Performance tests with Gatling
summary
Performance testing advice that actually helps: use Gatling because it implements a non-blocking asynchronous Scala DSL so one machine can generate huge load from a single machine; measure percentiles not means and beware the coordinated omission problem (Gatling can reduce it but not eliminate it); validate assumptions with monitoring, logs and one-bottleneck-at-a-time fixes; use feeders, throttling and built-in assertions, export the visual percentile-over-time reports, and run distributed agents or community plugins (Kafka Cassandra WebSockets) when local load is insufficient.
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chapters (18)
- 0:00Introduction
- 1:21Test environment
- 4:09Monitoring
- 7:43Metrics
- 10:56Coordinated emission problem
- 24:17Ifelse
- 24:39TryMax
- 25:24Scenario setup
- 26:13Global assertions
- 26:51Throttling
- 27:22Feeders
- 27:56Configuration
- 28:40Launching Gatling
- 28:52Aspect of Gatling
- 30:06My personal experience
- 30:40Visual report
- 32:03Use existing services
- 32:45Conclusion
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