About the service
I do load and performance testing on Gatling and treat every scenario as code, not as a one-off run. When you give me an API or web flow, I parse real user scenarios and traffic distribution, then write simulations in Scala that honestly reproduce them: pauses between steps, correlated tokens, dynamic request bodies, and feeders with believable data. Then I ramp up virtual users in steps so I can see where the system starts to bend, not just the point where it finally fails.
My job is to find bottlenecks before your customers do. I run soak and spike profiles and read what matters: response time percentiles, throughput, and error rate. I don't trust averages: p50 lies, and all the pain lives in p95 and p99. When a request slows down, I trace it to the layer that owns the delay: thread pool, connection limit, slow query, or garbage collection pause. The output is a report you can act on, and CI integration with thresholds that fail the build on regression. Know your limits before your users do.
— Gatling simulations in Scala, versioned alongside code
— Realistic soak, spike, stress, and capacity-check profiles
— Percentile analysis, bottleneck discovery, and tuning advice
— CI integration with pass/fail thresholds and trend history
My job is to find bottlenecks before your customers do. I run soak and spike profiles and read what matters: response time percentiles, throughput, and error rate. I don't trust averages: p50 lies, and all the pain lives in p95 and p99. When a request slows down, I trace it to the layer that owns the delay: thread pool, connection limit, slow query, or garbage collection pause. The output is a report you can act on, and CI integration with thresholds that fail the build on regression. Know your limits before your users do.
— Gatling simulations in Scala, versioned alongside code
— Realistic soak, spike, stress, and capacity-check profiles
— Percentile analysis, bottleneck discovery, and tuning advice
— CI integration with pass/fail thresholds and trend history
