Table of Contents
Table of Contents
Further Reading
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What load testing measures
Load testing confirms that infrastructure and applications handle expected traffic. It differs from related performance tests in focus.
- Load testing: expected, realistic levels of concurrent users
- Stress testing: pushes systems past their limits to find breaking points
- Volume testing: large amounts of data rather than many users
Key elements of a load test
- Realistic scenarios: user journeys and load levels that match production
- Representative data: test data that reflects real usage
- Clear KPIs: response time, throughput, error rates, and resource use
How to run a load test
- Define the user scenarios and target load.
- Prepare test data and the target environment.
- Ramp traffic up to the target and hold it.
- Record response times, throughput, errors, and resource use.
- Compare results with targets and previous runs.
Common load testing patterns
Different patterns answer different questions about the same system.
Why load testing matters
Without load testing, performance problems often surface only in production. The result is outages, latency, and unhappy customers.
- Autoscaling validation: confirm pods and nodes scale when traffic rises
- Early regression detection: catch slowdowns in CI/CD before release
- Reliable peaks: keep the customer experience steady during busy periods
- Right-sized infrastructure: avoid paying for too much or too little capacity
Consider a SaaS company planning a product launch. A load test shows the login service slows past 2,000 concurrent users. The team adds capacity before launch, not during it.
Best practices for load testing
- Test production-like environments: match infrastructure, data, and configuration
- Set thresholds: define pass or fail limits for latency and errors
- Ramp gradually: increase load in stages to spot the knee in the curve
- Monitor the system: watch CPU, memory, and network alongside test metrics
- Run continuously: load test on a schedule and in pipelines, not once a year
Real-world examples
- Holiday readiness: an e-commerce platform runs JMeter load tests with Testkube across clusters before holiday sales
- Streaming peaks: a streaming service simulates thousands of concurrent video requests to validate CDN performance
How load testing relates to Testkube
Testkube simplifies Kubernetes-native load testing. Load generators run next to the services they test.
- Popular tools: JMeter, k6, and Artillery run directly inside clusters
- Distributed load: executions scale across nodes for large simulations
- CI/CD integration: continuous performance validation on every change
- Observability: centralized dashboards and stored artifacts