Using Gatling CSV Feeders for Data‑Driven Load Tests
Learn how to configure a CSV feeder in Gatling to give each virtual user unique credentials, understand memory limits, and avoid common pitfalls.
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Learn how to configure a CSV feeder in Gatling to give each virtual user unique credentials, understand memory limits, and avoid common pitfalls.
Scaling Gatling load tests across multiple machines is straightforward, but it requires careful design. This guide details the minimal architecture, trust boundaries, monitoring, and failure modes for Gatling’s Distributed Mode, and when to rethink the setup.
Learn how Gatling's asynchronous architecture overcomes the thread-per-user bottleneck to simulate thousands of concurrent users without crashing your injector.
Load testing real-time applications requires WebSocket support. Gatling's built-in WebSocket simulation handles bidirectional communication for chat apps, live dashboards, and collaborative tools with memory-efficient non-blocking I/O.
Learn how to feed each virtual user unique data from a CSV and validate responses with Gatling checks so your load test reflects real‑world behavior.
Learn how to set up Gatling assertions that automatically abort simulations when thresholds are breached, ensuring early detection of regressions in your API.
Learn how to load dynamic data from a CSV file into Gatling simulations, choose the right feeder strategy, and verify the behavior without risking memory overload.
Closed-model injection hides saturation behind polite virtual users. Here is how Gatling's open and closed steps differ, and how to pick one deliberately.
Learn how to combine Gatling’s CSV feeder with a constantUsersPerSec injection profile to run a steady‑rate load test, including setup, execution checks, and practical limitations.