running-performance-tests

# Performance Test Suite ## Overview Execute load testing, stress testing, and performance benchmarking to identify bottlenecks, establish baseline metrics, and verify SLA compliance. Supports k6 (recommended), Artillery, Apache JMeter, Locust (Python), and autocannon (Node.js). ## Prerequisites - Performance testing tool installed (`k6`, `artillery`, `locust`, `jmeter`, or `autocannon`) - Target application deployed in a production-like environment (not local dev) - Baseline performance metrics or SLA targets (e.g., p95 < 200ms, 99.9% availability) - Monitoring stack accessible (Grafana, CloudWatch, Datadog) for resource metrics during tests - Test data sufficient to avoid cache-only responses ## Instructions 1. Define performance test scenarios based on production traffic patterns: - **Load test**: Simulate expected peak traffic (e.g., 500 concurrent users for 10 minutes). - **Stress test**: Ramp beyond expected capacity to find the breaking point. - **Spike test**: Sudden burst of traffic (0 to 1000 users in 10 seconds). - **Soak test**: Sustained moderate load for extended duration (1-4 hours) to detect memory leaks. 2. Create test scripts targeting critical endpoints: - Identify the top 5-10 most-hit API endpoints from production access logs. - Include both read (GET) and write (POST/PUT/DELETE) operations. - Simulate realistic user behavior with think time between requests. - Use parameterized data to avoid cache-only hits (randomize query ...