#performance-testing

Implement automated performance testing using JMeter and cloud scaling for application performance validation. JMeter test automation: 1. Test plan structure: thread groups, samplers, listeners, assertions for validation. 2. Parameterization: CSV data files, random variables, dynamic request generation. 3. CI/CD integration: headless execution, result analysis, performance regression detection. 4. Distributed testing: master-slave configuration for high-load simulation. Performance test types: 1. Load testing: normal expected load (1000 concurrent users), steady state performance. 2. Stress testing: breaking point identification, failure mode analysis, recovery testing. 3. Spike testing: sudden traffic increases, autoscaling validation, resource exhaustion scenarios. 4. Volume testing: large data set processing, database performance, storage capacity. Metrics and SLAs: 1. Response time: 95th percentile <500ms, average <200ms, maximum <2s. 2. Throughput: requests per second targets, sustained load capability. 3. Error rate: <0.1% for successful operations, graceful degradation under load. 4. Resource utilization: CPU <70%, memory <85%, database connections <80% of pool. Cloud-based testing: 1. AWS Load Testing Solution: distributed load generation, real-time monitoring. 2. Azure DevOps Load Testing: cloud-scale testing, geographic distribution. 3. GCP Cloud Load Testing: global load simulation, auto-scaling validation. Automated analysis: 1. Baseline comparison: performance trends, regression detection, alerting. 2. Report generation: HTML reports, trend analysis, SLA compliance verification. Integration testing: API performance, database query optimization, caching effectiveness, CDN performance validation.