Performance and load testing guides — measure, benchmark, and optimize your app before it hits production.
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
Load Testing
Getting a load test running on Flood takes less than 30 minutes if you have a JMeter plan ready. If you don't, this guide walks you through creating one. By the end you'll have run an actual flood and know how to read the results. Account
Flame Graphs
Flame graphs visualize sampled call stacks: wider bars = more time spent. Continuous profiling captures these samples always-on in production, enabling post-hoc analysis of performance incidents. This guide covers reading flame graphs, differential (diff) flame graphs for comparing before/after, and setting up Pyroscope or Parca for continuous profiling.
SpeedCurve
Continuous performance monitoring catches regressions after they reach production. CI/CD integration catches them before. By running SpeedCurve tests against every pull request and blocking deploys when performance budgets are breached, teams can prevent regressions from shipping in the first place. This guide covers SpeedCurve's API, GitHub Actions
SLA Testing
An SLA (Service Level Agreement) is only as valuable as your ability to verify it. Most engineering teams define SLAs, deploy to production, and then find out they're violating them from a customer complaint. That's not SLA management — that's reactive damage control. This guide
Edge Computing
Edge-native applications live and die by latency. When your app processes data at the edge — inside a retail store, on a factory floor, at a cell tower — a 200ms round trip to the cloud is catastrophic. Testing these systems requires a fundamentally different mindset than testing cloud-first applications.