"""
Test Load Testing Baseline
Documents the implementation of load testing infrastructure for performance validation
"""

def test_load_testing_implementation():
    """Test load testing baseline documentation"""

    print("Load Testing Baseline - Implementation")
    print("=" * 70)
    print()

    print("P2 Performance Validation:")
    print("  No documented throughput/latency benchmarks")
    print("  → Cannot detect performance regressions")
    print("  → No baseline for capacity planning")
    print("  → Unknown breaking points")
    print("  → No SLA guarantees")
    print()
    print("=" * 70)
    print()

    print("Implementation Locations:")
    print()

    locations = [
        {
            'file': 'load-tests/k6-load-test.js',
            'type': 'k6 Load Test',
            'scenarios': 4,
            'description': 'JavaScript-based load testing with k6'
        },
        {
            'file': 'load-tests/locust-load-test.py',
            'type': 'Locust Load Test',
            'scenarios': 3,
            'description': 'Python-based load testing with Locust'
        },
        {
            'file': 'load-tests/PERFORMANCE_BASELINES.md',
            'type': 'Documentation',
            'sections': 10,
            'description': 'Performance targets and test procedures'
        }
    ]

    for i, loc in enumerate(locations, 1):
        print(f"{i}. {loc['file']}")
        print(f"   Type: {loc['type']}")
        if 'scenarios' in loc:
            print(f"   Scenarios: {loc['scenarios']}")
        if 'sections' in loc:
            print(f"   Sections: {loc['sections']}")
        print(f"   Description: {loc['description']}")
        print()

    print("=" * 70)
    print()

    print("Performance Targets (p50/p95/p99):")
    print()

    targets = [
        {
            'category': 'Overall',
            'p50': '< 100ms',
            'p95': '< 500ms',
            'p99': '< 1000ms',
            'notes': 'All requests combined'
        },
        {
            'category': 'Authentication',
            'p50': '< 80ms',
            'p95': '< 400ms',
            'p99': '< 800ms',
            'notes': 'Login/token validation'
        },
        {
            'category': 'REST Gateway',
            'p50': '< 150ms',
            'p95': '< 600ms',
            'p99': '< 1200ms',
            'notes': 'API proxying overhead'
        },
        {
            'category': 'GraphQL Gateway',
            'p50': '< 200ms',
            'p95': '< 800ms',
            'p99': '< 1500ms',
            'notes': 'Query/mutation processing'
        },
        {
            'category': 'SOAP Gateway',
            'p50': '< 250ms',
            'p95': '< 1000ms',
            'p99': '< 2000ms',
            'notes': 'XML processing overhead'
        },
        {
            'category': 'Health Check',
            'p50': '< 10ms',
            'p95': '< 50ms',
            'p99': '< 100ms',
            'notes': 'No database queries'
        }
    ]

    for target in targets:
        print(f"{target['category']}:")
        print(f"  p50: {target['p50']}")
        print(f"  p95: {target['p95']}")
        print(f"  p99: {target['p99']}")
        print(f"  Notes: {target['notes']}")
        print()

    print("=" * 70)
    print()

    print("k6 Load Test - Scenarios:")
    print()

    k6_scenarios = [
        {
            'name': 'Smoke Test',
            'vus': '1',
            'duration': '30s',
            'purpose': 'Verify basic functionality',
            'thresholds': 'p99 < 200ms, 100% success'
        },
        {
            'name': 'Load Test',
            'vus': '0→10→50',
            'duration': '9m',
            'purpose': 'Simulate realistic production load',
            'thresholds': 'p95 < 500ms, error rate < 1%'
        },
        {
            'name': 'Stress Test',
            'vus': '0→100→200',
            'duration': '16m',
            'purpose': 'Find system breaking point',
            'thresholds': 'p99 < 2000ms, error rate < 5%'
        },
        {
            'name': 'Spike Test',
            'vus': '10→200→10',
            'duration': '2m',
            'purpose': 'Test resilience to traffic spikes',
            'thresholds': 'No crashes, recovery < 30s'
        }
    ]

    for scenario in k6_scenarios:
        print(f"Scenario: {scenario['name']}")
        print(f"  Virtual Users: {scenario['vus']}")
        print(f"  Duration: {scenario['duration']}")
        print(f"  Purpose: {scenario['purpose']}")
        print(f"  Success Criteria: {scenario['thresholds']}")
        print()

    print("=" * 70)
    print()

    print("k6 Implementation Details:")
    print()
    print("  Custom Metrics:")
    print("    - auth_success_rate: Authentication success rate")
    print("    - rest_gateway_latency: REST proxy latency distribution")
    print("    - graphql_gateway_latency: GraphQL latency distribution")
    print("    - soap_gateway_latency: SOAP latency distribution")
    print("    - error_count: Total errors across all requests")
    print()
    print("  Built-in Metrics:")
    print("    - http_req_duration: Request latency (p50/p95/p99)")
    print("    - http_req_failed: Failed request rate")
    print("    - http_reqs: Total requests per second")
    print("    - vus: Active virtual users")
    print()
    print("  Thresholds (fail test if exceeded):")
    print("    - http_req_duration: p50<100ms, p95<500ms, p99<1000ms")
    print("    - http_req_failed: rate<0.05 (error rate < 5%)")
    print("    - auth_success_rate: rate>0.95 (95% success)")
    print("    - error_count: count<100 (max 100 errors)")
    print()
    print("=" * 70)
    print()

    print("Locust Load Test - Features:")
    print()

    locust_features = [
        {
            'feature': 'User Classes',
            'description': 'DoormanUser (realistic), StressTestUser (rapid-fire)',
            'benefit': 'Different load patterns for different scenarios'
        },
        {
            'feature': 'Task Weighting',
            'description': 'Tasks weighted by production traffic distribution',
            'benefit': 'Realistic workload simulation'
        },
        {
            'feature': 'Load Shapes',
            'description': 'Step, Spike, Wave patterns',
            'benefit': 'Custom load patterns for specific tests'
        },
        {
            'feature': 'Tag-based Execution',
            'description': 'Run specific test categories (auth, gateway, stress)',
            'benefit': 'Targeted testing of specific components'
        },
        {
            'feature': 'Web UI',
            'description': 'Real-time metrics dashboard',
            'benefit': 'Visual monitoring during tests'
        },
        {
            'feature': 'HTML Reports',
            'description': 'Comprehensive test result reports',
            'benefit': 'Easy sharing and archival'
        }
    ]

    for feature in locust_features:
        print(f"{feature['feature']}:")
        print(f"  Description: {feature['description']}")
        print(f"  Benefit: {feature['benefit']}")
        print()

    print("=" * 70)
    print()

    print("Locust Traffic Mix (Realistic Workload):")
    print()
    print("  Task Distribution:")
    print("    - 10% Authentication (login)")
    print("    - 20% List APIs")
    print("    - 15% List Users")
    print("    - 15% List Roles")
    print("    - 15% List Groups")
    print("    - 25% REST Gateway (API proxying)")
    print("    - 5% Health Check")
    print()
    print("  Based on production access patterns:")
    print("    - Gateway requests are most common (API proxying)")
    print("    - API management second (admin operations)")
    print("    - Authentication periodic (token refresh)")
    print("    - Health checks for monitoring")
    print()
    print("=" * 70)
    print()

    print("Running Tests:")
    print()
    print("  k6 - Basic Run:")
    print("    k6 run load-tests/k6-load-test.js")
    print()
    print("  k6 - Custom Configuration:")
    print("    k6 run --env BASE_URL=https://api.example.com \\")
    print("           --env TEST_USERNAME=admin \\")
    print("           --env TEST_PASSWORD=secret \\")
    print("           load-tests/k6-load-test.js")
    print()
    print("  k6 - Generate JSON Results:")
    print("    k6 run --out json=results.json load-tests/k6-load-test.js")
    print()
    print("  Locust - Web UI Mode:")
    print("    locust -f load-tests/locust-load-test.py \\")
    print("           --host=http://localhost:8000")
    print("    # Open browser to http://localhost:8089")
    print()
    print("  Locust - Headless (CI/CD):")
    print("    locust -f load-tests/locust-load-test.py \\")
    print("           --host=http://localhost:8000 \\")
    print("           --users 50 --spawn-rate 5 --run-time 5m \\")
    print("           --headless --html report.html")
    print()
    print("  Locust - Specific Scenario:")
    print("    locust -f load-tests/locust-load-test.py \\")
    print("           --host=http://localhost:8000 \\")
    print("           --tags authentication")
    print()
    print("=" * 70)
    print()

    print("Load Shapes (Locust Custom Patterns):")
    print()
    print("  1. StepLoadShape:")
    print("     - Gradually increase load in steps")
    print("     - 10 users added every 60 seconds")
    print("     - Total duration: 10 minutes")
    print("     - Use: locust --shape StepLoadShape")
    print()
    print("  2. SpikeLoadShape:")
    print("     - Sudden traffic spike pattern")
    print("     - 0-60s: 10 users (normal)")
    print("     - 60-120s: 200 users (spike)")
    print("     - 120-180s: 10 users (recovery)")
    print("     - Use: locust --shape SpikeLoadShape")
    print()
    print("  3. WaveLoadShape:")
    print("     - Sine wave load pattern")
    print("     - Baseline: 25 users")
    print("     - Amplitude: 50 users")
    print("     - Period: 2-minute waves")
    print("     - Use: locust --shape WaveLoadShape")
    print()
    print("=" * 70)
    print()

    print("CI/CD Integration:")
    print()
    print("  GitHub Actions Example:")
    print("    1. Start Doorman (docker-compose up -d)")
    print("    2. Run k6 load test")
    print("    3. Check performance thresholds")
    print("    4. Fail build if thresholds exceeded")
    print("    5. Upload results as artifacts")
    print()
    print("  Performance Regression Detection:")
    print("    - Save baseline results (JSON)")
    print("    - Compare with new test results")
    print("    - Fail if regression detected:")
    print("      • Latency increase > 20%")
    print("      • Error rate increase > 2%")
    print("      • Throughput decrease > 15%")
    print()
    print("  Automated Testing Schedule:")
    print("    - Daily: Smoke tests (5 minutes)")
    print("    - Weekly: Load tests (30 minutes)")
    print("    - Monthly: Stress/spike tests (1 hour)")
    print("    - Quarterly: Baseline review and update")
    print()
    print("=" * 70)
    print()

    print("Monitoring During Tests:")
    print()
    print("  Application Metrics:")
    print("    - Request latency (p50, p95, p99)")
    print("    - Request rate (RPS)")
    print("    - Error rate by status code")
    print("    - Active connections")
    print("    - Worker CPU/memory usage")
    print()
    print("  Database Metrics:")
    print("    - Connection pool usage")
    print("    - Query latency")
    print("    - Slow queries (> 100ms)")
    print("    - Lock wait time")
    print("    - Replication lag")
    print()
    print("  Cache Metrics:")
    print("    - Hit rate (target > 80%)")
    print("    - Eviction rate")
    print("    - Memory usage")
    print("    - Connection count")
    print()
    print("  Commands:")
    print("    # Monitor system resources")
    print("    htop")
    print()
    print("    # Monitor MongoDB")
    print("    mongotop --host localhost --port 27017")
    print()
    print("    # Monitor Redis")
    print("    redis-cli --stat")
    print()
    print("=" * 70)
    print()

    print("Performance Optimization Tips:")
    print()
    print("  Database:")
    print("    ✓ Index frequently queried fields")
    print("    ✓ Increase connection pool size")
    print("    ✓ Use secondaryPreferred for reads")
    print()
    print("  Cache:")
    print("    ✓ Enable LRU eviction policy")
    print("    ✓ Increase max memory for Redis")
    print("    ✓ Use persistent connections")
    print()
    print("  Application:")
    print("    ✓ Workers = (2 × CPU cores) + 1")
    print("    ✓ Increase HTTP connection pool size")
    print("    ✓ Use asyncio.gather() for parallel ops")
    print()
    print("  Scaling:")
    print("    ✓ Horizontal: Add Doorman instances")
    print("    ✓ Vertical: Increase CPU/RAM")
    print("    ✓ Database: Replica set for reads")
    print()
    print("=" * 70)
    print()

    print("Alert Thresholds:")
    print()

    alerts = [
        {
            'metric': 'p99 Latency',
            'warning': '> 1000ms',
            'critical': '> 2000ms',
            'action': 'Investigate slow queries'
        },
        {
            'metric': 'Error Rate',
            'warning': '> 2%',
            'critical': '> 5%',
            'action': 'Check logs, scale up'
        },
        {
            'metric': 'CPU Usage',
            'warning': '> 70%',
            'critical': '> 85%',
            'action': 'Add workers/instances'
        },
        {
            'metric': 'Memory Usage',
            'warning': '> 75%',
            'critical': '> 90%',
            'action': 'Increase RAM'
        },
        {
            'metric': 'Cache Hit Rate',
            'warning': '< 70%',
            'critical': '< 50%',
            'action': 'Review cache strategy'
        },
        {
            'metric': 'DB Connections',
            'warning': '> 80% pool',
            'critical': '> 95% pool',
            'action': 'Increase pool size'
        }
    ]

    for alert in alerts:
        print(f"{alert['metric']}:")
        print(f"  Warning: {alert['warning']}")
        print(f"  Critical: {alert['critical']}")
        print(f"  Action: {alert['action']}")
        print()

    print("=" * 70)
    print()

    print("Benefits:")
    print()
    print("  Performance Visibility:")
    print("    ✓ Know system capacity (max throughput)")
    print("    ✓ Understand latency under load")
    print("    ✓ Identify bottlenecks early")
    print("    ✓ Detect regressions before production")
    print()
    print("  Capacity Planning:")
    print("    ✓ Data-driven scaling decisions")
    print("    ✓ Predict resource needs")
    print("    ✓ Optimize infrastructure costs")
    print("    ✓ Plan for traffic growth")
    print()
    print("  SLA Guarantees:")
    print("    ✓ Set realistic performance targets")
    print("    ✓ Validate against baselines")
    print("    ✓ Prove compliance to customers")
    print("    ✓ Track improvement over time")
    print()
    print("  Risk Mitigation:")
    print("    ✓ Identify breaking points before production")
    print("    ✓ Verify resilience to traffic spikes")
    print("    ✓ Test graceful degradation")
    print("    ✓ Validate autoscaling behavior")
    print()
    print("=" * 70)
    print()

    print("Testing Best Practices:")
    print()
    print("  1. Test realistic workloads:")
    print("     - Use production traffic distribution")
    print("     - Include authentication overhead")
    print("     - Mix read/write operations")
    print()
    print("  2. Test incrementally:")
    print("     - Start with smoke test (verify basics)")
    print("     - Ramp up gradually (find saturation point)")
    print("     - Spike test (verify resilience)")
    print()
    print("  3. Monitor holistically:")
    print("     - Application metrics (latency, errors)")
    print("     - Infrastructure metrics (CPU, memory)")
    print("     - Database metrics (connections, queries)")
    print()
    print("  4. Document everything:")
    print("     - Test configuration")
    print("     - Results and observations")
    print("     - Issues identified")
    print("     - Optimization actions")
    print()
    print("  5. Automate regression testing:")
    print("     - Run daily smoke tests")
    print("     - Compare with baselines")
    print("     - Alert on performance degradation")
    print()
    print("=" * 70)
    print()

    print("Future Enhancements:")
    print()
    print("  1. Distributed load testing:")
    print("     - Run k6 across multiple machines")
    print("     - Coordinate with k6 Cloud or custom orchestration")
    print()
    print("  2. Real user monitoring (RUM):")
    print("     - Capture actual user latency")
    print("     - Compare with synthetic tests")
    print()
    print("  3. Chaos engineering:")
    print("     - Inject failures during load tests")
    print("     - Test resilience to database outages")
    print("     - Verify graceful degradation")
    print()
    print("  4. Cost-based testing:")
    print("     - Track infrastructure cost during tests")
    print("     - Optimize cost per request")
    print()
    print("=" * 70)
    print()

    print("P2 Impact:")
    print("  No documented throughput/latency benchmarks")
    print()
    print("Production Impact:")
    print("  ✓ Established performance baselines (p50/p95/p99)")
    print("  ✓ Automated load testing with k6 and Locust")
    print("  ✓ Multiple test scenarios (smoke, load, stress, spike)")
    print("  ✓ CI/CD integration for regression detection")
    print("  ✓ Comprehensive monitoring guidance")
    print("  ✓ Data-driven capacity planning")
    print()

if __name__ == '__main__':
    test_load_testing_implementation()