#!/usr/bin/env python3 """ Marketplace Performance Optimization Tests Phase 9.2: Marketplace Performance Optimization (Weeks 10-12) """ import pytest import asyncio import time import json import requests import psutil import statistics from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, field from datetime import datetime, timedelta import logging from concurrent.futures import ThreadPoolExecutor, as_completed import threading # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class OptimizationStrategy(Enum): """Performance optimization strategies""" GPU_ACCELERATION = "gpu_acceleration" DISTRIBUTED_PROCESSING = "distributed_processing" CACHING_OPTIMIZATION = "caching_optimization" LOAD_BALANCING = "load_balancing" RESOURCE_SCALING = "resource_scaling" QUERY_OPTIMIZATION = "query_optimization" class PerformanceMetric(Enum): """Performance metrics to monitor""" RESPONSE_TIME = "response_time" THROUGHPUT = "throughput" GPU_UTILIZATION = "gpu_utilization" MEMORY_USAGE = "memory_usage" CPU_UTILIZATION = "cpu_utilization" NETWORK_LATENCY = "network_latency" CACHE_HIT_RATE = "cache_hit_rate" @dataclass class PerformanceBaseline: """Performance baseline for comparison""" metric_name: str baseline_value: float target_value: float measurement_unit: str tolerance: float = 0.1 @dataclass class OptimizationResult: """Result of optimization test""" strategy: OptimizationStrategy baseline_performance: Dict[str, float] optimized_performance: Dict[str, float] improvement_percentage: float resource_usage_change: Dict[str, float] optimization_time: float success: bool @dataclass class LoadTestConfig: """Load testing configuration""" concurrent_users: int requests_per_second: int test_duration_seconds: int ramp_up_period_seconds: int think_time_seconds: float = 0.5 class MarketplacePerformanceTests: """Test suite for marketplace performance optimization""" def __init__(self, marketplace_url: str = "http://127.0.0.1:18000"): self.marketplace_url = marketplace_url self.baselines = self._setup_baselines() self.session = requests.Session() self.session.timeout = 30 self.performance_history = [] def _setup_baselines(self) -> Dict[PerformanceMetric, PerformanceBaseline]: """Setup performance baselines for optimization""" return { PerformanceMetric.RESPONSE_TIME: PerformanceBaseline( metric_name="response_time", baseline_value=200.0, # 200ms baseline target_value=50.0, # 50ms target measurement_unit="ms", tolerance=0.2 ), PerformanceMetric.THROUGHPUT: PerformanceBaseline( metric_name="throughput", baseline_value=100.0, # 100 req/s baseline target_value=1000.0, # 1000 req/s target measurement_unit="req/s", tolerance=0.15 ), PerformanceMetric.GPU_UTILIZATION: PerformanceBaseline( metric_name="gpu_utilization", baseline_value=0.60, # 60% baseline target_value=0.90, # 90% target measurement_unit="percentage", tolerance=0.1 ), PerformanceMetric.MEMORY_USAGE: PerformanceBaseline( metric_name="memory_usage", baseline_value=70.0, # 70% baseline target_value=85.0, # 85% target (efficient use) measurement_unit="percentage", tolerance=0.15 ), PerformanceMetric.CACHE_HIT_RATE: PerformanceBaseline( metric_name="cache_hit_rate", baseline_value=0.40, # 40% baseline target_value=0.85, # 85% target measurement_unit="percentage", tolerance=0.1 ) } async def measure_current_performance(self) -> Dict[str, float]: """Measure current marketplace performance""" try: # Test basic endpoint performance start_time = time.time() response = self.session.get(f"{self.marketplace_url}/v1/marketplace/status", timeout=10) response_time = (time.time() - start_time) * 1000 # Get system metrics cpu_percent = psutil.cpu_percent(interval=1) memory_info = psutil.virtual_memory() memory_percent = memory_info.percent # Get marketplace-specific metrics metrics_response = self.session.get(f"{self.marketplace_url}/v1/metrics/current", timeout=10) if metrics_response.status_code == 200: marketplace_metrics = metrics_response.json() else: marketplace_metrics = {} current_performance = { "response_time": response_time, "cpu_utilization": cpu_percent, "memory_usage": memory_percent, "gpu_utilization": marketplace_metrics.get("gpu_utilization", 0.0), "cache_hit_rate": marketplace_metrics.get("cache_hit_rate", 0.0), "throughput": marketplace_metrics.get("throughput", 0.0), "active_connections": marketplace_metrics.get("active_connections", 0), "error_rate": marketplace_metrics.get("error_rate", 0.0) } self.performance_history.append({ "timestamp": datetime.now(), "metrics": current_performance }) return current_performance except Exception as e: logger.error(f"Performance measurement failed: {e}") return {} async def test_gpu_acceleration_optimization(self) -> OptimizationResult: """Test GPU acceleration optimization for marketplace""" try: # Measure baseline performance baseline_performance = await self.measure_current_performance() # Enable GPU acceleration optimization_payload = { "optimization_strategy": "gpu_acceleration", "gpu_acceleration_config": { "enable_gpu_processing": True, "gpu_memory_fraction": 0.8, "batch_processing_size": 32, "cuda_streams": 4, "memory_optimization": True }, "target_endpoints": [ "/v1/marketplace/search", "/v1/marketplace/recommend", "/v1/analytics/marketplace" ] } response = self.session.post( f"{self.marketplace_url}/v1/optimization/apply", json=optimization_payload, timeout=30 ) if response.status_code == 200: optimization_result = response.json() # Wait for optimization to take effect await asyncio.sleep(5) # Measure optimized performance optimized_performance = await self.measure_current_performance() # Calculate improvements response_time_improvement = ( (baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0)) / baseline_performance.get("response_time", 1) * 100 ) throughput_improvement = ( (optimized_performance.get("throughput", 0) - baseline_performance.get("throughput", 0)) / baseline_performance.get("throughput", 1) * 100 ) gpu_utilization_change = ( optimized_performance.get("gpu_utilization", 0) - baseline_performance.get("gpu_utilization", 0) ) overall_improvement = (response_time_improvement + throughput_improvement) / 2 return OptimizationResult( strategy=OptimizationStrategy.GPU_ACCELERATION, baseline_performance=baseline_performance, optimized_performance=optimized_performance, improvement_percentage=overall_improvement, resource_usage_change={ "gpu_utilization": gpu_utilization_change, "memory_usage": optimized_performance.get("memory_usage", 0) - baseline_performance.get("memory_usage", 0), "cpu_utilization": optimized_performance.get("cpu_utilization", 0) - baseline_performance.get("cpu_utilization", 0) }, optimization_time=optimization_result.get("optimization_time", 0), success=True ) else: return OptimizationResult( strategy=OptimizationStrategy.GPU_ACCELERATION, baseline_performance=baseline_performance, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) except Exception as e: logger.error(f"GPU acceleration optimization test failed: {e}") return OptimizationResult( strategy=OptimizationStrategy.GPU_ACCELERATION, baseline_performance={}, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) async def test_distributed_processing_optimization(self) -> OptimizationResult: """Test distributed processing framework optimization""" try: # Measure baseline performance baseline_performance = await self.measure_current_performance() # Enable distributed processing optimization_payload = { "optimization_strategy": "distributed_processing", "distributed_config": { "enable_distributed_processing": True, "worker_nodes": 4, "task_distribution": "round_robin", "load_balancing_algorithm": "least_connections", "fault_tolerance": True, "replication_factor": 2 }, "target_workloads": [ "bulk_data_processing", "analytics_computation", "recommendation_generation" ] } response = self.session.post( f"{self.marketplace_url}/v1/optimization/apply", json=optimization_payload, timeout=30 ) if response.status_code == 200: optimization_result = response.json() # Wait for optimization to take effect await asyncio.sleep(5) # Measure optimized performance optimized_performance = await self.measure_current_performance() # Calculate improvements throughput_improvement = ( (optimized_performance.get("throughput", 0) - baseline_performance.get("throughput", 0)) / baseline_performance.get("throughput", 1) * 100 ) response_time_improvement = ( (baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0)) / baseline_performance.get("response_time", 1) * 100 ) overall_improvement = (throughput_improvement + response_time_improvement) / 2 return OptimizationResult( strategy=OptimizationStrategy.DISTRIBUTED_PROCESSING, baseline_performance=baseline_performance, optimized_performance=optimized_performance, improvement_percentage=overall_improvement, resource_usage_change={ "cpu_utilization": optimized_performance.get("cpu_utilization", 0) - baseline_performance.get("cpu_utilization", 0), "memory_usage": optimized_performance.get("memory_usage", 0) - baseline_performance.get("memory_usage", 0), "network_latency": optimized_performance.get("network_latency", 0) - baseline_performance.get("network_latency", 0) }, optimization_time=optimization_result.get("optimization_time", 0), success=True ) else: return OptimizationResult( strategy=OptimizationStrategy.DISTRIBUTED_PROCESSING, baseline_performance=baseline_performance, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) except Exception as e: logger.error(f"Distributed processing optimization test failed: {e}") return OptimizationResult( strategy=OptimizationStrategy.DISTRIBUTED_PROCESSING, baseline_performance={}, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) async def test_caching_optimization(self) -> OptimizationResult: """Test advanced caching and optimization""" try: # Measure baseline performance baseline_performance = await self.measure_current_performance() # Enable advanced caching optimization_payload = { "optimization_strategy": "caching_optimization", "caching_config": { "enable_redis_cache": True, "cache_ttl_seconds": 300, "cache_size_mb": 1024, "cache_strategy": "lru_with_write_through", "enable_query_result_cache": True, "enable_session_cache": True, "compression_enabled": True }, "cache_targets": [ "marketplace_listings", "agent_profiles", "pricing_data", "analytics_results" ] } response = self.session.post( f"{self.marketplace_url}/v1/optimization/apply", json=optimization_payload, timeout=30 ) if response.status_code == 200: optimization_result = response.json() # Wait for cache warming await asyncio.sleep(10) # Measure optimized performance optimized_performance = await self.measure_current_performance() # Calculate improvements cache_hit_rate_improvement = ( (optimized_performance.get("cache_hit_rate", 0) - baseline_performance.get("cache_hit_rate", 0)) * 100 ) response_time_improvement = ( (baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0)) / baseline_performance.get("response_time", 1) * 100 ) overall_improvement = (cache_hit_rate_improvement + response_time_improvement) / 2 return OptimizationResult( strategy=OptimizationStrategy.CACHING_OPTIMIZATION, baseline_performance=baseline_performance, optimized_performance=optimized_performance, improvement_percentage=overall_improvement, resource_usage_change={ "memory_usage": optimized_performance.get("memory_usage", 0) - baseline_performance.get("memory_usage", 0), "cache_hit_rate": optimized_performance.get("cache_hit_rate", 0) - baseline_performance.get("cache_hit_rate", 0), "response_time": baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0) }, optimization_time=optimization_result.get("optimization_time", 0), success=True ) else: return OptimizationResult( strategy=OptimizationStrategy.CACHING_OPTIMIZATION, baseline_performance=baseline_performance, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) except Exception as e: logger.error(f"Caching optimization test failed: {e}") return OptimizationResult( strategy=OptimizationStrategy.CACHING_OPTIMIZATION, baseline_performance={}, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) async def test_load_balancing_optimization(self) -> OptimizationResult: """Test load balancing optimization""" try: # Measure baseline performance baseline_performance = await self.measure_current_performance() # Enable advanced load balancing optimization_payload = { "optimization_strategy": "load_balancing", "load_balancing_config": { "algorithm": "weighted_round_robin", "health_check_interval": 30, "failover_enabled": True, "connection_pool_size": 100, "max_connections_per_node": 50, "sticky_sessions": False }, "backend_nodes": [ {"node_id": "node_1", "weight": 3, "max_connections": 50}, {"node_id": "node_2", "weight": 2, "max_connections": 40}, {"node_id": "node_3", "weight": 1, "max_connections": 30} ] } response = self.session.post( f"{self.marketplace_url}/v1/optimization/apply", json=optimization_payload, timeout=30 ) if response.status_code == 200: optimization_result = response.json() # Wait for load balancer configuration await asyncio.sleep(5) # Measure optimized performance optimized_performance = await self.measure_current_performance() # Calculate improvements throughput_improvement = ( (optimized_performance.get("throughput", 0) - baseline_performance.get("throughput", 0)) / baseline_performance.get("throughput", 1) * 100 ) response_time_improvement = ( (baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0)) / baseline_performance.get("response_time", 1) * 100 ) error_rate_improvement = ( (baseline_performance.get("error_rate", 0) - optimized_performance.get("error_rate", 0)) / max(baseline_performance.get("error_rate", 0.01), 0.01) * 100 ) overall_improvement = (throughput_improvement + response_time_improvement + error_rate_improvement) / 3 return OptimizationResult( strategy=OptimizationStrategy.LOAD_BALANCING, baseline_performance=baseline_performance, optimized_performance=optimized_performance, improvement_percentage=overall_improvement, resource_usage_change={ "active_connections": optimized_performance.get("active_connections", 0) - baseline_performance.get("active_connections", 0), "error_rate": baseline_performance.get("error_rate", 0) - optimized_performance.get("error_rate", 0), "response_time": baseline_performance.get("response_time", 0) - optimized_performance.get("response_time", 0) }, optimization_time=optimization_result.get("optimization_time", 0), success=True ) else: return OptimizationResult( strategy=OptimizationStrategy.LOAD_BALANCING, baseline_performance=baseline_performance, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) except Exception as e: logger.error(f"Load balancing optimization test failed: {e}") return OptimizationResult( strategy=OptimizationStrategy.LOAD_BALANCING, baseline_performance={}, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) async def test_adaptive_resource_scaling(self) -> OptimizationResult: """Test adaptive resource scaling for marketplace demand""" try: # Measure baseline performance baseline_performance = await self.measure_current_performance() # Enable adaptive scaling optimization_payload = { "optimization_strategy": "resource_scaling", "scaling_config": { "enable_auto_scaling": True, "scaling_policy": "demand_based", "min_instances": 2, "max_instances": 10, "scale_up_threshold": 80, # CPU/memory threshold "scale_down_threshold": 30, "cooldown_period": 300, "target_utilization": 70 }, "scaling_metrics": [ "cpu_utilization", "memory_usage", "request_rate", "response_time" ] } response = self.session.post( f"{self.marketplace_url}/v1/optimization/apply", json=optimization_payload, timeout=30 ) if response.status_code == 200: optimization_result = response.json() # Simulate load increase to trigger scaling await self._simulate_load_increase() # Wait for scaling to complete await asyncio.sleep(15) # Measure optimized performance optimized_performance = await self.measure_current_performance() # Calculate improvements throughput_improvement = ( (optimized_performance.get("throughput", 0) - baseline_performance.get("throughput", 0)) / baseline_performance.get("throughput", 1) * 100 ) resource_efficiency = ( (baseline_performance.get("cpu_utilization", 0) - optimized_performance.get("cpu_utilization", 0)) / baseline_performance.get("cpu_utilization", 1) * 100 ) overall_improvement = (throughput_improvement + resource_efficiency) / 2 return OptimizationResult( strategy=OptimizationStrategy.RESOURCE_SCALING, baseline_performance=baseline_performance, optimized_performance=optimized_performance, improvement_percentage=overall_improvement, resource_usage_change={ "cpu_utilization": optimized_performance.get("cpu_utilization", 0) - baseline_performance.get("cpu_utilization", 0), "memory_usage": optimized_performance.get("memory_usage", 0) - baseline_performance.get("memory_usage", 0), "active_instances": optimization_result.get("current_instances", 1) - 1 }, optimization_time=optimization_result.get("optimization_time", 0), success=True ) else: return OptimizationResult( strategy=OptimizationStrategy.RESOURCE_SCALING, baseline_performance=baseline_performance, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) except Exception as e: logger.error(f"Adaptive resource scaling test failed: {e}") return OptimizationResult( strategy=OptimizationStrategy.RESOURCE_SCALING, baseline_performance={}, optimized_performance={}, improvement_percentage=0, resource_usage_change={}, optimization_time=0, success=False ) async def test_real_time_performance_monitoring(self) -> Dict[str, Any]: """Test real-time marketplace performance monitoring""" try: # Start performance monitoring monitoring_payload = { "monitoring_config": { "enable_real_time_monitoring": True, "sampling_interval": 5, # seconds "metrics_to_track": [ "response_time", "throughput", "error_rate", "gpu_utilization", "memory_usage" ], "alert_thresholds": { "response_time": 100, # ms "error_rate": 0.05, # 5% "memory_usage": 90 # % } } } response = self.session.post( f"{self.marketplace_url}/v1/monitoring/start", json=monitoring_payload, timeout=15 ) if response.status_code == 200: monitoring_result = response.json() # Collect monitoring data for 30 seconds await asyncio.sleep(30) # Get monitoring summary summary_response = self.session.get( f"{self.marketplace_url}/v1/monitoring/summary", timeout=10 ) if summary_response.status_code == 200: summary_data = summary_response.json() return { "monitoring_active": True, "monitoring_period": 30, "metrics_collected": summary_data.get("metrics_collected", {}), "performance_summary": summary_data.get("performance_summary", {}), "alerts_triggered": summary_data.get("alerts", []), "data_points": summary_data.get("data_points", 0), "success": True } else: return { "monitoring_active": True, "error": f"Failed to get monitoring summary: {summary_response.status_code}", "success": False } else: return { "error": f"Failed to start monitoring: {response.status_code}", "success": False } except Exception as e: return { "error": str(e), "success": False } async def _simulate_load_increase(self): """Simulate increased load to trigger scaling""" try: # Generate concurrent requests to increase load with ThreadPoolExecutor(max_workers=20) as executor: futures = [] for i in range(50): future = executor.submit( self.session.get, f"{self.marketplace_url}/v1/marketplace/search", timeout=5 ) futures.append(future) # Wait for all requests to complete for future in as_completed(futures): try: future.result() except Exception: pass # Ignore errors during load simulation except Exception as e: logger.error(f"Load simulation failed: {e}") async def run_load_test(self, config: LoadTestConfig) -> Dict[str, Any]: """Run comprehensive load test""" try: results = { "test_config": asdict(config), "start_time": datetime.now(), "request_results": [], "performance_metrics": {} } # Calculate total requests total_requests = config.requests_per_second * config.test_duration_seconds # Run load test start_time = time.time() with ThreadPoolExecutor(max_workers=config.concurrent_users) as executor: futures = [] for request_id in range(total_requests): # Calculate delay for target RPS delay = request_id / config.requests_per_second future = executor.submit( self._make_request_with_delay, delay, request_id ) futures.append(future) # Collect results for future in as_completed(futures): try: result = future.result() results["request_results"].append(result) except Exception as e: results["request_results"].append({ "request_id": None, "success": False, "error": str(e), "response_time": 0 }) end_time = time.time() results["end_time"] = datetime.now() results["total_test_time"] = end_time - start_time # Calculate performance metrics successful_requests = [r for r in results["request_results"] if r["success"]] failed_requests = [r for r in results["request_results"] if not r["success"]] if successful_requests: response_times = [r["response_time"] for r in successful_requests] results["performance_metrics"] = { "total_requests": len(results["request_results"]), "successful_requests": len(successful_requests), "failed_requests": len(failed_requests), "success_rate": len(successful_requests) / len(results["request_results"]) * 100, "average_response_time": statistics.mean(response_times), "median_response_time": statistics.median(response_times), "min_response_time": min(response_times), "max_response_time": max(response_times), "p95_response_time": np.percentile(response_times, 95), "p99_response_time": np.percentile(response_times, 99), "requests_per_second": len(successful_requests) / (end_time - start_time), "error_rate": len(failed_requests) / len(results["request_results"]) * 100 } else: results["performance_metrics"] = { "total_requests": len(results["request_results"]), "successful_requests": 0, "failed_requests": len(failed_requests), "success_rate": 0, "error_rate": 100 } return results except Exception as e: return { "error": str(e), "success": False } def _make_request_with_delay(self, delay: float, request_id: int) -> Dict[str, Any]: """Make HTTP request with delay for load testing""" try: # Wait for calculated delay time.sleep(max(0, delay - time.time())) start_time = time.time() response = self.session.get( f"{self.marketplace_url}/v1/marketplace/status", timeout=10 ) end_time = time.time() return { "request_id": request_id, "success": response.status_code == 200, "status_code": response.status_code, "response_time": (end_time - start_time) * 1000, # Convert to ms "timestamp": datetime.now() } except Exception as e: return { "request_id": request_id, "success": False, "error": str(e), "response_time": 0, "timestamp": datetime.now() } # Test Fixtures @pytest.fixture async def performance_tests(): """Create marketplace performance test instance""" return MarketplacePerformanceTests() @pytest.fixture def load_test_config(): """Load test configuration""" return LoadTestConfig( concurrent_users=10, requests_per_second=50, test_duration_seconds=30, ramp_up_period_seconds=5 ) # Test Classes class TestGPUAcceleration: """Test GPU acceleration optimization""" @pytest.mark.asyncio async def test_gpu_optimization_enabled(self, performance_tests): """Test GPU acceleration optimization""" result = await performance_tests.test_gpu_acceleration_optimization() assert result.success, "GPU acceleration optimization failed" assert result.improvement_percentage > 0, "No performance improvement detected" assert "gpu_utilization" in result.resource_usage_change, "No GPU utilization change measured" assert result.optimized_performance.get("gpu_utilization", 0) > result.baseline_performance.get("gpu_utilization", 0), "GPU utilization not increased" @pytest.mark.asyncio async def test_gpu_memory_optimization(self, performance_tests): """Test GPU memory optimization""" result = await performance_tests.test_gpu_acceleration_optimization() assert result.success, "GPU memory optimization test failed" assert result.optimized_performance.get("memory_usage", 0) <= 90, "Memory usage too high after optimization" class TestDistributedProcessing: """Test distributed processing framework""" @pytest.mark.asyncio async def test_distributed_processing_setup(self, performance_tests): """Test distributed processing setup""" result = await performance_tests.test_distributed_processing_optimization() assert result.success, "Distributed processing optimization failed" assert result.improvement_percentage > 0, "No throughput improvement detected" assert result.optimized_performance.get("throughput", 0) > result.baseline_performance.get("throughput", 0), "Throughput not improved" @pytest.mark.asyncio async def test_fault_tolerance(self, performance_tests): """Test fault tolerance in distributed processing""" result = await performance_tests.test_distributed_processing_optimization() assert result.success, "Fault tolerance test failed" assert result.optimized_performance.get("error_rate", 1.0) < 0.05, "Error rate too high" class TestCachingOptimization: """Test advanced caching optimization""" @pytest.mark.asyncio async def test_cache_hit_rate_improvement(self, performance_tests): """Test cache hit rate improvement""" result = await performance_tests.test_caching_optimization() assert result.success, "Caching optimization failed" assert result.optimized_performance.get("cache_hit_rate", 0) > 0.8, "Cache hit rate too low" assert result.resource_usage_change.get("cache_hit_rate", 0) > 0.4, "Cache hit rate improvement too low" @pytest.mark.asyncio async def test_response_time_improvement(self, performance_tests): """Test response time improvement through caching""" result = await performance_tests.test_caching_optimization() assert result.success, "Response time optimization failed" assert result.optimized_performance.get("response_time", 1000) < result.baseline_performance.get("response_time", 1000), "Response time not improved" class TestLoadBalancing: """Test load balancing optimization""" @pytest.mark.asyncio async def test_load_balancer_setup(self, performance_tests): """Test load balancer setup and configuration""" result = await performance_tests.test_load_balancing_optimization() assert result.success, "Load balancing optimization failed" assert result.optimized_performance.get("throughput", 0) > result.baseline_performance.get("throughput", 0), "Throughput not improved" assert result.optimized_performance.get("error_rate", 1.0) < result.baseline_performance.get("error_rate", 1.0), "Error rate not reduced" @pytest.mark.asyncio async def test_connection_distribution(self, performance_tests): """Test connection distribution across nodes""" result = await performance_tests.test_load_balancing_optimization() assert result.success, "Connection distribution test failed" assert result.resource_usage_change.get("active_connections", 0) > 0, "No active connections change" class TestAdaptiveScaling: """Test adaptive resource scaling""" @pytest.mark.asyncio async def test_auto_scaling_triggered(self, performance_tests): """Test automatic scaling trigger""" result = await performance_tests.test_adaptive_resource_scaling() assert result.success, "Adaptive scaling test failed" assert result.resource_usage_change.get("active_instances", 0) > 0, "No scaling occurred" assert result.optimized_performance.get("throughput", 0) > result.baseline_performance.get("throughput", 0), "Throughput not improved after scaling" @pytest.mark.asyncio async def test_resource_efficiency(self, performance_tests): """Test resource efficiency after scaling""" result = await performance_tests.test_adaptive_resource_scaling() assert result.success, "Resource efficiency test failed" assert result.optimized_performance.get("cpu_utilization", 100) < 80, "CPU utilization too high after scaling" class TestRealTimeMonitoring: """Test real-time performance monitoring""" @pytest.mark.asyncio async def test_monitoring_setup(self, performance_tests): """Test real-time monitoring setup""" result = await performance_tests.test_real_time_performance_monitoring() assert result.get("success", False), "Real-time monitoring setup failed" assert result.get("monitoring_active", False), "Monitoring not active" assert result.get("data_points", 0) > 0, "No data points collected" @pytest.mark.asyncio async def test_performance_metrics_collection(self, performance_tests): """Test performance metrics collection""" result = await performance_tests.test_real_time_performance_monitoring() assert result.get("success", False), "Performance metrics collection failed" assert "performance_summary" in result, "No performance summary provided" assert "metrics_collected" in result, "No metrics collected" class TestLoadTesting: """Test comprehensive load testing""" @pytest.mark.asyncio async def test_basic_load_test(self, performance_tests, load_test_config): """Test basic load test functionality""" result = await performance_tests.run_load_test(load_test_config) assert "error" not in result, "Load test failed with error" assert result.get("performance_metrics", {}).get("success_rate", 0) > 95, "Success rate too low" assert result.get("performance_metrics", {}).get("average_response_time", 10000) < 1000, "Average response time too high" @pytest.mark.asyncio async def test_throughput_measurement(self, performance_tests, load_test_config): """Test throughput measurement accuracy""" result = await performance_tests.run_load_test(load_test_config) assert "error" not in result, "Throughput measurement failed" actual_rps = result.get("performance_metrics", {}).get("requests_per_second", 0) target_rps = load_test_config.requests_per_second assert abs(actual_rps - target_rps) / target_rps < 0.1, f"Throughput not accurate: expected {target_rps}, got {actual_rps}" @pytest.mark.asyncio async def test_response_time_distribution(self, performance_tests, load_test_config): """Test response time distribution""" result = await performance_tests.run_load_test(load_test_config) assert "error" not in result, "Response time distribution test failed" metrics = result.get("performance_metrics", {}) assert metrics.get("p95_response_time", 10000) < 500, "P95 response time too high" assert metrics.get("p99_response_time", 10000) < 1000, "P99 response time too high" if __name__ == "__main__": pytest.main([__file__, "-v", "--tb=short"])