""" Performance Benchmark Tests for AITBC Tests system performance under various loads and conditions """ import pytest import time import asyncio import threading from datetime import datetime, timedelta from unittest.mock import Mock, patch from concurrent.futures import ThreadPoolExecutor import statistics class TestAPIPerformance: """Test API endpoint performance""" def test_response_time_benchmarks(self): """Test API response time benchmarks""" # Mock API client client = Mock() # Simulate different response times response_times = [0.05, 0.08, 0.12, 0.06, 0.09, 0.11, 0.07, 0.10] # Calculate performance metrics avg_response_time = statistics.mean(response_times) max_response_time = max(response_times) min_response_time = min(response_times) # Performance assertions assert avg_response_time < 0.1 # Average should be under 100ms assert max_response_time < 0.2 # Max should be under 200ms assert min_response_time > 0.01 # Should be reasonable minimum # Test performance thresholds performance_thresholds = { 'excellent': 0.05, # < 50ms 'good': 0.1, # < 100ms 'acceptable': 0.2, # < 200ms 'poor': 0.5 # > 500ms } # Classify performance if avg_response_time < performance_thresholds['excellent']: performance_rating = 'excellent' elif avg_response_time < performance_thresholds['good']: performance_rating = 'good' elif avg_response_time < performance_thresholds['acceptable']: performance_rating = 'acceptable' else: performance_rating = 'poor' assert performance_rating in ['excellent', 'good', 'acceptable'] def test_concurrent_request_handling(self): """Test handling of concurrent requests""" # Mock API endpoint def mock_api_call(request_id): time.sleep(0.01) # Simulate 10ms processing time return {'request_id': request_id, 'status': 'success'} # Test concurrent execution num_requests = 50 start_time = time.time() with ThreadPoolExecutor(max_workers=10) as executor: futures = [ executor.submit(mock_api_call, i) for i in range(num_requests) ] results = [future.result() for future in futures] end_time = time.time() total_time = end_time - start_time # Performance assertions assert len(results) == num_requests assert all(result['status'] == 'success' for result in results) assert total_time < 1.0 # Should complete in under 1 second # Calculate throughput throughput = num_requests / total_time assert throughput > 50 # Should handle at least 50 requests per second def test_memory_usage_under_load(self): """Test memory usage under load""" import psutil import os # Get initial memory usage process = psutil.Process(os.getpid()) initial_memory = process.memory_info().rss / 1024 / 1024 # MB # Simulate memory-intensive operations data_store = [] for i in range(1000): data_store.append({ 'id': i, 'data': 'x' * 1000, # 1KB per item 'timestamp': datetime.utcnow().isoformat() }) # Get peak memory usage peak_memory = process.memory_info().rss / 1024 / 1024 # MB memory_increase = peak_memory - initial_memory # Memory assertions assert memory_increase < 100 # Should not increase by more than 100MB assert len(data_store) == 1000 # Cleanup del data_store class TestDatabasePerformance: """Test database operation performance""" def test_query_performance(self): """Test database query performance""" # Mock database operations def mock_query(query_type): if query_type == 'simple': time.sleep(0.001) # 1ms elif query_type == 'complex': time.sleep(0.01) # 10ms elif query_type == 'aggregate': time.sleep(0.05) # 50ms return {'results': ['data'], 'query_type': query_type} # Test different query types query_types = ['simple', 'complex', 'aggregate'] query_times = {} for query_type in query_types: start_time = time.time() result = mock_query(query_type) end_time = time.time() query_times[query_type] = end_time - start_time assert result['query_type'] == query_type # Performance assertions assert query_times['simple'] < 0.005 # < 5ms assert query_times['complex'] < 0.02 # < 20ms assert query_times['aggregate'] < 0.1 # < 100ms def test_batch_operation_performance(self): """Test batch operation performance""" # Mock batch insert def mock_batch_insert(items): time.sleep(len(items) * 0.001) # 1ms per item return {'inserted_count': len(items)} # Test different batch sizes batch_sizes = [10, 50, 100, 500] performance_results = {} for batch_size in batch_sizes: items = [{'id': i, 'data': f'item_{i}'} for i in range(batch_size)] start_time = time.time() result = mock_batch_insert(items) end_time = time.time() performance_results[batch_size] = { 'time': end_time - start_time, 'throughput': batch_size / (end_time - start_time) } assert result['inserted_count'] == batch_size # Performance analysis for batch_size, metrics in performance_results.items(): assert metrics['throughput'] > 100 # Should handle at least 100 items/second assert metrics['time'] < 5.0 # Should complete in under 5 seconds def test_connection_pool_performance(self): """Test database connection pool performance""" # Mock connection pool class MockConnectionPool: def __init__(self, max_connections=10): self.max_connections = max_connections self.active_connections = 0 self.lock = threading.Lock() def get_connection(self): with self.lock: if self.active_connections < self.max_connections: self.active_connections += 1 return MockConnection() else: raise Exception("Connection pool exhausted") def release_connection(self, conn): with self.lock: self.active_connections -= 1 class MockConnection: def execute(self, query): time.sleep(0.01) # 10ms query time return {'result': 'success'} # Test connection pool under load pool = MockConnectionPool(max_connections=5) def worker_task(): try: conn = pool.get_connection() result = conn.execute("SELECT * FROM test") pool.release_connection(conn) return result except Exception as e: return {'error': str(e)} # Test concurrent access with ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(worker_task) for _ in range(20)] results = [future.result() for future in futures] # Analyze results successful_results = [r for r in results if 'error' not in r] error_results = [r for r in results if 'error' in r] # Should have some successful and some error results (pool exhaustion) assert len(successful_results) > 0 assert len(error_results) > 0 assert len(successful_results) + len(error_results) == 20 class TestBlockchainPerformance: """Test blockchain operation performance""" def test_transaction_processing_speed(self): """Test transaction processing speed""" # Mock transaction processing def mock_process_transaction(tx): processing_time = 0.1 + (len(tx['data']) * 0.001) # Base 100ms + data size time.sleep(processing_time) return { 'tx_hash': f'0x{hash(str(tx)) % 1000000:x}', 'processing_time': processing_time } # Test transactions of different sizes transactions = [ {'data': 'small', 'amount': 1.0}, {'data': 'x' * 100, 'amount': 10.0}, # 100 bytes {'data': 'x' * 1000, 'amount': 100.0}, # 1KB {'data': 'x' * 10000, 'amount': 1000.0}, # 10KB ] processing_times = [] for tx in transactions: start_time = time.time() result = mock_process_transaction(tx) end_time = time.time() processing_times.append(result['processing_time']) assert 'tx_hash' in result assert result['processing_time'] > 0 # Performance assertions assert processing_times[0] < 0.2 # Small transaction < 200ms assert processing_times[-1] < 1.0 # Large transaction < 1 second def test_block_validation_performance(self): """Test block validation performance""" # Mock block validation def mock_validate_block(block): num_transactions = len(block['transactions']) validation_time = num_transactions * 0.01 # 10ms per transaction time.sleep(validation_time) return { 'valid': True, 'validation_time': validation_time, 'transactions_validated': num_transactions } # Test blocks with different transaction counts blocks = [ {'transactions': [f'tx_{i}' for i in range(10)]}, # 10 transactions {'transactions': [f'tx_{i}' for i in range(50)]}, # 50 transactions {'transactions': [f'tx_{i}' for i in range(100)]}, # 100 transactions ] validation_results = [] for block in blocks: start_time = time.time() result = mock_validate_block(block) end_time = time.time() validation_results.append(result) assert result['valid'] is True assert result['transactions_validated'] == len(block['transactions']) # Performance analysis for i, result in enumerate(validation_results): expected_time = len(blocks[i]['transactions']) * 0.01 assert abs(result['validation_time'] - expected_time) < 0.01 def test_sync_performance(self): """Test blockchain sync performance""" # Mock blockchain sync def mock_sync_blocks(start_block, end_block): num_blocks = end_block - start_block sync_time = num_blocks * 0.05 # 50ms per block time.sleep(sync_time) return { 'synced_blocks': num_blocks, 'sync_time': sync_time, 'blocks_per_second': num_blocks / sync_time } # Test different sync ranges sync_ranges = [ (1000, 1010), # 10 blocks (1000, 1050), # 50 blocks (1000, 1100), # 100 blocks ] sync_results = [] for start, end in sync_ranges: result = mock_sync_blocks(start, end) sync_results.append(result) assert result['synced_blocks'] == (end - start) assert result['blocks_per_second'] > 10 # Should sync at least 10 blocks/second # Performance consistency sync_rates = [result['blocks_per_second'] for result in sync_results] avg_sync_rate = statistics.mean(sync_rates) assert avg_sync_rate > 15 # Average should be at least 15 blocks/second class TestSystemResourcePerformance: """Test system resource utilization""" def test_cpu_utilization(self): """Test CPU utilization under load""" import psutil import os # Get initial CPU usage initial_cpu = psutil.cpu_percent(interval=0.1) # CPU-intensive task def cpu_intensive_task(): result = 0 for i in range(1000000): result += i * i return result # Run CPU-intensive task start_time = time.time() cpu_intensive_task() end_time = time.time() # Get CPU usage during task cpu_usage = psutil.cpu_percent(interval=0.1) # Performance assertions execution_time = end_time - start_time assert execution_time < 5.0 # Should complete in under 5 seconds assert cpu_usage > 0 # Should show CPU usage def test_disk_io_performance(self): """Test disk I/O performance""" import tempfile from pathlib import Path with tempfile.TemporaryDirectory() as temp_dir: temp_path = Path(temp_dir) # Test write performance test_data = 'x' * (1024 * 1024) # 1MB of data write_times = [] for i in range(10): file_path = temp_path / f"test_file_{i}.txt" start_time = time.time() with open(file_path, 'w') as f: f.write(test_data) end_time = time.time() write_times.append(end_time - start_time) # Test read performance read_times = [] for i in range(10): file_path = temp_path / f"test_file_{i}.txt" start_time = time.time() with open(file_path, 'r') as f: data = f.read() end_time = time.time() read_times.append(end_time - start_time) assert len(data) == len(test_data) # Performance analysis avg_write_time = statistics.mean(write_times) avg_read_time = statistics.mean(read_times) assert avg_write_time < 0.1 # Write should be under 100ms per MB assert avg_read_time < 0.05 # Read should be under 50ms per MB def test_network_performance(self): """Test network I/O performance""" # Mock network operations def mock_network_request(size_kb): # Simulate network latency and bandwidth latency = 0.01 # 10ms latency bandwidth_time = size_kb / 1000 # 1MB/s bandwidth total_time = latency + bandwidth_time time.sleep(total_time) return {'size': size_kb, 'time': total_time} # Test different request sizes request_sizes = [10, 100, 1000] # KB network_results = [] for size in request_sizes: result = mock_network_request(size) network_results.append(result) assert result['size'] == size assert result['time'] > 0 # Performance analysis throughputs = [size / result['time'] for size, result in zip(request_sizes, network_results)] avg_throughput = statistics.mean(throughputs) assert avg_throughput > 500 # Should achieve at least 500 KB/s class TestScalabilityMetrics: """Test system scalability metrics""" def test_load_scaling(self): """Test system behavior under increasing load""" # Mock system under different loads def mock_system_load(load_factor): # Simulate increasing response times with load base_response_time = 0.1 load_response_time = base_response_time * (1 + load_factor * 0.1) time.sleep(load_response_time) return { 'load_factor': load_factor, 'response_time': load_response_time, 'throughput': 1 / load_response_time } # Test different load factors load_factors = [1, 2, 5, 10] # 1x, 2x, 5x, 10x load scaling_results = [] for load in load_factors: result = mock_system_load(load) scaling_results.append(result) assert result['load_factor'] == load assert result['response_time'] > 0 assert result['throughput'] > 0 # Scalability analysis response_times = [r['response_time'] for r in scaling_results] throughputs = [r['throughput'] for r in scaling_results] # Check that response times increase reasonably assert response_times[-1] < response_times[0] * 5 # Should not be 5x slower at 10x load # Check that throughput degrades gracefully assert throughputs[-1] > throughputs[0] / 5 # Should maintain at least 20% of peak throughput def test_resource_efficiency(self): """Test resource efficiency metrics""" # Mock resource usage def mock_resource_usage(requests_per_second): # Simulate resource usage scaling cpu_usage = min(90, requests_per_second * 2) # 2% CPU per request/sec memory_usage = min(80, 50 + requests_per_second * 0.5) # Base 50% + 0.5% per request/sec return { 'requests_per_second': requests_per_second, 'cpu_usage': cpu_usage, 'memory_usage': memory_usage, 'efficiency': requests_per_second / max(cpu_usage, memory_usage) } # Test different request rates request_rates = [10, 25, 50, 100] # requests per second efficiency_results = [] for rate in request_rates: result = mock_resource_usage(rate) efficiency_results.append(result) assert result['requests_per_second'] == rate assert result['cpu_usage'] <= 100 assert result['memory_usage'] <= 100 # Efficiency analysis efficiencies = [r['efficiency'] for r in efficiency_results] max_efficiency = max(efficiencies) assert max_efficiency > 1.0 # Should achieve reasonable efficiency