"How do you approach performance testing?"
Performance issues cause real business impact — slow pages lose users, overloaded systems crash. This tests your ability to proactively ensure system reliability.
Distinguish between load testing, stress testing, and soak testing.
Describe how you establish baselines and set performance budgets.
Explain your tooling and test environment approach.
Share results-driven examples.
I approach performance testing in three phases. First, baseline: profile the current system under normal load to establish response time, throughput, and resource utilization benchmarks. Second, load test: gradually increase concurrent users to find the breaking point and identify bottlenecks — I use k6 or Locust for this. Third, soak test: run sustained load for 24+ hours to catch memory leaks and connection pool exhaustion. I always test in an environment that mirrors production as closely as possible. In my last project, performance testing revealed a database connection leak that only manifested after 4 hours of sustained traffic. We fixed it before launch and avoided what would have been a production outage during our busiest period.
Always test in production-like environments — local testing gives misleading results.
Focus on metrics that matter to users: response time percentiles (p95, p99), not just averages.
Include performance testing in CI/CD to catch regressions automatically.
Load testing verifies the system handles expected traffic. Stress testing pushes beyond expected limits to find the breaking point and observe failure behavior.
k6 for developer-friendly scriptable tests, JMeter for complex scenarios, Locust for Python teams, and Gatling for Scala/Java environments.
Based on SLAs, user expectations, and business requirements. Common targets: p95 response time under 200ms, 99.9% availability, and handling 2x expected peak traffic.
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