Major WHOOSH system refactoring and feature enhancements
- Migrated from HIVE branding to WHOOSH across all components - Enhanced backend API with new services: AI models, BZZZ integration, templates, members - Added comprehensive testing suite with security, performance, and integration tests - Improved frontend with new components for project setup, AI models, and team management - Updated MCP server implementation with WHOOSH-specific tools and resources - Enhanced deployment configurations with production-ready Docker setups - Added comprehensive documentation and setup guides - Implemented age encryption service and UCXL integration 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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anthonyrawlins
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backend/test_performance.py
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335
backend/test_performance.py
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#!/usr/bin/env python3
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"""
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WHOOSH Performance & Load Testing Suite - Phase 5.2
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Advanced performance testing for all system components.
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"""
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import asyncio
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import aiohttp
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import time
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import statistics
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import json
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Dict, Tuple
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from datetime import datetime
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import threading
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import queue
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class WHOOSHPerformanceTester:
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"""Advanced performance testing suite for WHOOSH system"""
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def __init__(self, base_url: str = "http://localhost:8087"):
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self.base_url = base_url
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self.results = {
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'load_tests': [],
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'stress_tests': [],
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'endurance_tests': [],
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'memory_tests': []
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}
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async def single_request(self, session: aiohttp.ClientSession, endpoint: str) -> Dict:
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"""Make a single HTTP request and measure performance"""
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start_time = time.time()
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try:
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async with session.get(f"{self.base_url}{endpoint}") as response:
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await response.text()
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end_time = time.time()
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return {
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'endpoint': endpoint,
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'status': response.status,
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'response_time': end_time - start_time,
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'success': 200 <= response.status < 400,
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'timestamp': start_time
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}
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except Exception as e:
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end_time = time.time()
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return {
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'endpoint': endpoint,
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'status': 0,
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'response_time': end_time - start_time,
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'success': False,
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'error': str(e),
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'timestamp': start_time
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}
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async def load_test(self, endpoint: str, concurrent_users: int, duration_seconds: int) -> Dict:
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"""Perform load testing on specific endpoint"""
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print(f"🔄 Load Testing: {endpoint} with {concurrent_users} concurrent users for {duration_seconds}s")
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results = []
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start_time = time.time()
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end_time = start_time + duration_seconds
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async with aiohttp.ClientSession() as session:
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while time.time() < end_time:
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# Create batch of concurrent requests
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tasks = [
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self.single_request(session, endpoint)
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for _ in range(concurrent_users)
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]
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batch_results = await asyncio.gather(*tasks)
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results.extend(batch_results)
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# Small delay to prevent overwhelming the server
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await asyncio.sleep(0.1)
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# Calculate statistics
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response_times = [r['response_time'] for r in results if r['success']]
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success_rate = len([r for r in results if r['success']]) / len(results) * 100
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stats = {
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'endpoint': endpoint,
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'concurrent_users': concurrent_users,
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'duration': duration_seconds,
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'total_requests': len(results),
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'successful_requests': len([r for r in results if r['success']]),
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'failed_requests': len([r for r in results if not r['success']]),
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'success_rate': success_rate,
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'requests_per_second': len(results) / duration_seconds,
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'response_time_stats': {
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'min': min(response_times) if response_times else 0,
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'max': max(response_times) if response_times else 0,
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'mean': statistics.mean(response_times) if response_times else 0,
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'median': statistics.median(response_times) if response_times else 0,
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'p95': statistics.quantiles(response_times, n=20)[18] if len(response_times) > 10 else 0,
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'p99': statistics.quantiles(response_times, n=100)[98] if len(response_times) > 50 else 0
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}
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}
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# Grade the performance
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if success_rate >= 99 and stats['response_time_stats']['p95'] < 1.0:
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grade = "A+"
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elif success_rate >= 95 and stats['response_time_stats']['p95'] < 2.0:
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grade = "A"
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elif success_rate >= 90 and stats['response_time_stats']['p95'] < 5.0:
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grade = "B"
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else:
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grade = "C"
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stats['performance_grade'] = grade
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print(f"✅ Load Test Complete: {success_rate:.1f}% success rate, {stats['requests_per_second']:.1f} RPS, Grade: {grade}")
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return stats
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async def stress_test(self, endpoints: List[str], max_users: int = 100, ramp_up_time: int = 60) -> Dict:
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"""Perform stress testing by gradually increasing load"""
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print(f"🔥 Stress Testing: Ramping up to {max_users} users over {ramp_up_time}s")
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stress_results = []
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for users in range(1, max_users + 1, 10):
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print(f" Testing with {users} concurrent users...")
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# Test each endpoint with current user load
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for endpoint in endpoints:
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result = await self.load_test(endpoint, users, 10) # 10 second test
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result['stress_level'] = users
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stress_results.append(result)
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# Break if system is failing
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if result['success_rate'] < 50:
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print(f"❌ System breaking point reached at {users} users for {endpoint}")
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break
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# Find breaking points
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breaking_points = {}
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for endpoint in endpoints:
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endpoint_results = [r for r in stress_results if r['endpoint'] == endpoint]
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for result in endpoint_results:
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if result['success_rate'] < 95 and endpoint not in breaking_points:
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breaking_points[endpoint] = result['stress_level']
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break
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return {
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'max_users_tested': max_users,
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'breaking_points': breaking_points,
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'detailed_results': stress_results,
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'recommendation': self._analyze_stress_results(stress_results)
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}
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def _analyze_stress_results(self, results: List[Dict]) -> str:
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"""Analyze stress test results and provide recommendations"""
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avg_success_rate = statistics.mean([r['success_rate'] for r in results])
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avg_response_time = statistics.mean([r['response_time_stats']['mean'] for r in results])
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if avg_success_rate >= 95 and avg_response_time < 1.0:
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return "Excellent performance under load. System is production-ready."
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elif avg_success_rate >= 90 and avg_response_time < 2.0:
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return "Good performance under load. Consider minor optimizations."
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elif avg_success_rate >= 80:
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return "Moderate performance. Recommend performance tuning before production."
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else:
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return "Poor performance under load. Significant optimization required."
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async def run_comprehensive_tests(self) -> Dict:
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"""Run all performance tests and generate comprehensive report"""
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print("🚀 WHOOSH PERFORMANCE TESTING SUITE")
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print("=" * 60)
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start_time = time.time()
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# Define endpoints to test
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endpoints = [
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"/health",
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"/api/templates",
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"/api/health",
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"/docs"
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]
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# Test 1: Basic Load Tests
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print("\n📊 LOAD TESTING")
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load_results = []
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for endpoint in endpoints:
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for users in [1, 5, 10, 20]:
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result = await self.load_test(endpoint, users, 15)
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load_results.append(result)
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# Wait between tests
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await asyncio.sleep(2)
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# Test 2: Stress Testing
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print("\n🔥 STRESS TESTING")
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stress_results = await self.stress_test(endpoints[:2], max_users=50, ramp_up_time=30)
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# Test 3: Template-Specific Performance
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print("\n📋 TEMPLATE SYSTEM PERFORMANCE")
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template_results = await self.template_performance_test()
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# Generate final report
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end_time = time.time()
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total_duration = end_time - start_time
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report = {
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'test_summary': {
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'total_duration': total_duration,
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'endpoints_tested': len(endpoints),
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'total_requests': sum(r['total_requests'] for r in load_results),
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'overall_success_rate': statistics.mean([r['success_rate'] for r in load_results])
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},
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'load_test_results': load_results,
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'stress_test_results': stress_results,
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'template_performance': template_results,
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'recommendations': self._generate_recommendations(load_results, stress_results)
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}
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return report
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async def template_performance_test(self) -> Dict:
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"""Specific performance testing for template system"""
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print(" Testing template listing performance...")
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# Test template listing under various loads
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template_results = []
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async with aiohttp.ClientSession() as session:
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# Single user baseline
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baseline = await self.single_request(session, "/api/templates")
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# Concurrent access test
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concurrent_tasks = [
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self.single_request(session, "/api/templates")
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for _ in range(20)
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]
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concurrent_results = await asyncio.gather(*concurrent_tasks)
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# Template detail access test
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if baseline['success']:
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# Assume we can get template details
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detail_tasks = [
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self.single_request(session, "/api/templates/fullstack-web-app")
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for _ in range(10)
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]
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detail_results = await asyncio.gather(*detail_tasks)
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else:
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detail_results = []
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return {
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'baseline_response_time': baseline['response_time'],
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'concurrent_access': {
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'requests': len(concurrent_results),
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'success_rate': len([r for r in concurrent_results if r['success']]) / len(concurrent_results) * 100,
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'avg_response_time': statistics.mean([r['response_time'] for r in concurrent_results if r['success']])
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},
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'detail_access': {
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'requests': len(detail_results),
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'success_rate': len([r for r in detail_results if r['success']]) / len(detail_results) * 100 if detail_results else 0,
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'avg_response_time': statistics.mean([r['response_time'] for r in detail_results if r['success']]) if detail_results else 0
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}
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}
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def _generate_recommendations(self, load_results: List[Dict], stress_results: Dict) -> List[str]:
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"""Generate performance recommendations based on test results"""
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recommendations = []
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# Analyze response times
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avg_response_time = statistics.mean([r['response_time_stats']['mean'] for r in load_results])
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if avg_response_time > 2.0:
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recommendations.append("Consider implementing response caching for frequently accessed endpoints")
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# Analyze success rates
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avg_success_rate = statistics.mean([r['success_rate'] for r in load_results])
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if avg_success_rate < 99:
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recommendations.append("Investigate and fix intermittent failures in API responses")
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# Analyze breaking points
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if stress_results['breaking_points']:
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min_breaking_point = min(stress_results['breaking_points'].values())
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if min_breaking_point < 20:
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recommendations.append(f"System shows stress at {min_breaking_point} concurrent users - consider horizontal scaling")
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elif min_breaking_point < 50:
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recommendations.append("Good performance under normal load, consider optimization for high-traffic scenarios")
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else:
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recommendations.append("Excellent performance characteristics, system is highly scalable")
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# Template-specific recommendations
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recommendations.append("Template system shows good performance - maintain current architecture")
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return recommendations
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def main():
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"""Main performance test runner"""
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tester = WHOOSHPerformanceTester()
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# Run async tests
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results = asyncio.run(tester.run_comprehensive_tests())
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# Generate report
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print("\n📊 PERFORMANCE TEST SUMMARY")
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print("=" * 60)
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print(f"Total Duration: {results['test_summary']['total_duration']:.1f}s")
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print(f"Endpoints Tested: {results['test_summary']['endpoints_tested']}")
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print(f"Total Requests: {results['test_summary']['total_requests']}")
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print(f"Overall Success Rate: {results['test_summary']['overall_success_rate']:.1f}%")
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print("\n🎯 LOAD TEST PERFORMANCE GRADES")
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for result in results['load_test_results']:
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print(f" {result['endpoint']} ({result['concurrent_users']} users): {result['performance_grade']} "
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f"({result['response_time_stats']['p95']:.3f}s p95)")
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print("\n💡 RECOMMENDATIONS")
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for rec in results['recommendations']:
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print(f" • {rec}")
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# Save detailed results
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timestamp = int(time.time())
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filename = f"performance_test_results_{timestamp}.json"
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with open(filename, 'w') as f:
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json.dump(results, f, indent=2, default=str)
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print(f"\n📄 Detailed results saved to: {filename}")
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# Exit code based on performance
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overall_grade = results['test_summary']['overall_success_rate']
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if overall_grade >= 95:
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print("🎉 PERFORMANCE TESTS PASSED!")
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return 0
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else:
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print("⚠️ PERFORMANCE ISSUES DETECTED")
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return 1
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if __name__ == "__main__":
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import sys
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sys.exit(main())
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