K6

How k6 works

k6 scripts are written in JavaScript. Each script defines virtual users that send requests to APIs or web endpoints.

As load grows, k6 records how the system responds. Developers can run it locally, in CI/CD, or at scale in a cluster.

• Response time and errors: latency and failure rates under different loads
• Throughput: how many requests and concurrent users the system can handle
• Bottlenecks: which components become constrained first
• Degradation points: when and how the app begins to fail during spikes

k6 also exports results to monitoring and observability tools for deeper analysis.

How to run a k6 load test

1. Write a script that defines requests and virtual user behavior.
2. Set stages for ramp-up, steady load, and ramp-down.
3. Add thresholds for latency and error rate.
4. Run the test locally, in CI/CD, or in Kubernetes.
5. Review metrics and compare them with your baseline.

Common k6 test types

• Smoke tests: minimal load to confirm the script and system work
• Load tests: expected traffic to confirm normal performance
• Stress tests: rising load to find the breaking point
• Spike tests: sudden bursts of traffic
• Soak tests: steady load over hours to expose leaks

Why k6 matters

Applications now serve thousands or millions of users. Performance testing confirms they can handle production-scale traffic without slowdowns or outages.

Consider a SaaS team launching a new pricing page backed by an API. A k6 test in the pipeline shows that latency doubles past 500 users. They fix a slow query before launch day.

k6 vs. JMeter

Both tools are open source and widely used. They fit different teams.

Best practices for k6

• Clear thresholds: set max latency and error rates that match business goals
• Realistic scenarios: use real data and include edge cases
• System monitoring: watch CPU, memory, and network alongside k6 results
• CI/CD checks: run k6 in pipelines with alerts to block regressions
• Gradual ramps: increase load in stages to find breaking points safely
• Baselines: capture metrics in quiet periods to compare against
• Production-like environments: test on infrastructure that mirrors production
• Documentation: record scenarios, settings, and results for future reference

Common pitfalls

• Unrealistic tests: traffic patterns that do not match real usage give false confidence
• No infrastructure monitoring: resource limits go unnoticed during tests
• Overloaded clusters: poor resource allocation affects other workloads
• Unversioned scripts: changes are hard to track without version control
• One-off testing: load tests treated as an event, not an ongoing practice
• Happy paths only: errors, timeouts, and degraded services go untested
• No business check: results never compared with user experience goals
• Unsafe production tests: load against production without safeguards or approval

How k6 relates to Testkube

Testkube runs k6 tests directly inside Kubernetes clusters. Teams get scalable, containerized load testing on infrastructure they already have.

• Distributed tests: k6 load spread across environments with Kubernetes orchestration
• Unified dashboard: logs, metrics, and artifacts in one place
• Continuous testing: performance checks run on every deployment
• Data control: tests stay inside private infrastructure for security and compliance

Frequently Asked Questions (FAQs)