool.resize(new_size)
            
            # Optimize connection timeout
            if metrics.avg_wait_time > 100:  # ms
                pool.connection_timeout = min(pool.connection_timeout * 1.2, 5000)

class ResourceOptimizer:
    def __init__(self):
        self.memory_profiler = MemoryProfiler()
        self.cpu_profiler = CPUProfiler()
    
    async def optimize_service_resources(self, service_name):
        # Memory optimization
        memory_profile = await self.memory_profiler.profile_service(service_name)
        
        if memory_profile.heap_usage > 0.85:
            # Increase heap size or optimize memory usage
            await self.optimize_memory_usage(service_name, memory_profile)
        
        # CPU optimization
        cpu_profile = await self.cpu_profiler.profile_service(service_name)
        
        if cpu_profile.cpu_usage > 0.8:
            # Scale horizontally or optimize CPU-intensive operations
            await self.optimize_cpu_usage(service_name, cpu_profile)
    
    async def optimize_memory_usage(self, service_name, profile):
        # Identify memory hotspots
        hotspots = profile.get_memory_hotspots()
        
        optimizations = []
        for hotspot in hotspots:
            if hotspot.type == 'cache':
                # Optimize cache size and eviction policy
                optimizations.append(self.optimize_cache_memory(hotspot))
            elif hotspot.type == 'object_pool':
                # Optimize object pool size
                optimizations.append(self.optimize_object_pool(hotspot))
        
        await asyncio.gather(*optimizations)

class MultiTenantMonitoring:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_manager = AlertManager()
        self.tenant_thresholds = {}
    
    async def collect_tenant_metrics(self, store_id):
        metrics = {
            'requests_per_second': await self.get_tenant_rps(store_id),
            'response_time_p95': await self.get_tenant_latency(store_id),
            'error_rate': await self.get_tenant_error_rate(store_id),
            'database_connections': await self.get_tenant_db_connections(store_id),
            'cache_hit_ratio': await self.get_tenant_cache_ratio(store_id)
        }
        
        # Check against tenant-specific thresholds
        await self.check_tenant_alerts(store_id, metrics)
        
        return metrics
    
    async def check_tenant_alerts(self, store_id, metrics):
        thresholds = self.tenant_thresholds.get(store_id, self.default_thresholds)
        
        for metric_name, value in metrics.items():
            threshold = thresholds.get(metric_name)
            if threshold and value > threshold:
                await self.alert_manager.trigger_alert(
                    store_id, metric_name, value, threshold
                )

class PredictiveScaler:
    def __init__(self):
        self.ml_model = ScalingPredictionModel()
        self.historical_data = HistoricalDataStore()
    
    async def predict_scaling_needs(self, service_name, time_horizon_hours=2):
        # Gather historical data
        historical_metrics = await self.historical_data.get_metrics(
            service_name, days=30
        )
        
        # Include external factors
        external_factors = {
            'day_of_week': datetime.now().weekday(),
            'hour_of_day': datetime.now().hour,
            'is_holiday': await self.is_holiday(),
            'marketing_campaigns': await self.get_active_campaigns(),
            'seasonal_events': await self.get_seasonal_events()
        }
        
        # Predict resource needs
        prediction = await self.ml_model.predict(
            historical_metrics, external_factors, time_horizon_hours
        )
        
        # Pre-scale if high confidence prediction
        if prediction.confidence > 0.8 and prediction.scale_factor > 1.5:
            await self.pre_scale_service(service_name, prediction.scale_factor)
        
        return prediction