Performance and load testing guides — measure, benchmark, and optimize your app before it hits production.
Performance Testing
Gatling (Java/Scala/Kotlin, async actors), Locust (Python, coroutines), and k6 (JavaScript, Go-based VUs) are the three dominant open-source load testing tools in 2026. Each has a clear use case: Gatling excels at high-throughput API testing with rich HTML reports, Locust is the easiest entry point for
Performance Testing
JMeter has been the default load testing tool for over two decades. Gatling arrived later with a fundamentally different architecture and a code-first philosophy. Both are open source, both test HTTP APIs and web applications, and both generate detailed reports. But they make very different tradeoffs that affect everything
Performance Testing
Performance testing is no longer optional. As applications scale and user expectations rise, knowing how your system behaves under load is fundamental to shipping reliable software. Gatling is one of the most powerful open-source load testing tools available—built on Scala and Akka, it handles massive concurrency with minimal
Performance Testing
The Gatling bundle is fine for exploration, but production load testing belongs in your build system. Maven and Gradle integration gives you dependency management, versioned simulations alongside application code, and clean CI/CD pipeline integration. This guide covers both build tools with complete, working configurations. Why Build Tool Integration Matters
Performance Testing
A single Gatling machine can generate impressive load—10,000 to 50,000 concurrent users depending on hardware and workload. But some systems need more. Global applications require load from multiple geographic regions. Financial platforms need to simulate six-figure concurrent user counts. Distributed testing is where you go when
Performance Testing
Running Gatling in CI/CD transforms load testing from a one-off activity into a continuous performance gate. This guide shows the exact Maven plugin configuration, GitHub Actions YAML, and Jenkinsfile needed to run Gatling simulations automatically, archive the HTML report, and fail the build when your p95 or error
Performance Testing
Gatling is a high-performance open-source load testing tool built on Akka and Netty that lets developers write realistic API simulations in Java, Kotlin, or Scala. This guide walks you from zero to a running load test in under 30 minutes, covering Maven setup, your first simulation, and how
Performance Testing
Once you have a basic Gatling simulation running, feeders let you inject realistic test data, checks validate and extract values from responses, and throttling lets you control request rate independently from virtual user count. Together these three features let you model production traffic patterns with high fidelity. Key Takeaways * Feeders
Testing Strategy
Chaos engineering tools inject failures into systems. Game days are the events where teams deliberately experience those failures together, practicing their response before a real incident forces the issue. The combination — technical failure injection and human readiness practice — is what separates organizations that handle outages well from organizations that scramble.
Testing
Capacity planning is the practice of ensuring your infrastructure can handle future load before that load arrives. SRE teams use a combination of load testing, resource profiling, and traffic modeling to answer: "How much load can we handle, and when do we need to add more capacity?" The
Load Testing
Choosing a load testing platform comes down to a few key questions: what scripts do you already have, how often do you test, what's your budget model (per-test vs. subscription), and how much do you care about developer experience vs. enterprise features. This comparison covers Flood.io
Load Testing
Load testing at scale means either managing your own infrastructure or paying someone else to do it. Flood.io is firmly in the second camp. You write the test scripts in tools you already know — JMeter, Gatling, or Puppeteer — and Flood handles spinning up hundreds of nodes, distributing load across