Performance Testing

Master the five core performance testing types and learn to measure RPS, latency percentiles, error rate, and throughput to set data-driven performance baselines.

Install k6, write a complete load test script with virtual users, stages, groups, checks, and SLO thresholds, then interpret the terminal summary output.

Configure k6 executor types and multi-scenario scripts to run all five performance test types, model open workloads with arrival-rate executors, and identify system saturation.

Master k6 thresholds with abortOnFail and custom metrics, connect k6 to InfluxDB and Grafana for real-time dashboards, and tag requests for endpoint-level SLO monitoring.

Build data-driven k6 tests using SharedArray and CSV files, implement realistic user think time, parameterize requests with random and sequential data, and avoid the thundering herd anti-pattern.

Extend k6 with xk6 custom extensions for browser testing and gRPC, configure Grafana Cloud k6 for distributed cloud test execution, and analyze results in the Grafana web interface.

Run Lighthouse CLI audits, measure LCP, CLS, INP, and FCP, set performance budgets in JSON, and block deployments when Core Web Vitals regress below thresholds.

Profile a NestJS API with clinic.js flame graphs and 0x to identify CPU hot paths, memory leaks, and I/O bottlenecks revealed by k6 load tests.

Use EXPLAIN ANALYZE, pg_stat_statements, and k6 to identify slow queries, missing indexes, and N+1 patterns in PostgreSQL-backed NestJS APIs.

Analyze Next.js bundle sizes with next/bundle-analyzer, identify and eliminate large dependencies, implement route-based code splitting, and measure impact on Lighthouse performance scores.

Detect and fix memory leaks in NestJS services using heap snapshots, EventEmitter listener tracking, and WeakMap patterns for cache implementations.

Build a complete automated performance gate in GitHub Actions integrating k6 load tests, Lighthouse CI, bundle size checks, and database query timing as deployment blockers.