metodologia-performance-engineering

To establish a performance baseline, you profile the system to capture p50, p95, and p99 latency metrics. This identifies bottlenecks and provides actionable data to guide load testing and capacity planning.

The best way to design load tests is creating strategies for baseline, ramp, stress, spike, and soak scenarios. These tests can be integrated into CI/CD pipelines to ensure microservices and serverless architectures meet latency targets.

USL capacity planning uses Universal Scalability Law models to calculate system runway and predict throughput limits. It ensures architectures handle projected traffic loads without violating latency or reliability targets.

You need to design SLOs with error budgets and burn-rate alerts when your system requires strict reliability targets. This process tracks service level agreements and manages the trade-off between feature velocity and system stability.

Yes, you can use caching and CDN rules to optimize edge latency and cost. Designing a targeted edge strategy reduces origin load, improves response times across monoliths and microservices, and lowers infrastructure expenses.

Performance engineering produces actionable artifacts including templates, dashboards, playbooks, and capacity models. These deliverables support decision-making by translating percentile metrics and load testing results into operational guidelines.