Configuring Gatling HTTP Checks and Assertions for Reliable CI/CD Gatekeeping
Learn how to add status, JSONPath and global assertions to Gatling simulations, verify failures in CI, and recover with targeted reruns.
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Learn how to add status, JSONPath and global assertions to Gatling simulations, verify failures in CI, and recover with targeted reruns.
Choosing between Gatling's open and closed injection profiles changes whether your test measures system capacity or user experience under contention. Here's how to pick the right one and avoid hiding saturation.
Choosing between constantUsersPerSec and constantConcurrentUsers changes how latency is measured and how saturation appears. Learn to pick the right model for accurate results.
Learn how Gatling’s Expression Language lets you pull tokens, IDs, or other dynamic data from HTTP responses and reuse them in later requests—making your load tests realistic and maintainable.
Thread-per-user load generators create false bottlenecks. Gatling’s async engine, injection profiles and feeders let you measure real p95/p99 latency without generator saturation.
Learn how to choose between Open and Closed workload models in Gatling to avoid coordinated omission and simulate realistic user traffic for APIs and internal systems.
Learn how to combine per‑request checks that validate responses and extract data with global assertions that enforce performance SLOs, so your load tests catch functional regressions as well as slowdowns.
Data‑driven authentication tests in Gatling are easy with CSV feeders. Learn how to set up per‑user feeders, avoid contention, and validate your results in this practical guide.
Learn how Gatling's Closed Model maintains fixed concurrent virtual users for predictable load testing, and when to use it instead of the Open Model.